Intelligent fire-fighting equipment self-inspection and fault early warning system based on AI algorithm

Abstract

The invention provides an intelligent fire-fighting equipment self-inspection and fault early warning system based on an AI algorithm. The intelligent fire-fighting equipment self-inspection and fault early warning system comprises a sensing layer used for integrating a multi-source heterogeneous sensing unit; the edge layer is used for deploying intelligent preprocessing nodes; the transmission layer is used for constructing a safe and reliable transmission channel; the platform layer is used for establishing an intelligent analysis center; the application layer is used for providing intelligent decision service; and the five layers of architectures work cooperatively. According to the invention, full-dimension monitoring of fire-fighting equipment is realized through a multi-physics field cooperative sensing framework of a sensing layer, and the fault detection capability is remarkably improved. The multi-source heterogeneous sensing unit integrated in the sensing layer can capture various physical quantity information such as acoustics, vibration and thermal fields at the same time, information of different physical fields is complementarily enhanced through a cross-domain feature complementation enhancement mechanism, and more comprehensive equipment state features are obtained. In particular, the sensor self-adaptive excitation technology dynamically adjusts the excitation energy of the sensor according to the environmental interference degree, and ensures that high-quality signals can be obtained under different working conditions.

Description

Technical Field

[0001] The present invention relates to the field of intelligent fire safety technology, and particularly to an intelligent fire-fighting facility self-checking and fault warning system based on an AI algorithm. Background Art

[0002] Fire-fighting facilities, as the key guarantee for building safety, their reliability and effectiveness are directly related to the safety of personnel and property. However, there are still many deficiencies in the existing fire-fighting facility monitoring and maintenance technologies, which are difficult to meet the requirements of modern building fire safety. At present, the management of fire-fighting facilities mainly relies on regular manual inspections and simple alarm systems. This method not only consumes a large amount of human resources, but also has problems such as low inspection frequency and limited coverage. Most of the existing automated monitoring systems are based on single-parameter monitoring (such as single physical quantities like temperature, smoke, etc.), and it is difficult to comprehensively capture the early fault characteristics of equipment. For example, early faults such as abnormal heating of water pump bearings, tiny leaks in pipelines, and degradation of electrical insulation performance are often ignored due to the lack of obvious characteristic parameters, and are not discovered until the faults develop to a serious stage.

[0003] Traditional fire-fighting facility monitoring systems generally have the problem of a lagging warning mechanism. Most systems can only alarm after a fault occurs and lack predictive ability. The simple threshold judgment mechanism leads to a high false alarm rate, resulting in a reduced sensitivity of management personnel to alarm signals, and potential dangers may be ignored. At the same time, the data of each subsystem is isolated from each other, forming "data islands", lacking the ability of collaborative analysis, and it is difficult to identify the interactive effects and potential risks between equipment. For example, the data of the fire water pump control system and the water supply network monitoring system cannot be correlated and analyzed, resulting in the degradation of system performance that is difficult to be evaluated as a whole.

[0004] In terms of maintenance management, the currently widely used regular maintenance method has obvious drawbacks. Either premature maintenance wastes resources, or late maintenance causes faults. According to statistics, under the time-based preventive maintenance strategy, about 30% of the maintenance work is unnecessary, and at the same time, more than 20% of sudden faults cannot be prevented. In complex fire-fighting systems, it is difficult to locate the cause of faults. Maintenance personnel usually need to spend a lot of time checking possible fault points one by one, and the maintenance efficiency is low. The existing systems lack an effective knowledge accumulation and sharing mechanism. Expert experience is difficult to be systematically accumulated and applied. The training cycle for new employees is long, and the inheritance of technology is difficult.

[0005] The long process from anomaly detection to decision-making is the key factor restricting the emergency response ability of existing systems. When potential faults are discovered, the data needs to be reported layer by layer, manually analyzed and decided, delaying the golden processing time. According to industry data, the time window from the initial appearance of fault characteristics to complete failure of the fire-fighting system is usually 24 - 72 hours, while the average response time of traditional systems is 12 - 24 hours, often missing the best prevention opportunity.

[0006] In terms of emergency training and drills, traditional methods are difficult to simulate complex fire scenarios and equipment failures, limiting the training effect. Especially for low-frequency high-risk events, personnel lack practical experience and may react slowly or make operational mistakes in real emergencies. At the same time, most existing maintenance record systems are static documents, which are difficult to support dynamic queries and trend analysis, and are not conducive to managers formulating scientific maintenance strategies and resource allocation plans.

[0007] With the increase in building scale and functional complexity, the number of fire protection facilities has increased rapidly and the types have become increasingly diverse. The traditional decentralized management mode is difficult to cope with. The fire protection systems in large commercial complexes, super high-rise buildings and other places may include tens of thousands of monitoring points, and it is difficult for traditional technologies to achieve efficient and unified management. In addition, the interoperability between equipment of different manufacturers is poor, and the data formats and communication protocols are not unified, resulting in difficult system integration and high maintenance costs.

[0008] These technical pain points severely restrict the safe and reliable operation of fire protection facilities. There is an urgent need for new intelligent technologies to break through the existing limitations, establish an integrated solution for full-dimensional monitoring, intelligent analysis, prediction and early warning, and scientific decision-making, realize the active prevention and precise maintenance of fire protection facilities, so as to improve the overall level of building fire safety, reduce the incidence of fire accidents, and ensure the safety of people's lives and property. Summary of the Invention

[0009] To overcome the deficiencies of the prior art, the present invention proposes an intelligent fire protection facility self-check and fault warning system based on AI algorithms, which can significantly reduce the common false alarms and missed alarms of traditional fire monitoring systems, which is crucial for fire safety management. The adversarial noise suppression framework deployed at the edge layer uses conditional generative adversarial networks to accurately model environmental noise, and combines local-global noise decomposition technology to accurately separate noise from abnormal signals.

[0010] To achieve the above object, the present invention proposes an intelligent fire protection facility self-check and fault warning system based on AI algorithms, including a perception layer, an edge layer, a transmission layer, a platform layer and an application layer;

[0011] The perception layer is used to integrate multi-source heterogeneous sensing units, including conventional parameter monitoring units, voiceprint feature acquisition units, three-dimensional vibration analysis units, dynamic infrared thermal imaging arrays, gas chromatography analysis units and high-frequency electrical characteristic acquisition units;

[0012] The edge layer is used to deploy intelligent preprocessing nodes, including an adaptive noise suppression module, a feature space reconstruction module, a device-level micro-diagnosis model set and an emergency inference engine;

[0013] The transmission layer is used to build a secure and reliable transmission channel, integrating a protocol adaptive converter, a multipath redundant transmission controller, a quantum key encryption module, and a network topology self-optimization unit;

[0014] The platform layer is used to establish an intelligent analysis center, including a spatio-temporal data lake, a hybrid enhanced intelligence engine, a federation of device knowledge graphs, distributed digital twins, and a cross-domain transfer learning framework;

[0015] The application layer is used to provide intelligent decision-making services, integrating a device health prediction model, a fault causal chain analysis system, a maintenance strategy generator, an emergency plan deduction platform, and a dynamic adaptation module for regulations;

[0016] The five-layer architecture works together.

[0017] Further, the sensing layer includes:

[0018] a) The voiceprint feature acquisition unit adopts a fusion architecture of a microphone array and an ultrasonic sensor to achieve wide-band acoustic feature extraction in the frequency range of 10 Hz - 80 kHz;

[0019] The voiceprint feature acquisition unit uses a multi-scale Hilbert-Huang transform algorithm to extract non-stationary acoustic features, and calculates the energy time-frequency distribution through the following formula:

[0020]

[0021] Where, : The instantaneous amplitude of the i-th intrinsic mode function, representing the energy size of the mode at time t;

[0022] : The instantaneous frequency of the i-th intrinsic mode function, reflecting the main frequency component of the mode at time t;

[0023] : The complex exponential form of the analytic signal obtained by the Hilbert transform, representing the phase information of the signal;

[0024] : The wavelet basis function, responsible for frequency localization, making the decomposition have better time-frequency resolution;

[0025] : The adaptive bandwidth parameter, dynamically adjusting the scale of the analysis window according to the local characteristics of the signal;

[0026] n: The total number of intrinsic mode functions, that is, the number of signal components decomposed in the empirical mode decomposition process;

[0027] The energy time-frequency distribution calculation algorithm realizes the extraction of acoustic features through multi-step processing. The original acoustic signal is subjected to empirical mode decomposition (EMD), and the signal is decomposed into 5 - 12 intrinsic mode function (IMF) components, and the specific number is determined according to the signal complexity. Subsequently, the Hilbert transform is performed on each IMF component to extract the instantaneous amplitude and instantaneous frequency information.

[0028] The determination of the adaptive bandwidth parameter is in a way proportional to the instantaneous frequency, and the proportionality coefficient is usually set between 0.05 and 0.2, which can be dynamically adjusted according to the signal characteristics. The algorithm uses the Morlet wavelet as the basis function, and its parameter is set to 6, and this choice provides a good balance in time-frequency resolution.

