Perimeter alarm false alarm filtering control method and system

Abstract

The invention provides a perimeter false alarm filtering control method and system, and relates to the technical field of perimeter security, after original sampling data corresponding to a plurality of continuous sampling points of an area perimeter are acquired, two times of judgment are carried out to screen out sampling data with an instantaneous impact characteristic and a tension continuous jump characteristic, and the sampling data are subjected to false alarm filtering control according to the instantaneous impact characteristic and the tension continuous jump characteristic. After screening is completed, an actual tension attenuation data sequence in a preset time period is collected, the actual tension attenuation data sequence and a theoretical tension attenuation data sequence are subjected to coincidence comparison, and corresponding operation is executed according to a coincidence comparison result. Therefore, the method has the advantages that tension change and intrusion behaviors caused by the thermodynamic effect can be effectively distinguished, and false alarms caused by non-invasive factors such as environment temperature difference are remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of perimeter security, and in particular to a perimeter alarm false alarm filtering control method and system. Background Art

[0002] Electronic tension fencing is widely used as a key perimeter alarm device in the security systems of critical infrastructure. This device detects intrusion by monitoring changes in the tension of steel cables laid along the perimeter. When the cables are climbed, sheared, or pulled, the tension sensor detects the abnormal signal change and triggers an alarm. Existing tension alarm systems typically have a certain level of signal processing capability, capable of filtering out overall tension drift caused by factors such as slow changes in ambient temperature or a constant breeze, thereby reducing false alarm rates under normal weather conditions. However, in some specific applications, conventional filtering mechanisms still face challenges, especially when dealing with complex signals caused by non-invasive human activities.

[0003] For example, at a critical infrastructure facility located near a large reservoir, a highly sensitive electronic tension fence forms the core of its security system. Stretching for several kilometers along the plant's perimeter, the system consists of multiple parallel, taut steel cables, tension sensors mounted on posts, and a central signal processing unit. Under normal alert conditions, each cable maintains a precise reference tension. To ensure the system's long-term reliability, the plant regularly uses mobile high-pressure water cannons to clean the fence posts and cables. During planned maintenance, operators pre-program the work zone into "maintenance mode," temporarily ignoring specific signals and preventing false alarms. However, a unique and unexpected situation exposed the system's limitations. Imagine one autumn afternoon when a tanker truck carrying chemicals leaked a minor spill within the plant, splashing a small amount of liquid onto a section of the fence. For safety reasons, a maintenance worker must immediately conduct an emergency cleanup of the leaked area. Without time to request maintenance mode from the security center, he simply activated a nearby high-pressure water cannon. The water plant is unique in that its clean water is drawn directly from the adjacent reservoir. In autumn, the surface temperature of the steel wire rope, exposed to sunlight during the day, can still remain above 20°C, while the water temperature in the reservoir has dropped to around 8°C, creating a significant temperature difference of over 10°C. When this high-pressure, low-temperature water stream impacted the fence's wire rope, which was already in standard alert mode, the central signal processing unit received a highly complex set of signals. First, the immense kinetic energy of the water stream generated violent, high-frequency transient vibrations, with signal characteristics similar to those caused by a tool striking a fence. Second, and more crucially, the water stream, at a temperature of only 8°C, sprayed onto the wire rope, which was already over 20°C, causing a dramatic localized temperature drop. This instantaneous "cold contraction" effect of the metal triggered a sharp contraction in the impacted section of the wire rope, resulting in a sudden, substantial increase in tension that remained elevated for several seconds. The resulting signal pattern was nearly identical to that of an intruder using a tool such as a jack to continuously pull on the wire rope.

[0004] Existing alarm systems, when analyzing this combination of "impact followed by sustained pressure," would strongly associate it with a highly destructive, violent intrusion (such as the use of heavy shears), as this intrusion also produces the signal characteristics of impact and sustained pressure. The system was unable to distinguish the coincidental combination of "kinetic shock" and "thermal shock" caused by an unplanned cleaning operation from a true violent intrusion, ultimately resulting in a false alarm of the highest level, wasting security resources and unnecessary tension.

[0005] Therefore, providing a perimeter alarm false alarm filtering control method and system is a problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide a perimeter alarm false alarm filtering control method, which has the advantage of being able to effectively distinguish tension changes caused by thermodynamic effects from intrusion behavior, and significantly reduce false alarms caused by non-invasive factors such as ambient temperature differences.

[0007] Based on the above objectives, the technical solutions provided by the present invention are as follows: A perimeter alarm false alarm filtering control method is applied to an electronic tension alarm system to detect intrusion scenarios, comprising the following steps: Obtaining original sampling data corresponding to multiple consecutive sampling points around the perimeter of the suspected area; First, determine whether the tension change value between two adjacent continuous sampling points is greater than the tension change threshold. If the first determination result is yes, then secondly determine whether the raw sampling data after moving average processing jumps above the high tension alarm threshold within a preset time period and remains at a high level. If the second determination result is yes, it is defined as a pending high-risk event; Acquire an actual tension attenuation data sequence of the original sampled data after moving average processing in the preset time period; thirdly, determining whether the actual tension decay data sequence is consistent with a preset theoretical tension decay sequence; According to the third judgment result, a corresponding false alarm filtering operation or an alarm operation is performed.

[0008] Through the above scheme, it is possible to effectively distinguish between tension changes caused by thermodynamic effects and intrusion behaviors, and significantly reduce false alarms caused by non-invasive factors such as ambient temperature differences.

[0009] Preferably, the first step of determining whether the tension change value between two adjacent continuous sampling points is greater than a tension change threshold comprises the following steps if the first determination result is yes: Calculate the difference between the original sampling data corresponding to two adjacent continuous sampling points respectively; Within a first preset window length, first determining whether a difference between the original sampling data corresponding to two adjacent continuous sampling points is greater than the tension change threshold; If the first judgment result is yes, it is defined that a rapid impact feature exists.

[0010] Preferably, the second step of judging whether the raw sampled data after the moving average processing jumps above the high tension alarm threshold within a preset time period and remains at a high level, and if the second judgment result is yes, it is defined as a high-risk event to be confirmed, including the following steps: Performing moving average processing on the original sampled data according to a second preset window length to obtain a quasi-static tension value; within a preset time period, secondly determining whether the quasi-static tension value jumps above the high tension alarm threshold and remains high; If the second judgment result is yes, it is defined that there is a tension continuous jump feature; When the rapid impact feature and the tension continuous jump feature exist at the same time, it is defined as the high-risk event to be confirmed.

[0011] Through the above scheme, a two-time judgment mechanism is further introduced to filter out data that meets the conditions from the original sampling data, providing a basis for subsequent signal judgment and execution operations.

[0012] Preferably, the third step of determining whether the actual tension decay data sequence is consistent with a preset theoretical tension decay sequence comprises the following steps: Traversing each data point of the actual tension attenuation data sequence, and calculating the square of the difference between the actual tension attenuation data corresponding to each data point and the theoretical tension attenuation data; Accumulate the square of the difference of each data point to obtain the residual sum of squares of the tension decay data; thirdly, determining whether the residual sum of squares of the tension attenuation data is less than a preset residual sum of squares threshold; The theoretical tension decay data of each data point is obtained according to a preset theoretical tension decay sequence.

[0013] Through the above scheme, we can further determine whether the actual tension decay data sequence is consistent with the preset theoretical tension decay sequence in the thermodynamic model. Based on the matching results, we can identify whether the event conforms to the physical recovery law of thermal shock and improve the recognition accuracy.

