Integrated intelligent monitoring method for frontier defense important places

Abstract

The invention discloses a frontier defense important place integrated intelligent monitoring method, and relates to the technical field of intelligent monitoring, aiming at the monitoring requirements of multiple types of sensors under extreme climate and complex terrain, the method comprises the following steps: firstly, preprocessing the working state data of the environment and the sensors, and establishing an initial working base line; when the deviation between the actual output and the ideal working condition is detected, automatically correcting the gain and the threshold value by adopting a dynamic calibration model; then potential faults are identified based on failure aggregation measurement and a historical fault library, and once high risks are confirmed, early warning is triggered, and standby monitoring equipment is switched to; finally, deviation is further checked through multi-source synchronization and feature fusion, the decision weight is dynamically adjusted, and robust and efficient monitoring of the frontier defense important place is achieved. According to the scheme, an expandable parameter adjustment domain is provided in the identification and adjustment and optimization link, edge and center cooperation is considered, high adaptability and expandability are achieved, and therefore the adaptability and operation efficiency of the system in changeable weather are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to an integrated intelligent monitoring method for key border defense areas. Background Art

[0002] In strategic border areas, around critical infrastructure, or in high-risk terrain, multiple sensor types (such as optical cameras, infrared imaging, radar, sonar, and laser scanning) are often deployed to provide around-the-clock monitoring of potential threats, including personnel, vehicles, and even micro-aircraft (MAVs). These areas often experience extreme climatic conditions (such as high altitude, extreme cold, sandstorms, and heavy snowfall) and complex geographical environments (mountains, vast deserts, dense jungles, and waterways). This makes sensors susceptible to external interference and places high demands on monitoring coverage, data collection stability, and real-time performance. If sensors experience deviations or malfunctions due to environmental influences, blind spots or serious false alarms or missed alarms may occur. Furthermore, criminals or potential threats often exploit terrain blind spots or extreme weather conditions to infiltrate through camouflaged routes, small vehicles, and drone swarms, posing significant challenges to traditional surveillance systems. Therefore, while ensuring the effectiveness of large-scale, multi-dimensional monitoring, it is also necessary to take into account dynamic calibration and intelligent recognition capabilities, so as to quickly make reliable predictions of abnormal events in emergency environments and form a highly robust and high-precision comprehensive monitoring and defense system.

[0003] Chinese patent publication number CN117176555A discloses an intelligent integrated information monitoring method for marine platforms. It ensures the effective transmission of platform data collected by the first-level nodes by wirelessly communicating with several first-level nodes and a second-level node, and uses the second-level nodes to pre-process the abnormal data of the platform data collected by several first-level nodes to reduce the data processing volume. With the cooperation of the third-level nodes, the abnormal data is judged by building a fault library to ensure the timeliness of data transmission and processing while realizing the rapid location of the abnormal type. It can not only realize the integrated monitoring of the corresponding platform data of the marine platform, but also realize the intelligent judgment of marine platform abnormalities, thereby providing convenient guarantee for the rapid response to abnormal situations of the marine platform.

[0004] Based on the aforementioned application scenarios, to achieve continuous monitoring of key border areas and complex terrain, there is an urgent need to address the technical challenges of reliability assessment and dynamic tuning of multiple sensors in extreme environments. Existing solutions often rely on single-sensor or static threshold calibration, lacking mechanisms for joint calibration and fault identification across different meteorological conditions and sensor modalities, and are unable to cope with frequent and drastic environmental fluctuations. When sensors experience laser attenuation at high altitudes and low air pressure, widespread signal loss due to interference in snowstorms or dust storms, or unstable component performance due to significant diurnal temperature fluctuations, relying solely on fixed thresholds and manual troubleshooting often leads to significant increases in false positives and false negatives. Furthermore, in a widely dispersed monitoring network, cumulative sensor deviations or failure modes can directly impact subsequent target identification and threat assessment. The lack of comprehensive calibration methods for sensor operating conditions, environmental noise, and multi-source data fusion prolongs fault detection and repair times, increases monitoring blind spots, and further increases security risks.

[0005] To this end, the present invention provides an integrated intelligent monitoring method for key border defense areas. Summary of the Invention

[0006] (1) Technical problems solved To address the shortcomings of existing technologies, the present invention provides an integrated intelligent monitoring method for key border locations. This method, tailored to the monitoring needs of multiple sensor types in extreme climates and complex terrain, first preprocesses environmental and sensor operating status data and establishes an initial operating baseline. When deviations between actual output and ideal operating conditions are detected, a dynamic calibration model is used to automatically correct gains and thresholds. Potential faults are then identified based on failure aggregation metrics and a historical fault database. Once high-risk conditions are confirmed, an early warning is triggered and the system switches to backup monitoring equipment. Finally, multi-source synchronization and feature fusion are used to further verify deviations and dynamically adjust decision weights, achieving robust and efficient monitoring of key border locations. By establishing a dynamic calibration mechanism and fault diagnosis process for multiple sensors in extreme environments, supplemented by multi-source data fusion and decision-making models, the present invention seeks to significantly enhance the system's adaptive monitoring and stable protection capabilities under harsh conditions.