[0029] During the calculation process, the instantaneous amplitude, instantaneous frequency of each IMF component and the adaptive bandwidth parameter are substituted into the energy distribution calculation formula to obtain the complete time-frequency energy distribution diagram. Finally, the system sets a threshold (usually three times the standard deviation boundary of historical healthy data) to detect abnormal energy distribution and achieve early fault identification. This algorithm can effectively capture the weak acoustic wave characteristics emitted by fire-fighting equipment such as water pump bearings and valves at the initial stage of faults.

[0030] b) The three-dimensional vibration analysis unit integrates a triaxial MEMS accelerometer and a laser vibrometer to construct a time-frequency-spatial domain feature matrix of the mechanical state of the equipment, and realizes the decoupling of multi-source vibration signals through tensor decomposition methods:

[0031]

[0032] Among them, : The third-order vibration tensor, representing the time-frequency - spatial domain feature matrix of the vibration signal;

[0033] NC: The rank of tensor decomposition, representing the number of main components of the signal;

[0034] : The weight of the r-th component, measuring the contribution degree of this component to the overall vibration signal;

[0035] : The spatial mode vector, describing the vibration modes at different positions;

[0036] : The frequency mode vector, representing the distribution of the vibration signal at different frequencies;

[0037] : The time mode vector, reflecting the change trend of the vibration signal over time;

[0038] : Represents the outer product, used to construct tensors;

[0039] :The residual term, representing the noise or error that the model fails to explain;

[0040] Construct a third-order tensor from the time series data collected by a triaxial accelerometer and a laser vibrometer, with its dimensions corresponding to the spatial, frequency, and time dimensions respectively. The preprocessing stage includes mean removal, normalization, and outlier handling to ensure data quality.

[0041] The decomposition process uses the alternating least squares method for Tucker decomposition, with an initial setting of 100 iterations. The key number of decomposition components R is determined by the kernel consistency index, and the smallest R value with a kernel consistency greater than 85% is selected, usually between 3 and 8. The system calculates the weights of each component and the corresponding spatial, frequency, and time modal vectors, and controls the Frobenius norm of the residual tensor not to exceed 5% of the norm of the original tensor.

[0042] The decomposed modal vectors are matched with a pre-established fault mode library, and potential faults are identified by calculating the similarity. This method can effectively separate the normal operating vibration and abnormal vibration signals, and is particularly suitable for vibration state monitoring of rotating equipment such as fire pumps and fans, and can identify problems such as bearing wear and impeller imbalance in the incipient stage of faults.

[0043] c) The dynamic infrared thermal imaging array adopts a programmable scanning strategy to achieve sub-pixel level displacement compensation monitoring of the temperature field of key components, and the motion compensation of the thermal image sequence is realized through a residual attention deep learning model.

[0044] Furthermore, the perception layer further includes:

[0045] a) A multi-physical field collaborative perception framework that dynamically adjusts the sensor excitation energy according to the degree of environmental interference:

[0046]

[0047] Among them, : The basic excitation energy, the initial energy under no interference;

[0048] : The modulation depth, controlling the variation amplitude of the excitation energy, with a value range of 0.1 - 0.6;

[0049] : The modulation frequency, determining the period of energy modulation;

[0050] : The current signal-to-noise ratio, representing the degree of influence of environmental noise on the signal;

[0051] : The target signal-to-noise ratio, used to guide the energy adjustment to maintain better signal quality;

[0052] : Adjustment parameter to control the attenuation rate when the signal-to-noise ratio deviates from the target value;

[0053] b) Cross-domain feature complementary enhancement mechanism to achieve complementary enhancement of multi-physical field information such as acoustic, vibration, and thermal fields:

[0054] Among them, : The original feature data of the i-th physical field

[0055] : Complementary weight between physical fields, measuring the contribution degree of the j-th physical field to the i-th physical field

[0056] : Feature transformation function, used to adjust the data of different physical fields to make them complementary and suitable for fusion.

[0057] c) Gas-phase feature self-correction detection technology, adopting dynamic baseline correction and peak recognition algorithm to achieve ppb-level gas concentration change detection.

[0058] Furthermore, the edge layer includes:

[0059] a) The adaptive noise suppression module uses a generative adversarial network to achieve learning and elimination of noise features related to working conditions, and its optimization objective is:

[0060] Among them : Working condition variable, such as environmental temperature, pressure, etc.

[0061] : Clean signal, that is, the signal after target denoising.

[0062] : Noise prior distribution, representing the noise distribution characteristics under different working conditions.

[0063] : Discriminator D is used to judge whether the input signal is a real clean signal.

[0064] : Generator G attempts to generate a denoising result close to the real signal;

[0065] Based on the conditional generative adversarial network (cGAN) architecture, including two parts: a generator and a discriminator. The generator adopts a U-Net structure, including five layers of convolutional-transposed convolutional structure and skip connections; the discriminator adopts a PatchGAN architecture, and outputs a 70×70 feature map for local authenticity judgment.

[0066] The training process first collects paired clean signals and noise signals under different working conditions, and constructs a working condition vector, which contains information such as the operating state of the device and environmental parameters. In the pre-training stage, the mean squared error loss function is used to train the generator for 10 epochs, with a batch size of 64 and a learning rate of 0.0002.

[0067] In the adversarial training stage, the discriminator and the generator are trained alternately. The discriminator learns to distinguish between real clean signals and generated signals, while the generator tries to generate signals that the discriminator cannot distinguish, and at the same time minimizes the difference from the real clean signals. The complete loss function combines the conditional GAN loss and the L1 regularization loss, and the weight of the L1 loss is set to 100. The training lasts for 50 epochs, with a batch size of 32 and the learning rate reduced to 0.0001.

[0068] After the model training is completed, the complex model is compressed to a scale that can be deployed on edge devices through knowledge distillation technology, with a compression rate of 85% while maintaining the core performance. During the inference process, the current working condition vector and the noisy signal are input, and the generator outputs the denoised signal, and post-processing adjustments are applied, including amplitude correction and spectrum restoration, to ensure the physical interpretability of the output signal.

[0069] b) The feature space reconstruction module establishes a mapping relationship from high-dimensional data to low-dimensional eigenfeatures through a manifold learning algorithm, and adopts a manifold consistency constraint reconstruction formula:

[0070]

[0071] where, : The high-dimensional input data of the i-th sample,

[0072] : Encoder Responsible for mapping high-dimensional data to a low-dimensional feature space;

[0073] : Decoder Is responsible for reconstructing the low-dimensional features back to the original data;

[0074] : Sample similarity matrix, which controls the preservation of the local data structure;

[0075] : Jacobian matrix, which measures the local smoothness of the mapping function;

[0076] Both the encoder and the decoder are implemented using a multi-layer perceptron. The encoder has a three-layer structure (input dimension - 128 - 64 - 32), and the decoder also has a three-layer structure (32 - 64 - 128 - output dimension).

[0077] Construct a sample similarity matrix and a K-nearest neighbor graph based on Euclidean distance. The value of K is usually set between 10 and 15. The optimization process includes three core components: reconstruction error, manifold consistency loss, and regularization term. The reconstruction error ensures that the low-dimensional reconstruction can retain the original data information; the manifold consistency loss guarantees that similar samples are still close in the feature space, and the manifold consistency weight is set between 0.1 and 1.0; the regularization term prevents overfitting, and the weight is set between 0.01 and 0.1.

[0078] Training uses the Adam optimizer with an initial learning rate of 0.001. Usually, 500 - 1000 rounds of iteration are required to reach convergence. This algorithm can effectively compress high-dimensional sensing data into a low-dimensional feature space, significantly reducing the data transmission volume while retaining key fault features, and is suitable for feature extraction and anomaly detection in an edge computing environment.

[0079] c) When the communication is interrupted, the emergency inference engine starts the digital twin simulation and deduction based on the device physical model, using a hybrid prediction method that combines the physical model and neural network residual correction.

[0080] Furthermore, the edge layer also includes:

[0081] a) A multi-scale anomaly-preserving dimensionality reduction algorithm. For high-dimensional sensing data, the feature weight calculation formula is:

[0082]

[0083] where : The weight of the i-th feature, measuring its importance in the dimensionality reduction process;

[0084] : The i-th eigenvalue,

[0085] and : The mean and standard deviation of this feature in the entire dataset, used for normalization,

[0086] : Local outlier measure, used to measure the anomaly degree of a data point in the local neighborhood;

[0087] : Outlier degree threshold, controlling the influence of outliers on the feature weight;

[0088] : Scaling factor, adjusting the change range of the feature weight;

[0089] : Sensitivity parameter for adjusting the influence of the outlier degree on the feature weight;

[0090] First, calculate the mean and standard deviation of each feature, and then calculate the outlier degree of each sample on each feature (the multiple of the standard deviation deviating from the mean). Calculate the local outlier degree of each sample based on the k-nearest neighbor algorithm (k = 5), and set the outlier threshold to the 95th percentile of the local outlier degree of the training dataset.

