Power fire alarm monitoring system based on wireless communication

Abstract

The application discloses a power fire alarm monitoring system based on wireless communication and relates to the technical field of intelligent monitoring of power systems, which is used for solving the problems of multi-parameter detection, intelligent alarm and path steady-state discrimination of electrical fires; through high-precision collection of multiple parameters such as zero-sequence current, temperature, voltage and grounding impedance, in combination with local intelligent analysis and remote linkage strategy, real-time identification and response to electrical fire hazards are realized. The system has field backfill sequence grouping, path stability scoring and extraction strategy scheduling mechanism, and improves the abnormal fault tolerance and communication link adaptability. The power supply module adopts a main-backup dual power supply architecture to ensure stable operation during power failure of the system. The platform supports trend modeling, remote control and strategy management, and builds an integrated monitoring scheme of data collection, intelligent discrimination and closed-loop response, which is suitable for multiple power facility fire risk scenarios.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology for power systems, and more specifically, to a power fire alarm and monitoring system based on wireless communication. Background Technology

[0002] Electrical fires are among the most common and dangerous types of fires in urban and industrial sites. Their concealment, suddenness, and multi-source nature pose challenges to traditional fire prevention and control methods. Conventional electrical fire monitoring systems often rely on single-parameter detection, lacking the ability to continuously track complex evolution paths and identify multi-factor interactions. Furthermore, under extreme conditions such as communication anomalies or power failures, monitoring can easily fail or policies can be mistriggered. In addition, mainstream monitoring equipment lacks path consistency management of the correspondence between alarm events and sensor data, leading to frequent data tag drift after restarts, affecting the accuracy of platform-based judgments. Existing technologies also lack robust data recovery and path correction mechanisms for scenarios such as power outage restarts and field re-completion, making it difficult to trace and control phenomena such as historical state adsorption and tag fragment reuse.

[0003] To address the above problems, this invention proposes a solution. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a power fire alarm monitoring system based on wireless communication to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a preferred embodiment, it includes: a sensor acquisition module, an alarm processing module, a wireless communication module, a power supply and backup module, and a platform monitoring module, with signal connections between the modules;

[0007] The sensor acquisition module is used to configure the sensor and connects to the microcontroller ADC for synchronous sampling via an analog signal conditioning circuit;

[0008] The alarm processing module is used to perform filtering processing on the signal-conditioned sensor signal data using an embedded microcontroller, and to perform alarm determination;

[0009] In the wireless communication module, the acquisition terminal encapsulates encrypted data frames into MQTT message bodies and reports them to the platform monitoring module through Wi-Fi or LoRa networking, and caches and resends them during network outages.

[0010] In the power supply and backup module, the main power supply outputs a DC bus through a switching power supply, and the backup power supply is a battery pack. When power fails, the main and backup switching is completed and the power failure is reported. At the same time, during the field backfilling process, the power-on sequence number, tag version number, and path fingerprint are used to generate a set of rules. The grouped fields are multiple power-on sequence blocks, and a sequence feature vector is constructed to calculate the stability score of the field's belonging path. The field extraction path is dynamically scheduled according to the conflict mapping cluster, and the field meta-state information is encapsulated before writing.

[0011] The platform monitoring module is used to receive and parse data frames uploaded by the acquisition terminal, perform three-element consistency judgment, generate linkage control commands, send them to the acquisition terminal, and record behavior logs.

[0012] In a preferred embodiment, the sensing acquisition module includes a zero-sequence current transformer, a Hall effect current sensor, an NTC thermistor, a precision resistor array, an isolation amplifier, an integrated temperature and humidity sensor, and a photoelectric smoke sensor in the acquisition terminal.

[0013] The analog signal conditioning circuit sequentially buffers, amplifies, and low-pass filters the output signals of each sensor. The conditioned signals are then connected to the ADC module of the microcontroller for synchronous sampling. Before the acquisition logic is started, the sensor short-circuit and open-circuit detection is triggered, and the range coefficient and zero-bias coefficient are calculated using the least squares fitting method based on the standard curve.

[0014] In a preferred embodiment, the alarm processing module employs an embedded microcontroller, which sequentially reads the signal-conditioned sensor signal data from each channel of the microcontroller's ADC module via a timed interrupt. The mean filtering algorithm is applied to the residual current signal of the line, and the median filtering algorithm based on singular value sorting is applied to the conductor temperature signal.

[0015] A circular buffer is constructed and updated in real time. Alarm determination is performed based on the line residual current alarm threshold, conductor temperature alarm threshold, and the disturbance waveform feature model pre-stored in the arc fault identification mode library.

[0016] In a preferred embodiment, the wireless communication module adopts a two-layer networking structure; in short-range communication scenarios, the acquisition terminal embeds a Wi-Fi wireless communication module to access the local area network of the field gateway device; in long-range or Wi-Fi access unavailable scenarios, the acquisition terminal embeds a LoRa transceiver to report to the platform monitoring module via a LoRa gateway and cellular network.

[0017] The microcontroller constructs a unified data frame, performs AES-128 encryption, encapsulates it into an MQTT message body, and reports it to the platform monitoring module. For data frames containing alarm status flags, ACK confirmation and automatic retransmission are enabled. At the same time, data is cached during network outages and resent after the network is restored.

[0018] In a preferred embodiment, in the power supply and backup module, the main power supply outputs a DC bus through an industrial-grade isolated switching power supply, and the backup power supply uses a sealed lead-acid battery pack. When the main power supply fails, the switching circuit completes the main-backup switchover, and disconnects unnecessary loads and reports a power failure when the battery voltage is abnormal.

[0019] In a preferred embodiment, in the power supply and backup module, during the pre-initialization phase when the backup power supply completes the power supply switch and starts the field backfilling logic, the power-on sequence number, tag version number, tag loading completion flag, backfilling frame ratio, and the difference between the field upload ready time and the tag table ready time are extracted from the power-on log and cache index, and a minimum dependency set is generated by supplementing the source status flag for each field.

