Intelligent power supply service system and method based on voice recognition technology

Abstract

The invention discloses an intelligent power supply service system and method based on a voice recognition technology, and the method comprises the following steps: a user can send a voice instruction through an intelligent device, and the system collects a voice signal through a high-precision voice capturing technology, and converts the voice signal into a text instruction through a multi-mode voice recognition technology. The system analyzes a text instruction by using a natural language processing technology, generates a corresponding power supply control command in combination with a current environment and user requirements, and transmits the control command to the quantum superconducting power supply module to realize efficient power transmission. The system also comprises a self-healing nano sensor network which is used for monitoring the state of the equipment in real time and automatically repairing when a potential fault occurs. The system dynamically adjusts power load distribution through the self-learning module according to user habits and environment changes, and high efficiency and stability of the system are ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent power management and control technology, and in particular to an intelligent power supply service system and method based on voice recognition technology. Background Art

[0002] With the rapid development of science and technology and the widespread application of intelligent technology, modern power management systems have made significant progress in improving energy efficiency and user convenience. However, traditional power supply service systems still face many challenges, especially in terms of intelligence and automation, where there is significant room for improvement. The power supply systems in the existing technologies usually rely on preset control strategies and manual operations. These systems have limitations in responding to user needs and environmental changes, which affects the overall performance of the power supply system and user experience.

[0003] Traditional power supply systems usually adopt fixed power supply control strategies, which are often not flexible enough when facing different user needs and environmental conditions. For example, traditional systems may not be able to adjust power distribution according to real-time user instructions, resulting in waste of power resources or insufficient power supply. This fixed control method cannot fully adapt to the dynamic changes in user needs, and it is difficult to deal with the impact of complex environmental conditions on the power supply system. The existing power supply system has a low level of intelligence in voice recognition and user interaction. Although voice recognition technology has made certain progress, its application in power supply systems is still limited to simple command input. Existing technologies usually lack a deep understanding and intelligent analysis of voice commands, and cannot effectively convert user voice commands into specific power supply control commands. In addition, existing systems have deficiencies in the processing of environmental noise and user voice characteristics, resulting in limited accuracy and reliability of voice recognition.

[0004] Traditional power supply systems also have deficiencies in real-time monitoring and fault self-healing. Although some advanced systems have basic monitoring functions, the ability to monitor the status of power supply equipment in real time and self-heal from faults is still limited. Existing technologies often lack efficient self-healing materials and intelligent sensor networks, and cannot repair equipment in a timely manner when microcracks or other damage occurs. Such systems lacking self-healing capabilities often lead to longer downtime and maintenance costs when faced with potential problems during equipment aging and use.

[0005] Existing power supply management systems also have deficiencies in adaptive load adjustment and user behavior learning. Although some systems have basic load adjustment capabilities, they are usually unable to perform intelligent optimization based on user behavior habits and real-time environmental data. Existing technologies often lack in-depth analysis of user behavior patterns and environmental conditions, resulting in the inability to effectively improve the efficiency and stability of power supply load distribution.

[0006] Therefore, how to provide an intelligent power supply service system and method based on speech recognition technology is a problem that technical personnel in this field urgently need to solve. Summary of the invention

[0007] One purpose of the present invention is to propose an intelligent power supply service system and method based on voice recognition technology. The present invention uses photoacoustic effect technology to capture voice commands and combines quantum computing and natural language processing technology to generate accurate power supply control commands. The system also integrates self-healing materials and real-time load regulation technology to improve the reliability and adaptability of power supply equipment. It describes in detail how to improve the accuracy of voice recognition, realize intelligent power supply management, and enhance the stability and self-healing ability of the system.

[0008] According to an embodiment of the present invention, a smart power supply service system and method based on speech recognition technology includes the following steps:

[0009] S1. The user issues a voice command through a smart device, and the system uses photoacoustic effect voice capture technology to detect the pressure changes of sound waves in the air through a laser beam to collect voice at long distances and in complex environments;

[0010] S2, input the collected voice signal into the multimodal voice recognition module, combine the voice features, background noise analysis and user voice model to perform voice recognition and generate corresponding text instructions;

[0011] S3. The generated text instructions are semantically parsed through natural language processing technology, and combined with environment and intention synchronization technology, the current environmental variables and user physiological signals are analyzed to generate power supply control commands that match user needs;

[0012] S4, transmitting the generated power supply control command to the quantum superconducting power supply module, using the zero resistance characteristic of the superconductor to perform power transmission, and adjusting the power flow within microseconds to adapt to real-time power supply load changes;

[0013] S5. After the power supply operation is completed, the system will provide feedback to the user through voice feedback and visual interface, and provide further power supply operation suggestions and adjustment options in combination with real-time environmental data;

[0014] S6. The system monitors the operating status of the power supply equipment through an embedded self-healing nanosensor network, which can detect fatigue and microcracks in the equipment materials at the micro level, and repair the damage and prevent failure by activating the self-healing material mechanism;

[0015] S7. When the system detects potential power supply failures and abnormal conditions, the embedded self-healing nanosensor network triggers the local repair function to complete the repair before the microcracks propagate;

[0016] S8. The system uses a self-learning module, combined with the user's operating habits and real-time environmental data, and a self-organizing criticality optimization algorithm to dynamically adjust the power load distribution, so that the system remains in a critical state and ensures the efficiency and stability of the power supply system.

[0017] Optionally, the S1 specifically includes:

[0018] S11. The user issues a voice command through the smart device, and the voice command is transmitted to the voice input interface of the system, which includes a microphone array and a photoacoustic effect capture device;

[0019] S12. The system uses photoacoustic effect voice capture technology to illuminate a specific area in the air with a laser beam. The propagation path of the laser beam in the air is described by the function P(x, y, z, t), where x, y, z are spatial coordinates and t is a time variable. P(x, y, z, t) is used to describe the energy distribution of the laser beam in different spaces and times.