[0091] In the feature weight calculation formula, the enhancement coefficient beta is set between 1.5 and 3.0 to amplify the contribution of abnormal features; the steepness parameter gamma is set between 5.0 and 10.0 to control the steepness of the weight change. The calculated weights are normalized to ensure that the sum of the weights is equal to the number of features and maintain the overall scale.

[0092] The dimensionality reduction is implemented based on a variant of the t-SNE algorithm. Modify the distance calculation of samples in the high-dimensional space to the weighted Euclidean distance, and adjust the gradient calculation accordingly to introduce feature weights. To prevent over-optimization, set the number of iterations to 1000 - 2000, and set the perplexity parameter to 30 - 50. For samples with high local outlier degrees, increase the repulsive force by 1.5 - 2.0 times in the low-dimensional space to prevent them from being "absorbed" by normal samples.

[0093] The effect is evaluated by two indicators: the nearest neighbor retention rate and the outlier separation degree. Dynamically adjust the parameters according to the evaluation results to achieve the best dimensionality reduction effect. This method is particularly suitable for the extraction and retention of early fault features of fire-fighting equipment and can effectively capture weak but important abnormal patterns.

[0094] b) Local-global noise decomposition technique, which uses multi-scale analysis to separate noise from abnormal signals:

[0095] Among them, : The original signal, which contains noise and abnormal components;

[0096] : The denoised signal, which only contains valid information;

[0097] : The number of layers of multi-scale decomposition;

[0098] : The noise filter at the j-th level is used to extract noise components at different scales;

[0099] : The adaptive weight, which determines the contribution degree of noise filtering at different scales;

[0100] : The current working condition descriptor, which affects the adaptive strategy of noise decomposition;

[0101] c) Knowledge distillation compression diagnosis model, which migrates the knowledge of the complex model in the cloud to the edge through the teacher-student network architecture, reducing the computing resource requirements.

[0102] Further, the transport layer includes:

[0103] a) The protocol adaptive converter supports bidirectional semantic conversion between industrial protocols and AIoT protocols, which is achieved through a cross-domain protocol semantic mapping framework:

[0104]

[0105] Among them, and : respectively represent the message samples of the source protocol and the target protocol;

[0106] : the target protocol message after conversion;

[0107] : semantic distance metric, which measures the accuracy of the converted message;

[0108] : mapping complexity regularization term, which prevents the model from overfitting and improves the generalization ability;

[0109] : regularization weight, which controls the influence of the complexity constraint;

[0110] b) The quantum key encryption module adopts a one-time pad encryption system based on quantum random numbers, integrating quantum key distribution and traditional encryption algorithms:

[0111]

[0112] Among them, : the message data to be encrypted;

[0113] : quantum key, which is generated by a quantum random number generator and has the characteristics of unpredictability and one-time pad;

[0114] : classical key, which is used for additional encryption to enhance data security;

[0115] : quantum encryption function, which uses quantum key distribution for encryption;

[0116] : classical encryption function, such as AES, which encrypts the data after quantum encryption again;

[0117] The quantum key encryption module consists of three parts: a quantum random number generator, a quantum key distribution protocol, and a hybrid encryption architecture. The quantum random number generator is based on the phase noise measurement of optical quantum states, providing a true random bit stream of 6 Mbps, and its randomness is verified through all 15 tests of the NIST SP800-22 test suite.

[0118] The quantum key distribution protocol adopts an improved BB84 protocol, supports point-to-point and networked deployments. The key generation rate exceeds 2 kbps at a transmission distance of 50 km and exceeds 200 bps at 100 km. The tolerance threshold for the quantum bit error rate is set at 11%. The implementation method is based on the phase encoding of attenuated laser pulses and uses single-photon detectors for reception.

[0119] The hybrid encryption architecture combines quantum one-time pad and classical symmetric encryption. Quantum one-time pad is used to encrypt the symmetric key, which is achieved through exclusive-or operation; classical symmetric encryption uses the AES-256-GCM algorithm to encrypt the message content. The encryption process first generates a session key, encrypts the session key using the quantum key, then encrypts the message with the session key, and finally transmits the ciphertext pair containing the encrypted session key and the encrypted message.

[0120] The key management system controls the key life cycle for a maximum of 24 hours, and the number of uses of a single key does not exceed 3 times. It uses a security hardware module certified by FIPS 140-2 Level 3 to store keys. The system also integrates NIST-approved post-quantum cryptography algorithms (such as Kyber, Dilithium), supports the hybrid encryption mode of quantum keys and post-quantum algorithms, has algorithm agility, and supports algorithm updates without changing the system architecture.

[0121] c) The network topology self-optimization unit dynamically adjusts the transmission path weights based on reinforcement learning, and simultaneously optimizes multiple constraints such as latency, reliability, energy consumption, and security through deep reinforcement learning.

[0122] Furthermore, the transport layer also includes:

[0123] a) A multipath redundant transmission controller that adopts a predictive redundant transmission control strategy and adaptively adjusts the redundancy according to the prediction of the network state:

[0124]

[0125] where, : The number of redundant transmissions required at time t;

[0126] : The desired transmission success rate;

[0127] : The predicted value of the single transmission success probability under the current network state;

[0128] : Round up to ensure that the redundancy is an integer;

[0129] b) Cognitive security dynamic defense strategy, an active defense mechanism based on attack intention understanding:

[0130]

[0131] Among them, : The current security state of the system;

[0132] : Defense actions (such as IP blocking, intrusion detection rule update, etc.);

[0133] : The value function of taking defense action in state s;

[0134] : Temperature parameter, controlling the exploration of the strategy (high makes the selection more uniform, low is more biased towards high-value actions);

[0135] c) Protocol structure-aware compression coding technology, designing an adaptive compression coding strategy according to different protocol characteristics to reduce the amount of transmitted data.

[0136] Furthermore, the platform layer includes:

[0137] a) The hybrid enhanced intelligent engine integrates deep reinforcement learning and an expert rule system to build an interpretable fault diagnosis model, and its loss function includes:

[0138] Physical constraint loss:

[0139]

[0140] Among them, : Data-driven loss, measuring the error between the predicted value of the neural network and the real data;

[0141] : Physical consistency loss, making the neural network conform to physical laws, including:

[0142] : Ensuring that the predicted value is close to the calculation result of the physical model;

[0143] : Ensuring the gradient;

[0144] : Regularization loss, preventing overfitting;

[0145] and : Weight parameter, which controls the importance of different loss terms.

[0146] b) The device knowledge graph federation realizes privacy - protected knowledge sharing of cross - institutional device data, adopting the differential privacy protection mechanism:

[0147]

[0148] Among them

[0149] : Feature data after adding privacy - protected noise;

[0150] : Original feature data;

[0151] : Gaussian noise, which protects data privacy;

[0152] : Data sensitivity, indicating the impact of data changes on the result;

[0153] : Privacy budget. The smaller the value, the stronger the privacy protection but the decrease in data availability;

[0154] : Noise intensity coefficient;

[0155] c) The distributed digital twin adopts multi - scale modeling technology to fuse the device mechanism model and the data - driven model, constructing a multi - scale digital twin from components to systems:

[0156]

[0157] Among them, : The state of the entire system;

[0158] : The state of the i - th subsystem (such as sensors, control units, etc.);

[0159] : System coupling matrix, which describes the mutual relationship between different subsystems;

[0160] : Scale - to - scale mapping function, which integrates models at different levels.

[0161] Furthermore, the platform layer also includes:

[0162] a) Spatiotemporal data lake, which supports the unified storage and efficient retrieval of structured, semi - structured and unstructured data;

[0163] b) Multi-granularity attention diagnosis mechanism, an interpretable diagnosis framework based on hierarchical attention, realizes transparent interpretability of fault diagnosis by analyzing attention distribution; the multi-granularity attention diagnosis mechanism adopts a hierarchical network structure to achieve multi-level feature attention. The input layer receives multi-source sensing data, and the data structure is a three-dimensional tensor (batch size × time step × feature dimension). The feature-level attention layer uses the self-attention mechanism to calculate the internal correlation of features, and calculates the feature correlation weight through query / key / value matrix transformation and scaled dot-product attention.

[0164] The time-level attention layer uses bidirectional LSTM to extract temporal features, uses the attention mechanism to calculate the importance weights of each time step, and generates a weighted temporal feature representation. The device-level attention layer uses a graph attention network to model the relationships between devices. The node features are device state vectors, and the edges are physical or logical relationships between devices. The multi-head attention mechanism is used to focus on different types of relationships between devices.

[0165] Interpretability enhancement techniques include attention visualization, counterfactual explanation generation, and prototype matching explanation. Attention visualization shows the attention distribution at different levels through a heatmap, shows key time points on the waveform diagram on the time axis, and provides feature importance ranking and explanation. Counterfactual explanation perturbs the features or time points with high attention, observes the change in the diagnosis result, quantifies the impact of the features on the result, and generates explanations in the form of "if... then...". Prototype matching explanation maintains a fault prototype library, where each fault corresponds to a specific attention pattern, calculates the similarity between the current attention pattern and the prototype, and provides an explanation based on the most matching prototype.

