Building fire safety multi-dimensional dynamic evaluation method based on internet of things

Abstract

This application relates to the technical field of fire safety assessment, and discloses a multi-dimensional dynamic assessment method for building fire safety based on the Internet of Things (IoT). Addressing the shortcomings of traditional assessment methods, which rely on manual inspections, cannot capture dynamic risks, and have limited dimensions, this application continuously collects event stream data generated by fire IoT sensors. It extracts characteristic indicators from these data across three dimensions: the operational status of fire protection facilities, maintenance compliance, and the standardization of fire safety management. Dimensional assessment values ​​are calculated for each dimension, followed by safety baseline constraint judgment, weighted aggregation, and correction of the building's inherent risk coefficient. The resulting comprehensive assessment value and differentiated rectification strategies are then dynamically updated according to a preset cycle. This method can automatically identify silent fault detectors, track the degradation trend of linkage links, and accurately determine the physical state of control valves through cross-subsystem verification. Simultaneously, it avoids masking weak points with a single comprehensive score, thereby achieving continuous, multi-dimensional, and quantitative assessment of fire safety.

Description

Technical Field

[0001] This application relates to the technical field of fire safety assessment, and in particular to a multi-dimensional dynamic assessment method for building fire safety based on the Internet of Things. Background Technology

[0002] In recent years, with the acceleration of urbanization, the fire risk of various buildings, including high-rise buildings, large commercial complexes, underground spaces, and older residential areas, has increased significantly. Building fire safety management has become a core concern in the field of public safety. Traditional building fire safety assessment methods mainly rely on periodic manual inspections and static testing of fire protection facilities, such as monthly checks of fire extinguisher pressure and quarterly tests of fire pumps. These methods have the following inherent drawbacks: First, the assessment frequency is low, making it impossible to capture dynamic risks. Fire risks are affected in real time by multiple factors such as equipment aging, personnel activities, and environmental changes (such as temperature and humidity), while the manual inspection cycle is usually several days to several months, and sudden changes in risk between two inspections cannot be detected in a timely manner.

[0003] Second, there is a serious problem of data silos and a lack of multi-dimensional integration. Existing fire protection systems (fire alarm systems, gas extinguishing systems, evacuation guidance systems, etc.) operate independently, and their data is not interconnected, making it impossible to comprehensively assess the safety level from multiple dimensions such as equipment status, environmental parameters, personnel behavior, and spatial structure.

[0004] Third, it relies on human experience and lacks quantitative models. Even when data is available, it is often based on subjective judgment by on-duty personnel, lacking scientific quantitative indicators and dynamic weighting mechanisms, which can easily lead to misjudgments or omissions.

[0005] Fourth, the response is delayed and proactive warnings are not possible. Traditional methods only take action after a fire alarm occurs or equipment malfunctions are discovered, failing to issue warnings and coordinate control measures during periods of escalating risk.

[0006] While some research has attempted to apply IoT technology to fire protection, most studies remain focused on the remote transmission of single parameters, such as transmitting smoke alarm signals alone, without constructing a truly multi-dimensional dynamic assessment model. Therefore, how to utilize IoT to collect multi-source heterogeneous data in real time and integrate it to establish a dynamic quantitative assessment method for building fire safety is a pressing technical problem that needs to be solved in this field. Summary of the Invention

[0007] To address the technical problems of existing technologies, such as single evaluation dimensions, poor real-time performance, lack of dynamic weights and early warning capabilities, this application provides a multi-dimensional dynamic evaluation method for building fire safety based on the Internet of Things.

[0008] Firstly, this application provides a multi-dimensional dynamic assessment method for building fire safety based on the Internet of Things, employing the following technical solution: A multi-dimensional dynamic assessment method for building fire safety based on the Internet of Things, the method comprising: S1. Collect event stream data generated by fire protection IoT sensors deployed in the building, and perform timestamp calibration, deduplication, and classification labeling on the event stream data; S2. Extract feature indicators of the fire protection facility operation status dimension from the event stream data, including: detector operation health assessment indicators, silent failure identification indicators, linkage link effectiveness assessment indicators, water supply system operation status assessment indicators, and control valve status cross-verification indicators. S3. Extract feature indicators of fire protection facility maintenance dimension from the event stream data, including: detector function test completion rate, manual alarm button test completion rate, linkage control function test completion rate, fire pump start test completion rate, and end test valve and alarm valve water discharge test coverage rate. S4. Extract characteristic indicators of fire safety management dimension from the event flow data and management records, including: operational behavior standardization assessment indicators, alarm information processing timeliness indicators, fire inspection compliance indicators, and fire responsibility implementation and record improvement indicators; S5. Calculate the dimension evaluation values ​​for the dimensions of fire protection facility operation status, maintenance compliance, and fire safety management standardization, and based on the dimension evaluation values, make a safety bottom line constraint judgment, calculate the comprehensive evaluation value, and output differentiated rectification strategies. S6. Automatically execute S1 to S5 according to the preset cycle, dynamically update the evaluation values ​​of each dimension and the comprehensive evaluation value, and track the changing trend of the building's fire safety status.

