An electronic fence state monitoring method and system based on digital twin mapping

Abstract

The application discloses an electronic fence state monitoring method and system based on digital twin mapping, which comprises multi-source data acquisition, digital twin model construction, adaptive weight construction, monitoring index quantification, comprehensive situation score calculation and electronic fence safety state monitoring. The application belongs to the field of data processing, and specifically refers to an electronic fence state monitoring method and system based on digital twin mapping. According to the scheme, the monitoring index is dynamically weighted in double dimensions based on information dispersion weight and fluctuation degree weight, and a redundancy correction coefficient is introduced to control information overlap. A virtual-real state mapping function is constructed based on the definition of a dynamic threshold of the monitoring index, so as to improve the accuracy of risk quantification. A risk time attenuation model is constructed to distinguish the subsiding characteristics of different monitoring indexes. Through digital twin comprehensive situation score calculation, the synergistic effect between multi-source indexes is captured, and the effect of electronic fence safety situation monitoring is improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for monitoring the status of electronic fences based on digital twin mapping. Background Technology

[0002] Electronic fence status monitoring is a traditional security monitoring method that collects vibration, infrared, environmental, and equipment status data, judges intrusion and anomalies based on fixed thresholds, and directly outputs alarm results using simple, fixed-weight fusion indicators. However, general electronic fence status monitoring methods suffer from several drawbacks: one-sided analysis of monitoring indicators, lack of redundant control, poor adaptability of virtual-real mapping, leading to low reliability of risk prediction; rigid state boundaries, inability to adapt to continuous and gradual changes in risk, neglect of physical evolution processes, inaccurate virtual-real mapping, and consequently, poor monitoring effectiveness. Summary of the Invention

[0003] To address the above issues and overcome the shortcomings of existing technologies, this invention provides a method and system for monitoring the status of electronic fences based on digital twin mapping. Addressing the problems of general electronic fence status monitoring methods, such as one-sided analysis of monitoring indicators, lack of redundant control, poor adaptability of virtual-real mapping, and consequently low reliability of risk prediction, this solution dynamically assigns weights to monitoring indicators based on information dispersion weights and volatility weights, balancing anomaly differentiation capability and environmental sensitivity. Simultaneously, a redundancy correction coefficient is introduced to control information overlap, capturing both the inherent patterns of physical entity operational data and adapting to the differentiated mapping characteristics of the digital twin model; thereby improving the reliability of electronic fence security situation assessment. This addresses the shortcomings of general electronic fence status monitoring methods. Rigid state boundaries cannot adapt to the continuous and gradual changes in risk, ignore the physical evolution process, and result in inaccurate virtual-real mapping, leading to poor monitoring effectiveness. This solution constructs a virtual-real state mapping function based on dynamic threshold definitions of monitoring indicators to improve the accuracy of risk quantification; it adapts to the gradual evolution characteristics of electronic fences from slight disturbances to confirmed intrusions, achieving accurate quantification of different indicator types and avoiding the defects of rigid thresholds; by constructing a risk time decay model, it accurately depicts the dynamic evolution of security risks over time, while distinguishing the decay characteristics of different monitoring indicators; through digital twin comprehensive situational assessment calculation, it captures the synergistic effect between multi-source indicators and outputs regionalized situational scores using spatial interpolation technology; thereby improving the effectiveness of electronic fence security situational monitoring.

[0004] The technical solution adopted by this invention is as follows: This invention provides an electronic fence status monitoring method based on digital twin mapping, which includes the following steps: Step S1: Multi-source data acquisition; Step S2: Digital twin model construction; Step S3: Adaptive weight construction; Step S4: Quantify monitoring indicators; Step S5: Calculate the overall situation score; Step S6: Monitoring the security status of the electronic fence.

[0005] Furthermore, in step S1, the multi-source data acquisition involves obtaining electronic fence status data, including intrusion behavior data, environmental interference factors, and equipment health status; and performing spatiotemporal benchmark unification and data standardization on the acquired data.

