Fire-fighting monitor full life cycle management system based on scene adaptive early warning

Abstract

The application provides a fire-fighting monitor full-life cycle management system based on scene adaptive early warning, comprising a data acquisition module, an adaptive early warning module, a scene algorithm module, a full-life cycle management module and an output module, and aims to solve problems such as insufficient monitoring accuracy of the fire-fighting monitor, poor scene adaptability, rigid early warning, difficulty in upgrading old equipment and the like in a complex scene, and through multi-source sensing fusion, scene customized algorithm, reinforcement learning adaptive early warning and full-life cycle management, a data-driven full-link value closed loop is constructed, and the use unit, the production enterprise and the industry level are empowered, and the fire-fighting prevention and control safety and the industrial intelligent level are improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent fire protection technology, and in particular to a fire monitor full lifecycle management system based on scene adaptive early warning. Background Technology

[0002] With the development of IoT and AI technologies, fire protection equipment has been gradually upgraded, from traditional fire monitors that relied on manual inspections in the early days, to basic intelligent devices with single sensor monitoring, and now to advanced systems that pursue multi-data fusion and dynamic decision-making, realizing the transformation from passive response to proactive early warning. In recent years, the increasing complexity of industrial scenarios (such as high temperature, salt spray, and dust environments) and the large stock of old equipment have driven a surge in demand for equipment lifecycle management and scenario adaptability in the industry.

[0003] Existing technologies typically rely on single sensors or manual inspections, failing to comprehensively integrate data on the water cannon itself, the environment, and fire characteristics. Analysis is limited in scope and lacks customized algorithms for special environments such as high temperatures and salt spray, resulting in high equipment failure rates. Furthermore, fixed warning times cannot be dynamically adjusted, leading to high false alarm rates or delayed warnings. The data also fails to effectively empower user decision-making, manufacturing R&D, and industry standard setting, resulting in idle data value and hindering support for industrial upgrading and improved public safety governance.

[0004] Therefore, there is an urgent need in this field for a scenario-adaptive early warning-based fire monitor full lifecycle management system to solve the above problems. Summary of the Invention

[0005] This invention provides a fire monitor full lifecycle management system based on scene adaptive early warning, which aims to solve problems such as insufficient accuracy of fire monitor monitoring, poor scene adaptability, rigid early warning, and difficulty in upgrading old equipment in complex scenarios. It empowers users, manufacturers and industries to improve fire prevention and control safety and the level of industrial intelligence.

[0006] This invention provides a scene-adaptive early warning-based fire monitor full lifecycle management system, comprising:

[0007] The data acquisition module is used to collect data on the status of the fire monitor, environmental data, and fire characteristic data.

[0008] An adaptive early warning module, which is connected to the data acquisition module, is used to dynamically adjust the fault early warning time and fire early warning time based on a reinforcement learning model.

[0009] A scenario-based algorithm module, which is connected to the data acquisition module and the adaptive early warning module, is used to generate customized state assessment algorithms and risk prediction algorithms according to different application scenarios.

[0010] The full lifecycle management module, which is connected to the adaptive early warning module and the scenario-based algorithm module, is used to perform closed-loop management of the entire process of fire monitors from operation and maintenance to scrapping based on the collected data and algorithm output.

[0011] The output module, which is connected to the full lifecycle management module, is used to output monitoring information, early warning information, maintenance decision suggestions, and data value empowerment reports to the user terminal.

[0012] Compared with the prior art, the beneficial effects of this application are as follows:

[0013] 1. This invention dynamically adjusts the warning time through a reinforcement learning adaptive warning model, combined with a scenario-based customized algorithm, effectively reducing the false alarm and missed alarm rates, improving the timeliness of fault handling, and shortening the fire response time;

[0014] 2. This invention optimizes costs by managing the entire lifecycle, reducing excessive repairs and stockpiling through maintenance planning and spare parts inventory optimization, thereby lowering the operating costs for users.

[0015] 3. This invention constructs a closed loop of data value across the entire chain, providing manufacturing enterprises with product defect analysis and improvement suggestions, shortening the R&D cycle, and improving product quality and customer satisfaction;

[0016] 4. This invention can help upgrade the industry and improve public safety governance, provide quantitative data support for the revision of industry standards, promote the standardization of the fire protection industry, and improve emergency response capabilities and reduce public safety incidents through data sharing on the smart fire protection platform.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention, but do not constitute a limitation thereof; in the drawings:

[0019] Figure 1 This is a schematic diagram of the structure of a fire monitor full life cycle management system based on scene adaptive early warning provided by the present invention. Detailed Implementation

[0020] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0021] Example 1:

[0022] This invention provides a scene-adaptive early warning-based fire monitor full lifecycle management system. Please refer to [link / reference]. Figure 1 ,include:

[0023] The data acquisition module is used to collect data on the status of the fire monitor, environmental data, and fire characteristic data.

[0024] An adaptive early warning module, which is connected to the data acquisition module, is used to dynamically adjust the fault early warning time and fire early warning time based on a reinforcement learning model;

[0025] The scenario-based algorithm module, which is connected to the data acquisition module and the adaptive early warning module, is used to generate customized status assessment algorithms and risk prediction algorithms according to different application scenarios.

[0026] The full lifecycle management module, which connects with the adaptive early warning module and the scenario-based algorithm module, is used to perform closed-loop management of the entire process of fire monitors from operation and maintenance to decommissioning based on the collected data and algorithm output.

[0027] The output module, which connects to the full lifecycle management module, is used to output monitoring information, early warning information, maintenance decision suggestions, and data value empowerment reports to user terminals.

