Fire-fighting equipment intelligent diagnosis remote maintenance system based on Internet of Things

Abstract

The invention provides a fire-fighting facility intelligent diagnosis remote maintenance system based on the Internet of Things, and belongs to the technical field of fire-fighting facility maintenance, the fire-fighting facility intelligent diagnosis remote maintenance system comprises a sensor module, an edge computing node and a cloud platform, collecting pressure, flow, valve state, power supply voltage and environment temperature and humidity data in real time; the edge computing node is connected with the sensor module, is internally provided with a fault diagnosis model, and performs local preprocessing and preliminary fault analysis on original data; the cloud platform receives the data uploaded by the edge computing node through the encryption communication module and comprises an intelligent diagnosis engine, a predictive maintenance module and a remote maintenance interface, and the intelligent diagnosis engine fuses the multi-dimensional data to generate a facility health degree score. A closed loop of data acquisition-local diagnosis-cloud decision is realized through a three-layer architecture of a sensor, edge computing and a cloud platform, and the contradiction between poor real-time performance and limited computing power of a traditional system is broken through.

Description

Technical Field

[0001] The present invention relates to the technical field of fire protection facility maintenance, and in particular to an intelligent diagnosis and remote maintenance system for fire protection facilities based on the Internet of Things. Background Art

[0002] With the development of society, the number of buildings of all types is increasing. As an important equipment to ensure building fire safety, the normal operation of fire protection facilities is of vital importance. However, the traditional fire protection facility maintenance system has the following limitations:

[0003] 1. Lack of real-time performance: Data processing and fault response are delayed, making it difficult to quickly detect early hidden dangers.

[0004] 2. Limited computing power: Relying on a single threshold or simple logical judgment, it is unable to process multi-dimensional data under complex working conditions.

[0005] 3. High false alarm rate: The traditional solution has poor recognition capabilities for scenarios such as slow leaks and environmental interference. The false alarm rate of traditional solutions is >30% under complex working conditions.

[0006] 4. Low maintenance efficiency: Manual inspection is the main method, lacking intelligent diagnosis and prediction methods, and fault location and handling rely on experience.

[0007] Therefore, an intelligent diagnosis and remote maintenance system for fire protection facilities based on the Internet of Things is proposed. Summary of the Invention

[0008] The present invention aims to solve the problems raised in the background technology and provides an intelligent diagnosis and remote maintenance system for fire protection facilities based on the Internet of Things.

[0009] The specific technical solutions are as follows:

[0010] An intelligent diagnosis and remote maintenance system for fire protection facilities based on the Internet of Things, comprising:

[0011] Sensor modules are deployed at key nodes of fire protection facilities to collect real-time data on pressure, flow, valve status, power supply voltage, and ambient temperature and humidity;

[0012] Edge computing nodes, connected to sensor modules, have built-in fault diagnosis models to perform local pre-processing of raw data and preliminary fault analysis;

[0013] The cloud platform receives data uploaded by the edge computing node through the encrypted communication module, including:

[0014] Intelligent diagnostic engine that integrates multi-dimensional data to generate facility health scores;

[0015] Predictive maintenance module, which predicts component failure probability based on a time series model trained with historical data;

[0016] Remote maintenance interface, pushes maintenance instructions to authorized terminals and receives feedback.

[0017] As a preferred solution of the present invention, the intelligent diagnosis engine includes:

[0018] Fault decision tree unit, which determines the immediate fault type according to preset rules;

[0019] LSTM neural network unit, which analyzes the temporal characteristics of sensor data and outputs the remaining life prediction value;

[0020] The alarm classification module dynamically triggers the first-level sound and light alarm, the second-level SMS notification, and the third-level platform pop-up response strategy based on the severity of the fault.

[0021] As a preferred solution of the present invention, the edge computing node performs the following operations:

[0022] Wavelet transform algorithm is used to filter out high-frequency noise in sensor data;

[0023] When data anomalies are detected, the local diagnostic mode is activated and the preset fault feature library is called for pattern matching;

[0024] If the match fails, upload the original data to the cloud platform and request in-depth analysis.

