Information security protection method and system for home energy management system

Abstract

The present application relates to the technical field of information encryption, and particularly relates to a method and system for information security protection of a home energy management system. The method comprises the following steps: obtaining smart home data; performing communication simulation according to the smart home data, and identifying electromagnetic leakage in the communication simulation process to generate electromagnetic leakage data; identifying side channel attacks based on the electromagnetic leakage data; testing material shielding effectiveness by using the side channel attacks; predicting the service life of the shielding material 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; performing transient electromagnetic pulse simulation based on the multi-layer shielding data, and detecting inverter breakdown damage in the simulation process to obtain inverter breakdown damage data. The present application improves the security protection capability of the smart home system when facing electromagnetic leakage and side channel attacks based on information encryption technology, and significantly improves the shielding effectiveness and the anti-interference capability of the system.

Description

TECHNICAL FIELD

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

[0002] The information security protection of traditional smart home exposes many shortcomings when facing increasingly complex security threats, mostly relying on simple encryption technology and firewall means, which cannot comprehensively cope with complex multi-element data flow and multi-dimensional attacks in modern home energy management systems. These methods usually lack effective preventive measures for new attacks such as side channel attacks and electromagnetic leakage attacks, and are difficult to discover and respond to new security threats in a timely manner. Traditional protection measures often do not fully consider the risk of electromagnetic leakage, and the communication between home devices produces leakage signals, and lack an electromagnetic leakage detection mechanism, making it easy to become a weak link for attacks. Traditional methods ignore the aging effect of devices or shielding materials, only focusing on the current security protection state, without fully considering the durability of shielding materials, which leads to a decline in protection effect after long-term use. Furthermore, traditional shielding designs usually use a single shielding means (such as a metal shell), without fully considering electromagnetic interference and side channel attacks in the home environment, resulting in poor protection effect. Traditional methods fail to combine dynamic simulation and optimization, lack of pre-judgment and response measures for sudden situations such as electromagnetic pulses and inverter damage, and cannot effectively respond to system failures or attacks. SUMMARY

[0003] Therefore, it is necessary 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 purpose, an information security protection method for a home energy management system comprises the following steps:

[0005] Step S1: obtaining smart home data; performing communication simulation according to the smart home data, and identifying electromagnetic leakage in the communication simulation process to generate electromagnetic leakage data;

[0006] Step S2: identifying side channel attacks based on the electromagnetic leakage data; testing the shielding effectiveness of the material using the side channel attacks; predicting the service life of the shielding material of the smart home data according to the shielding effectiveness of the material; performing multi-layer shielding design based on the service life of the shielding material 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 in the simulation process to obtain inverter breakdown damage data; identifying the parasitic effect characteristics of the smart home data according to the inverter breakdown damage data;

[0008] Step S4: Based on the parasitic effect characteristics, electromagnetic shielding optimization is performed to obtain electromagnetic shielding optimization data; the electromagnetic shielding optimization data is encrypted for electromagnetic shielding information, and is uploaded to the home energy management system to perform electromagnetic shielding information security protection tasks.

[0009] The present application can effectively detect potential side-channel attack risks and improve the ability to prevent new attacks by focusing on traditional security protection measures in data communication processes and identifying electromagnetic leakage between smart home devices. In addition, by identifying side-channel attacks and combining shielding effectiveness of test materials, the service life of shielding materials can be accurately predicted, and shielding design can be optimized to avoid the problem of reduced shielding effect caused by long-term use. Transient electromagnetic pulse simulation based on multi-layer shielding structure can simulate dynamic changes in the electromagnetic environment and detect inverter breakdown during the simulation process, which helps to detect potential system failures in advance and improve the reliability of the device. For parasitic effects in smart home data, the method can identify parasitic currents and their effects by analyzing electromagnetic coupling data, accurately assess parasitic effect characteristics, and avoid information leakage problems caused by parasitic effects. Based on parasitic effect characteristics, electromagnetic shielding optimization can effectively improve electromagnetic compatibility, allowing smart home systems to remain stable in complex electromagnetic environments. An encryption mechanism for electromagnetic shielding information is introduced to ensure that the optimized electromagnetic shielding data has higher security during transmission and storage. Electromagnetic shielding optimization data is uploaded to the home energy management system, allowing it to perform information security protection tasks and achieve overall electromagnetic safety optimization of the smart home environment. Compared to 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, and parasitic effect analysis to build a more comprehensive electromagnetic security protection system. It not only improves resistance to sudden electromagnetic interference, but also more effectively prevents side-channel attacks, electromagnetic leakage, and other complex security threats, enhancing the data security and reliability of the home energy management system.

[0010] Preferably, step S1 is specifically:

[0011] Step S11: Obtain smart home data;

[0012] Step S12: Extract the communication protocol of the smart home data, and set the communication process based on the communication protocol;

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

[0014] Step S14: Perform communication simulation based on 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 application overcomes the limitations of traditional information security protection methods by analyzing the communication characteristics of smart home data and constructing an efficient detection mechanism for electromagnetic leakage. 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, the communication channel characteristics are accurately identified, making the channel state in the communication process more transparent, providing more reliable data support for subsequent security protection. Based on the communication channel characteristics and communication process, communication simulation is performed, which enables the smart home system to detect potential security risks in advance in a simulation environment, improving the adaptability and predictability of protection measures. Through this simulation process, the communication mode between smart home devices can be analyzed in depth, and the information leakage risk can be detected, providing data basis for accurate identification of electromagnetic leakage. Finally, electromagnetic leakage in communication simulation data can be efficiently identified, and electromagnetic leakage data can be generated, thereby establishing an electromagnetic safety warning mechanism for the smart home system.

[0017] Preferably, step S15 specifically comprises:

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

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

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

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

[0022] Step S155: Count the abnormal strong signals in the electromagnetic energy concentration area, and identify the stray signals based on the abnormal strong signals;

[0023] Step S156: Determine the electromagnetic leakage of the stray signals to obtain electromagnetic leakage data.

[0024] The application accurately identifies the signal propagation path of communication simulation data, making the analysis of electromagnetic leakage more accurate and traceable, and helping to locate potential leakage sources. The electromagnetic field strength of the signal propagation path is calculated, enabling the system to quantify the degree of electromagnetic interference at different positions and provide data support for subsequent leakage risk assessment. The electromagnetic field strength is converted into an electromagnetic intensity spectrum, enabling the system to intuitively analyze the electromagnetic energy distribution in different frequency ranges and improve the resolution of abnormal signals. The electromagnetic energy concentration area is identified, enabling the system to focus on the area where leakage occurs and improve the targeting and efficiency of detection. The abnormal strong signals in the electromagnetic energy concentration area are counted, effectively excluding normal signal interference and ensuring the reliability of the detection results. Based on the abnormal strong signals, stray signals are identified, enabling the system to further filter out electromagnetic leakage sources and improve the accuracy of detection. Finally, the stray signals are subjected to electromagnetic leakage judgment to ensure that the real electromagnetic leakage data can be accurately identified and extracted, thereby enhancing the security protection capability of the system in a complex smart home environment.

[0025] Preferably, the step S2 comprises:

[0026] reverse speculation of the encryption key of the electromagnetic leakage data;

[0027] recovery of the full key space based on the encryption key;

[0028] identification of the attack fingerprint of the electromagnetic leakage data using the full key space;

[0029] tracking of the attack path based on the attack fingerprint;

[0030] positioning of the key attack fingerprint of the attack path;

[0031] obtaining of a side channel attack fingerprint library;

[0032] fingerprint matching of the side channel attack fingerprint library based on the key attack fingerprint to obtain a side channel attack fingerprint;

[0033] identification of the side channel attack of the electromagnetic leakage data according to the side channel attack fingerprint.

[0034] The application can enable the system to infer key information based on the leaked signal by inferring the encryption key of the electromagnetic leakage data, so as to analyze the encryption weakness used by the attacker. The full key space is recovered based on the encryption key, so that the system can restore all key combinations and improve the inference ability of potential attack modes. Attack fingerprints of electromagnetic leakage data are identified using the full key space, so that the system can extract features from multiple attack modes and improve the accuracy of attack type judgment. The attack path is tracked based on the attack fingerprint, so that the system can reconstruct the action track of the attacker, thereby effectively tracing the attack source. The key attack fingerprint of the attack path is located, so that the system can focus on the most critical attack features and improve the accuracy and efficiency of subsequent detection. The side-channel attack fingerprint library is obtained, so that the system can compare based on the existing attack data set, improve the breadth and adaptability of detection. The side-channel attack fingerprint library is matched based on the key attack fingerprint, so that the system can accurately identify the matching attack type, thereby improving the reliability and automation level of identification. The side-channel attack of the electromagnetic leakage data is identified according to the side-channel attack fingerprint, so that the system can intelligently analyze the electromagnetic leakage data, quickly judge the attack type, and improve the real-time response capability and early warning capability of the protection system.

