Intelligent three-proofing control system for civil air defense engineering

Abstract

The invention relates to the technical field of civil air defense engineering, and discloses an intelligent three-proofing control system for civil air defense engineering, which comprises a data acquisition module for dividing the civil air defense engineering into a plurality of sub-regions S; periodically acquiring environment data of each sub-region S in the civil air defense project through a plurality of environment sensors, and periodically acquiring communication link state data of each communication device in each sub-region S; and the data analysis module is used for acquiring an environment early warning coefficient E of each sub-region S according to the environment data, and acquiring an operation state marking value of each communication device in each sub-region S according to the communication link state data. The method comprises the following steps: acquiring environment data and communication link state data of each sub-region of the civil air defense project, and acquiring an environment early warning coefficient of each sub-region, an environment risk judgment result of an adjacent region and a communication equipment operation state marking value of each sub-region according to the environment data and the communication link state data;

Description

Technical Field

[0001] This invention relates to the field of civil defense engineering technology, specifically to an intelligent three-defense control system for civil defense engineering. Background Technology

[0002] Currently, the NBC (nuclear, biological, chemical) defense control systems in civil defense projects generally adopt a centralized control architecture. The core control unit is typically deployed in a control room and connected to various ventilation devices, sensors, and actuators via multiple independent control cables. While this architecture had some applicability in early civil defense projects, it has gradually revealed numerous problems in practical applications. Existing traditional systems have weak integrated monitoring capabilities for ventilation equipment, environmental parameters, and engineering safety status. Most systems only have basic on / off control functions and lack the ability to collect and analyze key parameters such as temperature and humidity, concentration of harmful gases, water immersion status, and the position of protective doors in real time. When environmental anomalies or equipment failures occur, the system response is not timely enough, making it difficult to achieve effective protection and control in the first instance, thus affecting the emergency protection effectiveness of civil defense projects. Furthermore, existing three-proof control systems generally lack autonomous inspection and fault diagnosis mechanisms. The system's operating status relies on manual inspection and regular maintenance, which makes it impossible to achieve real-time status assessment of equipment and communication links, resulting in delayed maintenance work and difficulty in guaranteeing equipment integrity. Therefore, this invention proposes an intelligent three-defense control system for civil defense projects to address the shortcomings of existing technologies. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent three-defense control system for civil defense projects, in order to solve the problems mentioned in the background art.

[0004] The objective of this invention can be achieved through the following technical solutions: A smart tri-proof control system for civil defense projects includes the following modules: The data acquisition module divides the civil defense project into multiple sub-areas. ; The internal sub-areas of the civil defense project are periodically monitored using multiple environmental sensors. Environmental data, periodically acquired from each sub-region Communication link status data of each communication device within the system; The data analysis module obtains information for each sub-region based on the environmental data. Environmental warning coefficient Obtain each sub-region based on the communication link status data. Operating status indicators of each communication device within the system; The intelligent inspection module, based on each sub-area The operational status indicators of each communication device within the civil defense project are used to plan the inspection routes for each communication device inside the project. The intelligent control module will control each sub-region. The environmental warning coefficient is compared with the preset environmental threshold, and the ventilation mode inside the civil defense project is switched according to the comparison result.

[0005] Preferably, the data acquisition module operates as follows: The civil defense project is divided into multiple sub-regions of varying sizes. ; Through each sub-region The internally installed environmental sensors periodically acquire environmental data and construct time series based on the acquisition time. Environmental data include temperature, humidity, oxygen content, carbon dioxide content, carbon monoxide content, and formaldehyde content; Sub-regions obtained based on time series and periodicity Environmental data acquisition and environmental data transformation curves; The communication link status data is obtained through cyclic redundancy check. Communication link status data includes link connectivity, data transmission error rate, and link stability.

[0006] Preferably, the data analysis module operates as follows: Preset the weighting coefficients for each environmental data; Calculate each sub-region The deviation between the real-time value of environmental data and the preset safety standard value; if the real-time value of environmental data is within the range of the preset safety standard value, the deviation of environmental data is 0. Based on the weighting coefficients and deviations of each environmental data point, the values ​​of each sub-region are calculated and obtained. Environmental early warning coefficient; When a certain sub-region When an environmental anomaly occurs, acquire adjacent sub-regions. Environmental data, and adjacent sub-regions Environmental data transformation curves during the same period; Computational environment anomaly sub-region With each adjacent sub-region The similarity of the environmental data transformation curves is calculated, and a preset similarity threshold is set. If the similarity exceeds the preset threshold; It was determined that there was a risk of the same environmental problems occurring in adjacent sub-regions; If the similarity is lower than the preset threshold, it is determined that the same environmental problem will not occur in adjacent sub-regions for the time being. Preset communication link status standards for normal operation of communication equipment, for each sub-area Each communication device within the device is checked one by one. When all communication link status standards meet the preset standards, the operating status indicator value of the communication device is set to 1, which means that it is operating normally. If any core data fails to meet the preset standard, the operating status indicator value of the communication device is set to 0, indicating an abnormal operation.

[0007] Preferably, each sub-region Environmental warning coefficient The method for obtaining it is as follows: Get the The actual deviation of a single environmental data point from its standard value is calculated. ; Preset number Risk amplification factor for individual environmental data And calculate the contribution value of abnormal environmental data; Weighting coefficients are assigned based on the degree of impact on data security. The weights are multiplied by the individual data anomaly contribution values ​​and then summed. The data coupling correction factor is calculated by multiplying the deviations of various related environmental data. Correcting the risk of anomalies in multi-data collaboration; Determine the regional environmental sensitivity coefficient based on the functional attributes of the sub-region. Correcting the differences in environmental risk thresholds across different regions; Calculate the theoretical maximum warning value The results of the previous calculations are normalized to obtain the final environmental warning coefficient. .

