A perfluorohexanone fire-fighting monitoring system and method based on an internet of things

Abstract

This invention relates to the field of fire equipment control technology, specifically to a perfluorohexanone (PFH) fire monitoring system and method based on the Internet of Things (IoT). The system includes a monitoring and acquisition module, a fire identification module, a discharge verification module, a discharge organization module, and a monitoring confirmation module. This invention adds the occurrence sequence of multi-source fire signals and performs missing verification, enabling fire-related information to form a traceable structure in the time dimension. This avoids the risk of misjudgment caused by isolated judgments. By comparing smoke temperature changes with trigger limits and analyzing their sequential relationships, the fire evolution process can be characterized. A fingerprint-like consistency verification is established between the discharge command and the execution feedback, forming a closed-loop correlation between the discharge action and the actual response. Simultaneously, by combining the protected area status and cylinder availability information to sequence actions, the entire process has continuous verification capabilities, improving the reliability of monitoring and judgment and the controllability of discharge execution, and reducing the fire risks caused by abnormal triggering and execution deviations.

Description

Technical Field

[0001] This invention relates to the field of fire protection equipment control technology, specifically to a perfluorohexanone fire monitoring system and method based on the Internet of Things. Background Technology

[0002] The field of fire protection equipment control technology includes related technologies for monitoring, judging, and controlling the release devices of extinguishing media and their linkage facilities in fire prevention and control systems. The core lies in continuously collecting, transmitting, and judging fire-related physical quantities and controlling the start-up, shutdown, and operation status of fire protection equipment accordingly. Its overall technical system typically includes fire parameter acquisition devices, communication transmission means, control logic execution devices, and drive and linkage structures adapted to specific extinguishing media. It mainly serves application scenarios such as gas extinguishing systems, automatic extinguishing systems, and centralized fire monitoring systems.

[0003] The Internet of Things (IoT) based perfluorohexanone (PFH) fire monitoring system refers to a system technology that uses PFH gas extinguishing devices as the target to monitor and control the fire status through IoT communication. The technical aspects involved include the collection of fire environmental parameters and the monitoring of the working status of PFH extinguishing devices, the transmission of fire signals and control commands in the network, and the start-up and shutdown control of the extinguishing devices. Specifically, it involves deploying sensors to collect parameters such as temperature, smoke, and pressure, transmitting the collected data to the monitoring terminal via a communication network, and sending control commands to the PFH extinguishing devices according to preset judgment rules to complete the monitoring and control of the fire equipment operation process.

[0004] Existing methods rely heavily on single or scattered parameter acquisition results for judgment. When sensor signals are delayed, missing, or fluctuating, they struggle to reflect the sequential relationship of fire changes, easily leading to discrepancies between judgments and actual evolution. Furthermore, the focus on discharge commands and execution processes is concentrated on start-up and stop states, lacking systematic verification of intermediate processes and feedback sequences. When network transmission is unstable or equipment responses are abnormal, it is difficult to identify control deviations in a timely manner, potentially causing a mismatch between discharge actions and the state of the protected area, thus affecting fire extinguishing effectiveness and equipment safety. Therefore, these methods do not meet existing needs. To address this, we propose an IoT-based perfluorohexanone fire monitoring system and method. Summary of the Invention

[0005] The purpose of this invention is to provide a perfluorohexanone fire monitoring system and method based on the Internet of Things (IoT) to address the problems mentioned in the background art, which rely heavily on single or scattered parameter acquisition results for judgment. These systems struggle to reflect the sequential relationship of fire changes when sensor signals are delayed, missing, or fluctuating, leading to discrepancies between judgment results and actual evolution. Furthermore, the focus on discharge commands and execution processes is concentrated on start-up and stop states, lacking systematic verification of intermediate processes and feedback sequences. When network transmission is unstable or equipment responses are abnormal, it is difficult to identify control deviations in a timely manner, potentially causing a mismatch between discharge actions and the state of the protected area, thus affecting fire extinguishing effectiveness and equipment safety.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a perfluorohexanone fire monitoring system and method based on the Internet of Things, the system comprising:

[0007] The monitoring and acquisition module calls the signals from the smoke detector, temperature detector, bottle group pressure detection, discharge valve opening and closing, and electromagnetic drive circuit on / off. It adds acquisition sequence marks to each signal and arranges them in the order of occurrence. It performs a missing check on the arranged signals and generates perfluorohexanone monitoring and acquisition records.

[0008] Fire detection module: Based on the monitoring and data collection records of perfluorohexanone, the module makes fire trigger judgments on the responses of smoke detectors and heat detectors, and analyzes the sequential relationship between the two judgment results. When the sequential relationship shows a continuous change, the module records the corresponding location and generates a perfluorohexanone fire evolution record.

[0009] The discharge verification module: Based on the perfluorohexanone fire evolution record, it organizes the discharge area, valve opening, discharge duration, and closing buffer to form an instruction fingerprint. At the same time, it arranges the execution feedback returned during the discharge process in the same order to form an execution fingerprint. The two sets of fingerprints are compared item by item to generate a perfluorohexanone discharge compliance record.

[0010] Discharge organization module: Based on the perfluorohexanone discharge compliance record, it calls the protection zone closure confirmation, cylinder group availability mark and perfluorohexanone fire evolution record stage mark, organizes the discharge action sequence, and generates perfluorohexanone discharge execution plan.

[0011] Monitoring and confirmation module: Based on the perfluorohexanone release execution schedule, it verifies the start of release, the completion of release, valve reset, and bottle group marking, organizes and confirms the items according to time, and finally generates the perfluorohexanone fire monitoring results.

[0012] Preferably, the perfluorohexanone monitoring and acquisition records specifically include time series identifiers, signal integrity status, and multi-source response association indexes;

[0013] The perfluorohexanone fire evolution record includes fire stage division markers, trigger continuity characteristics, and evolution inflection point location indexes;

[0014] The perfluorohexanone release conforms to the record of the consistency conclusion of specific instruction execution, the matching status of the release process, and the abnormal deviation judgment mark;

[0015] The perfluorohexanone release execution schedule includes the action priority sequence, release coordination relationship identifier, and execution phase logical structure;

[0016] The specific results of the perfluorohexanone fire monitoring include confirmation of discharge completion, equipment operation status identification, and conclusion on the overall protection effectiveness.

[0017] Preferably, the monitoring and acquisition module includes:

[0018] Signal acquisition submodule: Acquires response signals from smoke detectors, heat detectors, fire extinguishing agent cylinder pressure detection signals, discharge valve opening / closing signals, and electromagnetic drive circuit on / off signals deployed in the perfluorohexanone protected area. Records the acquisition time value, signal amplitude, and status code for each signal. Performs numerical summarization calculations on the acquisition time value to generate response signal quantity.

[0019] Sequence Marking Submodule: Based on the response semaphore, it calls the acquisition time values ​​of each signal, performs a sequential sorting judgment on all signals, performs a comparison operation on the time difference of adjacent signals, appends the corresponding sequence number and forms a continuous arrangement structure, and generates a time sequence arrangement based on the arrangement result;

[0020] Missing signal check submodule: Based on the time sequence, it calls the signal type quantity and sequential number continuous value, performs gap judgment on each number, records the number difference for the gap position and summarizes the quantity results, and combines the arranged signal content to form a complete record structure to generate perfluorohexanone monitoring and acquisition record.