[0009] Optionally, the following steps may be included before step S5: Risk correction is performed on the assessment results based on the building's inherent characteristics. This risk correction includes determining the building's functional risk coefficient based on its intended use. And determine the risk factor for the configuration of the building's fire protection system based on the type of fire protection system actually configured in the building. .

[0010] Optionally, the detector's operational health assessment index is the degradation score value corresponding to each detector, and the degradation score value is obtained in the following way: The specific expression is: ; in, Let be the degradation score of the detector, with a value ranging from [0, 1]. Let be the alarm frequency of the i-th detector during the evaluation period, i.e., the number of alarms divided by the period duration. This is an environmental factor correction factor, preset according to season and spatial location, with a value range of 0.5 to 1.5. This is the highest alarm frequency value among all detectors.

[0011] Optionally, the silent failure identification index is a silent failure flag bit corresponding to each detector, and the value of the silent failure flag bit is either yes or no; The criteria for determining the flag to be "yes" are: the detector's online duration exceeds the preset threshold, no valid alarm events are generated within the evaluation period, and it is not a device that generates test signals during maintenance testing, is not a device in a known shielded state, or is not a device whose installation location does not meet the triggering conditions. The effectiveness evaluation indicators for the linkage link include the linkage success rate and the degradation warning flag. The linkage success rate is obtained by a per-event time-series pairing method: within a preset time window after each triggered event, it is searched to see if there is a corresponding linkage feedback event, and the ratio of the number of successful linkages to the total number of triggered events is calculated. Optionally, the degradation warning flag is set to a warning when the linkage success rate is lower than a preset threshold or when the linkage response rate shows a downward trend for multiple consecutive evaluation cycles; The water supply system operation status evaluation indicators include pressure abnormality flag, water pump power supply status code, and water pump control mode code. The pressure anomaly flag is obtained based on pressure time-series data and cross-validation of multiple sensors. It is set to anomaly when the actual pressure is abnormal, and normal otherwise. The water pump power supply status code indicates the power supply status of the fire pump. The water pump control mode code indicates whether the fire pump is in automatic or manual mode. The cross-validation metric for the control valve status is the physical shut-off flag of the control valve, and the value of the physical shut-off flag is either yes or no. The specific value of this flag is provided by an external system and is used directly in this method without further limitation.

[0012] Optionally, the safety baseline constraint determination in step S5 specifically includes: The detection includes the presence of confirmed critical safety defects, such as: all fire pumps losing power, all fire pumps being in manual control mode, fire system failure, and critical control valves being physically closed after cross-verification, resulting in water supply interruption. If any of the aforementioned critical safety deficiencies are present, the overall building safety rating will be limited to no higher than the minimum safety level, regardless of the assessment results of other dimensions.

[0013] Optionally, the operational behavior compliance assessment includes the calculation of nighttime reset rate and net shielding: The proportion of alarm reset operations that occur during non-working hours to all reset operations is statistically analyzed, and this proportion is output as the nighttime reset rate. Calculate the difference between the cumulative number of blocked events and the number of blocked recovery events, and output the difference as the net blocked quantity, which reflects the number of devices that are continuously disabled.

[0014] Optionally, the weighted aggregation calculation of the comprehensive evaluation value and the output of the differentiated rectification strategy in step S5 specifically includes: The assessment value of the operational status dimension of fire protection facilities Multiply by the risk factor of the building fire protection system configuration The corrected facility operation dimension score is obtained by performing the correction. The specific expression is: ; Corrected facility operation dimension scores Maintenance and upkeep dimension assessment value Safety management dimension assessment value The weighted sum is calculated according to the preset weights to obtain the weighted aggregation result. The specific expression is: ;in, , , These are the weighting coefficients, and + + =1; The weighting coefficients are adaptively adjusted based on the scores of each dimension in the previous evaluation period, and the specific expression is as follows: ;in The score of the i-th dimension in the previous evaluation period is normalized to the interval [0, 1]. This represents the score of the j-th dimension in the previous evaluation period; dimensions with lower scores receive higher weights in the next period. The weighted aggregation result Multiply by the building's functional risk coefficient The comprehensive fire safety assessment value of the building was obtained. The specific expression is: ; Simultaneously, by comparing the evaluation values ​​of the three dimensions, the weakest dimension with the lowest score is identified, and corresponding rectification suggestions are output according to the type of the weak dimension: if the weak dimension is the operating status of fire protection facilities, suggestions for equipment repair or replacement are output; if the weak dimension is maintenance, suggestions for increasing the frequency of testing and compliance management are output; if the weak dimension is safety management, suggestions for improving operating procedures and duty systems are output.