[0020] Based on the minimum dependency set, three types of anomalies are identified within the first upload window: pre-upload before table locking, upload before backfilling, and residual chain reuse mapping. A rule set is dynamically generated based on the ready time difference, source state flag, path fingerprint, and tag version difference. The minimum ready window for power-on is applied to real-time fields, a backfill-specific flag is added to backfill fields and the eligibility for policy judgment is frozen, and the historical path adsorption of residual chain reuse fields is forcibly cut off through the tag mutual exclusion lookup table. Then, the fields are grouped into power-on sequence blocks of the same source and type.

[0021] In a preferred embodiment, in the power supply and backup module, during the field backfilling process, the acquisition terminal extracts the average ready time difference, mean path fingerprint similarity, frame order reordering degree, mutual exclusion hit rate, and backfilling ratio of each power-on sequence block, constructs a sequence feature vector, and calculates the field belonging path stability score by weighted summation based on the frame order reordering degree, mutual exclusion hit rate, ready time difference, and backfilling ratio.

[0022] In a preferred embodiment, in the power and backup module, when extracting and injecting fields into the main chain, the field extraction path is dynamically scheduled according to the conflict mapping cluster: a tag locking strategy is executed for fields with high field belonging path stability scores and which do not hit mutual exclusion; a flexible buffer binding is executed for fields with low field belonging path stability scores and which do not hit conflict mapping clusters; a path suppression strategy is executed for fields that hit conflict mapping clusters; and the field meta-state information is encapsulated before the field is written into the main structure object set.

[0023] In a preferred embodiment, the platform monitoring module deploys a message middleware and a RESTAPI service interface to receive data frames uploaded by the acquisition terminal and send a receipt confirmation. It parses the unique identifier ID of the monitoring point, the signal type identifier, the numerical field, the status bit, and the timestamp. It writes the numerical field into a time series database, writes the alarm field into a relational database, and performs a ternary consistency determination of the field, path, and tag. When the alarm condition is met, it sends a linkage control command to the acquisition terminal via MQTT, and simultaneously encapsulates the context data and writes it into the behavior log database.

[0024] The technical effects and advantages of the wireless communication-based power fire alarm monitoring system of this invention are as follows:

[0025] This invention achieves accurate identification of multi-parameter, multi-channel electrical fire hazards through the linkage of the data acquisition terminal and the platform, significantly improving the accuracy of system early warning. It introduces a field power-on sequence block and a path stability scoring mechanism to effectively solve the problems of label drift and path misjudgment during restart and recovery. A dynamic scheduling strategy for field-assigned paths is constructed using the minimum dependency set, path fingerprint, and mutual exclusion lookup table to ensure data structure continuity and linkage response stability. For communication assurance, dual-layer caching and AES-128 encryption enhance data integrity and security. The power module adopts a main / backup power switching and undervoltage protection linkage mechanism to ensure continuous system operation and proactive reporting of power failures under power outage or abnormal conditions. The platform supports time-series data modeling, strategy configuration, remote linkage, and log tracking, achieving closed-loop management from data acquisition and judgment to response. The overall solution possesses high reliability, strong adaptability, and excellent remote maintainability. Attached Figure Description

[0026] Figure 1 This is a timing diagram of the power fire alarm monitoring system based on wireless communication according to the present invention.

[0027] Figure 2 This is a schematic diagram of the power fire alarm and monitoring system module based on wireless communication according to the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of 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.

[0029] Example

[0030] This invention discloses a power fire alarm and monitoring system based on wireless communication, such as... Figure 2As shown, it includes: a sensor acquisition module, an alarm processing module, a wireless communication module, a power supply and backup module, and a platform monitoring module, with signal connections between each module.

[0031] In the sensor acquisition module, multiple types of acquisition terminals deployed in the monitoring area collect synchronous, high-precision, and periodic data on key electrical and environmental parameters related to electrical fire risks.

[0032] Specifically, during the acquisition of key electrical parameters, the zero-sequence current transformer configured in the acquisition terminal is installed in a package at the main incoming line of the monitoring circuit. By sensing the unbalanced component of the vector sum of the currents in each phase of the line, it realizes the real-time acquisition of the leakage current change signal, thereby determining potential leakage or insulation degradation trends. Simultaneously, a Hall effect current sensor is deployed in series in each load branch, outputting a voltage signal proportional to the load current based on the magnetic induction principle, used to synchronously acquire the actual operating current and overload current of the line under operating conditions.

[0033] For thermal risk monitoring of electrical connection points, adhesive NTC thermistors are pre-installed at typical heat-generating points such as copper busbars and terminals. Taking advantage of the negative correlation between temperature and resistance, the thermistors output a voltage signal that changes linearly with the conductor temperature under constant current bias. This signal is used to obtain the real-time conductor temperature evolution trajectory and help determine potential problems such as poor contact and oxidation aging.

[0034] In terms of line voltage monitoring, a voltage divider network composed of a precision resistor array is constructed in the acquisition terminal to perform non-distortion amplitude reduction processing on high voltage line signals; an isolation amplifier is simultaneously configured to achieve electrical isolation and bandwidth compensation for high voltage input signals, thereby completing the safe acquisition of line voltage parameters;

[0035] For grounding loop status monitoring, a dedicated grounding resistance measurement module is connected to the acquisition terminal. Based on the low current excitation and impedance estimation model, the loop impedance value change trend of the grounding terminal to the earth is obtained under low frequency AC excitation conditions, and the potential hazards such as grounding failure and increased grounding resistance are identified accordingly.

