Civil air defense command engineering protection facility monitoring system

Abstract

The invention relates to the technical field of alarm systems, in particular to a civil air defense command engineering protection facility monitoring system, which comprises a data acquisition preprocessing module for receiving environmental monitoring sensor readings, obtaining sensor identifiers, geographic coordinates and timestamp information, and grouping according to a preset time window and geographic proximity. According to the invention, the environmental parameters and the equipment state information collected by the sensor are dynamically grouped according to the time window and the geographical proximity, and the space-time correlation signal group is constructed, so that the misjudgment probability of isolated signals is reduced, and the abnormal event positioning precision is improved. Multi-type sensor measurement values are extracted under a unified space-time framework, a deviation threshold value is set for cross validation, abnormal data caused by equipment faults or local interference are filtered, and the credibility and effectiveness of alarm events are ensured. The position, the type and the timestamp information of the alarm event are packaged based on a standardized format, rapid analysis and cross-node transmission of a data stream are realized by adopting a communication protocol, and the response delay of a central station is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of alarm systems, and in particular to a monitoring system for protective facilities of civil air defense command engineering projects. Background Art

[0002] The technical field of alarm systems covers core technologies such as real-time monitoring, anomaly identification, multi-source data fusion and emergency response. It focuses on collecting physical environmental parameters (such as temperature, pressure, gas concentration) and equipment status information (such as access control opening and closing, structural stress) through sensor networks, combining communication protocols to realize cross-node data transmission and provide graded warnings for potential risks.

[0003] Existing technologies use unstructured data formats for alarm information transmission, requiring additional parsing and field matching at the receiving end. This increases data processing latency and can potentially lead to protocol compatibility issues. Quantitative analysis models for the spatial density and temporal frequency of alarm signals have not been established, presenting them only as discrete points. This makes it difficult to quickly identify threat concentration areas, impacting the rationality and prioritization of emergency resource allocation. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a civil air defense command engineering protection facility monitoring system.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: The civil air defense command engineering protection facility monitoring system includes:

[0006] The data acquisition and preprocessing module receives environmental monitoring sensor readings, obtains sensor identifiers, geographic coordinates, and timestamp information, groups them according to preset time windows and geographic proximity, and establishes spatiotemporally correlated sensor signal groups;

[0007] An event consistency check module extracts measurement values ​​of different types of sensors within the same group based on the spatiotemporally correlated sensor signal group, sets a numerical deviation threshold for comparison, obtains a sensor consistency determination flag, filters the signal group based on the sensor consistency determination flag, eliminates signal groups determined to be inconsistent, retains consistent signal groups, and obtains confirmed alarm event units;

[0008] The alarm information transmission module extracts the event location coordinates, event type code and confirmation timestamp information based on the confirmed alarm event unit, encapsulates them into a standard format data unit, creates an alarm information packet to be sent, calls the communication interface, sends the alarm information packet to the target central station network address, records the sending log, and obtains the central station alarm location signal;

[0009] The situation aggregation presentation module calculates the spatial aggregation measurement of the signals in the map display area based on the alarm positioning signals received from multiple central stations, generates the alarm spatiotemporal density distribution results, adjusts the display level of the alarm points on the interface based on the alarm spatiotemporal density distribution results, and establishes the aggregated alarm results.

[0010] Preferably, the steps of acquiring the spatiotemporal correlation sensor signal group are:

[0011] Receive environmental monitoring sensor readings and structural stress sensor readings, extract sensor identifiers, geographic coordinates, and timestamp information, filter sensor data based on a preset time window threshold, filter sensor data based on a geographic proximity threshold, and generate a preliminary spatiotemporal grouping set;

[0012] Based on the preliminary spatiotemporal grouping set, calculating the spatiotemporal correlation between the protection door state sensor signal and the monitoring sensor within the same group;

[0013] Based on the spatiotemporal correlation, a group with a correlation greater than a threshold is selected, and the sensor identifier and the signal type are combined to establish a spatiotemporal correlation sensor signal group.

[0014] Preferably, the steps of obtaining the sensor consistency determination flag are:

[0015] Based on the spatiotemporal correlation sensor signal group, extract the value set of the operating status parameters of the gas filtering and ventilation equipment and the value set of the air quality parameters, match the parameter types in the same time window, and generate a parameter pairing set;

[0016] Calculating normalized deviations between parameters of different types of sensors based on the parameter pairing set;

[0017] Based on the normalized deviation, if the normalized deviation is greater than or equal to a preset deviation threshold, it is determined to be inconsistent; otherwise, it is determined to be consistent, and a sensor consistency determination flag is generated.

[0018] Preferably, the steps of obtaining the confirmed alarm event unit are:

[0019] Based on the sensor consistency determination flag, traverse all spatiotemporally correlated sensor signal groups, filter signal groups with consistent or inconsistent flags, and generate a classified signal group set;

[0020] According to the classified signal group set, sensor identifiers and timestamp information of all inconsistent signal groups are extracted, batch elimination is performed, and a confirmed alarm event unit is generated.

[0021] Preferably, the steps of obtaining the alarm information packet to be sent are:

[0022] Based on the confirmed alarm event unit, the event occurrence location coordinate field, the event type code field and the confirmation timestamp field are parsed, and the coordinate latitude and longitude values, the event type code string and the timestamp value are separated to generate an event attribute data set;

[0023] According to the event attribute dataset, the location coordinates are converted into a decimal floating point format, the event type code is converted into a preset protocol encoding value, and the timestamp is converted into a UTC standard time string, and mapped to fields according to the JSON-LD specification to generate a standard format data unit;

[0024] Based on the standard format data unit, communication protocol header information is added, encapsulated into a binary data stream, and an alarm information packet to be sent is generated.

[0025] Preferably, the steps of obtaining the central station alarm location signal are:

[0026] Call the communication interface, establish a Socket connection with the target center station network address through the TCP / IP protocol, send the binary data stream of the alarm information packet to be sent, and generate the communication interface sending result status;

[0027] Based on the sending result status of the communication interface, extract the sending timestamp, target network address and data packet length information, write them into the log file, and generate an alarm information sending log entry;

[0028] According to the alarm information sending log entry, the event location coordinates and event type code in the successfully sent data packet are parsed, mapped into geographic grid code and event level identifier, and a central station alarm positioning signal is generated.

[0029] Preferably, the steps for obtaining the alarm spatiotemporal density distribution result are:

[0030] Based on the alarm location signals received from multiple central stations, the geographic grid code, event level identifier and timestamp information in the signals are parsed, and the alarm signal set within a unit time is filtered according to the preset time window to generate an alarm location data set;

[0031] Calculating a spatial aggregation metric within a map display area based on the alarm location dataset;

[0032] Based on the spatial aggregation metric, the number of alarm signal arrivals per unit time is counted to generate a frequency parameter, and the intrusion detector signal source ID grouping tag is associated to generate an alarm spatiotemporal density distribution result.

[0033] Preferably, the steps of obtaining the aggregated alarm result are:

[0034] Based on the alarm spatiotemporal density distribution result, the spatial density and temporal frequency in the alarm spatiotemporal density distribution result are analyzed, the density level threshold is set to divide the display level interval, and the alarm point level division scheme is generated;

[0035] According to the alarm point hierarchical division scheme, the coordinates of the alarm points in the same level are merged according to the geographical grid area, and the grid numbers of the grids whose number of alarm points exceeds the aggregation threshold are counted to generate an aggregated alarm point set;

[0036] Based on the aggregated alarm point set, the display priority parameters in the alarm point hierarchical division scheme are associated, the high-density area alarm points are mapped to the top display, and the low-density area is mapped to the secondary display to generate an aggregated alarm result.

