Information security defense method and device for automatic fire alarm system

Abstract

The present invention discloses an information security defense method and device for an automatic fire alarm system, relating to the field of intelligent firefighting quantum security technology. The method includes calculating a normalized coupling coefficient based on dynamic light pattern vectors and current decay rates, outputting a security authentication flag when the normalized coupling coefficient is within a safe range, generating a network attack warning signal when the coefficient is below the safe range, and generating a physical tampering fuse signal when the coefficient is above the safe range. The method also maintains a normal alarm path based on the security authentication flag, shuts down external communication ports and increases monitoring density based on the network attack warning signal, and triggers an electrochemical reaction based on the fuse signal to corrode specific metal circuits and activate an audible and visual alarm device, generating a fuse feedback state. Through electrochemical fusing and quantum unclonable credential generation, the present invention achieves active defense and identity authentication against physical layer attacks, enhancing the information security protection and recovery capabilities of the fire alarm system.

Description

Technical Field

[0001] The present invention relates to the field of intelligent firefighting quantum security technology, and in particular to an information security defense method and device for an automatic fire alarm system. Background Art

[0002] With the acceleration of urbanization and the increasing complexity of buildings, automatic fire alarm systems have become a core component of modern building safety systems. Traditional automatic fire alarm systems primarily rely on physical sensors to respond to environmental changes and transmit alarm signals to fire control centers via wired or wireless communication networks. In recent years, with the development of the Internet of Things, cloud computing, and edge computing technologies, fire alarm systems have gradually evolved towards intelligence and networking, forming a cloud-based intelligent linkage alarm architecture. This intelligent linkage alarm architecture not only offers faster response times and remote monitoring capabilities, but also incorporates data fusion and machine learning algorithms to improve recognition accuracy and minimize false alarms.

[0003] Currently, the security defense mechanisms of most fire alarm systems remain at the passive detection level, lacking active defense capabilities. Specifically, existing technologies suffer from two significant flaws: first, they lack real-time identification and response mechanisms for cyberattacks. Most fire alarm systems can only record abnormal events but are unable to take immediate isolation and fuse measures when an attack occurs. Second, physical layer security is weakly designed. Existing anti-tampering mechanisms often rely on a single mechanical structure or electronic signal feedback, lacking self-repair or dynamic reconstruction capabilities, making it difficult to restore original functionality after damage. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an information security defense method for an automatic fire alarm system to solve the problems of weak active response capability and insufficient physical layer security in information security defense of existing fire alarm systems.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an information security defense method for an automatic fire alarm system, comprising: installing a laser scanning head at the end of a fire sprinkler pipe and pre-coating the detector with a nanocarbon layer, while applying a constant voltage to an ionization smoke sensor to generate a reference ionization current signal; when the ambient smoke concentration exceeds an alarm threshold, capturing scattered light spots from the nanocarbon layer and extracting real-time optical features to generate a dynamic light pattern vector, synchronously acquiring a tunneling current, and calculating a current decay rate; calculating a normalized coupling coefficient based on the dynamic light pattern vector and the current decay rate, outputting a security authentication mark when the normalized coupling coefficient is within a safe range, generating a network attack warning signal when the normalized coupling coefficient is below the safe range, and generating a physical tampering fuse signal when the normalized coupling coefficient is above the safe range; maintaining a normal alarm path based on the security authentication mark, closing external communication ports and increasing monitoring density based on the network attack warning signal, and triggering an electrochemical reaction to corrode specific metal circuits and activate an audible and visual alarm device based on the fuse signal, generating a fuse feedback state; controlling the combustion of a specific combustible medium to generate a new carbon deposit layer to cover the original nanostructure based on the fuse feedback state, resetting the reference ionization current signal after verifying the optical feature match, and generating an information security defense completion signal.

[0008] As a preferred solution of the information security defense method of the automatic fire alarm system of the present invention, wherein: the generation of the reference ionization current signal comprises the following specific steps:

[0009] An annular mounting base is embedded in the flange at the end of the fire sprinkler pipe, and a micro laser emitter and a scanning galvanometer are integrated, so that the light spot focus area of ​​the scanning galvanometer is aligned with the preset processing area of ​​the detector surface substrate;

[0010] The predetermined processing area is treated by argon plasma etching and a pulsed laser deposition process is used to generate a graphene-silicon carbide composite film.

[0011] The surface substrate of the graphene-silicon carbide composite film is assembled onto an ionization-type smoke sensor, and a constant voltage is applied to the ionization chamber electrodes to generate a reference ionization current signal.

[0012] As a preferred solution of the information security defense method of the automatic fire alarm system of the present invention, when the ambient smoke concentration exceeds the alarm threshold, the scattered light spots of the nanocarbonized layer are captured and the real-time optical characteristics are extracted to generate dynamic light pattern vectors, and the tunneling current is synchronously obtained and the current decay rate is calculated. The specific steps are as follows:

[0013] When the ambient smoke concentration exceeds the alarm threshold, a chaotic modulation signal is injected into the micro laser emitter to drive the scanning galvanometer to perform nonlinear trajectory scanning;

[0014] The dynamic scattered light spots of the nanocarbon layer are captured based on nonlinear trajectory scanning, and the light field gradient tensor is calculated by fractional differential operators.

[0015] Generate dynamic light pattern vectors based on the light field gradient tensor and synchronously trigger the high-speed ADC to collect the ionization chamber tunneling current waveform;

[0016] Based on the tunneling current waveform of the ionization chamber, the current decay rate is calculated using the third-order derivative penalty model.

[0017] As a preferred solution of the information security defense method of the automatic fire alarm system of the present invention, wherein: the normalized coupling coefficient is calculated based on the dynamic light pattern vector and the current attenuation rate, and a security authentication mark is output when the normalized coupling coefficient is in a safe range, a network attack warning signal is generated when it is below the safe range, and a physical tampering fuse signal is generated when it is above the safe range. The specific steps are as follows:

[0018] According to the dynamic light pattern vector and current decay rate, the quantum chaos tensor is generated by the tensor direct product of the quantum evolution operator and the chaotic synchronization derivative.

[0019] The quantum chaos tensor is decomposed into multiple fractals, and the normalized coupling coefficient is calculated through boundary integral and kernel space analysis.

