Intelligent fire protection facility self-inspection and fault warning system based on AI algorithm

Abstract

The present invention provides an intelligent fire-fighting facility self-inspection and fault warning system based on AI algorithms, including a perception layer for integrating multi-source heterogeneous sensing units; an edge layer for deploying intelligent pre-processing nodes; a transmission layer for building a safe and reliable transmission channel; a platform layer for establishing an intelligent analysis center; an application layer for providing intelligent decision-making services; and a five-layer architecture that works collaboratively. The present invention realizes full-dimensional monitoring of fire-fighting facilities through the multi-physical field collaborative perception framework of the perception layer, significantly improving the fault detection capability. The multi-source heterogeneous sensing units integrated in the perception layer are capable of simultaneously capturing information on multiple physical quantities such as acoustics, vibrations, and thermal fields, and complementarily enhance the information of different physical fields through a cross-domain feature complementary enhancement mechanism to obtain more comprehensive equipment status characteristics. In particular, the sensor adaptive excitation technology dynamically adjusts the sensor excitation energy according to the degree of environmental interference to ensure 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 in particular to an intelligent fire protection facility self-inspection and fault warning system based on an AI algorithm. Background Art

[0002] Firefighting facilities are a key safeguard for building safety, and their reliability and effectiveness are directly related to the safety of personnel and property. However, existing firefighting facility monitoring and maintenance technologies still have many shortcomings and are unable to meet the needs of modern building fire safety. Currently, firefighting facility management mainly relies on regular manual inspections and simple alarm systems. This approach not only consumes a large amount of human resources, but also has problems such as low inspection frequency and limited coverage. Existing automated monitoring systems are mostly based on single parameter monitoring (such as single physical quantities such as temperature and smoke), which makes it difficult to fully capture the early fault characteristics of equipment. For example, early faults such as abnormal heating of water pump bearings, small leaks in pipelines, and degradation of electrical insulation performance are often overlooked due to the lack of obvious characterizing parameters, and are not discovered until the fault develops to a serious stage.

[0003] Traditional firefighting facility monitoring systems commonly suffer from delayed early warning mechanisms. Most systems can only issue alarms after a fault occurs, lacking predictive capabilities. Simple threshold judgment mechanisms lead to high false alarm rates, reducing managers' sensitivity to alarm signals and potentially overlooking potential hazards. Furthermore, data from each subsystem is isolated, forming "data silos" and lacking collaborative analysis capabilities, making it difficult to identify interactions between devices and potential risks. For example, data from fire pump control systems and water supply network monitoring systems cannot be correlated and analyzed, making it difficult to comprehensively assess system performance degradation.

[0004] In terms of maintenance management, the currently widely used scheduled maintenance method has obvious drawbacks. Either premature maintenance wastes resources, or late maintenance causes failures. According to statistics, under a time-based preventive maintenance strategy, approximately 30% of maintenance work is unnecessary, while more than 20% of sudden failures remain unpreventable. In complex fire protection systems, locating the cause of failures is difficult, and maintenance personnel often need to spend a considerable amount of time troubleshooting possible fault points one by one, resulting in low maintenance efficiency. The existing system lacks an effective knowledge accumulation and sharing mechanism, making it difficult to systematically accumulate and apply expert experience. The training cycle for new employees is long, and technology transfer is difficult.

[0005] The lengthy process from anomaly detection to decision-making is a key factor limiting the emergency response capabilities of existing systems. When a potential fault is discovered, the data must be reported through multiple levels of escalation, manually analyzed, and finally decided, wasting valuable processing time. Industry data shows that the typical window from the initial signs of a fault to complete failure in a fire protection system is 24-72 hours, while the average response time for traditional systems is 12-24 hours, often missing the optimal opportunity for prevention.

[0006] In terms of emergency training and drills, traditional methods struggle to simulate complex fire scenarios and equipment failures, limiting training effectiveness. This is especially true for low-frequency, high-risk incidents, where personnel lack practical experience and can be slow to react or make operational errors in real emergencies. Furthermore, existing maintenance record systems are mostly static documents that struggle to support dynamic query and trend analysis, hindering managers from formulating sound maintenance strategies and resource allocation plans.

[0007] As buildings grow in size and become more complex, the number and variety of firefighting facilities are rapidly increasing, making traditional decentralized management models inadequate. Firefighting systems in large commercial complexes and high-rise buildings may include tens of thousands of monitoring points, making efficient, unified management difficult with traditional technologies. Furthermore, poor interoperability between equipment from different manufacturers, along with inconsistent data formats and communication protocols, complicates system integration and increases maintenance costs.

[0008] These technical pain points have seriously restricted the safe and reliable operation of fire-fighting facilities. New intelligent technologies are urgently needed to break through the existing limitations and establish an integrated solution for full-dimensional monitoring, intelligent analysis, prediction and early warning, and scientific decision-making to achieve proactive prevention and precise maintenance of fire-fighting facilities, thereby improving the overall level of building fire safety, reducing the incidence of fire accidents, and protecting people’s lives and property. Summary of the Invention

[0009] To overcome the shortcomings of existing technologies, this paper proposes an intelligent firefighting facility self-inspection and fault warning system based on AI algorithms. This system significantly reduces the false alarm and missed alarm issues common in traditional fire monitoring systems, which is crucial for fire safety management. The adversarial noise suppression framework deployed at the edge layer uses a conditional generative adversarial network to accurately model environmental noise and combines it with local-global noise decomposition technology to accurately separate noise from abnormal signals.

[0010] To achieve the above objectives, the present invention proposes an intelligent fire protection facility self-inspection and fault warning system based on AI algorithm, which includes 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 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 characteristics acquisition unit;

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

[0013] The transport 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 hub, including a spatiotemporal data lake, a hybrid enhanced intelligence engine, a device knowledge graph federation, a distributed digital twin, and a cross-domain transfer learning framework;

[0015] The application layer is used to provide intelligent decision-making services, integrating equipment health prediction models, fault causal chain analysis systems, maintenance strategy generators, emergency plan simulation platforms, and dynamic regulatory adaptation modules;

[0016] The five layers of architecture work together.

[0017] Furthermore, the perception layer includes:

[0018] a) The voiceprint feature acquisition unit uses a microphone array and ultrasonic sensor fusion architecture to achieve 10Hz-80kHz wide-band acoustic feature extraction;

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

[0020]

[0021] in, : The instantaneous amplitude of the i-th intrinsic mode function, which represents the energy of the mode at time t;

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

[0023] : The complex exponential form of the analytical signal obtained by Hilbert transform represents the phase information of the signal;

[0024] : Wavelet basis function, responsible for frequency localization, so that the decomposition has better time-frequency resolution;

[0025] : Adaptive bandwidth parameter, dynamically adjusts 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 during the empirical mode decomposition process;

[0027] The energy time-frequency distribution algorithm extracts acoustic features through a multi-step process. Empirical mode decomposition (EMD) is performed on the original acoustic signal, breaking it down into 5-12 intrinsic mode function (IMF) components, depending on the signal complexity. A Hilbert transform is then performed on each IMF component to extract the instantaneous amplitude and frequency information.

[0028] The adaptive bandwidth parameter is determined proportionally to the instantaneous frequency. The proportionality factor is typically set between 0.05 and 0.2 and can be dynamically adjusted based on signal characteristics. The algorithm uses the Morlet wavelet as the basis function with a parameter of 6, which provides a good balance between time and frequency resolution.