[0037] Compared with the prior art, the advantages and positive effects of the present invention are:

[0038] In the present invention, by dynamically grouping the environmental parameters and equipment status information collected by sensors according to time windows and geographical proximity, a spatiotemporal correlation signal group is constructed to reduce the probability of misjudgment of isolated signals and improve the accuracy of locating abnormal events. Under a unified spatiotemporal framework, multi-type sensor measurement values ​​are extracted and deviation thresholds are set for cross-validation to filter abnormal data caused by equipment failure or local interference, ensuring the credibility and effectiveness of alarm events. Based on the standardized format to encapsulate the location, type and timestamp information of the alarm event, a communication protocol is used to achieve rapid parsing and cross-node transmission of data streams, shortening the response delay of the central station. A spatial aggregation metric algorithm is introduced to dynamically adjust the display level in combination with the time frequency and geographical density distribution of the alarm signal to assist command personnel in quickly identifying high-threat areas and optimizing resource scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0041] See also Figure 1 The present invention provides a technical solution: a monitoring system for civil air defense command engineering protection facilities includes:

[0042] The data acquisition and preprocessing module receives environmental monitoring sensor readings, obtains sensor identifiers, geographic coordinates, and timestamp information, groups them according to preset time windows and geographic proximity, and establishes spatiotemporally correlated sensor signal groups;

[0043] The event consistency verification module extracts the measurement values ​​of different types of sensors in the same group based on the temporal and spatial correlation of sensor signal groups, sets a numerical deviation threshold for comparison, obtains the sensor consistency judgment mark, and filters the signal groups based on the sensor consistency judgment mark, eliminates the signal groups judged to be inconsistent, retains the consistent signal groups, and obtains the confirmed alarm event unit;

[0044] The alarm information transmission module extracts the event location coordinates, event type code, and confirmation timestamp information based on the confirmed alarm event unit, encapsulates them into a standard format data unit, creates an alarm information packet to be sent, calls the communication interface, sends the alarm information packet to the target central station network address, records the sending log, and obtains the central station alarm location signal;

[0045] The situation aggregation presentation module calculates the spatial aggregation measurement of the signals in the map display area based on the alarm positioning signals received from multiple central stations, generates the alarm spatiotemporal density distribution results, adjusts the display level of the alarm points on the interface based on the alarm spatiotemporal density distribution results, and establishes the aggregated alarm results.

[0046] The steps for obtaining the spatiotemporal correlation sensor signal group are as follows:

[0047] Receive environmental monitoring sensor readings and structural stress sensor readings, extract sensor identifiers, geographic coordinates, and timestamp information, filter sensor data based on a preset time window threshold, filter sensor data based on a geographic proximity threshold, and generate a preliminary spatiotemporal grouping set;

[0048] Based on the preliminary spatiotemporal grouping set, the spatiotemporal correlation between the protection door status sensor signal and the monitoring sensor within the same group is calculated. The calculation formula is:

[0049]

[0050] Among them, R is the spatiotemporal correlation, Δt k is the timestamp difference between the kth sensor and the protective door status sensor, T w is the preset time window threshold, d k is the geographical distance between the kth sensor and the protective door status sensor, D is the geographical proximity threshold, and m is the number of sensors in the current group;

[0051] Based on the spatiotemporal correlation, the groups with correlation greater than the threshold are selected, the sensor identifiers and signal types are merged, and the spatiotemporal correlation sensor signal groups are established.

[0052] Specifically, based on the real-time reading streams received from environmental monitoring sensors (such as temperature, humidity, air pressure, and toxic gas concentration sensors) and structural stress sensors (such as fiber Bragg gratings or piezoelectric stress sensors deployed on key load-bearing structures or protective door frames), these raw data streams are first parsed to extract the unique sensor identifier (such as a string in UUID format), precise geographic coordinates (using the WGS84 coordinate system, including longitude, latitude, and altitude values), and high-precision timestamp information (using Unix timestamp, accurate to milliseconds) associated with each data point. Subsequently, the sensor data is preliminarily screened based on a preset time window threshold, which is set based on historical data analysis or signal synchronization requirements during exercises. For example, by analyzing the sensor response time distribution of related events (such as simulated impacts or airflow changes) in similar protective projects in the past month, it was found that 95% of the related sensor signal times were The difference between the two is within 5 seconds, so the time window threshold is set to 5 seconds. During the specific screening, the current processing time point is used as the benchmark, and data points with timestamps falling within the interval [current time - 5 seconds, current time] are retained. Then, the sensor data is further filtered according to the preset geographic proximity threshold. This threshold is determined based on the physical layout of the protection unit and the effective monitoring range of the sensor. For example, by consulting the engineering design drawings, it is determined that the area within 10 meters around the key protection door is the area most directly related to changes in environmental and structural status. Therefore, the geographic proximity threshold is set to 10 meters. During the specific filtering, the geographic spatial distance between each sensor is calculated, and the subset of sensor data with a distance less than or equal to 10 meters between each other is retained. Finally, the sensor data that passes the dual screening conditions of time and geographic proximity are organized into preliminary spatiotemporal grouping sets in the form of sensor sets. Each set represents a group of sensor readings with similar geographical locations within a specific time window.

[0053] formula: The formula is useful in that it quantifies the correlation strength between the door status sensor signal and other monitoring sensor signals in the same preliminary group by combining information in both time and space dimensions. It also takes into account the time difference (Δt k ) and the preset time window (T w ), and the geographical distance between sensors (d k ) to the predetermined geographic proximity (D). By adding the squares of these two ratios to the denominator 1 / (1+x 2) structure, when the time and space differences are small relative to the threshold, the corresponding factor approaches 1 and the correlation is high; when the difference increases, the factor smoothly approaches 0 and the correlation decreases. The use of square terms increases the weight of the influence of large deviations. The product of the two factors ensures that only sensors that are close enough in time and space will be judged as strongly correlated. Compared with using only time or space thresholds for hard judgment, this comprehensive measurement method can more accurately identify the sensor signal combination that is truly related to the change in the state of the protective door, effectively reducing the misjudgment caused by coincidence of sensor signals, and providing a more reliable data basis for subsequent event consistency verification, thereby improving the accuracy of the alarm of the entire monitoring system.

[0054] Parameter Acquisition Steps: m: The number of monitoring sensors in the current group (excluding the protective door status sensor). This parameter is obtained by counting the sensors in the "Preliminary Spatiotemporal Grouping Set," excluding the protective door status sensor itself, which serves as the baseline. Acquisition Example: Process a preliminary group containing one protective door status sensor (ID: DoorSensor01), two temperature sensors (IDs: TempSensorA, TempSensorB), and one structural stress sensor (ID: StressSensorX). The number of monitoring sensors in this group, m, is 3.

[0055] Δt k : The absolute value of the time stamp difference between the kth monitoring sensor and the protective door status sensor, in seconds (s). This value is obtained by extracting the time stamp t of the kth monitoring sensor in the same group. k and the timestamp t of the protective door status sensor door , calculate |Δt k |=|t k -t door |Get. Get example: guard door sensor timestamp t door =1714300005.500 seconds, and the timestamp t1 of the first temperature sensor TempSensorA in the group is 1714300006.200 seconds. Then |Δt1|=

[0056] |1714300006.200-1714300005.500|=0.7 seconds.

[0057] T w :Preset time window threshold, in seconds. This threshold is set based on statistical analysis of the time differences of related sensor signals when related events occur in historical data or simulation exercises. By analyzing historical data, it is determined that 95% of the time differences of related signals are less than 4 seconds. To ensure coverage and a certain degree of fault tolerance, T is set. w= 5.0 seconds. Specific setting process example: Collect 50 guard door state change events and their associated environmental / stress sensor response time data in the past three months, calculate the time difference between the guard door signal and each associated sensor signal in each event, and obtain the time difference data set {δt1, δt2, ..., δt N}, sort the data set and find the 95th percentile value, for example, 3.85 seconds. Considering factors such as network delay and sensor response drift, increase a certain margin (for example, increase by about 30%) and set T w =ceil(3.85×1.3)=5.0 seconds.