[0007] (2) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: An integrated intelligent monitoring method for border defense key areas includes: when the monitoring module captures the working status data and environmental information of multiple types of sensors, a vector combination of environmental indicators is generated. Perform data preprocessing and generate an initial working baseline at the output stage that can characterize the coupling between the sensor and the environment; When the initial working baseline is resolved and the actual output of the sensor is detected to be different from the ideal working condition, the calibration deviation is measured. Establish a dynamic calibration model and calculate the calibration coefficient and update parameter vector , adaptively modify the sensor’s gain, threshold, and sensitivity; After completing sensor calibration and feedback data accumulation, based on failure aggregation metrics and historical fault database , using pattern recognition to identify potential failure signs or abnormal drift. If a high-risk event is found, an early warning signal is triggered and the operation and maintenance end is instructed to dispatch backup monitoring equipment; When receiving warning information and identifying suspicious or failed sensors, multi-source data synchronization and space-time mapping are used to integrate different modal observations into a unified reference, and the multi-source feature fusion operator is used to generate the corresponding Consistency across sensors Detect data deviations and adjust the proportion of faulty sensors in decision-making in real time and activate backup monitoring equipment; Preferably, the external environment data is continuously collected through monitoring modules deployed at key border locations. , pre-process and clean the original environmental data; collect the working status data of various field sensors at a given moment , perform preliminary noise filtering and outlier removal; If the working status data If a certain indicator is inconsistent with the environmental conditions to a greater extent than expected, it is judged as a suspected sensor failure or interference information. Basic upper and lower limit truncation and logical consistency checks are performed on each state parameter, and the suspicious parts are marked and eliminated.

[0008] Preferably, the environmental data , working status data Conduct joint analysis to establish the initial working baseline of each sensor under different climatic conditions, introduce a multi-scale analysis method to comprehensively measure the degree of coupling between the environment and the sensor, and define the following coupling baseline indicators , to measure the Coupling differences between internal environment and sensor status:

[0009] Where: Indicates the time range used in this step to build the baseline; represents the wave transform operator; is a weight function used to highlight specific moments The degree of impact on the system in extreme environments; Preferably, read environmental data Working status data of the sensor , and the coupled baseline metrics for the initial work baseline metrics By comparing the ideal output of the sensor under the standard environment with the actual output corresponding to the current environment, the calibration coefficient is calculated and written into the sensor; Define a new operator To measure the degree of deviation between the ideal output and the actual output, the result is recorded as the calibration deviation metric ; The ideal output can be based on the pre-calibrated calibration curve or the coupled baseline index The calibration deviation metric is generated to measure the degree of deviation of the sensor's measured value during continuous operation. :

[0010] Where: Indicates the sensor's corresponding time under reference conditions The ideal output; In the current actual environment Next, the real-time measurement value of the sensor; is a logarithmic contrast operator, which can be defined as:

[0011] in, It is an adjustable sensitivity parameter to amplify or suppress deviations in certain intervals.

[0012] Preferably, when obtaining the calibration deviation metric Then calculate the calibration coefficient , and written into the sensor or host system based on the calibration coefficient , dynamically adjust the core parameters of the sensor in real time or periodically; define the parameter vector , each Corresponding to the adjustable points inside different sensors, combined with the calibration coefficients , the following formula can be used to update the adaptive parameters:

[0013] Where: is the parameter adjustment cycle, Indicates the Adjustable parameters at the new time Update value when the calibration coefficient Determines the gain or loss; After updating the parameter vector After that, it is written back to the corresponding sensor or configuration file in real time and used in the next parameter adjustment cycle. Continue to measure deviations from the latest calibration Make a comparison.

[0014] Preferably, the actual measurement value collected after calibration , is the new key parameter vector for each sensor Real-time observation output under action; obtain sensor calibration log , including parameter update content and update amplitude , environmental data , and the characteristics of past fault trends are summarized to form a reference database; Introducing failure discrimination operator and failure aggregation metrics , used to evaluate the time interval Are there any suspicious signs of failure in the internal sensor?

[0015] Where: is the actual measured value at time α after calibration; A historical feature library containing typical data fragments under known failure modes, providing an environmental reference at the corresponding moment; is the independent variable of the time integration or iterative process; Can be designed as a multi-dimensional comparison and similarity analysis function; When the failure aggregation metric If the pre-set failure threshold is exceeded, it is determined that there is a high probability of potential failure or performance degradation.

[0016] Preferably, if the failure aggregation metric Exceeding the set threshold It is considered as high risk and generates fault warning signal immediately. If the fluctuation is near, it is considered suspicious and will be continuously observed; Combined with sensor calibration log , environmental data And the identification results of specific failure modes are used to comprehensively judge the alarm level; if the early warning is triggered, the backup monitoring equipment is automatically called or the multi-node redundancy mode is started. For minor suspicious situations, the weight of the faulty equipment in the overall monitoring results is reduced.