[0029] During the calculation process, the instantaneous amplitude, instantaneous frequency, and adaptive bandwidth parameters of each IMF component are substituted into the energy distribution formula to obtain a complete time-frequency energy distribution diagram. Finally, the system sets a threshold (typically three standard deviations of the historical healthy data) to detect abnormal energy distributions and achieve early fault identification. This algorithm effectively captures the weak acoustic wave characteristics emitted by firefighting equipment such as pump bearings and valves at the initial stage of a fault.

[0030] b) The 3D vibration analysis unit integrates a triaxial MEMS accelerometer and a laser vibrometer to construct a time-frequency-spatial feature matrix of the equipment's mechanical state. It decouples multi-source vibration signals through tensor decomposition.

[0031]

[0032] in, : The third-order vibration tensor represents the time-frequency-spatial feature matrix of the vibration signal;

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

[0034] : The weight of the rth component, which measures the contribution of this component to the overall vibration signal;

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

[0036] : frequency modal vector, representing the distribution of vibration signals at different frequencies;

[0037] : Time modal vector, reflecting the changing trend of the vibration signal over time;

[0038] : represents the outer product, used to construct a tensor;

[0039] : Residual term, which represents the noise or error that the model cannot explain;

[0040] A third-order tensor is constructed from time series data collected by a triaxial accelerometer and a laser vibrometer, with dimensions corresponding to the spatial, frequency, and time dimensions. Preprocessing includes de-meaning, normalization, and outlier handling to ensure data quality.

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

[0042] The decomposed modal vectors are matched against a pre-established fault pattern library, and potential faults are identified by calculating similarities. This method effectively separates normal operating vibration from abnormal vibration signals and is particularly suitable for vibration status monitoring of rotating equipment such as fire pumps and fans. It can identify problems such as bearing wear and impeller imbalance at their earliest stages.

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

[0044] Furthermore, the perception layer further comprises:

[0045] a) Multi-physics collaborative perception framework, dynamically adjusting sensor excitation energy according to the degree of environmental interference:

[0046]

[0047] in, : basic excitation energy, the initial energy in the absence of interference;

[0048] : Modulation depth, which controls the amplitude of the excitation energy change, with a value range of 0.1-0.6;

[0049] : Modulation frequency, which determines the period of energy modulation;

[0050] : Current signal-to-noise ratio, indicating the degree of influence of environmental noise on the signal;

[0051] : Target signal-to-noise ratio, used to guide energy adjustment to maintain good signal quality;

[0052] : Adjust the parameters to control the decay 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 acoustics, vibration, and thermal fields:

[0054] in, : The original characteristic data of the i-th physical field

[0055] : The complementary weight between physical fields, which measures the contribution of the ,th physical field to the i-th physical field

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

[0057] c) Gas phase characteristic self-correction detection technology uses dynamic baseline correction and peak recognition algorithms to achieve ppb-level gas concentration change detection.

[0058] Furthermore, the edge layer comprises:

[0059] a) The adaptive noise suppression module uses a generative adversarial network to learn and eliminate noise features related to working conditions. Its optimization objectives are:

[0060] in : Working condition variables, such as ambient temperature, pressure, etc.

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

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

[0063] : The discriminator D is used to determine whether the input signal is a real clean signal.

[0064] : Generator G attempts to generate denoised results close to the real signal;

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

[0066] The training process first collects paired clean signals and noisy signals under different operating conditions to construct an operating condition vector containing information such as device operating status and environmental parameters. During the pre-training phase, the generator is trained for 10 epochs using the mean squared error loss function, with a batch size of 64 and a learning rate of 0.0002.

[0067] During the adversarial training phase, the discriminator and generator are trained alternately. The discriminator learns to distinguish between real clean signals and generated signals, while the generator attempts to generate signals that the discriminator cannot distinguish while minimizing the difference from the real clean signals. The full loss function combines the conditional GAN ​​loss and the L1 regularization loss, with the L1 loss weight set to 100. Training lasts for 50 epochs, with a batch size of 32 and a learning rate reduced to 0.0001.

[0068] After model training is complete, knowledge distillation technology is used to compress the complex model to a scale deployable on edge devices, achieving an 85% compression rate while maintaining core performance. During inference, the current operating condition vector and a noisy signal are input. The generator outputs a denoised signal and applies post-processing adjustments, 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 intrinsic features through a manifold learning algorithm, and adopts the manifold consistency constraint reconstruction formula:

[0070]

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

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

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

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

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

[0076] Both the encoder and decoder are implemented using multi-layer perceptrons. 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] A sample similarity matrix is ​​constructed using a K-nearest neighbor graph based on Euclidean distance, with K typically set to 10 to 15. The optimization process consists of three core components: reconstruction error, manifold consistency loss, and regularization. The reconstruction error ensures that the low-dimensional reconstruction preserves the original data information; the manifold consistency loss ensures that similar samples remain close in feature space, with a manifold consistency weight set between 0.1 and 1.0; and the regularization term prevents overfitting, with a weight set between 0.01 and 0.1.

[0078] Training uses the Adam optimizer with an initial learning rate of 0.001, typically requiring 500-1000 iterations to reach convergence. This algorithm effectively compresses high-dimensional sensor data into a low-dimensional feature space, significantly reducing data transmission while preserving key fault characteristics. It is suitable for feature extraction and anomaly detection in edge computing environments.

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

[0080] Furthermore, the edge layer further comprises:

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

[0082]

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

[0084] : the i-th eigenvalue,

[0085] and : The mean and standard deviation of the feature in the entire data set are used for normalization.

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

[0087] : Outlier threshold, controls the impact of outliers on feature weights;

[0088] : Scaling factor, adjusts the range of variation of feature weights;

[0089] : Adjust the sensitivity parameter of the outlier degree on the feature weight;

[0090] First, we calculated the mean and standard deviation of each feature, then calculated the outlier of each sample on each feature (the number of standard deviations from the mean). We also calculated the local outlier of each sample using the k-nearest neighbor algorithm (k=5), and set the outlier threshold to the 95th percentile of the local outlier 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, and 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 total weight equals the number of features and maintain the overall scale.

[0092] Dimensionality reduction is based on a variation of the t-SNE algorithm, modifying the distance calculation of samples in high-dimensional space to a weighted Euclidean distance. The gradient calculation is adjusted accordingly to incorporate feature weights. To prevent over-optimization, the number of iterations is set to 1000-2000, and the perplexity parameter is set to 30-50. For samples with high local outliers, the repulsive force in low-dimensional space is increased by 1.5-2.0 times to prevent them from being "absorbed" by normal samples.

[0093] The effectiveness of the method is evaluated using two metrics: neighbor retention rate and outlier separation. Parameters are dynamically adjusted based on the evaluation results to achieve optimal dimensionality reduction. This method is particularly suitable for extracting and retaining early-stage fault features of firefighting equipment, effectively capturing subtle but significant abnormal patterns.