[0021] Preferably, the fire detection module includes:

[0022] The smoke detection control submodule: Based on the monitoring and data acquisition records of perfluorohexanone, it performs a comparison judgment between the response changes of smoke detectors and the fire triggering threshold, and obtains the response values ​​of smoke detectors at each sampling time, which are recorded as sequences. And set fire trigger thresholds. Values For each sequence Click to execute A Boolean logic judgment, if the condition is true, then at that moment... The number of out-of-bounds points in the state position is recorded as 1, otherwise it is recorded as 0. The system further counts the length of consecutive 1s. Only when the number of consecutive out-of-bounds points exceeds 3 sampling periods, that is, the duration is greater than 1.5 seconds, to avoid instantaneous smoke interference, is it confirmed as a valid out-of-bounds. The start and end timestamps of all valid out-of-bounds states are recorded to form binary state waveform data and generate smoke response out-of-bounds sequence values.

[0023] The temperature control submodule: Based on the monitoring and acquisition records of perfluorohexanone, it calls the smoke response out-of-bounds sequence value to obtain the response change data of the temperature detector at the same sampling time. The system locks the time axis interval with state 1 in the smoke response out-of-bounds sequence. Based on this, a 30-second backward and 30-second forward analysis window is used to extract the temperature value sequence of the temperature sensor within this window. And set an absolute temperature threshold. and temperature rise rate threshold The temperature rise rate is calculated using the following formula. :

[0024]

[0025] in, For the rate of temperature rise, The temperature value at the i-th second;

[0026] The temperature response value at each moment is compared with the corresponding trigger limit value, and the comparison formula is as follows:

[0027]

[0028] When both conditions in the formula are met simultaneously, the temperature sensing trigger condition is determined to be valid at that moment, and it is marked as a state node. The time series of temperature-triggered events and the time series of smoke-triggered events are placed on the same time axis, and the time difference between the two is calculated. If the temperature sensor triggers before the smoke sensor, the difference is negative. The system records the time interval of each pair of associated triggering events to obtain the dual detection timing difference.

[0029] Evolution Recording Submodule: Based on the monitoring and acquisition records of perfluorohexanone, it calls the dual detection time difference to continuously detect the order of smoke and heat detection results, identifies the sampling positions where the order relationship changes continuously, extracts the corresponding position index and time span parameters, summarizes and records them, and generates a perfluorohexanone fire evolution record.

[0030] Preferably, the discharge verification module includes:

[0031] Command fingerprint submodule: Obtain the perfluorohexanone fire evolution record, and send the discharge area identification content, valve drive opening content, discharge continuity arrangement content, and closure buffer arrangement content to the discharge bottle group. Perform an arrangement operation on the above content in chronological order to form a content sequence with a fixed order, and assign position numbers to each identifier in the sequence to generate a discharge command fingerprint sequence value.

[0032] The fingerprint execution submodule acquires the valve opening feedback, drive circuit response, discharge start marker, and discharge end marker transmitted back during the discharge process. Based on the order rules adopted by the discharge command fingerprint sequence value, it arranges each transmitted item in the same order and performs numbering and calibration on the corresponding positions to obtain the discharge execution fingerprint sequence value.

[0033] Consistency determination submodule: Calls the fingerprint sequence value of the release command and the fingerprint sequence value of the release execution, performs equality judgment on each corresponding numbered item in the sequence, counts the number of consistent items and the total number of items, calculates the consistency ratio, records the position number of inconsistent items, and generates perfluorohexanone release compliance record.

[0034] Preferably, the discharge organization module includes:

[0035] Recording and verification submodule: acquire perfluorohexanone release compliance records, collect protection zone closure confirmation signals, monitor bottle group availability indicators, perform sequential comparison of the timestamps of the three records, determine the order of each record and form a corresponding number sequence, and generate release record sequence values;

[0036] Signal integration submodule: Based on the order value of the discharge record, it calls the corresponding stage content of the fire evolution record, detects the start and end time of the stage and performs a consistency comparison with the numbered sequence, filters matching stages and aggregates them into a continuous sequence to obtain the stage matching sequence value;

[0037] Sequential arrangement submodule: Based on the stage matching sequence value, it calls the protection zone closure confirmation signal and the bottle group availability identifier, performs judgment based on time sequence and availability status, arranges the trigger order of the release action and forms a time arrangement sequence, and generates the perfluorohexanone release execution schedule.

[0038] Preferably, the monitoring and confirmation module includes:

[0039] The execution tag acquisition submodule acquires the perfluorohexanone release execution schedule, collects the release start tag, release completion tag, valve reset tag, and bottle group status tag returned by the IoT nodes, records the corresponding timestamps of each tag and the execution schedule time point, forms a tag time sequence collected by node number, and generates release tag time sequence values.

[0040] The timing verification submodule, based on the emission mark timing value, calculates the time difference between the emission start mark time and the execution schedule start time, and then calculates the actual start delay. and discharge duration deviation The formulas are as follows:

[0041]

[0042]

[0043] in, This refers to the actual start-up time of the spray. The start time of the spraying plan. This refers to the actual end time of the spray. This is the end time of the discharge plan;

[0044] Set the time consistency tolerance range as follows For each action node, if its execution time deviation is within the tolerance range, then the timing consistency score of that node is... It is denoted as 1; otherwise, it decreases linearly according to the magnitude of the deviation, as shown in the formula:

[0045]

[0046] Where 2000 is the maximum permissible deviation, in milliseconds;

[0047] The same judgment is made on subsequent actions such as completion of spraying and reset, and the number and specific number of inconsistent nodes are accumulated to generate a timing consistency coefficient.

[0048] The monitoring results aggregation submodule: Based on the time-series consistency coefficient, it calls the time difference values ​​marked by each node for aggregation and sorting. The system then aggregates and sorts the consistency coefficients of each node obtained above. Statistical analysis is performed to calculate the overall task execution confidence score, using the following formula:

[0049]

[0050] in Assigning weights to critical actions, the weight for the discharge action is set to 0.5, and the weight for the reset action is set to 0.1. Simultaneously, a full-process time sequence diagram is plotted based on the actual action trajectories of all nodes along the timeline, and all coefficients are statistically analyzed. If the percentage of nodes exceeds 95%, the fire monitoring task is considered to be effective; if it is less than 60%, it is considered to be an abnormal system. A set of monitoring values ​​arranged in chronological order is formed to generate perfluorohexanone fire monitoring results.

[0051] A perfluorohexanone fire monitoring method based on the Internet of Things includes the following steps:

[0052] S1: Acquire the smoke response signal, temperature response signal, bottle group pressure detection signal, discharge valve opening and closing signal, and electromagnetic drive circuit on / off signal within the perfluorohexanone protection zone. Mark the acquisition order of each signal and sort them in order. Perform missing verification on the sorting results and generate perfluorohexanone monitoring and acquisition records for post-event traceability analysis.

[0053] S2: Based on the monitoring and data collection of perfluorohexanone, the response changes of smoke detectors are compared with the fire triggering limit, and the response changes of heat detectors are compared with the activation limit. The order of the two types of judgment results is sorted. When the ordering relationship changes continuously, the corresponding position is recorded to form a perfluorohexanone fire evolution record.