[0020] S13. When the sound wave propagates in the air, the interaction between the sound wave and the laser beam causes a pressure change, and the pressure change is described by the formula ΔP=ρ·c·ΔV, where ΔP is the pressure change, ρ is the air density, c is the speed of sound, and ΔV is the change in air volume caused by the sound wave;

[0021] S14, the captured light signal S(t) is converted into an electrical signal by a photoelectric conversion device, and the waveform of the electrical signal is represented by S e (t) = k·S(t), where S e (t) is the electrical signal intensity, k is the photoelectric conversion coefficient;

[0022] S15. The converted electrical signal is input to the signal processing module, where S e (t) Noise filtering and signal enhancement are performed, and the resulting filtered signal is represented by E ′ (t).

[0023] Optionally, the S2 specifically includes:

[0024] S21, the filtered signal E obtained by the photoacoustic effect speech capture technology ′ (t) inputting into a multimodal speech recognition module, which includes a speech feature extraction unit, a background noise analysis unit and a user speech model library;

[0025] S22, in the speech feature extraction unit, filtering the signal E ′ (t) extract features and generate feature vector F(t). The feature vector F(t) describes the frequency characteristics of the speech signal, and its dimension is determined by the frequency distribution and time window of the signal;

[0026] S23, using quantum state coherent feature extraction technology to filter the signal E ′ (t) Enhanced processing is performed. Under the quantum computing framework, multiple dimensional features of the speech signal are encoded simultaneously through quantum state coherence, and an enhanced feature vector F is generated. q (t):

[0027]

[0028] Among them, α and β are quantum superposition coefficients, Q i (t) is the signal feature obtained by quantum state encoding, γ i is the weight coefficient related to the coherence of the quantum state, n is the number of dimensions of the quantum state;

[0029] S24, the enhanced feature vector F q (t) is transmitted to the background noise analysis unit and compared with F q (t) is subjected to noise filtering with the noise model N(t) to generate the feature vector F after noise filtering ′ (t);

[0030] S25, the feature vector F after noise filtering ′ (t) Input into the user voice model library and use the similarity function to calculate the feature vector F ′ (t) and each user's speech model M i and generates the corresponding text instruction T(t).

[0031] Optionally, the S3 specifically includes:

[0032] S31, inputting the text instruction T(t) generated by the multimodal speech recognition module into the natural language processing module, the natural language processing module includes a semantic parsing unit and a context management unit, the semantic parsing unit performs semantic parsing on the text instruction T(t) through a deep learning model, extracts the core intention I(T) and related operation parameters in the instruction, and generates a semantic vector V(T);

[0033] S32, the generated semantic vector V(T) is input to the context management unit, and the user's behavior pattern is analyzed by combining the semantic parsing results of the historical user instructions H(T) and the current text instruction T(t), and an extended context vector V is generated. C (T);

[0034] S33, context vector V C (T) is input to the environment and intention synchronization module, which combines the current environmental variables E(t) and the user's physiological signals B(t) to generate the environmental perception vector V E (t):

[0035]

[0036] Among them, σ is the activation function, W1, W2, W3 are weight matrices, λ k is the dynamically adjusted weight coefficient, φ k (V C (T), E(t), B(t)) are the features extracted from semantic vectors, environmental data, and user physiological signals;

[0037] S34. Under the framework of the environment and intention synchronization technology, a power supply control command C(t) matching the user's needs is generated. The power supply control command is generated by a multi-dimensional nonlinear combination function:

[0038]

[0039] Among them, ψ is a nonlinear activation function, α i is the dynamic weight coefficient related to user needs and environmental variables, g i (V E (t)) is a nonlinear combination of control laws;

[0040] S35 , transmitting the generated power supply control command C(t) to the power supply control module, and the power supply control module adjusts the operating state of the power supply device according to the command to respond to changes in user needs and environmental conditions.

[0041] Optionally, the S4 specifically includes:

[0042] S41, transmitting the generated power supply control command C(t) to the quantum superconducting power supply module, the quantum superconducting power supply module includes a superconductor power transmission unit and a real-time load adjustment unit, and the power supply control command C(t) is used to control the power transmission process of the superconductor;

[0043] S42. In the quantum superconducting power supply module, the zero resistance characteristic of the superconductor is used to perform power transmission:

[0044]

[0045] Where V(t) is the voltage, R sc (t) is the resistance of the superconductor. In the zero resistance state, R sc (t)≈0, so that the current I(t) is mainly controlled by the voltage V(t), It represents the instantaneous rate of change of voltage, β is the nonlinear regulation index used to control the impact intensity of voltage transients, and κ is the proportionality coefficient related to the characteristics of superconductor materials;

[0046] S43. During the power transmission process, the quantum superconducting power supply module dynamically adjusts the power flow in microseconds according to the power supply control command C(t) to adapt to the changes in the real-time power supply load. The power flow regulation process is achieved through a nonlinear combination of voltage and current to meet the needs under different load conditions.

[0047] S44, the real-time load regulation unit continuously monitors the power flow and dynamically adjusts the power transmission according to the real-time data of load changes to ensure the stability and response speed of the system under load fluctuations.

[0048] Optionally, the S6 specifically includes:

[0049] S61. The system monitors the operating status of the power supply equipment in real time through an embedded self-healing nanosensor network, which is composed of multiple distributed nanosensors. The multiple distributed nanosensors detect fatigue, microcracks and other potential structural damage of the equipment material at the micro level.