[0017] Preferably, after receiving suspicious or fault warning information, the real-time outputs from adjacent redundant sensors are obtained and synchronized, and a unified spatiotemporal mapping process is defined to record the output streams of several sensors as , and in a certain time window Aggregate into a unified coordinate system and time axis to form a fused input set .

[0018] Preferably, based on the fusion input set , identify the key features of the same target in multi-source observation data, and introduce a dedicated multi-source feature fusion operator and consistent metrics across sensors ; Multi-source feature fusion operator Integrate the perception modalities of different sensors to generate the feature vector of the target ; By comparing the sensors' observations of the same target at the same time The predicted feature vector is used to calculate the consistency across sensors , based on whether the fault is warned and the fault level and other fault information, combined with the consistency across sensors If there is a significant abnormal deviation that is consistent with the fault symptoms, the corresponding sensor will be listed as a high-suspect fault device.

[0019] Preferably, based on cross-sensor consistency The weight of each sensor in the overall monitoring decision is dynamically adjusted with fault information, and the weight vector is introduced To uniformly manage the contribution of each sensor in the decision-making layer at the current moment and activate backup monitoring equipment or execute maintenance mode when necessary; If a sensor has been confirmed as a highly suspected faulty device, its weight can be forced to be reset to a minimum value or completely eliminated, and a backup monitoring device can be enabled according to the system operation and maintenance strategy. After the weight adjustment is completed, the final fusion result will be output to the back-end monitoring platform. If the sensor is found to have completely failed or has a serious fault during the fusion and weight update process, this information will be transmitted back to the system operation and maintenance level to trigger maintenance mode or hardware replacement.

[0020] (3) Beneficial effects The present invention provides an integrated intelligent monitoring method for key border defense areas, which has the following beneficial effects: Through multi-dimensional data collection, dynamic calibration, failure identification and fusion decision-making, an integrated solution is provided for multi-sensor monitoring applications in extreme environments.

[0021] First, in the basic data phase, environmental and sensor status are preprocessed to establish a traceable baseline of environmental parameters and sensor operating conditions. This process effectively identifies and eliminates irrational data even in extreme environments such as extreme cold, high altitude, or sandstorms, maintaining a precise initial observation basis and reducing the risk of redundant calculations in subsequent steps due to noisy or erroneous data.

[0022] Subsequently, a calibration coefficient is calculated based on the difference between ideal and measured outputs. Combined with an adaptive parameter update strategy, this allows for timely adjustment of sensor gains and thresholds to maintain monitoring accuracy in a variety of conditions, including extreme cold, high altitude, and sandstorms. Furthermore, when identifying potential failures, the system compares the failure metrics with a database of historical fault signatures. Once a predetermined threshold is exceeded, an alert is triggered and backup monitoring equipment is deployed, significantly reducing troubleshooting time.

[0023] Finally, with the help of multi-source interpolation and space-time calibration methods, visible light, infrared, sonar, radar and other modal data are unified to the same reference. The consistency measurement and weight dynamic adjustment mechanism are used to minimize the impact of suspicious equipment, and the fusion output is applied to threat assessment and command decision-making.

[0024] This not only enhances system robustness in extreme climates but also balances the efficiency of collaboration between local edge and central backend systems, creating a reliable and scalable technical framework for security monitoring in key border areas. Furthermore, this solution incorporates a scalable parameter adjustment domain for identification and tuning, facilitating subsequent optimization for specific sensors or operational needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a structural diagram of the integrated intelligent monitoring method of the present invention. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0027] See also Figure 1 The present invention provides an integrated intelligent monitoring method for key border defense areas, comprising: Step 1: When the monitoring module captures the working status data and environmental information of multiple types of sensors, the vector combination of environmental indicators Perform data preprocessing and generate an initial working baseline at the output stage that can characterize the coupling between the sensor and the environment; The step 1 includes the following: Step 101: Environmental data collection and preliminary organization The monitoring modules deployed at the border defense locations continuously collect external environmental data such as temperature, humidity, air pressure, wind speed, etc. These environmental data are defined as ,in Can represent a period of time The vector combination of each environmental indicator, such as ; De-duplicate, time-align, and check consistency of the original environmental data, and perform brief cleaning operations on the local edge computing node (such as removing interference values generated during device self-test).

