A fire-fighting maintenance management system

Abstract

The application discloses a fire-fighting maintenance management system and relates to the technical field of fire-fighting equipment maintenance and artificial intelligence data processing. The system comprises a multi-modal data acquisition module, a feature alignment and fusion module, a risk index evaluation module, an intelligent early warning and dispatching module, a maintenance closed-loop management module and an applet interaction module. By collecting full-life-cycle multi-source heterogeneous data of fire extinguishers and completing standardized preprocessing, multi-dimensional feature extraction and correlation fusion are realized by using an improved multi-scale convolutional neural network, a quantitative maintenance risk evaluation index is output based on a continuous time hidden Markov model, and hierarchical early warning, intelligent dispatching and full-process maintenance closed-loop control are automatically completed according to the risk level. The application realizes the upgrade of fire-fighting extinguisher maintenance from passive reminding to active prediction and from manual management to intelligent decision-making, improves maintenance compliance and operation efficiency, and reduces safety hazards such as equipment overage and missed detection.

Description

Technical Field

[0001] This invention relates to the field of fire equipment maintenance and management technology, specifically to a fire maintenance and management system. Background Technology

[0002] Current fire extinguisher maintenance management generally relies on manual registration, paper ledgers, and fixed-date reminders. This results in inconsistent data collection, reliance on experience for status assessment, unquantifiable risks, a disconnect between maintenance dispatch and settlement, and chaotic management of certificates and retrieval. Traditional systems lack multi-source data fusion capabilities and intelligent evaluation mechanisms, making it impossible to predictively analyze the health status of fire extinguishers and prone to problems such as missed inspections, expired equipment, false alerts, and untraceable processes. Therefore, this invention proposes an intelligent, quantifiable, and closed-loop fire extinguisher maintenance management system. Summary of the Invention

[0003] The purpose of this invention is to provide a fire protection maintenance management system to solve the problems of existing technologies that cannot predictively analyze the health status of fire extinguishers, and are prone to missed inspections, expired products, false alerts, and untraceable processes.

[0004] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0005] A fire protection maintenance management system includes a multimodal data acquisition module, a feature alignment and fusion module, a risk index assessment module, an intelligent early warning and dispatch module, and a maintenance closed-loop management module. The multimodal data acquisition module collects relevant data throughout the entire lifecycle of fire extinguishers and performs cleaning, noise reduction, and normalization on the collected data to obtain standard multimodal data. The feature alignment and fusion module extracts multi-dimensional features from the standard multimodal data using an improved multi-scale convolutional neural network and performs alignment and fusion processing on the extracted features to obtain feature-rich multimodal data. The risk index assessment module constructs a continuous-time hidden Markov model, using the fire extinguisher's safety status as the hidden... The system calculates and outputs a quantitative maintenance risk assessment index based on the observed multimodal data of the status and features. The intelligent early warning and dispatch module is used to classify risk levels according to the maintenance risk assessment index, trigger graded early warnings according to the levels, and automatically generate maintenance work orders and personnel assignment instructions. The maintenance closed-loop management module is used to realize the closed-loop execution of the entire process of maintenance operation, cost settlement, certificate printing, scrap recycling, and data archiving. It adopts a modular architecture to realize the integrated collaboration of data collection, feature fusion, risk assessment, early warning dispatch, and maintenance closed loop, builds a complete technical link, and enables the system to have high stability and scalability, realizing the upgrade of fire extinguisher maintenance from passive management to intelligent prediction.

[0006] A further proposed solution includes at least four types of multi-source heterogeneous data related to the entire life cycle of the fire extinguisher: identification data, real-time status monitoring data, usage environment data, and historical maintenance record data. This comprehensive data collection from four dimensions—identity, status, environment, and history—covers key information throughout the entire life cycle of the fire extinguisher, avoiding biased assessments due to incomplete data and improving the comprehensiveness and accuracy of risk assessment.

[0007] In a further embodiment, the improved multi-scale convolutional neural network is configured with convolutional kernels of different receptive fields, and an attention mechanism is embedded after the convolutional layer. The fire extinguisher state trend features, spatial distribution features, and temporal periodic features are extracted by different convolutional kernels, and a cross-attention mechanism is used to complete the multi-feature association fusion. Multi-scale convolution adapts to the extraction of features of different dimensions, the attention mechanism strengthens key risk information, and cross-attention achieves deep feature association, effectively improving the accuracy of state recognition and reducing misjudgment and missed judgment.