[0008] Preferably, the method for obtaining curve similarity is as follows: For environmentally abnormal sub-regions and all adjacent sub-regions The environmental data transformation curve is denoised to remove abnormal fluctuation points; For each environmental data transformation curve after preprocessing, time-domain feature data and frequency-domain feature data are extracted respectively, and all extracted feature data are normalized. For each type of feature data, calculate the environmental anomaly sub-region separately. With all adjacent sub-regions Feature data similarity; Based on the degree of influence of various feature data on the correlation of environmental anomalies, a corresponding weight coefficient is assigned to each feature data. The overall similarity is calculated based on the similarity of individual feature data and the corresponding weight coefficient; Selecting environmental anomaly sub-regions Environmental data transformation curves for the three acquisition cycles before the anomaly occurred, the one acquisition cycle during the anomaly occurred, and the one acquisition cycle after the anomaly occurred were repeatedly calculated for the environmental anomaly sub-region within each cycle. and all adjacent sub-regions The overall similarity is calculated, and the mean and variance of the similarity are obtained. If the variance is less than or equal to the preset variance threshold, the mean similarity will be used as the final curve similarity. If the variance is greater than the preset variance threshold, the curve is denoised again, and the curve similarity is recalculated.

[0009] Preferably, the intelligent inspection module operates as follows: According to each sub-region The operating status indicators of all communication devices within the civil defense project are compared with the communication network topology. Each communication device is designated as an inspection node, and nodes with an operating status indicator value of 0 are designated as high-priority mandatory inspection nodes, while nodes with an indicator value of 1 are designated as regular sampling inspection nodes. Construct a network topology inspection map that includes all inspection nodes and communication links between nodes, and mark all upstream and downstream related links of high-priority mandatory inspection nodes; High-priority mandatory inspection nodes are set as mandatory inspection nodes, and regular random inspection nodes are embedded according to the density of network topology. The main network inspection path is planned to cover all mandatory inspection nodes and have the optimal link hop count. According to the planned main network inspection path, inspections are carried out sequentially. For nodes that must be inspected with a value of 0, the authenticity of their abnormal status is verified by multi-link polling. The network bandwidth and transmission latency of the inspection links are monitored in real time.

[0010] Preferably, the intelligent control module operates as follows: The environmental threshold of the civil defense project is preset, and each sub-area is divided into... The environmental early warning coefficient is compared with the preset threshold to complete the classification of environmental risk levels; Based on risk level and adjacent sub-regions Environmental risk assessment and special ventilation modes for civil defense and NBC protection; Based on the communication link, the ventilation mode is switched and the changes in ventilation equipment and environmental data are monitored in real time. The ventilation system's operation and control data are dynamically adjusted based on real-time environmental data.

[0011] Preferably, the ventilation modes include clean air ventilation, filtration ventilation, and isolation ventilation: Under clean ventilation, all fans and valves operate according to normal operating logic; When using a filtration-type ventilation system, the system activates the filtration equipment and adjusts the airflow direction. Isolation ventilation achieves self-sufficiency of the internal environment of the project by automatically closing all air intake and exhaust channels.

[0012] The beneficial effects of this invention are: 1. This invention acquires environmental data and communication link status data of each sub-area of ​​a civil defense project, and obtains environmental early warning coefficients, environmental risk assessment results of adjacent areas, and operational status indicators of communication equipment in each sub-area based on the environmental data and communication link status data. It switches between clean, filtered, and isolated three-proof ventilation modes and dynamically adjusts operating parameters based on the environmental early warning coefficients. Ultimately, it achieves early prediction and precise prevention and control of environmental safety risks in civil defense projects, as well as efficient inspection and maintenance of communication equipment. This significantly improves the intelligence and automation level of three-proof control in civil defense projects, effectively ensures the safety of the internal environment and the stability of the communication network, and reduces the cost and difficulty of manual operation and maintenance.

[0013] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a three-dimensional schematic diagram of an intelligent three-defense control system for civil defense engineering according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1As shown, this invention is an intelligent NBC (Nuclear, Biological, and Chemical) defense control system for civil defense projects. Its purpose is to provide a flexible, highly reliable, and adaptable intelligent NBC defense control system for civil defense projects, effectively solving problems such as complex wiring, difficult expansion, single-point failure affecting overall operation, inconvenient control logic adjustment, low protection level, weak environmental monitoring capabilities, lack of autonomous inspection mechanisms, and inability to achieve unified cloud management in existing centralized control architectures. By adopting a fully distributed control structure using fieldbus, peer-to-peer communication between control devices is achieved, significantly simplifying system wiring and improving the convenience of construction and maintenance. Simultaneously, the control logic is implemented by embedded software, replacing traditional hardware wiring methods, giving the system high programmability and scene adaptability, meeting the personalized needs of different civil defense projects.

[0018] The system includes a data acquisition module, a data analysis module, an intelligent inspection module, and an intelligent control module. The data acquisition module is used to divide the civil defense project into sub-areas, periodically collect environmental data, and verify communication link status data. Specifically, based on the functional layout, spatial structure, and environmental monitoring requirements of the civil defense project, the entire project is first divided into multiple independent sub-areas of varying sizes. For example, setting a 60㎡ command and control core area as A personnel shelter area of ​​180 square meters was set up The 40㎡ equipment room area is set up All sensors collect environmental data periodically, such as every 30 seconds, and simultaneously label each data point with a timestamp to construct a continuous environmental data time series. Then, based on the time series of each environmental data point and the corresponding collected environmental data, a smooth environmental data transformation curve is generated through fitting. The continuous change curve of oxygen content in the equipment room area within 1 hour intuitively reflects the dynamic trend of environmental data.