[0058] d k : The geographic distance between the kth monitoring sensor and the protective door status sensor, in meters (m). This value is obtained by obtaining the geographic coordinates (latitude and longitude or engineering coordinates) of the kth monitoring sensor and the protective door status sensor. For example, the coordinates of the protective door sensor are (39.90420°N, 116.40740°E), and the coordinates of the first stress sensor in the group, StressSensorX, are (39.90410°N, 116.40750°E). Use an online tool or GIS library function to calculate the distance between the two points, and you'll get d1 ≈ 15.7 meters.

[0059] D: Geographic proximity threshold, in meters (m). This threshold is set based on the physical layout of the protective project, the size of the monitoring area, and the range of possible impact of the event. Based on engineering drawings and sensor coverage analysis, it is determined that sensors directly related to the status of the protective door are usually deployed within a radius of 10 meters. To include boundary conditions, D is set to 10.0 meters. Specific setting process example: Consult the as-built drawings of a protective unit of a civil air defense project, determine the location of the protective door P1, and mark the installation locations of the surrounding environment sensors T1, T2 and stress sensors S1, S2 on the drawings. Measure the distances on the drawing and convert them to scale. It is found that T1 and S1 are 5 meters and 8 meters away from P1, respectively, and T2 and S2 are 12 meters and 15 meters away, respectively. Taking into account the potential shock wave or air pollutant diffusion model, it is believed that sensors within a 10-meter range have the strongest correlation, so D is set to 10.0 meters.

[0060] Calculation process: Now calculate based on a preliminary spatiotemporal grouping set, which includes a protective door status sensor and m = 2 monitoring sensors (sensor 1: temperature, sensor 2: stress). The parameter values ​​used (according to the above acquisition steps and examples): T w =5.0 seconds, D=10.0 meters, sensor data: sensor 1 (temperature): |Δt1|=0.7 seconds, d1=5.0 meters, sensor 2 (stress): |Δt2|=1.5 seconds, d2=8.0 meters;

[0061] Calculate the sum of the time components:

[0062]

[0063] Compute the sum of the spatial parts:

[0064]

[0065] Substitute the formula to calculate the spatiotemporal correlation R:

[0066]

[0067] The results show that the calculated spatiotemporal correlation R value is 0.4768. This value quantifies the comprehensive temporal and spatial proximity of the temperature and stress sensors to the protective door status sensor in the current group.

[0068] Based on the spatiotemporal correlation R values ​​of each preliminary spatiotemporal grouping set calculated in the previous step, the following screening and integration are performed to establish the final spatiotemporal correlation sensor signal group. First, a correlation judgment threshold needs to be set. The setting of this threshold is intended to distinguish strong correlation groups from weak correlation or irrelevant groups. The setting basis can be the best distinguishing point obtained by analyzing historical or simulation data through the receiver operating characteristic curve (ROC). For example, using a data set containing known real correlation events and irrelevant interference events, calculate the R values ​​of all groups, plot the true positive rate (TPR) and false positive rate (FPR) under different R thresholds, and select the threshold that ensures a relatively high R value. The R value with high TPR (for example, 90%) and low FPR (for example, less than 15%) is used as the threshold, or it can be set based on expert experience and the requirements for system sensitivity and false alarm rate. For example, for scenarios with high security requirements, a higher threshold may be set to sacrifice some sensitivity in exchange for an extremely low false alarm rate. The correlation threshold is set to 0.3. The specific setting process example: Use a test data set containing 100 known associated groups and 100 known non-associated groups, calculate the R value of each group, try different thresholds θ∈[0,1], calculate the TPR and FPR corresponding to each threshold, draw the ROC curve, and find the curve The threshold value of the point closest to the upper left corner (0, 1) is selected, or the threshold value is determined according to the point with the largest YoudenIndex = TPR-FPR. It is found that when θ = 0.32, TPR = 0.91, FPR = 0.14, and YoudenIndex reaches a maximum value of 0.77. Therefore, the correlation threshold value is selected as 0.32. Then, all the preliminary spatiotemporal grouping sets with calculated R values ​​are traversed, and the R value of each group is compared with the set threshold value of 0.32. If R ≥ 0.32, the group is determined to be a strongly correlated group and is retained. If R < 0.32, it is determined to be a weakly correlated or irrelevant group and is discarded. Then, for all groups that are judged to be strongly correlated, the identifiers of all sensors contained therein (such as: DoorSensor01, TempSensorA, StressSensorX) and the signal type corresponding to each identifier (such as: "protective door status", "temperature", "structural stress") are extracted, and this information is integrated into a structured data record, such as an object or dictionary containing a sensor list (each element contains ID and Type). Finally, the structured data records corresponding to all the strongly correlated groups that have passed the screening are collected together to form the final spatiotemporal correlation sensor signal group.

[0069] The steps to obtain the sensor consistency determination flag are:

[0070] Based on the spatiotemporal correlation sensor signal group, the numerical value set of the operating status parameters of the gas filtration and ventilation equipment and the numerical value set of the air quality parameters are extracted, the parameter types within the same time window are matched, and a parameter pairing set is generated;

[0071] According to the parameter pairing set, the normalized deviation between the parameters of different types of sensors is calculated. The calculation formula is:

[0072]

[0073] Among them, D′ is the normalized deviation, VP p AQ is the expected air quality parameter value based on the current operating status of the gas filtering and ventilation equipment in the pth pairing. p is the actual measured air quality parameter value in the pth pairing, T VP，max With T VP，min T is the upper and lower limits of the gas filtration and ventilation parameter thresholds, AQ，max With T AQ，min are the upper and lower limits of the air quality parameter threshold, and n is the number of parameter pairs;

[0074] Based on the normalized deviation, if the normalized deviation is greater than or equal to a preset deviation threshold, it is determined to be inconsistent; otherwise, it is determined to be consistent, and a sensor consistency determination flag is generated.

[0075] Specifically, based on the "spatiotemporal correlation sensor signal group" obtained in the previous step, the signal group has confirmed the temporal and spatial correlation of the sensors within the group. Now it is necessary to further verify whether the readings of different types of sensors within it conform to the expected physical or logical relationships. First, from the list of sensors contained in the signal group, identify and extract the numerical value sets of two types of key parameters: one is "gas filter ventilation equipment operating status parameters", such as fan speed (unit RPM), gas filter unit valve opening (unit %), equipment operating power (unit kW) and other parameters that can reflect the working status of the equipment; the other is "air quality parameters", such as carbon dioxide concentration (unit ppm), oxygen concentration (unit %), concentration of specific toxic and harmful gases (such as hydrogen cyanide HCN, chlorine Cl2) (unit mg / m 3 ), PM2.5 / PM10 particle concentration (unit: μg / m 3) and other parameters reflecting the environmental status within the protective space. The specific values ​​and precise timestamps of these parameters within the corresponding time window of the signal group are obtained. Next, according to the preset association rules (for example, when the ventilator is running at high speed, the carbon dioxide concentration should show a downward trend; after the gas filtration mode is activated, the concentration of specific toxic gases should be significantly reduced or maintained below the safety threshold), the relevant parameter types are matched within the same time window (the time window can be defined as the period between the earliest and latest timestamps in the signal group, or a small interval centered on the timestamp of the key event, such as 1 second). For example, the "ventilator speed" parameter is matched with the "carbon dioxide concentration" parameter, and the "gas filtration mode status" parameter (quantized as 1 for activated and 0 for inactivated) is matched with the "HCN concentration" parameter. During matching, the timestamps of the two parameters must be as close as possible (for example, the difference should not exceed 0.5 seconds). All successfully matched, intrinsically correlated parameter pairs (one from the gas filtration ventilation equipment status and one from the air quality) are collected. Each pair contains the specific parameter value and the corresponding timestamp. Finally, a parameter pair set is generated for subsequent quantitative consistency assessment.