[0054] S3: Based on the perfluorohexanone fire evolution record, extract the discharge area identifier and valve drive opening, discharge duration and closing buffer arrangement, generate discharge command fingerprint in sequence, synchronously obtain valve opening feedback and loop response, discharge start and end markers, form execution fingerprint in sequence, compare consistency, and generate discharge compliance record.

[0055] S4: Based on the perfluorohexanone release record, extract the protection zone closure confirmation signal, verify the availability of the bottle group, match the content of the fire evolution stage, compare the sequence of each signal, confirm that the bottle group is ready after the closure is completed, then proceed with the release initiation, and finally record the release completion and subsequent status, and monitor the feedback from the monitoring system to form a release execution plan.

[0056] S5: Based on the perfluorohexanone (PFH) release execution schedule, collect the release start marker, release completion marker, valve reset marker, and bottle group status marker transmitted back from the IoT nodes. Verify each transmitted marker and execution schedule item by item, summarize the verification results in chronological order, and form the PPH fire monitoring results.

[0057] Preferably, in step S1, when multiple sensor signals in the monitoring system are contradictory or missing, the following steps are performed:

[0058] S1.1: Collect signals from all currently available temperature and smoke sensors, assuming the sensor set is... 1. Each sensor Historical accuracy weight Based on its past The accuracy of this test is calculated as follows:

[0059]

[0060] in For sensors In the past The number of times a correct response is made in a test. For sensors Total number of responses;

[0061] S1.2: Define the current fire situation as follows Set up sensor In the fire The typical output value distribution when it occurs is as follows The typical output value distribution under no-fire conditions is as follows: According to the sensor Current reading Its degree of matching with the fire scenario hypothesis The calculation is as follows:

[0062]

[0063] in To observe under a given distribution D Probability estimation of nearby values, typically for this sensor Historical data statistics were obtained;

[0064] S1.3: The overall fire confidence level is calculated using the following formula. :

[0065]

[0066] in The number of missing signals. The total number of expected signals;

[0067] S1.4: If If the fire rate exceeds the preset threshold, it is considered a valid fire and triggers the subsequent fire discharge process; otherwise, it enters manual review or system self-check mode.

[0068] Compared with the prior art, the beneficial effects of the present invention are:

[0069] 1. This invention adds the occurrence sequence of multi-source fire signals and performs missing verification, so that fire-related information forms a traceable structure in the time dimension, avoiding the risk of misjudgment caused by isolated judgment. By comparing smoke temperature changes with trigger limits and analyzing their sequential relationship, the evolution process of the fire can be depicted. A fingerprint-like consistency check is established between the discharge command and the execution feedback, so that the discharge action and the actual response form a closed loop. At the same time, the action is sorted by combining the status of the protected area and the available information of the cylinder group, so that the status of the whole process has continuous verification capability, improving the reliability of monitoring and judgment and the controllability of discharge execution, and reducing the fire risk caused by abnormal triggering and execution deviation.

[0070] 2. This invention determines the confidence level of a fire by detecting the signal of each sensor, thus avoiding false fire situations caused by sensor damage leading to contradictory or missing signals. Attached Figure Description

[0071] Figure 1 This is a system flowchart of the present invention;

[0072] Figure 2 This is a flowchart of the method of the present invention;

[0073] Figure 3 This is a flowchart of the fire confidence detection method of the present invention. Detailed Implementation

[0074] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0075] Please see Figures 1 to 3 This invention provides a technical solution: a perfluorohexanone fire monitoring system based on the Internet of Things, the system comprising:

[0076] Monitoring and acquisition module: Acquires response signals from smoke detectors, heat detectors, fire extinguishing agent cylinder pressure detection signals, discharge valve opening and closing signals, and electromagnetic drive circuit on / off signals deployed in the perfluorohexanone protected area. Adds acquisition sequence marks to each signal and arranges them in chronological order. Performs missing checks on the arranged signal content and generates perfluorohexanone monitoring and acquisition records.

[0077] Fire identification module: Based on the monitoring and data collection records of perfluorohexanone, it compares the response changes of smoke detectors with the fire triggering limit and the response changes of heat detectors with the activation limit. It arranges the results of the two types of judgments in a sequential order. When the order changes continuously, it records the corresponding position and generates a perfluorohexanone fire evolution record.

[0078] Discharge verification module: Based on the perfluorohexanone fire evolution record, it obtains the discharge area identification, valve drive opening, discharge continuity arrangement, and closing buffer arrangement of the perfluorohexanone discharge bottle group, and arranges the above contents in a predetermined order to form a discharge command fingerprint. At the same time, it obtains the valve opening feedback, drive circuit response, discharge start mark, and discharge end mark transmitted back during the discharge process, and arranges them in the same order to form a discharge execution fingerprint. It performs consistency judgment on each of the two sets of fingerprints to generate a perfluorohexanone discharge compliance record.

[0079] Discharge organization module: Based on the perfluorohexanone discharge compliance record, obtain the protected area closure confirmation signal, the available bottle group identification, and the corresponding stage content in the fire evolution record. Compare the execution order of the above content, arrange the sequence of discharge-related actions according to the comparison results, and generate the perfluorohexanone discharge execution schedule.

[0080] Monitoring and confirmation module: Based on the perfluorohexanone (PFH) release execution schedule, it obtains the release start marker, release completion marker, valve reset marker, and bottle group status marker transmitted back from the IoT nodes. It performs corresponding checks on each transmitted marker and release execution schedule, and collects the check results in chronological order to generate PPH fire monitoring results.

[0081] The monitoring and data collection records for perfluorohexanone specifically include time series identifiers, signal integrity status, and multi-source response association indexes. The perfluorohexanone fire evolution records include fire stage division identifiers, trigger continuity characteristics, and evolution inflection point location indexes. The perfluorohexanone discharge compliance records specifically include instruction execution consistency conclusions, discharge process matching status, and abnormal deviation judgment identifiers. The perfluorohexanone discharge execution arrangements include action priority sequence, discharge coordination relationship identifiers, and execution stage logical structure. The perfluorohexanone fire monitoring results specifically include discharge completion confirmation status, equipment operation final state identifiers, and overall protection effectiveness conclusions.

[0082] The monitoring and data acquisition module includes:

[0083] Signal acquisition submodule: Acquires response signals from smoke detectors, heat detectors, fire extinguishing agent cylinder pressure detection signals, discharge valve opening / closing signals, and electromagnetic drive circuit on / off signals deployed in the perfluorohexanone protected area. Records the acquisition time value, signal amplitude, and status code for each signal. Performs numerical summarization calculations on the acquisition time value to generate response signal quantity.

[0084] The signal acquisition submodule acquires response signals from smoke detectors, heat detectors, extinguishing agent cylinder pressure sensors, discharge valve opening / closing signals, and electromagnetic drive circuit on / off signals deployed within the perfluorohexanone protected area. The system first initializes the multi-channel analog-to-digital converter, setting the sampling frequency to 200Hz. It then discretizes the various analog signals connected to the input ports, defining the smoke detector as channel CH1, the heat detector as channel CH2, the extinguishing agent cylinder pressure sensor as channel CH3, the discharge valve position sensor as channel CH4, and the electromagnetic drive circuit feedback as channel CH5. to Within the time window, the current or voltage values ​​of each channel are read synchronously. For the 4-20mA analog current signal output by the smoke detector, the current is converted into a voltage value through a resistor, and then the value is determined according to the sensor calibration curve. (in , This is converted back to a percentage value of smoke concentration. For example, when the sampling current is 12mA, the calculated smoke concentration is... This corresponds to a concentration value of 10.0% OBS / m. Similarly, the temperature sensor signal is converted from thermocouple potential to temperature, and the pressure signal of the extinguishing agent cylinder group is converted to pressure. The time of each acquisition action is accurate to the millisecond and recorded in the timestamp register. Simultaneously, the status registers of each sensor device are read to obtain the status code (e.g., 0x00 indicates normal operation, 0x01 indicates fault), and the converted physical quantity value is defined as the signal amplitude. All data from the aforementioned single sampling point are encapsulated into a single data frame, and then processed within a preset time period. The amplitude of each sampling point is calculated using an arithmetic mean, i.e. This is to eliminate instantaneous high-frequency noise interference. For example, 200 points are collected within 1 second, and the average pressure of these 200 points is calculated as the effective pressure reading for that second to generate a response signal.