[0166] The training adopts a multi-task learning objective, including diagnostic accuracy loss (cross-entropy or focal loss), attention sparsity loss (L1 regularization), and attention consistency loss (forcing the attention to be consistent with domain knowledge). The model also adopts attention-guided learning, uses the key features and time points annotated by experts to guide attention learning, and uses KL divergence to measure the difference between the model's attention and the expert's attention.

[0167] Adversarial training generates adversarial samples by adding perturbations in areas with high attention, improving the robustness of the model to noise and perturbations. In the inference stage, the system generates a hierarchical diagnostic report based on the attention at each layer, provides detailed explanations from the device level to the feature level, and at the same time supports interactive diagnostic exploration, allowing users to adjust the focus for hypothesis verification.

[0168] c) A secure multi-party computing framework based on zero-knowledge proof, realizing distributed secure computing based on homomorphic encryption:

[0169]

[0170] Among them, : The local calculation result of the i-th participant;

[0171] : The weight coefficient, which determines the contribution ratio of the calculation results of each participant;

[0172] : The homomorphic encryption function, which encrypts the calculation result C with the public key pk;

[0173] : It represents the multiplication aggregation of the encrypted results of all participants, and directly calculates the global result by using the homomorphic encryption property without decrypting the intermediate data;

[0174] The secure multi-party computation framework based on zero-knowledge proof is based on the Paillier semi-homomorphic encryption system, supports additive homomorphism, has a key length of 2048 bits, and uses the Chinese Remainder Theorem to accelerate the decryption process. The zero-knowledge proof protocol adopts the Sigma protocol framework, supports proof of knowledge and range proof, is implemented based on the discrete logarithm and commitment scheme, and uses the Fiat-Shamir transform to achieve non-interactive conversion.

[0175] The secure multi-party computation process is divided into two main roles: the data provider and the aggregator. The data provider first locally calculates the intermediate result, generates a random mask, encrypts the masked intermediate result, and generates a zero-knowledge proof to prove that the result is within the valid range, and then sends the encrypted result and the proof. The aggregator verifies all zero-knowledge proofs, calculates the encrypted aggregation result through the homomorphic property, requests the homomorphic computation result of the masked part from the participants, and decrypts to obtain the final aggregation result after removing the mask.

[0176] The weight allocation mechanism is adaptively allocated based on data quality and integrity. The weight range is 0 - 1 and the sum is 1. It is calculated through exponential normalization, and the temperature parameter is set to 0.1 - 1.0 to control the smoothness of the allocation. The framework also includes multiple privacy protection enhancement measures, including differential privacy mechanism, threshold encryption, and secure communication channels.

[0177] This framework guarantees the computational security based on the discrete logarithm hard problem and the decisional composite residue assumption, provides ε-differential privacy protection (ε is set to 0.1 - 1.0), and can resist collusion attacks by no more than one-third of the participants, providing a reliable guarantee for secure data sharing between different building fire protection systems.

[0178] d) Cross-domain transfer learning framework to achieve knowledge transfer and reuse between different buildings and regions.

[0179] Furthermore, the application layer includes:

[0180] a) Equipment health prediction model, which uses the physical constraint deep Bayesian time series network algorithm to realize the fault prediction and health status assessment of fire protection equipment at multiple time scales:

[0181]

[0182] wherein, : the equipment failure state at a future moment ;

[0183] : the historical state data of equipment sensors;

[0184] : a latent variable representing the current potential health state of the system (such as internal aging, stress accumulation, etc.)

[0185] : the influence of the latent state on the failure probability;

[0186] : the relationship between sensor data and the latent state;

[0187] b) The fault causal chain analysis system constructs a dynamic inference model of the fault propagation path based on the Bayesian network and calculates the causal strength using a multi-scale spatio-temporal causal inference network:

[0188]

[0189] wherein: : two fault events (such as abnormal temperature → sensor failure);

[0190] : conditional independence test, measuring whether still affects ;

[0191] : time priority score, indicating whether precedes in occurrence;

[0192] : spatial similarity index, measuring the degree of physical space correlation between two events;

[0193] The fault causal chain analysis system includes three main stages: causal graph construction, causal strength calculation, and root cause reasoning. Causal graph construction first performs data preprocessing, including time alignment, anomaly detection, and feature extraction. An initial causal skeleton is constructed using the time-priority-based PC algorithm, with time lags set at multiple levels (1 second, 5 seconds, 10 seconds, 30 seconds, 1 minute, 5 minutes), and partial correlation coefficients are used for conditional independence testing with a significance level set at 0.05.

[0194] The initial causal graph is refined by combining domain knowledge, removing physically impossible causal edges, verifying causal relationships through historical intervention records, and strengthening the causal relationships between physically connected devices by incorporating device topology constraints. Causal strength calculation consists of three components: conditional independence test, time precedence score, and spatial similarity index.

[0195] The conditional independence test calculates conditional mutual information using a non-parametric kernel method. The Gaussian kernel is selected as the kernel function, and the bandwidth parameter is determined by the median heuristic method. The result is normalized to the 0-1 interval to represent the statistical dependence strength. The time precedence score is calculated based on the temporal order of causal events, and the time constant is set to one-third of the system response time (usually 1-30 seconds). The spatial similarity index is calculated based on the physical location distance between devices, and the characteristic distance parameter is set to the system characteristic distance (usually 10-50 meters).

[0196] In the root cause reasoning stage, causal centrality is calculated, and the top three events with the highest centrality are selected as candidate root causes. Then, counterfactual simulation is performed through the digital twin to evaluate the system performance when each candidate root cause does not occur, calculate the impact scope and severity, and select the final root cause through comprehensive scoring. The system provides a visualization interface with a hierarchical layout, where the node size represents the event severity, the edge thickness represents the causal strength, and the color coding represents the time order.

[0197] c) The maintenance strategy generator uses a multi-objective optimization algorithm to balance device reliability, maintenance cost, and regulatory requirements:

[0198]

[0199] where: π: maintenance strategy (such as replacing parts, regular inspections, sensor calibration);

[0200] : reliability index of the device (such as mean time between failures MTBF);

[0201] : maintenance cost (such as cost of replacing parts, labor cost, etc.);

[0202] : regulatory compliance (such as national fire standards, equipment certification requirements)

[0203] It is solved by an improved Pareto multi-objective optimization algorithm.

[0204] Furthermore, the application layer also includes:

[0205] a) The emergency plan deduction platform combines virtual reality technology to achieve immersive simulation training for fire scenarios:

[0206]

[0207] Among them, : The virtual scene state at the current moment t;

[0208] : The operations of the user at moment t (such as using a fire extinguisher, evacuation route selection, etc.);

[0209] : The historical performance of the user (such as past training data, reaction speed, accuracy, etc.);

[0210] : The physical fire simulation model calculates the scene changes based on physical laws such as heat conduction and smoke diffusion;

[0211] : The adaptive adjustment function dynamically optimizes the scene in combination with the user's performance, such as increasing the training difficulty, providing hints, etc.;

[0212] b) Equipment life dynamic prediction model, predicting the remaining life considering the usage history and environmental impacts:

[0213]

[0214] Among them, : The basic remaining life, that is, the expected life of the equipment in an ideal environment;

[0215] : The change of the influencing factor over time;

[0216] : The time-varying weight of the influencing factor, indicating the influence degree of the factor at different time points;

[0217] : The global weight of each influencing factor, characterizing its long-term influence degree;

[0218] c) Regulatory dynamic adaptation module, automatically analyzing the requirements of the latest fire regulations and adjusting the system operation and maintenance strategy to ensure that the system operation always complies with the regulatory standards.

[0219] Furthermore, the system further includes:

[0220] a) Root cause localization mechanism, identifying the root cause of the failure through the maximum causal centrality:

[0221] ;

[0222] Among them, : Represents the event The causal centrality score as the root cause;

[0223] : It is a causal flow indication function, which indicates whether event j propagates causal influence to event k when i is the hypothesized root cause.

[0224] The outer summation traverses all events directly related to , and the inner summation traverses events with further influence ;

[0225] b) An interactive causal chain visualization decision-making system that assists experts in decision-making based on visualization technology;

[0226] c) A run-design two-way feedback mechanism that realizes two-way information flow between the digital twin and the physical device:

[0227]

[0228] where : The current digital twin model, i.e., the virtual device model designed initially;

[0229] : The operating state of the actual device (such as sensor data, fault records);

[0230] : The performance difference between the model and the real device is used to adjust the parameters of the twin model;

[0231] : The update function, which corrects the model based on real-time data to improve the prediction accuracy.

[0232] Compared with the prior art, the beneficial effects of the present invention are:

[0233] 1. The present invention provides an intelligent fire-fighting facility self-check and fault warning system based on AI algorithms, which breakthroughly realizes the early warning and accurate prediction capabilities of fire-fighting facility faults, and significantly extends the time window of preventive maintenance. The multi-time-domain health assessment system at the application layer integrates physical laws and probability prediction methods, and establishes a mapping relationship between the current state of the device and the future fault probability through a physically constrained deep Bayesian time series network. This network can not only give deterministic predictions, but also quantify the uncertainty of the predictions, providing a basis for risk assessment for maintenance decisions. The system also includes a dynamic prediction model for the remaining life of the device, which takes into account factors such as the usage history and environmental impact of the device, and conducts real-time assessment and update of the remaining service life. Combining the root cause analysis ability of the spatio-temporal causal reasoning network, the system can identify potential problems at the early symptom stage of faults and predict their development trends, transforming the traditional passive response-based maintenance into active predictive maintenance, and significantly reducing the unexpected shutdowns and emergency repair events of fire-fighting facilities.

