Intelligent fire-fighting equipment situation monitoring and centralized management and control system and method

Abstract

The application provides a smart fire-fighting equipment situation monitoring and centralized management and control system and method, relates to the technical field of fire-fighting equipment monitoring and control, and comprises a fire-fighting portrait establishing module, a fire-fighting equipment situation label analysis module, a false online risk label determining module and a fire-fighting equipment control execution and feedback verification module. The application can solve the problem that the traditional heartbeat mechanism can only detect network layer connectivity and cannot verify whether the fire-fighting equipment normally executes tasks at the application layer in real time, and reduces the safety hidden danger caused by the false online state of the fire-fighting equipment.

Description

Technical Field

[0001] This invention relates to the field of fire protection equipment monitoring and control technology, and in particular to an intelligent fire protection equipment situation monitoring and centralized management system and method. Background Technology

[0002] Existing intelligent fire protection equipment includes, but is not limited to, fire pump frequency converter control cabinets, fire valve controllers, gas extinguishing control panels, and automatic fire sprinkler system controllers. Fire pump frequency converter control cabinets are used to control the start and stop of fire pumps, frequency regulation, and pressure monitoring to ensure timely water supply to the fire protection system in case of fire. The equipment is typically equipped with sensors to monitor pump operating status, pressure, flow rate, and other parameters, and allows for remote start and stop control via a control interface. Fire valve controllers are used to control the opening and closing of valves in the fire protection system, ensuring the smooth flow of extinguishing media. Valve controllers can adjust valve positions via control commands and are equipped with actuators and sensors to provide feedback on valve status and position, supporting remote control and status monitoring. Gas extinguishing control panels are used to control the start and operation of gas extinguishing systems. This equipment automatically starts the gas extinguishing system by monitoring environmental parameters (such as temperature and air pressure) and detecting fire signals, ensuring the safety of the fire area. Automatic fire sprinkler system controllers are used to control the operating status of the fire sprinkler system, including start-up, shutdown, and water flow regulation, ensuring the sprinkler system can be put into use promptly in the event of a fire.

[0003] Currently, communication between fire-fighting equipment and the control center platform is typically achieved through communication networks (such as wireless or wired communication). This ensures that the fire-fighting equipment can transmit status data and receive control commands in real time, thereby enabling functions such as fire monitoring, alarm, and automatic fire suppression. After establishing a continuous communication connection, it is also necessary to check whether the fire-fighting equipment can continue to respond to commands normally. This ensures that while the network layer connection is normal, the fire-fighting equipment can also execute control tasks and return feedback information at the application layer. Currently, the common methods for detecting the connection status of fire-fighting equipment are TCP keepalive or MQTT heartbeat mechanisms. The online status of the fire-fighting equipment is confirmed by monitoring whether the connection is in the ESTABLISHED state. If the fire-fighting equipment responds to the heartbeat packet, the system considers the fire-fighting equipment to be online and capable of executing control commands or providing feedback information normally.

[0004] However, communication network connectivity and heartbeat mechanisms primarily monitor the connectivity of the network and transport layers. They can only guarantee the connection status between fire-fighting equipment and the platform, but cannot directly verify whether the fire-fighting equipment can execute tasks or respond to control commands normally at the application layer. In practical applications, fire-fighting equipment may be in a false online state due to various reasons. Common problems include deadlock and process blocking. Deadlock typically occurs in multi-tasking systems. When multiple tasks wait for each other to release resources, some critical processes of the equipment may be blocked, thus affecting the equipment's ability to respond to instructions from the control platform in a timely manner. Process blocking usually occurs when the task scheduling system malfunctions. The execution of some high-priority tasks may be delayed due to improper system resource allocation, causing the fire-fighting equipment to be unable to process subsequent control requests in a timely manner. These problems occur at the application layer of the fire-fighting equipment, but because the heartbeat mechanism mainly detects network layer connectivity, these faults are often undetected. Even if the fire-fighting equipment is in a deadlock or process blocking state, the platform may mistakenly believe that the equipment is online and continue to send control commands. In addition to deadlock and process blocking, fire-fighting equipment may also fail to execute tasks normally due to other faults (such as memory leaks, hardware failures, application layer software crashes, etc.). Although the network layer connection remains normal, the platform may continue to send control commands, mistakenly believing the equipment is functioning normally. In this situation, even if the platform has received confirmation that the control commands were successfully sent, the fire-fighting equipment has not actually performed the corresponding operation. This can lead to a failure to respond promptly in critical moments, missing the optimal opportunity for human intervention, and potentially severely impacting emergency response and safety assurance. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide an intelligent fire protection equipment status monitoring and centralized management system and method, which solves the problem that the traditional heartbeat mechanism can only detect network layer connectivity and cannot verify in real time whether the fire protection equipment is performing its tasks normally at the application layer, thereby reducing the safety hazards caused by the false online status of fire protection equipment.

[0006] This invention provides an intelligent fire protection equipment status monitoring and centralized management system, comprising: The fire protection profile creation module is used to automatically collect metadata sets of fire protection equipment and detect the capabilities of fire protection equipment, thereby generating profile entries for the fire protection equipment.

[0007] The fire equipment status label analysis module is used to access the time-series data of fire equipment, generate a reliable data stream with quality tags, calculate the status index set of fire equipment, and analyze the status labels of fire equipment. The status labels of fire equipment include health status labels and preliminary false online risk labels.

[0008] The false online risk label determination module is used to determine the probe execution frequency and type based on the preliminary false online risk label of fire protection equipment, thereby verifying the probe of fire protection equipment, obtaining the probe results of fire protection equipment, and thus determining the false online risk label of fire protection equipment.

[0009] The fire equipment control execution and feedback verification module is used to generate control commands based on the health status label and false online risk label of the fire equipment. The control commands include routine control or maintenance operation or emergency response of the fire equipment. The module sends the control commands and obtains the execution receipt of the fire equipment, and performs read-back verification to ensure that the fire equipment executes according to the predetermined requirements and provides the correct execution results.

[0010] This application also provides a method for intelligent fire protection equipment status monitoring and centralized management, including: The system automatically collects metadata about fire-fighting equipment and performs capability detection on the equipment, thereby generating profile entries for the fire-fighting equipment.

[0011] The system accesses time-series data from fire protection equipment, generates a reliable data stream with quality tags, calculates a set of status indicators for the fire protection equipment, and analyzes the status tags of the fire protection equipment, which include health status tags and preliminary false online risk tags.

[0012] Based on the preliminary false online risk labels of fire protection equipment, the probe execution frequency and type are determined, and the probe verification of fire protection equipment is carried out to obtain the probe results of fire protection equipment, thereby determining the false online risk labels of fire protection equipment.