[0025] As a preferred solution of the present invention, the cloud platform further includes:

[0026] Digital twin module, which builds a three-dimensional virtual model of fire protection facilities and maps the status of physical equipment in real time;

[0027] Maintain knowledge graph, store component relationships and repair cases, and assist in generating maintenance plans.

[0028] As a preferred solution of the present invention, the sensor module includes:

[0029] Self-diagnostic unit, periodically detects sensor zero drift and sensitivity attenuation;

[0030] Redundant communication interface supports RS-485 and LoRaWAN dual-channel data transmission, and automatically switches when a single channel fails.

[0031] As a preferred solution of the present invention, the remote maintenance interface implements:

[0032] Maintenance work order notarization based on blockchain technology records the operator, timestamp and hash value of the maintenance result;

[0033] AR-assisted maintenance function pushes device disassembly animations and operation instructions to mobile terminals.

[0034] As a preferred solution of the present invention, the encryption communication module adopts:

[0035] Lightweight national encryption algorithm SM4 encrypts transmitted data;

[0036] Dynamic key distribution mechanism, the cloud platform generates a session key based on the device ID, which is valid for a single communication cycle.

[0037] As a preferred solution of the present invention, the training method of the LSTM neural network unit includes:

[0038] The input layer receives normalized multi-source sensor time series data;

[0039] The hidden layer weights key features through the attention mechanism;

[0040] The output layer generates the probability of component failure, and when the probability is >85%, a preventive replacement recommendation is triggered.

[0041] As a preferred embodiment of the present invention, it also includes:

[0042] Adaptive threshold adjustment module, dynamically corrects the alarm threshold according to the age of the facility:

[0043] For equipment that has been in service for more than 5 years, the water pressure alarm threshold will be lowered by 10%;

[0044] For equipment in highly corrosive environments, the power supply voltage fluctuation tolerance is improved by 15%.

[0045] As a preferred solution of the present invention, the edge computing node and the cloud platform adopt:

[0046] Differential data transmission protocol, only uploading abnormal data fragments and complete data of the previous and subsequent related periods;

[0047] The breakpoint resume mechanism caches data packets when the network is interrupted and gives priority to transmitting alarm information after recovery.

[0048] The present invention has the following beneficial effects:

[0049] 1. Improved diagnostic capabilities

[0050] Taking into account both immediate fault response and long-term risk prediction, it can accurately identify hidden micro-leaks and other hidden dangers that are difficult to detect with traditional methods;

[0051] Integrate multi-dimensional data (pressure, flow, vibration, etc.) with intelligent algorithms (decision tree, LSTM, attention mechanism) to improve the accuracy of fault diagnosis under complex working conditions.

[0052] 2. Maintenance efficiency optimization

[0053] Digital twin visualization locates fault points, and intelligently recommends solutions based on maintenance knowledge graphs, shortening maintenance personnel's response time and skill threshold.

[0054] Predictive maintenance provides early warning of component failure risks, transforming reactive maintenance into proactive prevention and reducing downtime due to sudden failures.

[0055] 3. Enhanced system reliability

[0056] Mechanisms such as sensor self-diagnosis and redundant communication, edge computing local caching and breakpoint resuming, and dynamic threshold adaptive adjustment can reduce misjudgments and missed detections caused by device drift, communication interruptions, or environmental changes.

[0057] Blockchain work order notarization and AR standardized operation instructions reduce human errors and ensure the traceability of the maintenance process;

[0058] 4. Balancing communication and security: Lightweight encryption algorithms and dynamic key mechanisms adapt to the resource limitations of IoT terminals, improving communication efficiency while ensuring data security.