[0085] Sequence Marking Submodule: Based on the response semaphore, it calls the acquisition time values ​​of each signal, performs a sequential sorting judgment on all signals, performs a comparison operation on the time difference of adjacent signals, appends the corresponding sequence number and forms a continuous arrangement structure, and generates a time sequence arrangement based on the arrangement result;

[0086] The sequence marker submodule, based on the response semaphore, calls the acquisition time values ​​of each signal, and the system establishes a sequence marker with a length of [length missing]. A dynamic array that aggregates signals from different sensor sources according to their carried values. The timestamps are filled into an array, and the quicksort algorithm is used to sort the elements in the array in ascending order based on the timestamp values, thus satisfying the following conditions: For two adjacent signal items after sorting and Extract its timestamp and Perform interpolation Set the minimum time resolution benchmark as ,like If the two signals are determined to be concurrent events, they are assigned the same primary sequence number. Then assign an increasing sequential number, for example... The time is 10:00:01.005. The time is 10:00:01.006, the difference is 1ms which is less than 5ms, so both belong to the same timing group, and the number is SEQ_001. The time is 10:00:01.020, the difference is 14ms, and the number is incremented to SEQ_002. A unique 64-bit integer sequential ID is assigned to all signals in the entire monitoring period, forming a linked list structure with index key values ​​that is strictly distributed according to the time axis. A time sequence arrangement is generated based on the arrangement result.

[0087] Missing signal check submodule: Based on the time sequence, it calls the signal type quantity and sequential number continuous value, performs gap judgment on each number, records the number difference for the gap position and summarizes the quantity results, and combines the arranged signal content to form a complete record structure to generate perfluorohexanone monitoring and acquisition record.

[0088] Based on the time sequence, the system calls upon the number of signal types and sequential numbers. First, it reads the preset system configuration table to determine the total number of devices that should be online in the current protected area. (For example, configure 5 detectors, 2 bottle groups, and a total of 7 signal sources), traverse the sequence number IDs in the time sequence arrangement, and check the number sequence. Does a numerical fault exist? Execute the following logical judgment: If Then determine the position. and There are missing values; calculate the missing value. For example, if the current record number is 1050 and the next record number is 1053, the difference is 3, indicating that records numbered 1051 and 1052 are missing. The system further analyzes the expected heartbeat frequency of each signal source within this time period and compares it with the types of signals actually received. If a certain type of signal (such as the pressure of the fire extinguishing agent cylinder group) has not appeared within 3 consecutive numbering cycles, then the signal of this type is marked as "missing". All detected missing numbers, missing quantities and corresponding estimated time intervals are written to the log. At the same time, the sorted signal data stream after integrity verification is encapsulated. See Table 1. Table 1 lists the verification data of some monitoring and acquisition signals. By checking the integrity and continuity of each field of the data in Table 1 and combining it with the sorted signal content to form a complete record structure, perfluorohexanone monitoring and acquisition records are generated.

[0089] Table 1. Monitoring and Acquisition Signal Verification Data Table

[0090] Sequential numbering Timestamp Signal Source ID signal type Amplitude Status verification 1024 14:20:01.050 SD_01 Smoke Concentration 0.05 dB / m normal 1025 14:20:01.100 TD_01 temperature 25.5 °C normal 1027 14:20:01.200 PG_01 Bottle group pressure 4.2 MPa Missing (1026)

[0091] The fire detection module includes:

[0092] Smoke detection control submodule: Based on the monitoring and acquisition records of perfluorohexanone, it performs a comparison judgment on the response changes of smoke detectors and the fire triggering limit, obtains the response values ​​of smoke detectors at each sampling time, compares the response value at each time with the set fire triggering threshold point by point, records the response value exceeding the limit and not exceeding the limit state sequence, and generates the smoke response exceeding the limit sequence value.

[0093] The smoke detection control submodule, based on perfluorohexanone monitoring and data acquisition records, performs a comparison judgment between the response changes of smoke detectors and the fire triggering threshold, obtains the response values ​​of smoke detectors at each sampling time, and retrieves all data items marked "smoke detector" from the monitoring records to extract their concentration amplitude sequences. Set fire trigger threshold This threshold is set according to the "Code for Design of Automatic Fire Alarm Systems" and in conjunction with the environment of the perfluorohexanone protected area, and is set to a value of [value missing]. (This value is set based on the average measurement of visible smoke produced at a distance of 3 meters from the fire source in a laboratory standard paper burning test.) Traversal sequence Execute on each point A Boolean logic judgment, if the condition is true, then at that moment... The state is set to 1 (out of bounds), otherwise it is set to 0 (not out of bounds). For example, when The sequence is At that time, the control result sequence was The system further counts the length of consecutive "1" occurrences. Only when the number of consecutive boundary crossings exceeds 3 sampling periods (i.e., the duration is greater than 1.5 seconds to avoid instantaneous smoke interference) is it confirmed as a valid boundary crossing. The start and end timestamps of all valid boundary crossing states are recorded to form binary state waveform data and generate smoke response boundary crossing sequence values.

[0094] Temperature control submodule: Based on the monitoring and acquisition records of perfluorohexanone, call the smoke response out-of-bounds sequence value, obtain the response change data of the temperature detector at the same sampling time, perform item-by-item comparison between the temperature response value at each time and the corresponding start-up limit value, mark the start-up state change node, and arrange it in time order with the smoke out-of-bounds node to obtain the dual detection time sequence difference.

[0095] The temperature control submodule, based on the perfluorohexanone monitoring and acquisition records, calls the smoke response out-of-bounds sequence value to obtain the response change data of the temperature detector at the same sampling time. The system then locks the time axis interval with a state of "1" in the smoke response out-of-bounds sequence. Based on this, a 30-second backward and 30-second forward analysis window is used to extract the temperature value sequence of the temperature sensor within this window. Setting the start-up threshold value includes two indicators: absolute temperature threshold. With temperature rise rate threshold The temperature rise rate is calculated by performing a first-order difference operation on the sequence data, i.e. Compare item by item: If If the temperature sensing trigger condition is met at that moment, it is marked as a state node. The time series of temperature-triggered events and the time series of smoke-triggered events are placed on the same time axis, and the time difference between the two is calculated. For example, if the smoke sensor is triggered at 14:20:05 and the heat sensor is triggered at 14:20:15, then... If the temperature sensing triggers before the smoke sensing triggers, the difference is negative. The system records the time interval value of each pair of associated triggering events to obtain the dual detection timing difference.

