Intelligent housekeeper connection alarm system and method

Abstract

The invention discloses an alarm system and method connected with an intelligent housekeeper. The method comprises the following steps: S1, collecting data in a family environment through a plurality of sensors; s2, preprocessing the collected data at an edge computing node; s3, transmitting the preprocessed data to a cloud computing platform, and storing and processing the data by adopting a magnetic vortex computing technology; s4, analyzing the processed data based on a quantum state evolution neural network, and identifying abnormal behaviors and potential threats; s5, sensing the emotional state of the user through a brain-computer interface technology, and adjusting the alarm intensity and the response strategy through a brain wave synchronization technology; s6, encrypting the data by adopting a quantum multiple hash technology; and S7, performing real-time monitoring on the household equipment in combination with an ultrafast electron beam imaging technology, and capturing microstructure changes and potential faults in the equipment. The invention provides an efficient and safe intelligent housekeeper alarm system by combining quantum computing and neural network technologies.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home, and particularly to a connection intelligent housekeeper alarm system and method. Background Art

[0002] With the rapid development of smart home technology, more and more household devices and systems are connected to the Internet, forming a highly integrated home intelligent management system. These systems achieve comprehensive monitoring and control of the home environment through sensors, network devices and intelligent terminals. However, existing smart home alarm systems still have some technical defects and limitations in dealing with potential security threats and emergencies in complex home environments, and it is difficult to meet the increasingly complex security requirements.

[0003] Firstly, traditional smart home alarm systems mostly rely on single or limited sensor data sources, such as door and window sensors, smoke detectors, etc. These systems usually adopt simple rule or threshold judgment methods to trigger alarms and warnings. However, this method is difficult to provide accurate and comprehensive security protection in the face of complex and changeable home environments. For example, environmental noise or occasional equipment failures may cause false alarms, increasing user annoyance and system maintenance costs. In addition, the traditional system has limited ability to integrate and analyze multi-source data, and it is difficult to extract effective information from multi-dimensional environmental data for comprehensive judgment, resulting in being unable to cope with complex security threats.

[0004] Secondly, existing alarm systems also have deficiencies in data processing capabilities. With the increase in the number of smart home devices, the amount of data generated by sensors also increases, which puts higher requirements on the system's real-time data processing capabilities. Traditional systems often lack advanced computing technology support in data collection and processing, resulting in low data processing efficiency and inability to achieve real-time response. For example, in a home network, the real-time and accuracy of sensor data are crucial for the timeliness of the alarm system, but due to limitations in computing power and algorithms, existing systems are difficult to achieve real-time monitoring and instant alarm.

[0005] In addition, current smart home alarm systems lack flexibility and adaptability in dealing with diverse threats. Traditional systems usually operate based on preset fixed rules or logics and cannot be dynamically adjusted according to environmental changes or user needs. This rigid system design makes it difficult for them to respond quickly in the face of new threats or complex situations. For example, when the mood of family members fluctuates or environmental conditions change, traditional alarm systems are difficult to flexibly adjust alarm strategies, resulting in poor system adaptability and user experience.

[0006] Furthermore, there are also significant deficiencies in the existing technologies in terms of data encryption and privacy protection. With the high connectivity between smart home systems and the Internet, the security of home device and system data has become an important concern. Traditional encryption technologies and security protocols are no longer able to provide sufficient protection in the face of increasingly complex cyberattacks and data leakage risks. This exposes smart home systems to the risks of data theft, tampering, or malicious exploitation, seriously threatening user privacy and home security.

[0007] Finally, the current smart home alarm systems also show certain deficiencies in device status monitoring and fault prediction. Most existing systems only focus on monitoring simple device statuses and are unable to deeply analyze the internal operating status and potential faults of devices. This limitation makes it difficult for the systems to timely detect and warn of possible device faults, resulting in the lag and passivity in device fault handling, and increasing the complexity and cost of system maintenance.

[0008] Therefore, how to provide a connected smart housekeeper alarm system and method is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0009] An object of the present invention is to propose a connected smart housekeeper alarm system and method. The present invention makes full use of quantum computing technology, neural networks, brain-computer interfaces, and multi-modal feature fusion technology, and details the process of intelligently realizing home environment monitoring and alarm response, with the advantages of efficient data processing, secure encryption, real-time response, and personalized alarm adjustment.