[0020] When the normalized coupling coefficient is within the safety range, gradient verification is performed and the safety certification flag is activated;

[0021] When the normalized coupling coefficient is lower than the lower limit of the safety interval, the network attack warning signal is activated;

[0022] When the normalized coupling coefficient is higher than the upper limit of the safety interval, the physical tamper fuse signal is activated.

[0023] As a preferred solution of the information security defense method of the automatic fire alarm system of the present invention, wherein: the normal alarm path is maintained according to the security authentication mark, the external communication port is closed according to the network attack warning signal, and the monitoring density is increased, the specific steps are as follows:

[0024] When the security authentication mark is activated, a quantum trust chain is established and a Bell inequality test is performed through entangled photon distribution. When the test value is continuously greater than or equal to the critical value of quantum nonlocality, the normal alarm path is maintained;

[0025] When the network attack warning signal is activated, all external communication ports are closed and the quantum monitoring state is maintained through the traffic entropy dynamic fuse mechanism;

[0026] While the quantum monitoring state is maintained, if the port traffic entropy returns to normal, the communication link is reconstructed through gradual port opening and quantum trust verification. If it fails, it returns to the fuse state, and if it succeeds, the monitoring density is enhanced.

[0027] As a preferred solution of the information security defense method of the automatic fire alarm system of the present invention, wherein: the electrochemical reaction triggered by the fuse signal corrodes the specific metal circuit and activates the sound and light alarm device to generate the fuse feedback state, the specific steps are as follows:

[0028] When the fusing signal is activated, an indium gallium alloy electrolyte is sprayed onto the target metal line and a programmable voltage sequence is applied to generate a dynamic corrosion current;

[0029] When the dynamic corrosion current exceeds the critical current of quantum phase transition, the quantum dot laser pulse is triggered to excite the acoustic wave signal;

[0030] Perform quantum state analysis on the acoustic signal and generate an unclonable certificate for the fuse area using the Bloch sphere mapping algorithm;

[0031] After binding the unclonable credential to the acoustic signal feature, obtain the circuit breaker feedback status.

[0032] As a preferred solution of the information security defense method of the automatic fire alarm system of the present invention, wherein: according to the feedback state of the fuse, the specific combustible medium is controlled to burn to generate a new carbon deposition layer covering the original nanostructure, the reference ionization current signal is reset after verifying the optical feature matching, and an information security defense completion signal is generated. The specific steps are as follows:

[0033] According to the feedback state of the melting, a liquid crystal-carbon-rich precursor mixture is injected into the melting area, and an alternating electric field is applied to induce molecular alignment;

[0034] After the molecular alignment is completed, the laser ignition is triggered to generate a directional carbon deposition layer covering the original nanostructure, and the optical characteristic data is obtained through the quantum dot fluorescence lifetime detection;

[0035] The optical characteristic data is subjected to quantum authentication standards, and the quantum annealing algorithm is used to optimize and reset the baseline ionization current signal to generate an information security defense completion signal.

[0036] In a second aspect, the present invention provides an information security defense device for an automatic fire alarm system, comprising a signal acquisition module, a feature extraction module, a security assessment module, a response execution module, and a self-repair module. The signal acquisition module is configured to install a laser scanning head at the end of a fire sprinkler pipe and pre-coat the detector with a nano-carbonized layer, while applying a constant voltage to an ionization smoke sensor to generate a reference ionization current signal.

[0037] The feature extraction module is used to capture the scattered light spots of the nanocarbonized layer and extract real-time optical features when the ambient smoke concentration exceeds the alarm threshold, generate dynamic light pattern vectors, synchronously obtain tunneling current and calculate the current decay rate; the security assessment module is used to calculate the normalized coupling coefficient based on the dynamic light pattern vectors and the current decay rate, and output a security authentication mark when the normalized coupling coefficient is in the safe range. If it is lower than the safe range, a network attack warning signal is generated, and if it is higher than the safe range, a physical tampering fuse signal is generated; the response execution module is used to maintain the normal alarm path according to the security authentication mark, close the external communication port and enhance the monitoring density according to the network attack warning signal, and trigger an electrochemical reaction to corrode specific metal lines and activate the sound and light alarm device according to the fuse signal to generate a fuse feedback state; the self-repair module is used to control the combustion of a specific combustible medium to generate a new carbon deposit layer to cover the original nanostructure according to the fuse feedback state, reset the baseline ionization current signal after verifying the optical feature match, and generate an information security defense completion signal.

[0038] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the information security defense method of the automatic fire alarm system as described in the first aspect of the present invention is implemented.

[0039] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the information security defense method of the automatic fire alarm system as described in the first aspect of the present invention.

[0040] The beneficial effects of the present invention are: by combining laser scanning with a nano-carbonized layer, dynamic light pattern vectors and current attenuation rates are generated, thereby realizing multi-dimensional perception of environmental disturbances; further, through electrochemical fusing and quantum unclonable credential generation, active defense and identity authentication under physical layer attacks are realized, thereby improving the information security protection capability and recovery capability of the fire alarm system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 This is a flowchart of the information security defense method for the automatic fire alarm system.

[0043] Figure 2 Flowchart for generating a reference ionization current signal.

[0044] Figure 3 Flowchart for normalized coupling coefficient calculation and response.

[0045] Figure 4 This is the flowchart of fuse feedback self-repair. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0049] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides an information security defense method for an automatic fire alarm system, comprising the following steps:

[0050] S1: Install a laser scanning head at the end of the fire sprinkler pipe and pre-coat the detector with a nano-carbonized layer. At the same time, apply a constant voltage to the ionization smoke sensor to generate a reference ionization current signal.

[0051] S1.1: An annular mounting base is embedded in the end flange of the fire sprinkler pipe, and a micro laser emitter and a scanning galvanometer are integrated so that the light spot focus area of ​​the scanning galvanometer is aligned with the preset processing area of ​​the detector surface substrate.

[0052] The specific process includes embedding an annular mounting seat at the end flange of the fire sprinkler pipe. The annular mounting seat is processed and formed from 316L stainless steel. A slot structure is set on the inner circumference of the annular mounting seat. The micro laser emitter is fixed to the radial center position of the annular mounting seat through the slot structure. The scanning galvanometer is installed on the end face of the annular mounting seat through a four-point bolt connection. The normal direction of the scanning galvanometer reflector surface is at a 45-degree angle to the outgoing light beam of the micro laser emitter. The micro laser emitter outputs a continuous laser beam with a wavelength of 532 nanometers. The laser beam is reflected by the scanning galvanometer to form a circular light spot with a diameter of 2 mm. The focusing area of ​​the light spot accurately covers the preset processing area of ​​the detector surface substrate after plasma cleaning. The preset processing area is a circular area with a diameter of 5 mm at the center of the surface substrate.