[0050] S62, the sensor unit detects the rate of change of strain energy density inside the material To identify fatigue and microcrack formation:

[0051]

[0052] Where E is the elastic modulus of the material, ∈(t) is the strain of the material, Describes the direct effect of material elastic modulus and strain on strain energy density over time. The cumulative effect of strain acceleration on energy density is described by integration, α and β are the adjustment coefficient and time decay factor respectively;

[0053] ˙

[0054] S63, when the sensor detects that the strain energy density change rate W(t) exceeds a preset threshold, the sensor network transmits damage information D(t) to the self-healing material activation unit through the wireless communication module. The damage information includes the crack location, size and development trend. The sensor network updates these data in real time.

[0055] S64, the self-healing material activation unit activates the self-healing material mechanism according to the damage information D(t) transmitted by the sensor network, and triggers the polymerization reaction of the self-healing material through local heating and chemical reaction:

[0056] Mn+C→Mn +1 +Q;

[0057] Among them, M n represents the polymer chain in the self-healing material, C is the catalyst, M n+1 is the longer polymer chain generated by the reaction, and Q is the by-product released during the reaction;

[0058] S65. After the repair is completed, the self-healing nanosensor network continues to monitor the strain energy density change in the repair area.

[0059] ˙

[0060] The repair rate W(t) is calculated, and the monitored repair data is compared with the original damage information D(t). Through further material stress adjustment mechanism, the internal stress distribution of the material is adjusted to prevent the formation of new damage and the recurrence of the original damage.

[0061] Optionally, the S8 specifically includes:

[0062] S81. Through the self-learning module, the system collects and analyzes the user's operating habits and real-time environmental data, including the user's power usage pattern, device usage frequency, ambient temperature, humidity, and power demand fluctuations;

[0063] S82. The system constructs the user behavior feature vector U(t) and the environment state vector E(t) based on the collected data, where:

[0064] U(t)=[u1(t),u2(t),…,un(t)];

[0065] E(t)=[E1(t),E2(t),…,Em(t)];

[0066] S83, input the user behavior feature vector U(t) and the environment state vector E(t) into the self-organized criticality optimization algorithm module:

[0067]

[0068] Among them, ζ(t) is the critical state of the system, λ is the adjustment coefficient, γ i and δ j is the weight coefficient of user behavior and environmental state, θ and φ are nonlinear adjustment indexes;

[0069] S84. According to the calculated critical state ζ(t), the system dynamically adjusts the power load distribution so that the system is kept in an operation mode close to the critical state. The adjusted power load distribution P(t) is dynamically adjusted according to the critical state ζ(t) so that the system operates stably under different load conditions.

[0070] S85, the self-learning module monitors the adjustment effect of the power load, and updates the user behavior feature vector U(t) and the environmental state vector E(t) based on real-time feedback, and continuously optimizes the weight coefficient γ of the self-organized criticality optimization algorithm i and δ j .

[0071] Optional modules include:

[0072] Voice input interface: including microphone array and photoacoustic effect capture device, responsible for receiving user's voice commands and collecting voice at long distance and in complex environment;

[0073] Multimodal speech recognition module: including speech feature extraction unit, background noise analysis unit and user speech model library, responsible for processing speech signals and generating text instructions;

[0074] Natural language processing module: including semantic parsing unit and context management unit, responsible for parsing text instructions and generating power supply control commands;

[0075] Environment and intention synchronization module: combines current environmental variables and user physiological signals to generate power control commands that match user needs;

[0076] Quantum superconducting power supply module: including superconductor power transmission unit and real-time load adjustment unit, responsible for power transmission and dynamic adjustment of power flow;

[0077] Self-healing nanosensor network: monitors the operating status of power supply equipment, detects potential damage such as fatigue and microcracks in materials, and activates self-healing material mechanisms for repair;

[0078] Self-learning module: Combines user operation habits and real-time environmental data, uses self-organizing criticality optimization algorithm to dynamically adjust power load distribution and keep the system running in a critical state;

[0079] Signal processing module: processes the electrical signal obtained from the voice input interface, performs noise filtering and signal enhancement, and generates a filtered signal.

[0080] The beneficial effects of the present invention are:

[0081] By introducing photoacoustic effect technology and quantum state coherent feature extraction methods, the system can capture and analyze the user's voice commands with higher accuracy. This combination of technologies makes speech recognition no longer limited by traditional noise interference and signal ambiguity, improves the accuracy and robustness of recognition, thereby reducing the possibility of misoperation and enhancing the user's interactive experience with the system.

[0082] The present invention realizes intelligent analysis of complex voice commands through a multimodal voice recognition module and a natural language processing module. This innovation enables the system to accurately understand the user's needs and generate precise power supply control commands. By analyzing the user's historical commands and real-time environmental data, the system can dynamically adjust the power supply strategy to adapt to changing needs and environmental conditions, thereby improving the flexibility and adaptability of power supply services.

[0083] The present invention uses self-healing materials and nanosensor networks to monitor and automatically repair power supply equipment in real time. This technology effectively extends the service life of the equipment, reduces maintenance costs, and can quickly repair the equipment when it is damaged, maintaining stable operation of the equipment. This innovation reduces the downtime and maintenance difficulty that may occur in traditional equipment maintenance, and improves the reliability and durability of the system. The introduction of self-learning modules and self-organizing criticality optimization algorithms enables the system to dynamically optimize the power load distribution based on user behavior characteristics and environmental conditions. This method not only improves the efficiency of the use of power resources, but also maintains the stability and efficiency of the system. In the case of load fluctuations and environmental changes, the system can adjust quickly to ensure the stability and reliability of power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0085] Figure 1 A flow chart of an intelligent power supply service system and method based on speech recognition technology proposed by the present invention;

[0086] Figure 2 Schematic diagram of a voice capture and signal processing module based on photoacoustic effect technology in the present invention;