[0008] In a further proposed solution, the risk index assessment module divides the hidden state into four levels: healthy, requiring attention, requiring maintenance, and requiring scrapping. It uses a Gaussian mixture model to complete the probability mapping of the observation sequence and uses an iterative optimization algorithm to complete the adaptive update of the model parameters. The four-level state division fits the actual maintenance scenario, the probability mapping improves the model's adaptability, and the iterative optimization enables the system to continuously evolve with the accumulation of data, maintaining high assessment accuracy over the long term.

[0009] In a further embodiment, the risk index assessment module determines the most matching hidden state sequence through the optimal path reasoning algorithm, and maps the hidden state to a quantitative maintenance risk assessment index in the range of 0-100. The optimal path reasoning ensures that the state judgment is most consistent with the actual working conditions, and the quantitative index is intuitive and easy to understand, which facilitates automatic decision-making by the system and quick identification of risk levels by management personnel.

[0010] In a further proposed solution, the intelligent early warning and dispatch module divides risks into four levels based on a quantitative maintenance risk assessment index. Each level corresponds to a different handling strategy: routine monitoring, alerts and reminders, maintenance dispatch, and scrapping and recycling. This tiered approach avoids blanket alarms, ensures precise resource allocation, reduces ineffective interventions, and improves response efficiency and the economics of maintenance operations.

[0011] In a further embodiment, the maintenance closed-loop management module includes at least a personnel assignment unit, a settlement control unit, a certificate printing unit, a scrap recycling unit, and a data traceability unit. These multiple units work together to cover all aspects of maintenance operations, achieving standardized, paperless, and traceable processes, thereby improving management compliance and operational efficiency.

[0012] Further solutions include a mini-program interaction module, which enables fire extinguisher information scanning and collection, maintenance data reporting, early warning message reception, maintenance progress inquiry, and electronic certificate viewing. The lightweight entry point lowers the barrier to entry, supports mobile on-site operations, enables real-time data synchronization, and improves the convenience of data collection and the system's accessibility.

[0013] In a further embodiment, the multimodal data acquisition module adopts a combination of preprocessing methods, including missing value completion, outlier removal, and noise filtering, to ensure the integrity and accuracy of standard multimodal data. This combined preprocessing effectively solves problems such as missing data, anomalies, and high noise in actual acquisition scenarios, providing a high-quality data foundation for subsequent AI evaluation.

[0014] A further proposed solution is to adopt an edge caching and cloud-based distributed storage architecture, which supports offline caching and online synchronization, enabling full traceability and immutability of maintenance data. The edge caching is adapted to unstable on-site network scenarios to prevent data loss, while the distributed storage enhances security and traceability, meeting fire safety supervision and compliance requirements.

[0015] The present invention has the following beneficial effects:

[0016] This invention ensures data authenticity and integrity through multimodal data acquisition and standardized preprocessing, improves the accuracy of status identification through multi-scale feature extraction and cross-modal fusion, achieves quantitative risk assessment through probabilistic models, enhances response accuracy through hierarchical early warning and intelligent dispatching, and integrates maintenance, settlement, printing, recycling, and archiving through closed-loop management throughout the entire process. This transforms fire extinguisher maintenance from passive response to proactive prediction and from manual operation to intelligent decision-making, significantly improving management efficiency and compliance levels while reducing safety hazards. Attached Figure Description

[0017] Figure 1 This is a block diagram of the overall system structure of the present invention;

[0018] Figure 2 This is a flowchart of the data processing and feature fusion process of the present invention;

[0019] Figure 3 This is a flowchart illustrating the risk assessment and graded early warning system of this invention. Detailed Implementation

[0020] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0021] refer to Figures 1-3As shown, a fire protection maintenance management system consists of a data acquisition layer, a data processing layer, an AI evaluation layer, a business execution layer, and an interaction layer. Data flows unidirectionally between these layers, forming a closed loop. The data acquisition layer is responsible for acquiring multi-source heterogeneous data such as fire extinguisher identity, status, environment, and historical maintenance through hardware interfaces, mini-program input, and equipment detection. The data processing layer cleans the raw data, removes anomalies, fills in missing values, filters noise, and normalizes the data, unifying the heterogeneous data into a standard format. The AI ​​evaluation layer extracts features using a multi-scale convolutional neural network and performs state reasoning using a continuous-time hidden Markov model, outputting a quantitative risk index. The business execution layer automatically completes graded early warning, personnel assignment, work order generation, maintenance confirmation, cost settlement, certificate printing, scrap recycling, and data archiving based on risk levels. The interaction layer provides a lightweight entry point through a WeChat mini-program, supporting on-site data acquisition, mobile approval, message reception, progress inquiry, and electronic certificate viewing, achieving real-time data synchronization between the mobile and management terminals.