[0096] Evolution Recording Submodule: Based on the monitoring and acquisition records of perfluorohexanone, it calls the dual detection time difference to continuously detect the order of smoke and heat detection results, identifies the sampling positions where the order relationship changes continuously, extracts the corresponding position index and time span parameters, summarizes and records them, and generates a perfluorohexanone fire evolution record.

[0097] The evolution recording submodule, based on perfluorohexanone monitoring and data acquisition, utilizes dual-detection timing difference data to continuously detect the sequence of smoke and heat detection results. The system establishes a state machine model containing three states: "smoke only," "heat only," and "dual trigger." It scans the dual-detection timing difference data in time steps, identifying state transition points, such as transitioning from "no signal" to "smoke only," and then to "dual trigger." It records the number of frames maintained in each state. If the "smoke only" state lasts for a certain duration... If the smoldering stage exceeds the preset threshold (e.g., 60 seconds) and is immediately followed by a "double trigger," it is determined to be a typical smoldering-to-open flame process. If both trigger almost simultaneously ( If the fire is identified as a rapid, explosive fire, the key inflection point index (i.e., the array index where the state value changes) is extracted during the evolution process. The time span between inflection points is calculated, and a time-state mapping table for the fire development stages is constructed. The process of the fire from its initial stage to its full-scale triggering is digitized into a series of discrete stage identifiers, generating a perfluorohexanone fire evolution record.

[0098] The discharge verification module includes:

[0099] Command fingerprint submodule: Obtain the perfluorohexanone fire evolution record, and send the discharge area identification content, valve drive opening content, discharge continuity arrangement content, and closure buffer arrangement content to the discharge bottle group. Perform an arrangement operation on the above content in chronological order to form a content sequence with a fixed order, and assign position numbers to each identifier in the sequence to generate a discharge command fingerprint sequence value.

[0100] The command fingerprint submodule acquires the perfluorohexanone fire evolution record and sends it to the discharge cylinder group, including the discharge area identification, valve actuation opening information, discharge duration schedule, and shutdown buffer schedule. The system parses the fire extinguishing command package generated by the central control logic. This command package includes the target area code (e.g., Zone_A), the solenoid valve action type (e.g., Open_High_Pressure), and the designed discharge duration (e.g., ...). ) and valve closing delay (e.g. The parameters are converted into a standardized hexadecimal instruction code stream according to the logical sequence of the actions, for example: region identifier 0x0A. Valve open 0xFF Maintain 0x0A for 10 seconds. Valve closed at 0x00, assigning an incrementing position number to each byte in the bitstream. We construct an ideal instruction execution template, which represents the undisturbed theoretical control timing, in which each action node is bound to a time interval relative to the start time. The theoretical delay value, such as the valve opening time, is... The shutdown should be done in This forms a fixed sequence of content, generating a fingerprint sequence value for the spray command.

[0101] The fingerprint execution submodule acquires the valve opening feedback, drive circuit response, discharge start marker, and discharge end marker transmitted back during the discharge process. Based on the order rules adopted by the discharge command fingerprint sequence value, it arranges each transmitted item in the same order and performs numbering and calibration on the corresponding positions to obtain the discharge execution fingerprint sequence value.

[0102] The fingerprint submodule executes to acquire valve opening feedback, drive circuit response, discharge start marker, and discharge end marker transmitted during the discharge process. The system monitors feedback messages on the CAN bus or RS485 bus in real time, and records the moment when it receives the "valve in position switch" closing signal. and type code; when a sudden change in drive circuit current is detected (e.g., from 0mA to 2A), record the time. and type code; when the pipeline pressure switch activates to indicate the start of discharge, record the time. The system rearranges these discrete feedback events according to the definition rules adopted by the spray command fingerprint sequence value. If there is an out-of-order situation (such as the spray start mark being earlier than the valve opening feedback), the system records and marks the abnormal bit according to the actual occurrence order. Each feedback item is assigned a function number corresponding to the command fingerprint. For example, the actual received "valve opening feedback" is mapped to position number 2 of "valve drive opening" in the command sequence. If no feedback is received at a certain position, the position is left empty, thus obtaining the spray execution fingerprint sequence value.

[0103] Consistency determination submodule: Calls the fingerprint sequence value of the release command and the fingerprint sequence value of the release execution, performs equality judgment on each corresponding numbered item in the sequence, counts the number of consistent items and the total number of items, calculates the consistency ratio, records the position number of inconsistent items, and generates perfluorohexanone release compliance record.

[0104] The consistency determination submodule calls the ejection command fingerprint sequence value and the ejection execution fingerprint sequence value, and performs an equality check on each corresponding numbered item in the sequence. The system sets a comparison cursor. ,from Traverse to the total length of the sequence Compare instruction sequence items With execution sequence items The content matching degree is judged by the following logic: if and (in If the allowable time deviation is set to 200ms, then the item is considered to be consistent, and the counter is activated. Add 1; otherwise, record it as inconsistent and record the corresponding number. For example, if a command instructs the valve to open at 50ms, and the actual feedback is received at 60ms, the deviation is 10ms < 200ms, which is considered consistent. If the feedback is received after 300ms, it is considered a timeout inconsistency, and the consistency ratio is calculated. See Table 2, which shows an example of consistency determination between instructions and execution fingerprints. The table clearly lists the time comparison and determination results for each instruction and feedback. Through computational examples, it is verified that if... and If the consistency is 80%, this percentage reflects the accuracy of the system execution and generates perfluorohexanone release compliance records.

[0105] Table 2. Consistency Judgment Table between Command and Execution Fingerprint

[0106] Step number Instruction content Theoretical time (ms) Feedback content Actual time (ms) Deviation (ms) Judgment result 01 Drive output 0 loop current 10 +10 Consistent 02 Valve open 50 Valve in place 120 +70 Consistent 03 Discharge begins 100 pressure switch 450 +350 Inconsistent (timeout)

[0107] The ejection organization module includes:

[0108] Recording and verification submodule: acquire perfluorohexanone release compliance records, collect protection zone closure confirmation signals, monitor bottle group availability indicators, perform sequential comparison of the timestamps of the three records, determine the order of each record and form a corresponding number sequence, and generate release record sequence values;

[0109] The record verification sub-module obtains the perfluoroketone spraying compliance record, collects the confirmation signal of the closure of the protected area, monitors the available identification of the cylinder group. The system first checks the input ports DI_01 (status of the door and window magnetic switch) and DI_02 (status of the ventilation louvers). Only when both are at the closed logic level "1", it is confirmed that the protected area closure signal is valid, and the timestamp is recorded as Tseal. At the same time, the data of the weighing device or the liquid level gauge is read. If the current agent weight Wcurr ≥ 95% × Wrated (rated weight), the available identification of the cylinder group is set, and the timestamp is recorded as Tready. Sort the three values of Tseal, Tready, and the generation time Tcheck of the spraying compliance record. The ideal logic should satisfy Tseal < Tready < Tcheck (that is, first close the door, then confirm the cylinder group, and finally perform the spraying inspection). If the actual order violates this logic, for example, the closure signal is received after the spraying action starts, a specific error number code is assigned to this abnormal order, forming a number sequence describing the action preconditions, and the spraying record sequence value is generated.