[0076] formula: The formula is useful in that it provides a standardized way to measure the degree of consistency between the operating status of a gas-filtering ventilation device and the air quality parameters it affects. It calculates the normalized deviation D' between paired parameters. The key advantage lies in its normalization process: by dividing the square of the parameter difference (VP p -AQ p ) 2 Divide by the larger of the squares of the respective ranges of the pair of parameters max

[0077] (T VP，max -T VP，min ) 2 ,(T AQ，max -T AQ，min ) 2 ), eliminating the impact of different parameter types having different physical units and numerical ranges, making the deviations between different parameter pairs comparable. Using the square of the difference can amplify the deviation, and using the maximum value of the denominator for normalization can ensure that the deviation is constrained by the parameter with a wider dynamic range, avoiding excessive influence of the parameter with a small range on the result. Finally, a comprehensive, dimensionless average deviation indicator is obtained by taking the root mean square root. This allows the system to use a unified deviation threshold to determine whether the system status reflected by various sensor combinations is "consistent", greatly simplifying the subsequent judgment logic and improving the robustness of the judgment. It can effectively identify abnormal situations where the operating status of the equipment does not match the environmental feedback, such as equipment failure or external abnormal event interference.

[0078] Parameter acquisition steps:

[0079] n: Number of parameter pairs. Calculated by counting the number of valid parameter pairs in the "parameter pairing set" generated in the previous step. Example: Analyzing a "spatiotemporally correlated sensor signal group" successfully matches two pairs of intrinsically correlated (expected air quality, measured air quality) parameters, then n = 2.

[0080] VP p : The expected air quality parameter value based on the current operating state of the gas-filtering ventilation equipment in the pth pair. This value needs to be derived based on the ventilation equipment's operating model, environmental parameters, and target air quality standards.

[0081] AQ p : The actual measured air quality parameter value in the p-th pair. This value is directly extracted from the corresponding air quality sensor reading in the "parameter pairing set". Acquisition example: For the first pair (p=1), the measured CO2 concentration sensor reading is AQ1=580ppm. For the second pair (p=2), the measured HCN concentration sensor reading is AQ2=0.8mg / m 3 .

[0082] T VP，max With T VP，min : In the pth pairing, the expected air quality parameter (VP p ) is the upper and lower limits of the reasonable fluctuation range of the expected air quality parameter value under the specific ventilation equipment state, and is set according to design specifications, safety standards or historical operating data statistics. Acquisition example: For the first pair (expected CO2 concentration), set the normal fluctuation range to [450,550] ppm, that is, T VP，min，1 =450, T VP，max，1 =550. For the second pair (expected HCN concentration), the normal fluctuation range is set to [0.3, 0.7] mg / m 3 , that is, T VP，min，2 =0.3, T VP，max，2 =0.7.

[0083] T AQ，max With T AQ，min : In the pth pairing, the actual air quality parameter (AQ p ) sensor measurement range or the upper and lower limits of the limit range under normal environmental conditions. Usually set according to the sensor's technical specifications or environmental safety standards. Acquisition example: For the first pair (measured CO2 concentration), the CO2 sensor range used is [0,5000]ppm, that is, T AQ，min，1 =0, T AQ，max，1= 5000. For the second pair (measured HCN concentration), the HCN sensor used has a range of [0,10] mg / m 3 , that is, T AQ，min，2 =0, T AQ，max，2 =10.

[0084] Substituting the above parameters into the formula, the normalized deviation D' calculated is approximately 0.02404. This value represents the average normalized deviation between all matching (expected air quality, measured air quality) parameter pairs in this spatiotemporally correlated sensor signal group.

[0085] Based on the normalized deviation D' calculated in the previous step (for example, D'≈0.02404), it is necessary to compare it with a preset deviation threshold to ultimately determine whether the state reflected by the spatiotemporal correlation sensor signal group is consistent or inconsistent. The setting of this preset deviation threshold is a key link, which determines the system's sensitivity to abnormal states. Setting the threshold too low may lead to an increase in false alarms, while setting the threshold too high may miss real abnormal conditions. The determination of the threshold should be based on statistical analysis of historical operating data and known fault scenarios. The specific method can be: collecting a large number of historical "spatiotemporal correlation sensor signal groups" and their corresponding D' calculation results, Divide these data into two categories: "normal / consistent" state and "abnormal / inconsistent" state (the classification basis can be manual verification, exercise records or confirmed fault events), draw the probability distribution diagram or cumulative distribution function of the D' value under the two types of data, and select a D' value that can effectively distinguish the two types of distribution as the threshold. For example, you can choose the point that makes the misclassification rate (the sum of the false alarm rate + the missed alarm rate) the lowest, or according to the Neyman-Pearson criterion, while controlling one error rate (for example, the false alarm rate is less than 5%), maximize the accuracy of another judgment (for example, the detection rate), set the preset deviation threshold to 0.1, and set the specific process example: analyze the recent 10,000 sets of D' data were recorded over 6 months, including 50 confirmed minor inconsistency events (such as sensor drift and temporary decrease in ventilation efficiency) and 10 major inconsistency events (equipment failure and external pollutant intrusion). Statistics show that under normal conditions, 99% of the D' values ​​are lower than 0.08, the D' values ​​of minor inconsistency events are mainly distributed between 0.1 and 0.2, and the D' values ​​of major inconsistency events are all greater than 0.2. In order to ensure a low false alarm rate (accept fluctuations below 0.08) while promptly detecting minor and above inconsistencies, 0.1 is selected as the threshold for distinguishing "consistent" from "inconsistent". The calculated D' is used as the threshold for distinguishing "consistent" from "inconsistent". ′≈0.02404 is compared with the preset deviation threshold of 0.1, and the judgment logic is executed: if D′≥0.1, it is judged as "inconsistent", and if D′<0.1, it is judged as "consistent". In this example, because 0.02404<0.1, the judgment result is "consistent". Finally, according to the judgment result, a sensor consistency judgment flag is generated. This flag (for example, a Boolean value True represents consistency, False represents inconsistency, or a string "Consistent" / "Inconsistent") is attached to the currently processed spatiotemporal correlation sensor signal group for use in subsequent steps.

[0086] The steps to obtain the confirmed alarm event unit are:

[0087] Based on the sensor consistency judgment flag, all temporally and spatially correlated sensor signal groups are traversed, signal groups with consistent or inconsistent flags are filtered out, and a classified signal group set is generated;

[0088] According to the classified signal group set, the sensor identifiers and timestamp information of all inconsistent signal groups are extracted, batch elimination is performed, and confirmed alarm event units are generated.

[0089] Specifically, based on the "sensor consistency determination flag" added to each "spatiotemporal correlation sensor signal group" in the previous step (the flag is "consistent" or "inconsistent", which is obtained by comparing the normalized deviation with the preset deviation threshold), these signal groups need to be classified and processed next. First, the system will traverse all "spatiotemporal correlation sensor signal groups" generated in the current processing cycle, access each signal group and its associated "sensor consistency determination flag", and then perform a filtering operation based on the value of the flag. Specifically, check the flag of each signal group. If the flag is "consistent", the signal group is classified into the set of "consistent signal groups". If the flag is "inconsistent", it is classified into the set of "inconsistent signal groups". This process is equivalent to dividing the original signal group set into two mutually exclusive subsets according to the results of the consistency check. These two subsets (the "consistent signal group" set and the "inconsistent signal group" set) together constitute the classified signal group set.

[0090] According to the "classified signal group set" generated in the previous step, especially the subset marked as "inconsistent signal group", key information needs to be extracted from it to form a preliminary alarm event record. The system will specifically process all "temporal and spatial correlation sensor signal groups" that are judged to be "inconsistent". For each inconsistent signal group, all sensor identifiers contained therein (for example, 'Sensor_A_Temp', 'Sensor_B_Stress',

[0091] 'Door_1_Status') and key timestamp information that can represent the temporal characteristics of the signal group (for example, the earliest sensor reading timestamp in the signal group can be selected, or the average of all sensor timestamps in the group can be calculated as the representative timestamp). The extracted sensor identifier list and representative timestamp information are combined to construct a structured data record. This record represents the basic information of a potential abnormal event that requires attention. This process implicitly "eliminates" all signal groups judged to be "consistent" from the current alarm generation process because they are considered to reflect normal or expected system states and do not need to trigger an alarm. By repeating this extraction and construction process for all inconsistent signal groups, a series of structured data records are ultimately obtained, each of which is a confirmed alarm event unit.