[0014] Preferably, performing the corresponding false alarm filtering operation or alarm operation according to the third judgment result includes the following steps: If the third judgment result is yes, the actual tension decay data sequence is defined to be consistent with the preset theoretical tension decay sequence, the to-be-confirmed high-risk event is filed as an identified interference event, and a false alarm filtering operation is performed; If the third judgment result is no, it is defined that the actual tension attenuation data sequence does not match the preset theoretical tension attenuation sequence, the high-risk event to be confirmed is filed as a real intrusion event, and an alarm operation is executed.

[0015] Through the above solution, it is further determined whether the event to be confirmed is an identified interference event or a real intrusion event based on the matching results, and corresponding operations are performed, thereby improving the accuracy of event analysis.

[0016] Preferably, after defining that the actual tension decay data sequence does not match the preset theoretical tension decay sequence if the third judgment result is no, the method further includes the following steps: Fourthly, determining whether the actual tension attenuation data sequence has a macro-attenuation trend. If the fourth determination result is yes, fifthly determining whether the macro-attenuation process is stable. If the fifth determination result is yes, filing the to-be-confirmed high-risk event as an identified interference event and performing a false alarm filtering operation. If either the fourth judgment result or the fifth judgment result is negative, the high-risk event to be confirmed will be filed as a real intrusion event, and an alarm operation will be executed.

[0017] Preferably, the fourth step of determining whether the actual tension attenuation data sequence has a macroscopic attenuation trend comprises the following steps if the fourth determination result is yes: Dividing all data points of the actual tension decay data sequence into a first part of data points and a second part of data points; respectively obtaining a first average value of actual tension decay data corresponding to the first portion of data points and a second average value of actual tension decay data corresponding to the second portion of data points; Fourth, determining whether a difference between an average value of the first actual tension decay data and an average value of the second actual tension decay data is greater than a preset average value difference threshold; If the fourth judgment result is yes, it is defined that the actual tension decay data sequence has a macro decay trend.

[0018] Preferably, the fifth step of determining whether the macroscopic attenuation process is stable, if the fifth determination result is yes, then filing the to-be-confirmed high-risk event as an identified interference event and performing a false alarm filtering operation, comprises the following steps: Obtaining an average of absolute values ​​of differences between actual tension attenuation data corresponding to two adjacent continuous sampling points in all data points of the actual tension attenuation data sequence; Fifth, determining whether the average value of the absolute value of the difference falls within a preset stable range; If the result of the fifth judgment is yes, the macroscopic attenuation process is defined as stationary; When the actual tension attenuation data sequence has a macro-attenuation trend and the macro-attenuation process is stable, the high-risk event to be confirmed is filed as an identified interference event, and a false alarm filtering operation is performed.

[0019] Through the above steps, the macro-attenuation trend and macro-attenuation stability are introduced to further determine whether it is a real intrusion signal or a composite false signal, thereby effectively solving the event recognition accuracy of this special scenario where the two effects of "thermal shock shrinkage" and "surface sticky rust inhibitor layer being peeled off and emulsified" caused by water flow impact are superimposed.

[0020] Preferably, after obtaining the actual tension attenuation data sequence of the original sampled data after moving average processing in the preset time period, the method further includes the following steps: Traversing each data point of the actual tension decay data sequence, calculating the second-order derivative of the actual tension decay data corresponding to each data point, and obtaining a tension change acceleration sequence; Obtaining a variance value of the tension change acceleration sequence; Sixth, determining whether the variance value of the tension change acceleration sequence is less than a preset smoothing threshold; According to the sixth judgment result, a corresponding false alarm filtering operation or an alarm operation is performed.

[0021] Through the above steps, the smoothing threshold is introduced, which can identify the signal characteristics more precisely and distinguish non-invasive behaviors such as crystallization process from invasive behaviors.

[0022] A perimeter alarm false alarm filtering control system, comprising: A data acquisition module is used to obtain original sampling data corresponding to multiple continuous sampling points around the perimeter of the suspected area; A pending high-risk event definition module is configured to first determine whether a tension change value between two adjacent continuous sampling points is greater than a tension change threshold. If the first determination result is yes, then secondly determine whether the raw sampling data after moving average processing jumps above a high-tension alarm threshold within a preset time period and remains at a high level. If the second determination result is yes, the event is defined as a pending high-risk event. An actual tension attenuation data sequence acquisition module, configured to acquire an actual tension attenuation data sequence of the original sampled data after moving average processing in the preset time period; a sequence matching judgment module, configured to thirdly judge whether the actual tension decay data sequence matches a preset theoretical tension decay sequence; The execution module is used to execute a corresponding false alarm filtering operation or an alarm operation according to the third judgment result.

[0023] The perimeter alarm false alarm filtering control method provided by the present invention effectively distinguishes non-invasive factors such as thermodynamic effects from real intrusion behaviors by analyzing the attenuation characteristics of the composite tension signal and comparing it with the theoretical model. It has the advantage of being able to effectively distinguish tension changes caused by thermodynamic effects from intrusion behaviors, and significantly reducing false alarms caused by non-invasive factors such as ambient temperature differences.

[0024] The present invention also provides a perimeter alarm false alarm filtering control system, which solves the same technical problem as this method, belongs to the same technical concept, and should have the same beneficial effects, so it will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0026] Figure 1 A flowchart of a perimeter alarm false alarm filtering control method provided by an embodiment of the present invention; Figure 2 A flowchart of the first judgment in step S2 provided in an embodiment of the present invention; Figure 3 A flowchart of the second determination in step S2 provided in an embodiment of the present invention; Figure 4 A flowchart of step S4 provided in an embodiment of the present invention; Figure 5 A flowchart for a specific scenario of rust inhibitor emulsification provided by an embodiment of the present invention; Figure 6 A flowchart of determining the existence of a macroscopic attenuation trend according to an embodiment of the present invention; Figure 7 A flow chart of determining the smoothness of a macroscopic attenuation process according to an embodiment of the present invention; Figure 8 A flow chart of the invention embodiment for the heat absorption of surface attachment dissolution and subsequent solution evaporation to change the heat exchange mode; Figure 9 This is a structural diagram of a perimeter alarm false alarm filtering control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] 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 them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0028] The embodiments of the present invention are written in a progressive manner.

[0029] When monitoring perimeter tension changes to detect intrusions, traditional existing electronic tension fences typically have the ability to filter out overall tension drifts caused by slow changes in ambient temperature or continuous breezes. However, existing filtering mechanisms have limitations when dealing with complex signals caused by non-invasive human activities. Specifically, how to effectively distinguish between a real violent intrusion with both impact and sustained pressurization characteristics, and a composite false signal formed by the superposition of two physical effects, kinetic impact vibration and local thermal shock contraction, caused by an unplanned high-pressure, low-temperature fluid impacting a high-temperature steel wire rope, is a technical problem faced by current technology. This lack of differentiation affects the signal processing accuracy and false alarm control performance of the perimeter alarm system.