[0059] 5. Architectural flexibility: The three-layer architecture (sensors + edge computing + cloud platform) supports modular expansion to meet the intelligent upgrade needs of fire protection facilities of different scales. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A schematic diagram of the composition of an IoT-based intelligent diagnosis and remote maintenance system for fire protection facilities provided in an embodiment of the present invention;

[0061] Figure 2 A flowchart of the multi-source feature fusion leakage probability equation provided by an embodiment of the present invention;

[0062] Figure 3 A graph showing the leak detection capability of the IoT-based fire protection facility intelligent diagnosis and remote maintenance system provided by an embodiment of the present invention;

[0063] Figure 4 A curve chart showing the reduction in false alarm rate of the IoT-based fire protection facility intelligent diagnosis and remote maintenance system provided by an embodiment of the present invention;

[0064] Figure 5 A graph showing the reduction in maintenance response time for the IoT-based fire protection facility intelligent diagnosis and remote maintenance system provided by an embodiment of the present invention;

[0065] Figure 6 A graph showing the improvement in communication efficiency of the IoT-based firefighting facility intelligent diagnosis and remote maintenance system provided by an embodiment of the present invention;

[0066] Figure 7Graph showing the LSTM neural network prediction accuracy for the IoT-based firefighting facility intelligent diagnosis and remote maintenance system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The technical solution of the present invention will be further described below with reference to the accompanying drawings and through specific implementation methods.

[0068] Among them, the drawings are only used for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting this patent; in order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0069] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "inside", "outside" and the like indicate an orientation or position relationship based on the orientation or position relationship shown in the drawings, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting this patent. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0070] In the description of the present invention, unless otherwise expressly specified or limited, when the term "connection" or the like appears to indicate a connection relationship between components, such term should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be internal communication between two components or an interaction between two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood in specific circumstances.

[0071] Example

[0072] The fire protection facility intelligent diagnosis and remote maintenance system based on the Internet of Things provided in this embodiment is as follows: Figure 1-Figure 7 As shown, Figure 3 The change of the detection rate of small leaks over time or in different scenarios is demonstrated, which shows the system's high efficiency in detecting small leaks. Figure 4 It reflects the change of false alarm rate over time or under different working conditions, indicating that the system effectively reduces the false alarm rate through multi-source feature fusion; Figure 5The system demonstrates how maintenance response time changes over time or with different fault types, demonstrating how the system significantly shortens maintenance response time by visually locating fault points through digital twins and combining maintenance knowledge graphs with intelligent recommendations. Figure 6 This reflects the changes in communication efficiency over time or with different encryption algorithms, indicating that the system improves communication efficiency while ensuring data security through lightweight encryption algorithms and dynamic key mechanisms; Figure 7 It shows how the accuracy of LSTM neural network in predicting failure probability changes over time or for different components, and demonstrates that the system uses LSTM neural network units combined with attention mechanism to analyze time series data, thereby improving the accuracy of maintenance decisions.

[0073] The IoT-based intelligent diagnosis and remote maintenance system for fire protection facilities includes sensor modules, edge computing nodes, and a cloud platform.

[0074] The sensor module is used to be deployed at key nodes of fire-fighting facilities to collect real-time data on pressure, flow, valve status, power supply voltage, and ambient temperature and humidity. The sensor module includes a pressure sensor, a temperature sensor, a humidity sensor, a smoke sensor, a liquid level sensor, and a current and voltage sensor. The pressure sensor is used to collect pressure data in the fire-fighting pipe, the temperature sensor is used to collect temperature data of the environment in which the fire-fighting facilities are located, the humidity sensor is used to collect humidity data of the environment in which the fire-fighting facilities are located, the smoke sensor is used to collect smoke concentration data around the fire-fighting facilities, the liquid level sensor is used to collect liquid level data of the fire-fighting water tank or pool, and the current and voltage sensor is used to collect current and voltage data of the fire-fighting equipment.