[0087] Figure 3 This is a schematic diagram of the quantum superconducting power supply module in the present invention. DETAILED DESCRIPTION

[0088] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0089] refer to Figure 1-3 , an intelligent power supply service system and method based on speech recognition technology, comprising the following steps:

[0090] S1. The user issues a voice command through a smart device, and the system uses photoacoustic effect voice capture technology to detect the pressure changes of sound waves in the air through a laser beam to collect voice at long distances and in complex environments;

[0091] S2, input the collected voice signal into the multimodal voice recognition module, combine the voice features, background noise analysis and user voice model to perform voice recognition and generate corresponding text instructions;

[0092] S3. The generated text instructions are semantically parsed through natural language processing technology, and combined with environment and intention synchronization technology, the current environmental variables and user physiological signals are analyzed to generate power supply control commands that match user needs;

[0093] S4, transmitting the generated power supply control command to the quantum superconducting power supply module, using the zero resistance characteristic of the superconductor to perform power transmission, and adjusting the power flow within microseconds to adapt to real-time power supply load changes;

[0094] S5. After the power supply operation is completed, the system will provide feedback to the user through voice feedback and visual interface, and provide further power supply operation suggestions and adjustment options in combination with real-time environmental data;

[0095] S6. The system monitors the operating status of the power supply equipment through an embedded self-healing nanosensor network, which can detect fatigue and microcracks in the equipment materials at the micro level, and repair the damage and prevent failure by activating the self-healing material mechanism;

[0096] S7. When the system detects potential power supply failures and abnormal conditions, the embedded self-healing nanosensor network triggers the local repair function to complete the repair before the microcracks propagate;

[0097] S8. The system uses a self-learning module, combined with the user's operating habits and real-time environmental data, and a self-organizing criticality optimization algorithm to dynamically adjust the power load distribution, so that the system remains in a critical state and ensures the efficiency and stability of the power supply system.

[0098] In this implementation, S1 specifically includes:

[0099] S11. The user issues a voice command through the smart device, and the voice command is transmitted to the voice input interface of the system, which includes a microphone array and a photoacoustic effect capture device;

[0100] S12. The system uses photoacoustic effect voice capture technology to illuminate a specific area in the air with a laser beam. The propagation path of the laser beam in the air is described by the function P(x, y, z, t), where x, y, z are spatial coordinates and t is a time variable. P(x, y, z, t) is used to describe the energy distribution of the laser beam in different spaces and times.

[0101] S13. When the sound wave propagates in the air, the interaction between the sound wave and the laser beam causes a pressure change, and the pressure change is described by the formula ΔP=ρ·c·ΔV, where ΔP is the pressure change, ρ is the air density, c is the speed of sound, and ΔV is the change in air volume caused by the sound wave;

[0102] S14, the captured light signal S(t) is converted into an electrical signal by a photoelectric conversion device, and the waveform of the electrical signal is represented by S e (t) = k·S(t), where S e (t) is the electrical signal intensity, k is the photoelectric conversion coefficient;

[0103] S15. The converted electrical signal is input to the signal processing module, where S e (t) Noise filtering and signal enhancement are performed, and the resulting filtered signal is represented by E ′ (t).

[0104] In this implementation, S2 specifically includes:

[0105] S21, the filtered signal E obtained by the photoacoustic effect speech capture technology ′ (t) inputting into a multimodal speech recognition module, which includes a speech feature extraction unit, a background noise analysis unit and a user speech model library;

[0106] S22, in the speech feature extraction unit, filtering the signal E ′ (t) extract features and generate feature vector F(t). The feature vector F(t) describes the frequency characteristics of the speech signal, and its dimension is determined by the frequency distribution and time window of the signal;

[0107] S23, using quantum state coherent feature extraction technology to filter the signal E ′ (t) Enhanced processing is performed. Under the quantum computing framework, multiple dimensional features of the speech signal are encoded simultaneously through quantum state coherence, and an enhanced feature vector F is generated. q (t):

[0108]

[0109] Among them, α and β are quantum superposition coefficients, Q i (t) is the signal feature obtained by quantum state encoding, γ i is the weight coefficient related to the coherence of the quantum state, n is the number of dimensions of the quantum state;

[0110] S24, the enhanced feature vector F q (t) is transmitted to the background noise analysis unit and compared with F q (t) is subjected to noise filtering with the noise model N(t) to generate the feature vector F after noise filtering ′ (t);

[0111] S25, the feature vector F after noise filtering ′ (t) Input into the user voice model library and use the similarity function to calculate the feature vector F′ (t) and each user's speech model M i and generates the corresponding text instruction T(t).

[0112] In this implementation, S3 specifically includes:

[0113] S31, inputting the text instruction T(t) generated by the multimodal speech recognition module into the natural language processing module, the natural language processing module includes a semantic parsing unit and a context management unit, the semantic parsing unit performs semantic parsing on the text instruction T(t) through a deep learning model, extracts the core intention I(T) and related operation parameters in the instruction, and generates a semantic vector V(T);

[0114] S32, the generated semantic vector V(T) is input to the context management unit, and the user's behavior pattern is analyzed by combining the semantic parsing results of the historical user instructions H(T) and the current text instruction T(t), and an extended context vector V is generated. C (T);

[0115] S33, context vector V C (T) is input to the environment and intention synchronization module, which combines the current environmental variables E(t) and the user's physiological signals B(t) to generate the environmental perception vector V E (t):

[0116]

[0117] Among them, σ is the activation function, W1, W2, W3 are weight matrices, λ k is the dynamically adjusted weight coefficient, φ k (V C (T), E(t), B(t)) are the features extracted from semantic vectors, environmental data, and user physiological signals;

[0118] S34. Under the framework of the environment and intention synchronization technology, a power supply control command C(t) matching the user's needs is generated. The power supply control command is generated by a multi-dimensional nonlinear combination function:

[0119]

[0120] Among them, ψ is a nonlinear activation function, α i is the dynamic weight coefficient related to user needs and environmental variables, g i (V E (t)) is a nonlinear combination of control laws;

[0121] S35 , transmitting the generated power supply control command C(t) to the power supply control module, and the power supply control module adjusts the operating state of the power supply device according to the command to respond to changes in user needs and environmental conditions.