[0092] The steps to obtain the alarm information package to be sent are:

[0093] Based on the confirmed alarm event unit, the event location coordinate field, event type code field and confirmation timestamp field are parsed, and the coordinate latitude and longitude values, event type code string and timestamp value are separated to generate the event attribute data set;

[0094] According to the event attribute dataset, the location coordinates are converted to decimal floating point format, the event type code is converted to the preset protocol encoding value, and the timestamp is converted to the UTC standard time string. These are mapped to fields according to the JSON-LD specification to generate standard format data units.

[0095] Based on the standard format data unit, the communication protocol header information is added, encapsulated into a binary data stream, and the alarm information packet to be sent is generated.

[0096] Specifically, based on the set of "confirmed alarm event units" obtained in the previous step, the information contained in each unit needs to be parsed and extracted for subsequent formatting. The system will process each "confirmed alarm event unit" in turn, first accessing the event-related information stored inside the unit, including the sensor location information associated with the event, the event type determined by the sensor combination and inconsistency type, and the confirmation timestamp representing the time when the event occurred. During specific execution, the "event location coordinate field" is searched and read from the event unit. This field may store the geographic coordinates of the dominant sensor or the coordinates of the event center point obtained by calculation, and then the specific longitude and latitude values ​​are separated from it. Then, search and read the "event type code field", which stores the internal event classification identifier previously assigned based on the inconsistency analysis results, such as "high stress exceedance" or "toxic gas leakage", and extract the coded string representing the nature of the event. Finally, search and read the "confirmation timestamp field" to obtain the precise value previously determined that can represent the time when the inconsistent event occurred (such as Unix millisecond timestamp). The longitude value, latitude value, event type coding string, and timestamp value separated for each alarm event unit are collected together to form a temporary structured collection, namely the event attribute data set, in which each record contains the core original attributes of an alarm event.

[0097] According to the "event attribute data set" generated in the previous step, which contains the original location, type and time information of each alarm event, it is now necessary to convert this original information into a standard format that meets the receiving protocol requirements of the target central station. The system traverses each record in the "event attribute data set" and performs conversion operations on each attribute in the record. First, the location coordinates are processed, and the separated longitude and latitude values ​​are uniformly converted into decimal floating point format, for example, ensuring that their accuracy reaches at least six decimal places, such as longitude 116.407390, latitude 39.904211. Then, the event type code is processed according to a pre-defined "event type code protocol mapping table" (the table is jointly formulated by the communicating parties and specifies the correspondence between the internal event code and the standard protocol code. For example, the internal code "high stress exceedance" is mapped to the standard protocol code value "ALA-STR-01", and the internal code "toxic gas leakage" is mapped to "ALA-TOX-03"). The event type encoding string is converted into the corresponding "preset protocol encoding value". Then, the timestamp value is processed and converted from the Unix timestamp format to the international standard UTC (Coordinated Universal Time) time string, and follows the ISO8601 format specification, such as converting it to "2025-04-28T10:39:57.123Z". After completing the format conversion of each attribute value, according to the JSON-LD (JSON-based linked data) specification, these standardized values ​​are mapped to predefined fields to construct a JSON object. The object must contain the necessary context (@context) and type (@type) declarations, as well as standard fields containing the converted location coordinates (for example, nested under the geo field), the event type protocol code (for example, in the eventType field) and the UTC time string (for example, in the eventTime field). Each JSON-LD object constructed in this way is a standard format data unit.

[0098] Based on the "standard format data unit" generated in the previous step (that is, a JSON object that complies with the JSON-LD specification), it needs to be encapsulated into a final data packet that can be transmitted over the network. The system processes each "standard format data unit" and first adds the necessary communication protocol header information at its front end. The header information is constructed based on the communication protocol agreed with the target central station (for example, an application layer protocol based on TCP or a specific message queue protocol such as MQTT), which contains control information for network routing, message identification, data verification, etc., which may include: message sequence number (for packet loss prevention and reordering), total message length (indicating the size of the entire data packet), sender system identifier, receiver central station network address identifier, message type code (clearly indicating that this is an alarm Information), and a checksum (such as CRC32 or MD5 digest, used by the receiving end to verify data integrity). These header fields and their values ​​are filled in according to the protocol. Then, the entire structure with the header added - that is, the protocol header information and the "standard format data unit" as the payload (JSON-LD object, usually first encoded in UTF-8 and converted into a byte sequence) - is encapsulated as a continuous binary data stream. This means that the various fields of the header (which may be integers, short integers, etc.) are converted into bytes according to the agreed byte order (big endian or little endian), and the byte sequence of the JSON object is appended to it to form a complete byte array. The final binary byte array is the alarm information packet to be sent that can be sent directly through the network interface.

[0099] The steps for obtaining the central station alarm positioning signal are as follows:

[0100] Call the communication interface, establish a Socket connection with the target center station network address through the TCP / IP protocol, send the binary data stream of the alarm information packet to be sent, and generate the communication interface sending result status;

[0101] Based on the sending result status of the communication interface, extract the sending timestamp, target network address and data packet length information, write them into the log file, and generate the alarm information sending log entry;

[0102] According to the alarm information sending log entry, the event location coordinates and event type code in the successfully sent data packet are parsed, mapped into geographic grid code and event level identifier, and the central station alarm positioning signal is generated.

[0103] Specifically, the system calls the network communication interface function provided by the operating system or a specific communication library, and prepares to send the previously encapsulated "alarm information package to be sent" to the specified target central station. According to the preset configuration, the network address of the target central station (including IP address and port number) is obtained, and the TCP / IP protocol is specified for communication. First, the program tries to create a TCP type Socket descriptor. If the creation is successful, the descriptor and the network address information of the target central station are used to call the connection function (such as connect) to initiate a TCP connection request. The system will execute the TCP three-way handshake process to try to establish a connection, during which a connection timeout is set (for example, 10 seconds). If the connection cannot be successfully established within the timeout (for example, the target host is unreachable or the port is unreachable), the system will automatically connect to the target central station. The system will record the connection failure status and perform retries according to the strategy (for example, retry twice with an interval of 5 seconds). If the connection is successfully established, the Socket enters the ESTABLISHED state. The system then calls the sending function (for example, send) to send the complete binary data stream of the "alarm information package to be sent" through the established Socket connection. During the sending process, the actual number of bytes sent will be monitored to ensure that the data packet is sent completely, and possible sending errors will be handled (for example, the connection is reset by the other party). After the sending is completed (whether successful or failed), the system will record the result status of this communication attempt, including whether all data is sent successfully, whether the connection is successfully established, and any error codes encountered, and finally generate the communication interface sending result status.

[0104] Based on the "communication interface sending result status" generated in the previous step and the relevant information associated with the sending attempt, the system needs to record a detailed sending log. First, extract a clear operation result identifier from the "communication interface sending result status" (for example, "send successful", "connection failed", "send timeout", etc.), and at the same time, obtain the precise system timestamp when the sending operation is executed (for example, the millisecond timestamp obtained using System.currentTimeMillis()), and record the target central station network address (IP address and port number combination) of this sending and the actual data packet length (in bytes) of the "alarm information packet to be sent" to be sent. Send result status code - organized according to the predefined log format, for example, in JSON format: {"timestamp":"2025-04-28T10:40:15.123Z","target":"192.168.1.100:5050","length":1024,"status":"SUCCESS"}, or in plain text format: [2025-04-2810:40:15.123][INFO]Sentpacketto192.168.1.100:5050,Length:1024,Status:SUCCESS, then, open the system-specified alarm information sending log file in append mode (for example

[0105] / var / log / civil_defense_alarm.log) and write the formatted log string as a new line to the end of the file. The write operation must ensure persistence. After each write, close the file handle or flush the buffer. In this way, each record successfully written to the file constitutes an alarm information sending log entry.