[0006] Further, in step S2, the construction of the digital twin model includes: Geometric space mapping defines the geometric topology and spatial coordinate system of the fence; The data interface layer is designed to encapsulate the access protocol and standardized interface for electronic fence status data in S1; The logic rule engine is designed with an embedded weight calculation matrix and dynamic threshold rule library to support S3's adaptive weighting mechanism. The state evolution operator, with the time decay function and comprehensive score calculation preset in S5, enables dynamic projection of risk over time.

[0007] Further, in step S3, the adaptive weight construction specifically includes: The entropy value of the indicator is calculated, and then the information dispersion weight is obtained; The volatility weight is calculated by taking the mean and standard deviation of the monitoring indicators. Weight fusion is used to introduce a redundancy correction coefficient to correct the product of information dispersion weight and volatility weight, thus obtaining the final index weight of the digital twin model.

[0008] Furthermore, in step S4, the quantification of the monitoring indicators specifically includes: The dynamic threshold definition for monitoring indicators is based on the digital twin mapping logic and the risk orientation of the indicators. Construct a virtual-real state mapping function to map the monitoring values ​​in the physical space to the risk scores in the digital twin.

[0009] Further, in step S5, the calculation of the comprehensive situational assessment specifically includes: Risk decay rate fitting: Based on the historical operation and alarm closed-loop data of the electronic fence, the risk decay rate in the virtual model is fitted; Risk time decay model design; Design an indicator-level risk time decay model specific to the digital twin model, while introducing the risk decay critical time; Digital twin comprehensive situational assessment calculation: Based on the indicator weights and risk scores in the virtual model, the comprehensive situational assessment of the physical entity in the digital twin space is obtained.

[0010] Furthermore, in step S6, the electronic fence security status monitoring is based on the comprehensive situation score output by the digital twin model, and performs risk score-alarm level mapping of the virtual-real mapping layer, thereby realizing predictive monitoring of the security status of the physical entity.

[0011] The present invention provides an electronic fence status monitoring system based on digital twin mapping, including a multi-source data acquisition module, a digital twin model construction module, an adaptive weight construction module, a monitoring index quantification module, a comprehensive situation score calculation module, and an electronic fence security status monitoring module; The multi-source data acquisition module acquires electronic fence status data and performs preprocessing. The digital twin model construction module constructs a digital twin of the electronic fence, defining geometric space mapping, data interface layer, logical rule engine and state evolution operator; The adaptive weight construction module calculates weights based on information dispersion and data fluctuation, and introduces a redundancy correction coefficient to determine the dynamic weights of monitoring indicators in the digital twin model. The monitoring indicator quantification module sets dynamic thresholds according to the risk orientation of the monitoring indicators, constructs a virtual-real state mapping function, and transforms physical monitoring values ​​into standardized risk scores. The comprehensive situation score calculation module fits the risk time decay rate, designs a risk time decay model, and calculates the digital twin comprehensive situation score by combining the weights of monitoring indicators. The electronic fence security status monitoring module monitors the security status of the electronic fence based on a digital twin comprehensive situational assessment.

[0012] The beneficial effects achieved by the present invention using the above solution are as follows: (1) In view of the problems that the general electronic fence status monitoring method has, such as one-sided analysis of monitoring indicators, lack of redundant control, poor adaptability of virtual and real mapping, and thus low reliability of risk prediction, this solution dynamically assigns two dimensions to the monitoring indicators based on information dispersion weight and fluctuation degree weight, taking into account the ability to distinguish anomalies and environmental sensitivity; at the same time, a redundancy correction coefficient is introduced to control information overlap, which not only captures the inherent laws of physical entity operation data, but also adapts to the differentiated mapping characteristics of digital twin models; thereby improving the reliability of electronic fence security status assessment.