[0122] In this implementation manner, the S4 specifically includes:

[0123] S41, transmitting the generated power supply control command C(t) to the quantum superconducting power supply module, the quantum superconducting power supply module includes a superconductor power transmission unit and a real-time load adjustment unit, and the power supply control command C(t) is used to control the power transmission process of the superconductor;

[0124] S42. In the quantum superconducting power supply module, the zero resistance characteristic of the superconductor is used to perform power transmission:

[0125]

[0126] Where V(t) is the voltage, R sc (t) is the resistance of the superconductor. In the zero resistance state, R sc (t)≈0, so that the current I(t) is mainly controlled by the voltage V(t), It represents the instantaneous rate of change of voltage, β is the nonlinear regulation index used to control the impact intensity of voltage transients, and κ is the proportionality coefficient related to the characteristics of superconductor materials;

[0127] S43. During the power transmission process, the quantum superconducting power supply module dynamically adjusts the power flow in microseconds according to the power supply control command C(t) to adapt to the changes in the real-time power supply load. The power flow regulation process is achieved through a nonlinear combination of voltage and current to meet the needs under different load conditions.

[0128] S44, the real-time load regulation unit continuously monitors the power flow and dynamically adjusts the power transmission according to the real-time data of load changes to ensure the stability and response speed of the system under load fluctuations.

[0129] In this implementation manner, S6 specifically includes:

[0130] S61. The system monitors the operating status of the power supply equipment in real time through an embedded self-healing nanosensor network, which is composed of multiple distributed nanosensors. The multiple distributed nanosensors detect fatigue, microcracks and other potential structural damage of the equipment material at the micro level.

[0131] S62, the sensor unit detects the rate of change of strain energy density inside the material To identify fatigue and microcrack formation:

[0132]

[0133] Where E is the elastic modulus of the material, ∈(t) is the strain of the material, Describes the direct effect of material elastic modulus and strain on strain energy density over time. The cumulative effect of strain acceleration on energy density is described by integration, α and β are the adjustment coefficient and time decay factor respectively;

[0135] S63, when the sensor detects the strain energy density change rate When the preset threshold is exceeded, the sensor network transmits the damage information D(t) to the self-healing material activation unit through the wireless communication module. The damage information includes the crack location, size and development trend. The sensor network updates this data in real time.

[0136] S64, the self-healing material activation unit activates the self-healing material mechanism according to the damage information D(t) transmitted by the sensor network, and triggers the polymerization reaction of the self-healing material through local heating and chemical reaction:

[0137] Mn+C→Mn +1 +Q;

[0138] Among them, M n represents the polymer chain in the self-healing material, C is the catalyst, M n+1 is the longer polymer chain generated by the reaction, and Q is the by-product released during the reaction;

[0139] S65. After the repair is completed, the self-healing nanosensor network continues to monitor the strain energy density change in the repair area.

[0140] ˙

[0141] The repair rate W(t) is calculated, and the monitored repair data is compared with the original damage information D(t). Through further material stress adjustment mechanism, the internal stress distribution of the material is adjusted to prevent the formation of new damage and the recurrence of the original damage.

[0142] In this implementation manner, S8 specifically includes:

[0143] S81. Through the self-learning module, the system collects and analyzes the user's operating habits and real-time environmental data, including the user's power usage pattern, device usage frequency, ambient temperature, humidity, and power demand fluctuations;

[0144] S82. The system constructs the user behavior feature vector U(t) and the environment state vector E(t) based on the collected data, where:

[0145] U(t)=[u1(t),u2(t),…,un(t)];

[0146] E(t)=[E1(t),E2(t),…,Em(t)];

[0147] S83, input the user behavior feature vector U(t) and the environment state vector E(t) into the self-organized criticality optimization algorithm module:

[0148]

[0149] Among them, ζ(t) is the critical state of the system, λ is the adjustment coefficient, γ i and δ j is the weight coefficient of user behavior and environmental state, θ and φ are nonlinear adjustment indexes;

[0150] S84. According to the calculated critical state ζ(t), the system dynamically adjusts the power load distribution so that the system is kept in an operation mode close to the critical state. The adjusted power load distribution P(t) is dynamically adjusted according to the critical state ζ(t) so that the system operates stably under different load conditions.

[0151] S85, the self-learning module monitors the adjustment effect of the power load, and updates the user behavior feature vector U(t) and the environmental state vector E(t) based on real-time feedback, and continuously optimizes the weight coefficient γ of the self-organized criticality optimization algorithm i and δ j .

[0152] In this embodiment, the following modules are included:

[0153] Voice input interface: including microphone array and photoacoustic effect capture device, responsible for receiving user's voice commands and collecting voice at long distance and in complex environment;

[0154] Multimodal speech recognition module: including speech feature extraction unit, background noise analysis unit and user speech model library, responsible for processing speech signals and generating text instructions;

[0155] Natural language processing module: including semantic parsing unit and context management unit, responsible for parsing text instructions and generating power supply control commands;

[0156] Environment and intention synchronization module: combines current environmental variables and user physiological signals to generate power control commands that match user needs;

[0157] Quantum superconducting power supply module: including superconductor power transmission unit and real-time load adjustment unit, responsible for power transmission and dynamic adjustment of power flow;

[0158] Self-healing nanosensor network: monitors the operating status of power supply equipment, detects potential damage such as fatigue and microcracks in materials, and activates self-healing material mechanisms for repair;

[0159] Self-learning module: Combines user operation habits and real-time environmental data, uses self-organizing criticality optimization algorithm to dynamically adjust power load distribution and keep the system running in a critical state;

[0160] Signal processing module: processes the electrical signal obtained from the voice input interface, performs noise filtering and signal enhancement, and generates a filtered signal.