[0013] Based on the health status labels and false online risk labels of fire protection equipment, control instructions are generated. These instructions include routine control, maintenance operations, or emergency response for the fire protection equipment. The control instructions are sent and execution receipts from the fire protection equipment are obtained. The receipts are then read back for verification to ensure that the fire protection equipment executes according to the predetermined requirements and provides correct execution results.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: 1. This invention provides an intelligent fire protection equipment status monitoring and centralized management system and method. By automatically generating fire protection equipment profiles, it creates precise profile entries for each piece of equipment, providing a more accurate data foundation for subsequent health status analysis and risk label assessment. It accesses equipment time-series data and performs quality marking and consistency verification on the data stream to ensure high data reliability. Through real-time assessment of the operating status, it can determine the health status of the equipment and promptly provide feedback on whether the equipment is in a controllable state or a false online state, thereby automatically generating adaptive control commands to ensure the fire protection equipment can effectively perform its tasks. A probe verification module further enhances the system's reliability. For equipment false online risk labels, it dynamically adjusts the execution frequency and type of probes to ensure continuous monitoring and verification of the equipment. In the event of equipment anomalies, it can respond through maintenance operation commands or emergency response commands, thereby avoiding safety hazards caused by the inability to respond to control commands in a timely manner. Overall, this invention improves the reliability and real-time response capability of intelligent fire protection equipment through multi-level and multi-dimensional monitoring and control, solving the false online misjudgment problem existing in traditional methods, and ensuring the safety and efficiency of the fire protection system.

[0015] 2. This invention, by analyzing health status tags and preliminary false online risk tags, can identify whether fire-fighting equipment can normally perform control tasks and provide feedback at the application layer. Specifically, based on health status tags, the system can determine whether the equipment is in normal working condition, thereby ensuring that the equipment can execute normal control commands as expected; while through the analysis of preliminary false online risk tags, the system can further identify whether the equipment is in a false online state, avoiding the platform from mistakenly believing that the equipment is in normal working condition and continuing to send control commands, thus improving the reliability of fire-fighting equipment, ensuring timely response and execution of tasks at critical moments, and improving the safety and emergency response capabilities of the fire-fighting system.

[0016] 3. This invention determines the probe execution frequency and type based on the initial false online risk label of fire-fighting equipment, and verifies the probes accordingly. This allows for flexible adjustment of the monitoring strategy when equipment status changes. When equipment is marked as potentially falsely online, the accuracy of equipment status verification is improved by increasing the number of dual-read consistency probes and tightening the probe execution frequency. This enables the system to promptly detect execution faults or abnormal statuses of equipment when it may be in a false online state, thereby avoiding the sending of invalid control commands, improving the accuracy and reliability of the system, ensuring that fire-fighting equipment can respond to control commands truthfully and effectively at critical moments, and guaranteeing the stability and safety of the fire-fighting system. Attached Figure Description

[0017] Figure 1 This is a framework diagram of the intelligent fire protection equipment situation monitoring and centralized management system provided in the embodiments of the present invention; Figure 2 This is a flowchart of the intelligent fire protection equipment status monitoring and centralized management method provided in the embodiments of the present invention; Figure 3 This is a flowchart of probe verification and false online risk label determination; Figure 4 This is a flowchart for generating control instructions based on the health status labels and false online risk labels of fire-fighting equipment. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on the present invention. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In the following description, references to "some embodiments" refer to a subset of all possible embodiments. However, it is understood that "some embodiments" may be the same or different subsets of all possible embodiments and may be combined with each other without conflict. Embodiments of the present invention provide an intelligent fire protection equipment situation monitoring and centralized management system and method, such as... Figure 1 The diagram shown is a framework diagram of an intelligent fire protection equipment status monitoring and centralized management system. This system includes: Example 1: The intelligent fire protection equipment status monitoring and centralized management system connects the platform side and the fire protection equipment side through an existing communication network. The platform side automatically collects the communication parameters, protocols, interface addresses, authentication methods, equipment identifiers, equipment models, firmware versions, and reporting cycles of each fire protection device, and completes connectivity verification and identity verification to form a metadata set for the fire protection equipment. It parses the publicly available message structures or interface descriptions of the fire protection equipment (including self-reporting structures, historical reporting samples, and manufacturer structure descriptions). While extracting field names, data types, value ranges, timestamp fields, control entry points, and their parameter constraints, it simultaneously parses the minimum polling interval field, response timeout field, and acceptance receipt field.

[0020] Example A: Taking a fire pump frequency converter control cabinet (model VFD-FP300, firmware version v2.3.1) as an example, during the network access phase, the platform side obtains communication parameters (message broker address, port, certificate fingerprint, quality of service level, keep-alive period of 60 seconds) and message topics (status reporting topic fp / VFD-FP300 / tele, control command topic fp / VFD-FP300 / cmd, receipt topic fp / VFD-FP300 / cmd) through a connection based on message queue telemetry transmission (MQTT) and using transport layer security (TLS) during the network access phase. The authentication methods include p / VFD-FP300 / ack, interface address (local REST diagnostic interface path / api / v1 / diag), authentication method (device unique identifier device_uuid and token based on HMAC-SHA256 dual factor), device unique identifier (128-bit UUID), device model and firmware version (VFD-FP300 and v2.3.1), and reporting period (status 5 seconds, alarm 1 second, log 60 seconds). Connectivity verification is completed through handshake and application layer echo commands. The platform parses the publicly available message structure description of the control cabinet and extracts the field names, data types, and value ranges: running status (run_status, enumeration: stopped, running), work mode (work_mode, enumeration: automatic, manual), motor current (motor_current, floating-point, 0.0–120.0 A), discharge pressure (discharge_pressure, floating-point, 0.00–1.60 MPa), frequency (vf_frequency, floating-point, 0.0–50.0 Hz), execution counter (exec_counter, unsigned integer, 0–4294967295), last command time (last_cmd_ts, millisecond timestamp), running time (run_time_s, unsigned integer, in seconds), operation log cursor (log_index, unsigned integer), alarm code (alarm_code, integer, according to manufacturer's code table), and unified timestamp field (ts, millisecond timestamp). Simultaneously, the control entry points and parameter constraints are parsed: start (Boolean, only accepted when work_mode is automatic and minimum anti-jitter time is 3 seconds), stop (Boolean, accepted when run_status is running), mode switch set_mode (enumeration: automatic, manual, frequency setting is prohibited within 2 seconds after switching), set frequency set_freq (floating point, 10.0-50.0 Hz, only accepted when work_mode is manual and pressure closed loop is not taken over, step not less than 0.5 Hz), reset reset_alarm (Boolean, accepted when alarm code is in resettable level).