[0013] (2) To address the problems of rigid state boundaries in general electronic fence status monitoring methods, which cannot adapt to the continuous and gradual changes in risk, ignore the physical evolution process, and result in inaccurate virtual-real mapping, leading to poor monitoring effects, this solution constructs a virtual-real state mapping function based on the dynamic threshold definition of monitoring indicators to improve the accuracy of risk quantification; adapts to the gradual evolution characteristics of electronic fences from slight disturbances to confirmed intrusions, and achieves accurate quantification of different indicator types, avoiding the rigid threshold defect; accurately depicts the dynamic evolution law of security risks over time by constructing a risk time decay model, while distinguishing the decay characteristics of different monitoring indicators; captures the synergistic effect between multi-source indicators through digital twin comprehensive situational scoring calculation, and outputs regional situational scores by combining spatial interpolation technology; thereby improving the effect of electronic fence security situational monitoring. Attached Figure Description

[0014] Figure 1 A flowchart illustrating an electronic fence status monitoring method based on digital twin mapping provided by the present invention; Figure 2 This is a schematic diagram of an electronic fence status monitoring system based on digital twin mapping, provided by the present invention.

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. Detailed Implementation

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

[0017] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the system or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0018] Example 1, see Figure 1 This invention provides a method for monitoring the status of electronic fences based on digital twin mapping, the method comprising the following steps: Step S1: Multi-source data acquisition to obtain electronic fence status data and perform preprocessing; Step S2: Digital twin model construction, constructing a digital twin of the electronic fence, defining geometric space mapping, data interface layer, logical rule engine and state evolution operator; Step S3: Adaptive weight construction, calculate weights based on information dispersion and data fluctuation, and introduce a redundancy correction coefficient to determine the dynamic weights of monitoring indicators in the digital twin model; Step S4: Quantify monitoring indicators, set dynamic thresholds according to the risk orientation of monitoring indicators, construct a virtual-real state mapping function, and convert physical monitoring values ​​into standardized risk scores; Step S5: Calculate the comprehensive situation score, fit the risk time decay rate, design the risk time decay model, and calculate the digital twin comprehensive situation score by combining the weights of monitoring indicators. Step S6: Electronic fence security status monitoring, based on digital twin comprehensive situational assessment to monitor the security status of the electronic fence.

[0019] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, multi-source data acquisition involves obtaining electronic fence status data, including intrusion behavior data, environmental interference factors, and device health status. The intrusion behavior data includes the waveform amplitude and frequency of the vibrating optical fiber, and the blocking intensity of the infrared beam. The environmental interference factors include wind speed, wind direction, rainfall, and ambient temperature. The device health status includes power supply voltage, remaining power, and signal strength. The acquired data is then standardized by unifying the spatiotemporal reference: data from different sampling frequencies are unified to the same timestamp and subjected to maximum and minimum normalization processing.

[0020] Example 3, see Figure 1 This embodiment is based on the above embodiment. In step S2, the construction of the digital twin model aims to break down the information silos between the physical entity and the virtual monitoring system, and to construct a multi-dimensional digital twin of the electronic fence, including: Geometric space mapping defines the geometric topology and spatial coordinate system of the fence; The data interface layer is designed to encapsulate the access protocols (industrial bus protocol, IoT communication protocol) and standardized interfaces (message queue, data serialization standard, real-time stream processing) for the electronic fence status data in S1. The logic rule engine is designed with an embedded weight calculation matrix W and a dynamic threshold rule library to support the adaptive weighting mechanism of S3. The state evolution operator, with the time decay function and comprehensive score calculation preset in S5, enables dynamic projection of risk over time.