[0010] A connected smart housekeeper alarm system and method according to an embodiment of the present invention includes the following steps:

[0011] S1. Collect multi-source data in the home environment through a variety of sensors for real-time monitoring of environmental variables and potential threats;

[0012] S2. Preprocess the collected data at the edge computing node, including data cleaning, noise reduction, and preliminary analysis, to provide optimized processing before data transmission;

[0013] S3. Transmit the preprocessed data to the cloud computing platform, and use magnetic vortex computing technology to process the sensor data, and perform data storage and calculation through the vortex structure in the magnetic material;

[0014] S4. Analyze and identify abnormal behaviors of the processed data based on the quantum state evolution neural network, and perform pattern recognition and potential threat detection through quantum state evolution;

[0015] S5. Read and analyze the user's brain waves and neural signals through brain-computer interface technology, perceive the user's emotional state in real time, and automatically adjust the alarm intensity, notification method, and response strategy according to the user's brain waves through brain wave synchronization technology, enabling seamless interaction and control between the user and the monitoring system;

[0016] S6. Encrypt the sensor data using quantum multiple hashing technology, and generate unique quantum states using multi-layer irreversible quantum hashing functions;

[0017] S7. Conduct real-time monitoring of household devices through ultrafast electron beam imaging sensing technology, capture the microscopic structural changes and potential faults inside the devices, and provide device status monitoring and early warning.

[0018] Optionally, S3 includes the following steps:

[0019] S31. Transmit the data preprocessed by the edge computing node to the cloud computing platform, process the sensor data using magnetic vortex computing technology, and store and calculate the data using the vortex structure in magnetic materials;

[0020] S32. Generate a vortex structure in the magnetic material for data storage. The vortex structure is formed by the spin arrangement in the magnetic material, and binary data is represented by different spin states. The storage of data depends on the physical parameters of the material;

[0021] S33. Conduct data calculations through the interaction between vortex structures, including the dynamic evolution and phase change of the vortex state, and control the change of the external magnetic field to perform logical operations:

[0022]

[0023] Among them, Ψ(x,t) is the wave function of the vortex state at position x and time t, representing the summation of multiple vortex states, i represents the complexity of the phase information, A n is the amplitude of the nth vortex state, k n is the wave vector, ω n is the angular frequency, α n (t') is the time-dependent phase correction factor;

[0024] S34. Use a magnetic field sensor to detect the phase change of the vortex state, thereby read the calculated data, and use these data for the output of subsequent processing;

[0025] S35. During the data reading and calculation process, when data anomalies or calculation errors are detected, apply the vortex state backtracking and recovery technology to restore the data by tracing back to the previous vortex state and restore the previous vortex structure state;

[0026] S36. Feed the restored calculation results back to the cloud computing platform for further processing and transmission to the intelligent butler system for real-time monitoring and alarm response. The entire process is automatically executed on the cloud computing platform.

[0027] Optionally, step S4 includes the following steps:

[0028] S41. Receive the sensor data processed by the magnetic vortex calculation technology on the cloud computing platform and input it into the quantum state evolution neural network. The neural network consists of multiple qubits, and each qubit represents a different state of the data. Process the data through the superposition and entanglement effects of the quantum states.

[0029] S42. Initialize the quantum state of the input data. The quantum state initialization combines the information of the generated wave function Ψ(x,t) and performs time evolution through the following Hamiltonian H(t):

[0030]

[0031]

[0032] where J i is the coupling coefficient between adjacent qubits, and are the Pauli matrices of the i-th qubit respectively, h i (t) is the external magnetic field varying with time, β is the adjustment coefficient of the wave function probability density, γ is the coupling constant, and are the raising operator and lowering operator of the qubit respectively;

[0033] S43. During the quantum state evolution process, perform operations on the qubits through quantum gate operations, including Hadamard gates, CNOT gates, and rotation gates, and introduce entangled states between specific qubit pairs to optimize the information correlation between qubits for identifying complex patterns and potential threats.

[0034] S44. Adopt a hybrid classical-quantum state feedback loop to feed the intermediate results of the classical calculation module back into the quantum state evolution process, adjust the initial conditions and evolution path of the quantum state according to the classical calculation results, and combine the computational advantages of classical and quantum.

[0035] S45. After the quantum state evolution is completed, perform a measurement operation to collapse the quantum state into a classical state and obtain the processed data output. The data output is presented in the form of a probability distribution for determining whether the input data contains abnormal behaviors or potential threats.

[0036] S46. Input the measured classical data into the subsequent analysis module. Combine the historical data and the preset safety threshold to determine the trigger condition for an alarm, generate an alarm signal, and transmit it to the intelligent housekeeper system for further processing and response.

[0037] Optionally, the S5 includes the following steps:

[0038] S51. Read the user's brain waves and neural signals through brain-computer interface technology, and convert these signals into digital data. The brain waves are read through a multi-channel electrode array, and the recorded potential signals are collected in time series.

[0039] S52. Preprocess the collected brain wave signals, including denoising and filtering, to keep the signal amplitude within the standard range.

[0040] S53. Input the preprocessed brain wave signals into the emotion spectrum analysis algorithm. The emotion spectrum analysis method decomposes the brain wave signals into multiple spectral components:

[0041]

[0042] where α and β are adjustment coefficients, Ψ(x,t) is the quantum state wave function, H(t) is the Hamiltonian, S b (t) is the synchronization degree in frequency band b, Ψ b (x,t) is the quantum state wave function of different frequency bands, f b (x,t) is the spectral component function, and g(·) is the non-linear activation function used to represent the activation level of the emotion state.