[0106] According to the "alarm information sending log entries" recorded in the alarm information sending log file, the system filters out those entries with the status marked as "successfully sent", and performs information conversion and refinement based on the data packet content implicitly associated in these successful sending records. For each successfully sent log entry, the system needs to trace back or access the corresponding sent data (that is, the original "standard format data unit" or the information it contains), and parse out the previously standardized event location coordinates (decimal latitude and longitude values) and event type code (values ​​that comply with the preset protocol encoding, such as "ALA-STR-01"), and then perform two key mapping conversions: the first is to map the precise latitude and longitude coordinates into standardized "geographic grid codes", which requires calling the corresponding conversion function based on a predefined geographic grid system (such as the national standard geographic grid or the military general grid system MGRS), and input Input the longitude and latitude, and output the corresponding grid unit code (for example, "50SLG1234598765"). The second item is to map the specific event type code to a general "event level identifier". This requires reference to a pre-established "event type-level mapping rule" (this rule is based on the civil air defense emergency plan and risk assessment results, and divides different types of events into different levels of urgency or importance). For example, "ALA-STR-01" (high stress exceedance) is mapped to "Level 1 (Emergency)" and "ALA-TEMP-WARN" (temperature warning) is mapped to "Level 3 (General)". Query the rule to obtain the level identifier corresponding to the event type code. Finally, the converted "geographic grid code", "event level identifier" and the event timestamp (which can be obtained from the original data) are combined to form a structured signal, namely the central station alarm positioning signal.

[0107] The steps to obtain the alarm spatiotemporal density distribution results are as follows:

[0108] Based on the alarm location signals received from multiple central stations, the geographic grid code, event level identifier and timestamp information in the signals are parsed, and the alarm signal set within a unit time is filtered according to the preset time window to generate an alarm location data set;

[0109] Based on the alarm location dataset, the spatial aggregation metric within the map display area is calculated using the following formula:

[0110]

[0111] Where H is the spatial aggregation metric, (lat i ,lon i ) is the current grid center coordinate, (lat j ,lon j ) is the coordinate of the jth alarm point, S is the preset spatial aggregation radius, Δtj is the time difference of the jth alarm point in the current time window, T u is the unit time threshold, N is the number of alarm points;

[0112] Based on spatial aggregation measurement, the frequency parameter is generated by counting the number of alarm signal arrivals per unit time, and the source ID grouping tags of intrusion detector signals are associated to generate the alarm spatiotemporal density distribution results.

[0113] Specifically, based on the "central station alarm location signal" stream continuously received by the communication interface or log system, these signals contain the geographic grid code, event level identifier, and event confirmation timestamp generated in the previous step. The system needs to first parse these signals and extract structured data. The specific operation is to identify and separate the three key information fragments contained in each received signal: the "geographic grid code" representing the area where the event occurred (for example, "50SLG1234598765"), the "event level identifier" indicating the severity of the event (for example, "Level 1 (Emergency)"), and the precise "timestamp information" of the event (for example, "2025-04-28T10:40:15.123Z" in UTC format). Next, these parsed signals need to be filtered according to a "preset time window". This time window defines the time range for situation aggregation analysis, for example, it is set to the last 5 minutes. The duration of this window is the "unit time threshold" T in the subsequent formula. u , set T u The basis is the response requirements of combat command or situation monitoring, and it is necessary to balance real-time and trend stability. For example, if the regional risk situation is required to be updated every 5 minutes, T u 300 seconds. When filtering, the current system time is used as the reference point, and the timestamp of each signal is compared with it, and only those timestamps that fall within [current time - T u ,The signals within the current time] interval, all signals that pass the time filter (including their geographic grid code, event level identifier and timestamp) are collected to form an alarm location dataset for current period calculation.

[0114] formula: The formula is beneficial in that it combines Gaussian kernel density estimation with time-decay weighting to calculate the spatial aggregation metric H of alarm events around any point on the map (or grid center point i), thus achieving a spatiotemporal dynamic assessment of alarm hotspot areas. The first part of the formula is a two-dimensional Gaussian kernel function, which is weighted according to the geographic distance between the alarm point j and the assessment point i. The closer the distance, the greater the weight, and the weight decays smoothly with distance according to the Gaussian curve. The parameter S controls the degree of spatial smoothing, which avoids the boundary effect and information loss caused by simple area counting. The second part of the formula introduces the time decay factor, which makes the closer in time (Δt j The smaller the alarm point is, the greater the contribution to the current aggregation metric is. The older the alarm point is (Δt j Close to T u ) contribution is smaller. This design makes the calculation results reflect not only the spatial aggregation but also the temporal freshness, which can more accurately capture the clusters of events that are developing or have just occurred. u The contribution of alarm point j (a total of N) within the evaluation point is summed up, and the obtained H value can quantify the spatiotemporal alarm density near the evaluation point i, providing a continuous and dynamically changing quantitative basis for subsequent situation presentation and risk assessment.

[0115] Coordinate description: The formula uses longitude and latitude coordinates (lat, lon) to calculate the distance. i -lat j ) 2 +(lon i -lon j ) 2 When the latitude and longitude coordinates are converted to plane coordinates (such as meters) using an appropriate map projection (such as UTM projection), the sum of the squares of the plane coordinate differences (x i -x j ) 2 +(y i -y j ) 2 , or directly use the square of the spherical distance (such as Haversine distance) between two points d(i, j) 2 .

[0116] Parameter acquisition steps:

[0117] H: Spatial aggregation metric. This is a value calculated for each evaluation point (or grid center point) i on the map, representing the spatiotemporal density of alarm events around that point. A larger value indicates a higher density. (lat i ,lon i): Current grid center coordinates. These are the geographic coordinates of the center point of map grid cell i, for which density calculations are being performed, expressed in decimal degrees. These coordinates are determined by the map gridding method. For example, if the map is divided into a 100m x 100m grid, the center coordinates of the currently calculated grid cell are (39.905000°N, 116.408000°E).

[0118] (lat j ,lon j ): Coordinates of the jth alarm point. Refers to the geographic coordinates corresponding to the jth alarm signal in the "Alarm Location Dataset", expressed in decimal degrees. These coordinates must be calculated from the alarm signal's geographic grid code, typically taking the center coordinates of the grid cell. Example: The grid code for the j=1 alarm signal in the dataset is "50SLG1234598765", and the calculated center coordinates are (39.905120°N, 116.408150°E).

[0119] N: Number of alarm points. Refers to the total number of alarm signals contained in the "alarm location data set", that is, the number of alarm points in the preset time window T. u The number of alarm signals filtered within the time window. This value is obtained by counting the records in the dataset. Acquisition example: After filtering through the time window, the dataset contains N = 15 alarm signal records.

[0120] S: Preset spatial aggregation radius (bandwidth), in meters. This parameter defines the effective influence range of the spatial kernel function and controls the smoothness of the density estimation. Its value needs to be set according to the application scenario, map scale, and the impact range of events of general concern. Acquisition example: Considering the typical scale of protective projects and the need to identify street-level aggregations on the map, set S = 100.0 meters. Setting process example: Analyzing the spatial distribution characteristics of similar historical events (such as continuous air raid alerts and regional pollution diffusion), it is found that a meaningful local event cluster can usually be contained within a radius of 100 meters. At the same time, this radius corresponds to a better visualization granularity on the map, so S = 100.0 meters is selected.