[0234] 2. The present invention provides an intelligent fire-fighting facility self-check and fault warning system based on AI algorithms. While ensuring data integrity, the system significantly optimizes the utilization of communication resources and reduces the system response latency. The knowledge distillation and compression diagnosis model deployed at the edge layer uses the teacher-student network architecture to transfer the knowledge of the complex cloud model to the edge, achieving reasonable allocation of computing power resources and near-source computing. The protocol semantic adaptive conversion framework at the transport layer realizes the seamless docking and semantic-level conversion between industrial protocols and AIoT protocols, breaking through the communication barrier between traditional fire-fighting systems and modern Internet of Things architectures. The protocol structure-aware compression coding designs an adaptive compression strategy according to different protocol characteristics, reducing the amount of transmitted data. The multi-constraint self-optimizing routing algorithm dynamically selects the optimal network transmission path based on deep reinforcement learning, and the predictive redundant transmission control realizes adaptive redundant control according to network state prediction, jointly ensuring the real-time and reliable data transmission. The synergistic effect of these technologies enables the system to achieve a response time of milliseconds under limited bandwidth conditions, meeting the strict requirements of the fire safety system for real-time performance.

[0235] 3. The present invention provides an intelligent fire-fighting facility self-check and fault warning system based on AI algorithms. The five-layer collaborative architecture of this system forms an intelligent operation and maintenance system for fire-fighting facilities with self-evolution ability, which can adapt to different environmental and condition changes. The multi-scale digital twin system at the platform layer constructs a multi-level virtual model from components to the system, realizing the virtual-real mapping of the entire fire-fighting system. The operation-design two-way feedback mechanism forms an information closed-loop between the digital twin and the physical device, not only feeding back real-time operation data to the digital model, but also applying optimization suggestions and control strategies to the physical device. The physical model-driven lightweight twin inference engine at the edge layer ensures that the basic intelligent analysis ability can still be maintained in the case of communication interruption. By continuously learning environmental changes, equipment aging laws, and usage patterns, the system continuously adjusts and optimizes internal parameters and decision thresholds, enabling the entire system to exhibit adaptive capabilities and evolutionary characteristics similar to living organisms.

[0236] 4. The present invention provides an intelligent fire-fighting facility self-check and fault warning system based on AI algorithms. The system significantly reduces the maintenance cost of fire-fighting facilities and improves the operation and maintenance efficiency through scientific decision-making and intelligent assistance. The multi-objective optimization of maintenance strategies at the application layer uses an improved Pareto multi-objective optimization algorithm to find the best balance among reliability, cost, and regulatory requirements, and formulates a scientific and reasonable maintenance plan. The spatio-temporal causal inference network quickly and accurately locates the root cause of faults through causal intensity weight calculation and maximum causal centrality identification, shortening the traditional fault troubleshooting process that takes several hours to the minute level. The virtual reality enhanced pre-plan deduction provides an immersive training platform, improving the skill level and emergency response ability of maintenance personnel. These technologies work together to shift maintenance from passive response to active prediction, from experience-based decision-making to data-driven decision-making, greatly reducing unnecessary inspections and component replacements, reducing the consumption of human and material resources, and at the same time improving the maintenance quality and equipment availability.

[0237] 5. The present invention provides an intelligent fire-fighting facility self-check and fault warning system based on AI algorithms. The system enhances its adaptability in complex and harsh environments and solves the problem of insufficient reliability of traditional fire-fighting monitoring systems under extreme conditions. The non-stationary acoustic feature extraction algorithm in the perception layer uses a method combining multi-scale Hilbert-Huang transform and wavelet packet decomposition to extract key acoustic features in a strong background noise environment. The sub-pixel infrared thermal image registration algorithm realizes precise motion compensation of thermal image sequences through a deep learning model based on residual attention, overcoming interference factors such as environmental light changes and temperature fluctuations. The manifold consistency constraint reconstruction technology in the edge layer maintains the internal topological structure during the dimensionality reduction process of high-dimensional data, ensuring that key features are not lost during information compression. The predictive redundant transmission control in the transmission layer realizes adaptive redundant control according to network state prediction, ensuring reliable data transmission even in unstable network conditions. These technologies work together to enable the system to operate stably in harsh environments such as high noise, high humidity, high temperature, and strong electromagnetic interference, adapt to diverse application scenarios from high-rise buildings, underground spaces to industrial sites, and achieve reliable monitoring and warning all day long and in all scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0238] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0239] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0240] Figure 2It is the system transmission layer protocol conversion and secure transmission architecture;

[0241] Figure 3 It is a schematic diagram of the intelligent analysis center;

[0242] Figure 4 It is a framework diagram of the intelligent decision-making service at the application layer. Specific implementation manners

[0243] Next, the technical solution of the present invention will be more clearly and completely elaborated by combining the accompanying drawings and describing the preferred implementation manners of the present invention.

[0244] As a specific implementation manner, the overall system architecture is as Figure 1 shown, including five collaborative working levels: the perception layer, the edge layer, the transmission layer, the platform layer, and the application layer. The system is based on multi-physical field collaborative perception, and constructs a complete intelligent operation and maintenance system for fire-fighting facilities through edge intelligent preprocessing, secure and reliable transmission, platform intelligent analysis, and application layer decision-making.

[0245] As a specific implementation manner, the data from the perception layer to the edge layer adopts a structured binary message format, uses MessagePack encoding, and includes device identification, timestamp, sensor type, original numerical array, sampling rate, unit, and metadata (battery power, signal quality, environmental information, etc.). The data is compressed using the LZ4 algorithm, with a compression ratio of approximately 3:1, and the transmission frequency varies between 1Hz and 100Hz according to the sensor type.

[0246] The data from the edge layer to the transmission layer adopts feature vectors and metadata encoded by Protocol Buffers, including device identification, processing timestamp, feature vector list (including feature type, feature value array, and confidence), local diagnosis result, and relevant metadata. The data compression rate reaches 10%-25% of the original data, and the transmission frequency is usually 0.1Hz - 1Hz.

[0247] The data from the transmission layer to the platform layer uses encrypted feature packets, adopts the JSON-LD format to support semantic links, and includes device identification, timestamp, feature vector, diagnosis result, and security information (encryption type, signature, and timestamp). The data is encrypted using AES-256-GCM, and the key is updated through quantum key distribution. The transmission frequency is adjusted as needed, with a normal state of 0.01Hz - 0.1Hz and an abnormal state that can be increased to 1Hz.

[0248] Data from the platform layer to the application layer is realized through real-time push via RESTful API and WebSocket. The API structure includes endpoints such as device health status, diagnostic results, prediction results, maintenance plans, and causal chain analysis. The return format is JSON or XML, supporting HAL hypermedia links. The WebSocket push is based on the MQTT protocol to achieve real-time event streams.

[0249] The collaboration between the perception layer and the edge layer is achieved through adaptive sampling control. The edge layer sends sampling parameter adjustment instructions to the perception layer according to the analysis results, including sampling rate, gain adjustment, filtering parameters, and duration. After the sensor executes the instructions, it returns a confirmation message containing the actual execution parameters. The system also supports device-level collaborative sampling, and interconnected sensors adopt a synchronous sampling strategy.

[0250] The collaboration between the edge layer and the transport layer is mainly achieved through network-adaptive transmission. The transport layer provides network status information (bandwidth, latency, packet loss rate, connection quality) to the edge layer, and the edge layer adjusts the data sending strategy accordingly. The edge layer maintains multiple levels of priority queues, schedules the sending according to the importance and urgency of the data, and implements local caching when the network is unavailable and retransmits in the order of importance after the network recovers.

[0251] The collaboration between the transport layer and the platform layer includes secure channel establishment, session management, protocol adaptation, and two-way data flow. The secure channel is established through two-way authentication and key negotiation. Session management includes information such as session ID, establishment time, expiration time, encryption suite, and key rotation interval. The transport layer automatically identifies and converts different device protocols, provides a unified interface to the platform layer, and at the same time supports the platform layer to send configuration updates and control commands to the devices.

[0252] The collaboration between the platform layer and the application layer is based on user permission control and event subscription mechanisms. Permission control adopts fine-grained access control of RBAC and ABAC. Event subscription includes parameters such as subscription ID, event type list, device filtering conditions, notification channels, and notification thresholds. The system establishes a feedback loop. User feedback and operation records from the application layer are sent back to the platform layer for algorithm optimization. At the same time, it supports interactive analysis. The application layer provides user hypothesis scenarios, and the platform layer executes simulation analysis and returns the results.

[0253] Through these detailed algorithm implementation steps and cross-layer interaction mechanism descriptions, the system realizes the full-process intelligence from data collection to intelligent decision-making. Each component works together to form an intelligent operation and maintenance system for fire protection facilities that is self-evolving, highly reliable, and low-cost.