[0028] Specifically, this embodiment provides an intelligent fire monitor management system integrating multiple modules working collaboratively. Each module achieves full-process management through data transmission and interaction. The data acquisition module, as the data input source, collects data related to the fire monitor's own operational status, environmental data related to the deployment environment, and fire characteristic data related to the occurrence of a fire, providing basic data support for subsequent modules. The adaptive early warning module, based on a reinforcement learning model, receives input data from the data acquisition module and dynamically adjusts the early warning time for faults and fires through model calculations, solving the problem of rigidity in traditional early warning systems. The scenario-based algorithm module combines the environmental and equipment data from the data acquisition module with the data from the adaptive early warning module. The system generates customized status assessment and risk prediction algorithms for different application scenarios (such as high-temperature workshops, coastal salt spray areas, and dusty workshops) based on relevant early warning data, improving scenario adaptability. The full lifecycle management module integrates the early warning results from the adaptive early warning module and the assessment and prediction results from the scenario-based algorithm module to conduct closed-loop management of the entire lifecycle of fire monitors, from commissioning and daily maintenance to final scrapping, ensuring reliable operation of the equipment throughout the entire process. The output module outputs the management results of the full lifecycle management module to the user terminal in the form of monitoring information, early warning information, maintenance decision suggestions, and data value empowerment reports, providing decision-making basis for users, production enterprises, and industry management.

[0029] In one implementation, the data acquisition module includes:

[0030] The body status sensing unit is used to collect data on motor current, voltage, angle, valve status, and pipeline pressure of the fire monitor;

[0031] The environmental sensing unit is used to collect data on temperature, humidity, salt spray concentration, and dust concentration in the deployment area.

[0032] Fire feature sensing unit is used to collect infrared thermal imaging, visible light flame and smoke concentration data;

[0033] The data communication unit is used to transmit the collected multi-source data to the cloud platform.

[0034] Specifically, in this embodiment, the body status perception unit collects real-time data on motor current and voltage, water cannon spray angle, valve opening and closing status, and pipeline pressure through sensors deployed on key parts such as fire monitor motors, valves, and pipelines, comprehensively reflecting the equipment's own operating status. The environment perception unit collects data on temperature, humidity, salt spray concentration, and dust concentration in the fire monitor deployment area through environmental sensors, capturing the environmental factors affecting equipment operation. The fire feature perception unit collects infrared thermal imaging data, visible light flame image data, and smoke concentration data through devices such as infrared thermal imagers, visible light cameras, and smoke sensors, promptly capturing characteristic signals of fire occurrence. The data communication unit uses wired (e.g., Ethernet) or wireless (e.g., 5G, LoRa) communication methods to uniformly transmit the body status data, environmental data, and fire feature data collected by the above three perception units to the cloud platform, providing data support for subsequent module data analysis and processing, and ensuring the real-time performance and stability of data transmission.

[0035] In one implementation, the adaptive early warning module, the scenario-based algorithm module, the full lifecycle management module, and the output module are deployed on a cloud platform.

[0036] In one implementation, the adaptive early warning module includes:

[0037] The feature extraction unit is used to extract time-series features related to equipment failure and fire risk from the data collected by the data acquisition module.

[0038] The deep Q-network decision unit is used to take time-series characteristics and historical early warning feedback data as input, and calculate the optimal early warning time adjustment strategy through Q-value calculation.

[0039] The execution and feedback unit is used to execute the early warning time adjustment strategy and generate reward signals to feed back to the deep Q network decision unit based on the actual status of subsequent equipment or the occurrence of fire, so as to optimize the model parameters.

[0040] Specifically, in this embodiment, the feature extraction unit filters and extracts time-series features related to equipment failures (such as abnormal motor current, valve jamming, etc.) and fire risks (such as excessive smoke concentration, appearance of flame characteristics, etc.) from the multi-source data transmitted by the data acquisition module. These features include the trend of data change in the time dimension, abrupt change features, periodic features, etc., such as the increase of motor current in 10 consecutive minutes, the continuous change curve of smoke concentration, etc.

[0041] The calculation process of the decision unit in a Deep Q-Network (DQN) is as follows:

[0042] First, a state space is constructed, and the extracted time-series features (such as time-series vectors of parameters such as motor current, temperature, and smoke concentration) and historical early warning feedback data (such as whether historical early warnings are accurate and the deviation between the early warning time and the actual fault / fire occurrence time) are used as state inputs.

[0043] Then, the action space is constructed, and the actions include specific adjustment methods such as extending, shortening, and maintaining the current warning time;

[0044] The DQN model stores historical state-action-reward-next state data samples through its experience replay mechanism. The target network calculates the target Q value, and the current network calculates the predicted Q value. The difference between the target Q value and the predicted Q value is minimized through a loss function (such as the mean squared error loss function). The network parameters are iteratively updated, and finally, the optimal warning time adjustment strategy that maximizes the cumulative reward is output.

[0045] It should be noted that the DQN model is an existing mature model, and will not be described in detail in this embodiment;

[0046] After receiving the warning time adjustment strategy output by the DQN decision unit, the execution and feedback unit automatically executes the warning time adjustment operation, such as adjusting the original fault warning time from 1 month to 2 months. Simultaneously, it continuously monitors the actual operating status of subsequent equipment, such as whether a fault occurs, the time of the fault occurrence, or whether a fire actually occurs. Based on the monitoring results, it generates reward signals: if the warning adjustment accurately detects a fault / fire in advance, and the deviation between the warning time and the actual occurrence time is within a preset reasonable range (e.g., ±3 days), a positive reward is generated (e.g., reward value +10); if the warning is too early resulting in an invalid warning or too late resulting in a delayed warning, a negative reward is generated (e.g., reward value -5 for an early warning, reward value -8 for a late warning); if the warning time does not need adjustment and no fault / fire occurs, a neutral reward is generated (e.g., reward value 0).

[0047] The reward signal is fed back to the DQN decision-making unit, which further optimizes the model parameters based on the reward signal to improve the accuracy of subsequent early warning time adjustments.

[0048] In one implementation, the scenario-based algorithm module includes:

[0049] The scene recognition unit is used to identify the specific scene type where the fire monitor is located based on environmental data and area labels.