[0075] The edge computing node is connected to the sensor module and has a built-in fault diagnosis model to perform local pre-processing and preliminary fault analysis on the raw data;

[0076] The cloud platform receives data uploaded by edge computing nodes through an encrypted communication module. It includes an intelligent diagnosis engine, a predictive maintenance module, and a remote maintenance interface. The intelligent diagnosis engine integrates multi-dimensional data to generate a facility health score; the predictive maintenance module predicts the probability of component failure based on a time series model trained based on historical data; the remote maintenance interface pushes maintenance instructions to authorized terminals and receives feedback.

[0077] Through the three-layer architecture of sensors + edge computing + cloud platform, a closed loop of data collection-local diagnosis-cloud decision-making is achieved, breaking through the contradiction between the poor real-time performance of traditional systems and the limited computing power.

[0078] Specifically, in this embodiment, the intelligent diagnosis engine includes: a fault decision tree unit, an LSTM neural network unit, and an alarm classification module, wherein:

[0079] The fault decision tree unit is used to determine the immediate fault type according to preset rules;

[0080] The LSTM neural network unit is used to analyze the time series characteristics of sensor data and output the remaining life prediction value;

[0081] The alarm classification module is used to dynamically trigger the first-level sound and light alarm, the second-level SMS notification, and the third-level platform pop-up response strategy based on the severity of the fault.

[0082] Through the coordinated cooperation of the fault decision tree unit, LSTM neural network unit and alarm classification module, both immediate fault response and long-term risk prediction are taken into account, effectively improving alarm accuracy and handling efficiency.

[0083] Specifically, in this embodiment, the edge computing node performs the following operations:

[0084] Wavelet transform algorithm is used to filter out high-frequency noise in sensor data;

[0085] When data anomalies are detected, the local diagnostic mode is activated and the preset fault feature library is called for pattern matching;

[0086] If the match fails, upload the original data to the cloud platform and request in-depth analysis.

[0087] The edge computing node performs wavelet noise reduction → local matching → cloud backup, which can reduce network load while ensuring diagnostic integrity, thereby avoiding missed detection of critical faults due to communication interruptions.

[0088] Specifically, in this embodiment, the cloud platform also includes: a digital twin module and a maintenance knowledge graph, wherein:

[0089] The digital twin module is used to build a three-dimensional virtual model of firefighting facilities and map the status of physical equipment in real time;

[0090] The maintenance knowledge graph is used to store component relationships and maintenance cases, and assist in generating maintenance plans.

[0091] Through the collaborative cooperation of the digital twin module and the maintenance knowledge graph, fault points can be visually located and solutions can be intelligently recommended, effectively shortening the skill threshold and response time of maintenance personnel.

[0092] Specifically, in this embodiment, the sensor module includes: a self-diagnostic unit and a redundant communication interface, wherein:

[0093] The self-diagnosis unit is used to periodically detect the zero drift and sensitivity attenuation of the sensor;

[0094] The redundant communication interface supports RS-485 and LoRaWAN dual-channel data transmission, and automatically switches when a single channel fails.

[0095] Through the coordinated cooperation of the self-diagnostic unit and the redundant communication interface, data reliability is guaranteed from the source to prevent misjudgments caused by sensor drift or communication interruption.

[0096] Specifically, in this embodiment, the remote maintenance interface implements:

[0097] Maintenance work order notarization based on blockchain technology records the operator, timestamp and hash value of the maintenance result;

[0098] AR-assisted maintenance function pushes device disassembly animations and operation instructions to mobile terminals.

[0099] Through blockchain work order notarization + AR assistance, the maintenance process can be traced and the operation can be standardized, reducing the risk of human operational errors.

[0100] Specifically, in this embodiment, the encryption communication module adopts:

[0101] Lightweight national encryption algorithm SM4 encrypts transmitted data;

[0102] Dynamic key distribution mechanism, the cloud platform generates a session key based on the device ID, which is valid for a single communication cycle.

[0103] Through SM4 encryption + dynamic key distribution, it is possible to balance security and communication efficiency and adapt to scenarios where IoT terminal resources are limited.