[0021] Example 4, see Figure 1This embodiment is based on the above embodiment. In step S3, the adaptive weight construction is based on the fact that the contribution of different monitoring indicators to the fence security status varies in time and space (the weight of environmental factors on windy days should be automatically reduced). Therefore, a dynamic weighting mechanism is constructed within the digital twin model. The information dispersion reflects the indicator's ability to distinguish between intrusions or fault anomalies, and the data fluctuation reflects the indicator's sensitivity to environmental noise. Invalid and repetitive information in the virtual-real mapping is eliminated through redundancy correction, thereby achieving adaptive optimization of the digital twin model's logic layer. Specifically, this includes: The index entropy value is calculated and expressed as: ; ; and then the information dispersion weight is obtained, expressed as: ;in, It is the probability distribution value of the j-th monitoring indicator of the i-th sample; is the standardized value of the j-th monitoring indicator in the i-th sample; m is the total number of samples; It is the entropy value of the j-th monitoring indicator, used to quantify its ability to distinguish abnormal states in virtual space; The information dispersion weight of the j-th monitoring indicator; n is the total number of monitoring indicators; Calculate the weight of volatility and the mean of the monitoring indicators. and standard deviation , is represented as: ; ; and thus obtain the fluctuation degree weight. , is represented as: ; ;in, It is the fluctuation coefficient of the monitoring indicator; Weight fusion, introducing a redundancy correction coefficient This method characterizes the degree of information overlap among different monitoring indicators in the digital twin mapping process, corrects the product of information dispersion weight and fluctuation weight, avoids the repeated weighting of highly correlated sensors in the virtual model, and obtains the final indicator weights of the digital twin model. , is represented as: ; Where k is a monitoring indicator index that distinguishes it from j; It is the Pearson correlation coefficient between the j-th and k-th monitoring indicators, used to quantify their linear correlation in the digital twin space.

[0022] By performing the above operations, this solution addresses the problems of general electronic fence status monitoring methods, such as one-sided analysis of monitoring indicators, lack of redundant control, poor adaptability of virtual-real mapping, and consequently low reliability of risk prediction. It dynamically assigns weights to monitoring indicators based on information dispersion weights and volatility weights, balancing anomaly differentiation capability and environmental sensitivity. Simultaneously, it introduces a redundancy correction coefficient to control information overlap, capturing both the inherent patterns of physical entity operational data and adapting to the differentiated mapping characteristics of digital twin models. This improves the reliability of electronic fence security situation assessment.

[0023] Example 5, see Figure 1 This embodiment is based on the above embodiment. In step S4, the quantification of monitoring indicators is due to the continuous and gradual physical evolution of the electronic fence's operating state (there is no absolute binary boundary between slight environmental disturbances and confirmed intrusion behavior), and the risk orientation of different monitoring indicators differs (the higher the value of vibration / infrared indicators, the greater the risk, while the lower the value of power supply voltage indicators, the greater the risk). Therefore, a dynamic boundary mapping function is constructed in the digital twin virtual space. The risk threshold is determined based on the statistical analysis of the historical operating data of the physical entity. The physical monitoring value is mapped to a standardized risk score in the virtual space through a piecewise function. This accurately depicts the degree to which the physical state deviates from the safety threshold and adapts to the gradual evolution of electronic fence risks, solving the problems of rigid boundaries and poor adaptability of virtual-real mapping in traditional quantification methods. Specifically, it includes: The dynamic threshold definition for monitoring indicators is based on the digital twin mapping logic and the risk orientation of the indicators. Bottom boundary standards only (equipment health indicators: the lower the value, the higher the risk): x is the low threshold critical point (extremely high risk) of the risk monitoring indicator; y is the medium-low threshold critical point (risk begins to mitigate) of the risk monitoring indicator; z is the medium-high threshold critical point (starting point of the safety baseline) of the risk monitoring indicator; p is the high threshold critical point (completely safe) of the risk monitoring indicator. Top-level criteria only (intrusion behavior indicators: higher values ​​indicate higher risk): ; Two-dimensional standards (environmental disturbance indicators, wind speed, temperature: the higher the value outside the comfort range, the greater the risk): ; Construct a virtual-real state mapping function to map monitoring values ​​in the physical space to risk scores in the digital twin. The function is expressed as follows: ;in, It is the standardized risk score of the j-th monitoring indicator of the i-th sample in the digital twin virtual space (0 indicates that the virtual mapping state is safe, and 1 indicates that the virtual mapping state is high-risk). It is the standardized mapping input value of the measured values ​​of the monitoring indicators of physical entities.