[0050] The algorithm model library stores customized algorithm models corresponding to different scenario types, including a long short-term memory network model for corrosion prediction and a regression model for thermal failure prediction in high-temperature environments.

[0051] The model scheduling unit is used to call the corresponding algorithm model from the algorithm model library for data analysis and prediction based on the identified scene type.

[0052] Specifically, in this embodiment, the area label of the scene recognition unit is obtained in two ways. First, when the system is deployed, the user manually enters the attributes of the actual deployment location of the fire monitor into the system, such as industrial high-temperature workshop, coastal port storage area, dust workshop, etc. Second, the system automatically obtains the planning use label of the deployment area by associating with external geographic information system (GIS) data, such as industrial area, storage area, coastal area, etc.

[0053] The scene recognition process employs a dual recognition logic combining environmental data feature matching and area label verification. First, it extracts key features from the environmental data collected by the environmental sensing unit, such as temperatures consistently exceeding 40℃ in high-temperature scenes and salt spray concentrations consistently exceeding 0.05 mg / cm³ in salt spray scenes. 2 Dust concentration in dusty environments consistently exceeds 10 mg / m³ 3 These features are matched with different scene environment feature templates preset by the system to obtain a preliminary scene type. Then, the preliminary scene type is verified by combining it with the area label. If the preliminary scene type matches the area label, the final scene type is determined. If they do not match, the system issues a prompt for the user to confirm or re-enter the area label to ensure the accuracy of scene recognition.

[0054] The customized algorithm models stored in the algorithm model library are all dedicated models trained based on historical data for corresponding scenarios. Among them, the Long Short-Term Memory (LSTM) network model used for erosion prediction has the following specific formula:

[0055] Forgotten Gate: ;

[0056] Input Gate: ;

[0057] Cell status update: ;

[0058] Output gate: ;

[0059] in, This inputs environmental corrosion-related data (such as salt spray concentration, humidity, etc.) at the current moment. This is the output of the hidden layer from the previous time step. This represents the cell state at the previous moment. Here is the weight matrix for the corresponding gate. This is the bias term for the corresponding gate. It is the sigmoid activation function. For element-wise multiplication, The hyperbolic tangent activation function is used. The output of the hidden layer at the current moment is used to predict the degree of equipment corrosion and remaining lifespan. It should be noted that the basic principle of this LSTM model is an existing mature principle, and the above formula is only an example and can be adjusted based on actual applications and technological developments.

[0060] The regression model used for predicting thermal failure in high-temperature environments employs a multiple linear regression model, with the specific formula as follows: ,in This refers to thermal failure risk values, such as the probability of thermal failure of a motor and the remaining heat-resistant operating time of the equipment. Key influencing factors in high-temperature environments, such as ambient temperature, equipment operating load, and heat dissipation structure status, are determined based on the theoretical values ​​provided by the data acquisition module and the equipment itself. For the intercept term, These are the weighting coefficients for each influencing factor. For the random error term, the model is trained using historical equipment thermal failure data under high-temperature conditions to obtain the weight coefficients, thereby predicting the risk of equipment thermal failure in high-temperature environments. It should be noted that all the above weight coefficients can be preset and optimized based on feedback. This process can be implemented using any conventional means, and this embodiment is not limited to any particular method.

[0061] After the scene recognition unit determines the specific scene type, the model scheduling unit automatically calls the corresponding customized algorithm model from the algorithm model library, inputs the equipment and environmental data of the scene transmitted by the data acquisition module into the model, performs data analysis and risk prediction, and outputs equipment status assessment results and risk prediction results adapted to the scene.

[0062] In one embodiment, an legacy equipment compatibility module is also included for non-destructive status sensing and data access of non-intelligent fire monitors; the legacy equipment compatibility module includes:

[0063] The physical adapter unit uses an arc-shaped snap-on bracket combined with an external sensor to be installed in a non-destructive manner at a designated location on a traditional fire monitor.

[0064] The protocol conversion unit is used to convert the analog signals or non-standard digital signals collected by the physical adapter unit into a standard data protocol format that the system can recognize.

[0065] In one implementation, the full lifecycle management module includes:

[0066] The operation monitoring unit is used to monitor the operating status of fire monitors in real time and record the operation log;

[0067] The maintenance decision unit generates preventative maintenance plans, spare parts replacement suggestions, and maintenance guidelines based on the outputs of the adaptive early warning module and the scenario-based algorithm module.

[0068] The cost optimization unit calculates the optimal maintenance cycle and procurement strategy by combining equipment maintenance records and spare parts inventory data.

[0069] The product optimization feedback unit is used to summarize equipment failure data and performance data to generate product defect analysis reports and improvement suggestion reports.

[0070] Specifically, in this embodiment, the real-time monitoring data of the fire monitor by the operation monitoring unit are all the aforementioned physical status data, including motor current, voltage, angle, valve status, and pipeline pressure data. At the same time, environmental data and fire characteristic data from the data acquisition module are combined to assist in judging the operating status. The recorded operation log includes the real-time acquisition values ​​of the above data, data acquisition timestamps, equipment operating mode, whether a warning / fault event has occurred, the time of the event and its handling, etc., forming a data archive of the entire equipment operation process, providing a basis for subsequent maintenance and optimization.

[0071] The maintenance decision unit receives the early warning time adjustment strategy and fault / fire early warning information output by the adaptive early warning module, as well as the equipment status assessment results and risk prediction results output by the scenario-based algorithm module. Combined with equipment operation logs, it comprehensively judges the potential fault risks and maintenance needs of the equipment. Specifically, it generates corresponding preventative maintenance plans based on the risk level (high, medium, low), such as a comprehensive overhaul of high-risk equipment within one month, a special inspection of medium-risk equipment within three months, and routine maintenance of low-risk equipment within six months. Based on the remaining life prediction results of key equipment components, such as the scenario-based algorithm module predicting a motor's remaining lifespan of three months, it generates spare parts replacement suggestions, such as purchasing the same model motor in advance and planning to replace it two months later. Simultaneously, based on the equipment fault type (such as valve jamming, motor overheating, etc.), it provides detailed maintenance guidelines, including maintenance steps, required tools, and precautions. It should be noted that the specific maintenance plans mentioned above can directly use industry standards or be set by the system administrator; this embodiment does not impose any limitations.