[0015] Secondly, this application provides a multi-dimensional dynamic assessment method for building fire safety based on the Internet of Things, adopting the following technical solution: A computer device includes a processor running a program for the Internet of Things-based multidimensional dynamic assessment method for building fire safety as described in any one of the preceding claims.

[0016] Thirdly, this application provides a storage medium, which adopts the following technical solution: A storage medium storing a program for the IoT-based multi-dimensional dynamic assessment method for building fire safety as described in any one of the above-mentioned methods.

[0017] In summary, this application includes at least one of the following beneficial technical effects: This assessment method firstly fills monitoring blind spots during regular inspection intervals by automatically identifying silent fault detectors that are difficult to detect through traditional manual inspections. Secondly, through event-by-event pairing analysis, the system can quantify and track the success rate trends of various building linkage links, thereby issuing early warnings before the linkage function shows significant degradation, providing a basis for preventive maintenance. In addition, the cross-subsystem cross-verification mechanism for control valve status can eliminate misjudgments caused by management-induced shielding operations, thus accurately identifying real physical shutdown events. At the same time, the three-dimensional independent assessment architecture effectively distinguishes different risk modes such as mismatch between facility status and maintenance management quality, avoiding the masking of weaknesses by a single comprehensive score. This makes the output rectification suggestions more targeted, ultimately helping to improve the refinement and proactivity of building fire safety management. Attached Figure Description

[0018] Figure 1 This is the overall flowchart of the IoT-based multi-dimensional dynamic assessment method for building fire safety of the present invention.

[0019] Figure 2 This is the architecture diagram of the three-dimensional independent evaluation and comprehensive judgment of this invention.

[0020] Figure 3 This is a flowchart of the detector silence failure identification process of the present invention.

[0021] Figure 4 This is a flowchart of the event-by-event pairing verification process for the linkage link of the present invention.

[0022] Figure 5 This is a flowchart of the cross-subsystem verification process for the control valve status of the present invention.

[0023] Figure 6 This is the architecture diagram of the multi-dimensional dynamic assessment system for building fire safety of the present invention. Detailed Implementation

[0024] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0025] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0026] This application discloses an IoT-based multi-dimensional dynamic assessment method for building fire safety, referring to... Figures 1-6 ,include: S1. Collect event stream data generated by fire protection IoT sensors deployed in the building, and perform timestamp calibration, deduplication, and classification labeling on the event stream data; In a typical implementation scenario, the target building is equipped with the following fire protection IoT sensors and devices: automatic fire alarm system (including smoke detectors, heat detectors, manual alarm buttons, and fire linkage controllers), fire water supply system (including fire hydrant pumps, sprinkler pumps, pipeline pressure sensors, and water level sensors), smoke control system (including positive pressure fans and smoke exhaust fans), fire door monitoring system, electrical fire monitoring system, combustible gas detection and alarm system, and fire emergency lighting and evacuation guidance system.

[0027] Various sensors and devices connect to the fire protection IoT platform via user information transmission devices, continuously reporting event stream data. Event types include: alarm events (fire alarm, fault, monitoring), operation events (reset, shielding, shielding restoration, silencing), linkage events (linkage triggering, linkage feedback), status events (equipment online / offline, power supply / power failure, automatic / manual mode switching), and continuous measurement data (pressure value, water level value, electrical parameters). The above data serves as the input for step S1, and after timestamp calibration, deduplication, and classification labeling, it is used for subsequent feature index extraction in various dimensions.

