A fire protection equipment power supply monitoring method, system and medium

Abstract

The present invention discloses a method, system and medium for monitoring the power supply of fire-fighting equipment, which relates to the field of data processing technology. The method includes: interactively obtaining equipment information of fire-fighting equipment and establishing a time-series monitoring data set; obtaining node control signals and time-series load data in the time-series monitoring data set, performing dynamic and static mode discrimination, and generating dynamic and static mode discrimination results; using the discrimination results to divide the data set into dynamic and static modes, combining the equipment information to perform dynamic and static dual-state abnormality recognition, and establish a joint recognition abnormality; and performing fire-fighting equipment power supply monitoring and management based on the joint recognition abnormality. The present invention solves the technical problem that the existing method cannot accurately identify abnormalities of equipment in different working states, resulting in insufficient accuracy and reliability of equipment monitoring, and achieves the technical effect of accurately distinguishing abnormal performance of equipment in different working states through dynamic and static mode discrimination and joint recognition of abnormalities, thereby improving the accuracy and reliability of equipment monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method, system and medium for monitoring the power supply of fire-fighting equipment. Background Art

[0002] In modern firefighting systems, power supply monitoring for firefighting equipment is a critical component in ensuring reliable operation. The increasing complexity of firefighting systems, the networked deployment of equipment, and the diverse operational states of these equipment have placed higher demands on power supply monitoring. However, existing technologies primarily rely on static voltage and current monitoring, which can only detect obvious power failures and struggle to capture dynamic changes in equipment operating status, mode switching, and interactions between devices. This limitation prevents monitoring systems from promptly detecting potential failures, impacting the stability and safety of firefighting equipment. Summary of the Invention

[0003] The present application provides a fire protection equipment power supply monitoring method, system and medium, which are used to solve the technical problem that existing methods cannot accurately identify abnormalities of equipment in different working states, resulting in insufficient accuracy and reliability of equipment monitoring.

[0004] The first aspect of the present application provides a method for monitoring the power supply of fire-fighting equipment, the method comprising: interactively acquiring equipment information of the fire-fighting equipment, the equipment information comprising basic attributes and topological structure of the equipment; establishing a time-series monitoring data set after deploying monitoring sensors; acquiring node control signals and time-series load data of the time-series monitoring data set, performing dynamic and static mode discrimination based on the node control signals and the time-series load data, and establishing a dynamic and static mode discrimination result; performing dynamic and static mode division of the time-series monitoring data set using the dynamic and static mode discrimination result, and performing dynamic and static dual-state anomaly recognition based on the dynamic and static mode division result and the equipment information, and establishing a joint recognition anomaly; and performing equipment power supply monitoring and management of the fire-fighting equipment based on the joint recognition anomaly.

[0005] The second aspect of the present application provides a fire equipment power supply monitoring system, the system comprising: an equipment information interaction module, the equipment information interaction module being used to interactively obtain equipment information of fire equipment, the equipment information including basic equipment attributes and topological structure; a time series data monitoring module, the time series data monitoring module being used to establish a time series monitoring data set after deploying monitoring sensors; a dynamic and static mode discrimination module, the dynamic and static mode discrimination module being used to obtain node control signals and time series load data of the time series monitoring data set, perform dynamic and static mode discrimination based on the node control signals and the time series load data, and establish a dynamic and static mode discrimination result; a joint abnormality identification module, the joint abnormality identification module being used to perform dynamic and static mode division of the time series monitoring data set using the dynamic and static mode discrimination result, perform dynamic and static dual-state abnormality identification based on the dynamic and static mode division result and the equipment information, and establish a joint identification abnormality; a power supply monitoring management module, the power supply monitoring management module being used to perform equipment power supply monitoring and management of fire equipment according to the joint identification abnormality.

[0006] According to a third aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method of the first aspect is implemented.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The present application provides a fire-fighting equipment power supply monitoring method, system and medium, which relate to the field of data processing technology. By acquiring the basic properties and topological structure of the fire-fighting equipment, a time-series monitoring data set is established after deploying sensors, node control signals and load data are obtained, dynamic and static mode discrimination is performed, and the data set is divided into dynamic and static modes according to the discrimination results. Anomalies are identified in combination with equipment information, a joint identification anomaly is established, and power supply monitoring and management of the fire-fighting equipment is performed based on this. The method solves the technical problem that the existing method cannot accurately identify the anomalies of the equipment in different working states, resulting in insufficient accuracy and reliability of equipment monitoring. The method achieves the technical effect of accurately distinguishing the abnormal performance of the equipment in different working states through dynamic and static mode discrimination and joint identification of anomalies, thereby improving the accuracy and reliability of equipment monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1A flowchart of a method for monitoring power supply of fire-fighting equipment provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of the structure of a fire protection equipment power supply monitoring system provided in an embodiment of the present application.

[0012] Explanation of the accompanying drawings: device information interaction module 11, time series data monitoring module 12, dynamic and static mode discrimination module 13, joint abnormality identification module 14, power supply monitoring and management module 15. DETAILED DESCRIPTION

[0013] The present application provides a fire protection equipment power supply monitoring method, system and medium, which are used to solve the technical problem that existing methods cannot accurately identify abnormalities of equipment in different working states, resulting in insufficient accuracy and reliability of equipment monitoring.