[0022] Specifically, the following is a complete explanation of the full-process maintenance of 120 MFZ / ABC4 dry powder fire extinguishers in three office buildings in a certain industrial park.

[0023] Multimodal data acquisition and standardization processing: The system acquires data related to the entire lifecycle of fire extinguishers through the acquisition interface, including four categories:

[0024] Identification data: AB signature code, specifications, manufacturer, production date, factory pressure, total mass;

[0025] Condition monitoring data: current pressure value, cylinder appearance, last maintenance date, number of maintenance visits, and scrapping label;

[0026] Site environmental data: user unit, floor location, ambient temperature and humidity, storage scenario (office / power distribution / computer room);

[0027] Historical maintenance data: repair time, repair content, replaced parts, settlement records, and reminder records for each repair.

[0028] The collected data are subjected to standardized preprocessing in sequence: linear interpolation is used to complete short-term missing data in a time sequence to ensure that the data is continuously readable;

[0029] Abnormal data is identified and removed based on the 3σ principle, such as pressure values ​​exceeding the reasonable range and date logic errors.

[0030] Wavelet transform is used to remove acquisition noise and retain real status signals such as pressure surges and periodic maintenance.

[0031] Min-Max normalization is used to map various numerical values ​​to the [0,1] interval, eliminating dimensional differences.

[0032] The normalization calculation formula is:

[0033]

[0034] In the formula: This is the original collected data; It is the minimum value of the same type of data; The maximum value of the same type of data; These are the normalized standard data.

[0035] Specifically: If the pressure reading of a certain fire extinguisher is 0.8 MPa, and the normal range is 0.5–1.2 MPa, then after normalization:

[0036]

[0037] The system marks this data as a normal range. After preprocessing, it outputs standard multimodal data, providing high-quality input for subsequent feature extraction and risk assessment.

[0038] Multi-scale feature extraction and feature alignment fusion: The system employs an improved multi-scale convolutional neural network to process standard multimodal data. The network is configured with three convolutional kernels with different receptive fields to extract features respectively.

[0039] 1×3 convolution: Extracts state trend features such as pressure changes and aging trends using F1;

[0040] 3×3 convolution: Extracts the spatial distribution features F2 of fire extinguishers from the same floor, area, and batch;

[0041] 5×5 convolution: Extracts F3, a time-series periodic feature of annual inspection cycle, seasonal fluctuations, and maintenance patterns.

[0042] An SE attention mechanism is added after the convolutional layer to automatically strengthen the weights of key risk features such as pressure, service life, and maintenance interval, and suppress irrelevant noise effects such as small fluctuations in ambient temperature and humidity.

[0043] For features with different sampling frequencies and timestamps, linear interpolation with a uniform step size is used to complete feature alignment.

[0044]

[0045] In the formula: This is a feature alignment operation.

[0046] Then, by concatenating features along the channel dimension and learning the relationships between features through cross-attention mechanisms, deep fusion is achieved, resulting in multimodal feature data: In the formula: This is a channel-dimensional splicing operation.

[0047] The system extracts three types of features—trend, spatial, and temporal—from 120 fire extinguishers in the park. It identifies five fire extinguishers in the power distribution room area on the third floor whose pressure is showing a continuous downward trend and whose maintenance cycle is about to expire. By using a cross-attention mechanism, the spatial and temporal features are correlated, and these fire extinguishers are marked as high-attention objects.

[0048] Risk assessment based on continuous-time hidden Markov model: The system constructs a continuous-time hidden Markov model (CTHMM), and the complete model representation is as follows:

[0049]

[0050] In the formula: Here is the state transition probability matrix. , ;

[0051] B is the observation probability matrix. ;

[0052] Let be the initial state probability vector. .

[0053] The system categorizes the safety status of fire extinguishers into four hidden states: healthy, requiring attention, requiring maintenance, and requiring disposal. Using the fused feature F as the observation sequence O, a Gaussian mixture model (GMM) is employed to map the observation sequence into a probability distribution.