[0072] The cost optimization unit is implemented as follows: First, a maintenance cost model and an inventory cost model are established. The maintenance cost model covers single repair costs, maintenance downtime losses, and maintenance labor costs; the inventory cost model covers spare parts procurement costs, warehousing costs, spare parts backlog losses, and stockout losses. Then, combining equipment maintenance records (such as historical maintenance frequency, maintenance cycle, and maintenance costs) and spare parts inventory data (such as current spare parts inventory quantity, spare parts procurement cycle, and spare parts shelf life), a genetic algorithm is used to solve for the maintenance cycle and procurement strategy that minimizes the total cost (maintenance cost + inventory cost). For example, the algorithm calculates that the total cost is lowest when the maintenance cycle for a certain type of spare part is 4 months and the procurement quantity is 5 pieces each time. Based on this, the optimal maintenance cycle and procurement strategy are output.

[0073] The product optimization feedback unit is implemented as follows: First, it collects fault data (including fault type, fault occurrence time, environmental data at the time of fault occurrence, equipment runtime, fault handling method and result) and performance data (including equipment operating parameter compliance rate, response time, service life, and performance comparison data with similar equipment) of all fire monitors connected to the system. Then, it uses statistical analysis methods (such as fault frequency statistics, fault cause correlation analysis, and performance parameter distribution analysis) to locate product weaknesses. For example, statistics show that rust failure of solenoid valves accounts for 35% in humid areas of southern China, indicating that the solenoid valve material is a product weakness. Finally, it combines the environmental impact analysis results of the scenario-based algorithm module to form a product defect analysis report, clarifying the defect location, cause, scope and degree of defect impact, and making targeted improvement suggestions (such as changing the solenoid valve material from ordinary carbon steel to 304 stainless steel), forming an improvement suggestion report.

[0074] In one implementation, the output module includes:

[0075] The unit's decision support module is used to generate regional risk heat maps, operational cost optimization reports, and scenario-based fire emergency plans.

[0076] The R&D optimization unit of the manufacturing enterprise is used to generate product defect analysis reports, material and structural improvement suggestions, and supply chain optimization suggestions.

[0077] The industry-level empowerment unit is used to generate industry trend white papers and provide quantitative data support for the revision of industry standards and the construction of smart fire protection platforms.

[0078] Specifically, the output module in this embodiment empowers value at different levels through three functional units. The user unit's decision support unit, based on the operation monitoring data, maintenance decision results, and cost optimization data from the full lifecycle management module, uses heatmap generation technology to map the failure rate and fire risk prediction values ​​of each fire monitor within the area onto geographic space, generating a regional risk heatmap. Red areas represent high-risk areas, yellow represents medium-risk areas, and green represents low-risk areas, clearly identifying key prevention and control areas for the user unit. The output module also summarizes data such as equipment maintenance costs, spare parts procurement costs, and operating loss costs, and combines this with the optimal cost solution from the cost optimization unit to generate an operating cost optimization report, clarifying the current cost structure, optimizable aspects, and expected optimization effects. Finally, the output module, combined with the scenario type identified by the scenario algorithm module (such as high-temperature workshops or salt spray areas) and the environmental data and risk prediction results under that scenario, customizes scenario-based fire prevention plans, including fire warning thresholds, fire extinguishing response procedures, equipment operation specifications, and personnel evacuation routes.

[0079] The R&D optimization unit of the manufacturing enterprise receives product defect analysis reports and improvement suggestion reports from the product optimization feedback unit. Combining equipment operation data from multiple scenarios, such as integrating equipment failure data in different scenarios, it generates product defect analysis reports, clarifying the defect distribution, defect incidence rate, and defect influencing factors of different product models in various scenarios. Based on defect cause analysis (such as corrosion defects caused by insufficient material corrosion resistance, and thermal failure defects caused by unreasonable heat dissipation structure), it provides specific material selection suggestions (such as using 304 stainless steel for equipment in salt spray areas) and structural improvement suggestions (such as optimizing the heat dissipation duct design for equipment in high-temperature environments). According to spare parts consumption data (such as high monthly average consumption of a certain type of spare parts and serious inventory backlog), it analyzes the spare parts demand patterns and provides supply chain optimization suggestions for the manufacturing enterprise to adjust production plans and optimize supply chain procurement and inventory management (such as increasing the production batch of high-frequency consumable spare parts and reducing the production of backlogged spare parts).

[0080] At the industry level, the empowerment unit integrates multi-scenario fire monitor operation data (including equipment operation safety indicators, failure rates, and scenario adaptability data) from systems connected to the industry. It employs data analysis methods (such as trend analysis, comparative analysis, and cluster analysis) to summarize the current state of industry development, technological bottlenecks, and future trends, generating an industry trend white paper. It also extracts key quantitative data (such as fire response time, equipment lifespan prediction accuracy, and failure rates) to provide data support for revising industry standards (e.g., revising "fire response time ≤ 10s" in the original standard to "≤ 5s"). Through data sharing interfaces, it transmits equipment operation data, risk warning data, and maintenance management data to the industry-level smart fire protection platform, providing underlying data support for the platform's advanced functions such as situational awareness and collaborative command, thereby helping to improve the level of public safety governance.