[0024] Example 6, see Figure 1 This embodiment is based on the above embodiment. In step S5, the comprehensive situation score calculation is necessary because a single monitoring indicator cannot fully reflect the overall security status of the electronic fence, and security risks evolve dynamically with physical time (instantaneous wind and rain interference dissipates over time, while continuous intrusion attempts exacerbate the risk over time). Therefore, based on the indicator weights and quantified scores in the digital twin model, the synergistic effect between multi-source indicators is captured. At the same time, a risk time decay operator is introduced to characterize the dynamic evolution of physical risks in the digital twin virtual space, and a regionalized security situation score is output by combining spatial interpolation technology. Specifically, this includes: Risk decay rate fitting; Based on the historical operation and alarm closed-loop data of the electronic fence, the risk decay rate in the virtual model is fitted and expressed as: ;in, The actual risk score after time t (short-term observation verification value); It is the risk time decay rate of the j-th monitoring indicator in the digital twin model, used to quantify the forgetting rate of physical risks; It is a smoothing term; Risk time decay model design; A risk time decay model specific to the digital twin model is designed, and a risk decay critical time is introduced, expressed as: ;in, It is the critical time for the differential risk to dissipate (the higher the decay rate, the faster the risk disappears in the virtual model). This is the risk decay threshold, ranging from 0.03 to 0.10, where the risk score decreases to [a certain value]. The following is considered as the physical entity having returned to a safe state; It is the predicted risk score (long-term extrapolation value) of the j-th monitoring indicator of the i-th sample after time t in the virtual space. It is an estimated time interval; Digital twin comprehensive situational assessment calculation: Based on the indicator weights and risk scores in the virtual model, the comprehensive situational assessment of the physical entity in the digital twin space is obtained. , is represented as: .

[0025] By performing the above operations, this solution addresses the problems of rigid state boundaries in general electronic fence status monitoring methods, which fail to adapt to the continuous and gradual changes in risk, ignore the physical evolution process, and result in inaccurate virtual-real mapping, leading to poor monitoring effectiveness. It constructs a virtual-real state mapping function based on dynamic threshold definitions for monitoring indicators, improving the accuracy of risk quantification. It adapts to the gradual evolution of electronic fences from minor disturbances to confirmed intrusions, achieving accurate quantification of different indicator types and avoiding the rigidity of thresholds. By constructing a risk time decay model, it accurately depicts the dynamic evolution of security risks over time, while distinguishing the decay characteristics of different monitoring indicators. Through digital twin comprehensive situational assessment calculations, it captures the synergistic effects between multi-source indicators and outputs regionalized situational scores using spatial interpolation technology, thereby improving the effectiveness of electronic fence security situation monitoring.

[0026] Example 7, see Figure 1 This embodiment is based on the above embodiment. In step S6, the electronic fence security status monitoring is based on the comprehensive situation score output by the digital twin model. The risk score-alarm level mapping of the virtual-real mapping layer is performed to achieve predictive monitoring of the security status of the physical entity. The dynamic quantile division method is used to determine the risk score interval threshold in the virtual space and map it to the multi-level response mechanism of the physical execution layer: no risk (safe state) corresponds to 0~0.2; attention state (potential interference) corresponds to 0.2~0.4; warning state (suspected intrusion) corresponds to 0.4~0.6; alarm state (high threat) corresponds to 0.6~0.8; emergency state (confirmed intrusion) corresponds to 0.8~1.0. The warning state is reported, and the alarm state and emergency state are warned.