[0254] The system adopts a multi-source heterogeneous sensing unit integration strategy to construct a collaborative multi-physical field perception framework for fire-fighting equipment. This framework deploys a multi-modal sensor network, including a conventional parameter monitoring unit, a voiceprint feature acquisition unit, a three-dimensional vibration analysis unit, a dynamic infrared thermal imaging array, a gas chromatography analysis unit, and a high-frequency electrical characteristic acquisition unit. The voiceprint feature acquisition unit adopts a fusion architecture of an 8-channel microphone array and 4 ultrasonic sensors, covering the frequency band of 10Hz - 80kHz to achieve the extraction of non-stationary acoustic features. For different types of equipment, the system adopts a differential sampling rate configuration: 48kHz for large equipment such as pumps and fans, 96kHz for pipeline systems, and 192kHz for valve control units, effectively capturing the early fault features of the equipment.

[0255] The three-dimensional vibration analysis unit integrates a high-precision triaxial MEMS accelerometer array and a laser Doppler vibrometer, and adopts a multi-source vibration signal decoupling algorithm based on tensor decomposition. A vibration sensor array with 12 measurement points is deployed for key equipment such as fire pumps and compressors, continuously collecting data at a sampling interval of 1ms, and performing online analysis using a sliding window with a 2-second window and a 0.5-second step. The dynamic infrared thermal imaging array consists of multiple high-resolution uncooled infrared cameras, equipped with a thermal image sequence motion compensation algorithm based on a residual attention network, and the registration accuracy is better than 0.1 pixel. The thermal imaging array adopts adaptive frame rate control, 1fps in normal mode, and automatically increases to 10fps when the temperature is abnormal. The system sets differential temperature thresholds for different equipment components to achieve accurate anomaly detection.

[0256] The gas-phase feature self-calibration detection technology can detect gas concentration changes at the ppb level, and can detect abnormal gas concentration changes at the initial stage of thermal degradation of insulating materials. The collaborative multi-physical field perception framework dynamically adjusts the sensor excitation energy and realizes the complementary enhancement of multi-physical field information. For the fault diagnosis of fire pumps, the system combines acoustic features and vibration features; for bearing fault detection, it fuses acoustic high-frequency transient features and thermal imaging temperature change patterns, and the fault detection rate is increased by 27.8%.

[0257] To process the massive data collected by the perception layer, the system deploys an intelligent preprocessing node network. The adaptive noise suppression module uses a conditional generative adversarial network to realize the learning and elimination of noise features related to working conditions, constructs a generator and a discriminator using a multi-layer convolutional neural network, and trains the model through 12,000 groups of working condition-noise pairs. The edge device adopts a heterogeneous computing architecture, integrating a high-performance processor and an FPGA accelerator. The feature space reconstruction module is based on a hybrid model of variational autoencoder and manifold regularization, reducing the original high-dimensional feature vector to a low-dimensional eigen feature space, significantly reducing the false alarm rate.

[0258] The multi-scale anomaly-preserving dimensionality reduction algorithm enhances the contribution of anomaly-related features by dynamically adjusting feature weights, advancing the early fault detection time by 35% compared to traditional methods. The local-global noise decomposition technique uses a multi-scale decomposition framework, designs dedicated filters for different frequency bands, and dynamically adjusts filter weights through real-time SNR evaluation to achieve efficient anomaly feature extraction. The emergency inference engine integrates a simplified physical model and a lightweight neural network to enable low-latency inference on edge devices, maintaining the local fault diagnosis ability for up to 72 hours in case of communication interruption. The knowledge distillation technology is also applied at the edge layer to transfer the knowledge of complex cloud models to edge devices, significantly reducing the model size while maintaining a high diagnostic accuracy.

[0259] The system constructs an advanced communication architecture for the transport layer, such as Figure 2 shown. Figure 2 It shows the protocol conversion and secure transmission architecture of the system's transport layer. The figure shows that the upper part is the access layer for various heterogeneous protocols, the middle part is the protocol conversion module, and the lower part is the secure transmission channel. The industrial protocol domain and the IoT protocol domain are distinguished by different colors. The protocol adaptive converter supports seamless conversion between multiple industrial protocols and AIoT protocols. The system predefines a unified identification model to ensure efficient conversion between heterogeneous protocols. The protocol structure-aware compression coding technology designs different coding strategies for different data types, reducing the transmitted data volume by an average of 67.3% while maintaining a low system response time. The multi-path redundant transmission controller predicts the network state based on deep learning and implements different transmission strategies for data with different priorities, significantly improving the end-to-end reliability.

[0260] In terms of security, the system uses a quantum key encryption module to achieve high-strength data protection, integrating quantum random number generation and traditional encryption algorithms, and balancing security and performance through a hierarchical key update mechanism. The cognitive security dynamic defense strategy constructs a network behavior feature library and optimizes the selection of defense strategies through deep reinforcement learning to effectively resist known and unknown attacks. The network topology self-optimization unit continuously evaluates and adjusts network performance, significantly improving transmission efficiency and reducing energy consumption through self-learning optimization.

[0261] After the data converges to the platform layer, the intelligent analysis center as Figure 3 shown starts to work. Figure 3It shows the main components of the platform layer, including the spatio-temporal data lake, the hybrid enhanced intelligence engine, the federation of device knowledge graphs, distributed digital twins, and the cross-domain transfer learning framework. Different functional modules are represented by hexagons, and the data flow between modules is represented by connecting lines. The hybrid enhanced intelligence engine integrates deep learning and physical models to build an interpretable fault diagnosis framework. The system integrates knowledge in fields such as thermodynamics, fluid mechanics, and electromagnetics, imposes physical constraints on neural network predictions, and achieves high-accuracy fault classification. The multi-granularity attention diagnosis mechanism establishes a hierarchical attention model to achieve a transparent diagnosis process from the device level to the feature level, improving the efficiency of fault handling. The federation of device knowledge graphs constructs a fire-fighting equipment knowledge network containing 100,000 entities and 500,000 relationships, and realizes efficient knowledge reasoning through graph convolutional networks.

[0262] As shown in Table 1, the comparison between the physical knowledge fusion and the deep learning model, where the evaluation is based on 10 types of typical faults in fire pump fault diagnosis, and the F1 score is used as the accuracy metric. The comparison between the physical knowledge fusion and the deep learning model shows that in terms of accuracy, interpretability, data requirements, computing resources, and abnormal generalization ability, the physically constrained deep learning and the multi-physics field fusion model exhibit the best comprehensive performance. The distributed digital twins establish a multi-level virtual mirror from components to the system, support multiple modes such as historical playback, real-time mapping, and predictive simulation, and achieve high-precision system behavior simulation. To ensure data privacy, the system adopts a secure multi-party computing framework based on zero-knowledge proof, and realizes cross-institutional secure collaboration based on homomorphic encryption and key sharing. The cross-domain transfer learning framework solves the cold start problem of new building systems, efficiently migrates existing knowledge through domain adaptation technology, and significantly shortens the learning cycle.

[0263] Table 1

[0264] As Figure 4 shown, the application layer provides intelligent decision-making services. Figure 4 It shows the main functional modules and work processes of the application layer. The center is the main decision-making engine, and around it are functional units such as health prediction, fault diagnosis, and maintenance planning. The data and decision-making flows are represented by process-directed arrows. The device health prediction model uses a deep Bayesian network to achieve multi-time-scale prediction and provides a sufficient maintenance time window through a hierarchical warning mechanism. The fault causal chain analysis system constructs a dynamic Bayesian network to accurately identify the root cause in complex fault chains. The interactive visualization decision-making system supports multiple modes such as fault tracing, impact analysis, and hypothesis verification, and significantly shortens the fault diagnosis time.

[0265] Table 2 shows the comparison of different maintenance strategy types. The data is based on the 12 - month operation data statistics of a commercial building fire protection system. The maintenance strategy generator balances equipment reliability, maintenance cost, and regulatory requirements, and generates an optimal maintenance plan through a multi - objective optimization algorithm, reducing maintenance costs while improving equipment availability. The system supports three types of maintenance strategies: condition - based maintenance (CBM), risk - based maintenance (RBM), and reliability - centered maintenance (RCM). It dynamically selects the optimal strategy according to equipment importance and failure modes, reducing maintenance costs by 22.3% and increasing equipment availability by 3.7 percentage points.

[0266] Table 2

[0267] The system also integrates an emergency plan deduction platform, providing an immersive training environment through virtual reality technology, significantly improving the emergency response efficiency. The equipment life dynamic prediction model considers various influencing factors, provides a scientific basis for equipment renewal, and effectively reduces unplanned downtime. The regulatory dynamic adaptation module automatically analyzes the latest standard requirements to ensure that system operation and maintenance comply with regulatory regulations. The operation - design two - way feedback mechanism continuously optimizes the system design through real - time data, bringing significant improvements in aspects such as energy consumption, coverage uniformity, and false alarm rate, forming a self - optimizing fire protection facility health management ecosystem.