[0104] Specifically, in this embodiment, the training method of the LSTM neural network unit includes:

[0105] The input layer receives normalized multi-source sensor time series data;

[0106] The hidden layer weights key features through the attention mechanism;

[0107] The output layer generates the probability of component failure, and when the probability is >85%, a preventive replacement recommendation is triggered.

[0108] Through LSTM+attention mechanism training, we focus on key fault features and effectively improve the robustness of component failure prediction under complex working conditions.

[0109] Specifically, in this embodiment, the system further includes: an adaptive threshold adjustment module, equipment that has been in service for more than 5 years, and equipment in a highly corrosive environment, wherein:

[0110] The adaptive threshold adjustment module is used to dynamically modify the alarm threshold according to the age of the facility:

[0111] For equipment that has been in service for more than 5 years, the water pressure alarm threshold will be lowered by 10%;

[0112] For equipment in highly corrosive environments, the power supply voltage fluctuation tolerance is improved by 15%.

[0113] By adaptively adjusting thresholds based on age / environment, the alarm sensitivity can be dynamically optimized to avoid false alarms from older devices or missed alarms from newer devices.

[0114] Specifically, in this embodiment, the edge computing node and the cloud platform adopt: differential data transmission protocol and breakpoint resume mechanism, where:

[0115] The differential data transmission protocol only uploads abnormal data fragments and complete data of the preceding and following related periods;

[0116] The breakpoint resume mechanism caches data packets when the network is interrupted and gives priority to transmitting alarm information after recovery.

[0117] Through differential transmission + breakpoint resumption, critical data transmission can be prioritized and the system availability in weak network environments can be improved.

[0118] Specifically, in this embodiment, the fault decision tree unit uses a multi-source feature fusion leakage probability equation to diagnose pipeline leakage faults:

[0119]

[0120] in:

[0121] P leak : Real-time leakage probability (0-1);

[0122] ΔP: Pressure change rate (kPa / s), with sign and direction (negative value indicates pressure decrease);

[0123] σF: Flow fluctuation variance (L 2 / min 2 ), calculate the dispersion of traffic data in the last 60 seconds;

[0124] V corr : Correlation coefficient between vibration signal and flow signal, detecting abnormal vibration mode;

[0125] λ1,λ2,λ3: dynamic weight coefficients (satisfying λ1+λ2+λ3=1);

[0126] α: nonlinear amplification factor (default 1.8), which increases the sensitivity to small leaks;

[0127] ∈: smoothing constant (default 0.01), to avoid zero variance causing calculation overflow;

[0128] V ref : Baseline vibration correlation coefficient (default 0.7), calibrated by historical normal data.

[0129] The technical problems solved by the multi-source feature fusion leakage probability equation are:

[0130] Disadvantages of traditional methods: Existing technologies only use pressure thresholds or simple flow integrals to determine leaks, and are unable to distinguish between slow leaks (such as a pressure drop of 0.5 kPa per hour) and normal water usage fluctuations; nor can they distinguish between pipeline vibration noise and leakage impact vibration.

[0131] High false alarm rate: The false alarm rate of traditional solutions is >30% under complex working conditions.

[0132] The multi-source feature fusion leakage probability equation is:

[0133] sgn(ΔP) ensures that the contribution is enhanced when the pressure drops (the weight is doubled when ΔP < 0) and false positives are suppressed when the pressure rises.

[0134] ΔP α Amplify small pressure changes (such as 0.2kPa / s) to a significant range:

[0135] (0.2 1.8 ≈0.03→0.2 1.8 ≈0.08), improving the detection rate of micro leaks.

[0136] V corr / V ref Quantify the degree of abnormal decoupling of vibration and flow (increased vibration but unchanged flow in the event of a leak).