[0161] Embodiment 1:

[0162] In order to verify the feasibility of the present invention in implementation, the present invention is applied to a certain intelligent office building, where there are a variety of electrical equipment, including lighting systems, air conditioning systems, computer servers, and various office appliances. The power supply management of the building traditionally relies on manual operation and fixed schedules, and cannot flexibly respond to real-time changes in power demand and sudden equipment failures. This not only leads to energy waste, but also increases maintenance costs and the risk of power outages. The intelligent power supply service system based on voice recognition technology proposed in the present invention can effectively solve this problem and realize the intelligence and automation of power supply management.

[0163] In this office building, the deployment of this system includes multiple intelligent voice input interfaces and sensor networks. Employees only need to issue voice commands through devices such as smart speakers or mobile phones, such as "turn on the lights in the conference room" or "adjust the office temperature to 24 degrees", and the system can accurately capture and parse these commands. Photoacoustic effect technology can still ensure the accurate capture of voice commands in a noisy office environment, and then process these signals through quantum state coherent feature extraction technology, further improving the accuracy of speech recognition. The natural language processing module performs semantic analysis on the parsed text instructions, generates specific power supply control commands, and executes them in real time.

[0164] In actual application, during a staff meeting, the lighting and air conditioning systems of the conference room were activated through voice commands. According to the command, the system turned on the LED lights in energy-saving mode and set the air conditioning temperature to 22 degrees. During this process, the system continuously optimized the energy consumption configuration through the self-learning module to ensure that the equipment operated in the most energy-saving state. After the meeting, the system automatically turned off unnecessary electrical equipment and adjusted the lighting brightness and air conditioning temperature of the entire floor based on the information fed back by the sensors to adapt to the flow of people after the meeting.

[0165] During this period, the self-healing nanosensor network in the system monitored the status of the power supply equipment in real time. One night, the sensor detected a tiny crack inside a power supply device in the server room. Through the precise positioning of the nanosensor, the system automatically activated the repair function of the self-healing material, and successfully repaired the crack through local heating and polymerization reaction, avoiding possible equipment failure. The repair process was completed in just 10 minutes without human intervention and without any impact on the normal operation of the server.

[0166] During the peak period of winter electricity consumption, the system analyzed the electricity consumption patterns and weather data of the past month through the self-learning module and predicted future power demand. The system automatically adjusted the power supply load in the office area and public area, giving priority to the stable operation of core equipment, while reducing the power demand of non-critical equipment to avoid the waste of power resources. Through the quantum superconducting power supply module, the system still maintained stable power transmission under high load conditions, and there was no fluctuation in the power supply of lighting, air conditioning or servers, ensuring the normal operation of the office building.

[0167] Data analysis shows that since the introduction of the intelligent power supply service system of the present invention, the overall energy consumption of the building has dropped by about 20%. In the past year, the system has processed more than 1,000 user voice commands, with an average response time of less than 2 seconds. In addition, a total of 15 microcrack events were detected and repaired by the self-healing nanosensor network, avoiding at least 3 possible equipment downtime accidents. Compared with traditional systems, maintenance costs are reduced by about 30%, and the risk of power outages is significantly reduced.

[0168] During the hot summer month, the system successfully adjusted the operation mode of the air conditioning system by analyzing the temperature data, weather forecast and user habits in the building, so that the average indoor temperature was always between 24 and 26 degrees. Compared with before, power consumption was reduced by about 15%, and the comfort score of employees increased by 20%. Through intelligent power supply management, the building can flexibly respond to load fluctuations and environmental changes in various complex power consumption scenarios and maintain efficient and stable operation.

[0169] Table 1 System performance and optimization data table

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[0171]

[0172] Table 2 Overview of repair events in self-healing nanosensor networks

[0173]

[0174] Through the analysis of the above data, it can be seen that the intelligent power supply service system of the present invention has shown significant advantages in many aspects. The average voice command response time of the system is less than 2 seconds, which can quickly process the user's voice command, provide instant power supply control response, and significantly improve the user experience and system flexibility. The system has processed more than 1,000 commands in actual applications, proving its effectiveness and reliability in daily office environments. In addition, through intelligent power supply management and load optimization, the average energy consumption of the system has dropped by 20%, and under high temperature and high load conditions in summer, the power consumption has been reduced by 15%, while improving the employee comfort score. The self-healing nanosensor network had a total of 15 repair events, with an average repair time of 10 minutes, which effectively avoided the risk of equipment failure and downtime and ensured the stable operation of key facilities. Especially during peak power consumption, the system reduces energy consumption by 15% by intelligently adjusting the power load distribution, ensuring the continuous and stable power supply of key equipment. In summary, the present invention successfully realizes the intelligent management of the power supply system through advanced voice recognition technology, self-healing material application and intelligent load optimization algorithm, which not only improves energy efficiency, but also enhances the reliability and adaptability of the system, and has broad application prospects.

[0175] In summary, the present invention effectively solves the problems of high energy consumption, slow response, high maintenance cost, etc. in traditional power supply systems through an intelligent and automated power supply service system. Through the comprehensive application of speech recognition, natural language processing, self-healing materials and quantum superconducting technology, the system not only improves the accuracy and efficiency of power supply management, but also greatly reduces the risk of power outages and equipment failures, providing users with safer and more reliable power supply services.