[0053] S1.2: The predetermined processing area is treated as a processing object, and an argon plasma etching process is performed, and a pulsed laser deposition process is used to generate a graphene-silicon carbide composite film.

[0054] The specific process includes placing the preset processing area of ​​the surface substrate in a vacuum chamber, connecting the vacuum chamber to an argon gas inlet pipe and a vacuum pump group, and starting the vacuum pump group to reduce the chamber pressure to 5×10 -3 Pascal, 99.999% pure argon gas is introduced to maintain the chamber pressure at 10 Pascal, and 13.56 MHz radio frequency power is applied to generate argon plasma. The argon plasma bombards the surface of the preset treatment area at vertical incidence for 300 seconds, removing surface contaminants and forming an atomically flat substrate. After the argon gas inlet is turned off, the vacuum state is maintained. Graphite target and silicon carbide target are installed in the chamber. The graphite target and silicon carbide target are symmetrically arranged at a 45-degree angle on both sides of the preset treatment area. A Nd:YAG pulse laser was used to alternately bombard graphite targets and silicon carbide targets. The laser pulse energy density was set to 300 millijoules per square centimeter, the pulse width was 10 nanoseconds, and the repetition frequency was 10 Hz. After bombarding the graphite target with 1000 pulses, a single-layer graphene structure was deposited in the preset processing area. The silicon carbide target was switched to be bombarded with 500 pulses to form silicon carbide nanocrystals. After 20 cycles of alternating deposition, a graphene-silicon carbide composite film with a thickness of 200 nanometers was formed on the surface of the preset processing area.

[0055] S1.3: Assemble the surface substrate of the graphene-silicon carbide composite film to the ionization smoke sensor, and apply a constant voltage to the ionization chamber electrodes to generate a reference ionization current signal.

[0056] The specific process includes fixing the surface substrate to the inner wall of the ionization smoke sensor housing with conductive silver glue, with the graphene-silicon carbide composite film of the surface substrate facing the inside of the ionization chamber. The edge of the graphene-silicon carbide composite film is electrically connected to the metal electrode of the ionization smoke sensor through gold wire bonding. The interior of the ionization chamber is filled with a mixture of nitrogen and radon with a purity of 99.99% and a ratio of 1000:1. Stainless steel electrode plates are arranged in parallel on both sides of the ionization chamber with a spacing of 3 mm between the electrode plates. A constant voltage of 3.0 volts is applied to the ionization chamber electrodes through a precision DC power supply. The gas molecules in the ionization chamber are ionized under the action of the electric field, and free electrons form a stable conduction path on the surface of the graphene-silicon carbide composite film. The current value between the electrodes is measured using a picoammeter, and the average current lasting 60 seconds in the smoke-free state is recorded as the baseline ionization current signal.

[0057] S2: When the ambient smoke concentration exceeds the alarm threshold, the scattered light spots of the nanocarbonized layer are captured and the real-time optical features are extracted to generate dynamic light pattern vectors. The tunneling current is synchronously obtained and the current decay rate is calculated.

[0058] S2.1: When the ambient smoke concentration exceeds the alarm threshold, a chaotic modulation signal is injected into the micro laser emitter to drive the scanning galvanometer to perform nonlinear trajectory scanning.

[0059] The specific process includes: when the ionization smoke sensor detects that the ambient smoke concentration exceeds the alarm threshold, the signal processing unit generates a chaotic modulation signal based on the Lorenz equation. The chaotic modulation signal is transmitted to the driving circuit of the laser scanning head through a coaxial cable. The driving circuit converts the chaotic modulation signal into two scanning voltage signals with a phase difference of 90 degrees on the X-axis and Y-axis. The scanning voltage signal is input into the electromagnetic deflection coil of the scanning galvanometer. The electromagnetic deflection coil generates a nonlinear magnetic field driven by the chaotic voltage, driving the scanning galvanometer reflector surface to swing non-periodically. The reflector surface reflects the laser beam output by the micro laser emitter to form a scanning track with fractal characteristics. The laser beam scans the graphene-silicon carbide composite film area of ​​the detector surface substrate along the nonlinear track, and the scanning rate is synchronized with the Lyapunov exponent of the chaotic modulation signal.

[0060] The alarm threshold is set based on three times the standard deviation of the baseline ionization current in a clean environment, and is securely preset through hardware fuse storage and quantum key distribution.

[0061] S2.2: Based on nonlinear trajectory scanning, the dynamic scattered light spots of the nanocarbon layer are captured and the light field gradient tensor is calculated by the fractional differential operator. The expression is:

[0062] ;

[0063] in, represents the order of fractional differential, Represents fractional differential order The calculated light field gradient tensor, Represents the chaotic scanning trajectory At the moment The spatial reciprocal matrix of Indicates that along the image Caputo-type fractional differential operator in the direction of the coordinate axis, represents the light intensity distribution function, Indicates that along the image Caputo-type fractional differential operator in the direction of the coordinate axis, represents the Hadamard product, represents the natural exponential function, Indicates the memory decay coefficient (0.1≤ ≤0.5), represents the time variable, represents the integral time variable, Represented in image coordinates and the integration time variable The light intensity distribution function at Represents the average intensity of the light intensity distribution function in the spatial area.

[0064] The specific process includes: the laser beam output by the micro-laser emitter is reflected by the scanning galvanometer to form a nonlinear scanning trajectory to irradiate the surface of the nanocarbon layer; the high-speed CMOS image sensor captures the dynamic scattered light spot sequence at a rate of 1000 frames per second; each spot image is converted into a grayscale matrix to represent the light intensity distribution function; the fractional-order differential operator is applied to three consecutive frames of spot images for processing; the fractional-order differential operator adopts the Caputo definition to realize non-integer differential operations along the x-axis and y-axis directions of the image; the differential order is dynamically adjusted according to the Lyapunov exponent of the chaotic scanning trajectory; the calculation results of the Caputo-type fractional-order differential operator in the x-axis direction and the Caputo-type fractional-order differential operator in the y-axis direction are subjected to Hadamard product operation, and then point multiplied with the reciprocal matrix of the chaotic scanning trajectory space, and finally multiplied by the exponential function weighted by the memory decay coefficient; the integral operation performs time-varying convolution on the difference between the light intensity distribution function and the average intensity in the image space domain, and finally outputs a light field gradient tensor with spatiotemporal correlation characteristics.