[0035] Preferably, the shielding effectiveness of the test material in step S2 comprises:

[0036] Extracting smart home data of the smart camera data;

[0037] Attack simulation is performed on the smart camera data using side-channel attacks to obtain attack simulation data;

[0038] Collecting the electromagnetic signal waveform of the attack simulation data;

[0039] Determining electromagnetic leakage based on the electromagnetic signal waveform to obtain camera electromagnetic leakage data;

[0040] Filtering electromagnetic leakage cameras based on the camera electromagnetic leakage data, and performing shielding tests on the electromagnetic leakage cameras using a pre-set 0.1mm-0.3mm thin aluminum foil to obtain thin aluminum foil shielding data;

[0041] Performing shielding tests on the electromagnetic leakage cameras using a pre-set 0.5mm-1mm thick aluminum foil to obtain thick aluminum foil shielding data;

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

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

[0044] Integrating the thin aluminum foil shielding effectiveness and the thick aluminum foil shielding effectiveness to obtain the material shielding effectiveness.

[0045] The application extracts intelligent home data of the intelligent camera, so that the system can focus on the electromagnetic leakage risk of the intelligent camera, and provide accurate data basis for subsequent analysis. The side channel attack is used to attack and simulate the intelligent camera data, so that a real attack scene can be constructed to help the system evaluate the electromagnetic leakage characteristics of the camera under attack. The electromagnetic signal waveform of the attack simulation data is collected, so that the system can accurately measure the electromagnetic radiation of the camera under attack, and provide high-precision data support for leakage judgment. The electromagnetic signal waveform is used to judge the electromagnetic leakage, which can efficiently identify the electromagnetic leakage of the camera, and improve the automation and accuracy of detection. Based on the camera electromagnetic leakage data, the electromagnetic leakage camera is screened, so that the system can accurately screen out the devices with leakage risk, avoid the interference of irrelevant devices, and improve the test efficiency. The preset 0.1mm-0.3mm thin aluminum foil is used for shielding test of the electromagnetic leakage camera, so that the shielding ability of the thin aluminum foil material to low-intensity electromagnetic leakage can be evaluated, and the thin aluminum foil shielding data is obtained. The preset 0.5mm-1mm thick aluminum foil is used for shielding test of the electromagnetic leakage camera, so that the shielding effect of the thick aluminum foil to high-intensity electromagnetic leakage can be further verified, and the thick aluminum foil shielding data is obtained. The shielding efficiency of the thin aluminum foil is calculated based on the thin aluminum foil shielding data, so that the system can quantify the shielding effect of the thin aluminum foil, and provide reference for protection of different leakage intensities. The shielding efficiency of the thick aluminum foil is calculated based on the thick aluminum foil shielding data, so that the system can evaluate the actual shielding effect of the higher-intensity protection material, and provide data support for selecting appropriate shielding material. The shielding efficiency of the thin aluminum foil and the shielding efficiency of the thick aluminum foil are integrated, so that the shielding performance of aluminum foils with different thicknesses can be compared comprehensively, and scientific basis can be provided for electromagnetic protection optimization of smart home devices.

[0046] Preferably, the step S2 includes:

[0047] Extracting low-efficiency material shielding data of the material shielding efficiency;

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

[0049] Calculating the electrical conductivity of the thermal simulation data;

[0050] Performing corrosion experiment based on the low-efficiency material shielding data to obtain corrosion data;

[0051] Identifying surface oxidation of the corrosion data to obtain surface oxidation data;

[0052] Predicting the service life of the shielding material of the intelligent home data according to the electrical conductivity and the surface oxidation data.

[0053] The present application extracts shielding data of low-efficiency materials, enabling the system to accurately identify materials with poor shielding effect, and providing basic data support for subsequent optimization screening. Based on the shielding data of low-efficiency materials, thermal simulation can simulate the performance changes of materials in high-temperature environments, evaluate their stability during long-term use, and provide the basis for optimizing heat resistance. Calculating the thermal simulation data of electrical conductivity enables the system to quantify the electrical conductivity of materials at different temperatures, ensuring that the electromagnetic shielding ability of the materials in smart home devices does not decrease significantly due to temperature changes. Based on the corrosion experiment of low-efficiency material shielding data, the corrosion resistance of materials under different environmental conditions can be analyzed, providing data support for the reliability of long-term use. Identifying the surface oxidation of corrosion data enables the system to quantify the degree of material oxidation, thereby evaluating its antioxidant ability and avoiding the decline of shielding efficiency due to material oxidation. According to the electrical conductivity and surface oxidation data, the service life of shielding materials is predicted, enabling the system to establish a shielding material aging prediction model based on the trend of key physical properties, and providing more scientific protection measures for smart home devices.

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

[0055] Statistically screening low-life material data of shielding material life;

[0056] Dividing the material level of low-life material data to obtain conductive metal layer, conductive polymer layer and wave-absorbing layer;

[0057] Designing nickel-aluminum alloy shielding material based on the conductive metal layer;

[0058] Designing conductive rubber shielding material based on the conductive polymer layer;

[0059] Designing carbon-based shielding material based on the wave-absorbing layer;

[0060] Integrating the shielding material, the conductive rubber shielding material and the carbon-based shielding material, and recording the material data after integration to obtain multi-layer shielding data.

[0061] The application can accurately identify the shielding materials with short service life by counting the low-life material data, and provide data support for optimizing material design. The material level of low-life material data is divided, so that different types of materials can be applied in shielding structures in a targeted manner, improving the stability and adaptability of the overall shielding effect. The nickel-aluminum alloy shielding material is designed based on the conductive metal layer, which can effectively improve the conductivity and corrosion resistance of the material, thereby enhancing the electromagnetic shielding effect and prolonging the service life. The conductive rubber shielding material is designed based on the conductive polymer layer, which can maintain good conductivity while being flexible, and is suitable for complex structures and flexible components in smart home devices. The carbon-based shielding material is designed based on the wave-absorbing layer, which can reduce electromagnetic wave reflection while having strong wave-absorbing ability, thereby reducing electromagnetic leakage and improving electromagnetic compatibility. Integrating different types of shielding materials and recording the material data after integration can form a multi-layer shielding structure, so that the system can provide effective shielding in high-frequency and low-frequency electromagnetic interference environments, improve the comprehensive protection capability and long-term stability of the shielding material, and overcome the limitations of traditional single shielding methods.

[0062] Preferably, step S3 is specifically:

[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 an inverter electromagnetic field distribution map;

[0065] Step S33: identifying the inverter electromagnetic overload area of the inverter electromagnetic field distribution map, wherein the magnetic field intensity threshold is set to 10 A / m, and the power density threshold is set to 10 W / m 2 ;

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

[0067] Step S35: calculating the dielectric loss of the inverter electromagnetic overload area;

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

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

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

[0071] Step S39: identifying the parasitic effect characteristics of the smart home data according to the parasitic current.

[0072] The present application can simulate the influence of different electromagnetic environments in a wide range of frequencies and intensities through transient electromagnetic pulse simulation, providing accurate electromagnetic interference data for subsequent analysis. The electromagnetic field of the inverter is calculated and its distribution map is drawn, making the spatial distribution of electromagnetic interference visualized and facilitating the identification of high-risk areas. Identifying the electromagnetic overload area of the inverter helps to find the key electromagnetic anomalies that cause equipment failure and accurately locate the high-risk area based on the set magnetic field intensity and power density threshold. The magnetic field concentration is calculated, allowing the system to quantify the local aggregation of electromagnetic fields and identify potential overload risks. The dielectric loss is calculated, allowing the system to evaluate the electromagnetic absorption characteristics of materials and determine whether they will cause local overheating or damage due to excessive loss. The inverter breakdown condition is determined based on the magnetic field concentration and dielectric loss, allowing the system to accurately predict the failure risk of the inverter in a high-intensity electromagnetic environment and enhance electromagnetic protection capabilities. Electromagnetic coupling data is extracted, allowing the system to analyze the electromagnetic interference characteristics between smart home devices and provide data support for subsequent optimization. Based on the inverter breakdown data, parasitic current is identified, allowing the system to detect unintended current caused by electromagnetic interference and improve device safety. According to the parasitic current, the parasitic effect characteristics are identified, allowing the system to 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 specifically comprises:

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

[0075] Step S42: dividing the frequency band of the electromagnetic wave frequency 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 carbon nanotube composite material based on high-frequency electromagnetic waves;

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

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

[0080] The present application provides an effective frequency identification means for subsequent electromagnetic shielding optimization by calculating the parasitic effect characteristics and the electromagnetic wave frequency, ensuring that the shielding material can effectively shield the electromagnetic waves of a specific frequency band. Dividing the electromagnetic wave frequency into low and high frequency bands helps to more accurately select materials suitable for the characteristics of each frequency band, optimizing the shielding effect. Based on the low frequency band, ferrite material is selected to provide efficient absorption and shielding capability for low frequency electromagnetic waves, which helps to reduce the impact of low frequency interference on equipment. Based on the high frequency band, carbon nanotube composite material is selected to enable better shielding and anti-interference capability for high frequency electromagnetic waves, improving the protection effect of the high frequency band. Integrating ferrite material and carbon nanotube composite material forms a composite shielding material with more extensive applicability, ensuring effective protection in different electromagnetic wave frequency bands. The integrated material data is encrypted for electromagnetic shielding information, improving 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, effectively preventing electromagnetic leakage and side channel attacks, and improving the overall information security protection capability of the smart home system.