[0028] S2. Extract feature indicators of the fire protection facility operation status dimension from the event stream data, including: detector operation health assessment indicators, silent failure identification indicators, linkage link effectiveness assessment indicators, water supply system operation status assessment indicators, and control valve status cross-verification indicators. Optionally, the degradation warning flag is set to a warning when the linkage success rate is lower than a preset threshold or when the linkage response rate shows a downward trend for multiple consecutive evaluation cycles; The water supply system operation status evaluation indicators include pressure abnormality flag, water pump power supply status code, and water pump control mode code. The pressure anomaly flag is obtained based on pressure time-series data and cross-validation of multiple sensors. It is set to anomaly when the actual pressure is abnormal, and normal otherwise. The water pump power supply status code indicates the power supply status of the fire pump. The water pump control mode code indicates whether the fire pump is in automatic or manual mode. The cross-validation metric for the control valve status is the physical shut-off flag of the control valve, and the value of the physical shut-off flag is either yes or no. As one specific implementation method, the silent failure identification index is the silent failure flag bit corresponding to each detector, and the determination process specifically includes: Determining the evaluation target: Smoke detectors with an online duration exceeding a preset threshold are selected as evaluation targets. For example, this threshold could be 6 months, but this is merely an example and does not constitute a limitation of this solution. The basis for selecting this threshold is that, in a normally operating building environment, the probability of a smoke detector triggering at least one alarm event due to non-fire factors such as cooking fumes, construction dust, and changes in ambient temperature and humidity within this time span is extremely high; detectors that fail to generate any alarm event after exceeding this preset time have a reasonable suspicion of functional failure. Exclusion criteria: Exclude the following from the assessment objects: detectors that have generated maintenance test signals during the assessment period (indicating that they still have sensing capabilities); detectors that are in a known shielded state (their failure to alarm is the result of management operations rather than a loss of function); detectors installed in special locations where there are no triggering conditions (such as sealed pipe wells or equipment mezzanines where no one enters or exits). Silent Failure Determination: Detectors that have been online for more than 6 months, have no valid alarm events, and do not meet any exclusion criteria are marked as silent failure devices with their silent failure flag set to "yes".

[0029] Group statistical indicators: The proportion of suspected silent failure devices among all detectors in a building is counted as one of the key input indicators for the operational status of fire protection facilities. When the proportion of silent failures exceeds a preset threshold (exemplarily 20%), it indicates that the fire detection coverage of the building has been severely reduced. This example threshold refers to the mandatory requirement of a fire detection coverage rate of not less than 80% in the "Code for Design of Automatic Fire Alarm Systems" and is only an example, not a numerical limitation of the scheme.

[0030] The silent failure identification method described above can automatically detect detectors that have been online for a long time but have never generated an alarm. These devices are often misjudged as normal during traditional manual inspections. This identification mechanism effectively fills the monitoring blind spots during the intervals between regular inspections, thereby reducing the risk of missed fire alarms due to detector malfunction. As one specific implementation method, the detector's operational health assessment index is expressed through a degradation score. The quantification process, specifically the calculation procedure, is as follows: Calculate the alarm frequency of each detector within a sliding time window (e.g., every 3 months). This is the ratio of the number of alarms to the cycle duration; Introducing environmental factor correction coefficients Detectors on different floors and in different functional areas within the same building are affected differently by environmental factors; the correction coefficient is obtained through statistical analysis of the alarm frequencies of multiple detectors in the same area, used to eliminate the interference of common environmental factors on individual degradation judgment. The value of k ranges from 0.5 to 1.5, preset according to the season and spatial location; According to the expression Calculate the degradation score, where This is the highest alarm frequency value among all detectors. The value range is [0, 1], and the lower the score, the more severe the detector degradation. Linear regression analysis was performed on the corrected standardized alarm frequency sequence. If the regression slope of multiple consecutive windows is significantly positive and passes the trend validity test (e.g., the slope exceeds a preset threshold), the detector is determined to have a deterioration trend. The proportion of deteriorated detectors is used as an input indicator for the operational status dimension of fire protection facilities.

[0031] The degradation trend assessment result obtained from the linear regression analysis, together with the degradation score calculated above, constitutes the detector's operational health assessment index. The degradation score reflects the detector's current static degradation level, while the linear regression slope reflects the dynamic rate of degradation development. After weighting both at a 7:3 ratio, a comprehensive score for the operational health of a single detector is obtained. The average comprehensive score of all detectors is then used as the dimensional input value for this index.

[0032] As one specific implementation method, the linkage link effectiveness assessment includes linkage success rate values ​​and degradation warning flags, and its specific implementation method is as follows: Event-by-event pairing: Within a preset time window (e.g., 600 seconds) after the fire alarm control panel issues a trigger command, check whether the corresponding linked device has reported a feedback signal. If a feedback signal is generated, the linkage is considered successful; if no feedback signal is generated, the linkage is considered failed. Calculate the linkage success rate of each linkage link within the evaluation period, which is the ratio of the number of successful linkages to the total number of triggered events; Linkage degradation early warning: Track the time series changes of the linkage success rate of each linkage link. When the linkage success rate is lower than the preset threshold, or continues to decline in multiple consecutive evaluation periods and the decline exceeds the preset threshold, the degradation early warning flag will be set to early warning. Scope of assessment: The assessment objects include, but are not limited to, all linkage links such as elevator linkage, fire emergency broadcast linkage, emergency lighting linkage, non-fire power cut-off linkage, access control system linkage, fireproof roller shutter linkage, and smoke control system linkage.