[0176] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent power supply service method based on voice recognition technology, characterized in that: The steps include: S1. The user issues a voice command through a smart device, and the system uses photoacoustic effect voice capture technology to detect the pressure changes of sound waves in the air through a laser beam to collect voice at long distances and in complex environments; S2, input the collected voice signal into the multimodal voice recognition module, combine the voice features, background noise analysis and user voice model to perform voice recognition and generate corresponding text instructions; S3. The generated text instructions are semantically parsed through natural language processing technology, and combined with environment and intention synchronization technology, the current environmental variables and user physiological signals are analyzed to generate power supply control commands that match user needs; S4, transmitting the generated power supply control command to the quantum superconducting power supply module, utilizing the zero resistance characteristic of the superconductor to perform power transmission, and adjusting the power flow within microseconds to adapt to real-time power supply load changes; S5. After the power supply operation is completed, the system will provide feedback to the user through voice feedback and visual interface, and provide further power supply operation suggestions and adjustment options in combination with real-time environmental data; S6. The system monitors the operating status of the power supply equipment through an embedded self-healing nanosensor network, which can detect fatigue and microcracks in the equipment materials at the micro level, and repair the damage and prevent failure by activating the self-healing material mechanism; S7. When the system detects potential power supply failures and abnormal conditions, the embedded self-healing nanosensor network triggers the local repair function to complete the repair before the microcracks propagate; S8. The system uses a self-learning module, combined with the user's operating habits and real-time environmental data, and a self-organizing criticality optimization algorithm to dynamically adjust the power load distribution, so that the system remains in a critical state and ensures the efficiency and stability of the power supply system.

2. According to claim 1, a smart power supply service method based on voice recognition technology is characterized in that: The S1 specifically includes: S11. The user issues a voice command through the smart device, and the voice command is transmitted to the voice input interface of the system, which includes a microphone array and a photoacoustic effect capture device; S12. The system uses photoacoustic effect voice capture technology to illuminate a specific area in the air with a laser beam. The propagation path of the laser beam in the air is described by the function P(x, y, z, t), where x, y, z are spatial coordinates and t is a time variable. P(x, y, z, t) is used to describe the energy distribution of the laser beam in different spaces and times. S13. When the sound wave propagates in the air, the interaction between the sound wave and the laser beam causes a pressure change, and the pressure change is described by the formula ΔP=ρ·c·ΔV, where ΔP is the pressure change, ρ is the air density, c is the speed of sound, and ΔV is the change in air volume caused by the sound wave; S14, the captured light signal S(t) is converted into an electrical signal by a photoelectric conversion device, and the waveform of the electrical signal is represented by S e (t) = k·S(t), where S e (t) is the electrical signal intensity, k is the photoelectric conversion coefficient; S15. The converted electrical signal is input to the signal processing module, where S e (t) is subjected to noise filtering and signal enhancement, and the resulting filtered signal is denoted as E′(t).

3. The intelligent power supply service method based on voice recognition technology according to claim 1 is characterized in that: The S2 specifically includes: S21, inputting the filtered signal E′(t) obtained by the photoacoustic effect speech capture technology into a multimodal speech recognition module, which includes a speech feature extraction unit, a background noise analysis unit and a user speech model library; S22, in the speech feature extraction unit, extracting features from the filtered signal E′(t) to generate a feature vector F(t), wherein the feature vector F(t) describes the frequency characteristics of the speech signal, and its dimension is determined by the frequency distribution and time window of the signal; S23, using quantum state coherence feature extraction technology to enhance the filtered signal E′(t). Under the quantum computing framework, multiple dimensional features of the speech signal are encoded simultaneously through quantum state coherence, and an enhanced feature vector F is generated. q (t): Among them, α and β are quantum superposition coefficients, Q i (t) is the signal feature obtained by quantum state encoding, γ i is the weight coefficient related to the coherence of the quantum state, n is the number of dimensions of the quantum state; S24, the enhanced feature vector F q (t) is transmitted to the background noise analysis unit and compared with F q (t) performs noise filtering with the noise model N(t) to generate the noise-filtered feature vector F′(t); S25, input the noise-filtered feature vector F′(t) into the user voice model library, and use the similarity function to calculate the feature vector F′(t) and each user voice model M i and generates the corresponding text instruction T(t).

4. The intelligent power supply service method based on voice recognition technology according to claim 1 is characterized in that: The S3 specifically includes: S31, inputting the text instruction T(t) generated by the multimodal speech recognition module into the natural language processing module, the natural language processing module includes a semantic parsing unit and a context management unit, the semantic parsing unit performs semantic parsing on the text instruction T(t) through a deep learning model, extracts the core intention I(T) and related operation parameters in the instruction, and generates a semantic vector V(T); S32, the generated semantic vector V(T) is input to the context management unit, and the user's behavior pattern is analyzed by combining the semantic parsing results of the historical user instructions H(T) and the current text instruction T(t), and an extended context vector V is generated. C (T); S33, context vector V C (T) is input to the environment and intention synchronization module, which combines the current environmental variables E(t) and the user's physiological signals B(t) to generate the environmental perception vector V E (t): Among them, σ is the activation function, W1, W2, W3 are weight matrices, λ k is the dynamically adjusted weight coefficient, φ k (V C (T), E(t), B(t)) are the features extracted from semantic vectors, environmental data, and user physiological signals; S34. Under the framework of the environment and intention synchronization technology, a power supply control command C(t) matching the user's needs is generated. The power supply control command is generated by a multi-dimensional nonlinear combination function: Among them, ψ is a nonlinear activation function, α i is the dynamic weight coefficient related to user needs and environmental variables, g i (V E (t)) is a nonlinear combination of control laws; S35 , transmitting the generated power supply control command C(t) to the power supply control module, and the power supply control module adjusts the operating state of the power supply device according to the command to respond to changes in user needs and environmental conditions.