[0094] b) Local-global noise decomposition technology uses multi-scale analysis to separate noise and abnormal signals:

[0095] in, : The original signal, including noise and abnormal components;

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

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

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

[0099] : Adaptive weights, which determine the contribution of noise filtering at different scales;

[0100] : Current working condition descriptor, affecting the adaptive strategy of noise decomposition;

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

[0102] Furthermore, 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] in, and : Represents the message samples of the source protocol and the target protocol respectively;

[0106] : converted target protocol message;

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

[0108] : Mapping complexity regularization term to prevent model overfitting and improve generalization ability;

[0109] : Regularization weights to control the influence of complexity constraints;

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

[0111]

[0112] in, : Message data that needs to be encrypted;

[0113] : Quantum keys, generated by quantum random number generators, have unpredictable and sub-secret properties;

[0114] : Classic key, used for additional encryption to enhance data security;

[0115] : Quantum encryption function, using quantum key distribution for encryption;

[0116] : Classical encryption functions, such as AES, encrypt the data after quantum encryption;

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

[0118] The quantum key distribution protocol uses a modified BB84 protocol and supports both point-to-point and networked deployments. It achieves a key generation rate exceeding 2 kbps at a transmission distance of 50 km and over 200 bps at 100 km, with a quantum bit error rate tolerance threshold of 11%. This implementation is based on phase encoding of attenuated laser pulses, which are received using single-photon detectors.

[0119] This hybrid encryption architecture combines quantum one-time pads with classical symmetric encryption. The quantum one-time pad is used to encrypt the symmetric key using an XOR operation, while 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, and then encrypts the message using the session key. Finally, a ciphertext pair consisting of the encrypted session key and the encrypted message is transmitted.

[0120] The key management system controls key lifecycles to a maximum of 24 hours, with a single key usage limit of no more than three times. Key storage is achieved through a FIPS 140-2 Level 3 certified secure hardware module. The system also integrates NIST-approved post-quantum cryptography algorithms (such as Kyber and Dilithium), supports hybrid encryption modes combining quantum keys with post-quantum algorithms, and offers algorithm agility, enabling algorithm updates without requiring changes to 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 further comprises:

[0123] a) Multipath redundant transmission controller, which adopts a predictive redundant transmission control strategy and adaptively adjusts redundancy based on network status prediction:

[0124]

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

[0126] : expected transmission success rate;

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

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

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

[0130]

[0131] in, : The current security status of the system;

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

[0133] : the value function of taking a defensive action in state s;

[0134] : Temperature parameter, controls the exploratory nature of the strategy (high 7 makes the selection more uniform, low r makes it more biased towards high-value actions);

[0135] c) Protocol structure-aware compression coding technology: Adaptive compression coding strategies are designed based on 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 expert rule systems to build an explainable fault diagnosis model. Its loss function includes:

[0138] Physical Constraint Loss:

[0139]

[0140] in, : Data-driven loss, which measures the error between the neural network prediction value and the actual data;

[0141] : Physical consistency loss, which makes the neural network conform to the laws of physics, including:

[0142] : Ensure that the predicted value is close to the result calculated by the physical model;

[0143] : Ensure gradient;

[0144] : Regularization loss to prevent overfitting;

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

[0146] b) Device Knowledge Graph Federation enables privacy-preserving knowledge sharing of cross-institutional device data, using a differential privacy protection mechanism:

[0147]

[0148] in

[0149] : Feature data after adding privacy protection noise;

[0150] : original feature data;

[0151] : Gaussian noise, protecting data privacy;

[0152] : The sensitivity of the data, which indicates the impact of data changes on the results;

[0153] : Privacy budget, the smaller the value, the stronger the privacy protection but the lower the data availability;

[0154] : Noise intensity coefficient;

[0155] 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:

[0156]

[0157] in, : The status of the entire system;

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

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

[0160] : Inter-scale mapping function, integrating models at different levels.

[0161] Furthermore, the platform layer also includes:

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

[0163] b) A multi-granularity attention diagnosis mechanism, based on an interpretable diagnostic framework based on hierarchical attention, achieves transparent and explainable fault diagnosis by analyzing attention distribution. This mechanism employs a hierarchical network structure to implement multi-level feature attention. The input layer receives multi-source sensor data, structured as a three-dimensional tensor (batch size × time step × feature dimension). The feature-level attention layer uses a self-attention mechanism to calculate internal feature associations, calculating feature association weights through query / key / value matrix transformation and scaled dot-product attention.

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

[0165] Interpretability enhancement techniques include attention visualization, counterfactual explanation generation, and prototype matching explanation. Attention visualization uses heat maps to display the distribution of attention at different levels, and waveforms on a timeline to display key time points. It also provides feature importance ranking and explanations. Counterfactual explanations perturb high-attention features or time points, observe changes in diagnostic results, quantify the impact of features on the results, and generate "if...then..." explanations. Prototype matching explanations maintain a library of fault prototypes. Each fault corresponds to a specific attention pattern. The similarity between the current attention pattern and the prototype is calculated, and an explanation is provided based on the most closely matching prototype.

[0166] Training employs a multi-task learning objective, including a diagnostic accuracy loss (cross-entropy or focal loss), an attention sparsity loss (L1 regularization), and an attention consistency loss (enforcing attention to be consistent with domain knowledge). The model also employs attention-guided learning, leveraging expert-annotated key features and time points to guide attention learning, and using KL divergence to measure the difference between the model's attention and that of the expert.

[0167] Adversarial training generates adversarial examples by adding perturbations to areas of high attention, improving the model's robustness to noise and perturbations. During the inference phase, the system generates hierarchical diagnostic reports based on the attention levels of each layer, providing detailed explanations from the device level to the feature level. It also supports interactive diagnostic exploration, allowing users to adjust attention points and verify hypotheses.

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

[0169]

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

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

[0172] : Homomorphic encryption function, encrypting the calculation result C with the public key pk;

[0173] : Indicates that the encryption results of all participants are multiplied and aggregated, and the global result is directly calculated using the homomorphic encryption feature without decrypting the intermediate data;

[0174] The secure multi-party computation framework for zero-knowledge proofs 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 uses the Sigma protocol framework, supports knowledge proofs and range proofs, and is implemented based on discrete logarithms and commitment schemes, using the Fiat-Shamir transform for non-interactive conversion.

[0175] The secure multi-party computation process involves two main roles: the data provider and the aggregator. The data provider first computes the intermediate results locally, generates a random mask, encrypts the masked intermediate results, and generates a zero-knowledge proof proving that the results are within the valid range. The aggregator then sends the encrypted results and proof. The aggregator verifies all zero-knowledge proofs, computes the encrypted aggregated results using homomorphic properties, requests the masked homomorphic computation results from the participating parties, removes the mask, and decrypts the final aggregated results.

[0176] The weighted allocation mechanism adaptively allocates data based on data quality and completeness. Weights range from 0 to 1 and sum to 1. Weights are calculated using exponential normalization, with a temperature parameter set between 0.1 and 1.0 to control the smoothness of the allocation. The framework also incorporates several privacy-enhancing measures, including differential privacy, threshold encryption, and secure communication channels.

[0177] The framework ensures computational security based on the discrete logarithm problem and the deterministic composite remainder assumption, provides ε-differential privacy protection (ε is set to 0.1-1.0), can resist collusion attacks by no more than one-third of the participants, and provides reliable protection for secure data sharing between fire protection systems in different buildings.

[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, using a physically constrained deep Bayesian temporal network algorithm to achieve fault prediction and health status assessment of firefighting equipment at multiple time scales:

[0181]

[0182] in, :Future Moment Equipment failure status;

[0183] : Equipment sensor historical status data;

[0184] : Hidden variables, representing the current potential health status of the system (such as internal aging, stress accumulation, etc.)