[0081] Preferably, the present specification also provides an information security protection system for a home energy management system for executing 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 comprising:

[0082] A communication simulation module for obtaining smart home data; performing communication simulation based on the smart home data, and identifying electromagnetic leakage in the communication simulation process to generate electromagnetic leakage data;

[0083] A multi-layer shielding design module for identifying side channel attacks based on the electromagnetic leakage data; testing the shielding effectiveness of the material using side channel attacks; predicting the service life of the shielding material for smart home data based on the material shielding effectiveness; performing multi-layer shielding design based on the service life of the shielding material to obtain multi-layer shielding data;

[0084] A parasitic effect identification module for performing transient electromagnetic pulse simulation based on the multi-layer shielding data, and detecting inverter breakdown damage in the simulation process to obtain inverter breakdown damage data; identifying the parasitic effect characteristics of the smart home data based on the inverter breakdown damage data;

[0085] An electromagnetic shielding information encryption module for performing electromagnetic shielding optimization based on the parasitic effect characteristics to obtain electromagnetic shielding optimization data; encrypting the electromagnetic shielding optimization data for electromagnetic shielding information, and uploading to the home energy management system to perform electromagnetic shielding information security protection tasks.

[0086] The information security protection system for the home energy management system can realize any one of the information security protection methods for the home energy management system, is used for the medium for joint operation and signal transmission between various modules, and is used for the information security protection method for the home energy management system. BRIEF DESCRIPTION OF DRAWINGS

[0087] Other characteristics, objects and advantages of the present application will become more apparent from the following detailed description of non-restrictive embodiments, made with reference to the attached drawings:

[0088] Fig. 1 A step flow schematic diagram of the information security protection method for the home energy management system of the present application;

[0089] Fig. 2 A detailed step flow schematic diagram of step S1 in the present application;

[0090] Fig. 3 A detailed step flow schematic diagram of step S15 in the present application;

[0091] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the attached drawings. DETAILED DESCRIPTION

[0092] The technical method of the present application will be described clearly and completely below in combination with the drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0093] In addition, the drawings are only schematic illustrations of the present application, and are not necessarily drawn to scale. The same reference signs in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some block diagrams shown in the drawings are functional entities, and do not necessarily have to correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

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

[0095] To achieve the above object, please refer to Figs. 1 to 3 The present application provides an information security protection method for a home energy management system, comprising the following steps:

[0096] Step S1: Obtain smart home data; According to the smart home data, communication simulation is carried out, and electromagnetic leakage in the communication simulation process is identified, and electromagnetic leakage data is generated;

[0097] In this embodiment, real-time operation data of various devices in the home needs to be collected through a sensor array, including but not limited to temperature, humidity, power consumption, device status and other information. These data will be transmitted to the central control system through a wireless communication network. In this process, communication simulation software such as communication toolbox in MATLAB is used to simulate signal transmission between devices in the smart home system. By setting an electromagnetic wave frequency range (for example, 0.1 MHz to 10 GHz), the system can simulate and identify electromagnetic leakage in the communication process. Electromagnetic leakage data is generated by measuring signal leakage strength, frequency and direction and other parameters between devices. When recording the leakage data, a leakage threshold (such as signal strength below -60 dBm as the leakage judgment standard) needs to be set, and a spectrum analyzer is used for signal monitoring to record the time spectrum diagram of electromagnetic leakage, and generate electromagnetic leakage data for subsequent analysis.

[0098] Step S2: Identify side channel attacks based on electromagnetic leakage data; Test the shielding effectiveness of the material using side channel attacks; Predict the service life of the shielding material of the smart home data according to the shielding effectiveness of the material; Based on the service life of the shielding material, multi-layer shielding design is carried out, and multi-layer shielding data is obtained;

[0099] In this embodiment, based on the identification of electromagnetic leakage data, time domain signal analysis technology is used to analyze the waveform of electromagnetic leakage in detail and extract its spectral features. According to the spectral features (such as frequency and amplitude) of the leakage signal, the principle of side-channel attack is used to simulate the hacking process by setting the attack window (such as the time window is set to 500ms). At this time, use appropriate electromagnetic leakage attack tools (such as dedicated side-channel attack simulation software) to test the shielding effectiveness of different materials under the corresponding frequency band. In the shielding effectiveness test, considering the electromagnetic absorption rate of different materials, setting the material thickness (such as the thickness of the steel plate is 2mm, the thickness of the ferrite is 5mm) and the incident angle of electromagnetic wave, test the leakage signal strength after passing through the material. Using these test results, through model calculation, predict the service life of the shielding material in the set working environment. For example, when the frequency of the electromagnetic wave is 1GHz, the attenuation coefficient of the metal shielding material is 30dB, it can be predicted that the shielding effect of the material will decay to 50% after 5 years. According to the prediction results, complete the multi-layer shielding design, where each layer of shielding material needs to meet specific electromagnetic barrier standards, and finally obtain multi-layer shielding data.

[0100] Step S3: Based on the multi-layer shielding data, transient electromagnetic pulse simulation is performed, and the inverter breakdown damage in the simulation process is detected to obtain inverter breakdown damage data; and the parasitic effect features of the smart home data are identified according to the inverter breakdown damage data;

[0101] In this embodiment, based on the multi-layer shielding data, 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, the intensity range is 10V / m to 200kV / m, and different electromagnetic wave sources are selected for simulation. In the simulation process, the influence of electromagnetic wave on the smart home inverter is simulated, and the changes of current and voltage on the inverter circuit board are detected. According to the set electromagnetic pulse intensity (for example, the intensity is 50kV / m) and frequency, electromagnetic field simulation is performed using the finite element method, the electromagnetic field distribution is calculated, and whether the electrical breakdown of the inverter occurs is monitored. The threshold of inverter breakdown damage is set to the case where the voltage exceeds 30V. If the simulation data exceeds this threshold, it is recorded as breakdown damage, and inverter breakdown damage data is generated. Through analysis of the electromagnetic field distribution map, further identification of the potential parasitic effect features in the smart home system is performed, and data such as parasitic current and electric field strength are recorded.

[0102] Step S4: Based on the parasitic effect features, electromagnetic shielding optimization is performed to obtain electromagnetic shielding optimization data; the electromagnetic shielding optimization data is encrypted, and uploaded to the home energy management system to perform electromagnetic shielding information security protection tasks.

[0103] In this embodiment, the parasitic current characteristics identified according to the foregoing steps are used as input for optimization of the material and structure of the electromagnetic shield. During the shielding optimization process, ferrite material is selected as the shielding material for low-frequency electromagnetic waves, and carbon nanotube composite material is selected as the shielding material for high-frequency electromagnetic waves. The electromagnetic compatibility of the materials is analyzed in the simulation software. The relative permeability of the ferrite material is set to 1000, and the conductivity of the carbon nanotube composite material is 10^7 S / m. The simulation tool is used to simulate the shielding effect, and the target parameters are set to reduce signal leakage and improve the anti-electromagnetic interference capability of the material. Finally, the ferrite material and carbon nanotube composite material are integrated, and 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 through a secure network, and the system executes electromagnetic shielding information security protection tasks based on the encrypted data to respond to potential electromagnetic leakage attacks in a timely manner.

[0104] Preferably, step S1 is specifically:

[0105] Step S11: Obtain smart home data;

[0106] In this embodiment, data is collected by sensors installed in smart home devices. These sensors include temperature sensors, humidity sensors, motion sensors, light intensity sensors, etc. These data are transmitted to the central control system in a wireless manner through the communication module of the smart home device. During data collection, all device data are sorted and recorded according to the time stamp to ensure the time sequence and accuracy of the data. The data collection frequency is set to once per minute, and the collected data include device ID, device type, device status, sensor readings, etc. All data are encrypted and transmitted through Internet of Things protocols (such as MQTT, CoAP) to ensure data security.

[0107] Step S12: Extract the communication protocol of the smart home data, and set the communication process based on the communication protocol;

[0108] In this embodiment, the communication protocols of various smart home devices are analyzed, and the key fields such as device type identification, data transmission method (such as WIFI, ZigBee, Bluetooth, etc.), message format, etc. are extracted. The specific operation is to use network packet capture tools such as Wireshark to monitor the communication of the device and capture the communication data packets between the devices. By analyzing the content of the data packets, 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, which includes the handshake process between devices, the data transmission sequence, and the retransmission mechanism, etc. The specific design of the communication process refers to the standard communication protocol format, and considers the actual topology of the home network, ensuring that each device can exchange data according to the predetermined communication process.

[0109] Step S13: Identify the 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 appropriate frequency bands (such as 2.4GHz or 5GHz frequency bands), the quality and strength of signal transmission between devices are analyzed. For Wi-Fi signals, Signal Analyzer or other devices can be used to capture the frequency spectrum data of the signal, detect the signal attenuation, frequency drift and other characteristics. The specific method is to perform frequency spectrum analysis on each signal transmission period, and record the strength, frequency, noise ratio and other parameters of the signal to form channel characteristic data. Channel characteristics include signal propagation loss (such as setting the maximum signal attenuation to -60dBm), time delay (such as setting to 2ms), and frequency offset, etc. These characteristic data will be used as input parameters in subsequent communication simulation.