[0019] For each sub-region The communication link module of the internal communication equipment uses a cyclic redundancy check algorithm to collect status data. By sending a fixed-format check code to each communication link and receiving link feedback data, it extracts link connectivity, data transmission error rate, and link stability. For example, it performs CRC check on the communication link between the core switch of the S1 command area and the terminal. If the check code is not lost in the feedback data, the link connectivity is determined to be normal. If a 2-bit error is detected in 1000 bits of transmitted data, the data transmission error rate is recorded as 0.2%. At the same time, the link stability is determined by the link response delay fluctuation value of 10 consecutive checks, thus completing the collection of all communication link status data.

[0020] The data analysis module is used to calculate the values ​​of each sub-region based on the acquired environmental data through a standardized process. Environmental warning coefficient When an environmental anomaly occurs in a certain sub-region, the anomaly sub-region is calculated. With adjacent sub-regions The similarity of environmental data transformation curves is used to determine whether there is a risk of similar environmental problems occurring in adjacent sub-regions, providing predictive support for ventilation mode switching. Based on the obtained communication link status data of each sub-region, the operating status of each communication device is verified against preset standards, and an operating status indicator value is generated, providing core data basis for the network inspection path planning of the intelligent inspection module.

[0021] First, obtain the actual deviation of a single data point. First, extract the data from each sub-region transmitted by the data acquisition module. The system retrieves real-time measured values ​​for six environmental data items, along with corresponding preset environmental data safety standard values. The safety standard value for each environmental data item is set according to relevant civil defense engineering specifications. The actual deviation of each individual environmental data item is also recorded. The calculation method is as follows: ; in, For the first Real-time measured values ​​of environmental data. For the first The environmental data is preset with safety standard values; If the real-time measured value of a certain environmental data point is within the preset safety standard range, that is, the measured value does not exceed the upper limit of the standard value and is not lower than the lower limit of the standard value, then the actual deviation of the environmental data is considered to be within the range of the preset safety standard value. A direct value of 0 indicates that the data currently has no abnormal deviation. If the measured value exceeds the standard value range, the calculated value will be used instead. , The range of values ​​for is from 0 to positive infinity. The higher the value, the more serious the deviation of the environmental data from safety standards, and the higher the corresponding environmental risk. The values ​​range from 1 to 6, corresponding to temperature, humidity, oxygen content, carbon dioxide content, carbon monoxide content, and formaldehyde content, respectively. Each sub-region requires separate calculation of these six data points. value.

[0022] Obtain the actual deviation of each of the six environmental data points. Then, according to the preset corresponding number Risk amplification factor for individual environmental data , The values ​​are set based on the degree of impact of various environmental data on the internal safety of civil defense projects, including carbon monoxide content and oxygen content. The values ​​are the highest, ranging from 1.5 to 2.0, because they directly affect human life safety and the risk spreads rapidly. The carbon dioxide and formaldehyde content... The next best value is between 1.0 and 1.5. Long-term deviations from these values ​​can harm human health. Temperature and humidity... The value is the lowest, ranging from 0.5 to 1.0, and only affects environmental comfort and equipment operation in extreme cases. It should be noted that those skilled in the art can adjust the preset value according to the actual use scenario and safety level of the civil defense project. Adjustments are made based on the value; the contribution value of environmental data anomalies is determined using the formula: ,when When the value is small, the abnormal contribution value increases slowly, avoiding misjudging slight fluctuations as high risk; when When the value is large, the abnormal contribution value quickly approaches 1, highlighting the impact of high-risk environmental data.

[0023] Preset the weighting coefficients for each environmental data. , The value is also set based on the degree of impact of various environmental data on the safety of civil defense projects, and is consistent with... The value setting logic is consistent: data with greater risk impact receives a higher weight, and the sum of the weight coefficients for all environmental data is 1. In this application, the carbon monoxide content and oxygen content... Each value accounts for 0.25, totaling 0.5, for carbon dioxide content and formaldehyde content. Each value accounts for 0.15, totaling 0.3, for temperature and humidity. Each value accounts for 0.1, totaling 0.2, weighting coefficient. After preset, for each sub-region The weighting coefficients of the six environmental data items are determined. Multiply the calculated single-data anomaly contribution value by the weighted anomaly contribution value for each data item. Then, sum the weighted anomaly contribution values ​​of all six data items to obtain the weighted sum of the multiple data anomaly contribution values ​​for this sub-region. The calculation formula is as follows: .

[0024] Since the six environmental data points are not independent and some data points are interconnected, for example, increased temperature accelerates formaldehyde volatilization, leading to excessive formaldehyde levels, and decreased oxygen levels are often accompanied by increased carbon dioxide levels. Anomalies in a single data point can trigger a chain reaction of anomalies in other data points. Therefore, a data coupling correction factor is needed. To correct the risk bias caused by anomalies in multi-data collaboration, a data coupling correction factor is used. The actual deviation of each related environmental data The product is calculated by selecting data combinations that have mutual influence and calculating the product of each combination. The product of these factors is then summed over the products of all combinations to obtain the data coupling correction factor. , The value range is from 1 to positive infinity. When there are multiple data coordination anomalies... The more data with a value greater than 1, and the greater the degree of deviation, the more collaborative anomalies there are. The higher the value, the higher the collaborative risk; when there is only a single data anomaly or no data anomaly, A value of 1 indicates no risk of mutual influence.