[0185] : The impact of hidden state on failure probability;

[0186] : The relationship between sensor data and hidden states;

[0187] b) The fault causal chain analysis system builds a dynamic reasoning model of the fault propagation path based on a Bayesian network and uses a multi-scale spatiotemporal causal reasoning network to calculate causal strength:

[0188]

[0189] in: : Two fault events (e.g. temperature abnormality → sensor failure);

[0190] : Conditional independence test, measuring after removing other influencing factors, Is it still affecting ;

[0191] : Time priority score, indicating Is it before occur;

[0192] : Spatial similarity index, which measures the degree of physical spatial association between two events;

[0193] The fault causal chain analysis system consists of three main stages: causal graph construction, causal strength calculation, and root cause inference. Causal graph construction begins with data preprocessing, including time alignment, anomaly detection, and feature extraction. A time-priority-based PC algorithm is used to construct the initial causal skeleton. Time lags are set to multiple levels (1 second, 5 seconds, 10 seconds, 30 seconds, 1 minute, and 5 minutes). The conditional independence test uses partial correlation coefficients, with a significance level of 0.05.

[0194] The initial causal graph is refined by incorporating domain knowledge, removing physically impossible causal edges. Causal relationships are verified through historical intervention records, and device topology constraints are incorporated to strengthen causal relationships between physically connected devices. Causal strength calculations include three components: a conditional independence test, a temporal precedence score, and a spatial similarity index.

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

[0196] During the root cause inference phase, causal centrality is calculated and the top three events with the highest centrality are selected as candidate root causes. Counterfactual simulations are then conducted using the digital twin to evaluate system performance in the absence of each candidate root cause. The impact scope and severity are calculated, and the final root cause is selected based on a comprehensive score. The system provides a hierarchical visual interface, where node size indicates event severity, edge thickness indicates causal strength, and color coding indicates chronological order.

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

[0198]

[0199] Where: π: maintenance strategy (such as replacement of parts, regular inspection, sensor calibration);

[0200] : Equipment reliability indicators (such as mean time between failures (MTBF));

[0201] : Maintenance costs (such as replacement parts costs, labor costs, etc.);

[0202] Regulatory compliance (e.g., national fire protection standards, equipment certification requirements)

[0203] The solution is obtained through an improved Pareto multi-objective optimization algorithm.

[0204] Furthermore, the application layer also includes:

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

[0206]

[0207] in, : The virtual scene state at the current time t;

[0208] : User’s operation at time t (such as using a fire extinguisher, choosing an evacuation route, etc.);

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

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

[0211] : Adaptive adjustment function, dynamically optimizing the scenario based on user performance, such as increasing training difficulty, providing prompts, etc.

[0212] b) Dynamic prediction model for equipment life, which takes into account the remaining life prediction of usage history and environmental influences:

[0213]

[0214] in, : Basic remaining life, that is, the expected life of the equipment under ideal conditions;

[0215] : Changes in impact factors over time;

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

[0217] : The global weight of each influencing factor, representing its long-term impact;

[0218] 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.

[0219] Furthermore, the system further comprises:

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

[0221] ;

[0222] in, : Indicates an event Causal centrality score as the root cause;

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

[0224] The outer sum traverses all events directly related to , the inner layer sum is traversed Further impact events ;

[0225] b) An interactive causal chain visualization decision system, which uses visualization technology to assist expert decision-making;

[0226] c) Operation-design bidirectional feedback mechanism to achieve bidirectional information flow between digital twins and physical devices:

[0227]

[0228] in : The current digital twin model, that is, the originally designed virtual device model;

[0229] : Actual equipment operating status (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] : Update function, correct the model based on real-time data to improve prediction accuracy.

[0232] Compared with the prior art, the present invention has the following beneficial effects:

[0233] 1. The present invention provides an intelligent fire-fighting facility self-inspection and fault warning system based on AI algorithms, which has achieved breakthrough early warning and accurate prediction capabilities for fire-fighting facility failures, significantly extending the time window for preventive maintenance. The multi-time domain health assessment system at the application layer integrates physical laws and probabilistic prediction methods, and establishes a mapping relationship between the current state of the equipment and the probability of future failure through a physical constraint deep Bayesian time series network. The network can not only give deterministic predictions, but also quantify the uncertainty of the predictions, providing a risk assessment basis for maintenance decisions. The system also includes a dynamic prediction model for equipment life, which takes into account factors such as the equipment's usage history and environmental impact, and conducts real-time evaluation and updates of the remaining service life. Combined with the root cause analysis capability of the spatiotemporal causal reasoning network, the system can identify potential problems at the early stage of fault signs and predict their development trends, transforming traditional passive response maintenance into active predictive maintenance, significantly reducing unexpected downtime and emergency repair incidents of fire-fighting facilities.

[0234] 2. The present invention provides an intelligent fire-fighting facility self-inspection and fault warning system based on AI algorithm. While ensuring data integrity, the system significantly optimizes the utilization of communication resources and reduces system response delay. The knowledge distillation compression diagnosis model deployed at the edge layer uses the teacher-student network architecture to migrate the knowledge of complex models in the cloud to the edge end, realizing the reasonable allocation of computing resources and local computing. The protocol semantic adaptive conversion framework of the transport layer realizes the seamless connection and semantic-level conversion between industrial protocols and AIoT protocols, breaking through the communication barriers between traditional fire protection systems and modern Internet of Things architectures. Protocol structure-aware compression coding designs adaptive compression strategies according to the characteristics of different protocols, reducing the amount of transmitted data. The multi-constraint self-optimization routing algorithm dynamically selects the optimal network transmission path based on deep reinforcement learning, and the predictive redundant transmission control realizes adaptive redundant control based on network status prediction, jointly ensuring the real-time and reliability of data transmission. The synergistic effect of these technologies enables the system to achieve millisecond-level response time under limited bandwidth conditions, meeting the strict real-time requirements of the fire safety system.

[0235] 3. The present invention provides an intelligent fire-fighting facility self-inspection 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 capabilities, which can adapt to different environments and changes in conditions. The multi-scale digital twin system at the platform layer constructs a multi-level virtual model from components to systems, realizing the virtual-reality mapping of the entire fire-fighting system. The operation-design bidirectional feedback mechanism forms an information closed loop between the digital twin and the physical equipment, which can not only feed back real-time operation data to the digital model, but also apply optimization suggestions and control strategies to the physical equipment. The physical model of the edge layer drives the lightweight twin inference engine to ensure that basic intelligent analysis capabilities can be maintained in the event of communication interruption. The system continuously adjusts and optimizes internal parameters and decision thresholds by continuously learning environmental changes, equipment aging patterns and usage patterns, so that the entire system exhibits adaptive capabilities and evolutionary characteristics similar to those of organisms.

[0236] 4. The present invention provides an intelligent fire-fighting facility self-inspection and fault warning system based on AI algorithms. The system significantly reduces the maintenance cost of fire-fighting facilities and improves 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 between reliability, cost and regulatory requirements, and formulate a scientific and reasonable maintenance plan. The spatiotemporal causal reasoning network quickly and accurately locates the root cause of the fault through causal strength weight calculation and maximum causal centrality identification, shortening the traditional troubleshooting process that takes several hours to minutes. Virtual reality enhanced plan simulation provides an immersive training platform to improve the skills level and emergency response capabilities of maintenance personnel. These technologies work together to shift maintenance from passive response to active prediction, and from experience-based decision-making to data-driven, significantly reducing unnecessary inspections and component replacements, reducing manpower and material resource consumption, and improving maintenance quality and equipment availability.