[0014] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0015] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0016] Example 1, as Figure 1 As shown, the present application provides a method for monitoring power supply of fire-fighting equipment, the method comprising:

[0017] P10: Interactively obtain equipment information of fire-fighting equipment, which includes basic attributes and topology of the equipment.

[0018] Specifically, we first need to obtain the equipment information of the firefighting equipment through the interactive interface. Equipment information can be divided into two categories: basic equipment attributes and topology structure.

[0019] Basic equipment attributes refer to a collection of information inherent to firefighting equipment that identifies its fundamental characteristics. These attributes typically include the equipment model, specifications, rated power, rated voltage, manufacturer, production date, and installation location. For example, the equipment model clearly identifies the type and function of the equipment, while the rated power and rated voltage directly relate to the equipment's normal operating conditions. This basic attribute information provides the monitoring system with essential operating parameters and performance indicators for the equipment, serving as a crucial reference for subsequent data collection, analysis, and anomaly detection.

[0020] Topology refers to the connections and layout of firefighting equipment within a system. In complex firefighting systems, equipment often has a variety of connections, such as series, parallel, or networked. Topology reflects the hierarchical relationships, communication paths, and interdependencies between devices. For example, in a firefighting water pump system, multiple pumps may be connected in series or parallel through pipes. The topology determines how the water flow is distributed and controlled. By obtaining this topological information, the monitoring system can clearly understand the interactions between devices, allowing it to accurately locate fault points, analyze the propagation paths of abnormalities, and implement appropriate management measures.

[0021] During the interactive acquisition of device information, communication protocols and data interfaces are used to facilitate information exchange between the device and the monitoring system. The monitoring system interacts with the firefighting equipment's communication module, sending request instructions according to a pre-set communication protocol. The equipment responds accordingly with basic attributes and topology information. This information is transmitted to the monitoring system as data and stored in a database, providing foundational data support for subsequent monitoring steps. This interactive acquisition method not only ensures the accuracy and timeliness of information but also adapts to different types of firefighting equipment and complex system architectures, offering excellent compatibility and scalability.

[0022] P20: After deploying monitoring sensors, establish a time series monitoring dataset.

[0023] Furthermore, step P20 in this embodiment of the present application further includes:

[0024] P21: Performing a joint verification self-test on the monitoring sensor to generate a joint verification self-test compensation; P22: Correcting the monitoring result according to the joint verification self-test compensation to establish the time series monitoring data set.

[0025] Optionally, deploy monitoring sensors at key locations on firefighting equipment. These sensors are core components used to collect real-time data on equipment operating status. This data includes, but is not limited to, physical quantities such as voltage, current, load, temperature, and vibration. The selection and placement of sensors should be determined based on the equipment's actual needs and operating environment to ensure they comprehensively and accurately reflect the equipment's operating status.

[0026] After deploying sensors, a joint verification self-test is required to ensure their reliability and data accuracy. This verification involves comparing and coordinating data from multiple sensors to verify the operating status and data accuracy of each sensor. Specifically, a set of known standards or reference signals can be used to combine the data collected by different sensors to determine if there are any faults or deviations. If the data from a particular sensor is abnormal, the error can be identified and corrected by comparing and analyzing it with data from other sensors. This step is crucial to ensure that sensors function properly in real-world applications, avoiding low data accuracy caused by sensor failures, which could impact subsequent analysis and monitoring.

[0027] After the sensors complete the joint verification self-test, the monitoring results are corrected based on the generated joint verification self-test compensation. Self-test compensation refers to correcting the sensor output data based on the verification self-test results, making it more accurate and eliminating the effects of sensor errors, drift, or environmental changes. Compensation parameters are automatically calculated and generated by an algorithm and applied to correct real-time monitoring data. For example, if the measurement values ​​of a current sensor exhibit systematic deviation, joint verification self-test compensation can correct this deviation, bringing the corrected data closer to the true value. After data correction, the corrected data from each sensor can be aggregated and stored in chronological order, ultimately creating a complete time-series monitoring dataset. This dataset not only contains the raw data from the device's operation but also undergoes error correction, resulting in higher accuracy and reliability. This time-series monitoring dataset provides higher-quality data support for subsequent dynamic and static mode discrimination and anomaly identification, thereby improving the performance and reliability of the entire monitoring system.

[0028] P30: Obtaining the node control signal and the time series load data of the time series monitoring data set, performing dynamic and static mode discrimination according to the node control signal and the time series load data, and establishing a dynamic and static mode discrimination result.

[0029] Specifically, we first need to extract node control signals and time-series load data from the time-series monitoring dataset. Node control signals, obtained through the firefighting equipment's control system, reflect the equipment's operating instructions and state changes. These signals, such as on / off signals and operating mode switching signals, indicate whether the equipment is in operation, standby, or off. Time-series load data, on the other hand, is real-time equipment operating data collected by monitoring sensors. It primarily includes electrical parameters such as current and voltage, and reflects changes in the equipment's load.

[0030] Next, dynamic and static modes are determined based on the acquired node control signals and time-series load data. Specifically, by analyzing the node control signals, a preliminary determination can be made as to whether the device is in operation. If the control signal indicates that the device is in startup or operation mode, the device is preliminarily determined to be in dynamic mode. Conversely, if the control signal indicates that the device is in standby or stopped mode, the device is preliminarily determined to be in static mode. However, relying solely on node control signals for modal determination can lead to errors, so further verification requires the use of time-series load data. In dynamic mode, the device's time-series load data typically exhibits significant fluctuations or trends, such as increases or decreases in current and power. In static mode, however, time-series load data is relatively stable with minimal variation. By comprehensively analyzing the node control signals and time-series load data, the device's true operating mode can be more accurately determined. If the two results are consistent, the device's mode is confirmed. If they are inconsistent, further analysis of the data for anomalies is required to ensure the accuracy of the modal determination.