[0121] Δt j : The time difference of the jth alarm point in the current time window, in seconds. Refers to the absolute difference between the current calculation time point and the jth alarm signal timestamp in the "Alarm Location Dataset". Get example: The current time is T now , the timestamp of the j=1 alarm signal in the data set is T1, then Δt1=|T now -T1|. For example, if the current time is 17:00:00 and the alarm time is 16:58:30, then Δt1 = 90 seconds.

[0122] T u: Unit time threshold, in seconds. This is the length of the preset time window used to filter alarm signals. This value is set in the previous step. Example: According to the setting in the previous step, T u =300 seconds.

[0123] Calculation process: Calculate the map grid cell i (center coordinates (lat i ,lon i )=(39.905000,116.408000)). Parameter values ​​used: S=100.0 meters, T u = 300 seconds. For example, there are N = 2 alarm points in the alarm location data set within the current time window.

[0124] Alarm point j=1: coordinates (lat1, lon1) = (39.905120, 116.408150), time difference Δt1 = 90 seconds. Alarm point j=2: coordinates (lat2, lon2) = (39.904850, 116.407900), time difference Δt2 = 210 seconds.

[0125] First, calculate the square of the distance between the evaluation point i and each alarm point j (Unit: square meters). You need to use the geographic coordinate distance calculation method (for example, project to a plane coordinate system first and then calculate the square of the Euclidean distance). Here is a simplified demonstration, for example, the calculated plane distance square is:

[0126] Calculate the contribution of j = 1: Spatial part:

[0127]

[0128] Time part: Contribution 1: (1.560×10 -5 )

[0129] ×0.7692≈1.200×10 -5 ;

[0130] Calculate the contribution of j = 2: Spatial part:

[0131]

[0132] Time part: Contribution 2: (1.542×10 -5 )

[0133] ×0.5882≈0.907×10 -5 ;

[0134] Calculate the overall spatial aggregation measure H:

[0135] H=(1.200×10 -5 )+(0.907×10 -5 );

[0136] H=2.107×10 -5 ;

[0137] The results show that the calculated value of the spatial aggregation metric H of the map grid cell i (centered at (39.905000, 116.408000)) is 2.107×10 -5 This value represents the value of the set spatial aggregation radius S = 100 meters and time window T u = The sum of the spatiotemporal density contributions of nearby alarm points to the grid cell within 300 seconds. The H value itself is relative and needs to be compared with the H values ​​of other grid cells on the map. A higher H value (for example, an H value much greater than the background or average level) indicates that the area is a spatiotemporal hotspot of recent alarm events. This calculated H value will serve as one of the key inputs for the next step of generating the spatiotemporal density distribution of alarms, used to determine the alarm density level of the grid area.

[0138] Based on the "spatial aggregation metric" H value calculated for all grid cells in the map display area, it is also necessary to combine other information to generate the final alarm spatiotemporal density distribution results. First, it is necessary to calculate the number of each grid cell in the preset unit time T u The number of alarm signals actually received within (e.g. 300 seconds), that is, the number of alarm signals that fall within the geographical range of the grid unit and whose timestamps are between [current time - T u, the number of "central station alarm location signals" within the current time interval, this counting result is associated with the grid unit as an independent "frequency parameter", which reflects the original frequency of alarms in the area. Then, the system needs to pay special attention to specific types of alarm sources according to needs, such as "intrusion detectors". This requires accessing the original sensor information or event type code associated with each signal in the "alarm location dataset" and using a predefined mapping relationship (for example, a list stores all sensor IDs belonging to intrusion detectors, or event type codes contain a specific prefix such as "INT-" for intrusion) to identify the alarm signal from the intrusion detector. For each grid unit, you can record the following The number of intrusion alarm signals received per unit time, or a simple mark of whether there is an intrusion alarm signal in the grid (for example, setting a Boolean flag), integrates the three aspects of information: the spatial aggregation metric H value calculated for each grid cell, the statistically obtained frequency parameter (total number of alarms), and the associated intrusion detector signal source mark (such as intrusion alarm count or flag) to form a complete record describing the spatiotemporal alarm status of the grid cell. Aggregating such records of all grid cells constitutes the spatiotemporal density distribution of alarms, which provides the map with an indication of the alarm density (taking into account spatiotemporal attenuation), the original alarm frequency, and specific threats (such as intrusion) in each area.

[0139] The steps to obtain the aggregate alarm results are:

[0140] Based on the alarm spatiotemporal density distribution results, analyze the spatial density and temporal frequency in the alarm spatiotemporal density distribution results, set the density level threshold to divide the display level interval, and generate the alarm point level division plan;

[0141] According to the alarm point hierarchical division scheme, the coordinates of the alarm points in the same level are merged according to the geographical grid area, and the grid numbers of the grids whose number of alarm points exceeds the aggregation threshold are counted to generate an aggregated alarm point set;

[0142] Based on the aggregated alarm point set, the display priority parameters in the associated alarm point hierarchical division scheme are mapped to the high-density area alarm points on the top display, and the low-density area alarm points on the secondary display to generate the aggregated alarm result.

[0143] Specifically, based on the "alarm spatiotemporal density distribution results" calculated in the previous step, each geographic grid cell on the map is assigned a spatial aggregation metric (H value), a frequency parameter (number of alarms per unit time), and possible source characteristics (such as intrusion alarm markers). Based on this, a visualization hierarchy for the alarm points needs to be developed. First, the system analyzes the "alarm spatiotemporal density distribution results" to extract the H value and frequency count value for each grid cell. Then, a set of "density level thresholds" is set to map different H value intervals to different display levels. For example, three levels can be set: high density (Level 1), medium density (Level 2), and low density (Level 3). The thresholds can be set based on a statistical analysis of the H value distribution of historical alarm data. For example, the percentiles of the H values ​​of all grid cells can be calculated, defining the top 5% as the high-density interval, the next 15% as the medium-density interval, and the remaining 80% as the low-density interval. Alternatively, a fixed H value range can be set based on expert experience and understanding of the operational significance of different density levels. For example, through statistical analysis of H value data for a month, it is found that the 95% quantile of the H value is 4.5×10 -5 , the 80% quantile is 1.2×10 -5 , based on which the density level threshold is set as follows: high density (Level 1) corresponds to H>4.5×10 -5 , medium density (Level 2) corresponds to 1.2×10 -5 <H≤4.5×10 -5 , low density (Level 3) corresponds to H≤1.2×10 -5 ,The system then traverses all grid cells, compares its calculated H value with these thresholds, ,assigns a corresponding display level (Level 1, 2, or 3) to each grid cell, integrates ,all grid cells and their assigned display level information, and generates ,an alarm point level division scheme.

[0144] According to the "alarm point level division scheme" generated in the previous step, the scheme specifies the display level for each geographic grid unit. At the same time, it is also necessary to combine the frequency parameters in the "alarm spatiotemporal density distribution results" to decide whether to visually aggregate the alarm points. The system processes according to the geographic grid area. First, you need to set an "aggregation threshold", which is an integer representing the maximum number of independent alarm points allowed to be displayed in a single grid unit. If this number is exceeded, the aggregate display is triggered. The setting of this threshold is mainly based on map readability and avoiding visual confusion. For example, in a standard-sized grid unit, displaying more than 5 points at the same time may cause icons to overlap and be difficult to identify. Therefore, the aggregation threshold can be set to 5. The specific setting process example: at different map zoom levels, simulate placing different points in the grid. The number of alarm icons is evaluated by user interface design experts or end users for visual effects, and the maximum number of icons that a single grid can clearly accommodate without affecting quick interpretation is determined. For example, if the evaluation result is 5, the aggregation threshold is set to 5. The system then traverses all geographic grid cells, reads the frequency parameter (i.e., the number of alarms per unit time) recorded in the "alarm spatiotemporal density distribution results" of the cell, and compares the frequency parameter with the set aggregation threshold (e.g., 5). If the number of alarm points in the grid is greater than or equal to the aggregation threshold, the number (or identifier) ​​of the grid cell is recorded, and all grid numbers that meet this condition are collected together to generate an aggregated alarm point set. This set indicates which areas on the map need to be displayed with aggregate symbols instead of independent alarm points.