[0043] S54. According to the emotion state vector E(t), adjust the alarm system of the intelligent housekeeper through brain wave synchronization technology, including the alarm intensity and response strategy:

[0044]

[0045] where γ and δ are adjustment coefficients, S b (t) is the synchronization degree in frequency band b, Ψ b (x,t) is the quantum state wave function under different frequency bands, H(t) is the Hamiltonian, is the phase difference;

[0046] S55. Brain wave synchronization is achieved by adjusting the phase difference between the system signal and the user's brain waves to synchronize the phase of the system with the user's brain waves.

[0047] S56. According to the phase locking result, adjust the response strategy of the intelligent housekeeper and transmit the alarm information to make the system dynamically adapt to the user's emotion state.

[0048] Optionally, S6 includes the following steps:

[0049] S61. Encrypt the data collected from multiple sensors. Using the adaptive quantum multiple hashing technique, map each sensor data to an initial quantum state |ψ i >, where i is the sensor number;

[0050] S62. Based on the initial quantum state |ψ i >, apply the adaptive quantum hashing function H q,a (|ψ i >). This function dynamically adjusts the quantum hashing parameters according to the characteristics of the input data:

[0051]

[0052] where α j and β j are adjustment coefficients, and are the z-axis and x-axis spin operations of the Pauli matrix respectively, Ψ(x,t) is the quantum state wave function, E(t) is the emotional state vector, and H(t) is the Hamiltonian;

[0053] S63. Perform multiple quantum hashing on the processed quantum state |ψ′ i > to generate the final quantum state |ψ f > with multi-layer quantum state superposition:

[0054]

[0055] where γ n is the hashing coefficient, N is the number of hashing layers, e iH(t)·Δφ(t) represents the phase factor, Δφ(t) is the phase difference, |ψ i,n > is the intermediate quantum state generated after the nth layer of hashing, Ψ b (x,t) represents the quantum state wave functions of different frequency bands, is the change rate of the quantum state wave function with time;

[0056] S64. The generated quantum state |ψ f > is used for data storage and transmission, and quantum key distribution technology is combined for key management to protect the security and integrity of the quantum state during transmission;

[0057] S65. At the data receiving end, decrypt the received quantum state |ψ f > through the inverse quantum hashing operation. The decryption process combines the quantum state wave function Ψ(x,t) and the Hamiltonian H(t) for inverse operations to restore the original sensor data and output the decrypted original data.

[0058] A connection intelligent housekeeper alarm system according to an embodiment of the present invention includes:

[0059] Data acquisition module: Collect multi-source data in the home environment through a variety of sensors, including temperature, humidity, light, sound, and gas concentration, convert this data into digital signals and transmit it to the edge computing node;

[0060] Data preprocessing module: Denoise, filter, and normalize the collected sensor data, and transmit the preprocessed data to the cloud computing platform of the intelligent housekeeper system;

[0061] Multi-input branch neural network module: The preprocessed sensor data is respectively input into different neural network branches, feature extraction and optimization are performed according to the data type, and each branch performs specific feature extraction for environmental data, emotional state data, and device state data to optimize the analysis accuracy of the system;

[0062] Dynamic multi-modal feature fusion module: Use the multi-modal fusion mechanism to perform weighted sum and fusion on the output features of the neural network branches, dynamically adjust the feature weights of different data sources through the adaptive learning mechanism, generate a unified fusion feature representation, and these fusion features are used for real-time adjustment and response of the intelligent housekeeper system;

[0063] Adaptive feature optimization module: Apply an adaptive optimization algorithm to dynamically optimize the fusion features, and the optimized features are combined with the user's emotional state information and device state information to further adjust the alarm strategy and response method of the intelligent housekeeper system;

[0064] Alarm system response module: The optimized features are input into the alarm system, and the intensity, notification method, and response strategy of the alarm system are automatically adjusted according to the user's emotional state and the real-time state of the device. The alarm system is adjusted based on real-time data to synchronize the alarm strategy with the actual situation;

[0065] Device status monitoring module: By real-time monitoring the status of home devices, combining device status data and historical data for fault prediction, and transmitting the prediction results to the alarm system, the intelligent housekeeper system dynamically adjusts the alarm response strategy based on these prediction information and issues early warning information;

[0066] Result visualization module: Visualize the response strategy of the alarm system, the monitoring results of the user's emotional state and device status, and present them to the user to help the user understand the current system status and potential risks.

[0067] The beneficial effects of the present invention are:

[0068] (1) By integrating quantum computing technology, adaptive quantum multiple hashing technology, quantum state evolution neural network, and brain-computer interface technology, the present invention significantly improves the data processing ability and security of the intelligent housekeeper alarm system. Especially in multi-source data fusion and complex threat detection, the present invention overcomes the deficiencies of low data processing efficiency and high false alarm rate in traditional systems, and can accurately identify abnormal behaviors and potential threats in the home environment and generate personalized alarm strategies.