[0028] Step 102: Sensor working status data acquisition and noise filtering For various field sensors (infrared, laser scanning, radar, sonar, etc.), collect their working status data at a given moment, recorded as ,in Contains information such as voltage, current, internal temperature, accumulated operating time, etc. that may reflect the health of the sensor (e.g. Then, preliminary noise filtering and outlier removal are performed on these status data; According to the environmental data obtained in step 101 Perform cross validation, if the working status data If a certain indicator is extremely inconsistent with the environmental conditions (for example, the internal temperature of the sensor is significantly higher than the possible threshold), it can be judged as a suspected sensor failure or interference information. Basic upper and lower limit truncation and logical consistency checks are implemented on each status parameter (for example, the operating time cannot be rolled back, and the internal temperature cannot instantly jump to an unreasonable value). Suspicious parts are marked and eliminated; During use, relatively pure working status data of the sensor is obtained, ensuring that the subsequent step 103 can extract the sensor performance baseline based on relatively realistic status information. By associating steps 101 and 102, a preliminary distinction can be made between environmental anomalies and sensor anomalies, thereby reducing the misjudgment rate of subsequent analysis.

[0029] Step 103: Initial working baseline construction Environmental data and work status data A joint analysis is conducted to establish the initial working baseline of each sensor under different climatic conditions. In order to more precisely reflect the potential fluctuations of sensors under multi-dimensional environmental factors, a multi-scale analysis method (such as wavelet transform) is introduced to comprehensively measure the degree of coupling between the environment and the sensor. The following coupling baseline indicators are defined: , to measure the Coupling differences between internal environment and sensor status:

[0030] Where: Indicates the time frame (such as one week or one month) used to construct the baseline in this step; Represents a wave transform operator, which is used to extract the multi-scale features of environmental data and sensor working status data; is a weight function used to highlight specific moments The degree of impact on the system in extreme environments, such as giving greater weight during periods of extreme cold or peak dust storms; Coupling baseline indicators The smaller the value, the higher the matching degree between the environment and the sensor in the time window, that is, there is no obvious contradiction between the sensor working state and the environmental fluctuation; on the contrary, it means that significant differences have occurred in certain periods of time, which requires further attention or marking; The core of this step is to calculate and save The baseline value is used to determine the sensor calibration requirements and to form several coupled baseline indicators under multiple time periods and climate types by combining time series. Reference values lay the foundation for subsequent failure mode identification and multi-source data comparison.

[0031] During use, a more adaptable and stable initial working baseline is constructed through multi-scale analysis and integral measurement, which reduces the workload of constructing reference values from scratch in subsequent steps, clarifies the coupling relationship between the environment and sensor status, and provides data support for the subsequent rapid location of environmental anomalies or sensor anomalies. Wavelet transform is used in baseline construction With variable weight function Combined with the above, it can measure the fluctuations in different frequency bands or extreme scenarios in a differentiated manner, and through coupling the baseline indicators The introduction of is no longer limited to the traditional mean or range judgment method, but a comprehensive measurement of the multi-dimensional coupling between the environment and the sensor, which is both robust and interpretable.

[0032] Step 2: When the initial working baseline is analyzed and the actual output of the sensor is detected to be different from the ideal working condition, the calibration deviation is measured. Establish a dynamic calibration model and calculate the calibration coefficient and update parameter vector , adaptively modify the sensor’s gain, threshold, and sensitivity; Step 201: Calculate and write calibration coefficients Read the environment data output in the first step Working status data of the sensor , and the coupled baseline metrics for the initial working baseline metrics By comparing the ideal output of the sensor under a standard environment with the actual output corresponding to the current environment, the calibration coefficient is calculated and written into the sensor or its upper system; Define a new operator To measure the degree of deviation between the ideal output and the actual output, the result is recorded as the calibration deviation metric ; The ideal output here can be based on a pre-calibrated curve or a coupled baseline indicator The logarithmic form and the definition of the integral correction term are introduced to generate the calibration deviation metric , where the calibration deviation measure It is a key indicator to measure the degree of deviation between the actual value of the sensor and the ideal value under standard working conditions or manufacturer calibration during continuous operation:

[0033] Where: Indicates the sensor's corresponding time under reference conditions The ideal output; In the current actual environment Next, the real-time measurement value of the sensor; is a logarithmic contrast operator, which can be defined as:

[0034] in, It is an adjustable sensitivity parameter to amplify or suppress deviations in certain intervals; Indicates the ideal output value of the sensor under standard or manufacturer's nominal conditions; In the current extreme environment Or the actual output value measured in real working conditions; It depicts the degree of difference between the two; Calibration bias metric The larger the value, the greater the deviation between the current output of the sensor and the ideal value, and the higher the calibration amplitude is required; In obtaining calibration deviation metrics After that, the calibration coefficient can be calculated , and written into the sensor or host system to correct its internal threshold, gain, sensitivity and other key parameters. It is an extension of the traditional linear or static correction method. The example can be defined as:

[0035] in Used to control the steepness of the correction curve, it can perform exponential correction on the gain or sensitivity of different types of sensors, and is suitable for large drift in extreme climates or high altitude and low pressure environments; When used, the comparison between the ideal output and the actual output is quantified as a calibration deviation metric , and from this the calibration coefficient , it can automatically compensate for sensor deviations in extreme environments, and realize a unified calibration framework for different environments and sensor types without relying on a single statistical indicator, thereby enhancing the adaptability of the system.