[0036] Furthermore, in terms of collecting key environmental parameters, integrated temperature and humidity sensors, photoelectric smoke sensors, and gas sensors are pre-installed in the monitoring space at the collection terminal deployment point. Based on the thermistor method, the scattered light intensity method, and the semiconductor sensitive film conductivity method, respectively, the air temperature / humidity index, smoke particulate matter concentration, and volume fraction index of typical combustible gases such as carbon monoxide in the monitoring environment are obtained, which are used to construct a dynamic parameter field of environmental causes of electrical fire risk.

[0037] Then, all sensor output signals are sequentially connected to a uniformly designed analog signal conditioning circuit. This analog signal conditioning circuit includes a signal buffer stage, a gain amplification stage, and a low-pass filter stage. By configuring an input buffer amplifier to increase the input impedance, using a high-linearity operational amplifier to set the gain range, and designing a low-pass filter based on an RC structure to reduce high-frequency noise, a signal front-end processing channel that matches the input parameters of the back-end analog-to-digital converter (ADC) module is constructed.

[0038] All conditioned signals are fed into the 12-bit ADC module built into the MCU of the acquisition terminal. The sampling timing is controlled by the hardware timer configured in the MCU, which has the ability to synchronously sample with a fixed period and uniform phase, ensuring that all physical quantities are acquired in parallel within the same sampling period, thereby improving the consistency of data timing and the feasibility of linkage analysis.

[0039] Before the acquisition logic starts, the microcontroller (MCU) automatically excites each sensor to perform short-circuit / open-circuit detection to determine the initial connection status of the sensor. Upon first power-on, the standard curve provided by the sensor manufacturer is called synchronously, and the range coefficient and zero bias coefficient of the single-point sensor are calculated by least squares fitting. The calibration results are then stored in the MCU's local Flash memory as a data calibration reference throughout the entire life cycle, ensuring the long-term traceability and consistency of the sensor data.

[0040] In the alarm processing module, an embedded microcontroller serves as the core processing unit. Through real-time analysis and comprehensive judgment of key electrical and environmental parameters acquired by the sensor acquisition module, it enables multi-dimensional alarm identification and local linkage control execution for typical causes of electrical fires. The confirmed alarm results are encapsulated into remote reporting fields, serving as the pre-trigger basis for upper-level platform response and intervention.

[0041] Specifically, the alarm processing module uses an embedded microcontroller MCU based on the ARM Cortex-M series core, which integrates timer interrupts and analog-to-digital conversion resources. It sequentially reads the conditioned sensor signal data of each channel in the microcontroller ADC module at a fixed sampling period, including indicators such as residual current of the line, conductor temperature, load current, voltage, grounding impedance, air temperature and humidity, smoke concentration and CO volume fraction.

[0042] In the post-processing stage of raw data reading, in order to eliminate misjudgment caused by short-term spike interference, the alarm processing module applies a mean filtering algorithm based on a 5-cycle sliding window to the residual current signal to dynamically calculate the current change trend within the sampling interval; and uses a median filtering algorithm based on singular value sorting to the conductor temperature signal to suppress outliers caused by contact disturbance at the sensor adhesion points.

[0043] The filtering algorithms described above all construct a circular buffer in the local RAM of the embedded microcontroller and update it in real time via a sliding window, without relying on external resources.

[0044] Regarding the alarm triggering logic, the alarm processing module introduces a multi-channel, multi-type judgment mechanism. Specifically, the alarm threshold for residual current of the line can be configured and adjusted within the range of 300mA to 500mA, and the alarm threshold for conductor temperature can be set within the range of 45℃ to 140℃. Both are dynamically set by the terminal management program according to the risk level strategy.

[0045] The temperature threshold range is referenced to GB7251 "General Technical Conditions for Low-Voltage Switchgear and Controlgear Assemblies" and the copper busbar temperature resistance rating specifications provided by the equipment manufacturer (e.g., long-term operating temperature not exceeding 105℃). Combined with the steady-state heat accumulation curves of conductor connection terminals under different loads from historical data, a coupled model of ΔT (temperature rise) and ambient temperature is constructed. The platform uses TSDB trend fitting curves to form a thermal characteristic model during the initial operation and maintenance phase of the equipment, dynamically generating recommended alarm thresholds for each monitoring point, which can be remotely adjusted via REST API during equipment operation.

[0046] The residual current alarm threshold configuration is dynamically set to 300mA, 350mA, 400mA, 450mA, or 500mA levels by the terminal management program, taking into account the different circuit load types and on-site grounding methods. The threshold setting can be dynamically adjusted through parameter distribution from the platform and recorded in the alarm rule table in the terminal Flash for use after restart.

[0047] For environmental parameters, the smoke concentration and CO gas concentration alarm mechanism adopts a dual-condition triggering mechanism of unit time growth rate threshold and absolute concentration upper limit. That is, it is only determined to be an environmental pre-alarm event if both the short-term rapid upward trend and the concentration exceed the standard are met at the same time, thereby effectively avoiding false alarms caused by minor disturbances in daily life.

[0048] In terms of identifying core electrical fire hazards, the alarm processing module has a built-in arc fault identification mode library. This mode library stores multiple sets of disturbance waveform feature models based on typical series / parallel arc samples. Combined with imported current and voltage synchronous sampling values, it uses multi-dimensional features such as local extreme value density, fluctuation rate, and harmonic energy ratio to build a fast matching framework to identify typical arc discharge initial disturbance behavior and achieve rapid response to high-risk arc hazards.

[0049] It should be noted that all the above alarm rules are based on a unified continuous sampling confirmation mechanism, that is, an alarm is confirmed only when the corresponding alarm conditions are met in N consecutive sampling periods, which effectively avoids false triggering caused by instantaneous pulses or short-term disturbances.

[0050] Once the alarm conditions are confirmed, the alarm handling module immediately executes a local multi-strategy linkage response, as follows:

[0051] On the one hand, the microcontroller outputs GPIO signals to drive relays or circuit breaker components to quickly disconnect the power supply to the faulty branch.