[0030] For example, consider a water treatment plant that uses a highly sensitive electronic tension fence as the core of its security system. This system consists of steel cables arranged along the perimeter, tension sensors, and a central signal processing unit. Under normal alert conditions, the cables maintain a precise tension reference value. However, during an unplanned emergency cleaning operation, a high-pressure, low-temperature water jet is applied to the high-temperature, standard-alert cables. The central signal processing unit receives a composite signal. The kinetic energy of the water jet generates a transient vibration signal, while the low-temperature water jet sprays onto the high-temperature cables, causing a sharp local temperature drop and a sudden contraction of the cables. The tension value then increases dramatically and remains high for several seconds. This combination of initial impact followed by sustained pressure is highly similar to the signal characteristics of a violent intrusion in existing systems. Existing systems are unable to distinguish this composite false signal, resulting from the combined kinetic and thermal shocks, from a genuine violent intrusion.

[0031] If these issues are not addressed, the perimeter alarm system will be unable to accurately identify the signal source, potentially misinterpreting composite signals generated by non-invasive activities as actual intrusions, triggering the highest level of alarm. This wastes security resources, adds unnecessary emergency response burdens, and potentially reduces the perimeter alarm system's credibility in the event of a real intrusion. These false alarms are more frequent under certain environmental conditions (such as high-pressure fluid impacts caused by temperature differences), seriously impacting the system's practicality and reliability.

[0032] When faced with the above problems, the first thing that comes to mind for this application is to distinguish the source of the signal by analyzing the instantaneous impact amplitude or the peak value of continuous pressure of the signal. However, real violent intrusions and specific non-invasive operations may produce similar impact amplitudes and continuous pressure peak values, which cannot be effectively distinguished by this method. In this regard, the present application further considers that although the instantaneous impact and peak value of the signal are similar, there may be differences in the attenuation process of the signal after reaching the peak value. The physical mechanism of recovery or attenuation of the tension change caused by thermodynamic effects is different from that of the tension change caused by mechanical external forces. The contraction recovery process caused by thermodynamic effects should follow specific physical laws. Therefore, this application proposes an idea to determine the true source of the signal by analyzing the attenuation characteristics of the composite tension signal after reaching the peak value and comparing it with the theoretical model that characterizes the specific physical effect.

[0033] like Figure 1 As shown, a perimeter alarm false alarm filtering control method is applied to an electronic tension alarm system to detect intrusion scenarios, including the following steps: S1. Obtaining raw sampling data corresponding to multiple consecutive sampling points around the suspected area; S2. First, determine whether the tension change between two consecutive sampling points is greater than the tension change threshold. If the first determination is positive, then determine whether the raw sampled data, after moving average processing, jumps above the high tension alarm threshold within a preset time period and remains at a high level. If the second determination is positive, the event is defined as a pending high-risk event. S3 obtains the actual tension attenuation data sequence of the original sampled data after moving average processing in a preset time period; S4. Third, determine whether the actual tension decay data sequence matches the preset theoretical tension decay sequence; S5. Execute corresponding false alarm filtering operation or alarm operation according to the third judgment result.

[0034] The technical problem to be solved by this application is how to effectively distinguish a real violent intrusion behavior with both impact and continuous pressurization characteristics from a composite false signal formed by the superposition of two physical effects, "kinetic impact vibration" and "local thermal shock contraction", caused by an unplanned high-pressure, low-temperature fluid impacting a high-temperature steel wire rope.

[0035] The present application proposes a perimeter alarm false alarm filtering control method. After collecting and acquiring the original sampling data corresponding to multiple continuous sampling points on the perimeter of the area, two judgments are performed to screen out the sampling data with both instantaneous impact characteristics and continuous tension jump characteristics. After the screening is completed, the actual tension attenuation data sequence within a preset time period is collected. The actual tension attenuation data sequence is matched and compared with the theoretical tension attenuation data sequence, and corresponding operations are performed according to the matching comparison results.

[0036] Among them, sampling data with both instantaneous impact characteristics and continuous tension jump characteristics refers to monitoring the tension changes of the tension rope through tension sensors and other equipment. When the detected signal contains both short-term, high-amplitude fluctuations (instantaneous impact characteristics) and the subsequent phenomenon of a significant increase in tension value and maintenance of a high level for a period of time (continuous tension jump characteristics), the signal is identified and captured. The purpose is to identify abnormal tension events that may be caused by intrusion or specific environmental factors. Collecting the actual tension attenuation data sequence within a preset time period means that after the composite tension signal is captured and its tension reaches its highest point, continuous data points of the tension value changing with time are recorded to form a data sequence reflecting the process of gradual tension reduction. The purpose is to obtain the dynamic change characteristics after the signal peak and provide a basis for subsequent signal source determination; matching and comparing the actual tension attenuation data sequence with the theoretical tension attenuation data sequence means comparing the actual measured tension attenuation data curve with a theoretical tension change curve established based on physical principles (such as the temperature-tension relationship of metal materials, heat conduction rate, etc.), which describes the tension rope shrinking after cooling and recovering to its original state as the temperature rises, for mathematical similarity. The purpose is to quantify the degree of match between the measured attenuation process and the thermodynamic recovery process. Performing corresponding operations based on the matching results means judging, based on the degree of matching between the measured data and the theoretical model, whether the main cause of the composite tension signal is the tension change caused by the thermodynamic effect or the tension change caused by other reasons (such as physical pulling). The purpose is to distinguish signals generated by different physical mechanisms. When the main cause of the composite tension signal is the tension change caused by the thermodynamic effect, the conventional intrusion alarm is not triggered, or the event is marked as a non-intrusion event. The tension change caused by other reasons (such as physical pulling) will trigger an alarm. The purpose is to avoid false alarms caused by thermodynamic effects.

[0037] The core innovation of this application lies in that by collecting the measured tension attenuation data after the peak of the composite tension signal and comparing it with a preset theoretical tension recovery model, the main cause of the composite tension signal is determined based on the comparison results, thereby achieving the effect of effectively distinguishing false alarms caused by thermodynamic effects from real intrusion signals.

[0038] In some preferred embodiments, the tension signal is captured by a piezoelectric sensor or strain gauge sensor installed on the tension cable. After amplification, filtering, and digitization, the signal is analyzed by an embedded processor. When a rapid rise in the signal amplitude is detected, accompanied by sustained high tension, the processor records the sequence of tension values ​​after the signal reaches its peak. The processor internally stores a theoretical tension recovery model based on parameters such as the tension cable material properties, linear expansion coefficient, and thermal conductivity coefficient. The model can be a mathematical function or a lookup table. The processor performs least squares fitting or correlation analysis on the collected measured tension attenuation data points and the theoretical model curve to calculate a goodness of fit index. For example, if the goodness of fit exceeds a preset threshold (such as a correlation coefficient greater than 0.9), the processor determines that the signal source is a thermodynamic effect and sends a filtering instruction to the alarm control unit to prevent the generation of an alarm event.

[0039] This technical solution effectively identifies and filters out false alarms caused by complex tension signals due to non-invasive thermodynamic effects (e.g., high-pressure, low-temperature water impacting a high-temperature tension cable). By analyzing the signal's post-peak attenuation characteristics and comparing them with theoretical models of thermodynamic recovery, this method provides a basis for distinguishing true intrusion signals from false ones. This improves the perimeter alarm system's ability to mitigate false alarms, ensuring its reliability and accuracy, particularly in scenarios with specific environmental interference factors.

[0040] like Figure 2 As shown, preferably, the first step is to determine whether the tension change value between two adjacent continuous sampling points is greater than the tension change threshold. If the first determination result is yes, the following steps are included: A1. Calculate the difference between the original sample data corresponding to two adjacent continuous sampling points; A2. Within the first preset window length, the first determination is whether the difference between the original sampled data corresponding to two adjacent consecutive sampling points is greater than the tension change threshold; A3. If the first judgment result is yes, it is defined that a rapid impact feature exists.