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

[0112] In this embodiment, during the communication simulation process according to the communication channel characteristics and the communication process, MATLAB, NS3 and other simulation tools are used to input the communication protocol of the device, the channel characteristic data and the communication process, and to perform detailed signal transmission simulation. Communication simulation needs to consider factors such as wireless signal attenuation, interference and signal shielding. For each simulation, set the simulation parameters such as signal propagation loss to -50dB, signal interference source power to -40dBm, and simulation period to 5 minutes. During the simulation process, use channel models such as Rayleigh fading model or Nyquist model to simulate the transmission process of the signal, and the simulation data includes the transmission time, delay and packet loss rate of each data packet. Finally, the results obtained through communication simulation can provide information about network connection quality, data transmission stability, etc.

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

[0114] In this embodiment, the frequency spectrum of the communication signal is analyzed, and a spectrum analyzer (such as Keysight's X-Series Signal Analyzers) is used to monitor the frequency distribution of the signal. By setting a threshold for the strength of the leakage signal (such as -70 dBm), signals below the threshold are recorded as electromagnetic leakage signals. This process includes real-time monitoring of electromagnetic waves emitted by the device during each data transmission period of the communication simulation to determine whether the device exceeds the standard shielding limit. For example, when the signal strength exceeds -70 dBm, it is considered to be electromagnetic leakage. Through further analysis of the leakage signal, the frequency range, intensity, and relationship with the device location of the leakage are identified. The electromagnetic leakage data records the frequency, intensity, duration, and other parameters of the leakage signal. These data will be used in subsequent steps to analyze the effectiveness of the shielding material and optimize the shielding design.

[0115] Preferably, step S15 is specifically:

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

[0117] In this embodiment, when identifying the signal propagation path of the communication simulation data, it is necessary to first track the communication signal of the device in space. Using a high-precision signal positioning system (such as GPS-assisted positioning or radio frequency-based positioning technology), the propagation trajectory of the signal emitted by the device in space is determined. 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, and other parameters are obtained. For wireless signals, ray tracing methods are used for path modeling, and the propagation path of the signal is calculated based on the geometric layout of the device and the settings of obstacles such as walls and metal objects. For example, the Hata model or Okumura model is used to model signal propagation, and corrections are made based on actual environmental factors. For each signal propagation path, the maximum range of propagation is set to 200 meters, and the attenuation coefficient of the wall needs to be considered in the path calculation, with the attenuation coefficient of the wall set to -5 dB.

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

[0119] In this embodiment, when calculating the electromagnetic field strength of the signal propagation path, first, based on the propagation path parameters in the communication simulation data, electromagnetic field simulation software (such as COMSOL Multiphysics or ANSYS HFSS) is used for electromagnetic field calculation. According to the path data and the power value of the device used, the electric field strength of the signal at each transmission path point is calculated. The distribution calculation of the electric field strength is carried out in units of per meter, assuming that the output power of the transmitting device is 20dBm, and the electric field strength threshold is set to -50dBμV / m during the calculation process. The electromagnetic field strength during signal propagation is recorded. When simulating, environmental factors such as signal attenuation, reflection, etc. also need to be considered, assuming that the attenuation factor is 2.5dB / km, and the frequency of the electromagnetic wave 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: convert the electromagnetic field strength into electromagnetic intensity spectrum;

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

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

[0123] In this embodiment, when identifying the electromagnetic energy concentration area, first, clustering analysis needs to be performed on the electromagnetic spectrum data. The clustering algorithm (such as K-means algorithm or DBSCAN algorithm) is used to analyze the signal intensity in the electromagnetic spectrum, and the energy concentration area in the spectrum is identified. The threshold of signal intensity is set to -60dBm, and signals below this threshold are not considered as energy concentration areas. According to the strong signal interval in the electromagnetic spectrum, the frequency range corresponding to these areas is identified, and the frequency band of each energy concentration area is marked. The identification process of the electromagnetic energy concentration area needs to consider multiple interference sources, so the number of clusters is set to 5 to ensure that strong signal clusters at different frequency bandwidths can be captured. Finally, all identified energy concentration areas are saved as data files for subsequent processing.

[0124] Step S155: Count the abnormally strong signals in the electromagnetic energy concentration area, and identify the stray signals based on the abnormally strong signals.

[0125] In this embodiment, when counting the abnormally strong signals in the electromagnetic energy concentration area and identifying the stray signals based on the abnormally strong signals, first, the signal strength of the energy concentration area is subjected to outlier detection. Using statistical analysis methods (such as Z-score or threshold judgment based on standard deviation), abnormally strong signals whose signal strength deviates significantly from the normal range are identified. For each identified abnormally strong signal, set the signal strength threshold to signals exceeding -40 dBm, mark these signals as abnormal signals, and compare them with the surrounding signals to determine whether they are stray signals. To further improve the identification accuracy, time-frequency analysis methods can be used to analyze the abnormal signals in detail and filter out the frequency range of the stray signals. Assuming that the frequency range of the stray signals is between 2.3 GHz and 2.5 GHz, and the signal strength is higher than -40 dBm, it is considered as a stray signal.

[0126] Step S156: Electromagnetic leakage determination is performed on the stray signals to obtain electromagnetic leakage data.

[0127] In this embodiment, when determining the electromagnetic leakage of the stray signals, first, the standard of electromagnetic leakage needs to be set. Using electromagnetic compatibility (EMC) test equipment (such as Keysight's E4440A spectrum analyzer), the stray signals are subjected to frequency spectrum scanning to determine whether the signals exceed the specified electromagnetic leakage standard. The set electromagnetic leakage standard is a signal with a signal strength exceeding -50 dBm and lasting more than 1 second. For each stray signal, if its strength and duration both exceed the threshold, it is considered to belong to electromagnetic leakage. Through testing and comparison, the frequency, strength 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 identified side-channel attack in step S2 comprises:

[0129] Reverse speculation of the encryption key of the electromagnetic leakage data;

[0130] In this embodiment, when the encryption key of the reverse speculation electromagnetic leakage data is obtained, the collected electromagnetic leakage data is first subjected to signal decoding processing to extract useful information in 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. By comparing the frequency domain data of the leakage signal with the electromagnetic interference patterns of known encryption algorithms, the encryption key bits are speculated. For each encryption operation, the encryption algorithm type (such as AES, RSA, etc.) of the leakage signal is extracted, and the analysis window is set to 10 milliseconds. For each electromagnetic leakage signal, the relationship between the electric field intensity and the encryption bits is analyzed by linear regression to gradually speculate each part of the key. The bit width of the encryption key is set to 128 bits, and the time window width of the analysis is adjusted during the speculation process to ensure that the electromagnetic information related to the encryption operation is captured.

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

[0132] In this embodiment, when the full key space is recovered based on the encryption key, the partial encryption key is first used as the known input. The known partial key is brute-forced using a cryptography algorithm library (such as OpenSSL or Cryptlib), combined with the existing encryption algorithm type (such as AES or DES), and the speculated key bits are gradually increased to recover the full key space. This process uses the exhaustive method, and the maximum key space for brute-force cracking is set to 2^128 times, and the calculation time is limited to within 48 hours. By comparing the generated key with the actual leakage signal encryption result during each attempt, the recovered full key space is confirmed. During each operation, a time domain window is used for fine-grained segment analysis, and the window length is set to 50 ms with a maximum tolerance error of 5%.

[0133] Identifying the attack fingerprint of electromagnetic leakage data using the full key space;

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

[0135] Tracking the attack path based on the attack fingerprint;

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

[0137] Key attack fingerprints for locating attack paths;

[0138] In this embodiment, the tracked attack path is subjected to precise timing analysis, and signal points with strong electromagnetic fluctuations are selected. The electromagnetic intensity changes of these signal points are considered as key fingerprints of the attack path. Through high-precision signal analyzers such as Keysight 9000 series, spectral analysis is performed to determine the key frequency bands and intensity 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 time stamp and spectral features of the signal, it is determined which fingerprints have a decisive role in the location of the attack path, and the time position, frequency interval, and intensity change of each key attack fingerprint are recorded.

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

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

[0141] Based on the key attack fingerprints, the side-channel attack fingerprint library is matched with the fingerprints, and the side-channel attack fingerprints are obtained;

[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. Fast Fourier Transform (FFT) is used to convert the signal to the frequency domain, and then compared with each fingerprint in the library. The similarity threshold for fingerprint matching is set to 0.8, and only fingerprints with a similarity greater than this value are considered to be a match. Through the matching process, it can be quickly determined whether the leaked data is related to known side channel attack activities.

[0143] According to the side channel attack fingerprint, the electromagnetic leakage data is identified as a side channel attack.

[0144] In this embodiment, the fingerprints extracted from the electromagnetic leakage data are subjected to spectral analysis. Using the previously matched side channel attack fingerprint library, the spectral characteristics of each leaked data are compared to confirm whether there are fingerprints that match known attack patterns. The similarity threshold is set to 90%, and if a high-similarity attack fingerprint is matched, it is determined that a side channel attack has occurred. The identification process of each attack fingerprint needs to consider the noise level of the signal, and the signal-to-noise ratio (SNR) is set to be above 30 dB to ensure the accuracy of the data. Finally, by comparing the electromagnetic leakage signal with the known fingerprints, the corresponding side channel attack type is accurately identified, and the relevant attack data is recorded.