[0025] For example, oxygen content With carbon dioxide content This is a combination of related data, including carbon monoxide content. With formaldehyde content For the combination of related data, temperature With humidity For related data combinations, then Adding 1 is to avoid all When it is 0 The value is 0, and the calculation yields... After the value, with Multiplication is used to correct for anomalies in multi-data collaboration.

[0026] Due to the various sub-areas of the civil defense project Different functional attributes result in varying degrees of sensitivity to environmental anomalies. For example, the command and control core area and personnel shelter areas are far more sensitive to environmental data than equipment rooms and material storage areas. Therefore, it is necessary to introduce a regional environmental sensitivity coefficient. Correcting the differences in environmental risk thresholds across different sub-regions to ensure the environmental early warning coefficient. It can adapt to the actual needs of each sub-region, and the regional environmental sensitivity coefficient The value is preset based on the functional attributes of the sub-region, and the value range is 1.0 to 2.0, including the command and control core area and the personnel shelter area. The value is set to 1.8–2.0 for the equipment room area and material storage area. Set the value to 1.0~1.2, and other auxiliary areas The value is set to 1.3 to 1.7. After the value is preset, each sub-region corresponds to a unique value. Value, will With this sub-region Multiply the values ​​to obtain the corrected composite outlier. This allows for the differentiation of environmental risk sensitivities in different functional sub-regions, avoiding an abnormal underestimation of the environmental risks in sensitive areas and an abnormal overestimation of the environmental risks in non-sensitive areas.

[0027] To ensure the environmental early warning coefficient of each sub-region To ensure comparability and avoid excessively large numerical ranges due to data discrepancies, the theoretical maximum warning value needs to be calculated. and to Perform normalization processing, The range of values ​​is uniformly mapped to the interval 0 to 1, where This indicates that the sub-area environment is completely normal and there are no safety risks. This indicates a severely abnormal environment in the sub-region, reaching the highest level of safety risk. The closer the value is to 1, the higher the environmental risk; the theoretical maximum warning value. The calculation method is as follows: assuming all environmental data Take the theoretical maximum value and substitute it into The calculation formula yields the comprehensive outlier value. It should be noted that the theoretical maximum value is preset based on the maximum values ​​of various data during historical monitoring.

[0028] Finally, normalization is performed using the formula: The environmental early warning coefficient is calculated independently for each sub-region. After the calculation is completed, the sub-region is stored synchronously. Values ​​and intermediate calculation data; When a certain sub-region Environmental warning coefficient After the calculation is completed, the obtained The value is compared with the system's preset environmental warning threshold. If If the value exceeds a preset threshold, the sub-region is determined to be an environmentally abnormal sub-region and marked as such. ,like If the value does not exceed the preset threshold, the environment of this sub-region is determined to be normal, and no further risk assessment of adjacent regions is required. For sub-regions determined to have abnormal environments... It is also necessary to determine its adjacent sub-regions. Whether the same environmental problem will occur is determined based on the environmental anomaly sub-region. With adjacent sub-regions The similarity of environmental data transformation curves is used to determine the impact. Specifically, this involves first extracting anomalous sub-regions. and all its adjacent sub-regions The environmental data transformation curves are shown, where each curve corresponds to the same type of environmental data, i.e., temperature curves are compared with temperature curves, oxygen content curves are compared with oxygen content curves, and the collection period and time span of all curves are completely consistent, all covering the abnormal sub-regions. Environmental data collected in three acquisition cycles before the anomaly occurred, one acquisition cycle during the anomaly occurred, and one acquisition cycle after the anomaly occurred may contain abnormal fluctuations in the curves due to factors such as sensor errors and data transmission interference during the data acquisition process. Therefore, each curve needs to be denoised to remove abnormal fluctuations and retain the true trend of the curve. The denoising process uses a wavelet threshold denoising algorithm with a fixed preset wavelet basis and threshold. The algorithm filters out abnormal fluctuations in the curves and removes them. At the same time, the curves after removing abnormal points are smoothed and fitted to ensure that the preprocessed curves can truly reflect the dynamic changes of the environmental data in the corresponding sub-regions. The same denoising standard is used for all sub-region curves.

[0029] For each environmental data transformation curve after preprocessing, time-domain and frequency-domain feature data are extracted. The time-domain feature data includes the curve's mean, variance, range, peak value, trough value, mean rising slope, mean falling slope, and fluctuation frequency. The mean reflects the average level of the environmental data within that time period; the variance reflects the degree of dispersion in the environmental data fluctuation; the range reflects the maximum fluctuation range of the environmental data; the peak and trough values ​​reflect the highest and lowest values ​​of the environmental data, respectively; the mean rising slope and mean falling slope reflect the average rate of change during the rising and falling phases of the environmental data, respectively; and the fluctuation frequency refers to the average rate of change during the rising and falling phases of the environmental data. The number of times the curve completes one rise-fall cycle within a given time period reflects the drastic change in environmental data. Frequency domain feature data is extracted after converting the time domain curve into a frequency domain curve using a Fast Fourier Transform. This includes the dominant frequency amplitude, dominant frequency, secondary dominant frequency amplitude, and frequency domain energy distribution entropy. The dominant frequency amplitude reflects the energy intensity corresponding to the dominant frequency in the frequency domain curve, the dominant frequency reflects the frequency with the strongest energy in the frequency domain curve, the secondary dominant frequency amplitude reflects the energy intensity corresponding to the second strongest frequency in the frequency domain curve, and the frequency domain energy distribution entropy reflects the uniformity of the curve's energy distribution across different frequency bands, which can characterize the stability of environmental data changes.