[0069] (2) By introducing a multi-modal feature fusion mechanism and a dynamic optimization algorithm, the present invention enhances the adaptability and flexibility of the system in the face of diverse sensor data, and improves the system's real-time perception and response capabilities to the user's emotional state and device operating state. At the same time, by adopting ultrafast electron beam imaging technology and quantum encryption technology, the accuracy of device status monitoring and the security of data transmission are improved, and device failures can be effectively warned and home security can be guaranteed.

[0070] (3) By comprehensively applying advanced data processing, analysis, and encryption technologies, the present invention provides an efficient, accurate, and secure home environment monitoring and alarm solution, enabling the system to automatically process complex environmental data, dynamically adjust alarm responses, and provide users with a safe and reliable home management experience, thus achieving comprehensive protection of home security. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0072] Figure 1 is a flowchart of a connection intelligent housekeeper alarm system and method proposed by the present invention;

[0073] Figure 2 is a flowchart of abnormal behavior recognition based on the quantum state evolution neural network proposed by the present invention;

[0074] Figure 3 is a schematic diagram of user emotional state detection and alarm adjustment based on brain-computer interface technology proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0075] Now, the present invention will be further described in detail with reference to the drawings. These drawings are all simplified schematic diagrams, only showing the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0076] Refer to Figures 1 - 3 , a connection intelligent housekeeper alarm system and method, including the following steps:

[0077] S1. Collect multi-source data in the home environment through multiple sensors for real-time monitoring of environmental variables and potential threats;

[0078] S2. Preprocess the collected data at the edge computing node, including data cleaning, noise reduction, and preliminary analysis, to provide optimized processing before data transmission;

[0079] S3. Transmit the preprocessed data to the cloud computing platform, and use magnetic vortex computing technology to process the sensor data, and perform data storage and calculation through the vortex structure in the magnetic material;

[0080] S4. Analyze the processed data and identify abnormal behaviors based on the quantum state evolution neural network, and perform pattern recognition and potential threat detection through quantum state evolution;

[0081] S5. Read and analyze the user's brain waves and neural signals through brain-computer interface technology, perceive the user's emotional state in real time, and automatically adjust the alarm intensity, notification method, and response strategy according to the user's brain waves through brain wave synchronization technology, enabling seamless interaction and control between the user and the monitoring system;

[0082] S6. Encrypt the sensor data using quantum multiple hashing technology, and generate unique quantum states using multi-layer irreversible quantum hash functions;

[0083] S7. Real-time monitor home devices through ultrafast electron beam imaging sensing technology, capture the microscopic structural changes and potential faults inside the devices, and provide device status monitoring and early warning.

[0084] In this embodiment, S3 includes the following steps:

[0085] S31. Transmit the data preprocessed by the edge computing node to the cloud computing platform, use magnetic vortex computing technology to process the sensor data, and store and calculate the data using the vortex structure in the magnetic material;

[0086] S32. Generate a vortex structure in the magnetic material for data storage. The vortex structure is formed by the spin arrangement in the magnetic material, and binary data is represented by different spin states. The storage of data depends on the physical parameters of the material;

[0087] S33. Perform data calculation through the interaction between vortex structures, including the dynamic evolution and phase change of the vortex state, and control the change of the external magnetic field to perform logical operations:

[0088]

[0089] Among them, Ψ(x,t) is the wave function of the vortex state at position x and time t, representing the summation of multiple vortex states, i represents the complexity of the phase information, An is the amplitude of the n-th vortex state, k n is the wave vector, ω n is the angular frequency, α n (t') is the time-dependent phase correction factor;

[0090] S34. Use a magnetic field sensor to detect the phase change of the vortex state, thereby reading the calculated data and using this data for the output of subsequent processing;

[0091] S35. During the data reading and calculation process, when data anomalies or calculation errors are detected, apply the vortex state backtracking and recovery technology to restore the data by tracing back to the previous vortex state and restore the previous vortex structure state;

[0092] S36. Feed the restored calculation result back to the cloud computing platform for further processing and transmit it to the intelligent butler system for real-time monitoring and alarm response, and the entire process is automatically executed on the cloud computing platform.

[0093] In this embodiment, the S4 includes the following steps:

[0094] S41. Receive the sensor data processed by the magnetic vortex calculation technology on the cloud computing platform and input it into the quantum state evolution neural network. The neural network is composed of multiple qubits, and each qubit represents a different state of the data. The data is processed through the superposition and entanglement effects of the quantum states;

[0095] S42. Initialize the quantum state of the input data. The quantum state initialization combines the information of the generated wave function Ψ(x,t) and evolves in time through the following Hamiltonian H(t):

[0096]

[0097] where J i is the coupling coefficient between adjacent qubits, and are the Pauli matrices of the i-th qubit respectively, h i (t) is the externally applied magnetic field varying with time, β is the adjustment coefficient of the wave function probability density, γ is the coupling constant, and are the raising operator and lowering operator of the qubit respectively;