[0033] This event-by-event pairing verification method can automatically extract the success rate and trend of each linkage link from daily operation data without relying on regular manual testing. By continuously tracking the time series of linkage success rate, it can issue early warning signals before the linkage function shows obvious degradation, providing a quantitative basis for the preventive maintenance of the fire linkage system and effectively reducing the risk of fire expansion due to linkage failure.

[0034] As one specific implementation method, the water supply system operation status evaluation indicators include pressure anomaly flag, water pump power supply status code, water pump control mode code, and the control valve status cross-verification indicator is the physical closure flag. The specific implementation method is as follows: Fire pump status assessment: Real-time monitoring of the power supply status (power supply status code) and control mode (automatic or manual mode) of fire hydrant pumps and sprinkler pumps; when any fire pump is in a power failure state or all are in manual control mode, a critical safety defect flag is triggered. Pipeline pressure assessment: Continuously monitor the pressure of the main pipeline and terminal pipelines of the fire hydrant and sprinkler systems, and compare them with the pressure thresholds required by the standards. Based on pressure time-series data and multi-sensor cross-validation, distinguish between true pressure anomalies and false anomalies caused by individual sensor drift. When a true pressure anomaly occurs, mark the pressure anomaly position as abnormal. The specific value of the physical shut-off flag of the control valve is provided by the external system. This method uses it directly without further limitations.

[0035] The physical closure flag of the control valve is obtained from the output of the external fire protection network cross-system verification module. This module completes the valve physical state determination through multi-system fusion verification logic. This method directly calls the result of this flag and does not involve the internal calculation logic for valve physical state determination.

[0036] This method directly calls the physical shut-off flag of the control valve output by the external cross-system verification module and uses it as one of the input indicators for the comprehensive evaluation of the water supply system's operating status. Since this external flag distinguishes between administrative shielding and actual physical shut-off, the reliability evaluation results obtained by this method are more realistic and credible than the single judgment method that relies solely on the electrical signal of the signal valve.

[0037] S3. Extract feature indicators of fire protection facility maintenance dimension from the event stream data, including: detector function test completion rate, manual alarm button test completion rate, linkage control function test completion rate, fire pump start test completion rate, and end test valve and alarm valve water discharge test coverage rate. The maintenance and upkeep of fire protection facilities are monitored and the test completion rates of various fire protection facilities are automatically calculated using IoT event streams. Specifically, these include: Detector functional test completion rate: The proportion of detectors that generate valid test signals within the evaluation period. According to Article 6.0.5 of GB50166-2019, each detector should be tested at least once a year. Manual alarm button test completion rate: The coverage rate of effective alarm signals generated by manual alarm buttons within the evaluation period; Interlocking control function test completion rate: Statistical analysis of the proportion of interlocking equipment such as fire elevators, fireproof roller shutters, smoke control systems, and emergency lighting that generate effective feedback signals within the test period required by the specifications; Fire pump start-up test completion rate: Statistics on whether all fire pumps have completed manual start-up tests within the past month, automatically determined by pump feedback signals; Coverage of water discharge tests for end test valves and alarm valves: The completion rate of water discharge tests for end test valves and alarm valves is automatically determined by the water flow indicator signal. According to Article 9.0.6 of GB50261-2017, water discharge tests should be conducted on all end test valves every quarter. The completion rate of various tests is automatically calculated periodically according to their respective specifications and compared with compliance thresholds to output a comprehensive evaluation value for maintenance and upkeep. .

[0038] S4. Extract characteristic indicators of fire safety management dimension from the event flow data and management records, including: operational behavior standardization assessment indicators, alarm information processing timeliness indicators, fire inspection compliance indicators, and fire responsibility implementation and record improvement indicators; The specific quantification methods for each characteristic indicator in the fire safety management dimension are as follows: Nighttime reset rate: This is the percentage of alarm reset operations that occur during non-working hours (e.g., 7:00 PM to 8:00 AM the next day) out of all reset operations. A high nighttime reset rate indicates that on-duty personnel are processing alarm information during non-working hours, suggesting a delay in alarm processing during working hours.