[0110] The signal integration sub-module: According to the spraying record sequence value, call the corresponding stage content of the fire evolution record, detect the start and end times of the stage and perform a consistency comparison with the number sequence, screen the matching stages and gather them into a continuous sequence to obtain the stage matching sequence value;

[0111] The signal integration sub-module, according to the spraying record sequence value, calls the corresponding stage content of the fire evolution record, detects the start and end times of the stage and performs a consistency comparison with the number sequence. The system extracts the stage interval determined as "fire confirmed" in the fire evolution record , and performs an overlap analysis with the key action time points in the spraying record sequence value. The screening rule is: The spraying preparation actions (such as closure confirmation, cylinder group confirmation) must fall after and before the spraying instruction is issued. Calculate the matching degree score. If all safety interlock signals are completed within 5 seconds after the fire is confirmed, it is regarded as the "quick response stage". Gather these signal events that meet the logical time constraints,剔除 those isolated signals that occurred before the fire was confirmed (belonging to misoperations or tests), and construct a continuous event sequence including three dimensions of fire trigger, safety confirmation, and resource readiness to obtain the stage matching sequence value.

[0112] The sequence arrangement sub-module: Based on the stage matching sequence value, call the protected area closure confirmation signal and the available identification of the cylinder group, judge according to the time sequence and the available status, arrange the trigger order of the spraying action and form a time arrangement sequence, and generate the perfluoroketone spraying execution arrangement.

[0113] The sequence orchestration submodule, based on the phase matching sequence value, calls the protected area closure confirmation signal and the cylinder group availability indicator, and performs judgments based on time sequence and availability status. The system, according to a preset fire extinguishing tactical logic tree, finalizes the execution order of physical actions. The root node of the logic tree is "Fire Confirmation," the first-level branches are "Power Cut-off" and "Ventilation Shutdown," the second-level branches are "30-second Delay for Personnel Evacuation," and the third-level branches are "Cylinder Group Activation." The system traverses the phase matching sequence, verifying whether the preconditions of each branch are met. For example, if "Ventilation Shutdown" fails to respond successfully, the "Cylinder Group Activation" action is suspended and a "Re-shutdown Command" is inserted. A nanosecond-level absolute trigger time is assigned to each action to be executed. This ensures that high-priority actions (such as audible and visual alarms) are executed before low-priority actions (such as equipment self-tests), forming a control instruction queue that strictly increases in time and has no logical conflicts, thus generating a perfluorohexanone release execution schedule.

[0114] The monitoring and confirmation module includes:

[0115] The execution tag acquisition submodule acquires the perfluorohexanone release execution schedule, collects the release start tag, release completion tag, valve reset tag, and bottle group status tag returned by the IoT nodes, records the corresponding timestamps of each tag and the execution schedule time point, forms a tag time sequence collected by node number, and generates release tag time sequence values.

[0116] The execution tag acquisition submodule obtains the perfluorohexanone (PFH) release schedule, collects release start tags, release completion tags, valve reset tags, and bottle group status tags returned by IoT nodes, and the system listens to MQTT or CoAP protocol data packets reported by each distributed node (such as smart nozzles and solenoid valve controllers) through the IoT gateway, parses the opcode and timestamp in the packet body, and records the received "release start" (OpCode: 0xA1) time. "Discharge complete" (OpCode: 0xA2) time Valve reset (OpCode: 0xB1) time And the time for "bottle group pressure to zero" (OpCode: 0xC1) Extract the data and store the above time data in a hash table structure using the node ID (Node_ID) as the index key. For each planned node in the spray execution schedule, fill in the actual time value returned. If a node does not return, fill in NULL to form an actual execution time matrix distributed by node and generate spray mark time sequence values.

[0117] The timing verification submodule: Based on the timing value of the discharge mark, it calls the discharge start mark time and the execution schedule start time to calculate the time difference, and performs sequential judgment on the discharge completion mark, valve reset mark and bottle group status mark in turn, records the number of inconsistencies and the corresponding node number, and generates a timing consistency coefficient;

[0118] The timing verification submodule, based on the emission mark timing value, calculates the time difference between the emission start mark time and the execution schedule start time, and the system calculates the actual start delay. and the deviation in discharge duration Set the time consistency tolerance range to be For each action node, if its execution time deviation is within the tolerance range, then the timing consistency score of that node is... It is denoted as 1; otherwise, it decreases linearly according to the magnitude of the deviation, as shown in the formula. (Where 2000ms is the maximum allowable deviation), for example, if the planned start time is 10:00:00 and the actual start time is 10:00:00:20, the deviation is 200ms, and the score is 1; if the deviation is 1000ms, the score is 0.5. The same judgment is made for subsequent actions such as spray completion and reset, and the number and specific number of inconsistent nodes are accumulated to generate the timing consistency coefficient.

[0119] The monitoring results summary submodule: Based on the time sequence consistency coefficient, it calls the time difference values ​​of each node to collect and sort them, performs interval statistics on the consistency coefficient of all nodes under the same discharge task, forms a set of monitoring values ​​arranged in time order, and generates perfluorohexanone fire monitoring results.

[0120] The monitoring results aggregation submodule, based on the time-series consistency coefficient, calls the time difference values ​​marked by each node for aggregation and sorting. The system then aggregates and sorts the consistency coefficient sets of each node obtained above. Perform statistical analysis to calculate the overall task execution confidence level. (in Assigning weights to critical actions (e.g., the spraying action is weighted at 0.5, and the reset action at 0.1), and simultaneously plotting the actual action trajectories of all nodes along the timeline to create a full-process time sequence diagram, and statistically analyzing all coefficients. The percentage of nodes is determined. If the percentage exceeds 95%, the fire monitoring task is judged as "effective protection". If it is less than 60%, it is judged as "system abnormality". See Table 3. Table 3 lists the final monitoring results summary data, including the timing deviation of key nodes and the final judgment conclusion. It intuitively shows the final state of system operation. The result value directly quantifies the degree of consistency between the physical fire extinguishing process and the digital control logic, and generates the perfluorohexanone fire monitoring results.

[0121] Table 3 Summary of Perfluorohexanone Fire Monitoring Results

[0122] monitoring nodes Planned Time actual time Deviation (ms) Consistency coefficient State determination Startup command 12:00:00.000 12:00:00.050 +50 1.00 excellent spraying action 12:00:30.000 12:00:30.800 +800 0.60 good End of confirmation 12:00:40.000 12:00:40.900 +900 0.55 qualified

[0123] A perfluorohexanone fire monitoring method based on the Internet of Things includes the following steps:

[0124] S1. Acquire the smoke response signal, temperature response signal, bottle group pressure detection signal, discharge valve opening and closing signal, and electromagnetic drive circuit on / off signal within the perfluorohexanone protection zone. Mark the acquisition sequence of each signal and sort them in order. Perform missing verification on the sorting results and generate perfluorohexanone monitoring and acquisition records for post-event traceability analysis.

[0125] S2. Based on the monitoring and data collection of perfluorohexanone, the response changes of smoke detectors are compared with the fire triggering limit, and the response changes of heat detectors are compared with the activation limit. The order of the two types of judgment results is sorted. When the ordering relationship changes continuously, the corresponding position is recorded to form a perfluorohexanone fire evolution record.

[0126] S3. Based on the perfluorohexanone fire evolution record, extract the discharge area identifier and valve drive opening, discharge duration and closing buffer arrangement, generate discharge command fingerprint in sequence, synchronously obtain valve opening feedback and loop response, discharge start and end markers, form execution fingerprint in sequence, compare consistency, and generate discharge compliance record.