[0081] In one implementation, the process by which the maintenance decision-making unit generates a preventative maintenance plan employs a dynamic risk quantification model. This model calculates the maintenance urgency index through the following steps. :

[0082] S1, Calculate intrinsic fault risk :

[0083] ;

[0084] In the formula, For the first The actual values ​​of the item's state parameters. for The preset threshold, and for The historical maximum and minimum values, for The weights, and satisfying ; The preset fault observation period, Historical observation period Inner The number of times this type of failure occurred. Historical factors influencing failures;

[0085] S2, Calculate scene erosion risk :

[0086] ;

[0087] In the formula, This represents the current concentration of corrosive substances in the environment. This serves as a baseline concentration for corrosive substances in the environment. This is the critical harmful concentration. This is the concentration nonlinearity influence coefficient; For ambient temperature, For reference temperature, The coefficient representing the synergistic effect of temperature.

[0088] S3, Calculate operating cost risk :

[0089] ;

[0090] In the formula, This represents the average cost per maintenance for similar fire monitor equipment. The net asset value of the equipment represents its remaining economic value at the current point in time. To estimate downtime due to maintenance, The maximum allowable downtime, This is the maintenance cost burden coefficient. This is the downtime impact coefficient;

[0091] S4, Calculate the comprehensive maintenance urgency index :

[0092] ;

[0093] In the formula, Represents a risk fusion operator based on scenario type. These are the normalized benchmark values ​​for each risk dimension. These are risk sensitivity indices for various dimensions, and their values ​​are dynamically configured by the scenario-based algorithm module according to the identified scenarios.

[0094] The maintenance decision unit will calculate The value is compared with multiple preset level thresholds to trigger maintenance task instructions of different levels, and these instructions are used as the basis for the output module to generate maintenance guidelines.

[0095] Specifically, regarding intrinsic failure risk The calculation, where Data such as motor current, voltage, angle, valve status, and pipeline pressure are collected from the body status sensing unit; The system presets safety thresholds for various parameters based on the equipment's factory technical specifications, industry standards, and actual operating experience. For example, the motor's rated current is 12A, and the preset threshold is 14A. and This refers to the maximum and minimum values ​​of each parameter stored in the system during the historical operation of the device. The weights of each parameter determined based on the analytic hierarchy process (AHP) are as follows: for example, motor current has the greatest impact on faults, so its weight is set to 0.3; pipeline pressure has a weight of 0.2; valve status has a weight of 0.2; motor voltage has a weight of 0.15; and angle has a weight of 0.15. This satisfies the following conditions: ; The fault observation period preset for the system is usually set to 6 months or 1 year, which can be adjusted according to the service life of the equipment and the risk of the scenario. In order to be in During the period, the system records the number of times various equipment faults (such as motor faults, valve faults, pipeline faults, etc.) occur; This is the historical impact factor of faults, with a value ranging from 0.5 to 1.5. The longer the equipment has been in use, the more historical faults it has. The larger the value, the more automatically it will be set by the system based on the equipment's operating years and fault history.

[0096] In the formula, This is used to normalize the actual value of the i-th ontology state parameter, obtaining the relative degree to which the parameter deviates from the threshold. Approaching or exceeding The larger this value, the higher the failure risk corresponding to this parameter; multiplied by the weight Then, it reflects the contribution of different parameters to the failure risk; This represents the comprehensive fault risk value of the current system state parameters; The historical failure frequency multiplied by the historical failure impact factor. Then, it reflects the impact of historical failures on the risk of current failures.

[0097] Intrinsic Fault Risk By combining the current status of the equipment and historical failure information, the failure risk of the equipment itself is quantified. The higher the value, the higher the failure risk.

[0098] Risk of scene erosion The calculation, where The current concentration of corrosive substances in the environment, such as salt spray concentration and the corrosive equivalent concentration corresponding to humidity, is collected by the environmental sensing unit. This serves as the baseline safe concentration for corrosive substances in such scenarios, such as a preset salt spray concentration baseline value of 0.05 mg / cm³. 2 Determined based on industry standards or equipment manuals; This refers to the critical concentration at which corrosive substances can damage equipment, such as a critical salt spray concentration of 0.15 mg / cm³. 2 It is set according to the corrosion resistance test data of the equipment materials and industry standards; The concentration nonlinearity influence coefficient is preferably set between 1.2 and 2.0. It is obtained by fitting experimental data and reflects the nonlinear increase in risk after the concentration of corrosive substances exceeds the baseline value. The current ambient temperature is collected by the environmental sensing unit. For reference temperature, it is usually set to 25℃; The temperature synergistic effect coefficient is preferably set between 0.3 and 0.8, determined by experimental data, reflecting the synergistic effect of increased temperature accelerating the corrosion process and increasing the risk of erosion.

[0099] In the formula This represents the relative deviation of corrosive substance concentration, reflecting the degree to which the current concentration deviates from the baseline concentration, multiplied by... After exponentiation, the nonlinear effect of concentration on erosion risk is reflected; This reflects the impact of the deviation between the current temperature and the reference temperature on erosion; the higher the temperature, the larger this value, multiplied by the temperature synergistic effect coefficient. Subsequently, the synergistic contribution of temperature to erosion risk was demonstrated; This is a temperature-dependent influencing factor used to correct for the erosion risk corresponding to the concentration.

[0100] Scene erosion risk This value is used to quantify the risk of environmental corrosive factors to equipment. The higher the value, the higher the risk of environmental corrosion to the equipment.

[0101] Regarding operational cost risks The calculation, where The average single maintenance cost of similar fire monitor equipment under the same service life and the same scenario is calculated by aggregating maintenance records of a large number of equipment. The current net asset value of the equipment is calculated as follows: ,in The original purchase value of the equipment. This is the accumulated depreciation amount. (Cumulative failure loss amount). This is the estimated downtime for maintenance based on the type of equipment failure, the complexity of maintenance, and historical maintenance data. The maximum allowable downtime set by the user based on production and operation needs; The maintenance cost burden coefficient is preferably set between 0.4 and 0.6. The downtime impact coefficient is preferably set between 0.4 and 0.6, and the sum of the two is 1. It can be adjusted according to the user's sensitivity to cost and downtime.