[0021] Based on this, the capability detection of fire-fighting equipment is performed, which is divided into reading detection and control detection. Reading detection specifically involves the platform initiating read-only queries in sequence according to the security whitelist to check whether the following readable fields exist: running time, last command time, log fragment index or last time, working mode, valve position, pump status, loop current, pressure, execution counter, etc., and recording success and failure.

[0022] Control-type detection specifically involves: under conditions that do not change the on-site working status (only during drills or maintenance windows, and with whitelist approval, triggering self-tests or shadow acceptance tests; equipment such as gas extinguishing / delusion release links are permanently prohibited by default), probing whether the shadow control verification or intervention flag is set (e.g., only requesting acceptance without actual execution, or requesting and immediately withdrawing without changing the output bit), to identify whether the equipment has control entry points such as start / stop, reset, and mode switching. If the equipment does not support shadow mode, control-type detection is skipped, and subsequent verification relies primarily on readback evidence.

[0023] The whitelist is derived from manufacturer agreements and past audit records, and is maintained in a closed loop with rule generation, manual review, online verification, and periodic review. It is categorized by device model or version.

[0024] Semantic alignment and unit unification (e.g., current unit, pressure range) are performed on the detected readable fields, and valid value ranges and outlier judgment rules are established for the fields; timestamps are unified to the platform time base. Based on the actual capabilities of the fire-fighting equipment, a readback verification priority mapping is established for each control command: Priority 1 (direct effect quantity): quantities that can directly characterize the action result, such as valve opening (valve position = open, branch flow trigger, pressure change); pump start-up (operating status = running, current enters the working range, outlet pressure rises). Priority 2 (corroborating quantity): when the equipment lacks direct effect quantities or does not support acceptance receipts, completion traces are used for corroborating evidence, such as (execution counter +1, last command time is later than this command time, log shows new entries related to this command). The mapping is saved in a structured form for use by control loop and probes.

[0025] Without altering the on-site conditions, determine the permissible low-impact probes on the platform side for each fire-fighting equipment, including read-only probes, dual-read consistency probes, and lightweight self-test probes. Calculate the minimum execution cycle of the probes based on the reporting cycle, minimum polling interval, and action cooldown time, using the maximum value among the three. Determine the probe timeout parameters based on the response timeout field and the round-trip delay statistics of the acceptance test. Based on this, set an initial frequency upper limit, time window, and timeout parameters for each type of probe. Initiate an acceptance test (acceptance only, no execution) to the fire-fighting equipment. If acceptance confirmation is received within the preset safety window, mark it as supported for application-layer acceptance; otherwise, mark it as unsupported, and subsequent control loops directly enter readback verification. The acceptance test must not trigger on-site actions to ensure no actual execution by the equipment. Two consecutive failed acceptance tests will block the test path and trigger an alarm. Acceptance tests are only permitted during maintenance or drill windows.

[0026] The above results are organized into machine-readable profile entries, with fields including but not limited to: metadata set (device identifier, model, version, communication parameters, etc.); minimum execution cycle of the probe; probe timeout parameters; list of readable fields (including unit, range, outlier rules, timestamp fields); list of controllable instructions (including parameter constraints, risk level); instruction-readback verification mapping (direct effect quantity, circumstantial evidence quantity, verification priority and time window); list of permitted low-impact probes (frequency limit, sampling rhythm, timeout); acceptance receipt capability annotation; and restricted items (prohibited operations, drill window requirements, etc.).

[0027] Example B: Firefighting equipment with direct effect quantity and acceptance receipt, taking the high-pressure fine water mist zone solenoid valve controller as an example, its profile items are the verification sequence of the instruction to open the valve: valve position, branch flow, pressure, (symbol) counter, log; probe: read once every 30 seconds; double reading must be done before control; self-test is allowed once a day.

[0028] Example C: For fire-fighting equipment with direct effect quantities but not supporting acceptance receipts, taking the fire pump control cabinet as an example, the verification sequence for instructing pump start is: operating status, current, outlet pressure, (symbol) counter, log; probe: read once every 30 seconds; dual reading is enabled when the counter stops or the status is inconsistent with the current; self-test is not enabled.

[0029] Example D: No direct effect quantity, only circumstantial fire protection equipment is allowed. Taking the gas extinguishing control panel as an example, all controls that change the release link are prohibited; only querying and log retrieval are allowed; probe: read once every 60 seconds; dual reading is enabled when the log growth rate is insufficient; self-test is only allowed in the drill window.

[0030] A data access channel is established with the fire protection equipment. Time-series data is received according to a unified data structure. Each time-series data entry includes the following fields: operating status, control command, working mode, execution status, sensor reading, and operation log. The system performs time alignment and buffering on the arriving data. A fixed-length out-of-order buffer window is used to rearrange the order of cross-messages. A deduplication key is constructed using the equipment identifier, timestamp, and payload summary to remove duplicate messages, forming the original time-series sequence arranged chronologically. The system performs integrity and consistency checks on the original time-series sequence. Integrity checks include: field completeness checks (all six fields—operating status, control command, working mode, execution status, sensor reading, and operation log—must exist); timestamp validity checks (timestamp rollback is prohibited, and gaps caused by cross-window transitions must be eliminated by buffer rearrangement); and duplication checks (duplicates are determined based on the deduplication key and marked for discard). The results of the integrity checks are represented by Boolean bits and a reason code, including at least a field completeness flag, a timestamp validity flag, a duplication flag, and a corresponding reason code. Consistency verification is based on the correlation constraints of the same device within a sliding time window: First, the sequential relationship constraint between control commands and execution states, requiring the timestamp of the execution state to be later than the corresponding control command and appear within the window; second, the matching relationship constraint between working modes and operating states, requiring them to maintain an allowed combination mapping within the window and evolve synchronously when switching occurs; third, the causal relationship constraint between control commands and operation logs, requiring the operation log to have a new record corresponding to the control command after the control command appears; fourth, the response relationship constraint between execution states and sensor readings, requiring that within the response window after a change in execution state, the sensor reading shows a statistically significant amplitude or trend change relative to its baseline before the change, with the significance threshold adaptively calculated by the sliding median and median absolute deviation of the historical data of the same device. The results of the above consistency verification are also represented by Boolean bits and reason codes, including at least command-execution consistency flags, mode-operation consistency flags, command-log causal consistency flags, execution-sensor response consistency flags, and corresponding reason codes. After completing the integrity verification and consistency verification, the system binds each record with its quality information, generating a trusted data stream with quality tags. The quality tagging system consists of a set of integrity verification tags, a set of consistency verification tags, and a set of reason codes, along with device identifiers and window numbers for traceability. The trusted data stream serves as the sole data input for subsequent situation indicator calculations and tag analysis, ensuring that subsequent assessments and decisions are based on data with traceable sources, interpretable rules, and locatable defects.