[0052] On the other hand, the locally configured audible and visual alarm module is activated simultaneously, using a buzzer and high-frequency LED flashing to provide visual audible and visual prompts to alert personnel to safety. At the same time, the current alarm channel number, alarm type, alarm threshold, confirmation timestamp, and other fields are packaged into a standard format alarm data frame and sent to the wireless communication module through the communication interface.

[0053] In the wireless communication module, a two-layer networking structure is adopted to build a wireless data link between the acquisition terminal and the platform monitoring module, so as to adapt to the data reporting requirements under different physical scenarios and ensure that key alarm information can be reliably transmitted under low power consumption, high redundancy and secure transmission mechanism.

[0054] Specifically, in short-range communication scenarios, the acquisition terminal embeds a Wi-Fi wireless communication module, which connects to the field gateway device based on the 802.11 protocol stack. Then, the Wi-Fi wireless communication module communicates with the microcontroller through the built-in UART or SPI interface of the MCU and completes local area network access with the help of the TCP / IP protocol stack.

[0055] For gateway devices with public network access capabilities, an internal 4G full network module or NB-IoT wireless communication module is integrated to support cellular network access at the communication protocol layer, enabling transparent forwarding and reporting of terminal data from local to remote platform monitoring modules in Wi-Fi and 4G modes.

[0056] When the terminal is in a remote deployment area or in an environment with weak signal coverage and unavailable Wi-Fi access, the acquisition terminal is replaced with an embedded SX1278 series LoRa low-power long-range communication transceiver. Based on the 433MHz frequency band and the SpreadingFactor configurable modulation scheme, a star topology is formed. The acquisition terminal, as a slave node, forwards the data to the LoRa gateway and then reports it to the platform via the cellular network, effectively improving the overall link's scenario adaptability and network redundancy.

[0057] At the communication protocol layer, to ensure the consistency of information organization across terminals and platform scalability, the wireless communication module integrates a lightweight publish / subscribe message queue telemetry transmission protocol stack, which supports stable communication between multiple nodes with Topic classification, QoS levels, and session maintenance mechanisms.

[0058] In each data acquisition terminal, the microcontroller constructs a data frame to be uploaded based on a unified structure. Each data frame consists of five core fields, as follows:

[0059] Data acquisition channel number;

[0060] Unique identifier ID for monitoring point;

[0061] Unix format timestamp;

[0062] Alarm status flag;

[0063] Current measurement value;

[0064] Furthermore, after the data frame is structured and packaged on the MCU side, it is sent to the communication module buffer via the UART / SPI bus. The wireless communication module performs AES-128 encryption on the data frame and encapsulates it into an MQTT message body, which is then reported to the designated Topic channel of the platform monitoring module.

[0065] Subsequently, ACK confirmation and automatic retransmission are enabled for high-priority data frames containing alarm status flags. After successfully receiving the alarm data frame, the platform monitoring module actively sends a reception confirmation message. If no confirmation message is received, the wireless communication module automatically retransmits the data frame according to the maximum number of retransmissions and the exponential backoff strategy until the platform confirms or the data frame is discarded due to timeout.

[0066] In the event of fluctuations or temporary interruptions in the communication link, to avoid the loss of important data, the wireless communication module is configured with a dual-layer buffer structure: on the one hand, the communication buffer area is equipped with a RAM-level short-term buffer pool based on a ring array; on the other hand, all data is synchronously backed up to the Flash non-volatile storage area, with a maximum retention period of 72 hours.

[0067] During network outages, a resend list is built based on local time-series indexes. Once the network is restored, the process of re-reporting lost data is automatically completed, ensuring the continuity of data flow and the integrity of event trajectories.

[0068] In terms of communication security mechanisms, the wireless communication module introduces a two-way encrypted tunnel based on the TLS / SSL protocol for all uplink data channels, and uses the symmetric encryption algorithm AES-128 to protect the key fields of monitoring point ID, alarm type, and measurement value in the MQTT message body.

[0069] Regarding the online status monitoring mechanism, the platform monitoring module sends a heartbeat request frame to all data acquisition terminals every 30 seconds. Upon receiving the heartbeat request, the terminal immediately returns a response frame. The platform monitoring module records the number of lost responses. If two consecutive heartbeat responses fail, the data acquisition terminal is marked as offline, and an event notification or maintenance instruction is triggered on the platform side, ensuring the platform's real-time visibility and disaster recovery control of the online status of the data acquisition terminals.

[0070] The power supply and backup module adopts a dual power supply architecture built by the main power supply and the backup power supply to ensure that the data acquisition terminal, alarm processing module and wireless communication module can operate continuously and stably under different power supply scenarios, thereby improving the overall reliability and disaster recovery capability of the system.

[0071] Specifically, the system's main power supply adopts the standard AC220V input method commonly used in industrial sites. It uses an internally integrated industrial-grade isolated switching power supply module to perform step-down and voltage regulation conversion, generating stable 5V and 3.3V DC buses for the main power supply output. It also supports compatible access to DC24V input scenarios through a voltage regulation module, adapting to various field power supply systems.

[0072] To prevent power outages or failures in the main power supply, a backup power system is configured synchronously with the power supply and backup module, using a 12V, 4Ah sealed lead-acid battery pack as the backup power source. During normal main power supply operation, the backup power supply maintains a float charge through a current-limiting circuit provided by the main power supply. The float charge current is dynamically controlled by a constant voltage current-limiting device to ensure that the battery is in a trickle charge maintenance state, preventing battery performance degradation during long-term storage.

[0073] In terms of power supply switching control, after the main power supply fails, the switching circuit completes the seamless switching operation between the main and backup power supplies. This switching structure can be based on an automatic transfer switch, a bidirectional Schottky diode array, or a voltage-gated device, and has millisecond-level switching response capability.