[0065] S2.3: Generate dynamic light pattern vectors based on the light field gradient tensor, and synchronously trigger the high-speed ADC to collect the ionization chamber tunneling current waveform.

[0066] The specific process includes extracting the first three eigenvectors of the light field gradient tensor through principal component analysis, and arranging the eigenvectors in descending order of eigenvalue to form a dynamic light pattern vector. The dynamic light pattern vector is stored in the continuous address space of the dual-port RAM. At the same time, the trigger signal starts high-speed ADC sampling through the hardware trigger circuit of the FPGA. The high-speed ADC collects the tunneling current signal between the electrodes of the ionization chamber at a sampling rate of 1MHz. The tunneling current signal is processed by the anti-aliasing filter and converted into 12-bit digital waveform data. The digital waveform data is transmitted to the cache area of ​​the signal processor through the DMA channel. The dynamic light pattern vector and the tunneling current waveform data are strictly aligned in time stamp with an alignment accuracy of 100 nanoseconds.

[0067] S2.4: Based on the ionization chamber tunneling current waveform, the current decay rate is calculated using the third-order derivative penalty model. The expression is:

[0068] ;

[0069] in, represents the current decay rate, represents the length of the integration time window, represents the starting time of integration, represents the real part extraction operator of a complex number, represents the Hilbert transform operator, Represents the time variable The first derivative of represents the natural logarithm function, represents the time-dependent tunneling current function, Indicates the penalty item weight coefficient (0.15≤ ≤0.35), represents the hyperbolic compression function, Represents the time variable The third-order derivative of represents the tunneling current, represents an exponential decay function, An exponential decay function representing time gating.

[0070] The specific process includes: inputting the tunneling current waveform data collected by the high-speed ADC into the digital signal processor, the digital signal processor performing Hilbert transform on the tunneling current function to obtain an analytical signal, separating the instantaneous amplitude of the analytical signal through the real part extraction operator of the complex number, converting the instantaneous amplitude into a logarithmic attenuation through the natural logarithm function, and calculating the first-order derivative of the time variable of the tunneling current function to obtain the rate of change, multiplying the rate of change with the time-gated exponential attenuation function to obtain the primary attenuation component, and the digital signal processor further calculating the third-order derivative of the tunneling current function with respect to the time variable to characterize the high-order nonlinear characteristics, and normalizing the third-order derivative result through the hyperbolic compression function, and multiplying the normalized result by the penalty term weight coefficient to generate the penalty term, and performing weighted summation of the penalty term and the primary attenuation component within the length of the integration time window, and performing the integral operation to perform a sliding average on the time-dependent tunneling current function from the start moment of the integration, and finally outputting the current attenuation rate.

[0071] Furthermore, the training process of the third-order derivative penalty model is implemented using a supervised learning method: a tunneling current waveform dataset under various smoke concentration conditions is constructed, and each sample is annotated with the measured current decay rate as the training target; the penalty term weight coefficient is initialized to 0.25, and the tunneling current waveform is input into the model during the training phase. The gradient of the loss function with respect to the weight coefficient is analyzed through automatic differentiation, and the weight coefficient is iteratively updated using the Adam optimizer. The loss function is defined as the mean square error between the model output current decay rate and the labeled value plus the L2 regularization term. After each round of training, the model performance is tested on the validation set. Training is stopped when the validation loss stops decreasing for five consecutive rounds, and the weight coefficient with the smallest error on the validation set is retained as the penalty term weight coefficient.

[0072] S3: Based on the dynamic light pattern vector and current attenuation rate, the normalized coupling coefficient is calculated. When the normalized coupling coefficient is in the safe range, a security authentication mark is output. If it is lower than the safe range, a network attack warning signal is generated. If it is higher than the safe range, a physical tampering fuse signal is generated.

[0073] S3.1: Based on the dynamic light ripple vector and the current decay rate, the quantum chaos tensor is generated by the tensor direct product of the quantum evolution operator and the chaotic synchronization derivative.

[0074] The specific process includes: mapping the dynamic light pattern vector into a quantum state vector through unitary matrix transformation, the quantum state vector is input into the quantum evolution operator for unitary transformation, the quantum evolution operator is constructed using the linear combination of Pauli matrices, and the current decay rate generates a chaotic sequence through logistic mapping. The chaotic sequence is subjected to differential operation to obtain the chaotic synchronization derivative. The output tensor of the quantum evolution operator and the chaotic synchronization derivative are subjected to tensor direct product operation in Hilbert space. The tensor direct product operation realizes the extended combination of multidimensional arrays according to the Kronecker product rule. The combination result is processed by orthogonal normalization to eliminate redundant dimensions, and finally a quantum chaos tensor with non-local characteristics is generated.

[0075] S3.2: Perform multifractal decomposition on the quantum chaos tensor and calculate the normalized coupling coefficient through boundary integral and kernel space analysis. The expression is:

[0076] ;

[0077] in, represents the normalized coupling coefficient, represents the curve integral operator along a closed path, represents the characteristic spectrum fractal boundary, Represents the fractal boundary path parameter (0≤ ≤1), Represents the fractal boundary path parameters The composite weighting function of Represents the quantum chaos tensor at the fractal boundary path parameter The projection value at represents the imaginary unit, Represents the quantum chaos tensor at the fractal boundary path parameter The complex phase angle at represents the Hausdorff dimension, represents the dimension, represents the nonlinear coupling operator, represents the characteristic time scale, represents the time gradient operator, represents the quantum chaos tensor, Represents the Frobenius norm notation.

[0078] The specific process includes: decomposing the quantum chaos tensor into multiple fractal spectra through wavelet transform, constructing the characteristic spectrum fractal boundary on the complex plane, and using parameterization to represent the characteristic spectrum fractal boundary. The fractal boundary path parameters continuously change from zero to one. Curve integration is performed along the characteristic spectrum fractal boundary path. The curve integration operator acts on the product of the projection value of the quantum chaos tensor and the exponential function of the complex phase angle. The projection value is extracted by the Gram-Schmidt orthogonalization method to extract the component of the quantum chaos tensor at the fractal boundary path parameter. The complex phase angle is obtained by calculating the amplitude function of the quantum chaos tensor. During the integration process, the composite weighting function dynamically adjusts the fractal boundary path parameters. The composite weighting function adopts a Sigmoid weight distribution. The integration result is normalized by dividing it by the Frobenius norm of the quantum chaos tensor. At the same time, the nonlinear coupling operator performs a nonlinear transformation on the product of the characteristic time scale and the time gradient operator, and finally outputs the normalized coupling coefficient.