[0102] In the formula, The relative cost-bearing ratio of maintenance reflects the proportion of a single maintenance cost to the remaining value of the equipment. The higher the ratio, the lower the economic efficiency of maintenance and the higher the risk of operating costs. The ratio of downtime reflects the impact of maintenance downtime on the user's operations. The higher the ratio, the greater the operational losses and the higher the operational cost risk. Multiplying each ratio by the corresponding coefficients shows the contribution of maintenance costs and downtime to operational cost risk.

[0103] Operating cost risk This value is used to quantify the operational cost burden risk associated with equipment maintenance; the higher the value, the greater the operational cost risk.

[0104] For the comprehensive maintenance urgency index The calculation, where These are the normalized benchmark values ​​for each risk dimension, determined based on statistical analysis of risk data from a large number of devices within the industry. Set to 3, Set to 2, Set to 1; The risk sensitivity index for each dimension is preferably set between 0.8 and 1.2, and is dynamically configured by the scenario-based algorithm module according to the identified scenario. For example, in a high-risk fire scenario, A value of 1.2 increases the weight of intrinsic failure risk; in corrosive environments, A value of 1.2 increases the weight of scene erosion risk; in cost-sensitive areas, A value of 1.2 increases the weight of operating cost risk.

[0105] In the formula These are the normalized weighted values ​​for each risk dimension. The units of measurement for each risk dimension are unified by dividing by the normalized benchmark value. Then, the sensitivity of each risk dimension is adjusted by index calculation. This is a risk fusion operator based on scenario type, enabling the reasonable fusion of risks from various dimensions.

[0106] Comprehensive Maintenance Urgency Index This system integrates intrinsic failure risk, scenario erosion risk, and operational cost risk to quantify the urgency of equipment maintenance; it presets multiple threshold levels, for example, ≥4 is a high level, 2≤ <4 indicates a medium level. <2 indicates a low level; maintenance decision-making unit based on The level of maintenance triggers different levels of maintenance task instructions: high-level maintenance is arranged immediately, medium-level maintenance is arranged within a specified time, and low-level maintenance is carried out according to the regular maintenance plan. This level serves as the core basis for the output module to generate maintenance guidelines.

[0107] In one implementation, the risk fusion operator The calculation logic is as follows:

[0108] When the scenario type is a high-risk fire area ;

[0109] When the scene type is a corrosive environment, ;

[0110] When the scenario type is a cost-sensitive area .

[0111] Specifically, in this embodiment, the risk fusion operator Different fusion logics are adopted based on different scenario types to ensure the comprehensive maintenance urgency index. It can accurately reflect the core risk requirements in the corresponding scenario; when the scenario type is a high-risk fire area (such as a flammable and explosive material storage area, chemical workshop), equipment failure may directly cause a serious fire accident. Therefore, the maximum value fusion logic is adopted, taking the highest value among intrinsic failure risk, scenario erosion risk, and operating cost risk as the maximum value. Prioritize the most prominent risks to ensure timely identification and resolution of key hidden dangers. When the scenario type is a corrosive environment (such as coastal salt spray areas or humid industrial areas), environmental erosion is the main cause of equipment failure. At the same time, equipment failure and operating costs must also be considered. Therefore, a weighted summation fusion logic is adopted, assigning the highest weight of 0.5 to intrinsic failure risk, 0.3 to scenario erosion risk, and 0.2 to operating cost risk. This approach focuses on both equipment failure risk and the impact of environmental erosion. When the scenario type is a cost-sensitive area (such as ordinary warehouses or small processing plants), operating cost control is a core requirement. Therefore, a weighted summation fusion logic is adopted, assigning the highest weight of 0.5 to operating cost risk, 0.3 to intrinsic failure risk, and 0.2 to scenario erosion risk. This approach prioritizes controlling maintenance and operating costs while ensuring basic equipment operational safety. Through scenario-based risk fusion logic, The calculations are more aligned with the needs of real-world application scenarios, improving the accuracy of maintenance decisions.

[0112] In one implementation, the output module further includes:

[0113] For decision support units that use the unit, when When the level is high, a list of key equipment for monitoring and suggestions for activating emergency plans are generated; when When the level is medium, generate maintenance cycle optimization plans and spare parts inventory adjustment suggestions; when When the level is low, a standard operating cost analysis report is generated;

[0114] For the R&D optimization unit of a manufacturing enterprise, it is necessary to regularly analyze and optimize different scenarios with high performance. The common characteristic parameters of the equipment cluster are associated with the product model, and the defect distribution map is output according to the scenario and failure mode. The common characteristic parameters are: , The constructed feature vector; and based on The calculation formula is reversed to provide a quantitative indicator of the improvement in material corrosion resistance;

[0115] For industry-level enabling units, based on a large number of devices The spatiotemporal statistical analysis of the index outputs a map reflecting the overall health status of fire-fighting equipment in different regions and seasons, and provides statistical threshold suggestions for revising industry standards. These statistical threshold suggestions are correlated with... The values ​​of the index at different percentiles.

[0116] Specifically, for ease of understanding, this embodiment provides a concrete example:

[0117] 1. Example of output from a unit decision support module:

[0118] (1) When For higher levels (such as) When =4.5): An example of a key equipment monitoring list is as follows:

[0119] "Equipment No.: XF-2023-001, Equipment Model: PSKD20, Deployment Location: Flammable and Explosive Materials Storage Area, Zone A, High Risk Reason: Intrinsic Failure Risk" =4.2 (motor current remains close to the threshold), risk of scene erosion. =2.8 (salt spray concentration exceeds the standard), requiring real-time monitoring of motor current, voltage, and valve status every 2 hours;

[0120] An example of a suggested emergency response plan activation is as follows:

[0121] "Immediately activate the Level A emergency plan, arrange professional maintenance personnel to conduct a comprehensive overhaul of the equipment within 24 hours, activate the backup fire monitor during the overhaul to ensure that there are no blind spots in the fire protection coverage of Area A; if a fire warning is issued, immediately cut off the power supply to Area A, start the sprinkler system, and organize personnel to evacuate along the preset evacuation routes."