[0031] A set of situation indicators for assessing the operational status and health condition of fire protection equipment is calculated based on trusted data streams, including: The frequency of changes in the status of fire-fighting equipment (such as operating mode and running status) reflects whether the equipment frequently changes its operating status over a period of time. A low rate of change indicates stable equipment status, typically indicative of normal operation. For example, the equipment operates in a fixed mode without frequent status switching. A high rate of change indicates frequent switching of operating modes or statuses within a short period, potentially due to equipment malfunction or unstable operation, indicating an abnormal state. For example, frequent switching between operating and standby modes may indicate a problem. A moderate rate of change indicates stable equipment operation, meaning the equipment can respond to commands and is under control. An excessively high rate of change, where the equipment appears online but frequently switches statuses, may indicate a malfunction preventing normal response to control commands, thus being classified as a suspected false online status.

[0032] The execution counter records the number of times the fire protection equipment executes control commands or undergoes status changes. A low or stagnant growth rate may indicate that the fire protection equipment is not executing commands as expected or that its status has not changed. A healthy growth rate indicates that the fire protection equipment is continuously performing its tasks in response to control commands, its status changes are as expected, and the equipment is operating normally. A low or stagnant growth rate indicates that the fire protection equipment may not be responding to control commands, its status has not changed, and it may be malfunctioning or unable to perform control tasks. A stable increase in the execution counter indicates that the fire protection equipment is responding to control commands and its execution status is normal. No growth or abnormal growth in the execution counter may indicate that the equipment appears to be online but is unable to execute commands, suggesting a possible false online status.

[0033] Whether the equipment's running time continuously increases reflects the monotonicity of its operation, indicating whether there have been interruptions or shutdowns. If the equipment's running time consistently increases monotonically, it indicates continuous operation without interruptions or restarts, and the equipment is in normal condition. If the equipment's running time regresses or experiences intermittent interruptions, it indicates that the equipment has stopped or restarted, which is abnormal. A monotonically increasing running time indicates continuous normal operation and the equipment's ability to respond to control commands. If the running time regresses or there are frequent restarts, the equipment may not be able to respond to commands stably, posing a risk of false online status.

[0034] Whether a device's log file continuously increases over time reflects the device's activity and normal operation. Logs record information such as device operation events and control command execution. If the log file continues to grow, it indicates that the device is generating new logs during normal operation, suggesting that the device is functioning correctly. If the log file grows slowly or no new logs are generated, it may indicate that the device is not responding to new control commands or is not operating as expected, indicating an anomaly. Normal log growth means the device is executing control commands and generating corresponding logs, indicating that the device is functioning normally. If the log does not grow or grows slowly, it may indicate that the device is not executing commands, posing a risk of false online status.

[0035] Based on the aforementioned equipment profile library and trusted data streams with quality tags, the system establishes a status indicator verification set for each fire-fighting device. This set includes: the maximum permissible rate of change in the fire-fighting equipment's operating status, the minimum permissible growth rate of the execution counter, the minimum permissible growth rate of the log, and a criterion for determining the monotonicity of operating time. These indicator thresholds are registered in the configuration library according to equipment model and operating level, and undergo regression calibration at preset intervals. Each determination references the trusted data stream within the same sliding time window.

[0036] The system first calculates the rate of change of the operating status of fire-fighting equipment. The frequency of change is obtained by dividing the effective number of changes in operating status / mode within a sliding time window by the window length. This frequency is then compared with the maximum allowable rate of change of the operating status of the fire-fighting equipment. A rate of change less than or equal to the maximum allowable rate of change is considered a first criterion. Next, the system calculates the growth rate of the execution counter. The growth rate per unit time is obtained by dividing the difference between the window's final value and initial value by the window length. This growth rate is then compared with the minimum allowable growth rate of the execution counter. A growth rate greater than or equal to the minimum allowable growth rate of the execution counter is considered a second criterion. For determining the monotonicity of the running time, the system verifies whether the running time field strictly does not backtrack and does not decrease according to the sampling step size within the same sliding window. If this condition is met, a third criterion is considered met. For determining the log growth rate, the system counts the number of new log entries associated with control commands or running events within the statistical window. The log growth rate is obtained by dividing the number of new log entries by the statistical window duration and compared with the minimum allowable log growth rate. To avoid boundary ambiguity, this implementation uses a log growth rate not less than the minimum allowable log growth rate as a qualified criterion. If this condition is met, a fourth criterion is considered met. When a fire-fighting device simultaneously meets the first, second, third, and fourth judgment conditions within the same time window, the system labels the device's health status as normal and marks the initial false online risk label as controllable. If any condition is not met, the system labels the device's health status as abnormal and marks the initial false online risk label as suspected false online.

[0037] The system receives preliminary false online risk labels and equipment profile entries for fire protection equipment. It reads the minimum execution cycle, probe timeout parameters, acceptance receipt capability flags, and allowed probe types from the equipment profile entries, and retrieves the probe judgment window length and pass rate threshold from the configuration library. When the preliminary false online risk label indicates the equipment is controllable, the scheduler only enables read-only probes, with their execution frequency set to the minimum execution cycle recorded in the equipment profile entry. When the preliminary false online risk label indicates the equipment is suspected of being falsely online, the scheduler retains read-only probes at the minimum execution cycle while adding dual-read consistency probes, tightening their execution frequency to half the minimum execution cycle. This forms a probe plan for that label and initiates the verification phase.

[0038] like Figure 3 The diagram shows the flowchart for probe verification and false online risk label determination. First, the probe results of the fire equipment are obtained, and a judgment is made based on the verification results. If the probe verification is qualified, the qualification evidence is recorded; if the verification fails, the failure evidence is recorded, along with a reason code. Next, the system counts the number of qualified and unqualified probes and calculates the probe qualification rate. If the probe qualification rate is not less than a preset qualification rate threshold, the fire equipment is marked as controllable; if the probe qualification rate is lower than the threshold, the fire equipment is marked as false online.

[0039] Within a probe decision window, the platform continuously executes probes on the target device according to the aforementioned plan, and records three pieces of evidence for each probe. The first piece of evidence is that the instruction has been sent, recording the probe type and sending timestamp; the second piece of evidence is that the device has accepted the request, when the acceptance receipt capability of the device profile entry is marked as supported, waiting for the acceptance receipt within the probe timeout parameter and recording the acceptance result; the third piece of evidence is the read-back result, where the scheduler reads the verification field according to the instruction-result verification mapping in the device profile entry, compares it with the value of the same device in the trusted data stream at the previous moment, and generates the probe result for this time.