[0079] S3.3: When the normalized coupling coefficient is within the safety interval, perform gradient verification and activate the safety certification flag.

[0080] The specific process includes comparing the normalized coupling coefficient with the safety interval. When the normalized coupling coefficient falls within the safety interval, the gradient verification process is started. The gradient verification analyzes the first-order partial derivative of the normalized coupling coefficient with respect to the characteristic time scale. The partial derivative result is filtered through a Butterworth low-pass filter to eliminate high-frequency noise. The filtered signal is matched with the reference gradient template for correlation coefficient matching. When the normalized coupling coefficient exceeds the verification threshold, a 128-bit security authentication mark is generated. The security authentication mark is encrypted with AES-256 and transmitted to the security authentication unit of the fire linkage controller via the CAN bus. The fire linkage controller updates the device status register after decryption and verification to complete the security authentication process.

[0081] The safety interval is obtained based on 1000 hours of normal operating data. The normalized coupling coefficient mean range is taken and written into the tamper-proof storage after confirmation by the Kolmogorov-Smirnov test.

[0082] The verification threshold is determined through ROC curve analysis. Under the premise of ensuring 99.9% attack recognition rate, the optimal critical value of gradient template matching is selected, and HSM protection is adopted.

[0083] S3.4: When the normalized coupling coefficient is lower than the lower limit of the safety interval, the network attack warning signal is activated.

[0084] The specific process includes comparing the normalized coupling coefficient with the lower limit of the security interval. When the normalized coupling coefficient is lower than the lower limit of the security interval, the network attack detection process is triggered. The security authentication unit generates a 32-byte warning data packet containing a timestamp. The warning data packet analyzes the hash summary through the SHA-3 algorithm. The hash summary is encrypted by the elliptic curve digital signature algorithm to form a network attack warning signal. The network attack warning signal is transmitted to the firewall control unit through an isolated communication channel. The firewall control unit parses and verifies the digital signature and then executes the predefined defense strategy.

[0085] Predefined defense strategies are formulated based on attack signature libraries and expert experience. Rule sets are established by analyzing historical attack data, and machine learning is used to optimize response logic before being solidified in FPGA hardware.

[0086] S3.5: When the normalized coupling coefficient is higher than the upper limit of the safety interval, the physical tamper fuse signal is activated.

[0087] The specific process includes comparing the normalized coupling coefficient with the upper limit of the safety interval. When the normalized coupling coefficient exceeds the upper limit of the safety interval, the physical tampering judgment process is triggered. The security authentication unit generates a fuse instruction code containing the device serial number. The fuse instruction code is encrypted through AES-256-CBC to form a physical tampering fuse signal. The physical tampering fuse signal is transmitted to the electrochemical corrosion execution unit through the optocoupler isolation circuit. The electrochemical corrosion execution unit decrypts and verifies the instruction code and starts the preset corrosion voltage sequence. The corrosion voltage sequence is applied to the target metal circuit according to an increasing step waveform. The metal circuit undergoes controllable fracture under the action of the electrochemical reaction, and the impedance change generated by the fracture process is fed back to the security authentication unit to complete the closed-loop verification.

[0088] S4: Maintain the normal alarm path according to the security certification mark, close the external communication port and enhance the monitoring density according to the network attack warning signal, and trigger the electrochemical reaction to corrode the specific metal line and activate the sound and light alarm device according to the fuse signal to generate the fuse feedback state.

[0089] S4.1: When the security authentication mark is activated, a quantum trust chain is established and a Bell inequality test is performed through entangled photon distribution. When the test value is continuously greater than or equal to the critical value of quantum nonlocality, the normal alarm path is maintained.

[0090] The specific process includes: after the security authentication mark is activated, the quantum key distribution protocol is triggered, the laser diode generates entangled photon pairs, and the entangled photon pairs are transmitted to the quantum receiving ends of the fire linkage controller and the cloud platform respectively through polarization-maintaining optical fiber. The single-photon detector at the quantum receiving end measures the polarization state of the photon, and the measurement results are input into the Bell inequality test circuit. The test circuit calculates the CHSH correlation function value. When the measurement results for 10 consecutive times are greater than or equal to 2.7, it is determined that quantum non-locality holds, and the quantum trust chain is successfully established. The communication channel between the fire linkage controller and the cloud platform maintains the AES-GCM encryption state, the alarm data packet is continuously transmitted according to the standard protocol format, and the normal alarm path remains unobstructed.

[0091] S4.2: When the network attack warning signal is activated, close all external communication ports and maintain the quantum monitoring state through the traffic entropy dynamic fuse mechanism.

[0092] The specific process includes: the network attack warning signal triggers the hardware-level interrupt instruction, the firewall control unit immediately cuts off the physical connection between the Ethernet PHY chip and the RJ45 interface, and at the same time disables the RF front-end of the wireless communication baseband. The power management IC of all external communication ports enters the high-impedance state, the internal quantum monitoring channel remains activated, and the quantum random number generator continuously outputs entropy source data. The entropy source data analyzes the real-time traffic entropy value through the Shannon entropy algorithm. When the traffic entropy value is lower than the dynamic threshold, the optocoupler-isolated quantum monitoring loop is maintained. The single-photon counter in the monitoring loop continuously records the quantum dot fluorescence signal. The fluorescence signal generates a spectrum feature code after Fourier transform. The spectrum feature code is compared with the quantum fingerprint database to verify the communication integrity.

[0093] The dynamic threshold is determined based on the baseline entropy value range output by the quantum random number generator and the sum of the mean and three times the standard deviation is analyzed by a sliding window algorithm.

[0094] S4.3: When the quantum monitoring state is maintained, if the port traffic entropy returns to normal, the communication link is reconstructed through gradual port opening and quantum trust verification. If it fails, it returns to the fuse state. If it succeeds, the monitoring density is enhanced.