[0237] 5. This invention provides an intelligent firefighting facility self-inspection and fault warning system based on an AI algorithm. This system enhances its adaptability in complex and harsh environments, addressing the reliability issues of traditional fire monitoring systems under extreme conditions. The perception layer's non-stationary acoustic feature extraction algorithm utilizes a combination of multi-scale Hilbert-Huang transform and wavelet packet decomposition to extract key acoustic features in environments with strong background noise. A sub-pixel infrared thermal image registration algorithm utilizes a deep learning model based on residual attention to achieve precise motion compensation for thermal image sequences, overcoming interference factors such as ambient lighting changes and temperature fluctuations. The edge layer's manifold consistency constrained reconstruction technology preserves the inherent topological structure of high-dimensional data during dimensionality reduction, ensuring that key features are not lost during information compression. The transmission layer's predictive redundant transmission control implements adaptive redundant control based on network status predictions, ensuring reliable data transmission even in unstable networks. These technologies work together to enable the system to maintain stable operation in harsh environments such as high noise, high humidity, high temperature, and strong electromagnetic interference. It is adaptable to diverse application scenarios, from high-rise buildings and underground spaces to industrial sites, achieving reliable monitoring and early warning in all weather conditions and 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 is a brief introduction to the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

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

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

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

[0242] Figure 4 This is a diagram of the application layer intelligent decision-making service framework. DETAILED DESCRIPTION

[0243] The technical solutions of the present invention will be more clearly and completely explained below through description of preferred embodiments of the present invention in conjunction with the accompanying drawings.

[0244] As a specific implementation method, the overall system architecture is as follows Figure 1 As shown in the figure, it includes five collaborative layers: perception layer, edge layer, transmission layer, platform layer, and application layer. Based on multi-physics collaborative perception, the system builds a complete intelligent operation and maintenance system for fire protection facilities through edge intelligent preprocessing, secure and reliable transmission, platform intelligent analysis, and application layer decision-making.

[0245] As a specific implementation, data from the perception layer to the edge layer uses a structured binary message format encoded using MessagePack. This message contains the device identifier, timestamp, sensor type, raw value array, sampling rate, unit, and metadata (such as battery level, signal quality, and environmental information). The data is compressed using the LZ4 algorithm with a compression ratio of approximately 3:1. The transmission frequency varies depending on the sensor type, ranging from 1Hz to 100Hz.

[0246] Data transmitted from the edge layer to the transport layer uses Protocol Buffers to encode feature vectors and metadata. These include device identification, processing timestamp, feature vector list (including feature type, feature value array, and confidence level), local diagnostic results, and related metadata. Data compression reaches 10%-25% of the original data, and the transmission frequency is typically 0.1Hz-1Hz.

[0247] Data from the transport layer to the platform layer is encrypted using a signature package in JSON-LD format with semantic linking. This package contains device identification, timestamp, feature vector, diagnostic results, and security information (encryption type, signature, and timestamp). Data is encrypted using AES-256-GCM, with keys updated via quantum key distribution. The transmission frequency is adjusted as needed, ranging from 0.01Hz to 0.1Hz in normal conditions and up to 1Hz in abnormal conditions.

[0248] Data from the platform layer to the application layer is pushed in real time via RESTful APIs and WebSockets. 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. WebSocket push implements real-time event streaming based on the MQTT protocol.

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

[0250] The edge layer and transport layer collaborate primarily through adaptive network transmission. The transport layer provides the edge layer with network status information (bandwidth, latency, packet loss rate, and connection quality), which the edge layer then adjusts its data transmission strategy accordingly. The edge layer maintains a multi-level priority queue, scheduling data transmission based on importance and urgency. It also implements local caching in the event of network unavailability and retransmits data in order of importance upon network recovery.

[0251] The transport layer and platform layer collaborate to establish secure channels, manage sessions, adapt protocols, and implement bidirectional data flows. Secure channels are established through bidirectional authentication and key negotiation. Session management includes information such as session identifiers, establishment time, expiration time, cipher suites, and key rotation intervals. The transport layer automatically identifies and converts different device protocols, providing a unified interface to the platform layer and enabling the platform layer to send configuration updates and control commands to devices.

[0252] The collaboration between the platform and application layers is based on user permission control and event subscription mechanisms. Permission control utilizes fine-grained access control using RBAC and ABAC. Event subscriptions include parameters such as subscription identifiers, event type lists, device filtering conditions, notification channels, and notification thresholds. The system establishes a feedback loop, where user feedback and operation records from the application layer are transmitted back to the platform layer for algorithm optimization. Interactive analysis is also supported, with the application layer providing users with hypothetical scenarios, and the platform layer performing simulation analysis and returning 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. The various components work together to form a self-evolving, highly reliable, and low-cost intelligent operation and maintenance system for fire protection facilities.

[0254] The system adopts a multi-source heterogeneous sensing unit integration strategy to build a multi-physical field collaborative perception framework for firefighting equipment. The framework deploys a multimodal 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 10Hz-80kHz frequency band to achieve non-stationary acoustic feature extraction. For different equipment types, the system adopts differentiated sampling rate configurations: 48kHz for large equipment such as water pumps and fans, 96kHz for pipeline systems, and 192kHz for valve control units, effectively capturing early fault characteristics of equipment.

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

[0256] Gas-phase signature self-calibration detection technology enables detection of gas concentration changes at the ppb level, enabling detection of abnormal gas concentration changes at the early stages of thermal degradation in insulating materials. A multi-physics collaborative perception framework dynamically adjusts sensor excitation energy and achieves complementary enhancement of multi-physics information. For fire pump fault diagnosis, the system combines acoustic and vibration signatures. For bearing fault detection, it integrates acoustic high-frequency transient signatures with thermal imaging temperature change patterns, improving fault detection rates by 27.8%.

[0257] To process the massive amounts of data collected by the perception layer, the system deploys a network of intelligent preprocessing nodes. The adaptive noise suppression module uses a conditional generative adversarial network to learn and eliminate noise features related to operating conditions. A multi-layer convolutional neural network is used to construct the generator and discriminator, and the model is trained using 12,000 sets of operating condition-noise pairs. Edge devices utilize a heterogeneous computing architecture, integrating high-performance processors and FPGA accelerators. The feature space reconstruction module, based on a hybrid model of variational autoencoders and manifold regularization, reduces the original high-dimensional feature vectors to a low-dimensional intrinsic feature space, significantly reducing the false alarm rate.

[0258] The multi-scale anomaly-preserving dimensionality reduction algorithm dynamically adjusts feature weights to enhance the contribution of anomaly-related features, accelerating early fault detection by 35% compared to traditional methods. The local-global noise decomposition technology employs a multi-scale decomposition framework to design dedicated filters for different frequency bands. The filter weights are dynamically adjusted through real-time SNR evaluation to achieve efficient anomaly feature extraction. The emergency inference engine integrates simplified physical models with lightweight neural networks to achieve low-latency inference on edge devices, maintaining local fault diagnosis capabilities for up to 72 hours in the event of communication interruptions. The edge layer also applies knowledge distillation technology to migrate complex cloud-based model knowledge to edge devices, significantly reducing model size while maintaining high diagnostic accuracy.

[0259] The system builds an advanced transport layer communication architecture, such as Figure 2 shown. Figure 2 The system transport layer protocol conversion and secure transmission architecture are demonstrated. The diagram shows the access layer for various heterogeneous protocols at the top, the protocol conversion module in the middle, and the secure transmission channel at the bottom. Different colors are used to distinguish the industrial protocol domain from the IoT protocol domain. 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. Protocol structure-aware compression coding technology designs differentiated encoding strategies for different data types, reducing the amount of transmitted data by an average of 67.3% while maintaining a low system response time. The multipath redundant transmission controller predicts network status based on deep learning and implements differentiated transmission strategies for data of different priorities, significantly improving end-to-end reliability.