[0145] Based on the "aggregate alarm point set" generated in the previous step (including the grid numbers that need to be aggregated and displayed) and associated with the "alarm point hierarchical division scheme" (which contains the display level of each grid, which implies the display priority), the system begins to generate the final aggregate alarm result for display on the map interface. The system traverses all geographic grid units on the map and performs the following judgments and operations on each unit: Check whether the unit number exists in the "aggregate alarm point set". If so, it means that the grid needs to be aggregated and displayed. Then, the display level of the grid is obtained from the "alarm point hierarchical division scheme" (for example, Level 1-high density). According to this level, the visual attributes of the aggregation symbol are determined (for example, Level 1 uses a large, red, dynamically flashing aggregation icon and marks the total number of aggregated alarm points) and the display priority (for example, the highest priority, ensure that it is drawn at the top layer of the map). If the unit number is not in the "aggregate alarm point set", the system generates the final aggregate alarm result for display on the map interface. In the "combination", it means that the alarm points in the grid need to be displayed separately. The system will find all independent alarm points that fall within the geographical range of the grid from the original "alarm location data set". For each independent alarm point, its own "event level identifier" (such as "Level 1 (Emergency)") is obtained. The visual attributes of the independent alarm icon (for example, a red diamond icon is used for a Level 1 event) and the display priority (for example, the priority of a Level 1 event is higher than that of a Level 2 event, but may be lower than the highest density aggregate icon) are determined according to this event level. According to the determined visual attributes and priority rules, all aggregate icons and independent alarm icons are drawn to the corresponding positions on the map, among which graphic elements with high priority (such as high-density aggregate icons and Level 1 independent event icons) will be drawn above low-priority graphic elements. Finally, an alarm situation view combining density grading, quantity aggregation and event level is presented on the map interface. This is the aggregated alarm result.

Claims

1. The civil air defense command engineering protection facility monitoring system is characterized by: The system comprises: The data acquisition and preprocessing module receives environmental monitoring sensor readings, obtains sensor identifiers, geographic coordinates, and timestamp information, groups them according to preset time windows and geographic proximity, and establishes spatiotemporally correlated sensor signal groups; An event consistency check module extracts measurement values ​​of different types of sensors within the same group based on the spatiotemporally correlated sensor signal group, sets a numerical deviation threshold for comparison, obtains a sensor consistency determination flag, filters the signal group based on the sensor consistency determination flag, eliminates signal groups determined to be inconsistent, retains consistent signal groups, and obtains confirmed alarm event units; The alarm information transmission module extracts the event location coordinates, event type code and confirmation timestamp information based on the confirmed alarm event unit, encapsulates them into a standard format data unit, creates an alarm information packet to be sent, calls the communication interface, sends the alarm information packet to the target central station network address, records the sending log, and obtains the central station alarm location signal; The situation aggregation presentation module calculates the spatial aggregation measurement of the signals in the map display area based on the alarm positioning signals received from multiple central stations, generates the alarm spatiotemporal density distribution results, adjusts the display level of the alarm points on the interface based on the alarm spatiotemporal density distribution results, and establishes the aggregated alarm results.

2. The civil air defense command engineering protection facility monitoring system according to claim 1 is characterized in that: The steps for acquiring the spatiotemporal correlation sensor signal group are as follows: Receive environmental monitoring sensor readings and structural stress sensor readings, extract sensor identifiers, geographic coordinates, and timestamp information, filter sensor data based on a preset time window threshold, filter sensor data based on a geographic proximity threshold, and generate a preliminary spatiotemporal grouping set; Based on the preliminary spatiotemporal grouping set, calculating the spatiotemporal correlation between the protection door state sensor signal and the monitoring sensor within the same group; Based on the spatiotemporal correlation, a group with a correlation greater than a threshold is selected, and the sensor identifier and the signal type are combined to establish a spatiotemporal correlation sensor signal group.

3. The civil air defense command engineering protection facility monitoring system according to claim 1 is characterized in that: The steps for obtaining the sensor consistency determination flag are as follows: Based on the spatiotemporal correlation sensor signal group, extract the value set of the operating status parameters of the gas filtering and ventilation equipment and the value set of the air quality parameters, match the parameter types in the same time window, and generate a parameter pairing set; Calculating normalized deviations between parameters of different types of sensors based on the parameter pairing set; Based on the normalized deviation, if the normalized deviation is greater than or equal to a preset deviation threshold, it is determined to be inconsistent; otherwise, it is determined to be consistent, and a sensor consistency determination flag is generated.

4. The civil air defense command engineering protection facility monitoring system according to claim 1 is characterized in that: The steps for obtaining the confirmed alarm event unit are: Based on the sensor consistency determination flag, traverse all spatiotemporally correlated sensor signal groups, filter signal groups with consistent or inconsistent flags, and generate a classified signal group set; According to the classified signal group set, sensor identifiers and timestamp information of all inconsistent signal groups are extracted, batch elimination is performed, and a confirmed alarm event unit is generated.

5. The civil air defense command engineering protection facility monitoring system according to claim 1 is characterized in that: The steps for obtaining the alarm information packet to be sent are: Based on the confirmed alarm event unit, the event occurrence location coordinate field, the event type code field and the confirmation timestamp field are parsed, and the coordinate latitude and longitude values, the event type code string and the timestamp value are separated to generate an event attribute data set; According to the event attribute dataset, the location coordinates are converted into a decimal floating point format, the event type code is converted into a preset protocol encoding value, and the timestamp is converted into a UTC standard time string, and mapped to fields according to the JSON-LD specification to generate a standard format data unit; Based on the standard format data unit, communication protocol header information is added, encapsulated into a binary data stream, and an alarm information packet to be sent is generated.

6. The civil air defense command engineering protection facility monitoring system according to claim 1 is characterized in that: The steps for obtaining the central station alarm positioning signal are: Call the communication interface, establish a Socket connection with the target center station network address through the TCP / IP protocol, send the binary data stream of the alarm information packet to be sent, and generate the communication interface sending result status; Based on the sending result status of the communication interface, extract the sending timestamp, target network address and data packet length information, write them into the log file, and generate an alarm information sending log entry; According to the alarm information sending log entry, the event location coordinates and event type code in the successfully sent data packet are parsed, mapped into geographic grid code and event level identifier, and a central station alarm positioning signal is generated.

7. The civil air defense command engineering protection facility monitoring system according to claim 1 is characterized in that: The steps for obtaining the alarm spatiotemporal density distribution result are: Based on the alarm location signals received from multiple central stations, the geographic grid code, event level identifier and timestamp information in the signals are parsed, and the alarm signal set within a unit time is filtered according to the preset time window to generate an alarm location data set; Calculating a spatial aggregation metric within a map display area based on the alarm location dataset; Based on the spatial aggregation metric, the number of alarm signal arrivals per unit time is counted to generate a frequency parameter, and the intrusion detector signal source ID grouping tag is associated to generate an alarm spatiotemporal density distribution result.

8. The civil air defense command engineering protection facility monitoring system according to claim 1 is characterized in that: The steps for obtaining the aggregated alarm result are: Based on the alarm spatiotemporal density distribution result, the spatial density and temporal frequency in the alarm spatiotemporal density distribution result are analyzed, the density level threshold is set to divide the display level interval, and the alarm point level division scheme is generated; According to the alarm point hierarchical division scheme, the coordinates of the alarm points in the same level are merged according to the geographical grid area, and the grid numbers of the grids whose number of alarm points exceeds the aggregation threshold are counted to generate an aggregated alarm point set; Based on the aggregated alarm point set, the display priority parameters in the alarm point hierarchical division scheme are associated, the high-density area alarm points are mapped to the top display, and the low-density area is mapped to the secondary display to generate an aggregated alarm result.

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