[0031] Finally, based on the results of this comprehensive analysis, dynamic and static modal discrimination results are established. This result will serve as an important basis for subsequent steps, guiding operations such as anomaly identification and monitoring management. This process accurately distinguishes between the dynamic and static operating states of equipment, providing strong support for efficient power supply monitoring of firefighting equipment.

[0032] P40: After dividing the time series monitoring data set into dynamic and static modes using the dynamic and static mode discrimination results, dynamic and static dual-mode anomaly recognition is performed based on the dynamic and static mode division results and the device information, and a joint recognition anomaly is established.

[0033] Furthermore, step P40 in this embodiment of the present application further includes:

[0034] P41: Obtain the static modal division result, perform modal conversion association recognition of the static modal division result, and reconstruct the static modal period according to the association recognition result; P42: Set a time sliding window in the reconstructed static modal period, perform static feature recognition based on the time sliding window, and establish a static feature perception result; P43: Perform perception fluctuation recognition on the static feature perception result to establish a first recognition anomaly; P44: Perform common cause interference recognition on the static feature perception result according to the topological structure to establish a second recognition anomaly, and the second recognition anomaly is a position association anomaly; P45: Establish a static recognition anomaly based on the first recognition anomaly and the second recognition anomaly; P46: Use the static recognition anomaly to establish a joint recognition anomaly.

[0035] It should be understood that based on the aforementioned dynamic and static modal discrimination results, the time series monitoring data set is divided into dynamic and static modes, allowing for independent analysis and processing of data in different modes. By dividing the time series data into dynamic and static modes, the system can more accurately identify the behavioral characteristics of equipment in different operating states and provide a clear data distribution and processing basis for subsequent anomaly identification.

[0036] After completing the dynamic and static modal discrimination, the time series monitoring dataset is first divided into dynamic and static modes using the discrimination results. The static modal division results are then further processed for modal transition association identification. This is done to identify modal transitions that may occur when the device is in static mode, such as the transition from dynamic mode to static mode. Modal transition association identification allows analysis of the device's characteristic changes during modal switching and, based on this, reconstructs the static modal cycle. The reconstructed static modal cycle more accurately reflects the device's stable operation phase in static mode, providing a more precise timeframe for subsequent anomaly identification.

[0037] Next, a time sliding window is set within the reconstructed static mode period. Time sliding windows are a data processing technique that allows for local analysis of the data within a fixed-length time window within time series data. Static feature recognition based on time sliding windows can capture the stable characteristics of the device's power supply parameters in static mode, such as the mean and variance of current and voltage. This process establishes static feature perception results, providing foundational data for subsequent anomaly identification.

[0038] Next, the static feature sensing results are analyzed for fluctuation detection to detect abnormal fluctuations in the device's power supply parameters in static mode. For example, unexpected sudden changes or fluctuations in current or voltage in static mode may indicate a potential device failure. Fluctuation detection establishes the first level of anomaly recognition, identifying these anomalies based on fluctuations in the device's operating characteristics.

[0039] At the same time, common-cause interference identification is performed on the static feature perception results based on the topology structure, focusing on identifying location-related anomalies. Location-related anomalies occur when abnormal performance of a device is correlated with its location due to interactions between devices or interference from environmental factors. Due to the interconnectedness and hierarchical structure of firefighting equipment, an anomaly in one device may affect other connected devices. Common-cause interference identification can analyze the mutual influence between devices and identify location-related anomalies, the second identification anomaly.

[0040] Combine the first and second anomaly identification methods to create a static anomaly identification system. This system encompasses both abnormal fluctuations in the device's operating characteristics and related faults between devices. Based on this, a joint anomaly identification system is built using static anomalies to provide comprehensive and accurate anomaly information for subsequent fire protection equipment power monitoring and management.

[0041] Finally, the static anomaly identification results are combined with the dynamic anomaly identification results to form a joint anomaly identification system. This joint anomaly identification system not only identifies anomalies in static and dynamic modes separately, but also integrates the anomaly identification results in both static and dynamic modes to provide a comprehensive and accurate basis for anomaly judgment. This provides reliable technical support for subsequent fault warnings and power management of the system, ensuring that the equipment can respond promptly and take necessary maintenance measures when anomalies occur.

[0042] Furthermore, step P42 of the embodiment of the present application further includes:

[0043] P42-1: Move the time sliding window, which is a multi-scale time window, and each moment is covered by the multi-scale time window; P42-2: Within each time sliding window, perform voltage stability characteristics, current residual characteristics, temperature rise trend characteristics, resistance calculation characteristics, and time-frequency domain feature extraction, and establish static feature perception results based on the feature extraction results.

[0044] Specifically, the static feature recognition process can be further refined to achieve refined perception of the equipment operating status in static mode.