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

[0146] Extracting smart camera data of smart home data;

[0147] In this embodiment, the extraction of smart camera data needs to be done through direct access to the network interface or wireless transmission link of the camera. First, using the management interface of the device, the real-time video stream and sensor data of the camera are extracted using standard communication protocols (such as ONVIF or RTSP). The video stream data will be captured and saved as raw stream format (such as H.264 encoded stream). At the same time, the sensor data of the camera (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 encrypted data of the camera, the corresponding key needs to be used for decryption to ensure the integrity of the data.

[0148] Using 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 the smart camera. The specific operation is to capture the electromagnetic waves generated by the camera when processing data by configuring an attack simulation device (e.g., a radio frequency probe, a spectrum analyzer). The attack simulation process includes generating a series of abnormal inputs (such as random data packets or abnormal control signals) to the camera, simulating the electromagnetic wave emission changes of the camera when facing malicious inputs. These abnormal inputs will induce the camera to produce 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 10 kHz to 1 GHz, and the sampling rate should be 5 GHz to ensure that detailed signals are captured.

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

[0151] In this embodiment, first, set the radio frequency detection device (such as an oscilloscope or spectrum analyzer) to an appropriate frequency range, usually 100 MHz to 1 GHz, to ensure coverage of the electromagnetic signal frequency band generated by the camera. Then, collect the electromagnetic signal waveforms during the operation of the camera through the detection instrument. The duration of data collection should be set according to the camera processing period, usually 1 second to 5 seconds, to capture sufficient waveform changes. During the collection process, it is necessary to ensure that the electromagnetic signal interference is minimized and the device is working in ideal environmental conditions.

[0152] Determine electromagnetic leakage based on electromagnetic signal waveforms to obtain camera electromagnetic leakage data;

[0153] In this embodiment, the electromagnetic waveform signal is converted in time and frequency domain, the frequency domain features are extracted using Fourier transform, and then the spectrum analysis is used to evaluate whether there are abnormal peaks or periodic fluctuations. By setting a threshold (such as a signal peak exceeding 10 dB in amplitude), it is determined whether there is electromagnetic leakage. According to the frequency band and amplitude of the leakage, the source and characteristics of the leakage signal are further identified, and the electromagnetic leakage data of the camera is generated. This process needs to ensure that the signal analysis parameters (such as frequency, amplitude, etc.) are accurate and correct.

[0154] Based on the camera electromagnetic leakage data, filter the electromagnetic leakage cameras, and use the pre-set 0.1 mm-0.3 mm thin aluminum foil to perform shielding test on the electromagnetic leakage cameras, to obtain thin aluminum foil shielding data;

[0155] In this embodiment, the camera with serious electromagnetic leakage is screened according to the electromagnetic leakage data, and the screening standard is set as the electromagnetic leakage signal amplitude exceeding the set threshold value (such as 20 dB). Then, the screened camera is subjected to shielding test using the aluminum foil with the thickness of 0.1 mm-0.3 mm. In the specific implementation, the aluminum foil is uniformly wrapped outside the camera, and the aluminum foil is ensured to be in complete contact with the surface of the camera to avoid missing the electric leakage area. The electromagnetic signal waveform is collected again using the spectrum analyzer, and the electromagnetic signal data after the aluminum foil shielding is recorded. All the collected data should be compared with the baseline data without shielding to analyze the changes.

[0156] The electromagnetic leakage camera is subjected to shielding test using the preset aluminum foil with the thickness of 0.5 mm-1 mm, and the thick aluminum foil shielding data is obtained;

[0157] In this embodiment, the shielding test is performed using the thicker aluminum foil with the thickness of 0.5 mm-1 mm. The aluminum foil is wrapped outside the camera in the same way as the thin aluminum foil shielding, and the complete coverage without gaps is ensured. At this time, the spectrum analyzer is continuously used to collect the electromagnetic signal waveform, and the signal changes before and after the thick aluminum foil shielding are compared. The focus of this test is to evaluate the inhibitory effect of the thick aluminum foil shielding on the electromagnetic leakage, especially the influence on different frequency bands. All the data should be compared under the same environment to eliminate the interference of other external factors.

[0158] The shielding efficiency of the thin aluminum foil is calculated based on the thin aluminum foil shielding data;

[0159] In this embodiment, the electromagnetic signal strength data before and after the thin aluminum foil shielding needs to be collected. 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 measured again after the thin aluminum foil shielding. To ensure the accuracy of the measurement results, multiple measurements need to be performed, and the signal strength before and after shielding is recorded each time, and the average processing is performed. By comparing these signal strength data, the shielding efficiency of the thin aluminum foil can be obtained. Specifically, when calculating, the standard of signal strength measurement needs to be determined, such as using the same frequency band electromagnetic signal measurement equipment, testing in 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, and the average value is calculated to obtain the shielding efficiency of the thin aluminum foil. This process can quantify the effective inhibitory ability of the thin aluminum foil on the electromagnetic leakage.

[0160] The shielding efficiency of the thick aluminum foil is calculated based on the thick aluminum foil shielding data;

[0161] In this embodiment, the steps to calculate the shielding effectiveness of thick aluminum foil are similar to those of thin aluminum foil, but for thick aluminum foil, it is usually necessary to test in multiple frequency bands to ensure that its shielding effectiveness at all relevant frequencies meets the standard requirements. First, the electromagnetic leakage signal strength of the smart camera without shielding needs to be measured and recorded as the signal strength before shielding. Next, the smart camera is shielded using thick aluminum foil, ensuring that the aluminum foil completely covers the electromagnetic leakage source of the camera. Then, the electromagnetic leakage signal strength is re-measured and recorded as the signal strength after shielding. To obtain accurate shielding effectiveness data, the test needs to be conducted in multiple frequency bands. This is because different electromagnetic frequencies will have different effects on shielding effectiveness. Common frequency bands include low frequency, medium frequency and high frequency, and the specific frequency bands should be selected based on the frequency range of the electromagnetic signals emitted by the camera. Tests in each frequency band should be conducted under the same stable environmental conditions to avoid external electromagnetic interference affecting the measurement results. During testing, all equipment such as signal receivers and spectrum analyzers should be ensured to operate stably, and consistent standards should be used for signal strength measurement. Multiple measurements need to be taken repeatedly during the test, especially at different frequency bands, in order to obtain sufficient measurement data and perform averaging processing. By comparing the signal strengths before and after shielding, the shielding effectiveness of thick aluminum foil can be calculated. The data results need to be measured multiple times and averaged to ensure that the calculated shielding effectiveness reflects the actual protection effect of the thick aluminum foil. This process can effectively quantify the suppression ability of thick aluminum foil to electromagnetic leakage and ensure that it meets the standard requirements.

[0162] Integrate the shielding effectiveness of thin aluminum foil and the shielding effectiveness of thick aluminum foil to obtain the shielding effectiveness of the material.

[0163] In this embodiment, the shielding effectiveness of both thin and thick aluminum foils needs to be measured and calculated. This process involves testing the electromagnetic leakage signals of both materials at different frequency bands to obtain their respective shielding effectiveness values. During the measurement process, the stability of the test environment should be ensured to avoid any external electromagnetic interference affecting the results. Next, according to the specific application requirements, the shielding effectiveness of the thin and thick aluminum foils needs to be weighted and averaged. At this time, the corresponding weight factors w1 and w2 will be set according to the characteristics of each material (such as material thickness, shielding effect, and use environment), which reflect the relative importance of each material under different conditions. The selection of weight factors is usually based on the shielding ability of the two materials at different frequency bands, assuming that the thin aluminum foil has better shielding effect at certain frequency bands, while the thick aluminum foil is more effective at other frequency bands. For example, thin aluminum foil is more suitable for electromagnetic interference shielding in low frequency range, while thick aluminum foil performs better in high frequency range. Therefore, the weight w1 of thin aluminum foil and the weight w2 of thick aluminum foil should be set according to their shielding effectiveness in the relevant frequency band, usually w1+w2=1. Finally, the comprehensive shielding effectiveness value is calculated through the weighted average formula: comprehensive shielding effectiveness = w1 x thin aluminum foil effectiveness + w2 x thick aluminum foil effectiveness, where thin aluminum foil effectiveness and thick aluminum foil effectiveness are the measured shielding effectiveness values, and w1 and w2 are the corresponding weight factors. The comprehensive shielding effectiveness can provide a comprehensive evaluation of the overall shielding ability of the material in different application scenarios, providing a basis for selecting the appropriate shielding material.

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

[0165] extracting low-efficiency material shielding data of the material shielding effectiveness;

[0166] In this embodiment, the target material is selected and its shielding effectiveness at a specific frequency band is determined. Using standard shielding effectiveness test methods, the material is placed in a specified electromagnetic environment, and the electromagnetic leakage signal strength at a specific frequency band is recorded. The shielding effectiveness of the material is determined through the shielding effectiveness calculation formula (ratio of signal strength). For low-efficiency materials, the ratio of signal strength will be lower, and the obtained shielding effectiveness value will be lower than the standard requirement. On this basis, low-efficiency material shielding data of different thicknesses and different frequency bands are collected, repeated measurements are performed, 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 ISO 14234 (electromagnetic compatibility standard). Finally, the shielding effectiveness data of low-efficiency materials under different conditions is obtained, providing a data basis for subsequent steps.