[0040] The platform assigns a pass / fail conclusion to each probe according to a unified standard. For read-only probes, a probe is considered passable if its runtime and log cursor both move forward and the corresponding record's quality flag is passable; otherwise, it is considered failable and a reason code is written. For dual-read consistency probes, a probe is considered passable if the last command time and the last log timestamp move forward synchronously within a short time interval, and the time difference between them is not greater than the probe timeout parameter; otherwise, it is considered failable and a reason code is written if the above conditions are not met or the corresponding record's quality flag is failable. All evidence and judgment results are written to the device status database in chronological order, forming a traceable probe verification detail.

[0041] Example E: The initial online risk label indicates the fire equipment is controllable. Taking a wet sprinkler zone solenoid valve controller (model ZV-24D, firmware v1.8.0) as an example, the platform has obtained the equipment profile entry for this device, which records: a reporting cycle of 20 seconds, a minimum polling interval of 30 seconds, and an action cooldown time of 10 seconds, therefore the minimum execution cycle is 30 seconds; the probe timeout parameter is 2 seconds; the acceptance receipt capability is marked as supported; and the allowed probe types include read-only probes and dual-read consistency probes. When the initial online risk label indicates the equipment is controllable, the scheduler only enables read-only probes and sets the probe execution frequency to a minimum execution cycle of 30 seconds. Each probe is recorded with three pieces of evidence: a timestamp of the instruction being sent, a receipt from the equipment, and the read-back result. The read-back result is verified against the instruction-result verification mapping in the equipment profile entry, checking whether the run_time_s field has moved forward and whether the operation log cursor log_index has moved forward, while simultaneously verifying that the record quality is marked as qualified. Under this tag, dual-read consistency probes are not enabled, and read-only probes are kept running stably as minimal proof of continued health and controllability.

[0042] Example F: The initial false online risk label indicates that the fire-fighting equipment is suspected of being falsely online. Taking the smoke exhaust fan frequency converter control cabinet (model SFD-VFD200, firmware v3.2.0) as an example, the platform has obtained the equipment profile entry for this device, which records: a reporting cycle of 5 seconds, a minimum polling interval of 10 seconds, and an action cooldown time of 20 seconds, therefore the minimum execution cycle is 20 seconds; the probe timeout parameter is 3 seconds; the acceptance receipt capability is marked as supported; and the allowed probe types include read-only probes and dual-read consistency probes. When the initial false online risk label indicates that the equipment is suspected of being falsely online, the scheduler, while retaining the read-only probe and maintaining the execution frequency at the minimum execution cycle of 20 seconds, adds a dual-read consistency probe and tightens its execution frequency to half of the minimum execution cycle, i.e., 10 seconds. The dual-read consistency probe continuously reads the last command time (last_cmd_ts) and the last log timestamp (log_last_ts) within a short time interval, requiring them to move forward synchronously with a time difference not exceeding the probe timeout parameter of 3 seconds; simultaneously, it records the sending, acceptance, and reading back of three pieces of evidence. If the dual-read consistency probe continuously shows asynchronous or time-out events, the probe result is recorded as unqualified and accompanied by a reason code; after the window statistics are completed, the pass rate is used to update the false online risk label of the device.

[0043] After completing the execution of a probe judgment window, the platform counts the number of successful and unsuccessful attempts of the target device within that window, calculating the probe success rate. If the probe success rate is not less than the success rate threshold and there are no three consecutive unsuccessful attempts within the window, the device is marked as controllable under the false online risk label. If the probe success rate is less than the success rate threshold or there are three consecutive unsuccessful attempts within the window, the device is marked as falsely online under the false online risk label. After the label is determined, the system simultaneously writes back the window number, probe type and frequency, success rate, number of consecutive unsuccessful attempts, acceptance receipt statistics, and readback comparison details.

[0044] like Figure 4 The diagram shows a flowchart for generating control commands based on the health status label and false online risk label of fire equipment. First, the health status label and false online risk label of the fire equipment are obtained. Then, the combination of these two labels determines the type of control command to generate. If the equipment health status is normal and the false online risk label indicates the equipment is controllable, a normal control command is generated; if the equipment health status is normal and the false online risk label indicates the equipment is falsely online, a maintenance operation command is generated; if the equipment health status is abnormal and the false online risk label indicates the equipment is falsely online, an emergency response command is generated. The corresponding control command is sent and a timestamp is recorded. Execution receipts are received and read back for verification. If the verification result is satisfactory, the fire equipment status is updated; if the verification result is unsatisfactory, a failure is recorded and a warning is issued.

[0045] The system reads the health status label and the false online risk label, and determines the type of control command to be generated based on the combination of these two labels. The specific judgment logic is as follows: When the health status label indicates the equipment is normal and the false online risk label indicates the equipment is controllable, the platform generates normal control commands for normal equipment operation. These control commands include, but are not limited to, starting the pump, stopping the pump, opening and closing the valve, adjusting the pressure, and switching the operating mode. After the command is generated, the platform sends the command through the communication parameters and control interface in the equipment profile entry. When the health status label indicates the equipment is normal and the false online risk label indicates the equipment is falsely online, the platform generates maintenance operation commands. These commands include maintenance operations such as initiating self-tests, checking equipment operation logs, restarting the equipment, and sending fault reports. Because the equipment may be in a suspected false online state, the system performs additional monitoring and verification operations to ensure the effectiveness of equipment recovery. When the health status label indicates the equipment is abnormal and the false online risk label indicates the equipment is falsely online, the platform generates emergency response commands. These commands include, but are not limited to, immediately shutting down the equipment, switching to redundant equipment, starting the backup control system, or placing the equipment in emergency safety mode. Because the equipment is both in an abnormal state and suspected of being falsely online, the platform adopts a more conservative control strategy to ensure the safe operation of the system.

[0046] Example G: Firefighting equipment with a health status label of "Equipment Normal" and a false online risk label of "Equipment Controllable": Taking a fire pump frequency converter control cabinet (model VFD-FP300, firmware version v2.3.1) as an example, generate normal control instructions. The specific instruction is: Pump Start Instruction: The platform generates a pump start instruction based on the equipment's control instruction list. The instruction format is: Pump Start (start=true). The platform sends the instruction to the equipment via the MQTT protocol. After the equipment returns an execution receipt, the execution result is verified. If the equipment successfully responds and starts the pump, the equipment's execution status and current monitoring data will be reflected in the trusted data stream. The platform reads back and verifies based on the instruction-result verification mapping to ensure successful pump start-up.