[0045] When performing static feature recognition, the time window is first shifted, covering the time series data with a multi-scale time window. This multi-scale time window consists of windows of varying lengths. For example, short windows are used to capture rapidly changing features, while long windows are used to analyze long-term trends. By setting up multi-scale time windows and ensuring that each moment is covered by a multi-scale window, the operational characteristics of the device at different time scales in static mode can be fully captured. The core advantage of this approach is its ability to simultaneously analyze both subtle changes and larger fluctuations in the time series, helping to more accurately identify abnormal behavior during static device operation. It effectively addresses various anomalies that may arise during device operation, accurately detecting both instantaneous fluctuations and long-term trends. Specifically, the time window is shifted continuously, with each time point covered by at least one time window, thus ensuring data integrity and continuity.

[0046] Next, multiple feature extraction operations are performed within each time window to obtain detailed operational characteristics of the device in static mode. Specific feature extraction operations include the following aspects:

[0047] Voltage stability feature extraction: This feature analyzes voltage data within a time window and calculates statistical parameters such as the voltage fluctuation range, mean, and variance to assess voltage stability. Voltage stability is a key indicator of whether a device's power supply is functioning properly. Stable voltage indicates that the device is operating properly. If voltage fluctuations exceed a preset threshold, this may indicate unstable power supply conditions and warrants further attention.

[0048] Residual Current Feature Extraction: Calculates the residual current value within a time window, representing the difference between the measured and theoretical current values. This residual current feature can indicate abnormal equipment load conditions, such as leakage or short circuits. By comparing the difference between the actual and theoretical currents, potential electrical faults in the equipment can be identified in static mode.

[0049] Temperature Rise Trend Feature Extraction: This feature analyzes the changing trends of device temperatures within a time window and determines whether the device is at risk of overheating by calculating parameters such as the temperature rise rate and maximum temperature rise. Temperature rise trend features are crucial for early detection of potential equipment failures, as overheating is often a precursor to equipment failure. By monitoring temperature trends, it is possible to promptly identify overheating issues in static mode and take appropriate measures.

[0050] Resistance calculation feature extraction: Based on Ohm's law, this feature calculates the device's resistance by measuring voltage and current data and analyzes resistance trends. This feature can help identify issues such as poor connections or material aging within the device. By calculating the resistance value and monitoring its changes, it can be determined whether the device's electrical connections are functioning properly and whether there are any issues such as increased resistance due to aging.

[0051] Time-frequency domain feature extraction: Data within the time sliding window is transformed into the time-frequency domain, for example, using Fourier transform or wavelet transform, to extract the frequency components and time-frequency characteristics of the device's operating signal. This feature extraction can reveal hidden periodic or aperiodic changes in device operation, providing richer information for anomaly identification. By analyzing the frequency components of the signal, it is possible to identify abnormal frequency fluctuations or noise interference in the device's static mode.

[0052] Through the above feature extraction process, the system can comprehensively capture the performance of the equipment in a static working state from different dimensions. Based on the extraction results of these static features, the static feature perception results are ultimately established. Through this refined feature extraction method, the present application can more keenly capture abnormal conditions of the equipment in static mode, thereby improving the accuracy and reliability of anomaly identification and further ensuring the stable operation of firefighting equipment.

[0053] Furthermore, step P46 of the embodiment of the present application also includes:

[0054] P46-1: Establish a standard dynamic response behavior chain for fire-fighting equipment based on the equipment information; P46-2: Extract key feature indicators from the standard dynamic response behavior chain and establish a key feature indicator family; P46-3: After extracting the features of the dynamic modal division results based on the key feature indicator family, perform dynamic anomaly labeling and establish dynamic recognition anomalies; P46-4: Establish the joint recognition anomaly based on the static recognition anomaly and the dynamic recognition anomaly.

[0055] Optionally, the establishment process of the joint identification anomaly can be further improved to form a comprehensive joint identification anomaly.

[0056] During the joint anomaly identification process, a standard dynamic response behavior chain for firefighting equipment is first established based on device information. This information includes basic device attributes, topology, and historical operating data. This information provides the basis for defining the dynamic behavior pattern of the device under normal operating conditions. A standard dynamic response behavior chain is the typical sequence of changes in power supply parameters (such as current, voltage, and power) from startup to stable operation after receiving a control signal. For example, when a fire pump starts, the current increases momentarily and then gradually stabilizes. The parameter change sequence during this process is the standard dynamic response behavior chain. By analyzing the device's historical operating data and design parameters, a standard dynamic response behavior chain can be constructed for each device.

[0057] Next, key characteristic indicators (KPIs) are extracted from the standard dynamic response behavior chain to establish a KPI family. KPIs are parameters that significantly reflect the dynamic behavior characteristics of the device, such as startup time, current peak, and voltage stabilization time. By analyzing the standard dynamic response behavior chain, these KPIs are extracted and combined into KPI families. For example, for a fire pump, its KPI family might include startup current peak, current stabilization time, and voltage fluctuation range. These KPI families provide clear guidance for feature extraction from the dynamic mode segmentation results, ensuring accuracy and consistency in feature extraction.

[0058] Based on the key characteristic indicator family, feature extraction is performed on the dynamic mode segmentation results. In the dynamic mode, the operating state of the device changes dynamically, so features related to dynamic behavior need to be extracted. By analyzing the time series monitoring data in the dynamic mode segmentation results, the actual operating parameters corresponding to the key characteristic indicators are extracted. For example, parameters such as the actual current peak and current stabilization time of the device during startup are extracted and compared with the key characteristic indicators in the standard dynamic response behavior chain. If there is a significant deviation between the actual operating parameters and the key characteristic indicators in the standard dynamic response behavior chain, dynamic anomaly labeling is performed to establish dynamic anomaly identification. Dynamic anomaly labeling refers to marking operating states that do not conform to the standard behavior chain as abnormal states and recording the specific characteristics and occurrence time of the anomaly. For example, if the device's startup current peak exceeds the standard range or the current stabilization time is too long, these situations are labeled as dynamic anomalies, and the relevant parameters and time information are recorded to form dynamic anomaly identification.