[0041] like Figure 3 As shown, preferably, the second judgment is whether the raw sampled data after the moving average processing jumps above the high tension alarm threshold within a preset time period and continues to remain high. If the second judgment result is yes, it is defined as a high-risk event to be confirmed, including the following steps: B1. Perform moving average processing on the original sampled data according to the second preset window length to obtain the quasi-static tension value; B2. Within a preset time period, the second judgment is whether the quasi-static tension value jumps above the high tension alarm threshold and remains high; B3. If the second judgment result is yes, then the definition is that there is a continuous tension jump feature; B4. When both rapid impact characteristics and sustained tension surge characteristics are present, it is defined as a high-risk event to be confirmed.

[0042] Steps A1 to A3 and steps B1 to B3 of the present application are specific implementation details of step S2: they are: firstly judging whether the difference between the original sampling data corresponding to two adjacent continuous sampling points is greater than the tension change threshold within a first preset window length, defining the presence of a rapid impact feature, and preliminarily screening the sampling data that meets the first judgment condition from the original sampling data; secondly judging whether the quasi-static tension value jumps above the high tension alarm threshold and continues to remain high within a preset time period, defining the presence of a continuous tension jump feature, and again screening the sampling data that meets the second judgment condition from the preliminarily screened sampling data as sampling data with both instantaneous impact features and continuous tension jump features.

[0043] In some preferred embodiments, determining whether a rapid impact feature exists involves calculating the first-order difference between consecutive sampling points to obtain the rate of change of the signal. If the rate of change corresponds to a tension change of more than 50 Newtons within a 10-millisecond window, a rapid instantaneous impact component is determined to exist. Determining whether a sustained tension jump feature exists involves applying a 100-millisecond window-length moving average to the raw sampled data to obtain a smoothed quasi-static tension value. If, within 500 milliseconds after identifying the impact component, the quasi-static tension value jumps above the high-tension alarm threshold of 200 Newtons, a sustained tension jump feature is determined to exist. When both the rapid impact feature and the sustained tension jump feature exist, i.e., after selecting sampled data that simultaneously meets the above criteria, the event is defined as a pending high-risk event. At this point, the microcontroller does not immediately activate the alarm relay, but instead sets an internal flag, temporarily suspending the issuance of an external alarm command, and initiating the "thermal feature attenuation analysis program."

[0044] Through the above technical solution, it is possible to effectively filter out sampling data that has both instantaneous impact characteristics and continuous tension jump characteristics from the original sampling data, and define the event as a high-risk event to be confirmed, which provides a basis for the subsequent distinction between real intrusion signals and false signals, and further ensures the reliability and accuracy of executing corresponding false alarm filtering operations or alarm operations.

[0045] like Figure 4 As shown, preferably, the third step of determining whether the actual tension decay data sequence is consistent with the preset theoretical tension decay sequence comprises the following steps: C1. Traverse each data point in the actual tension decay data sequence and calculate the square of the difference between the actual tension decay data and the theoretical tension decay data corresponding to each data point; C2. Accumulate the square of the difference of each data point to obtain the residual sum of squares of the tension decay data; C3. The third judgment is whether the residual sum of squares of the tension decay data is less than the preset residual sum of squares threshold; The theoretical tension decay data of each data point is obtained according to a preset theoretical tension decay sequence.

[0046] Steps C1 to C3 of the present application are the specific implementation details of step S4: they are performed by traversing each data point in the actual tension attenuation data sequence, calculating the square of the difference between the actual tension attenuation data and the theoretical tension attenuation data of each data point, accumulating the square of the difference of each data point, obtaining the residual sum of squares of the tension attenuation data, and determining whether the residual sum of squares is less than a preset residual sum of squares threshold.

[0047] The actual tension decay data sequence refers to the microcontroller continuously collecting the quasi-static tension value at intervals of every 100 milliseconds for a total of 5 seconds, thereby obtaining a measured tension decay data sequence consisting of 50 data points and storing the sequence in the memory array of the microcontroller; the theoretical tension decay data refers to a mathematical model that describes the thermodynamic recovery process. This model can be specifically an exponential decay function: .in, For time The theoretical tension value at the moment, is the measured peak tension value, is the initial tension value before the event occurs, and the decay constant k is the key. The value is a specific value pre-calibrated through experiments based on the material properties and diameter of the fence wire rope and the heat transfer coefficient between it and low-temperature water (for example, 8 degrees Celsius). It directly reflects the heat exchange rate caused by significant temperature differences in scene-specific characteristics. In some preferred embodiments, the actual tension decay data sequence is traversed through 50 data points, and the square of the difference between each point and the theoretical model value at the same moment is calculated. These 50 squared differences are then accumulated. If the final residual sum of squares is less than a pre-set judgment threshold (for example, the threshold is set by calculating actual water gun impact test data), the measured data is considered to be highly consistent with the thermal shock physics model. If the residual sum of squares is greater than the threshold, for example, if the tension remains essentially unchanged after the peak value (due to continuous application of the crowbar) or if the tension suddenly drops to zero (due to the wire rope being cut), the measured data is determined to be inconsistent with the thermal shock physics model.

[0048] Through the above technical solution, the basis for judgment can be transferred from the vague "moment of event occurrence" to the clear "event recovery process", and the physical laws are used as the final judgment standard, which effectively improves the accuracy of judgment.

[0049] Preferably, according to the third judgment result, executing a corresponding false alarm filtering operation or an alarm operation includes the following steps: If the third judgment result is yes, the actual tension decay data sequence is defined to be consistent with the preset theoretical tension decay sequence, the high-risk event to be confirmed is filed as an identified interference event, and a false alarm filtering operation is performed; If the third judgment result is no, it is defined that the actual tension attenuation data sequence does not match the preset theoretical tension attenuation sequence, and the high-risk event to be confirmed is filed as a real intrusion event, and an alarm operation is executed.

[0050] The above steps are the specific implementation details of step S5: Steps C1 to C3 above determine whether the measured data matches the thermal shock physics model. If so, the event is determined to be a thermal shock contraction effect, a "false alarm filtering" operation is performed, the suspended alarm instruction is revoked, and the event is archived as an identified interference event. If the measured data does not match the thermal shock physics model, the event is determined to not conform to the physical recovery rules of thermal shock and is confirmed to be a real physical intrusion. At this time, the microcontroller immediately releases the suspended state, activates the alarm relay, and sends an alarm signal to the security center.

[0051] When sampled data exhibiting both instantaneous impact and a sustained increase in tension is screened, the system does not immediately identify an intrusion and trigger an alarm, as is done with traditional methods. Instead, it recognizes that the signal may be ambiguous, potentially due to shear or the impact of high-pressure, low-temperature water. Therefore, a brief observation and verification period is initiated, suspending the alarm decision while focusing on the signal's evolution after reaching its peak. The changes in tension are recorded, forming an actual decay curve. This recorded decay curve is then compared with a preset "theoretical standard thermal shock recovery curve" calculated based on the laws of heat conduction in physics to determine if they align. If the actual curve closely matches the theoretical standard curve, the event is determined to be due to natural "thermal recovery" from metal contraction, rather than a sustained external force. The event is then filtered out as a false alarm. Conversely, if the recorded tension remains high after its peak, fluctuates erratically, or suddenly disappears, completely inconsistent with the standard thermal recovery curve, it is considered a true intrusion and an alarm is immediately triggered.