[0047] Example H: Firefighting equipment with a health status label of "Equipment Normal" and a false online risk label of "Equipment False Online": Taking a fire valve controller (model: ZV-24D, firmware version: v1.8.0) as an example, maintenance operation instructions are generated. Specific operations include initiating a self-test, restarting the equipment, and checking the operation logs: Initiating Self-Test Instruction: The platform generates an initiating self-test instruction with the format: `start_self_test=true`. The instruction is sent to the equipment via the REST API. After the equipment performs the self-test, it returns the self-test result. If the equipment fails to perform the self-test successfully, the platform records the failure and automatically initiates a log check. Checking Equipment Logs Instruction: The platform sends a query logs instruction (`query_logs`) requesting the equipment to return the most recent operation logs and checking for any abnormal records. The platform analyzes the logs returned by the equipment to confirm whether there are any potential hardware failures or configuration errors. Restarting Equipment Instruction: The platform generates a equipment restart instruction with the format: `reboot=true` and sends it to the equipment. After the equipment performs the restart operation, the platform again obtains the equipment's execution receipt through the control interface and verifies the equipment's status to ensure that the equipment has returned to normal.

[0048] Example I: For fire-fighting equipment with a health status label of "Equipment Abnormal" and a false online risk label of "Equipment False Online": Using the fire-fighting gas extinguishing system control panel (model: GF-3000, firmware version: v3.5.0), generate emergency response instructions. These instructions include disabling the equipment, switching to redundant equipment, and activating the backup control system: **Disable Equipment Instruction:** The platform generates a disabling equipment instruction with the format: `disable equipment(disable=true)`, and sends it to the equipment via the MQTT protocol. Upon receiving the instruction, the equipment immediately stops all operations of the gas extinguishing system. The platform waits for the equipment to provide an execution receipt and records the result. **Switch to Redundant Equipment Instruction:** When the equipment cannot resume operation, the platform generates a switch to redundant equipment instruction with the format: `switch device(switch_device=true, device_id="redundant_device_001")`. This instruction is sent to the redundant equipment control system to ensure the system can still operate normally. **Activate Backup Control System Instruction:** When the redundant equipment is unavailable, the platform generates an activation instruction for the backup control system with the format: `start backup control system(start_backup=true)`. This instruction is sent to the backup control system. After the backup system is started, the platform obtains the execution receipt through the device interface and performs read-back verification to ensure that the backup control system is successfully activated.

[0049] The platform determines the sending method based on the communication parameters stored in the fire equipment profile entries, such as sending commands via MQTT message queues, REST APIs, Modbus, etc. When sending control commands, the platform records the sending timestamp and command content to ensure traceability. After executing the control command, the platform receives an execution receipt signal from the fire equipment. Upon receiving the equipment receipt signal, the platform performs readback verification to ensure that the equipment has executed the control command and that the execution result meets expectations. The specific steps are as follows: Based on the command-result verification mapping in the fire equipment profile entries, the fields that need to be verified are determined. These fields include, but are not limited to, operating status, valve position, current, pressure, and mode position. The platform reads these fields and compares them with the values ​​in the trusted data stream of the equipment at the previous moment. If the comparison results are consistent, the verification is considered successful, the platform updates the equipment status to normal and records the successful verification information; if the comparison results are inconsistent, the verification is considered unsuccessful, the platform records the failure type and issues a warning.

[0050] Example 2: Under the premise that the rest of the contents of Example 1 remain unchanged, the construction of fire equipment profiles can also include periodically updating the equipment profiles in the equipment profile library, and when the equipment firmware or configuration is triggered to update, performing differential comparison and regression verification, only reviewing the changed fields, and updating the fire equipment profile entries.

[0051] Example 3: Based on Example 1 or Example 2 above, the following steps can be performed to analyze false online risk tags: The platform side constructs an application layer latency baseline based on the acceptance receipt capability mark, probe timeout parameters, command-result verification mapping, and stored historical trusted data streams in the device profile entries. Within the historical judgment window, the system calculates the median and absolute deviation of three types of latency: acceptance receipt latency (acceptance receipt timestamp minus sending timestamp), direct effect latency (timestamp when the direct effect field (valve position, motor current, outlet pressure, mode position) corresponding to the command first reaches the judgment caliber minus sending timestamp), and circumstantial latency (timestamp when the execution counter, last command time, and operation log cursor first move forward minus sending timestamp). During online judgment, the system calculates the above three types of latency for each control command and generates a causal matching tag. When the delay in receiving the acceptance receipt is significantly lower than expected and neither the direct effect quantity nor the circumstantial evidence quantity appears within the probe timeout parameter, it is counted as one causal break. When the causal break occurs three times consecutively within the same judgment window, the false online risk label of the device is marked as the device is false online. When all three types of delay fall within the historical baseline allowable range and the direct effect quantity or circumstantial evidence quantity appears according to the mapping, the false online risk label is marked as the device is controllable.

[0052] Example 4: Based on Example 1 or Example 2 above, the following steps can be performed to analyze false online risk tags: When the aforementioned equipment profile entry contains a control command sequence number field (cmd_seq) and a queue depth field (queue_depth), and the profile entry has registered an execution counter (exec_counter) and an operation log cursor (log_index), the probe judgment window length, queue response ratio threshold, and continuous inactivation cumulative number threshold are read from the configuration library. The system first extracts four types of time series from the trusted data stream within a judgment time window (length equal to the probe judgment window length) that are for the same fire-fighting equipment and are marked as qualified. These are then aligned in ascending order by timestamp to form a control command sequence number sequence, a queue depth sequence, an execution counter sequence, and an operation log cursor sequence. The moment when the control command sequence number increments is used as the anchor point moment, and a response window is set for each anchor point (starting at the anchor point moment and ending at the anchor point moment plus the response window duration calculated from the probe timeout parameter). Within each response window, the system calculates and summarizes the following four activity quantities: Sequence monotonicity: Calculate the adjacent increments of the control instruction sequence number point by point within the decision time window. If there is no negative jump and at least one positive increment occurs, the sequence monotonicity is valid. At the same time, the sequence activity ratio (the ratio of the number of positive increments to the number of effective comparisons) is given.

[0053] Queue depth response: The queue depth is a non-negative integer, measured in lines, representing the number of unexecuted commands in the fire equipment control command queue. Within each response window [t0, t0+τ], the queue depth at the window's starting point is denoted as d0. After processing according to the debouncing parameters in the configuration library, if at any time t within the window, d(t) ≤ d0-1 (i.e., at least one command less than the starting point), then a queue depth decrease event is considered to have occurred at that anchor point, counted as a queue response hit; if d0 = 0 (the queue was initially empty), then that anchor point is recorded as a miss. The number of hits M and the total number of anchor points N are counted across all anchor points to obtain the queue response ratio R. p =M / N. When R p If the queue response ratio is less than the threshold (preset in the configuration library) and no queue depth decrease event occurs in any K consecutive anchor points (K is given by the configuration library), the queue is determined to be continuously not decreasing.