[0260] In terms of security, the system utilizes a quantum key encryption module for high-intensity data protection, integrating quantum random number generation with traditional encryption algorithms. A hierarchical key update mechanism balances security and performance. A cognitive security dynamic defense strategy builds a network behavior signature library and optimizes defense strategy selection through deep reinforcement learning, effectively defending against both known and unknown attacks. A 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 is aggregated to the platform layer, Figure 3 The intelligent analysis center shown starts working. Figure 3The main components of the platform layer are demonstrated, including a spatiotemporal data lake, a hybrid augmented intelligence engine, a device knowledge graph federation, a distributed digital twin, and a cross-domain transfer learning framework. Hexagons represent different functional modules, and lines represent data flows between modules. The hybrid augmented intelligence engine integrates deep learning and physical models to build an explainable fault diagnosis framework. The system integrates knowledge from fields such as thermodynamics, fluid mechanics, and electromagnetics, imposing physical constraints on neural network predictions to achieve high-accuracy fault classification. The multi-granularity attention diagnosis mechanism establishes a hierarchical attention model, enabling a transparent diagnostic process from the device to the feature level, improving fault handling efficiency. The device knowledge graph federation constructs a firefighting equipment knowledge network containing 100,000 entities and 500,000 relationships, and implements efficient knowledge reasoning through a graph convolutional network.

[0262] Table 1 shows a comparison of physical knowledge fusion and deep learning models. The evaluation evaluated 10 typical fault types based on fire pump fault diagnosis, using the F1 score as an accuracy metric. This comparison shows that physical constraint deep learning and multi-physics fusion models exhibit the best overall performance in terms of accuracy, interpretability, data requirements, computing resources, and anomaly generalization. Distributed digital twins establish a multi-level virtual mirror from components to systems, supporting multiple modes such as historical playback, real-time mapping, and predictive simulation to achieve high-precision simulation of system behavior. To ensure data privacy, the system uses a secure multi-party computation framework with zero-knowledge proof, enabling secure cross-institutional collaboration based on homomorphic encryption and key sharing. The cross-domain transfer learning framework addresses the cold start problem of new building systems, efficiently transferring existing knowledge through domain adaptation technology, and significantly shortening the learning cycle.

[0263] Table 1

[0264]

[0265] like Figure 4 As shown, the application layer provides intelligent decision-making services. Figure 4 The main functional modules and workflows of the application layer are demonstrated. The core is the main decision-making engine, surrounded by functional units such as health prediction, fault diagnosis, and maintenance planning. Process arrows indicate the flow of data and decisions. The equipment health prediction model uses a deep Bayesian network to achieve multi-timescale predictions, providing ample maintenance time windows through a hierarchical early warning mechanism. The fault causal chain analysis system utilizes a dynamic Bayesian network to accurately identify the root causes of complex fault chains. The interactive visual decision-making system supports multiple modes including fault tracing, impact analysis, and hypothesis verification, significantly reducing fault diagnosis time.

[0266] Table 2 compares different maintenance strategy types, based on 12 months of operational data from a commercial building's fire protection system. The maintenance strategy generator balances equipment reliability, maintenance costs, and regulatory requirements, generating an optimal maintenance plan through a multi-objective optimization algorithm. This reduces maintenance costs while improving equipment availability. The system supports three maintenance strategies: condition-based (CBM), risk-based (RBM), and reliability-based (RCM). Dynamically selecting the optimal strategy based on equipment criticality and failure modes can reduce maintenance costs by 22.3% while increasing equipment availability by 3.7 percentage points.

[0267] Table 2

[0268]

[0269] The system also integrates an emergency plan simulation platform, providing an immersive training environment through virtual reality technology, significantly improving emergency response efficiency. The dynamic equipment lifespan prediction model considers multiple influencing factors, providing a scientific basis for equipment upgrades and effectively reducing unplanned downtime. The regulatory dynamic adaptation module automatically analyzes the latest standards and ensures system operation and maintenance complies with regulatory requirements. The operation-design bidirectional feedback mechanism continuously optimizes system design using real-time data, resulting in significant improvements in energy consumption, coverage uniformity, and false alarm rates, forming a self-optimizing firefighting facility health management ecosystem.

[0270] The operation-design bidirectional feedback mechanism enables two-way information flow between the digital twin and the physical device. This mechanism establishes a closed-loop feedback system, continuously optimizing design models and parameters using real-time operational data. The system collects discrepancies between actual device operating parameters and theoretical model predictions and updates model parameters using Bayesian parameter estimation. Simultaneously, the system analyzes device degradation trends and automatically generates design improvement recommendations. In practical applications, this mechanism has optimized fire pump selection parameters, reducing energy consumption by 11.3%; improved the sprinkler system piping layout, increasing coverage uniformity by 8.7%; and optimized control strategies, reducing false alarm rates by 25.4%.

[0271] As a specific implementation example, in a high-rise commercial complex, the system covers five buildings over 200 meters tall, deploying approximately 1,200 multi-physics sensing units to monitor 212 critical firefighting equipment. The sensing layer installed multiple sensor types in key areas such as the pump room, power distribution room, and pipe shafts. The core monitoring target in the fire pump room is six high-power fire pumps, each equipped with an acoustic sensor array, a triaxial vibration sensor, a temperature sensor, a flow meter, and a pressure sensor.

[0272] During its three months of operation, the voiceprint feature acquisition unit detected an unusual high-frequency pulse signal on a main fire pump, a signal that would be nearly impossible to detect during routine maintenance inspections. The adaptive noise suppression module at the edge layer separated this weak signal from the complex background noise. The feature space reconstruction algorithm then compared it with historical health status data to generate an anomaly report. The system first cross-validated the system's data using multi-physics field data and found that the vibration sensor also detected the corresponding high-frequency vibration component, but with a much smaller amplitude, confirming the possibility of an anomaly.

[0273] The platform-level hybrid-enhanced intelligent engine, combined with analysis of the equipment's physical model, determined the fault type to be early-stage damage to the water pump bearing and deduced the damage location to be on the outer race of the non-drive-end bearing. Digital twin simulations predicted that, if left untreated, the bearing would suffer severe damage in approximately 25 days, potentially causing the water pump to shut down. The application-level maintenance strategy generator, after evaluating spare parts availability, maintenance personnel schedules, and the pump's criticality, recommended bearing replacement within seven days and suggested temporary adjustments to ensure fire water supply reliability during the replacement period.

[0274] After replacing the bearings as recommended, maintenance personnel disassembled and inspected the bearing outer rings and found initial pitting corrosion, consistent with the system's diagnosis. The entire process, from fault detection to troubleshooting, took just 11 days, avoiding a potential emergency downtime. The system estimates that this predictive maintenance effort reduced maintenance costs by approximately 67% compared to traditional planned maintenance. More importantly, it also avoided the potential risk of sudden pump failure. After one year of system operation, the emergency failure rate of firefighting equipment in the complex decreased by 83%, and maintenance costs were reduced by 42%.