[0095] The specific process involves immediately shutting down all external communication ports after the network attack warning signal is activated. The dynamic flow entropy fusing mechanism is activated and maintains the quantum monitoring state. The quantum random number generator continuously outputs entropy source data, and the Shannon entropy algorithm analyzes the flow entropy value in real time. If the port flow entropy value is detected to have risen back within the dynamic threshold while the quantum monitoring state is maintained, a single wired communication port is first restored with minimal privileges. The Ethernet PHY chip reestablishes a physical connection with the RJ45 interface, and the quantum fingerprint database compares the current spectrum signature with a pre-stored reference code. If the quantum trust verification fails, the port is immediately disconnected and returns to the fusing state. If the verification passes, the remaining wired ports and the wireless communication baseband RF front-end are sequentially enabled. After each activation, a single-photon counter is used to collect the quantum dot fluorescence signal, which is then Fourier transformed to generate a new spectrum signature for secondary verification. After all ports are successfully reconfigured, the Shannon entropy algorithm sampling frequency is increased, and the entropy source output bandwidth of the quantum random number generator is expanded to enhance the signal capture density of the quantum monitoring loop.

[0096] S4.4: When the fusing signal is activated, an indium gallium alloy electrolyte is sprayed onto the target metal line and a programmable voltage sequence is applied to generate a dynamic corrosion current.

[0097] The specific process includes: when the fuse signal is activated, the high-voltage microfluidic pump drives the indium gallium alloy electrolyte to be precisely sprayed onto the surface of the target metal circuit. At the same time, the programmable power supply outputs a positive and negative alternating step voltage sequence according to the preset timing. The indium gallium alloy electrolyte undergoes an oxidation-reduction reaction with the metal circuit to form a primary battery effect. The amplitude change of the voltage sequence regulates the electrolyte ion migration rate. The dynamic corrosion current shows a nonlinear growth with the increase of the voltage step. The impedance change of the metal circuit is fed back to the voltage sequence controller in real time. The controller dynamically adjusts the subsequent voltage pulse width according to the impedance increment. When the impedance reaches the fuse threshold, the voltage output is terminated and nitrogen purge is started to remove residual electrolyte.

[0098] S4.5: When the dynamic corrosion current exceeds the critical current of the quantum phase transition, the quantum dot laser pulse is triggered to excite the acoustic wave signal.

[0099] The specific process includes: when the dynamic corrosion current exceeds the critical current of the quantum phase change, the quantum dot laser immediately emits a femtosecond pulse beam of a specific wavelength. The beam is focused on the gallium arsenide quantum dot array pre-implanted on the surface of the target metal circuit. The quantum dots are stimulated to radiate and produce coherent phonon oscillations. The oscillation wave propagates through the metal lattice to form a surface acoustic wave signal. The acoustic wave signal is captured by the piezoelectric sensor array and converted into an electrical signal. After the characteristic frequency component of the electrical signal is extracted by the bandpass filter, it is matched and verified with the quantum phase change characteristic spectrum database. If the verification is passed, the fuse completion signal is triggered. If the verification fails, the secondary quantum dot laser pulse excitation cycle is started.

[0100] The quantum phase transition critical current is set based on the Coulomb blockade effect measurement of the GaAs quantum dot array and is determined by calibrating the single-electron tunneling threshold using a scanning tunneling microscope at a low temperature of 4.2K.

[0101] S4.6: Perform quantum state analysis on the acoustic wave signal and generate an unclonable certificate for the fuse area using the Bloch sphere mapping algorithm.

[0102] The specific process includes: the acoustic wave signal captured by the piezoelectric sensor array is converted into a quantum state probability amplitude through a superconducting quantum interferometer; the quantum state probability amplitude is input into the Bloch sphere mapping algorithm for three-dimensional Hilbert space projection; the projection coordinates are transformed by the Pauli matrix to generate an eigenstate parameter group containing the lattice vibration mode of the fuse area; the eigenstate parameter group is XORed with the entropy source output by the quantum random number generator to generate a 128-dimensional hash vector; the hash vector is transmitted to the verification terminal through the quantum key distribution channel; the verification terminal uses the same Bloch sphere mapping algorithm to reconstruct the eigenstate parameter group and compare the hash vector consistency; after a complete match, an unclonable certificate based on the acoustic fingerprint of the fuse area is generated.

[0103] S4.7: After binding the unclonable credential to the acoustic signal feature, obtain the circuit breaker feedback status.

[0104] The specific process includes: the unclonable certificate is transmitted to the acoustic signal feature database through a quantum entanglement channel; the database extracts the time-frequency domain Mel-frequency cepstral coefficients of the acoustic signal in the fuse area; the Mel-frequency cepstral coefficients are homomorphically encrypted with the 128-dimensional hash vector in the unclonable certificate under the lattice cryptographic basis to generate a joint authentication tag; after the joint authentication tag is verified by bilinear pairing, if the acoustic signal characteristics and the quantum state analysis results of the unclonable certificate meet the Pauli exclusion principle, the feedback status register writes the fuse success flag; otherwise, the secondary verification process of the quantum dot laser pulse excitation acoustic signal is triggered until a certain fuse feedback state is obtained.

[0105] S5: According to the feedback status of the fuse, the specific combustible medium is controlled to burn to generate a new carbon deposit layer to cover the original nanostructure. After verifying the optical feature matching, the baseline ionization current signal is reset and an information security defense completion signal is generated.

[0106] S5.1: According to the melting feedback state, a liquid crystal-carbon-rich precursor mixture is injected into the melting area, and an alternating electric field is applied to induce molecular alignment.

[0107] The specific process includes: when the fuse feedback status register shows a successful fuse mark, the microfluidic injection device injects the liquid crystal-carbon-rich precursor mixture into the recessed part of the fuse area in a laminar flow mode, and at the same time the programmable power supply applies a three-phase sinusoidal alternating electric field with a gradual frequency change, and the direction of the electric field intensity gradient is parallel to the long axis of the fuse area; the liquid crystal molecules are oriented along the direction of the electric field under the action of dielectric anisotropy, and the carbon-rich precursor undergoes an electropolymerization reaction under the induction of the electric field to form a carbon-based conductive network with a conjugated structure; the alternating electric field frequency is adaptively adjusted according to the real-time feedback of the impedance of the fuse area, and when the infrared thermal imaging detects that the regional temperature reaches the liquid crystal phase transition point, it switches to a DC bias voltage, and finally forms a composite repair layer with anisotropic conductive properties in the fuse area.