[0054] Execution count growth rate: The execution counter represents the cumulative number of control actions actually completed by the fire protection equipment. It is a non-negative integer and should only increase under normal circumstances. Using the time point when each control command sequence number increments as the anchor point, within the response window (length calculated from the probe timeout parameter) after that anchor point, if the execution counter increases by at least one value compared to the initial value of the anchor point at any given time, it is counted as one execution count hit. Within the same probe judgment window, the total number of anchor points and the number of hits are counted, and the count growth ratio (i.e., the proportion of hits to the total number of anchor points) is calculated. The count growth ratio threshold, the consecutive miss count threshold, and the minimum number of valid anchor points are read from the configuration library. If all of the following conditions are met simultaneously: the number of valid anchor points is not less than the minimum number of valid anchor points, the count growth ratio is not less than the count growth ratio threshold, and the longest consecutive miss count within the statistical window is less than the consecutive miss count threshold, then the execution count growth rate is considered valid; otherwise, it is not valid. If the number of valid anchor points in this statistical window is insufficient, the conclusion of this window is marked as invalid.

[0055] Log forward shift status: Using the time point when each control command sequence number increments as the anchor point, within the response window (length calculated from the probe timeout parameter) after this anchor point, if at any time the operation log cursor increases by at least one value compared to the anchor point's initial value, it is counted as a log forward shift hit. Within the same judgment window, the total number of anchor points and the number of hits are counted, and the log forward shift ratio (number of hits divided by the total number of anchor points) is calculated. The log forward shift ratio threshold, the consecutive non-forward shift count threshold, and the minimum number of valid anchor points are read from the configuration library. If all of the following conditions are met simultaneously: the number of valid anchor points is not less than the minimum number of valid anchor points, the log forward shift ratio is not less than the log forward shift ratio threshold, and the longest consecutive non-forward shift count within the judgment window is less than the consecutive non-forward shift count threshold, then the log growth rate is considered valid; otherwise, the judgment is invalid. If the number of valid anchor points in this judgment window is insufficient, the conclusion of this window is marked as ineffective.

[0056] After calculating the four activity parameters, the system performs queue inactivation determination: determination is only carried out if the sequence monotonicity is met within the determination time window. If the queue does not decrease continuously, the execution count growth rate is not met, and the log growth rate is not met within the same determination time window, it is counted as one queue inactivation. When the cumulative queue inactivation reaches the threshold of consecutive inactivation times within the same probe determination window length, the false online risk label of the fire protection equipment is marked as false online. If the queue depth response, execution count growth rate, or log growth rate is met within the effective determination window, the false online risk label is marked as controllable at the end of the determination time window. If the sequence monotonicity is not met, the determination window is marked as ineffective, the previous label remains unchanged, and the determination continues in the next window.

[0057] like Figure 2 As shown, this application also provides a flowchart of a method for intelligent fire protection equipment status monitoring and centralized management, including: The system automatically collects metadata about fire-fighting equipment and performs capability detection on the equipment, thereby generating profile entries for the fire-fighting equipment.

[0058] The system accesses time-series data from fire protection equipment, generates a reliable data stream with quality tags, calculates a set of status indicators for the fire protection equipment, and analyzes the status tags of the fire protection equipment, which include health status tags and preliminary false online risk tags.

[0059] Based on the preliminary false online risk labels of fire protection equipment, the probe execution frequency and type are determined, and the probe verification of fire protection equipment is carried out to obtain the probe results of fire protection equipment, thereby determining the false online risk labels of fire protection equipment.

[0060] Based on the health status labels and false online risk labels of fire protection equipment, control instructions are generated. These instructions include routine control, maintenance operations, or emergency response for the fire protection equipment. The control instructions are sent and execution receipts from the fire protection equipment are obtained. The receipts are then read back for verification to ensure that the fire protection equipment executes according to the predetermined requirements and provides correct execution results.

[0061] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0062] It should be understood that determining B based on A does not mean determining B solely based on A; it also means determining B based on A and / or other information.

[0063] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0064] The above description is only an optional embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An intelligent fire protection equipment situation monitoring and centralized control system, characterized in that, The system includes: The fire protection profile creation module is used to automatically collect metadata sets of fire protection equipment and detect the capabilities of fire protection equipment, thereby generating profile entries for fire protection equipment. The fire equipment status label analysis module is used to access the time-series data of fire equipment, generate a reliable data stream with quality tags, calculate the status index set of fire equipment, and analyze the status labels of fire equipment. The status labels of fire equipment include health status labels and preliminary false online risk labels. The false online risk label determination module is used to determine the probe execution frequency and type based on the preliminary false online risk label of the fire equipment, thereby verifying the probe of the fire equipment, obtaining the probe results of the fire equipment, and thus determining the false online risk label of the fire equipment; The fire equipment control execution and feedback verification module is used to generate control instructions based on the health status label and false online risk label of the fire equipment. The control instructions include the normal control or maintenance operation or emergency response of the fire equipment. The module sends the control instructions and obtains the execution receipt of the fire equipment, and performs read-back verification to ensure that the fire equipment executes according to the predetermined requirements and provides the correct execution results.

2. The intelligent fire protection equipment situation monitoring and centralized management system as described in claim 1, characterized in that, The fire protection profile creation module includes: Automatically acquire communication parameters, protocols, interface addresses, authentication methods, device identifiers, device models, firmware versions, and reporting cycles of fire protection equipment, and complete connectivity verification and identity verification to form a metadata set of fire protection equipment; The minimum execution cycle of the probe is calculated based on the reporting cycle, the minimum polling interval in the protocol description, and the action cooldown time obtained from capability detection. At the same time, based on the response timeout field and acceptance receipt field in the protocol description, and combined with the acceptance receipt results of the capability probe, the probe timeout parameters and acceptance receipt capability flags are determined. Parse the protocol description of the fire protection equipment, identify the readable fields and controllable command candidates of the fire protection equipment, including the name, data type, unit and value range of each field, and generate a list of readable fields and a list of controllable commands of the equipment; By initiating control tests and read requests, the system probes the readable fields and controllable commands supported by the fire protection equipment, verifies whether the fire protection equipment responds to these fields, confirms the validity and availability of the fields, and generates a list of capabilities supported by the equipment. The legality of detected controllable commands is verified by checking the parameter constraints, execution conditions and scope of influence of the commands to ensure their legality and validity, and a list of legal controllable commands is generated. Based on the collected metadata set, the minimum execution cycle of the probe, the probe timeout parameters, the acceptance receipt capability flag, the readable field list, the controllable instruction list, the capability list, and the instruction legality list, a profile entry for the fire-fighting equipment is generated and written into the equipment profile library to form a standardized equipment profile.