[0039] Net shielding: This is the difference between the cumulative number of shielding events and the number of shielding recovery events, reflecting the number of devices currently continuously disabled. The larger the net shielding, the more severe the reduction in building fire detection coverage.

[0040] Alarm information processing timeliness index: This index calculates the percentage of fire alarms, faults, and potential hazards that have been processed, outputting the processing completion rate. The presence of unprocessed fire alarms or faults indicates a serious oversight in fire safety management.

[0041] Fire safety inspection compliance indicators: The completion rate of daily fire prevention patrols and inspections is calculated based on inspection records.

[0042] Fire safety responsibility implementation and record-keeping indicators: These assess the implementation of the fire safety responsibility system and the completeness of fire safety records. The above indicators are weighted and aggregated to output a safety management dimension assessment value. .

[0043] S5. Calculate the dimension evaluation values ​​for the dimensions of fire protection facility operation status, maintenance compliance, and fire safety management standardization, and based on the dimension evaluation values, make a safety bottom line constraint judgment, calculate the comprehensive evaluation value, and output differentiated rectification strategies. S6. Automatically execute S1 to S5 according to the preset cycle, dynamically update the evaluation values ​​of each dimension and the comprehensive evaluation value, and track the changing trend of the building's fire safety status.

[0044] Optionally, the following steps may be included before step S5: Risk correction is performed on the assessment results based on the building's inherent characteristics. This risk correction includes determining the building's functional risk coefficient based on its intended use. And determine the risk factor for the configuration of the building's fire protection system based on the type of fire protection system actually configured in the building. .

[0045] Building use function risk factor Risk factor of building fire protection system configuration The specific value selection method is as follows: Building use function risk factor The specific function is determined based on the actual use of the building. In one particular embodiment... The value ranges from 0.9 to 1.0, for example: ordinary office buildings. =1.0 (benchmark value), residential =0.96, School =0.92, hospitals and nursing homes =0.92, public gathering places (shopping malls, hotels, sports venues and public entertainment venues) =0.9. The lower the value, the higher the fire risk of this type of building under the same fire protection facilities conditions, and the overall assessment value will be reduced accordingly.

[0046] Risk factor of building fire protection system configuration Determined based on the type of fire protection system actually configured in the building.

[0047] In one specific embodiment: both an automatic fire alarm system and an automatic sprinkler system are configured. =1.0; Only fire alarm system and fire hydrant system are configured. =0.8; Only fire alarm system configured =0.75; Only fire hydrant systems are configured. =0.6; Only equipped with stand-alone smoke detectors =0.5; No fire protection system installed. =0.2. The lower the value, the less perfect the building's fire protection system configuration, and the evaluation value of the fire protection facility operation status dimension is correspondingly compressed in the comprehensive evaluation.

[0048] As one specific implementation method, the specific process of calculating the dimensional evaluation value, determining the safety baseline constraint, and outputting the comprehensive evaluation value is as follows: Calculate the assessment values ​​for three dimensions: operational status of fire protection facilities, compliance of maintenance and upkeep, and standardization of fire safety management. , , The evaluation values ​​for each dimension are calculated based on the weighted aggregation of all indicators under that dimension, and standardized to a unified [0, 1] interval. Taking the operational status of fire protection facilities as an example, the weight allocation of each sub-indicator is as follows: detector operational health score (0.3), silent failure rate (0.25), linkage link success rate (0.2), water supply system operational status score (0.15), and control valve status score (0.1). Among these, Boolean indicators such as the silent failure rate and control valve status are calculated with 1 point for normal operation and 0 points for abnormal operation. The final weighted sum is then normalized to the [0, 1] interval. The maintenance and safety management dimensions use the same weighted aggregation method, and the weights of each sub-indicator can be fine-tuned according to the building's usage function.

[0049] Safety baseline constraint assessment: This involves detecting the presence of confirmed critical safety defects, including: complete loss of power to all fire pumps; all fire pumps being in manual control mode; fire protection system malfunction (e.g., all user information transmission devices being offline); and critical control valves being physically shut off, resulting in water supply interruption, as confirmed by cross-verification. If any of these critical safety defects are present, the overall building safety rating will be limited to no higher than the minimum safety level, regardless of the assessment results in other dimensions.