[0137] Example

[0138] Scenario: Pinhole leak in sprinkler system pipe (pore diameter <1mm);

[0139] 1. Data input:

[0140] ΔP = -0.3 kPa / s (slowly decreasing);

[0141] σF=2.1L 2 / min 2 (small flow fluctuation);

[0142] V corr =0.15 (correlation between vibration and flow rate drops sharply);

[0143] 2. Parameter settings:

[0144] λ1=0.6, λ2=0.3, λ3=0.1, α=1.8, V ref =0.7

[0145] 3. Calculation process:

[0146]

[0147] 4. Decision-making:

[0148] When P leak >0.5 for 5 minutes, triggering a hidden leak warning (level 2 alarm).

[0149] The technical effect of the multi-source feature fusion leakage probability equation

[0150] 1. Improved detection capability: The detection rate of small leaks (<0.5L / min) is significantly improved compared to traditional methods;

[0151] 2. Reduced false alarm rate: False alarms caused by normal water usage fluctuations are significantly reduced;

[0152] 3. Response speed: The average leak diagnosis time is significantly shortened compared to traditional methods.

[0153] This multi-source feature fusion leakage probability equation can accurately identify hidden micro-leaks and solve the pain point of traditional methods that confuse slow leaks with normal fluctuations.

[0154] Working principle process Figure 2 shown.

[0155] In summary, the IoT-based intelligent diagnosis and remote maintenance system for fire protection facilities provided in this embodiment has the following advantages:

[0156] 1. Improved diagnostic capabilities

[0157] Taking into account both immediate fault response and long-term risk prediction, it can accurately identify hidden micro-leaks and other hidden dangers that are difficult to detect with traditional methods;

[0158] Integrate multi-dimensional data (pressure, flow, vibration, etc.) with intelligent algorithms (decision tree, LSTM, attention mechanism) to improve the accuracy of fault diagnosis under complex working conditions.

[0159] 2. Maintenance efficiency optimization

[0160] Digital twin visualization locates fault points, and intelligently recommends solutions based on maintenance knowledge graphs, shortening maintenance personnel's response time and skill threshold.

[0161] Predictive maintenance provides early warning of component failure risks, transforming reactive maintenance into proactive prevention and reducing downtime due to sudden failures.

[0162] 3. Enhanced system reliability

[0163] Mechanisms such as sensor self-diagnosis and redundant communication, edge computing local caching and breakpoint resuming, and dynamic threshold adaptive adjustment can reduce misjudgments and missed detections caused by device drift, communication interruptions, or environmental changes.

[0164] Blockchain work order notarization and AR standardized operation instructions reduce human errors and ensure the traceability of the maintenance process;

[0165] 4. Balancing communication and security: Lightweight encryption algorithms and dynamic key mechanisms adapt to the resource limitations of IoT terminals, improving communication efficiency while ensuring data security.

[0166] 5. Architectural flexibility: The three-layer architecture (sensors + edge computing + cloud platform) supports modular expansion to meet the intelligent upgrade needs of fire protection facilities of different scales.

[0167] Working principle:

[0168] (1) System architecture and core modules

[0169] 1. Sensor module

[0170] Deployment location: Installed at key nodes of fire protection facilities (such as pipes, valves, power supplies, etc.).

[0171] Function: Real-time collection of data such as pressure, flow, valve status, power supply voltage, and ambient temperature and humidity; a self-diagnostic unit detects sensor drift and utilizes redundant communication interfaces (RS-485 and LoRaWAN dual channels) to ensure data transmission reliability.

[0172] 2. Edge computing nodes

[0173] Data preprocessing: Use wavelet transform algorithm to filter out high-frequency noise and extract effective features.

[0174] Local diagnosis: Call the preset fault feature library for pattern matching. If the match fails, upload the original data to the cloud platform.

[0175] Communication optimization: Through differential data transmission protocols (transmitting only abnormal data fragments) and breakpoint resume transmission mechanisms, network load is reduced and data integrity is guaranteed.