[0268] The operation - design two - way feedback mechanism realizes the two - way information flow between the digital twin and the physical equipment. This mechanism establishes a closed - loop feedback system, continuously optimizing the design model and parameters through real - time operation data. The system collects the differences between the actual operation parameters of the equipment and the predicted values of the theoretical model, and updates the model parameters through the Bayesian parameter estimation method. At the same time, the system analyzes the equipment degradation trend and automatically generates design improvement suggestions. In practical applications, this mechanism has optimized the selection parameters of the fire pump, reducing energy consumption by 11.3%; improved the pipe network layout of the sprinkler system, increasing the coverage uniformity by 8.7%; and optimized the control strategy, reducing the false alarm rate by 25.4%.

[0269] As a specific implementation case, in the implementation of a high - rise commercial building complex, the system covers 5 high - rise buildings over 200 meters, deploys about 1200 multi - physical - field sensing units, and monitors 212 key fire protection equipment. The perception layer installs multi - type sensors in key areas such as pump rooms, distribution rooms, and pipe shafts. Among them, the core monitoring objects in the fire pump room are 6 high - power fire pumps, and each pump is equipped with an acoustic sensor array, a three - axis vibration sensor, a temperature sensor, a flowmeter, and a pressure sensor.

[0270] During the three months of operation, the voiceprint feature acquisition unit captured abnormal high-frequency pulse signals on a main fire pump, which are almost impossible to be detected during routine maintenance inspections. The adaptive noise suppression module in the edge layer separated this weak signal from the complex background noise. After comparing it with the historical health status data through the feature space reconstruction algorithm, an anomaly report was generated. First, through cross-verification of multi-physical field data, the system found that the vibration sensor also detected the corresponding high-frequency vibration component, but with a very small amplitude, confirming the possibility of the anomaly.

[0271] After the hybrid enhanced intelligent engine in the platform layer analyzed in combination with the device physical model, it was determined that the fault type was the initial damage of the water pump bearing, and the damage location was deduced to be the outer ring of the non-drive end bearing. The digital twin simulation prediction showed that if not handled, the bearing would suffer serious damage in about 25 days, possibly leading to the shutdown of the water pump. After evaluating the spare part availability, maintenance personnel scheduling, and the importance of the water pump, the maintenance strategy generator in the application layer recommended replacing the bearing within 7 days and recommended a temporary adjustment plan to ensure the reliability of fire water supply during the replacement period.

[0272] After the maintenance personnel replaced the bearing as recommended, upon disassembly and inspection, it was found that there was indeed initial pitting on the outer ring of the bearing, which was consistent with the system diagnosis. The entire process from fault detection to elimination took only 11 days, avoiding a possible emergency shutdown event. The system estimated that compared with traditional planned maintenance, this predictive maintenance saved about 67% of the maintenance cost. More importantly, it avoided the potential sudden failure risk of the water pump. After one year of system operation in this building complex, the emergency failure rate of fire-fighting equipment decreased by 83%, and the maintenance cost decreased by 42%.

[0273] Based on the above specific implementation manners, the intelligent fire-fighting facility self-inspection and fault warning system based on AI algorithms realizes intelligent monitoring, diagnosis, and warning throughout the entire life cycle of fire-fighting facilities. The system comprehensively captures the operating status information of equipment through multi-physical field collaborative perception technology, uses edge intelligence technology to achieve efficient preprocessing of data, ensures data transmission security through a secure and reliable transmission layer, relies on the intelligent analysis engine in the platform layer to achieve equipment fault diagnosis and prediction, and finally provides intelligent decision-making support through the application layer. The actual deployment results of the system in multiple commercial buildings and public facilities show that the system can extend the early warning time of fire-fighting facility faults from 24 hours by traditional methods to 72 hours, improve the fault location accuracy rate from 85% to 94.3%, while reducing the maintenance cost by 24.7% and increasing the equipment availability by 5.1%, providing a reliable guarantee for fire safety.

[0274] The above specific embodiments only describe the preferred embodiments of the present invention, rather than limiting the protection scope of the present invention. Without departing from the design concept and spirit scope of the present invention, various deformations, substitutions, and improvements made by those of ordinary skill in the art to the technical solutions of the present invention based on the written description and drawings provided by the present invention shall all fall within the protection scope of the present invention. The protection scope of the present invention is determined by the claims.

Claims

1. An intelligent fire-fighting facility self-check and fault warning system based on an AI algorithm, characterized in that, It includes a perception layer, an edge layer, a transmission layer, a platform layer and an application layer; The perception layer is used to integrate multi-source heterogeneous sensing units, including conventional parameter monitoring units, voiceprint feature acquisition units, three-dimensional vibration analysis units, dynamic infrared thermal imaging arrays, gas chromatography analysis units and high-frequency electrical characteristic acquisition units; The edge layer is used to deploy intelligent preprocessing nodes, including an adaptive noise suppression module, a feature space reconstruction module, a device-level micro-diagnosis model set and an emergency inference engine; The transmission layer is used to build a secure and reliable transmission channel, integrating a protocol adaptive converter, a multi-path redundant transmission controller, a quantum key encryption module and a network topology self-optimization unit; The platform layer is used to establish an intelligent analysis center, including a spatio-temporal data lake, a hybrid enhanced intelligence engine, a device knowledge graph federation, a distributed digital twin and a cross-domain transfer learning framework; The application layer is used to provide intelligent decision-making services, integrating a device health prediction model, a fault causal chain analysis system, a maintenance strategy generator, an emergency plan deduction platform and a regulation dynamic adaptation module; The five-layer architecture works together.

2. The intelligent fire-fighting facility self-checking and fault warning system based on the AI algorithm according to claim 1, characterized in that, The perception layer includes: a) The voiceprint feature acquisition unit adopts a microphone array and ultrasonic sensor fusion architecture to realize wide-band acoustic feature extraction in the 10Hz-80kHz frequency band; The voiceprint feature acquisition unit uses a multi-scale Hilbert-Huang transform algorithm to extract non-stationary acoustic features, and calculates the energy time-frequency distribution through the following formula: Among them, : The instantaneous amplitude of the i-th intrinsic mode function, representing the energy magnitude of the i-th intrinsic mode at time t; : The instantaneous frequency of the i-th intrinsic mode function, which reflects the main frequency component of the i-th intrinsic mode at time t; :The complex exponential form of the analytic signal obtained by the Hilbert transform, representing the phase information of the signal; : Wavelet basis function, responsible for frequency localization, enabling the decomposition to have better time-frequency resolution; : An adaptive bandwidth parameter that dynamically adjusts the scale of the analysis window according to the local characteristics of the signal; n: The total number of intrinsic mode functions, that is, the number of signal components decomposed in the empirical mode decomposition process; b) The three-dimensional vibration analysis unit integrates a three-axis MEMS accelerometer and a laser vibrometer to construct a time-frequency space domain feature matrix of the device mechanical state, and realizes the decoupling of multi-source vibration signals through a tensor decomposition method: Among them, : The third-order vibration tensor, representing the time-frequency - spatial domain feature matrix of the vibration signal; NC: The rank of tensor decomposition, indicating the number of main components of the signal; : The weight of the r-th component, which measures the contribution degree of the r-th component to the overall vibration signal; : Spatial modal vector, describing the vibration modes at different positions; : Frequency modal vector, representing the distribution of vibration signals at different frequencies; : The time modal vector, which reflects the changing trend of the vibration signal over time; : Represents the exterior product, used to construct tensors; : The residual term, representing the noise or error that the model fails to explain; c) The dynamic infrared thermal imaging array adopts a programmable scanning strategy to realize sub-pixel level displacement compensation monitoring of the component temperature field, and the motion compensation of the thermal image sequence is realized through a residual attention deep learning model.

3. The intelligent fire-fighting facility self-checking and fault warning system based on the AI algorithm according to claim 1, wherein The perception layer also includes: a) A multi-physical field collaborative perception framework that dynamically adjusts the sensor excitation energy according to the degree of environmental interference: Among them, : The basic excitation energy, which is the initial energy under the condition of no interference; : Modulation depth, which controls the variation range of the excitation energy, with a value range of 0.1 - 0.6; : Modulation frequency, which determines the period of energy modulation; : Current signal-to-noise ratio, indicating the degree of influence of environmental noise on the signal; : The target signal-to-noise ratio, which is used to guide energy adjustment to maintain good signal quality; : Adjustment parameter to control the attenuation rate when the signal-to-noise ratio deviates from the target value; b) A cross-domain feature complementary enhancement mechanism to realize the complementary enhancement of multi-physical field information: Among them, : The original feature data of the i-th physical field; : The complementary weight between physical fields, which measures the contribution degree of the i-th physical field to the j-th physical field; : A feature transformation function for adjusting data of different physical fields; c) A gas phase feature self-calibration detection technology that adopts a dynamic baseline correction and peak recognition algorithm to realize the detection of gas concentration changes at the ppb level.