[0059] Finally, a joint anomaly recognition system is established based on static and dynamic anomaly recognition. Joint anomaly recognition is the result of a comprehensive analysis and integration of anomalies in both static and dynamic modes. By comparing static and dynamic anomalies, a comprehensive understanding of device anomalies in different operating modes is achieved, providing more comprehensive and accurate anomaly information for power supply monitoring and management of firefighting equipment. For example, if a device experiences abnormal voltage fluctuations in static mode and abnormal starting current in dynamic mode, the joint anomaly recognition system will include both anomalies and provide detailed anomaly characteristics and timing information, enabling the monitoring system to take appropriate action.

[0060] Furthermore, step P46-4 of the embodiment of the present application also includes:

[0061] P46-41: Perform abnormal time location on the static identification abnormality and the dynamic identification abnormality; P46-42: Perform static and dynamic abnormality correlation analysis based on the abnormal time location result, and generate the joint identification abnormality based on the static and dynamic abnormality correlation analysis result.

[0062] Specifically, in order to further improve the accuracy and practicality of joint anomaly identification, anomaly time location is first performed on static and dynamic anomalies. Anomaly time location refers to accurately locating the time point when the anomaly occurs by analyzing the anomaly data identified in static and dynamic modes. The core task of this step is to determine the specific location of these anomalies on the timeline based on the timestamp information in the monitoring data and the characteristics of static and dynamic anomalies. For example, the equipment may exhibit abnormal voltage fluctuations or load changes within a specific time period, and anomaly time location helps determine the time when these behaviors occur. Through precise time location, a clear time reference can be provided for the tracking and subsequent processing of the anomaly source, avoiding time ambiguity in the anomaly identification results.

[0063] Next, dynamic and static anomaly correlation analysis is performed based on the anomaly time location results. Dynamic and static anomaly correlation analysis involves analyzing the temporal correlation between static and dynamic anomalies to determine whether there is a causal relationship or mutual influence between them. For example, if a current anomaly is detected during device startup (dynamic mode), and a voltage fluctuation anomaly is detected after the device enters the stable operation phase (static mode), correlation analysis can determine whether these two anomalies are caused by the same fault source. If the two anomalies have a clear temporal sequence or overlapping time periods, and their correlation can be inferred from the device's operating logic, they can be considered correlated anomalies. For example, a current anomaly at device startup may cause voltage fluctuations during subsequent device operation. In this case, a causal relationship exists between the dynamic and static anomalies.

[0064] Finally, based on the results of the static and dynamic anomaly correlation analysis, a joint anomaly identification is generated. This joint anomaly identification is the final anomaly result, formed by comprehensively considering the correlation between static and dynamic anomalies. If a correlation exists between static and dynamic anomalies, they are combined into a single joint anomaly identification, along with detailed information such as the anomaly type, occurrence time, impact range, and possible fault cause. For example, if the correlation analysis indicates that abnormal current during device startup caused subsequent voltage fluctuations, the joint anomaly identification will include detailed information about both the startup current anomaly and the voltage fluctuation anomaly, and indicate the causal relationship between them. If there is no clear correlation between the static and dynamic anomalies, they are recorded as separate abnormal events and listed separately in the joint anomaly identification. In this way, the joint anomaly identification can comprehensively and accurately reflect the abnormal conditions of the device in different operating modes, providing more detailed and targeted anomaly information for power supply monitoring and management of fire protection equipment. This helps the monitoring system take timely and effective measures for troubleshooting and resolution, ensuring the safe operation of fire protection equipment.

[0065] Furthermore, the static recognition anomaly is used to establish a joint recognition anomaly. The embodiment of the present application further includes step P46a. Step P46a further includes:

[0066] P46-1a: Create a transition modal cycle based on the associated identification results; P46-2a: Analyze the modal conversion transition behavior of the fire-fighting equipment in the transition modal cycle and establish auxiliary identification anomalies; P46-3a: Use the auxiliary identification anomaly to compensate for the joint identification anomaly.

[0067] In a possible embodiment of the present application, the accuracy and completeness of anomaly identification can be further improved by creating a transitional modal cycle, analyzing modal conversion transition behavior, and compensating for joint identification anomalies.

[0068] First, a transition modal cycle is created based on the association identification results. A transition modal cycle refers to the dynamic characteristics exhibited by a device during the transition of the device state from one working mode to another. For example, the characteristic information of the device during the modal transition process is obtained by modal transition association identification of the static modal division results. These characteristic information reflects the behavioral changes of the device when it switches from one operating mode (such as a dynamic mode) to another mode (such as a static mode). Based on these association identification results, the time range of the device during the modal transition process can be determined, thereby creating a transition modal cycle. Creating a transition modal cycle can help the system model and analyze these changes, thereby better understanding the behavioral performance of the device during the transition period and providing a reference basis for subsequent abnormality identification.