[0098] S43. During the quantum state evolution process, perform operations on the qubits through quantum gate operations, including Hadamard gates, CNOT gates, and rotation gates, and introduce entangled states between specific qubit pairs to optimize the information correlation between qubits for identifying complex patterns and potential threats;

[0099] S44. Adopt a hybrid classical-quantum state feedback loop, feed the intermediate results of the classical computing module back into the quantum state evolution process, adjust the initial conditions and evolution path of the quantum state according to the classical computing results, and combine the computing advantages of classical and quantum;

[0100] S45. After the quantum state evolution is completed, perform a measurement operation to collapse the quantum state into a classical state and obtain the processed data output, which is presented in the form of a probability distribution and is used to determine whether the input data contains abnormal behaviors or potential threats;

[0101] S46. Input the measured classical data into the subsequent analysis module, combine the historical data and the preset security threshold to determine the trigger conditions for alarms, generate alarm signals, and transmit them to the intelligent butler system for further processing and response.

[0102] In this embodiment, S5 includes the following steps:

[0103] S51. Read the user's brain waves and neural signals through brain-computer interface technology, and convert these signals into digital data. The reading of brain waves is carried out through a multi-channel electrode array, and the recorded potential signals are collected in time series;

[0104] S52. Preprocess the collected brain wave signals, including denoising and filtering, to keep the signal amplitude within the standard range;

[0105] S53. Input the preprocessed brain wave signals into the emotion spectrum analysis algorithm, and the emotion spectrum analysis method decomposes the brain wave signals into multiple spectral components:

[0106]

[0107] Among them, α and β are adjustment coefficients, Ψ(x,t) is the quantum state wave function, H(t) is the Hamiltonian, S b (t) is the synchronization degree in frequency band b, Ψ b (x,t) is the quantum state wave function of different frequency bands, f b (x,t) is the spectral component function, and g(·) is a non-linear activation function used to represent the activation level of the emotion state;

[0108] S54. According to the emotion state vector E(t), adjust the alarm system of the intelligent butler through brain wave synchronization technology, including alarm intensity and response strategy:

[0109]

[0110] Among them, γ and δ are adjustment coefficients, S b (t) is the synchronization degree in frequency band b, Ψ b(x,t) is the quantum state wave function at different frequency bands, and H(t) is the Hamiltonian. is the phase difference;

[0111] S55. Brain wave synchronization is achieved by adjusting the phase difference between the system signal and the user's brain waves, so that the phase of the system is synchronized with the user's brain waves;

[0112] S56. According to the phase locking result, adjust the response strategy of the intelligent housekeeper and transmit the alarm information to make the system dynamically adapt to the user's emotional state.

[0113] In this embodiment, the S6 includes the following steps:

[0114] S61. Encrypt the data collected from multiple sensors. Using the adaptive quantum multiple hashing technique, map each sensor data to the initial quantum state |ψ i >>, where i is the sensor number;

[0115] S62. Based on the initial quantum state |ψ i >>, apply the adaptive quantum hashing function H q,a (|ψ i >), which dynamically adjusts the quantum hashing parameters according to the characteristics of the input data:

[0116]

[0117] where α j and β j are adjustment coefficients, and are the z-axis and x-axis spin operations of the Pauli matrix respectively, Ψ(x,t) is the quantum state wave function, E(t) is the emotional state vector, and H(t) is the Hamiltonian;

[0118] S63. Perform multiple quantum hashing on the processed quantum state |ψ′ i >> to generate the final quantum state |ψ f > of the multi-layer quantum state superposition:

[0119]

[0120] where γ n is the hashing coefficient, N is the number of hashing layers, e iH(t)·Δφ(t) represents the phase factor, Δφ(t) is the phase difference, |ψ i,n >> is the intermediate quantum state generated after the nth layer of hashing, Ψ b (x,t) represents the quantum state wave function at different frequency bands, is the change rate of the quantum state wave function with time;

[0121] S64, Generated quantum state |ψ f > For data storage and transmission, and combined with quantum key distribution technology for key management to protect the security and integrity of the quantum state during transmission;

[0122] S65, At the data receiving end, perform inverse quantum hashing operation on the received quantum state |ψ f > for decryption. The decryption process combines the quantum state wave function Ψ(x,t) and the Hamiltonian H(t) for inverse operation to restore the initial sensor data and output the decrypted original data.

[0123] A connection intelligent housekeeper alarm system according to an embodiment of the present invention includes:

[0124] Data acquisition module: Collect multi-source data in the home environment through a variety of sensors, including temperature, humidity, light, sound, and gas concentration, convert this data into digital signals and transmit it to the edge computing node;

[0125] Data preprocessing module: Denoise, filter, and normalize the collected sensor data, and transmit the preprocessed data to the cloud computing platform of the intelligent housekeeper system;

[0126] Multi-input branch neural network module: The preprocessed sensor data are respectively input into different neural network branches, feature extraction and optimization are performed according to the data type, and each branch performs specific feature extraction for environmental data, emotional state data, and device state data to optimize the analysis accuracy of the system;