[0036] Step 202: Adaptive parameter adjustment and real-time control Based on calibration coefficients , to dynamically adjust the core parameters of the sensor (such as threshold, gain, sensitivity, filter window size, etc.) in real time or periodically.

[0037] Define the parameter vector , used to uniformly represent the current moment The following settings have been written into the sensor; For example , where each Corresponding to the adjustable points inside different sensors, combined with the calibration coefficients , the following formula can be used to update the adaptive parameters:

[0038] Where: is the parameter adjustment cycle, Indicates the Adjustable parameters at the new time Update value at time; calibration coefficient Determines the gain or loss; After updating the parameter vector After that, it is written back to the corresponding sensor or configuration file in real time and used in the next parameter adjustment cycle. Continue to measure deviations from the latest calibration Compare and form a closed-loop adaptive calibration mechanism.

[0039] When using, the calibration coefficient The dynamic adjustment mechanism realizes the real-time update of key parameters such as sensor threshold and gain, ensuring that the monitoring method can maintain high accuracy and reliability in harsh environments; Using parameter vector The adjustable points of different types of sensors are then managed in a unified manner and controlled in the form of multiplicative updates, making it easy to quickly apply them under multiple extreme climate conditions.

[0040] By logarithmic comparison operator With exponential calibration coefficient A highly reliable dynamic calibration method is constructed by combining the linear correction method with the sensor parameter vector. This method significantly reduces the risk of misalignment of the traditional linear correction in extreme environments. Synchronous regulation helps to achieve large-scale corrections in a short period of time, and can also flexibly converge or diverge according to drastic changes in the environment.

[0041] By first calculating the calibration coefficient , and then the sensor key parameter vector Real-time updates are performed to maintain monitoring accuracy and stability in a changeable and extreme border environment. The new parameter vector configuration of the calibration log is finally generated to further determine whether the sensor may still show signs of failure after calibration, providing a more accurate reference basis for failure mode identification and early warning triggering, and achieving seamless connection between each step.

[0042] Step 3: After completing sensor calibration and feedback data accumulation, based on failure aggregation measurement and historical fault database , using pattern recognition to identify potential failure signs or abnormal drift. If a high-risk event is found, an early warning signal is triggered and the operation and maintenance end is instructed to dispatch backup monitoring equipment; The step three includes the following: Step 301: Failure mode identification In step 301, the following data sources need to be comprehensively referenced: the actual measurement values collected after calibration , which is the key parameter vector of each sensor in the new Real-time observation output under action; Sensor calibration log , including parameter update content and update amplitude Size and other historical operation information; Current environment status: Continue to use the environment data defined in the first step , combined with the latest environmental sampling; Previous failure trend characteristics: first step coupling baseline indicators Based on the partial trajectory in the second step of the correction process, combined with the sensor abnormal data under extreme scenarios such as high altitude, extreme cold, and sandstorms recorded in the past operation cycle, a reference database is formed ; In order to uniformly measure the failure characteristics of the above data, the failure discrimination operator is introduced and failure aggregation metrics , define the following integral form to evaluate the time interval Are there any suspicious signs of failure in the internal sensor?

[0043] Where: is the actual measurement value at time α after calibration (obtained from the sensor output updated in the second step); The historical feature library independently maintained in step 301 contains typical data fragments under known failure modes (for example, signal frequency drift, abnormal output signal jitter, severe deviation of data curves, failure curves under extreme environments, etc.), which does not change dynamically with α. Provides an environmental reference at the corresponding moment to facilitate the operator Distinguish between fluctuations caused by the environment and failures of the device itself; α is the independent variable of the time integration or iteration process; It can be designed as a multi-dimensional comparison and similarity analysis function, such as testing frequency domain features and instantaneous amplitude jumps one by one, and then integrating logarithmic or exponential difference metrics; failure aggregation metrics The bigger, the There is a more obvious accumulation of failure signs within the time interval; When the failure aggregation metric Exceeds a pre-set failure threshold, wherein the failure threshold may be based on Based on historical experience, it is determined that there is a high probability of potential failure or performance degradation; When using, The comparison of typical failure modes in the data can more accurately identify potential failure signs in different time periods and under different extreme climate conditions, and in the failure aggregation measurement Introducing environmental data , so that the system can eliminate normal fluctuations caused by transient changes in external extreme environments and reduce the false alarm rate.

[0044] Step 302: Early warning triggering and backup plan scheduling If the failure aggregation metric Exceeding the set threshold It is considered as high risk and generates fault warning signal immediately. If the fluctuation is near, it is considered suspicious and will be continuously observed; Combined with sensor calibration log , environmental data The system uses the identification results of specific failure modes to comprehensively determine the warning level. For example, a common and quickly repairable phenomenon such as short-term signal attenuation of a high-altitude radar in a snowstorm environment can trigger a low-level warning, while a serious abnormality in the output of a key sensor can trigger a high-level warning. If an early warning is triggered, backup monitoring equipment is automatically called or multi-node redundancy mode is activated to minimize the risk of failure. For minor suspicious situations, data from other healthy sensors can be temporarily weighted to reduce the weight of the faulty device in the overall monitoring results. Failure identification results, early warning levels, and scheduling information are packaged together. When in use, it can make differentiated responses in a timely manner according to the severity of different faults, rather than just reporting errors or shutting down equipment in a one-size-fits-all manner. Through the intelligent scheduling of backup plans and redundant sensors, it can minimize blind spots in border monitoring and ensure the continuous and stable operation of the system. The early warning classification mechanism provides clear risk guidance for subsequent fusion comparison and fault handling, allowing the system to be targeted when processing multi-source information.