[0069] Next, the modal conversion transition behavior of the fire-fighting equipment is analyzed in the transition modal period to establish auxiliary identification of abnormalities. Modal conversion transition behavior analysis refers to a detailed analysis of the changing characteristics of the power supply parameters of the equipment during the modal conversion process. For example, when the equipment switches from the dynamic mode to the static mode, the current and voltage may undergo a rapid change process, and the parameter changes in this process may contain abnormal information. By analyzing the changing trends, change rates and fluctuations of parameters such as current, voltage, and power in the transition modal period, it is possible to identify abnormal behaviors that may occur in the equipment during the modal conversion process, including sudden current changes, abnormal voltage fluctuations, abnormal power drops, etc. These abnormal behaviors are recorded as auxiliary identification abnormalities as a supplementary identification of abnormal conditions of the equipment during the modal conversion process, which can provide potential problems that may occur in the equipment during the dynamic and static mode switching process.

[0070] Finally, the established auxiliary identification anomaly is used to compensate for the joint identification anomaly. The joint identification anomaly is a combination of the anomaly identification results under static, dynamic and transition modes. However, anomalies in the transition period may be ignored in the static and dynamic identification processes. Therefore, by combining and compensating the auxiliary identification anomaly with the joint identification anomaly, it is possible to further accurately identify hidden problems that may occur in the equipment during the modal transition process. For example, if an abnormal behavior of current mutation is detected during the modal transition process, and this abnormal behavior is not fully considered in the static identification anomaly and dynamic identification anomaly, this auxiliary identification anomaly can be added to the joint identification anomaly to improve the content of the joint identification anomaly. In this way, the joint identification anomaly can more comprehensively reflect the abnormal conditions of the equipment in different operating modes and modal transition processes, provide more accurate and complete anomaly information for the power supply monitoring and management of fire protection equipment, and further improve the reliability and safety of the system.

[0071] P50: Perform equipment power supply monitoring and management of firefighting equipment based on the joint identification anomaly.

[0072] Furthermore, step P50 of the embodiment of the present application further includes:

[0073] P51: Establish a three-level warning discrimination index; P52: Use the three-level warning discrimination index to perform the joint identification of abnormalities to perform warning level discrimination, and establish a warning level discrimination result; P53: Establish a warning abnormality based on the warning level discrimination result.

[0074] Specifically, according to the joint identification of abnormalities, power supply monitoring and management of firefighting equipment is performed. That is, according to the abnormal identification results of the equipment status, corresponding management measures are taken to ensure the stable operation of the equipment and prevent potential failure risks in a timely manner.

[0075] Specifically, in the process of implementing power supply monitoring and management of fire-fighting equipment, a three-level early warning discrimination index is first established. The three-level early warning discrimination index divides the anomalies into three different warning levels, usually low, medium and high, based on factors such as the severity of the anomaly, the scope of impact and potential risks. Each level corresponds to different discrimination criteria, which are formulated based on factors such as the abnormal characteristics, frequency of occurrence, duration and degree of impact on equipment operation in the joint identification of anomalies. For example, a low-level warning may correspond to occasional slight voltage fluctuations, which have little impact on the normal operation of the equipment; a medium-level warning may correspond to frequent current anomalies or excessive local temperature rise, which may have a certain impact on the long-term stability of the equipment; a high-level warning corresponds to serious power failures, equipment overheating or serious deviations of key parameters from the normal range, which may cause the equipment to stop operating immediately or cause more serious safety problems.

[0076] Next, the established three-level warning discrimination index is used to determine the warning level of the jointly identified anomalies. That is, the various abnormal characteristics in the jointly identified anomalies are analyzed and compared with the three-level warning discrimination index to determine the warning level of each abnormal event. For example, if the jointly identified anomaly includes an abnormal equipment startup current, and the frequency of this anomaly is high and the duration is long, it may be judged as a medium-level warning based on the three-level warning discrimination index; if the jointly identified anomaly includes a sudden and significant drop in voltage during equipment operation, accompanied by equipment cessation of operation, it may be judged as a high-level warning based on the discrimination index. Through this systematic discrimination method, it can be ensured that the division of warning levels has a clear basis and consistency, providing accurate guidance for subsequent monitoring and management.

[0077] Finally, based on the warning level identification results, warning anomalies are established. This is the result of categorizing and recording the jointly identified anomalies according to the warning level, providing clear priorities and response strategies for monitoring and management. For example, for high-level warning anomalies, the monitoring system can immediately trigger an emergency alarm, notifying relevant personnel for on-site inspection and handling. It may also take automatic protective measures, such as power cutoff, to prevent equipment damage or safety accidents. For medium-level warning anomalies, the monitoring system can issue a warning and recommend regular inspection and maintenance to prevent further deterioration of the anomaly. For low-level warning anomalies, the monitoring system can record the anomaly for subsequent analysis and reference, but immediate action is generally not required. By establishing warning anomalies, the monitoring system can take appropriate management measures based on the severity of the anomaly, ensuring that power monitoring and management of firefighting equipment is both targeted and effective in ensuring the safe operation of the equipment.

[0078] In summary, through these steps, comprehensive power supply monitoring and management of fire-fighting equipment can be achieved. Not only can equipment anomalies be accurately identified, but also graded warnings can be issued according to the severity of the anomaly. Effective early warning measures can be taken to promptly respond to potential risks and ensure the continuous and stable operation of the equipment.