[0054] The EM algorithm is used to iteratively optimize the model parameter λ, with the objective of maximizing the log-likelihood function.

[0055]

[0056] The iteration termination condition is:

[0057]

[0058] In the formula: The convergence threshold, >0.

[0059] After the parameters converge, the Viterbi algorithm is used to solve for the optimal hidden state sequence:

[0060]

[0061] The optimal state is mapped to a 0-100 quantitative maintenance risk index R.

[0062] Specifically: Based on model analysis, the five fire extinguishers in the power distribution room on the 3rd floor of the park have a risk index of 68 points, corresponding to a status of needing maintenance; the normal fire extinguishers in the office area have a risk index of 15-25 points, corresponding to a healthy status; the fire extinguishers that have not been inspected and have insufficient pressure have a risk index of 89 points, corresponding to a status of needing to be scrapped.

[0063] Tiered early warning and intelligent task dispatch: The system classifies risks into four levels based on the risk index R and executes differentiated handling strategies accordingly.

[0064] Level 1 (0-20): Health status, routine monitoring, no alerts triggered;

[0065] Level 2 (21-40): Requires attention; a mini-program will provide a reminder to check on schedule.

[0066] Level 3 (41-70): Repair required. Work order will be automatically generated, and inspector and repairman will be assigned.

[0067] Level 4 (71-100): The equipment needs to be scrapped. Initiate the recycling process, lock the equipment, and notify the customer.

[0068] The system automatically matches personnel qualifications, working distance, and shift status to generate maintenance work orders and synchronize them to the mini-program and management backend.

[0069] Specifically: The risk index of the 5 fire extinguishers in the power distribution room on the 3rd floor is 68 points. The system automatically judges it as level three and generates a work order within 10 seconds, assigning the nearest inspector and maintenance personnel, and pushing the maintenance task, location information, fire extinguisher details and estimated working hours.

[0070] Repair process closed-loop management: The system performs closed-loop control over the entire repair process.

[0071] The maintenance process automatically records information such as pressure testing, cylinder repair, reagent replacement, and airtightness testing;

[0072] The bill is automatically generated based on the type of repair, including annual inspection fee, repair fee, parts fee, and tax.

[0073] Supports standardized printing of repair certificates and printing of custom templates, automatically including inspector, repair date, and test pressure;

[0074] Automatically enter scrapped equipment into the waste recycling ledger and generate recycling vouchers;

[0075] All data is written to the storage unit in real time, supporting traceability, auditing, and export.

[0076] The system employs edge caching and cloud-based distributed storage to meet the following requirements:

[0077]

[0078] Local caching occurs when the network is offline, and automatic synchronization occurs when the network is connected, ensuring that the data is not lost, is traceable, and cannot be tampered with.

[0079] Specifically: After the repair of the 5 fire extinguishers in the power distribution room is completed, the system automatically generates a settlement statement, prints a certificate of conformity, records the entire repair process, and synchronizes the data to the customer's account in the park. The customer can view the electronic voucher and the next repair date in the mini-program.

[0080] Mini Program Interaction and Lightweight Applications: The system is equipped with a WeChat Mini Program interaction module, supporting:

[0081] Scan to collect data: Scan the AB label to automatically read the fire extinguisher information;

[0082] Batch data entry: Supports count collection and batch uploading;

[0083] Repair reporting: Fill out the repair results on-site, take photos and upload them;

[0084] Early warning reception: Real-time push notifications for expiration, repair, and scrapping reminders;

[0085] Progress tracking: View work order status, repair history, and risk score;

[0086] Electronic certificates: View certificates of conformity, settlement statements, and return receipts.

[0087] The mini-program synchronizes data with the management terminal in real time, meeting the integrated needs of on-site operations, mobile office, and remote supervision, significantly reducing the barrier to entry and improving the convenience and coverage of operations.

[0088] Specifically: Park administrators can scan a QR code via a mini-program to view the risk index, maintenance records, and next annual inspection date of any fire extinguisher without having to consult ledgers; maintenance personnel can submit their work on-site with one click, and the system will automatically archive the data, making the entire process paperless.