[0045] Introducing historical feature libraries in the fault identification phase Compared with multi-source environment Coupling discriminant operator , can conduct multi-dimensional matching analysis on subtle failure signs of sensors, rather than just staying in the simple single-channel fluctuation detection. This type of cumulative measurement, combined with adjustable thresholds Provide graded early warning and are not easily affected by transient anomalies or short-term external extreme environment interference.

[0046] The actual measured value of the sensor after the second step calibration , calibration log , environmental data And historical fault feature library Based on the failure aggregation metric To assess whether the sensor has potential signs of failure and issue early warnings in grades when necessary; at the same time, backup sensors or redundant strategies can be called for emergency response according to different severities, providing a reliable basis for further eliminating abnormal information and improving overall monitoring accuracy; by introducing multi-dimensional feature matching and cumulative measurement methods in failure identification, the system's fault detection accuracy and response speed in harsh border defense environments can be significantly improved, achieving early detection and early handling of sensor attenuation and failures.

[0047] Step 4: When receiving warning information and identifying suspicious or failed sensors, multi-source data synchronization and space-time mapping are used to integrate different modal observations into a unified reference, and the multi-source feature fusion operator is used to generate the corresponding data. Consistency across sensors Detect data deviations and adjust the proportion of faulty sensors in decision-making in real time and activate backup monitoring equipment; The step 4 includes the following contents: Step 401: Multi-source data synchronization and space-time mapping After receiving the suspicious or fault warning information provided in the third step, it is necessary to obtain and synchronize the real-time outputs from adjacent redundant sensors for cross-device comparison. To avoid mismatching of the same physical target due to different sampling frequencies, different geographical locations or network delays, a unified spatiotemporal mapping process is defined, and the output streams of several sensors are recorded as (which indicates different sensor identifiers, such as infrared, visible light, sonar, radar, etc.) and within a certain time window Aggregate into a unified coordinate system and time axis to form a fused input set , specifically, custom multi-dimensional interpolation and timing calibration operators can be used , defined as follows:

[0048] Where: Indicates that the sensor is All sampling data of the interval; The synchronization window width is used to ensure that fast-moving targets or sudden changes can also be observed within the same time window; It is a multi-source interpolation and space-time calibration function that supports operations such as geographic coordinate alignment (such as GPS coordinate projection to a unified geographic reference system) and timestamp correction; Finally, the data of each sensor on the same target or the same area under the unified spatiotemporal reference are summarized. Observations within the time period will be used for feature matching and deviation identification based on this; When used, it solves the problems of asynchronous multi-sensor sampling and inconsistent coordinate systems, maintains continuous observation data on suspicious targets or areas, and avoids missing sudden or cross-time abnormal events; it lays a unified reference coordinate for subsequent multi-source feature analysis, making it easier to identify the degree of deviation or consistency between different sensors.

[0049] Step 402: Target feature matching and abnormal deviation determination Based on the fusion input set , it is necessary to identify the key features of the same target (or the same area) in multi-source observation data and compare the consistency of sensor output to determine whether there is an abnormal large deviation. To further quantify this process, a dedicated multi-source feature fusion operator is introduced and consistent metrics across sensors ; Multi-source feature fusion operator It can integrate the perception modalities of different sensors (such as infrared intensity, visible light image characteristics, sonar echo morphology, radar distance-speed information, etc.) to generate a feature vector for the target ; By comparing the sensors' observations of the same target at the same time The predicted feature vector is used to calculate the consistency across sensors :

[0050] Where: Indicates that at time α, the sensor After multi-source feature fusion operator The target feature vector extracted after processing; It is a feature vector matching operator, which can be based on logarithmic difference, phase spectrum comparison or high-dimensional similarity measurement; Cross-sensor consistency The larger the value, the higher the feature matching degree between sensors and the better the data consistency. If the value is significantly lower, it indicates that the output of some sensors is significantly different from that of most devices, and further evaluation of their effectiveness is required; In completing cross-sensor consistency After calculation, the fault information such as whether to warn the fault level is determined, combined with the consistency across sensors If there is a significant abnormal deviation that is consistent with the fault symptoms, the corresponding sensor will be listed as a high-suspect fault device; When used, cross-verification is performed on the same target or scene at the multi-source perception level, greatly reducing the impact of individual sensor failures or data drift on the overall judgment of the system; cross-sensor consistency It can provide a quantitative basis for the next step of weight allocation, especially in the determination of abnormal deviations, and has a strong ability to distinguish. It is compatible with the data features of multi-modal sensors and can be used through multi-source feature fusion operators. The feature expressions of different perceptual dimensions are integrated to make recognition more robust and generalizable.