[0030] After extracting all feature data, since different types of feature data have different dimensions, it is necessary to normalize all extracted feature data, mapping the value range of all feature data to the [0,1] interval to eliminate the influence of dimension differences. After normalization, for each type of feature data, the environmental anomaly sub-region is calculated separately. With each adjacent sub-region The similarity of feature data is calculated using the Euclidean distance algorithm for the mean, variance, peak, trough, range, mean of rising slope, and mean of falling slope. and The Euclidean distance between the normalized values ​​of the corresponding feature data is calculated, and then the Euclidean distance is converted into a similarity score. The similarity score is calculated as 1 - Euclidean distance divided by the maximum Euclidean distance. The smaller the Euclidean distance, the smaller the difference between the two feature data points, and the closer the similarity score is to 1. For the frequency-type time-domain feature data, fluctuation frequency, a cosine similarity algorithm is used to calculate... and The cosine similarity of the normalized values ​​of the corresponding feature data is calculated. The cosine similarity value ranges from [0,1]. The closer the value is to 1, the more consistent the frequency characteristics of the two data points are. The similarity of the dominant frequency amplitude, dominant frequency, and secondary dominant frequency amplitude is calculated using the cosine similarity algorithm. The frequency domain energy distribution entropy is calculated using the relative entropy algorithm. and The frequency domain energy distribution entropy difference is calculated, and then the difference value is converted into a similarity. The smaller the difference value, the closer the similarity is to 1.

[0031] Because multiple feature data have varying degrees of influence on the correlation of environmental anomalies, and some feature data can more accurately reflect the spread trend of environmental anomalies, it is necessary to assign corresponding weight coefficients to each type of feature data according to its degree of influence. The sum of the weight coefficients is 1, ensuring that the weight allocation reflects the differences in importance of the feature data. Specifically, frequency domain feature data accounts for 40% of the weight, with each item accounting for 10%; the four time domain feature data—peak value, trough value, mean of rising slope, and mean of falling slope—account for 35% of the weight, with each item accounting for 8.75%; and the four data—mean, variance, range, and fluctuation frequency—account for 25% of the weight, with each item accounting for 6.25%. It should be noted that those skilled in the art can preset the weight coefficients according to actual conditions. After the weight coefficients are preset, a weighted summation algorithm is used to calculate the similarity of individual feature data and their corresponding weight coefficients. With each The overall similarity is calculated by multiplying the similarity of a feature data point by its weight coefficient, and then summing the similarities of all feature data points. A value closer to 1 indicates a higher similarity. With each The smaller the difference in the environmental data transformation curves, the better. Appearance and The higher the risk of the same environmental problem, the closer the overall similarity value is to 0, indicating a greater difference between the two curves. The lower the risk of similar environmental problems occurring.

[0032] To avoid the randomness of a single comprehensive similarity calculation and ensure the reliability of the judgment results, the stability of the comprehensive similarity is verified, and the following selections are made: Environmental data transformation curves for 5 periods: 3 collection periods before the anomaly, 1 collection period during the anomaly, and 1 collection period after the anomaly. For each period, the aforementioned operation is repeated to calculate the changes within each period. With each The overall similarity is calculated, resulting in overall similarity values ​​for five periods. Then, based on the overall similarity values ​​for the five periods, the mean and variance of the similarity are calculated. The mean is the arithmetic mean of the overall similarity values ​​for the five periods, and the variance reflects the degree of fluctuation and dispersion of the overall similarity values ​​for the five periods. The smaller the variance, the more stable the overall similarity calculation result is, and vice versa.

[0033] The system presets a variance threshold, which is 0.05 in this application. The calculated variance is compared with the preset threshold: if the variance is less than or equal to 0.05, it indicates that the similarity calculation result is stable and without significant fluctuations. In this case, the average similarity over 5 periods is used as the similarity value. With that The final curve similarity is calculated; if the variance is greater than 0.05, it indicates the presence of abnormal interference factors, causing large fluctuations in the similarity calculation results. In this case, the similarity calculation needs to be recalculated. The environmental data transformation curve is denoised, the denoising threshold is adjusted, and the aforementioned steps are repeated to recalculate the comprehensive similarity for 5 periods until the variance is less than or equal to 0.05. The mean similarity is then used as the final curve similarity for all adjacent sub-regions. All of these must undergo the aforementioned stability verification to ensure the accuracy of the final curve similarity results.

[0034] All adjacent sub-regions After the final curve similarity calculation is completed, based on the system's preset similarity threshold, the... With each The final curve similarity is compared with a preset threshold. The preset value range in this application is 0.6 to 0.8. It should be noted that those skilled in the art can adjust it according to the actual situation of civil defense projects. If the similarity is greater than or equal to the preset threshold, the adjacent sub-region is determined. There is a risk of the same environmental problems occurring, so this should be marked simultaneously. The risk level is determined and the risk assessment result is fed back to the intelligent control module to provide data for advance adjustment of the ventilation mode. If the similarity is less than a preset threshold, the adjacent sub-area is identified. The same environmental problems are not expected to occur in the near future, so it is marked as a low-risk area. No preventive measures need to be taken in advance, but its environmental data changes need to be continuously monitored. After the risk assessment is completed, the risk assessment results of all adjacent sub-areas are stored.