[0079] In summary, the embodiments of the present application have at least the following technical effects:

[0080] Through dynamic and static mode discrimination, this application can accurately distinguish the abnormal performance of fire-fighting equipment in different working states, solving the problem that the existing technology cannot accurately identify the equipment status; combining time-series monitoring data and equipment information, it realizes real-time monitoring of the equipment power supply, and can timely discover and identify potential anomalies to ensure the stable operation of the equipment; based on the basic properties, topology and monitoring data of the equipment, the system can perform multi-dimensional anomaly identification, thereby improving the accuracy and comprehensiveness of anomaly detection; combining dynamic and static mode division and joint identification of anomaly methods, it provides comprehensive monitoring of the equipment in static and dynamic states, avoiding the traditional method of ignoring dynamic anomalies; through joint identification of anomaly results, it can perform intelligent power management according to the anomaly level, take timely response measures, and ensure the stability and safety of the equipment power supply.

[0081] The technical effect of accurately distinguishing abnormal performance of equipment under different working conditions through dynamic and static mode discrimination and joint identification of abnormalities has been achieved, thereby improving the accuracy and reliability of equipment monitoring.

[0082] Embodiment 2 is based on the same inventive concept as the fire-fighting equipment power supply monitoring method in the above embodiment. Figure 2 As shown, the present application provides a fire equipment power supply monitoring system. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0083] The device information interaction module 11 is used to interactively obtain device information of fire-fighting equipment, and the device information includes basic attributes and topological structure of the equipment.

[0084] The time series data monitoring module 12 is used to establish a time series monitoring data set after the monitoring sensors are deployed.

[0085] The dynamic and static mode discrimination module 13 is used to obtain the node control signal and the time series load data of the time series monitoring data set, perform dynamic and static mode discrimination according to the node control signal and the time series load data, and establish a dynamic and static mode discrimination result.

[0086] The joint anomaly identification module 14 is used to use the dynamic and static mode discrimination results to divide the time series monitoring data set into dynamic and static modes, and then perform dynamic and static dual-mode anomaly identification based on the dynamic and static mode division results and the device information to establish a joint identification anomaly.

[0087] The power supply monitoring and management module 15 is used to monitor and manage the power supply of the fire-fighting equipment according to the combined identification anomaly.

[0088] Furthermore, the time series data monitoring module 12 is further configured to perform the following steps:

[0089] Performing a joint verification self-test on the monitoring sensor to generate a joint verification self-test compensation; correcting the monitoring result according to the joint verification self-test compensation to establish the time series monitoring data set.

[0090] Furthermore, the joint anomaly identification module 14 is further configured to perform the following steps:

[0091] Obtain the static modal division result, perform modal conversion association recognition of the static modal division result, and reconstruct the static modal period according to the association recognition result; set a time sliding window in the reconstructed static modal period, perform static feature recognition based on the time sliding window, and establish a static feature perception result; perform perception fluctuation recognition on the static feature perception result to establish a first recognition anomaly; perform common cause interference recognition on the static feature perception result according to the topological structure to establish a second recognition anomaly, which is a position association anomaly; establish a static recognition anomaly based on the first recognition anomaly and the second recognition anomaly; and use the static recognition anomaly to establish a joint recognition anomaly.

[0092] Furthermore, the joint anomaly identification module 14 is further configured to perform the following steps:

[0093] The time sliding window is moved, and the time sliding window is a multi-scale time window, and each moment is covered by the multi-scale time window; within each time sliding window, voltage stability characteristics, current residual characteristics, temperature rise trend characteristics, resistance calculation characteristics, and time-frequency domain feature extraction are performed, and static feature perception results are established based on the feature extraction results.

[0094] Furthermore, the joint anomaly identification module 14 is further configured to perform the following steps:

[0095] A standard dynamic response behavior chain of fire-fighting equipment is established based on the equipment information; key feature indicators are extracted from the standard dynamic response behavior chain to establish a key feature indicator family; after feature extraction of the dynamic mode division results based on the key feature indicator family, dynamic anomaly labeling is performed to establish dynamic recognition anomalies; the joint recognition anomaly is established based on the static recognition anomaly and the dynamic recognition anomaly.

[0096] Furthermore, the joint anomaly identification module 14 is further configured to perform the following steps:

[0097] Anomaly time location is performed on the static recognition anomaly and the dynamic recognition anomaly; static and dynamic anomaly correlation analysis is performed based on the anomaly time location result, and the joint recognition anomaly is generated based on the dynamic and static anomaly correlation analysis result.

[0098] Furthermore, the joint anomaly identification module 14 is further configured to perform the following steps:

[0099] A transition modal cycle is created based on the associated identification result; a modal conversion transition behavior analysis of the fire-fighting equipment is performed on the transition modal cycle to establish an auxiliary identification anomaly; and the joint identification anomaly is compensated using the auxiliary identification anomaly.

[0100] Furthermore, the joint anomaly identification module 14 is further configured to perform the following steps:

[0101] Establishing a three-level early warning discrimination index; using the three-level early warning discrimination index to perform the joint identification of abnormalities to perform early warning level discrimination, and establishing an early warning level discrimination result; establishing an early warning abnormality according to the early warning level discrimination result.

[0102] Furthermore, the power monitoring management module 15 is further configured to perform the following steps:

[0103] Performing a joint verification self-test on the monitoring sensor to generate a joint verification self-test compensation; correcting the monitoring result according to the joint verification self-test compensation to establish the time series monitoring data set.

[0104] In the third embodiment, based on the same inventive concept as the method for monitoring the power supply of fire-fighting equipment in the aforementioned embodiment, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method in the first embodiment is implemented.