[0052] The creative nature of the above steps in this solution lies in its deep exploration of information dimensions and strategic shift in judgment focus. Rather than acquiring additional information by adding new hardware such as temperature sensors, this approach deciphers a new, implicit dimension of physical processes from the existing, single tension sensor signal. Conventional alarm logic interprets the signal as direct evidence of intrusion, while this approach views the signal as a complete record of a physical process. It innovatively proposes that the "second half" of the signal—the recovery phase after the disturbance—contains information that is as important as, or even more important than, the "first half" of the disturbance. Rather than addressing the ambiguous question of "whether the impact signal resembles an intrusion," it cleverly circumvents this issue by verifying the clear physical question of "whether the signal recovery process adheres to thermodynamic laws." This shift in thinking, from "instantaneous morphology matching" to "verification of process laws," redefines the nature of the problem, fundamentally shifts the focus of signal processing, and is remarkably non-obvious.

[0053] This method can extremely effectively filter out the complex false signals generated in the specific scenario of "high-pressure, low-temperature fluid impact", solving the problem that existing technologies are unable to distinguish such signals from real violent intrusion signals, and greatly reducing the false alarm rate under such special operating conditions.

[0054] like Figure 5 As shown, preferably, if the third judgment result is no, then after defining that the actual tension decay data sequence does not match the preset theoretical tension decay sequence, the following steps are further included: D1. Fourth, determine whether the actual tension decay data sequence shows a macro-decay trend. If so, then determine whether the macro-decay process is stable. If so, file the pending high-risk event as an identified interference event and perform false alarm filtering. D2. If either the fourth judgment result or the fifth judgment result is negative, the high-risk event to be confirmed will be filed as a real intrusion event, and an alarm operation will be executed.

[0055] Steps D1 and D2 in this application are applied in a more complex deployment environment. This involves the electronic tension fence being installed not at an inland water plant, but rather for perimeter security at a nuclear power plant located in a subtropical coastal region. This region experiences high humidity year-round, and the air contains high concentrations of salt, which significantly corrodes metal components. To ensure the fence's long-term structural stability, its routine maintenance procedures include a special procedure: After regular water cleaning, maintenance personnel use specialized equipment to spray a thin layer of highly adhesive, oil-based rust inhibitor onto all steel cables. This rust inhibitor solidifies on the cables, forming a dense protective film that isolates the salt spray from direct contact with the metal. Imagine that after a summer typhoon, a large amount of windblown plant debris and silt accumulates on some sections of the fence. Upon observing this, a security patrol officer, believing the contaminant could affect the sensitivity of the tension sensor, decides to immediately clean it. Instead of entering maintenance mode as per standard procedure, he activates the high-pressure water cannon at a nearby fire hydrant. At that moment, the surface temperature of the steel wire rope, scorched by the afternoon sun, reached a staggering 50°C, while the emergency water in the fire hydrant was only 15°C, creating a significant temperature difference of 35°C. When this high-pressure, low-temperature water stream impacted the rust-inhibitor-coated steel wire rope, as expected, the drastic temperature difference caused the wire rope to "cold contract," resulting in a sudden and sharp increase in tension. This triggered the "pending high-risk event" judgment logic and initiated the collection of post-peak tension decay data. However, during the subsequent recovery phase, the kinetic energy and scouring action of the high-pressure water stream began to destroy the protective film of oil-based rust inhibitor on the steel wire rope surface. This viscous oil film was not instantly washed away, but gradually emulsified and peeled off by the water flow. This process resulted in the formation of a physically unstable emulsion layer on the steel wire rope surface, composed of a mixture of water, oil, and air microbubbles. This emulsion layer acted as an unintended "insulator," significantly reducing the efficiency of heat exchange between the steel wire rope and the surrounding air. The process by which the wire rope absorbs ambient heat to restore its temperature no longer follows the smooth, predictable exponential decay characteristic of a clean metal surface in air. The recovery process becomes slow and irregular due to the dynamic changes in the latex layer (partially washed away, while others remain attached). This slow, fluctuating, non-exponential decay data was compared with a stored theoretical recovery model calibrated based on the physical conditions of a "clean wire rope." Due to the significant difference in the two curves, the sum of squared residuals far exceeded the judgment threshold. Therefore, the signal recovery process was determined to be inconsistent with the pre-defined physical laws of thermal shock, thus negating the possibility of thermal shock and misclassifying it as a true intrusion caused by a sustained, irregular external force, ultimately triggering a false alarm.

[0056] Steps D1 to D2 were designed to solve the technical problem of "how to effectively distinguish between two situations after encountering an unplanned high-pressure, low-temperature water flow impact: one is the real, high-tension maintenance caused by continuous external mechanical force; the other is the superposition of the two effects of "thermal shock shrinkage" and "surface sticky rust inhibitor layer being peeled off and emulsified" caused by the water flow impact, causing the actual recovery process of the tension signal to deviate from the preset theoretical recovery model, thus forming a composite false signal."

[0057] Steps D1 to D2 are to determine whether there is a macro-attenuation trend in the actual tension attenuation data sequence as a whole after the measured data does not match the thermal shock physical model. If there is a macro-attenuation trend, then the fifth step is to determine whether the macro-attenuation process is smooth. If the macro-attenuation process is smooth, the high-risk event to be confirmed is filed as an identified interference event, and a false alarm filtering operation is performed; if there is no macro-attenuation trend or the macro-attenuation process is not smooth, the high-risk event to be confirmed is filed as a real intrusion event, and an alarm operation is performed.

[0058] For this specific scenario, a "fault tolerance and feature re-identification" judgment step is added to the existing single-layer decision-making logic. If the tension decay curve fails to accurately compare with the "clean wire rope thermal recovery model," the conclusion is not considered final. Instead, the system takes a step back and considers whether this mismatched curve indicates a "refusal to recover" (e.g., due to continued external force) or an "abnormal but ongoing recovery" (e.g., insulation interference in this scenario). To this end, the "failed" curve is qualitatively examined in two dimensions: first, whether the overall tension is trending downward (i.e., determining whether a macroscopic decay trend exists); and second, the degree of "bumpiness" in this downward trend (i.e., determining whether the macroscopic decay process is smooth). These two checks are used to capture and identify the unique signal characteristics of the specific physical process of rust inhibitor emulsification.

[0059] like Figure 6 As shown, preferably, the fourth step of determining whether the actual tension attenuation data sequence has a macroscopic attenuation trend comprises the following steps if the fourth determination result is yes: E1. Divide all data points of the actual tension decay data sequence into a first part of data points and a second part of data points; E2. Obtain the average value of the first actual tension decay data corresponding to the first part of the data points and the average value of the second actual tension decay data corresponding to the second part of the data points respectively; E3. The fourth step is to determine whether the difference between the average value of the first actual tension decay data and the average value of the second actual tension decay data is greater than the preset average difference threshold; E4. If the fourth judgment result is yes, it is defined that the actual tension decay data sequence has a macro decay trend.

[0060] Steps E1 to E4 of the present application are the specific implementation details of the fourth judgment process in step D1: all data points in the measured data are divided into the first half of the data points and the second half of the data points; the average value of the tension attenuation data corresponding to the first half of the data points and the average value of the tension attenuation data corresponding to the second half of the data points are calculated; it is determined whether the difference between the average value of the tension attenuation data corresponding to the second half of the data points and the average value of the tension attenuation data corresponding to the first half of the data points is greater than a preset average value difference threshold; if the difference between the average value of the tension attenuation data corresponding to the second half of the data points and the average value of the tension attenuation data corresponding to the first half of the data points is greater than the preset average value difference threshold, it is defined that the actual tension attenuation data sequence has a macroscopic attenuation trend.