[0167] performing thermal simulation based on the 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. The specific parameters of the material, such as thermal conductivity, specific heat capacity, thickness, etc., are input, and the corresponding temperature field boundary conditions are set, for example, the environmental 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, and the simulation process needs to set the time step, calculation accuracy and heat source distribution (such as radiation heating). The thermal simulation results will include the temperature change curve of each point and the thermal conductivity characteristic data, and record the thermal stability of the material in a high temperature environment.

[0169] Calculate the electrical conductivity of the thermal simulation data;

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

[0171] Perform corrosion experiments based on the low-efficiency shielding data to obtain corrosion data;

[0172] In this embodiment, a known low-efficiency shielding material is selected for corrosion experiments. First, the experimental environment is set, including the corrosion 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, for example, in a salt spray test chamber for 48 hours of salt spray corrosion test. During the experiment, the corrosion changes on the surface of the material are recorded regularly, such as mass loss, formation of surface corrosion products, etc. Use corrosion evaluation standards (such as ASTM B117 salt spray corrosion test standard), and according to the surface state of the material after the test, calculate the corrosion rate and corrosion layer thickness, etc. Key parameters.

[0173] Identify the surface oxidation of the corrosion data to obtain surface oxidation data;

[0174] In this embodiment, scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS) analysis are performed on the material surface samples obtained from the corrosion experiments to identify the presence and characteristics of the surface oxide layer. The micro-morphology of the surface corrosion is observed by SEM to identify whether there is oxide deposition on the surface. Then, XPS is used to analyze the chemical composition of the material surface oxide layer to determine the type of oxide and its thickness. The identification of the oxide is based on the ratio of the oxygen element to the metal element (such as aluminum or copper) signal in the surface spectrum, and by setting a threshold for the thickness of the oxide layer (e.g., an oxide layer with a thickness of more than 0.5 μm is considered to have oxidation), the surface oxidation data is obtained.

[0175] The service life of the shielding material for smart home data is predicted based on the conductivity and surface oxidation data.

[0176] In this embodiment, a material life prediction model is constructed based on the conductivity data and surface oxidation data. First, the key factors affecting the service life of the material are set, such as the change in conductivity, the thickness of the oxide layer, the 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 the service life of the material and the conductivity and oxidation data. For example, a standard life evaluation model is set, in which the service life is inversely proportional to the decline rate of the conductivity and the change in the thickness of the oxide layer. Finally, by inputting the known conductivity and oxidation data into the model, the predicted service life of the material is obtained.

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

[0178] The low-life material data of the shielding material service life is statistically analyzed;

[0179] In this embodiment, the service life data of different materials in long-term use is collected and sorted, focusing on those shielding materials with lower service life. The service life of the material under these conditions is obtained through a series of standardized tests, such as high temperature, high humidity, salt spray corrosion, etc. The recorded data includes the service life of each material under different environments, failure modes, corrosion rates, and changes in physical properties (such as conductivity, hardness, etc.). By comparing the performance degradation of different materials, low-life materials are selected. For example, a standard is set to determine the standard of low-life materials by testing the conductivity change rate (e.g., more than 5% per year) and the frequency of failure modes (e.g., corrosion cracks occur once every 5 years).

[0180] The material hierarchy of the low-life material data is divided to obtain the conductive metal layer, the conductive polymer layer, and the wave-absorbing layer;

[0181] In this embodiment, the material is analyzed by layer according to the low-life material data. The performance of the material is classified according to factors such as conductivity, wave absorption characteristics, etc. using a standard layering method. The material selection of the conductive metal layer is a metal material with good conductivity, such as nickel-plated aluminum alloy. The conductive polymer layer should select conductive rubber or conductive plastic material with high electrical conductivity. The material selection of the wave absorption layer is a composite material with wave absorption characteristics, such as carbon-based material. It is divided by the electrical conductivity, wave absorption rate and thickness of each material. For example, the electrical conductivity of the conductive metal layer material should not be less than 10^6 S / m, the electrical conductivity of the conductive polymer layer should be between 10^3 to 10^4 S / m, and the wave absorption performance of the wave absorption layer should reach a reflection loss of more than 20 dB at a frequency of 2 GHz.

[0182] Designing a nickel-plated aluminum alloy shielding material based on the 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, which mainly contains aluminum and a proper amount of alloying elements such as copper and silicon. Then, a layer of nickel is plated on the surface of the aluminum alloy using electroplating technology, with a thickness of 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 plating layer. In the design, the electrical conductivity of the nickel-plated aluminum alloy shielding material should not be less than 10^6 S / m, and the uniformity and adhesion of the plating layer should be ensured.

[0184] Designing a conductive rubber shielding material based on the conductive polymer layer;

[0185] In this embodiment, the conductive polymer layer is designed, and conductive rubber is selected as the material. The selected substrate is a high molecular rubber, and conductive fillers such as carbon black and conductive fibers are added to achieve the conductive function. The proportion of the filler is adjusted according to the required electrical conductivity, and in general, the mass fraction of the conductive filler needs to be controlled between 15% and 40%. The filler is uniformly dispersed in the rubber substrate 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 device to ensure uniform distribution of the filler. The electrical conductivity of the conductive rubber should be controlled between 10^3 to 10^4 S / m, and the final material thickness is generally 1mm to 5mm.

[0186] Designing a carbon-based shielding material based on the wave absorption layer;

[0187] In this embodiment, a composite material containing carbon nanotubes, graphene or carbon black is selected. When selecting carbon-based materials, the emphasis is placed on their wave-absorbing properties, i.e., the wave-absorbing ability in a specific frequency band (e.g., 2-10 GHz). 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 preparation of the carbon-based shielding material is achieved by mixing carbon nanotubes or graphene with a matrix material (such as a polymer or rubber) to ensure uniform distribution of the carbon filler. During the molding process, the material surface is required to be flat, and the thickness is usually set to 2-4 mm. Finally, the reflection loss of the wave-absorbing material should reach more than 20 dB to ensure the shielding effectiveness at high frequencies.

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

[0189] In this embodiment, the conductive metal layer, conductive polymer layer and wave-absorbing layer are combined according to the specific order and ratio required by the design. When combining, the adhesion between layers and structural stability need to be ensured. Generally, the conductive metal layer is located at the outermost layer, the conductive polymer layer is in the middle, and the wave-absorbing layer is located at the innermost layer. In order to ensure the good combination between layers, 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 electrical conductivity, wave-absorbing rate, thickness and overall electromagnetic shielding effect. This data includes the specific parameters of each layer (such as thickness, electrical conductivity, etc.) and the comprehensive performance of the integrated material (such as shielding effectiveness and wave-absorbing ability). For example, the total shielding effectiveness of the integrated material should reach more than 30 dB, which can effectively block electromagnetic interference of different frequency bands.

[0190] Preferably, step S3 specifically comprises:

[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, transient electromagnetic pulse simulation is performed based on multi-layer shielding data. First, the frequency range of the electromagnetic pulse is set to 0.1 MHz to 10 GHz, and the electromagnetic pulse intensity is set to 10 V / m to 200 kV / m. The simulation is performed by electromagnetic field analysis tools such as COMSOL Multiphysics or ANSYS HFSS, and the data of the multi-layer shielding material is input, including its dielectric constant, magnetic permeability, wave absorption characteristics and other parameters. During the simulation, based on the set frequency range and intensity, the electromagnetic pulse signal is gradually applied to the multi-layer shielding structure. The finite difference time domain (FDTD) method is used to calculate the transient electromagnetic field, and the electric field and magnetic field data of the entire time sequence are obtained. During this process, appropriate time step, spatial grid accuracy and boundary conditions need to be set, such as using perfect matched layer (PML) boundary to reduce reflection. The simulation results include the propagation characteristics of electromagnetic pulse in multi-layer shielding structure, reflection coefficient and penetration effect, etc., and finally the electromagnetic pulse simulation data is generated.

[0193] Step S32: Calculate the inverter electromagnetic field of the electromagnetic pulse simulation data and draw the inverter electromagnetic field distribution map;

[0194] In this embodiment, the inverter electromagnetic field of the electromagnetic pulse simulation data is calculated and the electromagnetic field distribution map is drawn. First, the electromagnetic pulse simulation results are extracted, and the electric field and magnetic field data of the inverter region are calculated. The field analysis module in the electromagnetic field analysis tool is used to calculate the electric field distribution point by point in the inverter region, and the electric field intensity and direction distribution are obtained. Combined with the magnetic field data, the magnetic field distribution map around the inverter is calculated. The intensity, distribution and hot spot area of the magnetic field in the region are marked 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 field and magnetic field intensity, but also shows the electromagnetic wave propagation path inside the inverter.

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

[0196] In this embodiment, the electromagnetic overload area in the inverter electromagnetic field distribution map is identified. According to the magnetic field intensity data extracted from the electromagnetic field distribution map, the magnetic field intensity threshold is set to 10 A / m and the power density threshold is set to 10 W / m 2 . For each data point in the electromagnetic field, first calculate its magnetic field intensity and power density to determine whether it exceeds the set threshold. The magnetic field intensity exceeding 10 A / m and the power density exceeding 10 W / m 2The power density area of the electromagnetic overload region is considered as an electromagnetic overload region. During operation, all electromagnetic field data is screened using a threshold judgment method to form a list containing the coordinates and electromagnetic parameters of all electromagnetic overload regions. This step requires batch processing with the help of electromagnetic field simulation software to output electromagnetic overload region data that meets the conditions.