4. The intelligent fire-fighting facility self-checking and fault warning system based on the AI algorithm according to claim 1, characterized in that, The edge layer includes: a) The adaptive noise suppression module uses a generative adversarial network to realize the learning and elimination of noise features related to working conditions, and its optimization goal is: Among them : Condition variable : Clean signal, i.e., the signal after target denoising; : The noise prior distribution, representing the noise distribution characteristics under different working conditions; : The discriminator D is used to determine whether the input signal is a real clean signal; : The generator G attempts to generate a denoised result close to the real signal; b) The feature space reconstruction module establishes a mapping relationship from high-dimensional data to low-dimensional intrinsic features through a manifold learning algorithm, and adopts a manifold consistency constraint reconstruction formula: Among them, : The high-dimensional input data of the i-th sample, : Encoder Responsible for mapping high-dimensional data to a low-dimensional feature space; :Decoder is responsible for reconstructing the low-dimensional features back into the original data; : The sample similarity matrix, which controls the preservation of the local data structure; : Jacobian matrix, which measures the local smoothness of the mapping function; c) The emergency inference engine starts a digital twin simulation deduction based on the device physical model when the communication is interrupted, and adopts a hybrid prediction method combining the physical model and the neural network residual correction.

5. The intelligent fire-fighting facility self-check and fault warning system based on the AI algorithm according to claim 1, characterized in that The edge layer also includes: a) A multi-scale anomaly-preserving dimensionality reduction algorithm. For high-dimensional sensing data, the feature weight calculation formula is: Among them, : The weight of the i-th feature, which measures its importance in the dimensionality reduction process; : the i-th eigenvalue, and : The mean and standard deviation of the feature across the entire dataset, used for normalization; : Local Outlier Factor, which is used to measure the degree of abnormality of data points within a local neighborhood; : The outlier degree threshold controls the influence of outliers on the feature weights; : Scaling factor, which adjusts the range of variation of the feature weights; : Adjust the sensitivity parameter of the influence of outlier degree on feature weights; b) Local-global noise decomposition technology uses multi-scale analysis to separate noise from abnormal signals: Among them, : the original signal, including noise and abnormal components; : The denoised signal only contains valid information; : The number of layers of multi-scale decomposition; : The noise filter of the j-th stage is used to extract noise components at different scales; : Adaptive weight, which determines the contribution degree of noise filtering at different scales; : Current operating condition descriptor, affecting the adaptive strategy for noise decomposition; c) The knowledge distillation compression diagnosis model migrates complex model knowledge from the cloud to the edge through the teacher-student network architecture, reducing the demand for computing resources.

6. The intelligent fire-fighting facility self-checking and fault warning system based on the AI algorithm according to claim 1, characterized in that, The transport layer contains: a) The protocol adaptive converter supports bidirectional semantic conversion between industrial protocols and AIoT protocols, which is achieved through a cross-domain protocol semantic mapping framework: Among them, and : represent message samples of the source protocol and the destination protocol respectively; : The target protocol message after conversion; : Semantic distance metric, which measures the accuracy of the transformed message; : The mapping complexity regularization term prevents the model from overfitting and improves the generalization ability; : Regularized weight that controls the impact of the complexity constraint; b) The quantum key encryption module adopts a one-time encryption system based on quantum random numbers, integrating quantum key distribution with traditional encryption algorithms: Among them, : Message data to be encrypted; : Quantum key; : Classical key; : Quantum encryption function, using quantum key distribution for encryption; : Classical encryption function; c) The network topology self-optimization unit dynamically adjusts the transmission path weight based on reinforcement learning, and simultaneously optimizes multiple constraints such as latency, reliability, energy consumption, and security through deep reinforcement learning; The transport layer also includes: a) Multipath redundant transmission controller, adopts predictive redundant transmission control strategy, and adaptively adjusts redundancy according to network status prediction: Among them, : The number of redundant transmissions required at time t; : Desired transmission success rate; : Predicted value of the single - transmission success probability under the current network state; : Round up to ensure that the redundancy is an integer; b) Cognitive security dynamic defense strategy, active defense mechanism based on understanding of attack intent: Among them, : The current security state of the system; : Defense actions, including IP blocking and intrusion detection rule updates; : Value function for taking a defensive action in state s; : Temperature parameter; c) Protocol structure-aware compression coding technology: Design adaptive compression coding strategies based on different protocol characteristics to reduce the amount of transmitted data.

7. The intelligent fire-fighting facility self-checking and fault warning system based on the AI algorithm according to claim 1, characterized in that, The platform layer includes: a) The hybrid enhanced intelligent engine integrates deep reinforcement learning and expert rule system to build an explainable fault diagnosis model. Its loss function includes: Physical constraint loss: Among them, : data-driven loss, which measures the error between the predicted value of the neural network and the real data; : Physical consistency loss, which makes the neural network conform to physical laws, including: : Ensure that the predicted value is close to the calculation result of the physical model; : Ensure the gradient; : Regularization loss to prevent overfitting; and : a weight parameter that controls the importance of different loss terms; b) The device knowledge graph federation realizes privacy-preserving knowledge sharing of cross-institutional device data, using a differential privacy protection mechanism: Among them, : Feature data after adding privacy protection noise; : Original feature data; : Gaussian noise to protect data privacy; : The sensitivity of the data, indicating the impact of data changes on the result; : Privacy budget. The smaller the value, the stronger the privacy protection but the lower the data availability; : Noise intensity coefficient; c) Distributed digital twins use multi-scale modeling technology to integrate equipment mechanism models and data-driven models to build multi-scale digital twins from components to systems: Among them, : the state of the entire system; : The state of the i-th subsystem; : System coupling matrix, which describes the relationships between different subsystems; : Scale - level mapping function that integrates models at different levels; The platform layer also includes: a) Spatiotemporal database, supporting unified storage and efficient retrieval of structured, semi-structured and unstructured data; b) Multi-granularity attention diagnosis mechanism: Based on the interpretable diagnosis framework of hierarchical attention, transparent and interpretable fault diagnosis is achieved by analyzing the distribution of attention; c) A secure multi-party computing framework based on zero-knowledge proof, which implements distributed secure computing based on homomorphic encryption: Among them, : The local calculation result of the i-th participant; : Weight coefficient, which determines the contribution ratio of the calculation results of each participating party; : Homomorphic encryption function, encrypting the calculation result C with the public key pk; : It means multiplying and aggregating the encryption results of all participating parties, and directly calculating the global result using the property of homomorphic encryption without decrypting the intermediate data. d) Cross-domain transfer learning framework to achieve knowledge transfer and reuse between different buildings and regions.

8. The intelligent fire-fighting facility self-checking and fault warning system based on the AI algorithm according to claim 1, characterized in that, The application layer includes: a) Equipment health prediction model, using the physical constraint deep Bayesian temporal network algorithm to achieve fault prediction and health status assessment of fire equipment at multiple time scales: Among them, : the device failure state at a future moment ; : Historical status data of device sensors; : Latent variable, representing the current potential health state of the system; : Influence of hidden state on failure probability; : The relationship between sensor data and hidden states; b) The fault causal chain analysis system builds a dynamic reasoning model of the fault propagation path based on the Bayesian network and uses a multi-scale spatiotemporal causal reasoning network to calculate the causal strength: Wherein: : Two fault events; : Conditional independence test, which measures whether still has an impact after removing other influencing factors ; : The time priority score, indicating whether it precedes in occurrence; : Spatial similarity index, which measures the degree of physical space correlation between two events; c) The maintenance strategy generator uses a multi-objective optimization algorithm to balance equipment reliability, maintenance costs and regulatory requirements.

9. The intelligent fire-fighting facility self-checking and fault warning system based on the AI algorithm according to claim 1, characterized in that The application layer also includes: a) The emergency plan simulation platform combines virtual reality technology to realize immersive simulation training of fire scenes: Among them, : The virtual scene state at the current moment t; : The operation of the user at time t; : User historical performance; : The physical fire simulation model calculates the scene changes based on physical laws; : An adaptive adjustment function that dynamically optimizes the scenario based on the user's performance; b) Dynamic prediction model for equipment life, which takes into account the remaining life prediction of usage history and environmental influences: Among them : Basic remaining life, that is, the expected life of the equipment in an ideal environment; : Variation of the impact factor over time; : The time-varying weight of the impact factor, indicating the degree of influence of the impact factor at different time points; : The global weight of each influencing factor, representing its long-term influence degree; c) Dynamic regulatory adaptation module, which automatically analyzes the latest fire regulations and adjusts system operation and maintenance strategies to ensure that system operation always complies with regulatory standards.

10. The intelligent fire-fighting facility self-checking and fault warning system based on the AI algorithm according to claim 1, wherein, The system further comprises: a) Root cause location mechanism, identifying the root cause of the fault through maximum causal centrality: ； Among them, : represents an event The causal centrality score as the root cause; : represents an event to the event direct causal influence weight; : It is a causal flow indicator function, indicating whether event j propagates causal influence to event k when i is the hypothesized root cause; The outer summation iterates over all events that are "directly related" , and the inner summation then iterates over events that are further affected ; b) An interactive causal chain visualization decision-making system that assists experts in decision-making based on visualization technology; c) A run-design two-way feedback mechanism to achieve two-way information flow between digital twins and physical devices.

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