[0061] In some preferred embodiments, the data points of the actual tension decay data sequence are divided into two parts, the first 25 points (0-2.5 seconds) and the last 25 points (2.5-5 seconds), and the arithmetic mean of the tension decay data average values ​​corresponding to the data points in these two parts are calculated. The average value of the first part is recorded as The average value of the latter part is Then, judge Is it significantly less than For example, set a mean value difference threshold (such as 20 Newtons), if ( )>20 Newtons, it is believed that although the curve shape is not standard, a macroscopic and continuous attenuation trend objectively exists.

[0062] like Figure 7 As shown, preferably, the fifth step is to determine whether the macro attenuation process is stable. If the fifth determination result is yes, the high-risk event to be confirmed is filed as an identified interference event, and a false alarm filtering operation is performed, including the following steps: F1. Obtain the average of the absolute values ​​of the differences between the actual tension decay data corresponding to two consecutive sampling points in all data points of the actual tension decay data sequence; F2. Fifth, determine whether the average value of the absolute value of the difference falls within the preset stable range; F3. If the result of the fifth judgment is yes, the macroscopic attenuation process is defined as stable; F4. When the actual tension attenuation data sequence shows a macro-attenuation trend and the macro-attenuation process is stable, the high-risk event to be confirmed will be filed as an identified interference event and false alarm filtering will be performed.

[0063] Steps F1 to F4 of the present application are the specific implementation details of the fifth judgment process in step D1: the average value of the absolute value of the difference between the actual tension attenuation data corresponding to two adjacent continuous sampling points in all data points of the actual tension attenuation data sequence is obtained, and it is judged whether the average value of the absolute value of the difference falls within a preset stable range interval. If it falls within the stable range interval, the macro-attenuation process is defined as stable; only when there is a macro-attenuation trend in the actual tension attenuation data sequence and the macro-attenuation process is stable, the high-risk event to be confirmed is archived as an identified interference event, and a false alarm filtering operation is performed.

[0064] In some preferred embodiments, the stability of the decay process is analyzed by calculating the average value of the absolute value of the tension change between two adjacent sampling points (with a time interval of 100 milliseconds) in the entire 50-point data sequence. This average value reflects the degree of local "bumping" or "jittering" of the curve, and is recorded as A reasonable range of irregularity is preset, such as [1.0 Newton, 5.0 Newton]. The upper and lower limits of this range are obtained by calibrating the experimental data of the "rust inhibitor stripping and emulsification" process. If it falls within this range, it means that the degree of jitter of the curve is consistent with the physical characteristics of the rust inhibitor layer being slowly and unevenly peeled off (that is, the macroscopic attenuation process is stable). If it is less than 1.0 Newton, the curve is too smooth and more like a steady, slow release of mechanical force rather than a physical and chemical process. If it is greater than 5.0 Newtons, it means that the curve jitter is too severe, more like artificial, irregular shaking or tool scratching.

[0065] The innovation of the above steps in this solution lies in the following: when faced with a signal that does not conform to the pre-defined "clean wire rope" thermal recovery model, it is not hastily classified as an intrusion, but rather given a second chance for verification. For a real intrusion, such as an intruder applying sustained force with a crowbar, the tension signal would fail the "macro-confirmation of the decay trend" because the tension would remain high, with minimal difference between the average values ​​before and after. Therefore, a correct alarm is triggered. For a false signal unique to this scenario, such as high-pressure, low-temperature water impacting a wire rope coated with rust inhibitor, while its tension signal fails the first round of precise model comparison, it does pass the second round of verification: its overall tension is slowly decreasing, passing the "macro-confirmation of the decay trend." Furthermore, this decrease is accompanied by slight fluctuations caused by oil film emulsification, the amplitude of which falls well within the calibration range for "recovery process irregularities." Therefore, this benign, complex interference caused by a specific maintenance scenario is identified and filtered out as a false alarm.

[0066] Through this logical progression from "precise morphological matching" to "macro-feature verification," this solution improves the logical resolution capability in complex and special operating environments without increasing any hardware costs, and accurately solves the false alarm problem caused by the additional physical variables introduced by the protective coating.

[0067] like Figure 8 As shown, preferably, after obtaining the actual tension attenuation data sequence of the original sampled data after moving average processing in a preset time period, the following steps are also included: G1. Traverse each data point of the actual tension decay data sequence, calculate the second-order derivative of the actual tension decay data corresponding to each data point, and obtain the tension change acceleration sequence; G2. Obtain the variance value of the tension change acceleration sequence; G3. Sixth, determine whether the variance of the tension change acceleration sequence is less than a preset smoothing threshold; G4. Based on the sixth judgment result, execute corresponding false alarm filtering operation or alarm operation.

[0068] Steps G1 to G4 in this application are applied in another more complex application deployment environment, that is, the production process of a fertilizer production base will produce a large amount of urea dust with strong hygroscopicity. These dusts will inevitably settle and adhere to the surface of the electronic tension fence wire rope outside the factory area, forming a thin layer of solid powder. In dry weather, this layer of powder has little effect on the tension baseline value of the wire rope. Due to the high humidity in the air at night, the urea dust attached to the wire rope absorbs moisture in the air and does not completely dissolve. Instead, it forms a sticky, high-concentration urea saturated solution film on the surface of the wire rope. Subsequently, the sunlight increased, and the surface temperature of the wire rope quickly rose to 45 degrees Celsius. Part of the water evaporated, but the sticky solution film still wrapped around the wire rope. At that moment, a transport vehicle was involved in an accident on an internal factory road, causing a partial spill of raw materials. To prevent the situation from escalating, an on-site worker, without prior warning, immediately activated a fire hydrant and used fire water at a pressure of 0.6 MPa and a temperature of 20°C to urgently flush the ground and fences near the leak area. When this high-pressure water stream impacted the hot steel wire rope, which was coated with a thin film of highly concentrated urea solution, the combined impact and sudden temperature change triggered violent vibration and "cold contraction" in the wire rope, causing a significant increase in tension. The alarm detection program was suspended, and subsequent tension decay data collection began. However, during the subsequent recovery phase, the physical state of the wire rope deviated from the expected trajectory. The high-pressure water stream did not simply act as a heat exchange medium, as it would during a cleansing operation. It first diluted the highly concentrated urea solution on the wire rope surface. This dilution and dissolution process is a significant endothermic process. Beyond the "cold contraction" effect, it also rapidly absorbed a significant amount of heat from the wire rope itself, resulting in a much steeper downward slope in the initial tension recovery curve than in the standard process. The diluted urea solution then coated the entire impacted section of wire rope. Due to the presence of the solution, its evaporation boiling point is higher than that of pure water, and this liquid film persists on the wire rope surface for a longer period of time. The heat dissipation process from the wire rope to the surroundings shifts from the originally assumed "metal-to-air" convection heat transfer to a complex "metal-to-urea solution-to-air" heat transfer process, involving the latent heat of liquid evaporation. This process causes the tension recovery curve to exhibit a flat, non-exponential tailing characteristic in the middle and later stages. This measured decay curve, characterized by a rapid initial decrease and a flatter tail, was compared with a stored standard exponential decay curve calibrated to the physical conditions of "clean wire rope." Due to fundamental differences in the initial slope and overall shape of the two curves, the calculated sum of squared residuals far exceeded the judgment threshold. Consequently, the signal recovery process was determined to be inconsistent with the pre-defined physical laws of thermal shock, thus ruling out the possibility of thermal shock and mistakenly attributing it to an intrusion caused by an unknown, persistent external force, ultimately triggering a false alarm.