[0176] 3. Cloud Platform

[0177] Intelligent diagnostic engine

[0178] Fault decision tree unit: Determines the immediate fault type based on preset rules, such as accurately identifying pipeline leaks by fusing multi-source features into a leakage probability equation (fusing parameters such as pressure change rate, flow fluctuation variance, and vibration and flow correlation).

[0179] LSTM neural network unit: Combined with the attention mechanism to analyze time series data, predict the remaining life of the component and the probability of failure (triggering a replacement recommendation when the probability is >85%).

[0180] Alarm classification module: respond according to the severity of the fault (level 1 sound and light alarm, level 2 SMS notification, level 3 platform pop-up window).

[0181] Predictive maintenance module: Trains time series models based on historical data to predict component failure probability and implement preventive maintenance.

[0182] Digital Twins and Knowledge Graphs

[0183] Digital twin module: Builds a three-dimensional virtual model, maps equipment status in real time, and assists in visual fault location.

[0184] Maintenance knowledge graph: stores component relationships and maintenance cases, and intelligently recommends maintenance plans.

[0185] Secure communication: Uses lightweight national encryption algorithm SM4 to encrypt data, combined with a dynamic key distribution mechanism (generates a single communication key based on device ID) to balance security and communication efficiency.

[0186] Adaptive threshold adjustment: Dynamically adjust the alarm threshold to optimize sensitivity based on the equipment's age (e.g., the water pressure threshold is lowered by 10% if the equipment is over 5 years old) and the environment (e.g., the power supply voltage tolerance is increased by 15% in a highly corrosive environment).

[0187] (2) Key technology mechanisms

[0188] 1. Data closed-loop process: Sensors collect data → Edge computing performs local preprocessing and preliminary diagnosis → The cloud platform conducts in-depth analysis, generates health scores and maintenance strategies → The remote maintenance interface executes instructions and provides feedback, forming a closed loop of "collection-diagnosis-decision-maintenance".

[0189] 2. Multi-technology integration: Integrate technologies such as the Internet of Things (sensors and communications), edge computing (local real-time processing), cloud computing (big data analysis), artificial intelligence (LSTM, decision trees, attention mechanisms), digital twins, and blockchain (work order storage) to enhance the intelligence level of the system.

[0190] Workflow

[0191] 1. Data collection phase: The sensor module collects firefighting facility operation data in real time, the self-diagnosis unit synchronously detects its own status, and the redundant communication interface ensures stable data transmission to the edge computing node.

[0192] 2. Edge computing stage

[0193] 2.1 The edge nodes perform wavelet denoising on the data to filter out noise interference.

[0194] 2.2 Start local diagnosis: Match data through the fault feature library. If a known fault is identified, an alarm is directly triggered and the results are uploaded. If the match fails, the original data and abnormal fragments are uploaded to the cloud platform.

[0195] 3. Cloud platform processing stage

[0196] 3.1 Intelligent Diagnosis: The fault decision tree unit analyzes immediate faults (such as calculating the probability of pipeline leakage), and the LSTM unit predicts the remaining life of components. The results of both are input into the alarm classification module to generate a response strategy.

[0197] 3.2 Predictive maintenance: Train the model based on historical data to predict the probability of component failure, locate the fault point in combination with the digital twin model, and call the maintenance knowledge graph to generate a maintenance plan.

[0198] 3.3 Secure Communication: Data is transmitted through SM4 encryption and dynamic keys to ensure secure communication between the cloud and edge nodes and authorized terminals.

[0199] 4. Maintenance execution phase: The remote maintenance interface pushes maintenance instructions (such as AR-assisted disassembly animation) to the authorized terminal, and uses blockchain technology to store maintenance work orders (operator, timestamp, maintenance result hash value) to achieve process traceability.