[0197] Step S34: Calculate the magnetic field concentration of the inverter electromagnetic overload region;

[0198] In this embodiment, using electromagnetic overload region data, the magnetic field concentration is calculated for each overload region using the concentration formula. This formula can be determined by solving the spatial gradient of the magnetic field strength and the total amount of the magnetic field strength in the region. The specific method is to calculate the magnetic field strength distribution in the overload region, obtain the magnetic field focusing degree in the region, and further optimize the calculation process using the weighted average method. According to the calculation result, a magnetic field concentration distribution map is formed, which can reveal the focusing characteristics of the magnetic field in the electromagnetic overload region and its potential impact on the equipment.

[0199] Step S35: Calculate the dielectric loss of the inverter electromagnetic overload region;

[0200] In this embodiment, the electric field strength data needs to be extracted from the electromagnetic simulation data, and the electric field strength of each electromagnetic overload region is determined according to the distribution of the electric field strength given in the simulation results. Then, the dielectric constant of the materials used in the inverter needs to be combined, which should be obtained through experiments or material data books. For each overload region, the corresponding dielectric loss value is calculated according to 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 the dielectric constant are accurate, so as to ensure that the dielectric loss calculation results of each overload region are accurate. This step requires accurate analysis and combination of the dielectric properties of the material and the electric field strength in the region to avoid errors in the calculation affecting the electromagnetic overload region loss analysis of the inverter.

[0201] Step S36: Determine the inverter breakdown damage of the electromagnetic pulse simulation data according to the magnetic field concentration and the dielectric loss, and obtain the inverter breakdown damage data;

[0202] In this embodiment, the inverter breakdown damage of the electromagnetic pulse simulation data is determined according to the magnetic field concentration and the dielectric loss, and the magnetic field concentration and dielectric loss data obtained in steps S34 and S35 are used to set the criteria for determining the breakdown damage. Assuming that the magnetic field concentration exceeds a certain threshold value (for example: concentration greater than 0.5), and the dielectric loss reaches a certain standard (for example: loss exceeds 10 W / m 2If the electromagnetic overload region is determined to be the region where the inverter breakdown occurs, the electromagnetic parameters of the region are recorded as the inverter breakdown data. By analyzing all the electromagnetic overload regions according to the standard, the region where the breakdown occurs is finally identified, and the breakdown data of the breakdown, including the specific electromagnetic parameters and the breakdown, are recorded.

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

[0204] In this embodiment, the electromagnetic coupling data of the smart home data is extracted. First, based on the electromagnetic field data of the smart home device, the electromagnetic coupling relationship between devices is analyzed through the coupling model. Specifically, the electric field, magnetic field and current distribution data in the home device are extracted using electromagnetic field simulation software, and coupling analysis is performed using these data. By setting appropriate device spacing, dielectric constant and magnetic permeability, the interaction of electromagnetic waves is simulated, the electromagnetic coupling strength between devices is calculated, and the electromagnetic coupling data is obtained.

[0205] Step S38: Identify parasitic current of electromagnetic coupling data based on inverter breakdown data

[0206] In this embodiment, according to the inverter breakdown 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 electromagnetic simulation tools. Specifically, the identified electromagnetic overload region is simulated in detail using electromagnetic simulation tools, and the current distribution between devices is calculated. By accurately calculating the relationship between the electric field strength and the current density between devices, the region causing parasitic current is identified. For each device, according to the calculation results of its current distribution and electric field strength, further confirm which region's electromagnetic coupling data causes the generation of parasitic current. In this process, the data of electric field strength, current distribution and current density between all devices need to be accurately obtained and input into the simulation tool to ensure that the identified parasitic current region has sufficient accuracy, and then record the relevant data for subsequent analysis and processing.

[0207] Step S39: Identify parasitic effect characteristics of smart home data according to parasitic current

[0208] In this embodiment, the frequency and amplitude of the parasitic current and other data are used to analyze the impact on the performance of the device. In this process, by simulating and analyzing the parasitic current in detail, the interference and influence of the parasitic current on the internal electrical system of the smart home device are identified. For example, the parasitic current causes additional power consumption of the device, or generates electromagnetic interference, affecting the normal operation of the device. In specific operation, the working parameters (such as voltage, current, frequency) and electrical characteristics (such as resistance, capacitance, inductance, etc.) of each device need to be accurately measured and recorded, and then combined with these parameters to analyze the specific impact of the parasitic current on the device. By modeling the relationship between the parasitic current and the performance of the device, the corresponding parasitic effect characteristic data such as additional power consumption and electromagnetic interference of the device are extracted. 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:

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

[0211] In this embodiment, the parasitic current data need to be obtained, and then combined with the frequency characteristics of the current, the current signal is analyzed by Fourier transform to extract the frequency distribution of the current signal. This process determines the frequency range of the electromagnetic wave by analyzing the main frequency components in the parasitic current signal. The frequency range of the electromagnetic wave needs to be refined according to the variation of the current to obtain the frequency range of the electromagnetic wave. This frequency data will provide basic parameters for subsequent material selection.

[0212] Step S42: Divide the frequency band of the electromagnetic wave frequency to obtain low-frequency electromagnetic waves and high-frequency electromagnetic waves;

[0213] In this embodiment, the frequency band of the electromagnetic wave frequency is divided, and the frequency of the electromagnetic wave is divided into low-frequency and high-frequency bands according to the electromagnetic wave frequency data. The low-frequency electromagnetic wave is usually between tens of hertz and several megahertz, while the high-frequency electromagnetic wave is from several hundred megahertz to several gigahertz. In the division process, appropriate threshold values need to be set according to the distribution of the electromagnetic wave frequency to clearly define the low-frequency and high-frequency bands. This division standard is based on the actual electromagnetic interference characteristics and the response characteristics of the material to ensure that the frequency band division is reasonable and scientific.

[0214] Step S43: Select ferrite material based on low-frequency electromagnetic wave;

[0215] In this embodiment, ferrite materials are selected based on low-frequency electromagnetic waves. According to the divided frequency range of low-frequency electromagnetic waves, ferrite materials suitable for low-frequency bands are selected. Ferrite materials have good electromagnetic shielding performance, especially in low-frequency bands, they can effectively absorb electromagnetic waves. At this time, by consulting the electromagnetic performance data of the material, the wave absorption characteristics in the low-frequency band are determined. When selecting ferrite materials, the magnetic permeability, dielectric constant, and loss factor of the material should be considered 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: Select carbon nanotube composite material based on high-frequency electromagnetic waves;

[0217] In this embodiment, carbon nanotube composite materials are selected based on high-frequency electromagnetic waves. According to the divided frequency range of high-frequency electromagnetic waves, carbon nanotube composite materials suitable for high-frequency bands are selected. Carbon nanotube composite materials have excellent electrical conductivity and mechanical properties, especially in high-frequency electromagnetic wave absorption characteristics. When selecting this material, the length, diameter, electrical conductivity of carbon nanotubes, and the proportion of other components in the composite material should be considered to ensure that the material can effectively absorb and shield electromagnetic waves in high-frequency bands. Through experimental data analysis of different composite materials, the best combination of carbon nanotube composite materials is determined to achieve the goal of optimizing electromagnetic shielding effect.

[0218] Step S45: Integrate ferrite materials and carbon nanotube composite materials, and record the material data after integration to obtain electromagnetic shielding optimization data;

[0219] In this embodiment, ferrite materials and carbon nanotube composite materials are integrated, and the material data after integration is recorded to obtain electromagnetic shielding optimization data. In this step, the ferrite materials in the low-frequency band and the carbon nanotube composite materials 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 accurately controlled to ensure that the composite material has good electromagnetic shielding performance in a wide frequency band. After the material is integrated, the electromagnetic characteristic data of the integrated material is recorded, such as shielding effectiveness, wave absorption capacity, and heat resistance. Through experimental testing and simulation analysis, it is ensured that the integrated material meets the design requirements and forms the final electromagnetic shielding optimization data.

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

[0221] In this embodiment, the electromagnetic shielding optimization data is encrypted with electromagnetic shielding information encryption, and uploaded to the home energy management system to perform electromagnetic shielding information security protection tasks. In this stage, the integrated electromagnetic shielding optimization data is encrypted using encryption algorithms. The encryption method includes encrypting the data content to prevent data from being stolen or tampered with during transmission. The encrypted data will be uploaded to the home energy management system through a secure protocol to ensure the security of the electromagnetic shielding information. At the same time, the home energy management system will perform electromagnetic shielding information security protection tasks according to the encrypted data to protect household electrical equipment from external electromagnetic interference and attacks. The encryption process involves selecting appropriate encryption standards and algorithms to ensure data integrity and confidentiality.