[0127] S4. Based on the perfluorohexanone release record, extract the protection zone closure confirmation signal, verify the availability of the cylinder group, match the content of the fire evolution stage, compare the sequence of each signal, confirm that the cylinder group is ready after the closure is completed, then proceed with the release start, finally record the release completion and subsequent status, and monitor the feedback from the monitoring system to form a release execution plan.

[0128] S5. Based on the perfluorohexanone (PFH) release execution schedule, collect the release start marker, release completion marker, valve reset marker, and bottle group status marker transmitted back from the IoT nodes. Verify each transmitted marker against the execution schedule item by item, summarize the verification results in chronological order, and form the PPH fire monitoring results.

[0129] In step S1, when multiple sensor signals in the monitoring system are contradictory or missing, the following steps are executed:

[0130] S1.1: Collect signals from all currently available temperature and smoke sensors, assuming the sensor set is... 1. Each sensor Historical accuracy weight Based on its past The accuracy of this test is calculated as follows:

[0131]

[0132] in For sensors In the past The number of times a correct response is made in a test. For sensors Total number of responses;

[0133] S1.2: Define the current fire situation as follows Set up sensor In the fire The typical output value distribution when it occurs is as follows The typical output value distribution under no-fire conditions is as follows: According to the sensor Current reading Its degree of matching with the fire scenario hypothesis The calculation is as follows:

[0134]

[0135] in To observe under a given distribution D Probability estimation of nearby values, typically for this sensor Historical data statistics were obtained;

[0136] S1.3: The overall fire confidence level is calculated using the following formula. :

[0137]

[0138] in The number of missing signals. The total number of expected signals;

[0139] S1.4: If If the fire rate exceeds the preset threshold, it is considered a valid fire and triggers the subsequent fire discharge process; otherwise, it enters manual review or system self-check mode.

[0140] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A perfluorohexanone fire monitoring system based on the Internet of Things, characterized in that, It includes a monitoring and acquisition module, a fire identification module, a discharge verification module, a discharge organization module, and a monitoring and confirmation module; The monitoring and acquisition module is used to call signals from smoke detectors, temperature detectors, bottle group pressure detection, opening and closing of discharge valves, and on / off signals of electromagnetic drive circuits. It adds acquisition sequence marks to each signal and arranges them in the order of occurrence. It performs missing checks on the arranged signals and generates perfluorohexanone monitoring and acquisition records. The fire identification module is based on the monitoring and data collection records of perfluorohexanone. It makes fire trigger judgments on the responses of smoke detectors and heat detectors, and analyzes the sequential relationship between the two judgment results. When the sequential relationship shows a continuous change, it records the corresponding location and generates a perfluorohexanone fire evolution record. The discharge verification module is based on the perfluorohexanone fire evolution record. It organizes the discharge area, valve opening, discharge duration, and closing buffer to form an instruction fingerprint. At the same time, it arranges the execution feedback returned during the discharge process in the same order to form an execution fingerprint. The two sets of fingerprints are compared item by item to generate a perfluorohexanone discharge compliance record. Based on the perfluorohexanone release compliance record, the release organization module calls the protected area closure confirmation, the cylinder group availability mark and the perfluorohexanone fire evolution record stage mark, organizes the release action sequence, and generates the perfluorohexanone release execution plan. The monitoring and confirmation module is based on the perfluorohexanone (PFH) release execution schedule. It verifies the start of release, the completion of release, valve reset, and bottle group marking. It organizes and confirms the items according to time and finally generates the PFH fire monitoring results.

2. The perfluorohexanone fire monitoring system based on the Internet of Things according to claim 1, characterized in that: The perfluorohexanone monitoring and acquisition records specifically include time series identifiers, signal integrity status, and multi-source response association indexes. The perfluorohexanone fire evolution record includes fire stage division markers, trigger continuity characteristics, and evolution inflection point location indexes; The perfluorohexanone release conforms to the record of the consistency conclusion of specific instruction execution, the matching status of the release process, and the abnormal deviation judgment mark; The perfluorohexanone release execution schedule includes the action priority sequence, release coordination relationship identifier, and execution phase logical structure; The specific results of the perfluorohexanone fire monitoring include confirmation of discharge completion, equipment operation status identification, and conclusion on the overall protection effectiveness.

3. The perfluorohexanone fire monitoring system based on the Internet of Things according to claim 1, characterized in that: The monitoring and data acquisition module includes: Signal acquisition submodule: Acquires response signals from smoke detectors, heat detectors, fire extinguishing agent cylinder pressure detection signals, discharge valve opening / closing signals, and electromagnetic drive circuit on / off signals deployed in the perfluorohexanone protected area. Records the acquisition time value, signal amplitude, and status code for each signal. Performs numerical summarization calculations on the acquisition time value to generate response signal quantity. Sequence Marking Submodule: Based on the response semaphore, it calls the acquisition time values ​​of each signal, performs a sequential sorting judgment on all signals, performs a comparison operation on the time difference of adjacent signals, appends the corresponding sequence number and forms a continuous arrangement structure, and generates a time sequence arrangement based on the arrangement result; Missing signal check submodule: Based on the time sequence, it calls the signal type quantity and sequential number continuous value, performs gap judgment on each number, records the number difference for the gap position and summarizes the quantity results, and combines the arranged signal content to form a complete record structure to generate perfluorohexanone monitoring and acquisition record.

4. The perfluorohexanone fire monitoring system based on the Internet of Things according to claim 1, characterized in that: The fire detection module includes: The smoke detection control submodule: Based on the monitoring and data acquisition records of perfluorohexanone, it performs a comparison judgment between the response changes of smoke detectors and the fire triggering threshold, and obtains the response values ​​of smoke detectors at each sampling time, which are recorded as sequences. And set fire trigger thresholds. Values For each sequence Click to execute A Boolean logic judgment, if the condition is true, then at that moment... The number of out-of-bounds points in the state position is recorded as 1, otherwise it is recorded as 0. The system further counts the length of consecutive 1s. Only when the number of consecutive out-of-bounds points exceeds 3 sampling periods, that is, the duration is greater than 1.5 seconds, to avoid instantaneous smoke interference, is it confirmed as a valid out-of-bounds. The start and end timestamps of all valid out-of-bounds states are recorded to form binary state waveform data and generate smoke response out-of-bounds sequence values. The temperature control submodule: Based on the monitoring and acquisition records of perfluorohexanone, it calls the smoke response out-of-bounds sequence value to obtain the response change data of the temperature detector at the same sampling time. The system locks the time axis interval with state 1 in the smoke response out-of-bounds sequence. Based on this, a 30-second backward and 30-second forward analysis window is used to extract the temperature value sequence of the temperature sensor within this window. And set an absolute temperature threshold. and temperature rise rate threshold The temperature rise rate is calculated using the following formula. : in, For the rate of temperature rise, The temperature value at the i-th second; The temperature response value at each moment is compared with the corresponding trigger limit value, and the comparison formula is as follows: When both conditions in the formula are met simultaneously, the temperature sensing trigger condition is determined to be valid at that moment, and it is marked as a state node. The time series of temperature-triggered events and the time series of smoke-triggered events are placed on the same time axis, and the time difference between the two is calculated. If the temperature sensor triggers before the smoke sensor, the difference is negative. The system records the time interval of each pair of associated triggering events to obtain the dual detection timing difference. Evolution Recording Submodule: Based on the monitoring and acquisition records of perfluorohexanone, it calls the dual detection time difference to continuously detect the order of smoke and heat detection results, identifies the sampling positions where the order relationship changes continuously, extracts the corresponding position index and time span parameters, summarizes and records them, and generates a perfluorohexanone fire evolution record.