[0050] Comprehensive assessment value calculation: This includes the assessment values ​​for the operational status of fire protection facilities. Multiply by the risk factor of the building fire protection system configuration After correction, the following results were obtained. ; Then the corrected facility operation dimension score Maintenance and upkeep dimension assessment value Safety management dimension assessment value The weighted sum is calculated according to the preset weights to obtain the weighted aggregation result. ;in, + + =1; The weighting coefficients are adaptively adjusted based on the scores of each dimension in the previous evaluation period: The lower the score of a dimension, the higher its weight will be in the next cycle. The weighted aggregation result Multiply by the building's functional risk coefficient The comprehensive fire safety assessment value of the building was obtained. ; Differentiated rectification strategy output: Compare the evaluation values ​​of the three dimensions to identify the weakest dimension with the lowest score, and output corresponding rectification suggestions based on the type of the weak dimension: If the weak dimension is the operating status of fire protection facilities, output suggestions for equipment repair or replacement; if the weak dimension is maintenance, output suggestions for increasing the frequency of testing and compliance management; if the weak dimension is safety management, output suggestions for improving operating procedures and duty system.

[0051] This three-dimensional independent assessment framework outputs scores for facility status, maintenance, and safety management, rather than providing only a comprehensive score. This avoids the situation where excellent performance in one dimension masks serious deficiencies in another, such as a facility in good condition but with chaotic management systems. By identifying the weakest dimension with the lowest score, managers can directly pinpoint the root cause of the problem and take targeted corrective measures. Compared with traditional methods, this method improves the guidance of assessment results and the efficiency of corrective work.

[0052] The value range is 0 to 1, corresponding to the following safety levels: Excellent: 0.8 to 1.0, no major hidden dangers, routine maintenance is sufficient; Good: 0.6 to 0.8, some minor hidden dangers exist, rectification within a specified period is required; Average: 0.4 to 0.6, many hidden dangers exist, rectification within one month; Poor: 0.2 to 0.4, major hidden dangers exist, rectification within 7 days; Dangerous (minimum safety level): 0 to 0.2, fatal hidden dangers exist, immediate shutdown for rectification is required. When any critical safety defect exists, regardless of the scores in other dimensions, it is directly classified as dangerous.

[0053] This application also discloses a computer device, including a processor, wherein the processor runs a program for the IoT-based multi-dimensional dynamic assessment method for building fire safety as described in any one of the above embodiments.

[0054] This application also discloses a storage medium storing the program of the IoT-based multi-dimensional dynamic assessment method for building fire safety as described in any one of the above embodiments.

[0055] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A multi-dimensional dynamic assessment method for building fire safety based on the Internet of Things, characterized in that, The method includes: S1. Collect event stream data generated by fire protection IoT sensors deployed in the building, and perform timestamp calibration, deduplication, and classification labeling on the event stream data; S2. Extract feature indicators of the fire protection facility operation status dimension from the event stream data, including: detector operation health assessment indicators, silent failure identification indicators, linkage link effectiveness assessment indicators, water supply system operation status assessment indicators, and control valve status cross-verification indicators. S3. Extract feature indicators of fire protection facility maintenance dimension from the event stream data, including: detector function test completion rate, manual alarm button test completion rate, linkage control function test completion rate, fire pump start test completion rate, and end test valve and alarm valve water discharge test coverage rate. S4. Extract characteristic indicators of fire safety management dimension from the event flow data and management records, including: operational behavior standardization assessment indicators, alarm information processing timeliness indicators, fire inspection compliance indicators, and fire responsibility implementation and record improvement indicators; S5. Calculate the dimension evaluation values ​​for the dimensions of fire protection facility operation status, maintenance compliance, and fire safety management standardization, and based on the dimension evaluation values, make a safety bottom line constraint judgment, calculate the comprehensive evaluation value, and output differentiated rectification strategies. S6. Automatically execute S1 to S5 according to the preset cycle, dynamically update the evaluation values ​​of each dimension and the comprehensive evaluation value, and track the changing trend of the building's fire safety status.

2. The IoT-based multi-dimensional dynamic assessment method for building fire safety according to claim 1, characterized in that, The following steps are included before step S5: Risk correction is performed on the assessment results based on the building's inherent characteristics. This risk correction includes determining the building's functional risk coefficient based on its intended use. And determine the risk factor for the configuration of the building's fire protection system based on the type of fire protection system actually configured in the building. .

3. The IoT-based multi-dimensional dynamic assessment method for building fire safety according to claim 2, characterized in that, The detector's operational health assessment index is the degradation score value corresponding to each detector, which is obtained through the following method: The specific expression is: ; in, The degradation score for each detector. Let be the alarm frequency of the i-th detector during the evaluation period, i.e., the number of alarms divided by the period duration. Environmental factor correction coefficients are preset based on season and spatial location. This is the highest alarm frequency value among all detectors.