[0200] The above are only preferred embodiments of the present invention and do not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the description and illustrations of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent diagnosis and remote maintenance system for fire protection facilities based on the Internet of Things, characterized in that: include: Sensor modules are deployed at key nodes of fire protection facilities to collect real-time data on pressure, flow, valve status, power supply voltage, and ambient temperature and humidity; Edge computing nodes, connected to sensor modules, have built-in fault diagnosis models to perform local pre-processing of raw data and preliminary fault analysis; The cloud platform receives data uploaded by the edge computing node through the encrypted communication module, including: Intelligent diagnostic engine that integrates multi-dimensional data to generate facility health scores; Predictive maintenance module, which predicts component failure probability based on a time series model trained with historical data; Remote maintenance interface, pushes maintenance instructions to authorized terminals and receives feedback.

2. The system according to claim 1, wherein: The intelligent diagnosis engine includes: Fault decision tree unit, which determines the immediate fault type according to preset rules; LSTM neural network unit, which analyzes the temporal characteristics of sensor data and outputs the remaining life prediction value; The alarm classification module dynamically triggers the first-level sound and light alarm, the second-level SMS notification, and the third-level platform pop-up response strategy based on the severity of the fault.

3. The fire protection facility intelligent diagnosis and remote maintenance system based on the Internet of Things according to claim 1 is characterized in that: The edge computing node performs the following operations: Wavelet transform algorithm is used to filter out high-frequency noise in sensor data; When data anomalies are detected, the local diagnostic mode is activated and the preset fault feature library is called for pattern matching; If the match fails, upload the original data to the cloud platform and request in-depth analysis.

4. The fire protection facility intelligent diagnosis and remote maintenance system based on the Internet of Things according to claim 1 is characterized in that: The cloud platform also includes: Digital twin module, which builds a three-dimensional virtual model of fire protection facilities and maps the status of physical equipment in real time; Maintain knowledge graph, store component relationships and repair cases, and assist in generating maintenance plans.

5. The fire protection facility intelligent diagnosis and remote maintenance system based on the Internet of Things according to claim 1 is characterized in that: The sensor module comprises: Self-diagnostic unit, periodically detects sensor zero drift and sensitivity attenuation; Redundant communication interface supports RS-485 and LoRaWAN dual-channel data transmission, and automatically switches when a single channel fails.

6. The fire protection facility intelligent diagnosis and remote maintenance system based on the Internet of Things according to claim 1 is characterized in that: The remote maintenance interface implements: Maintenance work order notarization based on blockchain technology records the operator, timestamp and hash value of the maintenance result; AR-assisted maintenance function pushes device disassembly animations and operation instructions to mobile terminals.

7. The fire protection facility intelligent diagnosis and remote maintenance system based on the Internet of Things according to claim 1 is characterized in that: The encrypted communication module adopts: Lightweight national encryption algorithm SM4 encrypts transmitted data; Dynamic key distribution mechanism, the cloud platform generates a session key based on the device ID, which is valid for a single communication cycle.

8. The fire protection facility intelligent diagnosis and remote maintenance system based on the Internet of Things according to claim 2 is characterized in that: The training method of the LSTM neural network unit includes: The input layer receives normalized multi-source sensor time series data; The hidden layer weights key features through the attention mechanism; The output layer generates the probability of component failure, and when the probability is >85%, a preventive replacement recommendation is triggered.

9. The fire protection facility intelligent diagnosis and remote maintenance system based on the Internet of Things according to claim 1 is characterized in that: Also includes: Adaptive threshold adjustment module, dynamically corrects the alarm threshold according to the age of the facility: For equipment that has been in service for more than 5 years, the water pressure alarm threshold will be lowered by 10%; For equipment in highly corrosive environments, the power supply voltage fluctuation tolerance is improved by 15%.

10. The fire protection facility intelligent diagnosis and remote maintenance system based on the Internet of Things according to any one of claims 1 to 9, characterized in that: The edge computing nodes and the cloud platform use: Differential data transmission protocol, only uploading abnormal data fragments and complete data of the previous and subsequent related periods; The breakpoint resume mechanism caches data packets when the network is interrupted and gives priority to transmitting alarm information after recovery.

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