[0051] Introducing logarithmic or phase spectrum characteristic difference measurement (by Implementation), avoiding the simple reliance on mean or standard deviation for consistency comparison, and is more suitable for processing high-dimensional and multimodal data. The pairwise comparison results of multiple pairs of sensors are integrated into the cross-sensor consistency. This comprehensive indicator helps to quickly identify the degree of outliers between some sensors and group observations.

[0052] Step 403: Data weight adjustment and intelligent decision making Based on cross-sensor consistency The weight of each sensor in the overall monitoring decision is dynamically adjusted with fault information, and the weight vector is introduced To uniformly manage the contribution of each sensor in the decision-making layer at the current moment and activate backup monitoring equipment or execute maintenance mode when necessary, the following adaptive update mechanism is defined:

[0053] Where: Indicates that the sensor is in the next decision cycle The weight in is the difference between the sensor and the overall average consistency, which can be simply defined as ,in For sensor-based Sub-consistency obtained after comparison with other sensors; It is a regulating constant used to control the speed and amplitude of weight updates; If the difference of a sensor If the observed results are continuously greater than expected, it means that the observed results have long deviated from the group consensus. Will decay rapidly until it approaches zero; It is composed of the weight values of all sensors, namely , which means that when there is sensors, each sensor i corresponds to a weight , and finally form a vector by concatenating all weights .

[0054] If a sensor is confirmed to be a highly suspected faulty device, its weight can be reset to a minimum or completely eliminated, and a backup monitoring device can be activated according to the system's operation and maintenance strategy. After the weight adjustment is completed, the final fusion results are output to the back-end monitoring platform, serving as the primary basis for target identification, threat assessment, and command and dispatch. Furthermore, if a sensor is found to be completely faulty or seriously faulty during the fusion and weight update process, this information will be transmitted back to the system operation and maintenance level, triggering maintenance mode or hardware replacement.

[0055] The formula describes:

[0056] Not directly to , but for each component of the vector Calculate; once all Applying the update rules one by one, we get the new From a mathematical point of view, it can be written as:

[0057] Where ⊙ represents element-wise multiplication, is a vector of all 1s, For all Vector composed of.

[0058] When in use, the dynamic adjustment of weights is used to gradually reduce the impact of unreliable or faulty sensors on the overall judgment of the system, thereby maintaining a high level of monitoring accuracy. Combined with fault warning information, the system can flexibly compress the weight of suspicious sensors at the fusion level or enable backup monitoring equipment to achieve fault isolation. By outputting the final fusion decision on the monitoring platform, it ensures that commanders can timely understand the monitoring accuracy and risk level, and achieve continuous and efficient protection of key border areas.

[0059] Consistency across sensors Difference from individual sensors Combined with the above, adaptive weight allocation is achieved through an exponential update strategy, which makes the downgrading of abnormal sensors faster and smoother. It is compatible with a variety of fault scenarios and sensor types, and can be associated with the level of fault warning in the third step to form a closed-loop safety mechanism of warning-calibration-fusion-re-warning.

[0060] In sub-step 402 of this solution, the multi-source feature fusion operator It is necessary to unify the multimodal data from infrared, visible light, sonar, radar and other sensors into a relatively consistent feature space in order to calculate the consistency across sensors. To achieve this goal, the following "typical conversion methods" or corresponding relationship examples can be used: Geometric space corresponds to: Visible light image and infrared image: If the two imaging devices are installed on the same platform (or in approximately the same position), their intrinsic and extrinsic parameters can be calibrated, and then the infrared image can be registered to the visible light image plane using homogeneous coordinate transformation, thereby achieving a one-to-one correspondence between pixels in the same field of view. Radar range-velocity information: When fused with visible light / infrared data, the radar installation location (GPS coordinates) and observation direction are generally used to convert the "target polar coordinates" (range + azimuth) into "projected coordinates" that match the image pixel coordinates. Common methods include geodetic coordinate conversion or spatial mapping based on depth estimation. Sonar data: For underwater or semi-underwater scenes, the polar coordinate representation of the sonar echo intensity can be mapped to a two-dimensional grid corresponding to the geographic coordinates or surface image coordinates, and corrected according to the installation reference plane and tilt angle. After mapping each modal data to a unified or comparable coordinate or feature space, the multi-source feature fusion operator Key attributes (such as target shape, spatial position, energy intensity, etc.) can be aggregated to generate a unified feature vector , and on this basis with other sensors Do a similarity comparison.