[0035] The data analysis module will also analyze each sub-region The communication link status data of each communication device within the system is used to verify the operating status of each device against a preset standard, generating an operating status indicator value. This indicator value includes only 1 and 0, where 1 represents normal operation and 0 represents abnormal operation. Specifically, a pre-defined standard for the normal operation of the communication link is established. This standard sets clear qualification thresholds for link connectivity, data transmission error rate, and link stability acquired by the data acquisition module. All three data points must meet the pre-defined standard to determine that the communication equipment is operating normally. If any data point fails to meet the pre-defined standard, the communication equipment is deemed to be operating abnormally. The pre-defined standard process is as follows: The qualified standard for link connectivity is that the communication link is continuously connected without interruption. The link feedback data and the sent check code are completely matched by the Cyclic Redundancy Check (CRC-32) algorithm, with no loss or tampering. The qualified standard for data transmission error rate is that the error rate is less than or equal to 0.1%, that is, for every 10,000 bits of data transmitted, the error data does not exceed 10 bits. The qualified standard for link stability is that the link response delay fluctuation value is less than or equal to 50ms, that is, the difference between the maximum and minimum link response delay values ​​of 10 consecutive CRC checks does not exceed 50ms, ensuring stable link transmission without significant delay fluctuations.

[0036] Then each sub-region was analyzed one by one. The communication link status data of each communication device is compared and verified with the preset communication link status qualification standard. First, the link connectivity is verified. If the link connectivity verification result is continuous connectivity and the feedback data and the verification code match completely, the qualification standard is met. If the link connectivity verification result is interruption and the feedback data and the verification code do not match, the qualification standard is not met, and the communication device is directly judged to be malfunctioning.

[0037] After the link connectivity verification passes, the data transmission error rate is verified. The measured bit error rate of the device is compared with the preset 0.1% pass threshold. If the measured bit error rate is less than or equal to 0.1%, it meets the pass standard. If the measured bit error rate is greater than 0.1%, it does not meet the pass standard, and the communication device is directly judged to be malfunctioning. After the data transmission error rate verification passes, the link stability is verified. The link response delay fluctuation value of the device is compared with the preset 50ms pass threshold. If the fluctuation value is less than or equal to 50ms, it meets the pass standard, and the communication device is judged to be malfunctioning. If the fluctuation value is greater than 50ms, it does not meet the pass standard, and the communication device is judged to be malfunctioning.

[0038] Based on the verification results, a corresponding operating status indicator value is generated for each communication device: if the three core data of the device—link connectivity, data transmission error rate, and link stability—all meet the preset qualification standards, that is, the verification results are all qualified, the operating status indicator value of the communication device is set to 1, indicating that the device is operating normally; if any of the three data of the device does not meet the preset qualification standards, that is, the verification result is unqualified, the operating status indicator value of the communication device is set to 0, indicating that the device is operating abnormally.

[0039] Summary of work content for data analysis, intelligent inspection and intelligent control modules The data analysis module is the core data processing unit of the intelligent three-defense control system for civil defense projects. It receives all the raw data transmitted from the data acquisition module. Its core responsibilities are: calculating the environmental warning coefficient E for each sub-region, determining the risk of adjacent areas in sub-regions with abnormal environments, and generating communication equipment operating status indicators to provide data support for subsequent modules. All tasks are performed automatically without manual intervention. Its core functions include: obtaining the environmental warning coefficient E for each sub-region based on environmental data through deviation calculation, anomaly contribution value analysis, weighted summation, and multiple rounds of correction; determining the risk of adjacent sub-regions when an environmental anomaly occurs in a sub-region through curve denoising, feature extraction, similarity calculation, and stability verification; and verifying each device against preset standards based on communication link status data, generating an operating status indicator value of 1 or 0, and then, based on each sub-region... The inspection routes for each communication device within the civil defense project are planned based on the operational status indicators of the devices. Specifically, each communication device is first designated as an inspection node, and node priorities are defined. Nodes with an operational status indicator value of 0 are designated as high-priority mandatory inspection nodes, while nodes with an indicator value of 1 are designated as regular sampling inspection nodes. This ensures that abnormal devices are inspected first. A network topology inspection map containing all inspection nodes and communication links between nodes is constructed, and the upstream and downstream links of high-priority mandatory inspection nodes are marked to obtain the core inspection coverage area. Path planning prioritizes coverage of high-priority mandatory inspection nodes and minimizes link hops. High-priority mandatory inspection nodes are designated as mandatory inspection nodes, and regular sampling inspection nodes are appropriately embedded according to the density of the network topology. A main network inspection path covering all mandatory inspection nodes with the optimal link hop count is planned. During the inspection execution phase, remote inspection and detection are performed sequentially along the main path. For mandatory inspection nodes with an indicator value of 0, the authenticity of their abnormal status is verified through multi-link polling to avoid misjudgment. At the same time, the network bandwidth and transmission latency of the inspection links are monitored in real time to ensure uninterrupted inspection and stable data transmission.

[0040] The intelligent control module is used to determine the environmental early warning coefficient for each sub-region. Based on the risk results of adjacent areas, ventilation mode switching and parameter adjustment are carried out. First, it is necessary to preset environmental thresholds for the civil defense project. It should be noted that those skilled in the art can set the thresholds according to the actual environmental conditions of the civil defense project, and then set the environmental warning coefficients for each sub-area. Each value is compared with a preset threshold, and the environmental risk level is classified based on the comparison results to obtain the information for each sub-region. Environmental safety status, based on each sub-region Risk level, and referencing adjacent sub-regions. Based on the environmental risk assessment results, the corresponding special ventilation mode for civil defense and NBC protection is matched to ensure that the ventilation mode is adapted to the current environmental safety requirements and to avoid the risks caused by abnormal environmental diffusion in advance.