5. The intelligent power supply service method based on voice recognition technology according to claim 1 is characterized in that: The S4 specifically includes: S41, transmitting the generated power supply control command C(t) to the quantum superconducting power supply module, the quantum superconducting power supply module includes a superconductor power transmission unit and a real-time load adjustment unit, and the power supply control command C(t) is used to control the power transmission process of the superconductor; S42. In the quantum superconducting power supply module, the zero resistance characteristic of the superconductor is used to perform power transmission: Where V(t) is the voltage, R sc (t) is the resistance of the superconductor. In the zero resistance state, R sc (t)≈0, so that the current I(t) is mainly controlled by the voltage V(t), It represents the instantaneous rate of change of voltage, β is the nonlinear regulation index used to control the impact intensity of voltage transients, and κ is the proportionality coefficient related to the characteristics of superconductor materials; S43. During the power transmission process, the quantum superconducting power supply module dynamically adjusts the power flow in microseconds according to the power supply control command C(t) to adapt to the changes in the real-time power supply load. The power flow regulation process is achieved through a nonlinear combination of voltage and current to meet the needs under different load conditions. S44, the real-time load regulation unit continuously monitors the power flow and dynamically adjusts the power transmission according to the real-time data of load changes to ensure the stability and response speed of the system under load fluctuations.

6. The intelligent power supply service method based on voice recognition technology according to claim 1, characterized in that: The S6 specifically includes: S61. The system monitors the operating status of the power supply equipment in real time through an embedded self-healing nanosensor network, which is composed of multiple distributed nanosensors. The multiple distributed nanosensors detect fatigue, microcracks and other potential structural damage of the equipment material at the micro level. S62, the sensor unit detects the rate of change of strain energy density inside the material To identify fatigue and microcrack formation: Where E is the elastic modulus of the material, ∈(t) is the strain of the material, Describes the direct effect of material elastic modulus and strain on strain energy density over time. The cumulative effect of strain acceleration on energy density is described by integration, α and β are the adjustment coefficient and time decay factor respectively; S63, when the sensor detects the strain energy density change rate When the preset threshold is exceeded, the sensor network transmits the damage information D(t) to the self-healing material activation unit through the wireless communication module. The damage information includes the crack location, size and development trend. The sensor network updates this data in real time. S64, the self-healing material activation unit activates the self-healing material mechanism according to the damage information D(t) transmitted by the sensor network, and triggers the polymerization reaction of the self-healing material through local heating and chemical reaction: M n +C→M n+1 +Q; Among them, M n represents the polymer chain in the self-healing material, C is the catalyst, M n+1 is the longer polymer chain generated by the reaction, and Q is the by-product released during the reaction; S65. After the repair is completed, the self-healing nanosensor network continues to monitor the rate of change of strain energy density in the repaired area. The monitored repair data is compared with the original damage information D(t), and the internal stress distribution of the material is adjusted through further material stress adjustment mechanism to prevent the formation of new damage and the recurrence of the original damage.

7. The intelligent power supply service method based on voice recognition technology according to claim 1 is characterized in that: The S8 specifically includes: S81. Through the self-learning module, the system collects and analyzes the user's operating habits and real-time environmental data, including the user's power usage pattern, device usage frequency, ambient temperature, humidity, and power demand fluctuations; S82. The system constructs the user behavior feature vector U(t) and the environment state vector E(t) based on the collected data: U(t)=[u1(t),u2(t),…,u n (t)]; E(t)=[E1(t),E2(t),…,E m (t)]; S83, input the user behavior feature vector U(t) and the environment state vector E(t) into the self-organized criticality optimization algorithm module: Among them, ζ(t) is the critical state of the system, λ is the adjustment coefficient, γ i and δ j is the weight coefficient of user behavior and environmental state, θ and φ are nonlinear adjustment indexes; S84. According to the calculated critical state ζ(t), the system dynamically adjusts the power load distribution so that the system is kept in an operation mode close to the critical state. The adjusted power load distribution P(t) is dynamically adjusted according to the critical state ζ(t) so that the system operates stably under different load conditions. S85, the self-learning module monitors the adjustment effect of the power load, and updates the user behavior feature vector U(t) and the environmental state vector E(t) based on real-time feedback, and continuously optimizes the weight coefficient γ of the self-organized criticality optimization algorithm i and δ j .

8. An intelligent power supply service system based on voice recognition technology, characterized in that: Includes the following modules: Voice input interface: including microphone array and photoacoustic effect capture device, responsible for receiving user's voice commands and collecting voice at long distance and in complex environment; Multimodal speech recognition module: including speech feature extraction unit, background noise analysis unit and user speech model library, responsible for processing speech signals and generating text instructions; Natural language processing module: including semantic parsing unit and context management unit, responsible for parsing text instructions and generating power supply control commands; Environment and intention synchronization module: combines current environmental variables and user physiological signals to generate power control commands that match user needs; Quantum superconducting power supply module: including superconductor power transmission unit and real-time load adjustment unit, responsible for power transmission and dynamic adjustment of power flow; Self-healing nanosensor network: monitors the operating status of power supply equipment, detects potential damage such as fatigue and microcracks in materials, and activates self-healing material mechanisms for repair; Self-learning module: Combines user operation habits and real-time environmental data, uses self-organizing criticality optimization algorithm to dynamically adjust power load distribution and keep the system running in a critical state; Signal processing module: processes the electrical signal obtained from the voice input interface, performs noise filtering and signal enhancement, and generates a filtered signal.

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