[0222] Preferably, the present specification also provides an information security protection system for a home energy management system for performing 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 comprising:

[0223] A communication simulation module for obtaining smart home data; performing communication simulation based on smart home data, and identifying electromagnetic leakage in the communication simulation process to generate electromagnetic leakage data;

[0224] A multi-layer shielding design module for identifying side-channel attacks based on electromagnetic leakage data; testing material shielding effectiveness using side-channel attacks; predicting the service life of shielding materials for smart home data based on material shielding effectiveness; and performing multi-layer shielding design based on the service life of shielding materials to obtain multi-layer shielding data;

[0225] A parasitic effect identification module for performing transient electromagnetic pulse simulation based on multi-layer shielding data, and detecting inverter breakdown damage during simulation to obtain inverter breakdown damage data; and identifying parasitic effect characteristics of smart home data based on inverter breakdown damage data;

[0226] An electromagnetic shielding information encryption module for performing electromagnetic shielding optimization based on parasitic effect characteristics to obtain electromagnetic shielding optimization data; and encrypting electromagnetic shielding optimization data with electromagnetic shielding information encryption, and uploading to the home energy management system to perform electromagnetic shielding information security protection tasks.

[0227] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application being defined by the appended claims rather than the above description, and it is therefore intended to encompass all variations falling within the meaning and scope of the equivalent elements of the application file. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application being defined by the appended claims rather than the above description, and it is therefore intended to encompass all variations falling within the meaning and scope of the equivalent elements of the application file.

[0228] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and it is intended to embrace all such modifications and changes that fall within the scope of the application. Accordingly, the application is not to be restricted in scope to the specific embodiments disclosed herein but is to be accorded the full scope that the principles and novel features request appropriately granted.

Claims

1. A method for information security protection of a home energy management system, characterized in that, The method comprises the following steps: Step S1: obtaining smart home data; performing communication simulation based on the smart home data, and identifying electromagnetic leakage in the communication simulation process to generate electromagnetic leakage data; Step S2: identifying side channel attacks based on the electromagnetic leakage data; testing the shielding effectiveness of the material; and predicting the service life of the shielding material for the smart home data based on the shielding effectiveness of the material; Based on the service life of the shielding material, a multi-layer shielding design is performed to obtain multi-layer shielding data; Step S3: based on the multi-layer shielding data, transient electromagnetic pulse simulation is performed, and inverter breakdown damage in the simulation process is detected to obtain inverter breakdown damage data; and based on the inverter breakdown damage data, the parasitic effect characteristics of the smart home data are identified, and step S3 is specifically as follows: Step S31: based on the multi-layer shielding data, transient electromagnetic pulse simulation is performed, wherein the electromagnetic pulse frequency is set to 0.1MHz-10GHz, and the electromagnetic pulse intensity is set to 10V / m-200kV / m, to obtain electromagnetic pulse simulation data; Step S32: calculating the inverter electromagnetic field of the electromagnetic pulse simulation data, and drawing an inverter electromagnetic field distribution map; Step S33: identifying the inverter electromagnetic overload area of the inverter electromagnetic field distribution map, wherein the magnetic field intensity threshold is set to 10A / m, and the power density threshold is set to 10W / m²; Step S34: calculating the magnetic field concentration of the inverter electromagnetic overload area; Step S35: calculating the dielectric loss of the inverter electromagnetic overload area; Step S36: determining the inverter breakdown damage of the electromagnetic pulse simulation data according to the magnetic field concentration and the dielectric loss to obtain inverter breakdown damage data; Step S37: extracting electromagnetic coupling data of the smart home data; Step S38: identifying parasitic current of the electromagnetic coupling data based on the inverter breakdown damage data; Step S39: identifying the parasitic effect characteristics of the smart home data according to the parasitic current; Step S4: based on the parasitic effect characteristics, electromagnetic shielding optimization is performed to obtain electromagnetic shielding optimization data; the electromagnetic shielding optimization data is encrypted, and is uploaded to a 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, wherein, Step S1 is specifically as follows: Step S11: obtaining smart home data; Step S12: extracting the communication protocol of the smart home data, and setting the communication flow based on the communication protocol; Step S13: identifying the communication channel characteristics of the smart home data; Step S14: performing communication simulation based on the communication channel characteristics and the communication flow to obtain communication simulation data; Step S15: identifying electromagnetic leakage of the communication simulation data to generate electromagnetic leakage data.

3. The information security protection method for a home energy management system according to claim 2, wherein, Step S15 is specifically as follows: Step S151: identifying the signal propagation path of the communication simulation data; Step S152: calculating the electromagnetic field intensity of the signal propagation path; Step S153: converting the electromagnetic field intensity into an electromagnetic intensity spectrum; Step S154: identifying an electromagnetic energy concentration area; Step S155: counting abnormal strong signals in the electromagnetic energy concentration area, and identifying stray signals based on the abnormal strong signals; Step S156: determining electromagnetic leakage of the stray signals to obtain electromagnetic leakage data.

4. The information security protection method for a home energy management system according to claim 1, wherein, The identification of side channel attacks in step S2 comprises: Recovering an encryption key of reverse speculation electromagnetic leakage data; Recovering a full key space based on the encryption key; Identifying an attack fingerprint of the electromagnetic leakage data by using the full key space; Tracking an attack path based on the attack fingerprint; Locating a key attack fingerprint of the attack path; Obtaining a side channel attack fingerprint library; Fingerprint matching the side channel attack fingerprint library based on the key attack fingerprint, to obtain a side channel attack fingerprint; Identifying a side channel attack of the electromagnetic leakage data according to the side channel attack fingerprint.

5. The information security protection method for a home energy management system according to claim 1, wherein, The shielding effectiveness of the test material in step S2 includes: Extracting intelligent camera data of the smart home data; Attack simulation on the intelligent camera data by using the side channel attack, to obtain attack simulation data; Collecting electromagnetic signal waveforms of the attack simulation data; Determining electromagnetic leakage based on the electromagnetic signal waveforms, to obtain camera electromagnetic leakage data; Filtering electromagnetic leakage cameras based on the camera electromagnetic leakage data, and shielding test on the electromagnetic leakage cameras by using a preset 0.1mm-0.3mm thin aluminum foil, to obtain thin aluminum foil shielding data; Shielding test on the electromagnetic leakage cameras by using a preset 0.5mm-1mm thick aluminum foil, to obtain thick aluminum foil shielding data; Calculating the thin aluminum foil shielding effectiveness of the thin aluminum foil shielding data; Calculating the thick aluminum foil shielding effectiveness of the thick aluminum foil shielding data; Integrating the thin aluminum foil shielding effectiveness and the thick aluminum foil shielding effectiveness, to obtain the material shielding effectiveness.

6. The information security protection method for a home energy management system according to claim 1, wherein, The prediction of the shielding material life in step S2 includes: Extracting low-effectiveness material shielding data of the material shielding effectiveness; Thermal simulation based on the low-effectiveness material shielding data, to obtain thermal simulation data; Calculating the electrical conductivity of the thermal simulation data; Corrosion experiment based on the low-effectiveness material shielding data, to obtain corrosion data; Identifying surface oxidation of the corrosion data, to obtain surface oxidation data; Prediction of the shielding material life of the smart home data according to the electrical conductivity and the surface oxidation data.

7. The information security protection method for a home energy management system of claim 1, wherein, The multi-layer shielding design in step S2 includes: Statistical low-life material data of the shielding material life; Material level division of the low-life material data, to obtain a conductive metal layer, a conductive polymer layer and a wave-absorbing layer; Nickel-aluminum alloy shielding material design based on the conductive metal layer; Conductive rubber shielding material design based on the conductive polymer layer; Carbon-based shielding material design based on the wave-absorbing layer; Integration of the shielding material, the conductive rubber shielding material and the carbon-based shielding material, and recording the material data after integration, to obtain multi-layer shielding data.

8. The information security protection method for a home energy management system of claim 1, wherein, Step S4 specifically includes: Step S41: calculating the electromagnetic wave frequency based on the parasitic effect characteristics; Step S42: dividing the frequency band of the electromagnetic wave frequency, to obtain low-frequency electromagnetic waves and high-frequency electromagnetic waves; Step S43: selecting ferrite material based on the low-frequency electromagnetic waves; Step S44: selecting carbon nanotube composite material based on the high-frequency electromagnetic waves; Step S45: integrating the ferrite material and the carbon nanotube composite material, and recording the material data after integration, to obtain electromagnetic shielding optimization data; Step S46: encrypting the electromagnetic shielding information of the electromagnetic shielding optimization data, and uploading to the home energy management system, to perform electromagnetic shielding information security protection tasks.

9. An information security protection system for a home energy management system, characterized by, The application discloses a method for information security protection of a home energy management system, and belongs to the technical field of information security protection. The communication simulation module is used for 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. The multi-layer shielding design module is used for identifying side channel attacks based on the electromagnetic leakage data, testing material shielding efficiency by using the side channel attacks, predicting the service life of shielding materials of the smart home data according to the material shielding efficiency, and performing multi-layer shielding design based on the service life of the shielding materials to obtain multi-layer shielding data. The parasitic effect identification module is used for performing transient electromagnetic pulse simulation based on the multi-layer shielding data, detecting inverter breakdown damage in the simulation process, obtaining inverter breakdown damage data, and identifying parasitic effect characteristics of the smart home data according to the inverter breakdown damage data. The electromagnetic shielding information encryption module is used for performing electromagnetic shielding optimization based on the parasitic effect characteristics, obtaining electromagnetic shielding optimization data, performing electromagnetic shielding information encryption on the electromagnetic shielding optimization data, and uploading the electromagnetic shielding information encryption data to the home energy management system to perform an electromagnetic shielding information security protection task.

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