[0069] The purpose of designed steps G1 to G4 is to solve the technical problem of "how to design a false alarm screening control method that can effectively distinguish two situations after encountering an unplanned high-pressure water flow impact: one is the real high tension maintenance caused by continuous and irregular external mechanical force; the other is the superposition of two physical effects caused by the water flow impact, namely "thermal shock contraction" and "dissolution of surface attachments and absorption of heat and subsequent solution evaporation to change the heat exchange mode", resulting in the actual recovery curve of the tension signal presenting a non-standard form of "rapid drop in the initial stage and gentle in the latter stage", thus forming a composite false signal."

[0070] Steps G1 to G4 are to obtain the actual tension attenuation data sequence by traversing each data point, calculating the second-order inverse of the actual tension attenuation data corresponding to each data point, and obtaining the tension change acceleration sequence; calculating the variance value of the tension change acceleration sequence, and judging whether the variance value is less than the preset smoothing threshold value, and executing the corresponding false alarm filtering operation or alarm operation according to the judgment result.

[0071] For this specific scenario, the judgment basis shifts from the specific shape of the tension signal recovery curve (i.e., the signal's first-order rate of change) to the inherent smoothness of the curve's evolution (i.e., the stability of the signal's second-order rate of change). The basic judgment logic is that any state change governed by natural physical or chemical laws, no matter how complex the process or how bizarre the curve's shape, must be continuous and smooth on a macroscopic scale. In contrast, the application and variation of force in human-induced mechanical intrusions are inherently discontinuous and uneven, filled with abrupt starts, stops, and adjustments. Therefore, by verifying the "smoothness" of the tension variation process, the physical source of the event can be fundamentally distinguished. Based on this, in the existing control logic, when a high-risk composite event is detected and an alarm is suspended, a comparison with any preset curve shape is not performed. Instead, a dedicated "process smoothness verification procedure" is initiated.

[0072] In some preferred embodiments, when a high-risk event is triggered, the microcontroller sets an alarm and continuously collects data for 5 seconds at intervals of 100 milliseconds after the tension reaches its peak, obtaining a series of 50 data points of measured tension decay data, which is recorded as ; The microcontroller traverses the collected tension data sequence and calculates its second-order derivative. ( from 1 to 48), its second-order derivative value It can be approximated by the central difference formula: This calculation process will generate a "tension change acceleration" sequence A consisting of 48 points; the microcontroller calculates the discreteness of this "tension change acceleration" sequence A. The specific quantification method is to calculate the variance of the sequence. First calculate the average value of sequence A , then calculate the variance This variance directly reflects the degree of "bumpiness" or "jitter" during the tension change process. A pre-stored "physical process smoothness threshold" (i.e., a preset smoothness threshold), such as a specific variance value, is used internally. This threshold was determined through experiments on various real physical processes (such as pure thermal shock and thermal shock with coatings) and various simulated intrusion behaviors (such as pulling and shaking), resulting in a critical value that effectively distinguishes between the two types of events.

[0073] If the calculated variance If the value is less than the threshold, it indicates that the tension recovery process is smooth and continuous. The control unit determines that the event originates from a natural physical or chemical process (whether it is a standard exponential recovery or a complex urea dissolution recovery in this scenario), and therefore performs a "false alarm filtering" operation and cancels the pending alarm instruction; if the calculated variance If the value is greater than or equal to this threshold, it indicates that the tension recovery process is full of irregular and drastic acceleration and deceleration changes. The control unit determines that this process does not have the smooth characteristics of a natural physical process and is most likely caused by a discontinuous artificial mechanical force. Therefore, it is confirmed as a real intrusion and immediately activates the alarm relay.

[0074] The innovation of the above steps in this solution lies in the fact that, upon capturing a suspicious signal, the system no longer focuses on the appearance of the signal's recovery curve, but rather examines the smoothness of the recovery process itself. This smoothness is quantified by calculating the variance of the second-order derivative of the tension signal. For the complex recovery process in this scenario, caused by the combined heat absorption of urea dissolution and solution evaporation, although the curve shape (first-order derivative) is non-standard, the process itself is continuous, and the variance of its second-order derivative will be small. However, when an intruder exerts force continuously, fine-tuning the force will cause irregular fluctuations in the tension, resulting in a large variance in the second-order derivative. By setting a reasonable variance threshold, all smooth "natural processes" can be accurately distinguished from non-smooth "artificial processes," thus resolving the problem.

[0075] In this way, the present invention is no longer limited by the cognition of a specific curve shape, but by testing a more essential physical process property - "continuous smoothness", it successfully distinguishes irregular and intermittent interference caused by human power from all continuous processes, no matter how complex, dominated by natural laws, thereby accurately solving the problem of false alarms caused by complex physical and chemical effects in specific chemical environments.

[0076] like Figure 9 As shown, a perimeter alarm false alarm filtering control system includes: A data acquisition module is used to obtain original sampling data corresponding to multiple continuous sampling points around the perimeter of the suspected area; A module for defining pending high-risk events is used to first determine whether the tension change value between two consecutive sampling points is greater than a tension change threshold. If the first determination result is yes, then a second determination is made whether the raw sampling data after moving average processing jumps above a high-tension alarm threshold within a preset time period and remains at a high level. If the second determination result is yes, the event is defined as a pending high-risk event. An actual tension attenuation data sequence acquisition module is used to acquire an actual tension attenuation data sequence of the original sampling data after moving average processing in a preset time period; A sequence matching judgment module is used to thirdly judge whether the actual tension decay data sequence matches the preset theoretical tension decay sequence; The execution module is used to execute a corresponding false alarm filtering operation or an alarm operation according to the third judgment result.

[0077] The above solution provides a perimeter alarm false alarm filtering and control system. This system, through a modular design, assigns data acquisition, definition of high-risk time to be confirmed, acquisition of actual tension decay sequences, sequence matching determination, and operation execution to different units, enabling effective and reliable execution of the perimeter alarm false alarm filtering and control method. This system effectively distinguishes between non-invasive factors such as thermodynamic effects and actual intrusion behavior, thereby effectively distinguishing tension changes caused by thermodynamic effects from intrusion behavior, significantly reducing false alarms caused by non-invasive factors such as ambient temperature differences.

[0078] In the embodiments provided in this application, it should be understood that the disclosed methods and systems can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of modules is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0079] In addition, all functional modules in the embodiments of the present invention may be integrated into one processor, or each module may be a separate device, or two or more modules may be integrated into one device; the functional modules in the embodiments of the present invention may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0080] Those skilled in the art will understand that all or part of the steps of the above-mentioned method embodiment can be completed by program instructions and related hardware. The aforementioned program instructions can be stored in a computer-readable storage medium. When the program instructions are executed, the steps of the above-mentioned method embodiment are executed; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM), magnetic disks or optical disks, and other media that can store program codes.

[0081] It should be understood that the use of "system," "device," "unit," and / or "module" in this application is merely a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0082] As used herein, unless the context clearly indicates an exception, the terms "a," "an," "an," and / or "the" are not intended to refer to the singular and may include the plural, unless the context clearly indicates otherwise. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list; a method or apparatus may also include other steps or elements. The phrase "comprises a..." does not preclude the presence of other identical elements in the process, method, product, or apparatus that includes the elements.