[0105] Through the detailed description of the fire-fighting equipment power supply monitoring method described above, those skilled in the art will clearly understand the fire-fighting equipment power supply monitoring method, system, and medium of this embodiment. Therefore, for the sake of brevity, a detailed description is not given here. As the device disclosed in the embodiment corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method description.

[0106] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0107] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0109] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A method for monitoring power supply of fire-fighting equipment, characterized in that: The method comprises: Interactively obtain device information of fire-fighting equipment, the device information including basic attributes and topology of the equipment; After deploying monitoring sensors, establish a time series monitoring dataset; Obtaining node control signals and time series load data of the time series monitoring data set, performing dynamic and static modal discrimination according to the node control signals and the time series load data, and establishing a dynamic and static modal discrimination result; After dividing the time series monitoring data set into dynamic and static modes using the dynamic and static mode discrimination result, dynamic and static dual-mode anomaly recognition is performed based on the dynamic and static mode division result and the device information, and a joint recognition anomaly is established; Performing equipment power monitoring and management of firefighting equipment based on the joint identification anomaly; The abnormality recognition of the dynamic and static dual states based on the dynamic and static mode division result and the device information, and the establishment of a joint abnormality recognition method, include: Obtaining a static mode division result, performing modal conversion association recognition on the static mode division result, and reconstructing a static mode period according to the association recognition result; Setting a time sliding window in the reconstructed static modal period, performing static feature recognition based on the time sliding window, and establishing a static feature perception result; Performing perception fluctuation recognition on the static feature perception result to establish a first recognition anomaly; Performing common cause interference identification of the static feature perception result according to the topological structure, and establishing a second identification anomaly, where the second identification anomaly is a position correlation anomaly; Establishing a static recognition anomaly based on the first recognition anomaly and the second recognition anomaly; Establishing a joint recognition anomaly using the static recognition anomaly; The performing of static feature recognition based on the time sliding window and establishing a static feature perception result includes: The time sliding window is moved, wherein the time sliding window is a multi-scale time window, and each moment is covered by the multi-scale time window; In each time sliding window, voltage stability features, current residual features, temperature rise trend features, resistance calculation features, and time-frequency domain feature extraction are performed, and static feature perception results are established based on the feature extraction results; The step of using the static recognition anomaly to establish a joint recognition anomaly includes: Establishing a standard dynamic response behavior chain of fire-fighting equipment based on the equipment information; Extracting key characteristic indicators from the standard dynamic response behavior chain and establishing a key characteristic indicator family; After extracting the features of the dynamic mode segmentation results based on the key feature index family, dynamic anomaly annotation is performed to establish dynamic recognition anomalies; Establishing the combined identification anomaly according to the static identification anomaly and the dynamic identification anomaly; The step of establishing the joint identification anomaly based on the static identification anomaly and the dynamic identification anomaly includes: Performing abnormality time location on the statically identified abnormality and the dynamically identified abnormality; The dynamic and static anomaly correlation analysis is performed according to the abnormal time positioning result, and the joint recognition anomaly is generated according to the dynamic and static anomaly correlation analysis result.

2. A fire-fighting equipment power supply monitoring method according to claim 1, characterized in that: The method of using the static recognition anomaly to establish a joint recognition anomaly further includes: creating a transition modal cycle according to the association identification result; Performing modal conversion transition behavior analysis on the fire-fighting equipment in the transition modal period and establishing auxiliary abnormality identification; The auxiliary recognition anomaly is utilized to compensate for the joint recognition anomaly.

3. A fire-fighting equipment power supply monitoring method according to claim 1, characterized in that: The monitoring and management of the power supply of the fire-fighting equipment according to the combined identification anomaly includes: Establish three-level early warning identification indicators; Using the three-level warning discrimination indicators to perform the joint identification of abnormalities to perform warning level discrimination, and establish a warning level discrimination result; An early warning anomaly is established based on the early warning level determination result.

4. A fire-fighting equipment power supply monitoring method according to claim 1, characterized in that: After the monitoring sensors are deployed, a time series monitoring data set is established, including: performing a joint verification self-test of the monitoring sensor to generate a joint verification self-test compensation; After the monitoring results are corrected according to the joint verification self-test compensation, the time series monitoring data set is established.

5. A fire equipment power supply monitoring system, characterized in that: A method for monitoring power supply of fire-fighting equipment according to any one of claims 1 to 4, the system comprising: A device information interaction module, which is used to interactively obtain device information of fire-fighting equipment, including basic device attributes and topological structure; A time series data monitoring module, which is used to establish a time series monitoring data set after the monitoring sensors are deployed; a dynamic and static mode discrimination module, the dynamic and static mode discrimination module being used to obtain the node control signal and the time series load data of the time series monitoring data set, perform dynamic and static mode discrimination based on the node control signal and the time series load data, and establish a dynamic and static mode discrimination result; A joint anomaly recognition module, wherein the joint anomaly recognition module is used to divide the time series monitoring data set into dynamic and static modes using the dynamic and static mode discrimination results, and then perform dynamic and static dual-mode anomaly recognition based on the dynamic and static mode division results and the device information to establish a joint recognition anomaly; A power supply monitoring and management module is used to monitor and manage the power supply of fire-fighting equipment based on the joint identification anomaly.

6. A computer-readable storage medium, characterized in that The storage medium stores a computer program, which implements the steps of the method according to any one of claims 1 to 4 when executed by a processor.

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