[0275] Based on the above specific implementation methods, the AI ​​algorithm-based intelligent fire protection facility self-inspection and fault warning system realizes intelligent monitoring, diagnosis and warning of the entire life cycle of fire protection facilities. The system comprehensively captures equipment operating status information through multi-physical field collaborative sensing technology, uses edge intelligence technology to achieve efficient data preprocessing, ensures data transmission security through a secure and reliable transmission layer, relies on the platform layer's intelligent analysis engine to achieve equipment fault diagnosis and prediction, and finally provides intelligent decision 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 protection facility failures from the traditional method of 24 hours to 72 hours, and increase the fault location accuracy from 85% to 94.3%. At the same time, it reduces maintenance costs by 24.7% and improves equipment availability by 5.1%, providing reliable protection for fire safety.

[0276] The above-described specific embodiments merely describe preferred embodiments of the present invention and do not limit the scope of protection of the present invention. Any modifications, substitutions, and improvements made to the technical solution of the present invention by a person skilled in the art based on the textual description and drawings provided herein, without departing from the design concept and spirit of the present invention, shall fall within the scope of protection of the present invention. The scope of protection of the present invention is determined by the claims.

Claims

1. The intelligent fire protection facility self-inspection and fault warning system based on AI algorithm is characterized by: Includes perception layer, edge layer, transport layer, platform layer and 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 characteristics acquisition units; The edge layer is used to deploy intelligent pre-processing nodes, including adaptive noise suppression modules, feature space reconstruction modules, device-level micro-diagnostic model sets, and emergency reasoning engines; The transport 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; The platform layer is used to establish an intelligent analysis hub, including a spatiotemporal data lake, a hybrid enhanced intelligence engine, device knowledge graph federation, distributed digital twins, and a cross-domain transfer learning framework; The application layer is used to provide intelligent decision-making services, integrating equipment health prediction models, fault causal chain analysis systems, maintenance strategy generators, emergency plan simulation platforms, and dynamic regulatory adaptation modules; The five layers of architecture work together; The perception layer includes: a) The voiceprint feature acquisition unit uses a microphone array and ultrasonic sensor fusion architecture to achieve 10Hz-80kHz wide frequency band acoustic feature extraction; The voiceprint feature acquisition unit uses the multi-scale Hilbert-Huang transform algorithm to extract non-stationary acoustic features and calculates the energy time-frequency distribution using the following formula: Among them, a i (t): The instantaneous amplitude of the i-th natural mode function, which represents the energy of the i-th natural mode at time t; f i (t): instantaneous frequency of the i-th natural mode function, reflecting the main frequency component of the i-th natural mode at time t; The complex exponential form of the analytical signal obtained by Hilbert transform represents the phase information of the signal; ψ(·): wavelet basis function, responsible for frequency localization, making the decomposition have better time-frequency resolution; B i (t): Adaptive bandwidth parameter, dynamically adjusting 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 during the empirical mode decomposition process; b) The 3D vibration analysis unit integrates a triaxial MEMS accelerometer and a laser vibrometer to construct a time-frequency-spatial feature matrix of the equipment's mechanical state. It decouples multi-source vibration signals through tensor decomposition. in, The third-order vibration tensor represents the time-frequency-spatial feature matrix of the vibration signal; NC: rank of tensor decomposition, indicating the number of main components of the signal; ρ r : The weight of the rth component, which measures the contribution of the rth component to the overall vibration signal; v r : spatial modal vector, describing the vibration modes at different positions; b r : frequency modal vector, representing the distribution of vibration signals at different frequencies; c r : Time modal vector, reflecting the changing trend of the vibration signal over time; ο: represents the outer product, used to construct a tensor; ε: residual term, representing the noise or error that the model cannot explain; c) The dynamic infrared thermal imaging array uses a programmable scanning strategy to achieve sub-pixel displacement compensation monitoring of component temperature fields. Motion compensation of thermal image sequences is achieved through a residual attention deep learning model. The perception layer also includes: d) Multi-physics field collaborative perception framework, dynamically adjusting sensor excitation energy according to the degree of environmental interference: Among them, E base : basic excitation energy, the initial energy in the absence of interference; α: modulation depth, which controls the amplitude of the excitation energy change and has a value range of 0.1-0.6; f m : Modulation frequency, which determines the period of energy modulation; SNR(t): current signal-to-noise ratio, indicating the degree of influence of environmental noise on the signal; SNR target : Target signal-to-noise ratio, used to guide energy adjustment to maintain good signal quality; σ: Adjustment parameter to control the decay rate when the signal-to-noise ratio deviates from the target value; e) Cross-domain feature complementary enhancement mechanism to achieve complementary enhancement of multi-physics field information: in, The original characteristic data of the i-th physical field; W ij : The complementary weight between physical fields, which measures the contribution of the i-th physical field to the j-th physical field; T ij : Characteristic transformation function, used to adjust data of different physical fields; f) Gas phase characteristic self-correction detection technology uses dynamic baseline correction and peak recognition algorithms to achieve ppb-level gas concentration change detection.

2. The intelligent fire protection facility self-inspection and fault warning system based on AI algorithm according to claim 1 is characterized in that: The edge layer comprises: a) The adaptive noise suppression module uses a generative adversarial network to learn and eliminate noise features related to working conditions. Its optimization objectives are: Where x: working condition variable; y: clean signal, i.e. the target signal after denoising; z: noise prior distribution, indicating the noise distribution characteristics under different working conditions; D(x, y): The discriminator D is used to determine whether the input signal is a real clean signal; G(x, z): 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 the manifold consistency constraint reconstruction formula: Among them, X i : high-dimensional input data of the i-th sample, Φ(X i ): The encoder Φ is responsible for mapping high-dimensional data to low-dimensional feature space; Ψ(X i ): The decoder Ψ is responsible for reconstructing the low-dimensional features back to the original data; M i，j : Sample similarity matrix, controlling the preservation of local data structure; J Φ (X i ): Jacobian matrix, which measures the local smoothness of the mapping function; c) The emergency reasoning engine starts the digital twin simulation based on the physical model of the equipment when the communication is interrupted, and adopts a hybrid prediction method that combines the physical model with the neural network residual correction.

3. The intelligent fire protection facility self-inspection and fault warning system based on AI algorithm according to claim 1 is characterized in that: The edge layer further comprises: a) Multi-scale anomaly-preserving dimensionality reduction algorithm, for high-dimensional sensor data, the feature weight calculation formula is: Among them, W i : The weight of the i-th feature, which measures its importance in the dimensionality reduction process; x i : the i-th eigenvalue, μ i and σ i : The mean and standard deviation of the feature in the entire data set, used for normalization; L i : Local outlier metric, used to measure the degree of abnormality of a data point in a local neighborhood; L th : Outlier threshold, controls the impact of outliers on feature weights; β: scaling factor, which adjusts the range of feature weights; γ: sensitivity parameter for adjusting the effect of outlier degree on feature weight; b) Local-global noise decomposition technology uses multi-scale analysis to separate noise and abnormal signals: Among them, S raw : The original signal, including noise and abnormal components; S clean : The denoised signal only contains valid information; L: the number of layers of multi-scale decomposition; NF j : The j-th level noise filter is used to extract noise components at different scales; w j : Adaptive weights, which determine the contribution of noise filtering at different scales; ζ: current operating condition descriptor, affecting the adaptive strategy of noise decomposition; c) Knowledge distillation compression diagnosis model, which migrates complex model knowledge from the cloud to the edge through the teacher-student network architecture, reducing computing resource requirements.