[0108] S5.2: After the molecular alignment is completed, trigger laser ignition to generate a directional carbon deposition layer to cover the original nanostructure, and obtain optical characteristic data through quantum dot fluorescence lifetime detection.

[0109] The specific process includes: after the molecular directional arrangement is completed, the pulsed laser emits a nanosecond laser beam with a wavelength of 1064nm and focuses on the center point of the melting area. The laser energy density is precisely controlled above the thermal decomposition threshold of the liquid crystal-carbon-rich precursor mixture, triggering a local thermal decomposition reaction to generate a directional carbon deposition layer of the graphene structure; the carbon deposition layer extends outward in a concentric circle manner to cover the surface of the original nanostructure, and at the same time, the quantum dot probe emits 405nm excitation light to irradiate the repair area, and the fluorescence signal generated by the stimulated radiation of the cadmium selenide quantum dots is collected by a time-correlated single photon counter to obtain a fluorescence decay curve; the fluorescence decay curve is fitted by maximum likelihood estimation to extract the three exponential components, and the amplitude ratio of each component and the decay time constant are combined into optical characteristic data. The optical characteristic data is Manhattan distance matched with the repair reference spectrum pre-stored in the quantum fingerprint database to verify the structural integrity of the directional carbon deposition layer.

[0110] S5.3: The optical feature data is subjected to quantum authentication standards, and the quantum annealing algorithm is used to optimize and reset the baseline ionization current signal to generate an information security defense completion signal.

[0111] The specific process includes: inputting optical characteristic data into the quantum authentication standard processor, converting the amplitude ratio of the three exponential components and the decay time constant into a characteristic frequency vector through quantum Fourier transform; loading the characteristic frequency vector into the quantum annealing processor to construct the Ising model Hamiltonian, and solving the optimal solution of the spin configuration corresponding to the ground state energy through the quantum annealing process; the optimal solution is reconstructed into a reference ionization current signal waveform through a digital-to-analog converter, and the waveform parameters include rise time, peak amplitude and decay slope; the reconstructed reference ionization current signal is injected into the original fuse area through the Josephson junction array, and the quantum dot fluorescence lifetime detection continuously monitors the stability of the optical characteristic data; when the correlation coefficient between the reference ionization current signal and the optical characteristic data after three consecutive quantum annealing optimizations exceeds the quantum authentication standard, the quantum random number generator triggers the information security defense completion signal, which is transmitted to all defense nodes through the quantum key distribution channel.

[0112] The quantum authentication standard is set based on the three-exponential fluorescence decay characteristic spectrum of cadmium selenide quantum dots in standard repair samples, and the authentication threshold is determined by extracting the tensor product of the first three eigenvectors through quantum principal component analysis.

[0113] This embodiment also provides an information security defense device for an automatic fire alarm system, including: a signal acquisition module, a feature extraction module, a security assessment module, a response execution module, and a self-repair module. The signal acquisition module is used to install a laser scanning head at the end of a fire sprinkler pipe and pre-coat a detector nanocarbon layer, while applying a constant voltage to an ionization smoke sensor to generate a reference ionization current signal; the feature extraction module is used to capture the scattered light spots of the nanocarbon layer and extract real-time optical features when the ambient smoke concentration exceeds the alarm threshold, generate dynamic light pattern vectors, synchronously obtain tunneling currents, and calculate current decay rates; the security assessment module is used to calculate normalized light pattern vectors based on the dynamic light pattern vectors and current decay rates. Coupling coefficient, when the normalized coupling coefficient is in the safe range, a security authentication mark is output; if it is lower than the safe range, a network attack warning signal is generated; if it is higher than the safe range, a physical tampering fuse signal is generated; the response execution module is used to maintain the normal alarm path according to the security authentication mark, close the external communication port and enhance the monitoring density according to the network attack warning signal, and trigger the electrochemical reaction to corrode the specific metal line and activate the sound and light alarm device according to the fuse signal to generate a fuse feedback state; the self-repair module is used to control the combustion of a specific combustible medium to generate a new carbon deposit layer to cover the original nanostructure according to the fuse feedback state, reset the baseline ionization current signal after verifying the optical feature matching, and generate an information security defense completion signal.

[0114] This embodiment also provides a computer device suitable for the information security defense method of the automatic fire alarm system, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the information security defense method of the automatic fire alarm system proposed in the above embodiment.

[0115] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0116] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the information security defense method for an automatic fire alarm system as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0117] In summary, the present invention achieves multi-dimensional perception of environmental disturbances by combining laser scanning with a nanocarbonized layer to generate dynamic light pattern vectors and current decay rates. Furthermore, through electrochemical fusing and quantum unclonable credential generation, active defense and identity authentication under physical layer attacks are achieved, thereby enhancing the information security protection and recovery capabilities of the fire alarm system.