3. The intelligent fire protection equipment situation monitoring and centralized management system as described in claim 1, characterized in that, The fire equipment status label analysis module specifically includes: Access the timing data of fire protection equipment, including equipment operating status, control commands, working modes, execution status, sensor readings, and operation logs; The collected time-series data is subjected to integrity and consistency verification to generate a trusted data stream with quality tags; Based on trusted data stream computation, a set of situation indicators is used to assess the working status and health status of fire protection equipment, including the rate of change of working status of fire protection equipment, the growth rate of execution counter, the monotonicity of running time, and the growth rate of log. Based on the calculated status index set of fire protection equipment, the status labels of the fire protection equipment are analyzed and obtained. The status labels of the fire protection equipment include health status labels and preliminary false online risk labels. The health status label includes whether the device is normal or abnormal. The initial false online risk label includes whether the device is controllable or suspected of being falsely online.

4. The intelligent fire protection equipment situation monitoring and centralized management system as described in claim 3, characterized in that, The analysis yields status labels for the fire protection equipment, including: Extract the preset status indicator verification set of fire protection equipment, including the maximum allowable change rate of fire protection equipment working status, the minimum allowable growth rate of execution counters, and the minimum allowable growth rate of logs; The first criterion is that the rate of change of the working status of the fire-fighting equipment is less than the maximum allowable rate of change of the working status of the fire-fighting equipment. The second criterion is that the growth rate of the execution counter is greater than the minimum allowable growth rate of the execution counter. The result of the monotonicity of the running time is monotonically increasing, which is used as the third criterion. The fourth criterion is that the log growth rate is less than the minimum allowed log growth rate. If a fire-fighting device fully meets the above four judgment conditions, the health status label of the fire-fighting device will be marked as normal, and the preliminary false online risk label of the fire-fighting device will be marked as controllable. Conversely, if the fire-fighting device does not meet the above conditions, the health status label of the fire-fighting device will be marked as abnormal, and the preliminary false online risk label of the fire-fighting device will be marked as suspected false online.

5. The intelligent fire protection equipment situation monitoring and centralized management system as described in claim 1, characterized in that, The determination of probe execution frequency and type based on preliminary false online risk labels of fire protection equipment specifically includes: If the initial false online risk label of the fire equipment is that the equipment is controllable, then select the read-only probe and set the probe execution frequency to the minimum execution cycle recorded in the corresponding fire equipment profile entry; If the initial false online risk label of the fire protection equipment is that the equipment is suspected of being falsely online, then add a dual-read consistency probe and tighten its probe execution frequency to half of the minimum execution cycle, retain the read-only probe, and set the probe execution frequency of the read-only probe to the minimum execution cycle.

6. The intelligent fire protection equipment situation monitoring and centralized management system as described in claim 5, characterized in that, The process of verifying the probes of the fire-fighting equipment and obtaining the probe results specifically includes: Within the probe judgment window length, probe verification is performed on the fire-fighting equipment, and three pieces of evidence are recorded one by one, including that the instruction has been sent, the equipment has accepted the instruction, and the result has been read back. The readback result reads the verification field according to the instruction-result verification mapping and compares it with the value in the trusted data stream at the previous moment on the same device to form the probe result for this time. For each probe, a pass or fail conclusion is given. The pass criteria for read-only probes are that the runtime and log cursor are moved forward and the quality is marked as pass. The pass criteria for dual-read consistency probes are that the last command time and the last timestamp of the log are moved forward synchronously within a short time interval and the time difference between the two is not greater than the probe timeout parameter. When the corresponding quality mark is unqualified, record the probe result as unqualified and attach a reason code; When the corresponding quality mark is qualified, the probe result is recorded as qualified.

7. The intelligent fire protection equipment situation monitoring and centralized management system as described in claim 1, characterized in that, The determination of false online risk labels for fire protection equipment specifically includes: Within the probe judgment window, the number of times each fire-fighting equipment passes and the number of times it fails are counted, and the probe pass rate of the corresponding fire-fighting equipment is calculated. When the probe pass rate of a fire protection device is not less than the preset pass rate threshold, and there are no three consecutive failures within the probe judgment window, the false online risk label of the fire protection device is marked as controllable. When the probe pass rate of a fire protection device is less than the pass rate threshold, or when there are three consecutive failures within the probe judgment window, the false online risk label of the fire protection device will be marked as false online.

8. The intelligent fire protection equipment situation monitoring and centralized management system as described in claim 1, characterized in that, The control instructions generated based on the health status labels and false online risk labels of fire protection equipment specifically include: For fire-fighting equipment with a health status label of "equipment normal" and a false online risk label of "equipment controllable", generate normal control instructions; For fire-fighting equipment with a health status label of "equipment normal" and a false online risk label of "equipment false online", generate maintenance operation instructions; For fire-fighting equipment with a health status label of "Equipment Abnormal" and a false online risk label of "Equipment False Online", generate emergency response instructions.

9. The intelligent fire protection equipment situation monitoring and centralized management system as described in claim 1, characterized in that, The step of sending control commands and obtaining execution receipts from fire-fighting equipment, followed by read-back verification, specifically includes: Determine the instruction sending method and receiving format based on the communication parameters and protocols in the fire equipment profile entries, send control instructions, and record the instruction timestamp and instruction content; After receiving the readback verification signal, the verification field is read according to the instruction-result verification mapping in the fire equipment profile entry, and compared with the value of the corresponding equipment in the trusted data stream at the previous moment to form the readback verification result; If the verification result read back is satisfactory, record the verification as successful and update the status of the fire-fighting equipment; If the readback verification result is invalid, record the failure and issue a warning.

10. A method for intelligent fire protection equipment status monitoring and centralized control, used to implement the intelligent fire protection equipment status monitoring and centralized control system according to any one of claims 1-9, characterized in that, The method includes: Automatically collect metadata sets of fire-fighting equipment and perform capability detection on the fire-fighting equipment, thereby generating profile entries for the fire-fighting equipment; The system accesses time-series data of fire protection equipment, generates a reliable data stream with quality tags, calculates a set of status indicators for the fire protection equipment, and analyzes the status tags of the fire protection equipment, which include health status tags and preliminary false online risk tags. Based on the preliminary false online risk labels of fire protection equipment, the probe execution frequency and type are determined, and the probe verification of fire protection equipment is carried out to obtain the probe results of fire protection equipment, thereby determining the false online risk labels of fire protection equipment; Based on the health status tags and false online risk tags of the fire protection equipment, control instructions are generated. These control instructions include routine control, maintenance operations, or emergency response of the fire protection equipment. The control instructions are sent and the execution receipts of the fire protection equipment are obtained. The receipts are then read back for verification to ensure that the fire protection equipment executes according to the predetermined requirements and provides correct execution results.

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