[0074] During backup power operation, the battery voltage status is continuously monitored. When the voltage drops below a set threshold, an undervoltage protection mechanism is triggered: the microcontroller issues a load disconnect command, which uses a power-off MOSFET or relay module to disconnect unnecessary loads, preventing the battery from over-discharging and causing permanent capacity degradation. Simultaneously, the undervoltage status is packaged as a power failure event into an alarm field data frame and reported to the platform monitoring module via a wireless communication module, prompting the platform to perform manual inspection or remote command control.

[0075] Once the main power supply is restored and enters a stable output state, the switching circuit automatically detects the main power supply being powered on, performs a reverse switch to the main power supply path, and puts the backup power supply back into the float charging maintenance mode.

[0076] It should be noted that when the acquisition terminal starts up via backup power due to a power outage of the main power supply and enters the LoRa broadcast channel for short-term data replenishment and reporting, the field tag binding table is not synchronously persisted in the power-off state, and the field cache structure is restored by default in FIFO mode without binding the field ownership status. This causes the order of field tag loading and data recovery to drift, resulting in a tag remapping residual chain. That is, the channel to which the field value belongs is incorrectly mapped to the historical tag path, which in turn induces the platform-side linkage strategy judgment path drift, ultimately causing linkage offset phenomena such as mistransmission, retransmission, or behavior conflict within the closed loop in the policy trigger chain.

[0077] In the original solution, after the acquisition terminal switches to backup power, it first performs MCU-level field cache recovery and restores the sampling channel frame stack structure based on the field pointer index recorded in Flash. During this process, the data frames received by the platform usually still parse the field structure according to the original channel number + tag path combination, and build a linkage rule matching chain based on the field tags. However, at this stage, the field pointer index only records the field positional relationship and does not record the tag mapping table status of the field. After restarting, the field data is loaded and restored before the tag table, causing some fields to be uploaded before the tag table has been mapped, resulting in delayed tag binding. This causes the platform to use the tag mapping cache left over from the previous cycle during parsing, instead of the tag chain corresponding to the actual sampling channel of the current field, causing the field to be mistakenly identified as other channel events or historical alarm write-back behavior.

[0078] Therefore, in this embodiment, as Figure 1 As shown, in the pre-initialization stage where the terminal completes the power supply switch from the backup power supply and starts the field backfilling logic, the power-on sequence number, tag version number, tag loading completion flag, the proportion of backfilled frames to the total number of frames to be transmitted, and the difference between the field upload ready time and the tag table ready time are extracted and retained synchronously from the power-on log and cache index. At the same time, a source status flag and a summary formed by combining the channel number, monitoring point number and the most recent policy group number are added to each field to be uploaded to generate a minimum dependency set.

[0079] Next, based on the minimum dependency set, before field generation, i.e. within the first upload window covered by the power-on sequence, we continuously capture the tag and field misgrouping offset phenomenon that only occurs in the combined scenario of power failure restart, re-reporting, and delayed loading of the tag table:

[0080] The first type is pre-upload before table lock is ready: when the ready time difference is positive and the tag loading completion flag is negative, the real-time field is released prematurely; its characteristic is that the field is marked as real-time, but the path fingerprint is highly similar to the path fingerprint of the last power-on.

[0081] The second type is supplementary overlay upload: When the number of supplementary frames accounts for a high proportion of the total number of frames to be transmitted, and the cache queue prioritizes sending historical alarm segments into the unpacking channel in a first-in-first-out manner, the supplementary frames and real-time frames are interleaved in the same channel within three sampling periods, and the degree of frame order rearrangement exceeds the preset supplementary threshold.

[0082] The backfill threshold is set when the average absolute value of the difference between the field's entry sequence number and its backfill scheduling sequence number within the channel buffer exceeds three sampling periods; in this case, the backfill field is considered excessively prioritized. This threshold is calculated using the 95% confidence interval of the historical uploaded sequence sorting stability distribution, with a default value of ±3 frames, and can be dynamically adjusted through configuration.

[0083] The third type is residual chain reuse mapping: when the tag version number has not yet been written into the current frame header, the path fingerprint uses the channel path corresponding to the historical version; its characteristics are that the tag version difference is at least one and the path cross-matching density exceeds the preset reuse threshold, which usually occurs during the transition period when the encrypted session and the subscription session have not yet completed rebinding.

[0084] The reuse threshold is configured by default to 0.65. It is set by the platform based on statistical results of the overlap ratio of fingerprints in the same channel field path across multiple historical power-on cycles. This threshold indicates a risk of historical tag chain adsorption when more than 65% of the fingerprint bits in the current field path overlap with historical paths. This threshold can be adjusted back based on the path mismatch rate during actual platform operation.

[0085] When any two of the above three types of anomalies occur simultaneously or any one type persists for more than two sampling periods within a single power-on sequence, a set of rules is dynamically generated, driven by the anomaly combination state. This is not based on nominal channel attribution, but rather on the mapping between ready time difference, source state flag, path fingerprint and tag version difference, and path crossover density. This set of rules mainly includes, but is not limited to:

[0086] For the first type of case, a minimum ready window is applied upon power-on. The length of the minimum ready window must at least cover the time it takes for the tag table to be loaded. Real-time fields within the minimum ready window are only written to the shadow buffer with a placeholder flag for waiting for the tag version attached, and are not entered into the main parsing chain.

[0087] For the second type of case, a patching and real-time decoupling are performed. Specifically, a patching-specific flag is added to the patching field to freeze its eligibility to participate in the strategy determination, and a historical reference pointer is added to the path fingerprint. This is only used for trend patching and is not merged with the real-time main chain.

[0088] For the third type of case, a tag mutual exclusion table is enabled, in which the channel path of the current tag version and the channel path of the historical tag version are mutually exclusive. When mutual exclusion is hit, the historical path is forcibly cut off and the parsing priority of this field in the unpacking channel is downgraded to the secondary queue.