[0041] When switching ventilation modes, the relevant equipment performs the switching operation based on the communication link status. The entire process monitors the operating status of ventilation equipment and changes in environmental parameters of each sub-area in real time, promptly detecting equipment malfunctions or fluctuations in environmental parameters. At the same time, based on the real-time collected environmental data, the operating control parameters of the ventilation system are dynamically adjusted. There are three ventilation modes: clean, filtered, and isolated, each corresponding to different environmental scenarios: clean ventilation operates all fans and valves normally; filtered ventilation starts the filtration equipment and adjusts the airflow direction; isolated ventilation closes all air intake and exhaust channels to achieve self-sufficiency of the internal environment of the project.

[0042] This system also includes a central control unit, sub-control units, sensor components, display components, and a communication network. The central control unit and each sub-control unit are flexibly connected via a single control network cable, eliminating the need for a central host and improving system fault tolerance and stability. All control modules are installed locally near the controlled equipment, enabling distributed deployment of control functions and effectively isolating the impact of local faults on the overall system.

[0043] The central control unit incorporates an intelligent tri-proof controller and touchscreen, supporting automatic switching of ventilation modes. It integrates multiple environmental and safety sensors via an RS485 interface, enabling real-time monitoring and alarm functions for temperature, humidity, gas concentration, water immersion, fire, and security door status within the facility. The sub-control units include intake, exhaust, and communication control boxes, each equipped with control panel buttons for local operation of valves, fans, and ventilation mode switching. Remote linkage control is also supported. This invention includes a reserved cloud access interface, allowing environmental data, equipment status, and alarm information to be uploaded to a management platform via fiber optic or other communication methods, enabling remote monitoring and unified management of multiple civil defense projects. The central control unit (tri-proof central control box) measures 420 (±1) mm × 550 (±1) mm × 120 (±1) mm and weighs ≤20 kg. It is wall-mounted, with pre-installed protective connectors to accommodate different wiring requirements. The box integrates... The system integrates a smart tri-proof controller with a 7-inch touchscreen. The display component supports graphical interface operation and can show the ventilation mode status, equipment operation data, and alarm information in real time. The central control unit connects to various environmental and safety sensors via an RS485 interface, including temperature and humidity sensors, oxygen, carbon dioxide, carbon monoxide, and formaldehyde gas sensors, as well as water immersion, fire, and protective door status sensors. This enables comprehensive monitoring of the internal environmental data and safety status of the civil defense project. When the monitored data exceeds the set threshold, the system automatically triggers the alarm mechanism and sends instructions to the sub-control units to automatically switch the ventilation mode by linking the fans and valves.

[0044] The sub-control units include three types: an intake air three-proof control box (sub-control unit 1), an exhaust air three-proof control box (sub-control unit 2), and a control communication box (sub-control unit 3). Sub-control units 1 and 2 have dimensions of 500 (±1) mm × 660 (±1) mm × 140 (±1) mm and weigh ≤25 kg, and are also wall-mounted. The controller and control loop are integrated, allowing control of up to 6 valves and 3 fans. The control panel is equipped with physical buttons for local operation, including valve opening / closing, fan start / stop, and manual switching of ventilation modes. Sub-control unit 3 has dimensions of 420 (±1) mm × 550 (±1) mm × 120 (±1) mm and weighs ≤20 kg. It has the expansion capability to connect to more types of equipment, such as water pumps, sewage pumps, and protective door actuators, for online monitoring and remote control of equipment.

[0045] All control devices have an IP65 protection rating, making them suitable for high humidity, salt spray, and high temperature conditions in basement environments. The enclosure features a moisture-proof isolation chamber and a convex-concave door frame with rubber strip sealing structure. The controller module has passed relevant tests according to national military and national standards, possessing waterproof, moisture-proof, high-temperature resistance, salt spray corrosion resistance, and nuclear electromagnetic pulse interference resistance capabilities, ensuring stable operation in extreme environments.

[0046] To enable cloud access and unified management, the system reserves standard communication interfaces (such as fiber optic or Ethernet) to synchronously upload environmental monitoring data, equipment status information, and alarm events to the remote management platform via a data gateway. This platform supports multi-project data aggregation and analysis, remote control operation, and historical data querying, providing comprehensive operation and maintenance support for administrators.

[0047] The present invention can also be adapted and expanded according to specific engineering needs, such as adding more types of sensors, supporting wireless communication modules to replace some wired wiring, and using local edge computing devices to improve data processing efficiency, thereby further improving the flexibility and intelligence of the system.

[0048] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.

Claims

1. A smart three-defense control system for civil defense projects, characterized in that, Includes the following modules: The data acquisition module divides the civil defense project into multiple sub-areas. ; The internal sub-areas of the civil defense project are periodically monitored using multiple environmental sensors. Environmental data, periodically acquired from each sub-region Communication link status data of each communication device within the system; The data analysis module obtains information for each sub-region based on the environmental data. Environmental warning coefficient Obtain each sub-region based on the communication link status data. Operating status indicators of each communication device within the system; The intelligent inspection module, based on each sub-area The operational status indicators of each communication device within the civil defense project are used to plan the inspection routes for each communication device inside the project. The intelligent control module will control each sub-region. The environmental warning coefficient is compared with the preset environmental threshold, and the ventilation mode inside the civil defense project is switched according to the comparison result.

2. The intelligent three-defense control system for civil defense engineering according to claim 1, characterized in that, The data acquisition module operates as follows: The civil defense project is divided into multiple sub-regions of varying sizes. ; Through each sub-region The internally installed environmental sensors periodically acquire environmental data and construct time series based on the acquisition time. Environmental data include temperature, humidity, oxygen content, carbon dioxide content, carbon monoxide content, and formaldehyde content; Sub-regions obtained based on time series and periodicity Environmental data acquisition and environmental data transformation curves; The communication link status data is obtained through cyclic redundancy check. Communication link status data includes link connectivity, data transmission error rate, and link stability.