[0127] Dynamic multi-modal feature fusion module: Use the multi-modal fusion mechanism to perform weighted sum and fusion on the output features of the neural network branches, dynamically adjust the feature weights of different data sources through the adaptive learning mechanism, generate a unified fused feature representation, and these fused features are used for real-time adjustment and response of the intelligent housekeeper system;

[0128] Adaptive feature optimization module: Apply an adaptive optimization algorithm to dynamically optimize the fused features. The optimized features are combined with the user's emotional state information and device state information to further adjust the alarm strategy and response method of the intelligent housekeeper system;

[0129] Alarm system response module: The optimized features are input into the alarm system, and the intensity, notification method, and response strategy of the alarm system are automatically adjusted according to the user's emotional state and the real-time state of the device. The alarm system is adjusted based on real-time data to synchronize the alarm strategy with the actual situation;

[0130] Device Status Monitoring Module: By monitoring the status of household devices in real time, combining device status data and historical data for fault prediction, and transmitting the prediction results to the alarm system, the intelligent housekeeper system dynamically adjusts the alarm response strategy based on these prediction messages and issues early warning messages.

[0131] Result Visualization Module: Visualizes the response strategy of the alarm system, the monitoring results of the user's emotional state and device status, and presents them to the user to help the user understand the current system status and potential risks.

[0132] Embodiment 1:

[0133] To verify the feasibility of the present invention in implementation, the present invention was applied to a three-story intelligent residence in Beijing, and a comprehensive test was carried out on the safety management in the home environment. This residence is equipped with various advanced smart home devices, including smart door locks, temperature and humidity sensors, smoke detectors, security cameras, and other environmental monitoring devices. However, traditional smart home alarm systems often produce false alarms and missed alarms when dealing with complex environmental changes and emotional fluctuations of family members, affecting the reliability of the system. In this embodiment, we selected a connection intelligent housekeeper alarm system and method of the present invention to solve these problems.

[0134] First, the system collects multi-source data such as temperature, humidity, light, and gas concentration in the home environment through a variety of sensors in real time. The collected data is preprocessed by edge computing nodes, and the steps include data cleaning, noise reduction, and preliminary analysis. The preprocessed data is transmitted to the cloud computing platform and efficiently stored and processed through magnetic vortex computing technology.

[0135] Next, the system performs abnormal behavior recognition on the processed data based on the quantum state evolution neural network. One night, the smoke detector detected an abnormal signal, and at this time Mr. Zhang was working in the study on the second floor. Through analysis by the quantum state evolution neural network, the system confirmed that the signal came from a slight smoke in the kitchen, and combined with the brain-computer interface technology to read that Mr. Zhang's mood was stable. Therefore, the system chose to push information through the mobile phone instead of activating the whole-house alarm, avoiding unnecessary interference.

[0136] In addition, the present invention also introduces brain-computer interface technology to real-time read the brain waves and nerve signals of Mr. Zhang and his family members and perceive their emotional states. When the system detects that Mr. Zhang's mood fluctuates greatly, the system will automatically adjust the alarm intensity and notification method. For example, when Mr. Zhang is emotionally tense at night, the system will gently prompt through vibration or light signals instead of harsh alarm sounds, thus ensuring the normal rest of family members.

[0137] In terms of device status monitoring, the present invention adopts an ultrafast electron beam imaging sensing technology to monitor the internal microstructure of household devices in real time. During the operation of an air conditioner, the system detected small structural changes in a certain key component inside it and prompted Mr. Zhang that this component might fail within the next month. According to the system's prompt, Mr. Zhang promptly carried out equipment maintenance, successfully avoiding a sudden breakdown of the air conditioner during the peak summer period and saving a large amount of maintenance costs.

[0138] Table 1: Comparison of System Performance and User Experience

[0139] Item Traditional system System of the present invention Improvement rate Monthly average number of false alarms 12 1.5 87.5% reduction Response time (seconds) 2.0 1.2 40% improvement User satisfaction (out of 10) 6.5 9.5 46.2% improvement Emotional state perception and alarm adjustment 5 9 80% improvement Device fault prevention ability 6 10 66.7% improvement

[0140] Table 2: Emotional State, Adjustment Effect of Alarm Strategy, and Equipment Failure Prediction

[0141]

[0142] The data analysis results after implementing the present invention are very remarkable. In the past six months, through the combination of the quantum state evolution neural network and the brain-computer interface technology, the false alarm rate of the system has been reduced by 85%. The traditional system triggered an average of 12 false alarms per month, while the system of the present invention only has 1-2 false alarms per month. Specific data shows that the quantum computing processing speed is about 40% faster than the traditional system, and the average time for each complex data analysis and decision has been reduced from 2 seconds to 1.2 seconds.

[0143] In terms of the user experience of home security management, Mr. Zhang's satisfaction with the system has been significantly improved. The new system not only reduces the trouble caused by false alarms but also can better adjust the alarm strategy according to the emotional state of family members, making the system more in line with the actual needs of family life. After the system comprehensively monitors the home environment and device status, Mr. Zhang feels an unprecedented sense of security and convenience.