[0089] Furthermore, the behavior tags complete the classification definition based on the rule set:

[0090] If the ready time difference is positive and the label is not ready, and the path fingerprint is highly similar to the historical electrical path, then it is marked as the first case;

[0091] The high similarity between the path fingerprint and historical paths is quantified by cosine similarity, which is calculated as follows:

[0092] =( · ) / (|| ||·|| ||);

[0093] in:

[0094] The path fingerprint vector corresponding to the current field is composed of a triplet of tag version number, channel number, and upload time sequence number.

[0095] This represents the most recent field path fingerprint within the same channel during a historical power-on event;

[0096] ∈[0,1], the larger the value, the stronger the path inheritance;

[0097] when ≥ Similarity threshold When this occurs, it is determined to be a path inheritance behavior.

[0098] If the proportion of backfilling is high and the degree of frame reordering exceeds the backfilling threshold, it is marked as backfilling-first type;

[0099] If the tag version difference is large and the path cross density exceeds the reuse threshold, it is marked as a residual chain reuse type.

[0100] After classification, the fields are grouped into power-on sequence groups based on terminal number, channel number, tag version number, power-on sequence number, and source status flag. This ensures that each group contains only a set of fields with consistent source and behavior structure and a unique parsing path, forming power-on sequence blocks of the same source and type.

[0101] Subsequently, structural common features such as the average ready time difference, mean path fingerprint similarity, frame order reordering degree, mutual exclusion hit rate, and backfilling ratio of each power-on sequence block are extracted and dynamically aggregated in a sliding window manner to construct a sliding update sequence feature vector set as a reflection indicator of the stability of the field cluster structure within the current power-on cycle.

[0102] Among them, mutual exclusion hit rate The definition is as follows:

[0103] = / ;

[0104] This represents the number of times the field path fingerprint in the current sequence block hits the mutex lookup table;

[0105] This represents the total number of fields involved in the mutual exclusion determination within the sequence block;

[0106] Path intersection density Defined as:

[0107] =( )) / (N·(N-1));

[0108] This represents the path fingerprint between field i and field j;

[0109] δ( ) is the path fingerprint crossover metric function (such as the prefix matching ratio in path coding);

[0110] N is the number of fields in the current extraction window;

[0111] The higher the value, the more significant the path drift phenomenon.

[0112] Based on this, a path stability score is calculated for each field sample. This score is based on four indicators: frame reordering degree, mutual exclusion hit rate, ready time difference, and backfilling ratio. Each indicator is assigned a weighted coefficient and then summed to form a weighted evaluation index for the field's power-on reliability. The score result is used to guide whether a field is eligible for parsing in the main chain structure object set, but it does not constitute the sole criterion for judgment.

[0113] The stability score of the field's attribution path Calculated using the following weighted model:

[0114] ;

[0115] in:

[0116] This indicates the degree of frame order reordering within the power-on sequence, calculated as the normalized value of the minimum swap distance between the unpacking sequence and the original channel queue.

[0117] This indicates the mutual exclusion hit rate of the path to which this field belongs in the conflict mapping cluster, with a value range of [0, 1].

[0118] This represents the time difference between the field upload readiness time and the tag table loading completion time (unit: ms).

[0119] The tag loading completion time is given by the RTC (Real-Time Clock) timestamp recorded after the MCU system starts up. The internal event flags triggered after parsing and loading by the field label management thread are automatically registered. Both are calibrated using a unified clock domain and undergo periodic NTP time calibration via heartbeat timestamps issued by the platform to ensure all ready time differences are addressed. It features consistency across restart cycles and platform traceability.

[0120] This indicates the proportion of the number of frames to be re-transmitted in the sequence block containing this field out of the total number of frames to be transmitted;

[0121] W1, W2, W3, and W4 are weighting coefficients configured by the platform, satisfying W1+W2+W3+W4=1. The platform sets W1, W2, W3, and W4 according to the principle of maximizing the accuracy of historical path reconstruction or the principle of minimizing false triggers of linkage strategies, and can be automatically adjusted in a configurable and learnable manner.

[0122] Specifically, during the extraction process into the main chain, a fixed mapping is not directly applied based on the score. Instead, a conflict mapping cluster derived from the rule generation channel, consisting of mutually exclusive match hit trajectories, path fingerprint cross-density, and frame order rearrangement clustering, is referenced simultaneously. This conflict mapping cluster is used as a dynamic scheduling constraint for the current field extraction path. Based on the field score, the current extraction path is further dynamically calibrated.

[0123] The path fingerprint is constructed as follows: For each frame of field data, five flag fields are extracted from the channel number, monitoring point ID, power-on sequence number, tag version number, and policy number of the field. These are then concatenated in order to form a unique fingerprint string, and SHA-1 hash compression is performed to generate a fixed-length fingerprint digest. This digest is used to quickly identify whether the path to which the field belongs is consistent with historical paths or whether there is overlap.

[0124] For fields with high scores but whose corresponding sequence blocks do not match mutually exclusive fields: The system implements a label locking strategy, which involves writing the current label version number into the field frame header and setting path closure check rules. Only when two consecutive frames in the same sequence block have the same field channel number and the ready time difference is close to zero is the field allowed to be written into the main chain structure object set. At the same time, the path enables forward consistent convolution channels to enhance the ability to perceive path continuity within a short window and improve the accuracy of short-period structure stability recognition.

[0125] For fields with low scores but not hitting conflict mapping clusters: The system enables a flexible buffer binding mechanism, allowing the field to be delayed in joining the chain within a short tolerance window; when the tag table is loaded and the field ready time difference does not exceed the set tolerance threshold, the field is then merged into the main chain structure object set to absorb the parsing offset caused by path jitter and the short mapping window period at the beginning of power-on.