5. The perfluorohexanone fire monitoring system based on the Internet of Things according to claim 1, characterized in that: The discharge verification module includes: Command fingerprint submodule: Obtain the perfluorohexanone fire evolution record, and send the discharge area identification content, valve drive opening content, discharge continuity arrangement content, and closure buffer arrangement content to the discharge bottle group. Perform an arrangement operation on the above content in chronological order to form a content sequence with a fixed order, and assign position numbers to each identifier in the sequence to generate a discharge command fingerprint sequence value. The fingerprint execution submodule acquires the valve opening feedback, drive circuit response, discharge start marker, and discharge end marker transmitted back during the discharge process. Based on the order rules adopted by the discharge command fingerprint sequence value, it arranges each transmitted item in the same order and performs numbering and calibration on the corresponding positions to obtain the discharge execution fingerprint sequence value. Consistency determination submodule: Calls the fingerprint sequence value of the release command and the fingerprint sequence value of the release execution, performs equality judgment on each corresponding numbered item in the sequence, counts the number of consistent items and the total number of items, calculates the consistency ratio, records the position number of inconsistent items, and generates perfluorohexanone release compliance record.

6. The perfluorohexanone fire monitoring system based on the Internet of Things according to claim 1, characterized in that: The discharge organization module includes: Recording and verification submodule: acquire perfluorohexanone release compliance records, collect protection zone closure confirmation signals, monitor bottle group availability indicators, perform sequential comparison of the timestamps of the three records, determine the order of each record and form a corresponding number sequence, and generate release record sequence values; Signal integration submodule: Based on the order value of the discharge record, it calls the corresponding stage content of the fire evolution record, detects the start and end time of the stage and performs a consistency comparison with the numbered sequence, filters matching stages and aggregates them into a continuous sequence to obtain the stage matching sequence value; Sequential arrangement submodule: Based on the stage matching sequence value, it calls the protection zone closure confirmation signal and the bottle group availability identifier, performs judgment based on time sequence and availability status, arranges the trigger order of the release action and forms a time arrangement sequence, and generates the perfluorohexanone release execution schedule.

7. The perfluorohexanone fire monitoring system based on the Internet of Things according to claim 1, characterized in that: The monitoring and confirmation module includes: The execution tag acquisition submodule acquires the perfluorohexanone release execution schedule, collects the release start tag, release completion tag, valve reset tag, and bottle group status tag returned by the IoT nodes, records the corresponding timestamps of each tag and the execution schedule time point, forms a tag time sequence collected by node number, and generates release tag time sequence values. The timing verification submodule, based on the emission mark timing value, calculates the time difference between the emission start mark time and the execution schedule start time, and then calculates the actual start delay. and discharge duration deviation The formulas are as follows: in, This refers to the actual start-up time of the spray. The start time of the spraying plan. This refers to the actual end time of the spray. This is the end time of the spraying plan; Set the time consistency tolerance range as follows For each action node, if its execution time deviation is within the tolerance range, then the timing consistency score of that node is... It is denoted as 1; otherwise, it decreases linearly according to the magnitude of the deviation, as shown in the formula: Where 2000 is the maximum permissible deviation, in milliseconds; The same judgment is made on subsequent actions such as completion of spraying and reset, and the number and specific number of inconsistent nodes are accumulated to generate a timing consistency coefficient. The monitoring results aggregation submodule: Based on the time-series consistency coefficient, it calls the time difference values ​​marked by each node for aggregation and sorting. The system then aggregates and sorts the consistency coefficients of each node obtained above. Statistical analysis is performed to calculate the overall task execution confidence score, using the following formula: in Assigning weights to critical actions, the weight for the discharge action is set to 0.5, and the weight for the reset action is set to 0.

1. Simultaneously, a full-process time sequence diagram is plotted based on the actual action trajectories of all nodes along the timeline, and all coefficients are statistically analyzed. If the percentage of nodes exceeds 95%, the fire monitoring task is considered to be effective; if it is less than 60%, it is considered to be an abnormal system. A set of monitoring values ​​arranged in chronological order is formed to generate perfluorohexanone fire monitoring results.

8. A perfluorohexanone fire monitoring method based on the Internet of Things, characterized in that, The method, used in the IoT-based perfluorohexanone fire monitoring system according to any one of claims 1-7, includes the following steps: S1: Acquire the smoke response signal, temperature response signal, bottle group pressure detection signal, discharge valve opening and closing signal, and electromagnetic drive circuit on / off signal within the perfluorohexanone protection zone. Mark the acquisition order of each signal and sort them in order. Perform missing verification on the sorting results and generate perfluorohexanone monitoring and acquisition records for post-event traceability analysis. S2: Based on the monitoring and data collection of perfluorohexanone, the response changes of smoke detectors are compared with the fire triggering limit, and the response changes of heat detectors are compared with the activation limit. The order of the two types of judgment results is sorted. When the ordering relationship changes continuously, the corresponding position is recorded to form a perfluorohexanone fire evolution record. S3: Based on the perfluorohexanone fire evolution record, extract the discharge area identifier and valve drive opening, discharge duration and closing buffer arrangement, generate discharge command fingerprint in sequence, synchronously obtain valve opening feedback and loop response, discharge start and end markers, form execution fingerprint in sequence, compare consistency, and generate discharge compliance record. S4: Based on the perfluorohexanone release record, extract the protection zone closure confirmation signal, verify the availability of the bottle group, match the content of the fire evolution stage, compare the sequence of each signal, confirm that the bottle group is ready after the closure is completed, then proceed with the release initiation, and finally record the release completion and subsequent status, and monitor the feedback from the monitoring system to form a release execution plan. S5: Based on the perfluorohexanone (PFH) release execution schedule, collect the release start marker, release completion marker, valve reset marker, and bottle group status marker transmitted back from the IoT nodes. Verify each transmitted marker and execution schedule item by item, summarize the verification results in chronological order, and form the PPH fire monitoring results.

9. The perfluorohexanone fire monitoring method based on the Internet of Things according to claim 1, characterized in that: In step S1, when multiple sensor signals in the monitoring system are contradictory or missing, the following steps are executed: S1.1: Collect signals from all currently available temperature and smoke sensors, assuming the sensor set is...

1. Each sensor Historical accuracy weight Based on its past The accuracy of this test is calculated as follows: in For sensors In the past The number of times a correct response is made in a test. For sensors Total number of responses; S1.2: Define the current fire situation as follows Set up sensor In the fire The typical output value distribution when it occurs is as follows The typical output value distribution under no-fire conditions is as follows: According to the sensor Current reading Its degree of matching with the fire scenario hypothesis The calculation is as follows: in To observe under a given distribution D Probability estimation of nearby values, typically for this sensor Historical data statistics were obtained; S1.3: The overall fire confidence level is calculated using the following formula. : in The number of missing signals. The total number of expected signals; S1.4: If If the fire rate exceeds the preset threshold, it is considered a valid fire and triggers the subsequent fire discharge process; otherwise, it enters manual review or system self-check mode.

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