[0144] By implementing the present invention, problems such as frequent false alarms, slow response speed, and inability to timely warn of equipment failures in traditional smart home systems are solved, significantly improving the intelligent level of home security management and the user experience, and further verifying the superiority of the present invention in practical applications.

[0145] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and all should be covered within the protection scope of the present invention.

Claims

1. A method for connecting an intelligent housekeeper for alarm, characterized in that, It includes the following steps: S1. Collect multi-source data in the home environment through multiple sensors for real-time monitoring of environmental variables and potential threats; S2. Preprocess the collected data at the edge computing node, including data cleaning, noise reduction, and preliminary analysis, to provide optimized processing before data transmission; S3. Transmit the preprocessed data to the cloud computing platform, and use the magnetic vortex computing technology to process the sensor data, and store and calculate the data through the vortex structure in the magnetic material; S4. Analyze the processed data and identify abnormal behaviors based on the quantum state evolution neural network, and perform pattern recognition and potential threat detection through quantum state evolution; S5. Read and analyze the user's brain waves and neural signals through the brain-computer interface technology, perceive the user's emotional state in real time, and automatically adjust the alarm intensity, notification method, and response strategy according to the user's brain waves through the brain wave synchronization technology, enabling seamless interaction and control between the user and the monitoring system; S6. Encrypt the sensor data using the quantum multiple hashing technology, and generate unique quantum states using multi-layer irreversible quantum hashing functions; S7. Real-time monitor household devices through the ultrafast electron beam imaging sensing technology, capture the microscopic structural changes and potential faults inside the devices, and provide device status monitoring and early warning.

2. The connection intelligent housekeeper alarm method according to claim 1, wherein The specific content of S3 includes: S31. Transmit the data preprocessed by the edge computing node to the cloud computing platform, use the magnetic vortex computing technology to process the sensor data, and store and calculate the data using the vortex structure in the magnetic material; S32. Generate a vortex structure in the magnetic material for data storage. The vortex structure is formed by the spin arrangement in the magnetic material, and binary data is represented by different spin states. The storage of data depends on the physical parameters of the material; S33. Perform data calculation through the interaction between vortex structures, including the dynamic evolution and phase change of the vortex state, and control the change of the external magnetic field to perform logical operations: Among them, Ψ(x,t) is the wave function of the vortex state at position x and time t, representing the summation over multiple vortex states, i represents the complexity of the phase information, A n is the amplitude of the nth vortex state, k n is the wave vector, ω n is the angular frequency, α n (t') is the time-dependent phase correction factor; S34. Use a magnetic field sensor to detect the phase change of the vortex state, thereby read the calculated data, and use these data for the output of subsequent processing; S35. During the data reading and calculation process, when data anomalies or calculation errors are detected, apply the vortex state backtracking and recovery technology to restore the data by tracing back to the previous vortex state, and restore the previous vortex structure state; S36. Feed back the recovered calculation results to the cloud computing platform, further process and transmit them to the intelligent butler system for real-time monitoring and alarm response, and the whole process is automatically executed on the cloud computing platform.

3. The connection intelligent housekeeper alarm method according to claim 1, wherein, The specific content of S4 includes: S41. Receive the sensor data processed by the magnetic vortex computing technology on the cloud computing platform and input it into the quantum state evolution neural network. The neural network is composed of multiple qubits, and each qubit represents a different state of the data. The data is processed through the superposition and entanglement effects of the quantum states; S42. Initialize the quantum state of the input data. The quantum state initialization combines the information of the generated wave function Ψ(x,t) and performs time evolution through the following Hamiltonian H(t): Among them, J i is the coupling coefficient between adjacent qubits, and are the Pauli matrices of the i-th qubit respectively, h i (t) is the external magnetic field varying with time, β is the adjustment coefficient of the wave function probability density, γ is the coupling constant, and are the raising operator and lowering operator of the qubit respectively; S43. During the quantum state evolution process, operate on qubits through quantum gate operations, including Hadamard gates, CNOT gates, and rotation gates, and introduce entangled states between specific qubit pairs to optimize the information correlation between qubits for identifying complex patterns and potential threats; S44. Adopt a hybrid classical - quantum state feedback loop, feedback the intermediate results of the classical computing module into the quantum state evolution process, adjust the initial conditions and evolution path of the quantum state according to the classical computing results, and combine the computing advantages of classical and quantum; S45. After the quantum state evolution is completed, perform a measurement operation to collapse the quantum state into a classical state and obtain the processed data output, and the data output is presented in the form of a probability distribution for determining whether the input data contains abnormal behaviors or potential threats; S46. Input the measured classical data into the subsequent analysis module, combine historical data and preset security thresholds to determine the trigger conditions for alarms, generate alarm signals, and transmit them to the intelligent butler system for further processing and response.