3. The intelligent three-defense control system for civil defense projects according to claim 2, characterized in that, The data analysis module operates as follows: Preset the weighting coefficients for each environmental data; Calculate each sub-region The deviation between the real-time value of environmental data and the preset safety standard value; if the real-time value of environmental data is within the range of the preset safety standard value, the deviation of environmental data is 0. Based on the weighting coefficients and deviations of each environmental data point, the values ​​of each sub-region are calculated and obtained. Environmental early warning coefficient; When a certain sub-region When an environmental anomaly occurs, acquire adjacent sub-regions. Environmental data, and adjacent sub-regions Environmental data transformation curves during the same period; Computational environment anomaly sub-region With each adjacent sub-region The similarity of the environmental data transformation curves is calculated, and a preset similarity threshold is set. If the similarity exceeds the preset threshold; It was determined that there was a risk of the same environmental problems occurring in adjacent sub-regions; If the similarity is lower than the preset threshold, it is determined that the same environmental problem will not occur in adjacent sub-regions for the time being. Preset communication link status standards for normal operation of communication equipment, for each sub-area Each communication device within the device is checked one by one. When all communication link status standards meet the preset standards, the operating status indicator value of the communication device is set to 1, which means that it is operating normally. If any core data fails to meet the preset standard, the operating status indicator value of the communication device is set to 0, indicating an abnormal operation.

4. The intelligent three-defense control system for civil defense engineering according to claim 3, characterized in that, The sub-regions Environmental warning coefficient The method for obtaining it is as follows: Get the The actual deviation of a single environmental data point from its standard value is calculated. ; Preset number Risk amplification factor for individual environmental data And calculate the contribution value of abnormal environmental data; Weighting coefficients are assigned based on the degree of impact on data security. The weights are multiplied by the individual data anomaly contribution values ​​and then summed. The data coupling correction factor is calculated by multiplying the deviations of various related environmental data. Correcting the risk of anomalies in multi-data collaboration; Determine the regional environmental sensitivity coefficient based on the functional attributes of the sub-region. Correcting the differences in environmental risk thresholds across different regions; Calculate the theoretical maximum warning value The results of the previous calculations are normalized to obtain the final environmental warning coefficient. .

5. The intelligent three-defense control system for civil defense engineering according to claim 4, characterized in that, The method for obtaining curve similarity is as follows: For environmentally abnormal sub-regions and all adjacent sub-regions The environmental data transformation curve is denoised to remove abnormal fluctuation points; For each environmental data transformation curve after preprocessing, time-domain feature data and frequency-domain feature data are extracted respectively, and all extracted feature data are normalized. For each type of feature data, calculate the environmental anomaly sub-region separately. With all adjacent sub-regions Feature data similarity; Based on the degree of influence of various feature data on the correlation of environmental anomalies, a corresponding weight coefficient is assigned to each feature data. The overall similarity is calculated based on the similarity of individual feature data and the corresponding weight coefficient; Selecting environmental anomaly sub-regions Environmental data transformation curves for the three data acquisition cycles before the anomaly occurred, the one data acquisition cycle during the anomaly occurred, and the one data acquisition cycle after the anomaly occurred were repeatedly calculated for the environmental anomaly sub-region within each cycle. and all adjacent sub-regions The overall similarity is calculated, and the mean and variance of the similarity are obtained. If the variance is less than or equal to the preset variance threshold, the mean similarity will be used as the final curve similarity. If the variance is greater than the preset variance threshold, the curve is denoised again, and the curve similarity is recalculated.

6. The intelligent three-defense control system for civil defense engineering according to claim 1, characterized in that, The intelligent inspection module operates as follows: According to each sub-region The operating status indicators of all communication devices within the civil defense project are compared with the communication network topology. Each communication device is designated as an inspection node, and nodes with an operating status indicator value of 0 are designated as high-priority mandatory inspection nodes, while nodes with an indicator value of 1 are designated as regular sampling inspection nodes. Construct a network topology inspection map that includes all inspection nodes and communication links between nodes, and mark all upstream and downstream related links of high-priority mandatory inspection nodes; High-priority mandatory inspection nodes are set as mandatory inspection nodes, and regular random inspection nodes are embedded according to the density of network topology. The main network inspection path is planned to cover all mandatory inspection nodes and have the optimal link hop count. According to the planned main network inspection path, inspections are carried out sequentially. For nodes that must be inspected with a value of 0, the authenticity of their abnormal status is verified by multi-link polling. The network bandwidth and transmission latency of the inspection links are monitored in real time.

7. The intelligent three-defense control system for civil defense engineering according to claim 1, characterized in that, The intelligent control module operates as follows: The environmental threshold of the civil defense project is preset, and each sub-area is divided into... The environmental early warning coefficient is compared with the preset threshold to complete the classification of environmental risk levels; Based on risk level and adjacent sub-regions Environmental risk assessment and special ventilation modes for civil defense and NBC protection; Based on the communication link, the ventilation mode is switched and the changes in ventilation equipment and environmental data are monitored in real time. The ventilation system's operation and control data are dynamically adjusted based on real-time environmental data.

8. The intelligent three-defense control system for civil defense projects according to claim 7, characterized in that, The ventilation modes include clean air ventilation, filtration ventilation, and isolation ventilation: Under clean ventilation, all fans and valves operate according to normal operating logic; When using a filtration-type ventilation system, the system activates the filtration equipment and adjusts the airflow direction. Isolation ventilation achieves self-sufficiency of the internal environment of the project by automatically closing all air intake and exhaust channels.

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