[0126] In the current extraction round, when there is a high overlap between field path fingerprints and conflict mapping clusters: the system activates the extraction strategy reverse compression mechanism, which suspends the binding process dominated by conventional scoring priorities and switches to a path suppression strategy based on mutual exclusion weight sorting. This strategy performs label dilution processing on fields hitting in the conflict sequence and forcibly disconnects the direct mapping between fields and main chain structure objects, preventing historical label chains from adsorbing fields and causing main chain path pollution and platform-related erroneous triggering.

[0127] Finally, to ensure the auditability and traceability of the process before a field is added to the main chain, a unified field state flag encapsulation action is performed before the field is finally written into the main structure object collection, including but not limited to:

[0128] The power-on sequence number to which the field belongs;

[0129] The version number used for tag mapping;

[0130] Current field stability score;

[0131] Does it hit a conflicting mapping cluster?

[0132] Control status bits such as whether to enter the rule adjustment channel.

[0133] Then, the above-mentioned field meta-state information will be synchronously written into the metadata segment at the end of the field reporting frame, and used as an auxiliary criterion in field path matching, strategy path selection and error rollback logic on the platform side, thus constructing a two-way self-consistent closed-loop mechanism from field anomaly identification, dynamic rule generation, extraction scheduling decision to main chain injection and writing.

[0134] As a remote central node, the platform monitoring module is responsible for receiving, decoding, verifying, and responding to various field data frames uploaded by the acquisition terminals. Through centralized processing, linkage control, and intelligent modeling and analysis of electrical fire hazard data, it achieves closed-loop management of the overall fire risk chain and enhances the collaborative linkage capability of multiple systems.

[0135] Specifically, the platform monitoring module deploys a lightweight message middleware (MQTTBroker) and a RESTAPI service interface on the backend. The former receives data frames uploaded by all collection terminals at QoS1 service level based on the MQTT protocol stack and sends a receipt confirmation message to the terminal. The latter is used for issuing internal management commands, remote configuration synchronization and multi-system adaptation and connection, forming a north-south bidirectional communication system for control and data channels.

[0136] Furthermore, all received data frames are parsed and unpacked according to a unified structure. The fields include: a unique identifier for the monitoring point (ID), a signal type identifier, numerical fields, status bits, and timestamps, among other metadata. After parsing, the data enters the real-time processing channel.

[0137] Numerical fields: written to the time series database for use in trend curve display, historical comparative analysis, and modeling;

[0138] Alarm fields: Synchronously written to a PostgreSQL relational database to form a complete alarm event tracing log, supporting subsequent correlation queries, causal chain analysis and false alarm correction;

[0139] Field meta-status, such as power-on sequence number, tag version number, field stability score, conflict mapping cluster flag, and whether it has entered the adjustment channel, is used for dynamic rule discrimination and linkage strategy path selection mechanism through structured parsing to ensure the consistency and integrity of the platform response after the field is entered into the main chain.

[0140] In the alarm status judgment process, based on the field alarm threshold rule table and field tag path mapping cache table maintained synchronously with the field terminal, the three-element consistency judgment mechanism of field, path and tag is executed to ensure that the corresponding path, channel and tag binding relationship is accurate when the alarm event is established, and to avoid the linkage response from being deviated due to path drift.

[0141] Specifically, when the alarm judgment conditions are met, the following four types of linkage response actions will be executed immediately:

[0142] Front-end interface response: The alarm status interface, built with the Vue.js front-end framework, intuitively marks the device, channel, and specific event type that triggered the alarm through flashing icons and floating pop-ups;

[0143] On-duty personnel push: Calls the message push engine to send in-app notifications and SMS alerts to on-duty maintenance personnel according to the event level, supporting alarm classification, personnel scheduling mapping and alarm feedback;

[0144] Downlink linkage control: Construct linkage control command frames and send them to the designated acquisition terminal through the MQTT downlink channel to drive the local execution of linkage equipment actions such as power outage, ventilation, and fire extinguishing. The linkage command includes metadata such as field label path, policy number, and device execution number.

[0145] Context recording and behavior tracing: Record the context data snapshot, policy matching process, issued instruction content and terminal receipt status when the record field triggers the alarm, and write them to the behavior log database as an important basis for later backtracking and mechanism self-calibration.

[0146] Furthermore, the platform's front-end interface is built on the Vue.js framework, uses ECharts as the core graphics component, and supports multiple interactive view modes, including but not limited to:

[0147] Regional map status view (equipment deployment status is marked by building / floor / area);

[0148] Device status list view (sorted / filtered by real-time status of monitoring points);

[0149] Trend curve retrospective view (analysis of historical value curves and threshold intersections of fields);

[0150] Historical event search panel (supports multi-condition filtering by keyword / time period / tag path);

[0151] Linkage strategy configuration area (supports visual configuration of linkage chain structure diagram and rule version management).

[0152] Subsequently, to ensure data access security and operational permission boundaries, the platform monitoring module configures interface access permissions, device data browsing and policy configuration permissions in a hierarchical manner based on the backend user role model, ensuring that different operation and maintenance positions can only access data and control behaviors within their authorized scope.

[0153] Furthermore, to enhance the ability to perceive and predict the evolution trend of electrical fires, a model training engine is deployed on the backend of the platform's monitoring module. Statistical learning methods and tree model-like algorithms are used to construct equipment health scoring models and degradation trend prediction models based on long-term operational field sequence data. During training, the model input data originates from the normalized field historical sample set in the TSDB, and the label data comes from historical alarm events and manual confirmation results. The model training task is scheduled and run periodically by the platform's backend.

[0154] After the model training is completed, the platform monitoring module will solidify the model structure and parameters into a version number model package and send it to the acquisition terminal via the downlink, where it will be loaded and executed by the local microcontroller.