[0061] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0062] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0063] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only for some logical functions. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0064] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0065] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An integrated intelligent monitoring method for key border defense areas, characterized by: include, When the monitoring module captures the working status data and environmental information of multiple types of sensors, it performs data preprocessing on the vector combination of environmental indicators and generates an initial working baseline that can characterize the coupling status of sensors and the environment; When the initial working baseline is completed and the deviation between the actual output of the sensor and the ideal working condition is detected, a dynamic calibration model is established to adaptively correct the gain, threshold and sensitivity of the sensor by calculating the calibration coefficient and updating the parameter vector; Based on failure aggregation metrics and a historical fault database, pattern recognition is used to identify potential failure signs or abnormal drift. If a high-risk event is detected, an early warning signal is triggered and the operation and maintenance department is instructed to dispatch backup monitoring equipment. Multi-source data synchronization and space-time mapping are used to integrate different modal observations into a unified reference. The multi-source feature fusion operator and cross-sensor consistency are used to check data deviations, and the proportion of faulty sensors in decision-making is adjusted in real time, and backup monitoring equipment is activated.

2. The integrated intelligent monitoring method for key border defense areas according to claim 1 is characterized by: The deployed monitoring module continuously collects external environmental data for preprocessing and cleaning. It also collects working status data at a given moment for various on-site sensors. If a certain indicator in the working status data is more inconsistent with the environmental conditions than expected, it is judged as a suspected sensor failure or interference information, and the suspicious part is marked and removed.

3. The integrated intelligent monitoring method for key border defense areas according to claim 2 is characterized by: Environmental data and working status data are jointly analyzed to establish the initial working baseline of each sensor under different climatic conditions. A multi-scale analysis method is introduced to comprehensively measure the degree of coupling between the environment and the sensor, and a coupling baseline indicator is defined to measure the coupling difference between the environment and the sensor status within a certain time window.

4. The integrated intelligent monitoring method for key border defense areas according to claim 3 is characterized by: Read environmental data and sensor working status data, as well as the coupling baseline indicators of the initial working baseline measurement; calculate the calibration coefficient and write it into the sensor by comparing the ideal output of the sensor in a standard environment with the actual output corresponding to the current environment; construct a calibration deviation measurement to measure the degree of deviation between the ideal output and the actual output.

5. The integrated intelligent monitoring method for key border defense areas according to claim 4 is characterized in that: After obtaining the calibration deviation measurement, the calibration coefficient is calculated and written into the sensor or upper system. Based on the calibration coefficient, the core parameters of the sensor are dynamically adjusted in real time or periodically. Define the parameter vector and perform adaptive parameter updates. After the parameter vector is updated, it is written back to the corresponding sensor or configuration file in real time and compared with the latest calibration deviation measurement in the next parameter adjustment cycle.

6. The integrated intelligent monitoring method for key border defense areas according to claim 5 is characterized by: Calibrate the real-time observation output of each sensor under the action of the new key parameter vector; Obtain sensor calibration logs, including parameter update content, update amplitude, environmental data, and past fault trend characteristics, and compile them into a reference database; Failure discrimination operators and failure aggregation metrics are introduced. When the failure aggregation metric exceeds a preset failure threshold, it is determined that there is a potential fault or performance degradation.

7. The integrated intelligent monitoring method for key border defense areas according to claim 6 is characterized by: If the failure aggregation metric exceeds the set threshold, it is considered high risk and a fault warning signal is immediately generated. If it fluctuates around the set threshold, it is considered suspicious and is continuously observed. Combine sensor calibration logs, environmental data, and identification of specific failure modes to comprehensively determine the alarm level; If an early warning is triggered, the backup monitoring device is automatically called or the multi-node redundancy mode is started, or the weight of the faulty device in the overall monitoring results is reduced.

8. The integrated intelligent monitoring method for key border defense areas according to claim 7 is characterized in that: After receiving suspicious or fault warning information, we obtain and synchronize the real-time outputs from adjacent redundant sensors, define a unified spatiotemporal mapping process, and record the output streams of several sensors as , and in a certain time window Aggregate into a unified coordinate system and time axis to form a fused input set.

9. The integrated intelligent monitoring method for key border defense areas according to claim 8, characterized in that: A multi-source feature fusion operator and a cross-sensor consistency metric are introduced. The multi-source feature fusion operator integrates the perception modalities of different sensors to generate a feature vector for the target. The cross-sensor consistency is calculated by comparing the feature vectors predicted by each sensor for the same target at the same time. Fault information is determined based on whether a fault warning is issued, and preliminary screening is performed based on the consistency across sensors. If there is a significant abnormal deviation that matches the fault symptoms, the corresponding sensor will be listed as a high-suspect fault device.

10. The integrated intelligent monitoring method for key border defense areas according to claim 9, characterized in that: Based on cross-sensor consistency and fault information, the weight of each sensor in the overall monitoring decision is dynamically adjusted; and backup monitoring equipment is enabled according to the operation and maintenance strategy. If a sensor is found to have completely failed or has a serious fault, this information is transmitted back to the operation and maintenance level, triggering maintenance mode or hardware replacement.

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