[0122] (2) When Medium level (e.g.) When =3.0):

[0123] An example of a maintenance cycle optimization scheme is as follows:

[0124] "Equipment No.: XF-2023-005, original maintenance cycle was 6 months, based on the current..." Based on the risk and severity level analysis, it is recommended to adjust the maintenance cycle to 4 months, focusing on overhauling the motor cooling system and valve sealing performance, which is expected to reduce the failure rate by 30%.

[0125] Example of spare parts inventory adjustment suggestion:

[0126] "Currently, there are 2 spare motors for this equipment in stock. Based on the maintenance cycle optimization and equipment operation status forecast, 1 motor needs to be replaced in the next 6 months. It is recommended to replenish the stock of 1 motor, maintain the stock of 3 motors, avoid downtime losses due to stockouts, and reduce inventory backlog."

[0127] (3) When For lower levels (e.g.) When =1.5):

[0128] A sample of a standard operating cost analysis report is as follows:

[0129] "This quarter, all 10 fire monitors in the area were at a low maintenance urgency level, with a total operating cost of 80,000 yuan, including 20,000 yuan for maintenance, 30,000 yuan for spare parts procurement, and 30,000 yuan for energy consumption. Compared with the previous quarter, the maintenance cost decreased by 15%, mainly due to a reduction in equipment failures. It is recommended to continue with the current maintenance cycle and procurement strategy. It is expected that the operating cost in the next quarter can be controlled within the range of 75,000 to 85,000 yuan."

[0130] 2. Example of output from the R&D optimization unit of a manufacturing enterprise:

[0131] (1) Example of defect distribution map by scenario and failure mode: Taking the operating data of a certain type of fire monitor in the coastal salt fog area and the southern humid area as an example, the defect distribution map uses the scenario as the horizontal axis and the failure mode (solenoid valve corrosion, motor overheating, valve jamming) as the vertical axis, and uses different shades of color to represent the defect incidence rate; The figure shows that the defect incidence rate of solenoid valve corrosion in the coastal salt fog area is 32%, the defect incidence rate of solenoid valve corrosion in the southern humid area is 28%, and the incidence rate of other failure modes is less than 10%, which clearly shows that solenoid valve corrosion is the main defect of this type of equipment in high humidity and high corrosion scenarios.

[0132] (2) Example of quantitative indicators for improving material corrosion resistance: Based on the calculation formula of scenario erosion risk Re, it is deduced that a certain type of equipment in an environment with a salt spray concentration of 0.12 mg / cm², =2.5, corresponding to a solenoid valve corrosion rate of 30%; if you want to To reduce the salt spray corrosion resistance to below 1.0, the material's resistance to salt spray corrosion needs to be improved. Specifically, the quantitative indicator is that the salt spray resistance of the solenoid valve material needs to be increased from the current 0.1 mg / cm³. 2 Increased to 0.2 mg / cm³ 2 The above requirements can be met by using 304 stainless steel, and it is expected that the corrosion rate of the solenoid valve can be reduced to below 5%.

[0133] 3. Industry-level empowerment unit output example:

[0134] (1) Example of overall health status chart of fire protection equipment: taking each region as a geographical unit, the fire protection equipment of each province is statistically analyzed on a quarterly basis. The average value of the index is used, and the health level is represented by different colors on the trend chart (green: <2, Healthy; Yellow: 2≤ <4, General; Red: ≥4, unhealthy); the figure shows that in East China and South China, due to the high temperature and humidity environment in summer, the equipment health level in some provinces is yellow, while in North China and Northwest China, the equipment health level is mostly green; the distribution pattern of equipment health level in different regions and seasons is clarified, providing a basis for industry resource allocation.

[0135] (2) Example of statistical threshold recommendations for industry standard revision: based on 100,000 fire monitors in the industry. Index statistics The 90th percentile of the index is 3.8, and the 80th percentile is 3.0. It is recommended that when revising the "Safety Specification for the Operation of Intelligent Fire Monitors," the "Equipment Maintenance Urgency Warning Threshold" be set to 3.0, meaning that when the equipment... When the value is ≥3.0, special maintenance is required. This threshold covers 80% of high-risk equipment, which can effectively reduce the overall failure rate of the industry and improve the safety level of equipment operation.