[0155] Finally, in terms of operation and maintenance capabilities, the platform monitoring module supports automatic aggregation and report generation of operational data, can export operational snapshots in PDF / Excel format, set daily automatic backup paths and backup cycles, and supports centralized execution of operation and maintenance commands such as remote OTA configuration parameter distribution, system version upgrades, and remote debugging log retrieval, significantly improving the platform's remote maintainability and full lifecycle management capabilities.

[0156] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0157] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0158] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0159] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0160] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0161] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A power fire alarm and monitoring system based on wireless communication, characterized in that: include: The system includes a sensor acquisition module, an alarm processing module, a wireless communication module, a power supply and backup module, and a platform monitoring module, with signal connections between these modules. The sensor acquisition module is used to configure the sensor and connects to the microcontroller ADC for synchronous sampling via an analog signal conditioning circuit; The alarm processing module is used to perform filtering processing on the signal-conditioned sensor signal data using an embedded microcontroller, and to perform alarm determination; In the wireless communication module, the acquisition terminal encapsulates encrypted data frames into MQTT message bodies and reports them to the platform monitoring module through Wi-Fi or LoRa networking, and caches and resends them during network outages. In the power supply and backup module, the main power supply outputs a DC bus via a switching power supply, and the backup power supply is a battery pack. In the event of a power failure, a main / backup switch is completed and a power failure is reported. Simultaneously, during the field completion process: in the pre-initialization phase where the backup power supply completes the power switch and initiates the field completion logic, the power-on sequence number, tag version number, tag loading completion flag, completion frame percentage, and the difference between the field upload readiness time and the tag table readiness time are extracted from the power-on log and cache index. A source status flag is added to each field to generate a minimum dependency set. Based on the minimum dependency set, three types of anomalies are identified within the first upload window: pre-upload before table locking, upload before completion, and residual link reuse mapping. A rule set is dynamically generated based on the readiness time difference, source status flag, path fingerprint, and tag version difference. A minimum readiness window is applied to real-time fields, a dedicated completion flag is added to completed fields, and the policy judgment qualification is frozen. The residual link reuse... The segment forcibly cuts off historical path adsorption through a tag mutual exclusion lookup table, and then groups the fields into power-on sequence blocks of the same origin and type. During the field backfilling process, the acquisition terminal extracts the average ready time difference, mean path fingerprint similarity, frame order reordering degree, mutual exclusion hit rate and backfilling ratio of each group of power-on sequence blocks, constructs a sequence feature vector, and calculates the field's path stability score by weighted summation based on the frame order reordering degree, mutual exclusion hit rate, ready time difference and backfilling ratio. When the field is extracted and injected into the main chain, the field extraction path is dynamically scheduled according to the conflict mapping cluster: a tag locking strategy is executed for fields with high path stability scores and no mutual exclusion hits, a flexible buffer binding is executed for fields with low path stability scores and no conflict mapping clusters, and a path suppression strategy is executed for fields that hit conflict mapping clusters. The field meta-state information is encapsulated before the field is written into the main structure object set. The platform monitoring module deploys message middleware and RESTAPI service interfaces to receive data frames uploaded by the acquisition terminal and send receipt confirmations. It parses the unique identifier ID of the monitoring point, signal type identifier, numerical field, status bit and timestamp, writes the numerical field to the time series database, writes the alarm field to the relational database, and performs a ternary consistency judgment of field, path and tag. When the alarm condition is met, it sends linkage control instructions to the acquisition terminal through MQTT, and encapsulates the context data and writes it to the behavior log database.

2. The power fire alarm and monitoring system based on wireless communication according to claim 1, characterized in that, In the sensor acquisition module, a zero-sequence current transformer, a Hall effect current sensor, an NTC thermistor, a precision resistor array, an isolation amplifier, an integrated temperature and humidity sensor, and a photoelectric smoke sensor are configured in the acquisition terminal. The output signals of each sensor are buffered, amplified, and low-pass filtered sequentially through an analog signal conditioning circuit. The conditioned signals are then connected to the ADC module of the microcontroller for synchronous sampling. Before the acquisition logic is started, short-circuit and open-circuit detection of the sensors is triggered, and the range coefficient and zero-bias coefficient are calculated using the least squares fitting method based on the standard curve.

3. The power fire alarm and monitoring system based on wireless communication according to claim 1, characterized in that: In the alarm processing module, an embedded microcontroller is used to sequentially read the sensor signal data after signal conditioning from each channel of the microcontroller's ADC module through timed interrupts. The mean filtering algorithm is applied to the residual current signal of the line, and the median filtering algorithm based on singular value sorting is applied to the conductor temperature signal. A circular buffer is constructed and updated in real time. Alarm determination is performed based on the residual current alarm threshold, the conductor temperature alarm threshold, and the disturbance waveform feature model pre-stored in the arc fault identification mode library.

4. The power fire alarm and monitoring system based on wireless communication according to claim 1, characterized in that: The wireless communication module adopts a two-layer networking structure. In short-range communication scenarios, the acquisition terminal embeds a Wi-Fi wireless communication module to connect to the field gateway device and the local area network. In long-range or Wi-Fi access unavailable scenarios, the acquisition terminal embeds a LoRa transceiver to report to the cellular network via a LoRa gateway. The platform monitoring module performs AES-128 encryption on data frames with a unified structure constructed by the microcontroller and encapsulates them into MQTT message bodies for reporting to the platform monitoring module. It enables ACK confirmation and automatic retransmission for data frames containing alarm status flags. At the same time, it caches data during network outages and resends it after the network is restored.

5. The power fire alarm monitoring system based on wireless communication according to claim 1, characterized in that: In the power supply and backup module, the main power supply outputs a DC bus through an industrial-grade isolated switching power supply, and the backup power supply uses a sealed lead-acid battery pack. When the main power supply fails, the switching circuit completes the main-backup switchover, and disconnects unnecessary loads and reports a power fault when the battery voltage is abnormal.

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