[0089] Summary of working principles:

[0090] This system achieves comprehensive aggregation of fire extinguisher lifecycle information through multimodal data acquisition. Standardized preprocessing eliminates data noise, missing data, and format differences, forming a unified and standardized input foundation. An improved multi-scale convolutional neural network extracts and fuses three types of features: state trends, spatial distribution, and temporal cycles, fully representing the inherent changing patterns and correlations of equipment status. Based on a continuous-time hidden Markov model, abstract states are transformed into calculable and quantifiable risk indices, enabling objective assessment of fire extinguisher health. Early warning strategies and response procedures are automatically matched according to risk levels, achieving rapid response from risk identification to intelligent dispatch. Finally, through integrated closed-loop execution of maintenance, settlement, printing, recycling, and archiving, the entire fire protection maintenance process is standardized, digitized, and traceable. The overall workflow follows a progressive logic of data acquisition, feature fusion, risk assessment, tiered early warning, and closed-loop execution, transforming fire extinguisher maintenance from passive reminders to proactive prediction and from manual experience-based management to AI-powered intelligent decision-making. This significantly improves operational efficiency while enhancing the safety and compliance of fire equipment management.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A fire protection maintenance management system, characterized in that, The system includes a multimodal data acquisition module, a feature alignment and fusion module, a risk index assessment module, an intelligent early warning and dispatch module, and a maintenance closed-loop management module. The multimodal data acquisition module collects relevant data throughout the entire lifecycle of fire extinguishers and performs cleaning, noise reduction, and normalization on the collected data to obtain standard multimodal data. The feature alignment and fusion module extracts multi-dimensional features from the standard multimodal data using an improved multi-scale convolutional neural network and performs alignment and fusion processing on the extracted features to obtain feature-based multimodal data. The risk index assessment module constructs a continuous-time hidden Markov model, using the fire extinguisher's safety status as the hidden state and the feature-based multimodal data as the observation sequence, to calculate and output a quantified maintenance risk assessment index. The intelligent early warning and dispatch module classifies risks according to the maintenance risk assessment index, triggers graded early warnings based on the level, and automatically generates maintenance work orders and personnel assignment instructions. The maintenance closed-loop management module enables closed-loop execution of the entire process, including maintenance operations, cost settlement, certificate printing, scrap recycling, and data archiving.

2. The fire protection maintenance management system according to claim 1, characterized in that, The data related to the entire life cycle of the fire extinguisher includes at least four types of multi-source heterogeneous data: identification data, real-time status monitoring data, usage environment data, and historical maintenance record data.

3. The fire protection maintenance management system according to claim 1, characterized in that, The improved multi-scale convolutional neural network is configured with convolutional kernels with different receptive fields and an attention mechanism is embedded after the convolutional layer. The fire extinguisher state trend features, spatial distribution features and time-series periodic features are extracted by different convolutional kernels, and a cross-attention mechanism is used to complete the multi-feature association fusion.

4. The fire protection maintenance management system according to claim 1, characterized in that, The risk index assessment module divides the hidden state into four levels: healthy, requiring attention, requiring maintenance, and requiring scrapping. It uses a Gaussian mixture model to complete the probability mapping of the observation sequence and uses an iterative optimization algorithm to complete the adaptive update of the model parameters.

5. The fire protection maintenance management system according to claim 4, characterized in that, The risk index assessment module determines the most matching hidden state sequence through the optimal path reasoning algorithm and maps the hidden state to a quantitative maintenance risk assessment index in the range of 0-100.

6. The fire protection maintenance management system according to claim 1, characterized in that, The intelligent early warning and dispatch module divides risks into four levels based on a quantitative maintenance risk assessment index. Each level corresponds to a different handling strategy, namely, routine monitoring, alerts and reminders, maintenance dispatch, and scrapping and recycling.

7. The fire protection maintenance management system according to claim 1, characterized in that, The maintenance closed-loop management module includes at least a personnel assignment unit, a settlement control unit, a certificate printing unit, a scrap recycling unit, and a data traceability unit.

8. The fire protection maintenance management system according to claim 1, characterized in that, It also includes a mini-program interaction module, which is used to collect fire extinguisher information by scanning codes, report maintenance data, receive early warning messages, query maintenance progress, and view electronic certificates.

9. The fire protection maintenance management system according to claim 1, characterized in that, The multimodal data acquisition module employs a combination of preprocessing methods, including missing value completion, outlier removal, and noise filtering, to ensure the integrity and accuracy of standard multimodal data.

10. The fire protection maintenance management system according to claim 1, characterized in that, The system adopts an edge caching and cloud-based distributed storage architecture, supporting offline caching and online synchronization, enabling full traceability and tamper-proof maintenance data.

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