4. The IoT-based multi-dimensional dynamic assessment method for building fire safety according to claim 2, characterized in that, The silent failure identification index is the silent failure flag bit corresponding to each detector, and the value of the silent failure flag bit is either yes or no. The criteria for determining the flag to be "yes" are: the detector's online duration exceeds the preset threshold, no valid alarm events are generated within the evaluation period, and it is not a device that generates test signals during maintenance testing, is not a device in a known shielded state, or is not a device whose installation location does not meet the triggering conditions. The effectiveness evaluation indicators for the linkage link include the linkage success rate and the degradation warning flag. The linkage success rate is obtained through an event-by-event time-series pairing method: within a preset time window after each triggered event, a corresponding linkage feedback event is searched for, and the ratio of the number of successful linkages to the total number of triggered events is calculated.

5. The IoT-based multi-dimensional dynamic assessment method for building fire safety according to claim 4, characterized in that, The degradation warning flag is set as a warning when the linkage success rate is lower than a preset threshold or when the linkage response rate shows a downward trend for multiple consecutive evaluation cycles. The water supply system operation status evaluation indicators include pressure abnormality flag, water pump power supply status code, and water pump control mode code. The pressure anomaly flag is obtained based on pressure time-series data and cross-validation of multiple sensors. It is set to anomaly when the actual pressure is abnormal, and normal otherwise. The water pump power supply status code indicates the power supply status of the fire pump. The water pump control mode code indicates whether the fire pump is in automatic or manual mode. The cross-validation metric for the control valve status is the physical shut-off flag of the control valve, and the value of the physical shut-off flag is either yes or no.

6. The IoT-based multi-dimensional dynamic assessment method for building fire safety according to claim 2, characterized in that, The safety baseline constraint determination in step S5 specifically includes: The detection includes the presence of confirmed critical safety defects, such as: all fire pumps losing power, all fire pumps being in manual control mode, fire system failure, and critical control valves being physically closed after cross-verification, resulting in water supply interruption. If any of the aforementioned critical safety deficiencies are present, the overall building safety rating will be limited to no higher than the minimum safety level, regardless of the assessment results of other dimensions.

7. The IoT-based multi-dimensional dynamic assessment method for building fire safety according to claim 2, characterized in that, The operational behavior standardization assessment includes the calculation of nighttime reset rate and net shielding capacity: The proportion of alarm reset operations that occur during non-working hours to all reset operations is statistically analyzed, and this proportion is output as the nighttime reset rate. Calculate the difference between the cumulative number of blocked events and the number of blocked recovery events, and output the difference as the net blocked quantity, which reflects the number of devices that are continuously disabled.

8. The IoT-based multi-dimensional dynamic assessment method for building fire safety according to claim 2, characterized in that, The weighted aggregation calculation of the comprehensive evaluation value and the output of the differentiated rectification strategy in step S5 specifically include: The assessment value of the operational status dimension of fire protection facilities Multiply by the risk factor of the building fire protection system configuration The corrected facility operation dimension score is obtained by performing the correction. The specific expression is: ; Corrected facility operation dimension scores Maintenance and upkeep dimension assessment value Safety management dimension assessment value The weighted sum is calculated according to the preset weights to obtain the weighted aggregation result. The specific expression is: ;in, , , These are the weighting coefficients, and + + =1; The weighting coefficients are adaptively adjusted based on the scores of each dimension in the previous evaluation period, and the specific expression is as follows: ;in The score for the i-th dimension in the previous evaluation period; This represents the score of the j-th dimension in the previous evaluation period; dimensions with lower scores receive higher weights in the next period. The weighted aggregation result Multiply by the building's functional risk coefficient The comprehensive fire safety assessment value of the building was obtained. The specific expression is: ; Simultaneously, by comparing the evaluation values ​​of the three dimensions, the weakest dimension with the lowest score is identified, and corresponding rectification suggestions are output according to the type of the weak dimension: if the weak dimension is the operating status of fire protection facilities, suggestions for equipment repair or replacement are output; if the weak dimension is maintenance, suggestions for increasing the frequency of testing and compliance management are output; if the weak dimension is safety management, suggestions for improving operating procedures and duty systems are output.

9. A computer device, characterized in that, Includes a processor, wherein the processor runs a program for the Internet of Things-based multidimensional dynamic assessment method for building fire safety as described in any one of claims 1-8.

10. A storage medium, characterized in that, The program stores the Internet of Things-based multidimensional dynamic assessment method for building fire safety as described in any one of claims 1-8.

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