4. The intelligent fire protection facility self-inspection and fault warning system based on AI algorithm according to claim 1 is 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, A i and B i : Represents the message samples of the source protocol and the target protocol respectively; Map(A i ): converted target protocol message; d(B i ,Map(A i )): semantic distance metric, which measures the accuracy of the converted message; R(Map): Mapping complexity regularization term, which prevents model overfitting and improves generalization ability; K: regularization weight, controlling the influence of complexity constraints; b) The quantum key encryption module uses a one-time pad encryption system based on quantum random numbers, integrating quantum key distribution with traditional encryption algorithms: ENC(m,k q ,k c )=ENC c (ENC q (m, k q ),k c ) Where m: message data to be encrypted; k q :Quantum key; k c :classic key; ENC q (m,k q ): Quantum encryption function, using quantum key distribution for encryption; ENC c (·, k c ): Classic encryption function; c) The network topology self-optimization unit dynamically adjusts transmission path weights based on reinforcement learning, and simultaneously optimizes multiple constraints such as latency, reliability, energy consumption, and security through deep reinforcement learning; The transport layer further comprises: a) Multipath redundant transmission controller, which adopts a predictive redundant transmission control strategy and adaptively adjusts redundancy based on network status prediction: Where, R(t): the number of redundant transmissions required at time t; P target : expected transmission success rate; P success (t): predicted value of the success probability of a single transmission under the current network status; Round up to ensure that the redundancy is an integer; b) Cognitive security dynamic defense strategy, an active defense mechanism based on understanding attack intent: Among them, s t : The current security status of the system; a i : Defensive actions, including IP blocking and intrusion detection rule updates; Q(s t ,a i ): the value function of taking a defensive action in state s; τ: temperature parameter; c) Protocol structure-aware compression coding technology designs adaptive compression coding strategies based on different protocol characteristics to reduce the amount of transmitted data.

5. The intelligent fire protection facility self-inspection and fault warning system based on AI algorithm according to claim 1 is characterized in that: The platform layer includes: a) The hybrid enhanced intelligent engine integrates deep reinforcement learning and expert rule systems to build an explainable fault diagnosis model. Its loss function includes: Physical Constraint Loss: in, Data-driven loss, which measures the error between the neural network prediction and the real data; Physical consistency loss, which makes the neural network conform to the laws of physics, includes: ||f NN (x)-f physics (x)|| 2 : Ensure that the predicted value is close to the result calculated by the physical model; Ensure gradient; Regularization loss to prevent overfitting; ω phys and ω reg : Weight parameter, which controls the importance of different loss terms; b) Device knowledge graph federation enables privacy-preserving knowledge sharing of cross-institutional device data, using a differential privacy protection mechanism: in, Feature data after adding privacy-preserving noise; Original feature data; Gaussian noise, protecting data privacy; S: Sensitivity of the data, indicating the impact of data changes on the results; ∈: privacy budget, the smaller the value, the stronger the privacy protection but the lower the data availability; σ n : 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, S system : The status of the entire system; S i : The state of the i-th subsystem; System coupling matrix, which describes the interrelationships between different subsystems; Γ: inter-scale mapping function, integrating 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) A multi-granularity attention diagnosis mechanism, based on a hierarchical attention-based interpretable diagnosis framework, achieves transparent and interpretable fault diagnosis by analyzing attention distribution; c) A secure multi-party computing framework with zero-knowledge proof, which implements distributed secure computing based on homomorphic encryption: Among them, C i : The local calculation result of the i-th participant; w i : Weight coefficient, which determines the contribution ratio of each participant to the calculation results; HE pk (C): Homomorphic encryption function, encrypting the calculation result C with the public key pk; Π i : Indicates that the encryption results of all participants are multiplied and aggregated, and the global result is directly calculated using the homomorphic encryption feature without decrypting the intermediate data; d) Cross-domain transfer learning framework to achieve knowledge transfer and reuse between different buildings and regions.

6. The intelligent fire protection facility self-inspection and fault warning system based on AI algorithm according to claim 1 is characterized in that: The application layer includes: a) Equipment health prediction model, using a physical constraint deep Bayesian temporal network algorithm to achieve fault prediction and health status assessment of fire equipment at multiple time scales: P(F t+Δt |S t ,S t-1 ,...,S t-n )=∫P(F t+Δt |Z t )P(Z t |S t ,S t-1 ,...,S t-n )dZ t Among them, F t+Δt : Equipment failure status at future time t+Δt; S t , S t-1 ,...,S t-n : Equipment sensor historical status data; Z t : Hidden variable, indicating the current potential health status of the system; P(F t+Δt |Z t ):The impact of hidden state on failure probability; P(Z t |S t , S t-1 ,...,S t-n ): The relationship between sensor data and hidden state; b) The fault causal chain analysis system builds a dynamic reasoning model of the fault propagation path based on a Bayesian network and uses a multi-scale spatiotemporal causal reasoning network to calculate causal strength: BUT i，j =CIT(e i , it j |PA(e j )\{e i })×TPS(e i , it j )×SSI(e i , it j ) Among them: e i , e j : Two fault events; CIT(e i , e j |PA(e j )\{e i }): Conditional independence test, which measures the effect of e after removing other influencing factors. i Does it still affect e j ; TPS(e i , e j ): time priority score, indicating e i Is it before e j occur; SSI(e i , e j ): spatial similarity index, which measures the degree of physical spatial association between two events; c) The maintenance strategy generator uses a multi-objective optimization algorithm to balance equipment reliability, maintenance costs and regulatory requirements.

7. The intelligent fire protection facility self-inspection and fault warning system based on AI algorithm according to claim 1 is characterized in that: The application layer also includes: a) The emergency plan simulation platform combines virtual reality technology to achieve immersive simulation training of fire scenarios: S VR (t+Δt)=f phys (S VR (t),A user (t))+g adapt (S VR (t),A user (t),H user ) Among them, S VR (t): virtual scene state at the current time t; A user (t): user's operation at time t; H user :User historical performance; f phys (S VR (t), A user (t)): Physical fire simulation model calculates scene changes based on physical laws; g adapt (S VR (t), A user (t), H user ): Adaptive adjustment function, dynamically optimizing the scene based on user performance; b) Dynamic prediction model for equipment life, which takes into account the remaining life prediction of usage history and environmental influences: Where RUL(t): basic remaining life, that is, the expected life of the equipment under ideal conditions; IF i (τ): Changes in impact factor over time; v i (τ): the time-varying weight of the impact factor, which indicates the degree of influence of the impact factor at different time points; η i : The global weight of each influencing factor, representing its long-term impact; c) Dynamic regulatory adaptation module automatically analyzes the latest fire regulations and adjusts system operation and maintenance strategies to ensure that system operations always comply with regulatory standards.

8. The intelligent fire protection facility self-inspection and fault warning system based on AI algorithm according to claim 1 is characterized in that: The system further comprises: a) Root cause location mechanism, identifying the root cause of the fault through maximum causal centrality: Among them, C causal (e i ): indicates event e i Causal centrality score as the root cause; CI i，j : Indicates event e i For event e j The weight of direct causal influence; I(j→k|i): is the causal flow indicator function, indicating whether event j propagates causal influence to event k when i is the hypothetical root cause; the outer layer sum traverses all events e directly related to i , the inner sum is traversed e j Further impact events k ; b) Interactive causal chain visualization decision system, which uses visualization technology to assist expert decision-making; c) Operation - Design a two-way feedback mechanism to achieve two-way information flow between digital twins and physical devices.

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