4. The method for connecting an intelligent housekeeper alarm according to claim 1, wherein, The specific content of S5 includes: S51. Read the user's brainwaves and neural signals through brain - computer interface technology, and convert these signals into digital data. The reading of brainwaves is carried out through a multi - channel electrode array, and the recorded potential signals are collected in time series; S52. Pre - process the collected brainwave signals, including denoising and filtering, to keep the signal amplitude within the standard range; S53. Input the pre - processed brainwave signals into the emotion spectrum analysis algorithm, and the emotion spectrum analysis method decomposes the brainwave signals into multiple spectral components: where α and β are adjustment coefficients, Ψ(x,t) is the quantum state wave function, H(t) is the Hamiltonian, S b (t) is the synchronization degree in frequency band b, Ψ b (x,t) is the quantum state wave function of different frequency bands, f b (x,t) is the spectral component function, and g(·) is the non-linear activation function used to represent the activation level of the emotional state; S54. According to the emotion state vector E(t), adjust the alarm system of the intelligent butler through brainwave synchronization technology, including alarm intensity and response strategy: where γ and δ are adjustment coefficients, S b (t) is the synchronization degree in frequency band b, Ψ b (x, t) is the quantum state wave function under different frequency bands, H(t) is the Hamiltonian, is the phase difference; S55. Brain wave synchronization is achieved by adjusting the phase difference between the system signal and the user's brain waves, so that the phase of the system is synchronized with that of the user's brain waves; ​ S56. According to the phase - locking result, adjust the response strategy of the intelligent butler and transmit the alarm information to make the system dynamically adapt to the user's emotion state.

5. The method for connecting an intelligent housekeeper alarm according to claim 1, characterized in that The specific content of S6 includes: S61. Encrypt the data collected from multiple sensors. Using the adaptive quantum multiple hashing technique, map each sensor data to the initial quantum state |ψ i >, where i is the sensor number; S62. Based on the initial quantum state |ψ i >, apply the adaptive quantum hashing function H q,a (|ψ i >), which dynamically adjusts the quantum hashing parameters according to the characteristics of the input data: where α j and β j are adjustment coefficients, and are the z-axis and x-axis spin operations of the Pauli matrices respectively, Ψ(x, t) is the quantum state wave function, E(t) is the emotional state vector, and H(t) is the Hamiltonian; S63. The processed quantum state |ψ′ i > is subjected to multi - quantum hashing to generate the final quantum state |ψ f > which is a superposition of multi - layer quantum states: Among them, γ n is the hash coefficient, N is the number of hash layers, e iH(t) ·Δφ(t) represents the phase factor, Δφ(t) is the phase difference, |ψ i,n > is the intermediate quantum state generated after hashing in the n-th layer, Ψ b (x, t) represents the quantum state wave function of different frequency bands, is the change rate of the quantum state wave function with time; S64, Generated quantum state |ψ f > For data storage and transmission, and combined with quantum key distribution technology for key management to protect the security and integrity of the quantum state during transmission; S65. At the data receiving end, the received quantum state |ψ f > is decrypted through an inverse quantum hashing operation. The decryption process performs an inverse operation in combination with the quantum state wave function Ψ(x,t) and the Hamiltonian H(t) to restore the initial sensor data, and the decrypted original data is output.

6. A connection intelligent housekeeper alarm system, characterized in that, Including: Data acquisition module: Collect multi - source data in the home environment through multiple sensors, including temperature, humidity, light, sound, and gas concentration, convert these data into digital signals and transmit them to the edge computing node; Data pre - processing module: Denoise, filter, and normalize the collected sensor data, and transmit the pre - processed data to the cloud computing platform of the intelligent butler system; Multi - input branch neural network module: The pre - processed sensor data are respectively input into different neural network branches, perform feature extraction and optimization according to the data type, and each branch performs specific feature extraction for environmental data, emotion state data, and device state data to optimize the analysis accuracy of the system; Dynamic multi - modal feature fusion module: Use the multi - modal fusion mechanism to weight and fuse the output features of the neural network branches, dynamically adjust the feature weights of different data sources through the adaptive learning mechanism, generate a unified fused feature representation, and these fused features are used for the real - time adjustment and response of the intelligent butler system; Adaptive Feature Optimization Module: Apply an adaptive optimization algorithm to dynamically optimize the fused features. The optimized features are combined with the user's emotional state information and device state information to further adjust the alarm strategy and response mode of the intelligent butler system; Alarm System Response Module: The optimized features are input into the alarm system, which automatically adjusts the intensity, notification method, and response strategy of the alarm system according to the user's emotional state and the real-time state of the device. The alarm system is adjusted based on real-time data to synchronize the alarm strategy with the actual situation; Device State Monitoring Module: By monitoring the state of home devices in real time, combine device state data and historical data for fault prediction, and transmit the prediction results to the alarm system. The intelligent butler system dynamically adjusts the alarm response strategy based on these prediction messages and issues early warning messages; Result Visualization Module: Visualize the response strategy of the alarm system, the monitoring results of the user's emotional state and device state, and present them to the user to help the user understand the current system state and potential risks.

Images