[0083] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the quantity of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features.

[0084] If a flow chart is used in this application, the flow chart is used to illustrate the operations performed by the system according to the embodiments of the application. It should be understood that the previous or subsequent operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. At the same time, other operations can be added to these processes, or one or more operations can be removed from these processes.

[0085] The above describes in detail the perimeter alarm false alarm filtering control method and system provided by the present invention. The above description of the disclosed embodiments will enable those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A perimeter alarm false alarm filtering control method, applied to the electronic tension alarm system to detect intrusion scenarios, characterized by: The steps include: Obtaining original sampling data corresponding to multiple consecutive sampling points around the perimeter of the suspected area; First, determine whether the tension change value between two adjacent continuous sampling points is greater than the tension change threshold. If the first determination result is yes, then secondly determine whether the raw sampling data after moving average processing jumps above the high tension alarm threshold within a preset time period and remains at a high level. If the second determination result is yes, it is defined as a pending high-risk event; Acquire an actual tension attenuation data sequence of the original sampled data after moving average processing in the preset time period; thirdly, determining whether the actual tension decay data sequence is consistent with a preset theoretical tension decay sequence; According to the third judgment result, a corresponding false alarm filtering operation or an alarm operation is performed.

2. The perimeter alarm false alarm filtering control method according to claim 1, characterized in that: The first step of determining whether the tension change value between two adjacent continuous sampling points is greater than a tension change threshold, if the first determination result is yes, comprises the following steps: Calculate the difference between the original sampling data corresponding to two adjacent continuous sampling points respectively; Within a first preset window length, first determining whether a difference between the original sampling data corresponding to two adjacent continuous sampling points is greater than the tension change threshold; If the first judgment result is yes, it is defined that a rapid impact feature exists.

3. The perimeter alarm false alarm filtering control method according to claim 2, characterized in that: The second step of determining whether the raw sampled data after the moving average processing jumps above the high tension alarm threshold within a preset time period and remains at a high level, and if the second determination result is yes, defining it as a pending high-risk event, includes the following steps: Performing moving average processing on the original sampled data according to a second preset window length to obtain a quasi-static tension value; within a preset time period, secondly determining whether the quasi-static tension value jumps above the high tension alarm threshold and remains high; If the second judgment result is yes, it is defined that there is a tension continuous jump feature; When the rapid impact feature and the tension continuous jump feature exist at the same time, it is defined as the high-risk event to be confirmed.

4. The perimeter alarm false alarm filtering control method according to claim 1, characterized in that: The third step of determining whether the actual tension decay data sequence is consistent with a preset theoretical tension decay sequence comprises the following steps: Traversing each data point of the actual tension attenuation data sequence, and calculating the square of the difference between the actual tension attenuation data corresponding to each data point and the theoretical tension attenuation data; Accumulate the square of the difference of each data point to obtain the residual sum of squares of the tension decay data; thirdly, determining whether the residual sum of squares of the tension attenuation data is less than a preset residual sum of squares threshold; The theoretical tension decay data of each data point is obtained according to a preset theoretical tension decay sequence.

5. The perimeter alarm false alarm filtering control method according to claim 4, characterized in that: The step of performing a corresponding false alarm filtering operation or an alarm operation according to the third judgment result includes the following steps: If the third judgment result is yes, the actual tension decay data sequence is defined to be consistent with the preset theoretical tension decay sequence, the to-be-confirmed high-risk event is filed as an identified interference event, and a false alarm filtering operation is performed; If the third judgment result is no, it is defined that the actual tension attenuation data sequence does not match the preset theoretical tension attenuation sequence, the high-risk event to be confirmed is filed as a real intrusion event, and an alarm operation is executed.

6. The perimeter alarm false alarm filtering control method according to claim 5, characterized in that: After the step of defining that the actual tension decay data sequence does not match the preset theoretical tension decay sequence if the third judgment result is negative, the following steps are further included: Fourthly, determining whether the actual tension attenuation data sequence has a macro-attenuation trend. If the fourth determination result is yes, fifthly determining whether the macro-attenuation process is stable. If the fifth determination result is yes, filing the to-be-confirmed high-risk event as an identified interference event and performing a false alarm filtering operation. If either the fourth judgment result or the fifth judgment result is negative, the high-risk event to be confirmed will be filed as a real intrusion event, and an alarm operation will be executed.

7. The perimeter alarm false alarm filtering control method according to claim 6, characterized in that: The fourth step of determining whether the actual tension attenuation data sequence has a macroscopic attenuation trend comprises the following steps if the fourth determination result is yes: Dividing all data points of the actual tension decay data sequence into a first part of data points and a second part of data points; respectively obtaining a first average value of actual tension decay data corresponding to the first portion of data points and a second average value of actual tension decay data corresponding to the second portion of data points; Fourth, determining whether a difference between an average value of the first actual tension decay data and an average value of the second actual tension decay data is greater than a preset average value difference threshold; If the fourth judgment result is yes, it is defined that the actual tension decay data sequence has a macro decay trend.

8. The perimeter alarm false alarm filtering control method according to claim 7, characterized in that: The fifth step of determining whether the macroscopic attenuation process is stable, if the fifth determination result is yes, then filing the to-be-confirmed high-risk event as an identified interference event, and performing a false alarm filtering operation, includes the following steps: Obtaining an average of absolute values ​​of differences between actual tension attenuation data corresponding to two adjacent continuous sampling points in all data points of the actual tension attenuation data sequence; Fifth, determining whether the average value of the absolute value of the difference falls within a preset stable range; If the result of the fifth judgment is yes, the macroscopic attenuation process is defined as stationary; When the actual tension attenuation data sequence has a macro-attenuation trend and the macro-attenuation process is stable, the high-risk event to be confirmed is filed as an identified interference event, and a false alarm filtering operation is performed.

9. The perimeter alarm false alarm filtering control method according to claim 1, characterized in that: After obtaining the actual tension attenuation data sequence of the original sampled data after moving average processing in the preset time period, the method further includes the following steps: Traversing each data point of the actual tension decay data sequence, calculating the second-order derivative of the actual tension decay data corresponding to each data point, and obtaining a tension change acceleration sequence; Obtaining a variance value of the tension change acceleration sequence; Sixth, determining whether the variance value of the tension change acceleration sequence is less than a preset smoothing threshold; According to the sixth judgment result, a corresponding false alarm filtering operation or an alarm operation is performed.

10. A perimeter alarm false alarm filtering control system, characterized in that: include: A data acquisition module is used to obtain original sampling data corresponding to multiple continuous sampling points around the perimeter of the suspected area; A pending high-risk event definition module is configured to first determine whether a tension change value between two adjacent continuous sampling points is greater than a tension change threshold. If the first determination result is yes, then secondly determine whether the raw sampling data after moving average processing jumps above a high-tension alarm threshold within a preset time period and remains at a high level. If the second determination result is yes, the event is defined as a pending high-risk event. An actual tension attenuation data sequence acquisition module, configured to acquire an actual tension attenuation data sequence of the original sampled data after moving average processing in the preset time period; a sequence matching judgment module, configured to thirdly judge whether the actual tension decay data sequence matches a preset theoretical tension decay sequence; The execution module is used to execute a corresponding false alarm filtering operation or an alarm operation according to the third judgment result.

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