[0136] It should be noted that the above report is only an example, and different templates can be customized for different scenarios. This embodiment does not impose any limitations and mainly reflects... Its uses.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A scene-adaptive early warning-based fire monitor full lifecycle management system, characterized in that, include: The data acquisition module is used to collect data on the status of the fire monitor, environmental data, and fire characteristic data. An adaptive early warning module, which is connected to the data acquisition module, is used to dynamically adjust the fault early warning time and fire early warning time based on a reinforcement learning model. A scenario-based algorithm module, which is connected to the data acquisition module and the adaptive early warning module, is used to generate customized state assessment algorithms and risk prediction algorithms according to different application scenarios. The full lifecycle management module, which is connected to the adaptive early warning module and the scenario-based algorithm module, is used to perform closed-loop management of the entire process of fire monitors from operation and maintenance to scrapping based on the collected data and algorithm output. The full lifecycle management module includes: an operation monitoring unit for real-time monitoring of the fire monitor's operating status and recording operation logs; a maintenance decision unit for generating preventative maintenance plans, spare parts replacement suggestions, and maintenance guidelines based on the outputs of the adaptive early warning module and the scenario-based algorithm module; a cost optimization unit for calculating the optimal maintenance cycle and procurement strategy by combining equipment maintenance records and spare parts inventory data; and a product optimization feedback unit for summarizing equipment fault data and performance data to generate product defect analysis reports and improvement suggestion reports. The process by which the maintenance decision-making unit generates a preventative maintenance plan employs a dynamic risk quantification model. This model calculates the maintenance urgency index through the following steps. ; S1, Calculate intrinsic fault risk ; In the formula, For the first The actual values ​​of the item's state parameters. for The preset threshold, and for The historical maximum and minimum values, for The weights, and satisfying ; The preset fault observation period, Historical observation period Inner The number of times this type of failure occurred. Historical factors influencing failures; S2, Calculate scene erosion risk : In the formula, This represents the current concentration of corrosive substances in the environment. This serves as a baseline concentration for corrosive substances in the environment. This is the critical harmful concentration. This is the concentration nonlinearity influence coefficient; For ambient temperature, For reference temperature, The coefficient representing the synergistic effect of temperature. S3, Calculate operating cost risk : In the formula, The average cost per maintenance for similar fire monitor equipment. The net asset value of the equipment represents its remaining economic value at the current point in time. To estimate downtime due to maintenance, The maximum allowable downtime, This is the maintenance cost burden coefficient. This is the downtime impact coefficient; S4, Calculate the comprehensive maintenance urgency index : In the formula, Represents a risk fusion operator based on scenario type. These are the normalized benchmark values ​​for each risk dimension. The risk sensitivity index is defined for each dimension, and its value is dynamically configured by the scenario-based algorithm module according to the identified scenario. The maintenance decision unit will calculate The value is compared with multiple preset level thresholds to trigger maintenance task instructions of different levels, and these instructions are used as the basis for the output module to generate maintenance guidelines. The output module, which is connected to the full lifecycle management module, is used to output monitoring information, early warning information, maintenance decision suggestions, and data value empowerment reports to the user terminal.

2. The fire monitor lifecycle management system based on scene adaptive early warning as described in claim 1, characterized in that, The data acquisition module includes: The body status sensing unit is used to collect data on motor current, voltage, angle, valve status, and pipeline pressure of the fire monitor; The environmental sensing unit is used to collect data on temperature, humidity, salt spray concentration, and dust concentration in the deployment area. Fire feature sensing unit is used to collect infrared thermal imaging, visible light flame and smoke concentration data; The data communication unit is used to transmit the collected multi-source data to the cloud platform.

3. The fire monitor full lifecycle management system based on scene adaptive early warning as described in claim 1, characterized in that, The adaptive early warning module includes: The feature extraction unit is used to extract time-series features related to equipment failure and fire risk from the data collected by the data acquisition module. The deep Q-network decision unit is used to take the time-series features and historical early warning feedback data as input, and calculate the optimal early warning time adjustment strategy through Q value; The execution and feedback unit is used to execute the warning time adjustment strategy and generate a reward signal based on the actual status of the equipment or the occurrence of a fire, and feed it back to the deep Q network decision unit to optimize the model parameters.

4. A fire monitor lifecycle management system based on scene adaptive early warning as described in claim 1, characterized in that, The scenario-based algorithm module includes: The scene recognition unit is used to identify the specific scene type where the fire monitor is located based on environmental data and area labels. The algorithm model library stores customized algorithm models corresponding to different scenario types, including a long short-term memory network model for corrosion prediction and a regression model for thermal failure prediction in high-temperature environments. The model scheduling unit is used to call the corresponding algorithm model from the algorithm model library to perform data analysis and prediction based on the identified scene type.

5. A fire monitor lifecycle management system based on scene adaptive early warning as described in claim 1, characterized in that, It also includes an old equipment compatibility module for non-destructive status sensing and data access of non-intelligent fire monitors; the old equipment compatibility module includes: The physical adapter unit uses an arc-shaped snap-on bracket combined with an external sensor to be installed in a non-destructive manner at a designated location on a traditional fire monitor. The protocol conversion unit is used to convert the analog signals or non-standard digital signals collected by the physical adapter unit into a standard data protocol format that the system can recognize.

6. A fire monitor lifecycle management system based on scene adaptive early warning as described in claim 1, characterized in that, The output module includes: The unit's decision support module is used to generate regional risk heat maps, operational cost optimization reports, and scenario-based fire emergency plans. The R&D optimization unit of the manufacturing enterprise is used to generate product defect analysis reports, material and structure improvement suggestions, and supply chain optimization suggestions. The industry-level empowerment unit is used to generate industry trend white papers and provide quantitative data support for the revision of industry standards and the construction of smart fire protection platforms.

7. A fire monitor lifecycle management system based on scene adaptive early warning as described in claim 1, characterized in that, The risk fusion operator The calculation logic is as follows: When the scenario type is a high-risk fire area ; When the scene type is a corrosive environment, ; When the scenario type is a cost-sensitive area .

8. A fire monitor lifecycle management system based on scene adaptive early warning as described in claim 6, characterized in that, The output module also includes: For the aforementioned user unit decision support unit, when When the level is high, a list of key equipment for monitoring and suggestions for activating emergency plans are generated; when When the level is medium, generate maintenance cycle optimization plans and spare parts inventory adjustment suggestions; when When the level is low, a standard operating cost analysis report is generated; For the R&D optimization unit of the aforementioned manufacturing enterprise, it is necessary to periodically optimize the R&D of products under different scenarios that have high performance. The common characteristic parameters of the equipment cluster are associated with the product model, and a defect distribution map is output according to the scenario and failure mode. The common characteristic parameters are: The constructed feature vector; and based on The calculation formula is reversed to provide a quantitative indicator of the improvement in material corrosion resistance; For the aforementioned industry-level enabling unit, based on a large number of devices The spatiotemporal statistical analysis of the index outputs a map reflecting the overall health status of fire-fighting equipment in different regions and seasons, and provides statistical threshold suggestions for revising industry standards. These statistical threshold suggestions are correlated with... The values ​​of the index at different percentiles.

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