[0118] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An information security defense method for an automatic fire alarm system, characterized by: include, A laser scanning head is installed at the end of the fire sprinkler pipe and a nano-carbonized layer is pre-coated on the detector. At the same time, a constant voltage is applied to the ionization smoke sensor to generate a reference ionization current signal. When the ambient smoke concentration exceeds the alarm threshold, the scattered light spots of the nanocarbonized layer are captured and the real-time optical features are extracted to generate dynamic light pattern vectors. The tunneling current is simultaneously obtained and the current decay rate is calculated. Based on the dynamic light pattern vector and current attenuation rate, the normalized coupling coefficient is calculated. When the normalized coupling coefficient is in the safe range, a security authentication mark is output. If it is lower than the safe range, a network attack warning signal is generated. If it is higher than the safe range, a physical tampering fuse signal is generated. Maintain normal alarm paths based on security certification marks, shut down external communication ports and increase monitoring density based on network attack warning signals, and trigger electrochemical reactions based on fuse signals to corrode specific metal circuits and activate audible and visual alarm devices, generating fuse feedback status; According to the feedback status of the fuse, the specific combustible medium is controlled to burn to generate a new carbon deposit layer to cover the original nanostructure. After verifying the optical feature matching, the baseline ionization current signal is reset to generate an information security defense completion signal; When the ambient smoke concentration exceeds the alarm threshold, the scattered light spots of the nanocarbonized layer are captured and the real-time optical features are extracted to generate dynamic light pattern vectors, and the tunneling current is synchronously obtained and the current decay rate is calculated. The specific steps are as follows: When the ambient smoke concentration exceeds the alarm threshold, a chaotic modulation signal is injected into the micro laser emitter to drive the scanning galvanometer to perform nonlinear trajectory scanning; The dynamic scattered light spots of the nanocarbon layer are captured based on nonlinear trajectory scanning, and the light field gradient tensor is calculated by fractional differential operators. Generate dynamic light pattern vectors based on the light field gradient tensor and synchronously trigger the high-speed ADC to collect the ionization chamber tunneling current waveform; Based on the tunneling current waveform of the ionization chamber, the current decay rate is calculated using the third-order derivative penalty model; The method calculates the normalized coupling coefficient based on the dynamic light pattern vector and the current attenuation rate. When the normalized coupling coefficient is in a safe range, a security authentication mark is output. When the normalized coupling coefficient is lower than the safe range, a network attack warning signal is generated. When the normalized coupling coefficient is higher than the safe range, a physical tampering fuse signal is generated. The specific steps are as follows: According to the dynamic light pattern vector and current decay rate, the quantum chaos tensor is generated by the tensor direct product of the quantum evolution operator and the chaotic synchronization derivative. The quantum chaos tensor is decomposed into multiple fractals, and the normalized coupling coefficient is calculated through boundary integral and kernel space analysis. When the normalized coupling coefficient is within the safety range, gradient verification is performed and the safety certification flag is activated; When the normalized coupling coefficient is lower than the lower limit of the safety interval, the network attack warning signal is activated; When the normalized coupling coefficient is higher than the upper limit of the safety interval, the physical tamper fuse signal is activated.

2. The information security defense method for an automatic fire alarm system according to claim 1, wherein: The specific steps of generating the reference ionization current signal are as follows: An annular mounting base is embedded in the flange at the end of the fire sprinkler pipe, and a micro laser emitter and a scanning galvanometer are integrated, so that the light spot focus area of ​​the scanning galvanometer is aligned with the preset processing area of ​​the detector surface substrate; The predetermined processing area is treated by argon plasma etching and a pulsed laser deposition process is used to generate a graphene-silicon carbide composite film. The surface substrate of the graphene-silicon carbide composite film is assembled onto an ionization-type smoke sensor, and a constant voltage is applied to the ionization chamber electrodes to generate a reference ionization current signal.

3. The information security defense method for an automatic fire alarm system according to claim 1, wherein: The specific steps of maintaining the normal alarm path according to the security authentication mark, closing the external communication port and increasing the monitoring density according to the network attack warning signal are as follows: When the security authentication mark is activated, a quantum trust chain is established and a Bell inequality test is performed through entangled photon distribution. When the test value is continuously greater than or equal to the critical value of quantum nonlocality, the normal alarm path is maintained; When the network attack warning signal is activated, all external communication ports are closed and the quantum monitoring state is maintained through the traffic entropy dynamic fuse mechanism; While the quantum monitoring state is maintained, if the port traffic entropy returns to normal, the communication link is reconstructed through gradual port opening and quantum trust verification. If it fails, it returns to the fuse state, and if it succeeds, the monitoring density is enhanced.

4. The information security defense method for an automatic fire alarm system according to claim 3, wherein: The electrochemical reaction triggered by the fuse signal corrodes the specific metal circuit and activates the sound and light alarm device to generate the fuse feedback state. The specific steps are as follows: When the fusing signal is activated, an indium gallium alloy electrolyte is sprayed onto the target metal line and a programmable voltage sequence is applied to generate a dynamic corrosion current; When the dynamic corrosion current exceeds the critical current of quantum phase transition, the quantum dot laser pulse is triggered to excite the acoustic wave signal; Perform quantum state analysis on the acoustic signal and generate an unclonable certificate for the fuse area using the Bloch sphere mapping algorithm; After binding the unclonable credential to the acoustic signal feature, obtain the circuit breaker feedback status.

5. The information security defense method for an automatic fire alarm system according to claim 4, characterized in that: According to the feedback state of the fuse, the specific combustible medium is controlled to burn to generate a new carbon deposit layer to cover the original nanostructure, the reference ionization current signal is reset after verifying the optical feature matching, and an information security defense completion signal is generated. The specific steps are as follows: According to the feedback state of the melting, a liquid crystal-carbon-rich precursor mixture is injected into the melting area, and an alternating electric field is applied to induce molecular alignment; After the molecular alignment is completed, the laser ignition is triggered to generate a directional carbon deposition layer covering the original nanostructure, and the optical characteristic data is obtained through the quantum dot fluorescence lifetime detection; The optical characteristic data is subjected to quantum authentication standards, and the quantum annealing algorithm is used to optimize and reset the baseline ionization current signal to generate an information security defense completion signal.

6. An information security defense device for an automatic fire alarm system, based on the information security defense method for an automatic fire alarm system according to any one of claims 1 to 5, characterized in that: Including signal acquisition module, feature extraction module, security assessment module, response execution module and self-repair module, The signal acquisition module is used to install a laser scanning head at the end of the fire sprinkler pipe and pre-coat the detector with a nano-carbonized layer. At the same time, a constant voltage is applied to the ionization smoke sensor to generate a reference ionization current signal; The feature extraction module is used to capture the scattered light spots of the nanocarbon layer and extract real-time optical features when the ambient smoke concentration exceeds the alarm threshold, generate dynamic light pattern vectors, and simultaneously obtain the tunneling current and calculate the current decay rate; The security assessment module is used to calculate the normalized coupling coefficient based on the dynamic light pattern vector and the current decay rate. When the normalized coupling coefficient is within the safe range, a security certification mark is output. If it is below the safe range, a network attack warning signal is generated. If it is above the safe range, a physical tampering fuse signal is generated. The response execution module is used to maintain the normal alarm path according to the security authentication mark, shut down the external communication port and increase the monitoring density according to the network attack warning signal, and trigger the electrochemical reaction to corrode the specific metal circuit and activate the sound and light alarm device according to the fuse signal to generate the fuse feedback status; The self-repair module is used to control the combustion of a specific combustible medium to generate a new carbon deposit layer to cover the original nanostructure according to the fuse feedback status, reset the baseline ionization current signal after verifying the optical feature matching, and generate an information security defense completion signal.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the information security defense method for the automatic fire alarm system according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the information security defense method for the automatic fire alarm system according to any one of claims 1 to 5 are implemented.

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