[0027] Example 8, see Figure 2 Based on the above embodiments, this embodiment provides an electronic fence status monitoring system based on digital twin mapping, including a multi-source data acquisition module, a digital twin model construction module, an adaptive weight construction module, a monitoring indicator quantification module, a comprehensive situation score calculation module, and an electronic fence security status monitoring module. The multi-source data acquisition module acquires electronic fence status data and performs preprocessing. The digital twin model construction module constructs a digital twin of the electronic fence, defining geometric space mapping, data interface layer, logical rule engine and state evolution operator; The adaptive weight construction module calculates weights based on information dispersion and data fluctuation, and introduces a redundancy correction coefficient to determine the dynamic weights of monitoring indicators in the digital twin model. The monitoring indicator quantification module sets dynamic thresholds according to the risk orientation of the monitoring indicators, constructs a virtual-real state mapping function, and transforms physical monitoring values ​​into standardized risk scores. The comprehensive situation score calculation module fits the risk time decay rate, designs a risk time decay model, and calculates the digital twin comprehensive situation score by combining the weights of monitoring indicators. The electronic fence security status monitoring module monitors the security status of the electronic fence based on a digital twin comprehensive situational assessment.

[0028] It should be noted that, in this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0029] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

[0030] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for monitoring the status of an electronic fence based on digital twin mapping, characterized in that: The method includes the following steps: Step S1: Multi-source data acquisition to obtain electronic fence status data and perform preprocessing; Step S2: Digital twin model construction, constructing a digital twin of the electronic fence, defining geometric space mapping, data interface layer, logical rule engine and state evolution operator; Step S3: Adaptive weight construction, calculate weights based on information dispersion and data fluctuation, and introduce a redundancy correction coefficient to determine the dynamic weights of monitoring indicators in the digital twin model; Step S4: Quantify monitoring indicators, set dynamic thresholds according to the risk orientation of monitoring indicators, construct a virtual-real state mapping function, and convert physical monitoring values ​​into standardized risk scores; Step S5: Calculate the comprehensive situation score, fit the risk time decay rate, design the risk time decay model, and calculate the digital twin comprehensive situation score by combining the weights of monitoring indicators. Step S6: Electronic fence security status monitoring, based on digital twin comprehensive situational assessment to monitor the security status of the electronic fence.

2. The electronic fence status monitoring method based on digital twin mapping according to claim 1, characterized in that: In step S3, the adaptive weight construction specifically includes: The entropy value of the indicator is calculated, and then the information dispersion weight is obtained; The volatility weight is calculated by taking the mean and standard deviation of the monitoring indicators. Weight fusion is used to introduce a redundancy correction coefficient to correct the product of information dispersion weight and volatility weight, thus obtaining the final index weight of the digital twin model.

3. The electronic fence status monitoring method based on digital twin mapping according to claim 1, characterized in that: In step S4, the quantification of the monitoring indicators specifically includes: The dynamic threshold definition for monitoring indicators is based on the digital twin mapping logic and the risk orientation of the indicators. Construct a virtual-real state mapping function to map the monitoring values ​​in the physical space to the risk scores in the digital twin.

4. The electronic fence status monitoring method based on digital twin mapping according to claim 1, characterized in that: In step S5, the comprehensive situation score calculation specifically includes: Risk decay rate fitting: Based on the historical operation and alarm closed-loop data of the electronic fence, the risk decay rate in the virtual model is fitted; Risk time decay model design; Design an indicator-level risk time decay model specific to the digital twin model, while introducing the risk decay critical time; Digital twin comprehensive situational assessment calculation: Based on the indicator weights and risk scores in the virtual model, the comprehensive situational assessment of the physical entity in the digital twin space is obtained.

5. The electronic fence status monitoring method based on digital twin mapping according to claim 1, characterized in that: In step S1, the multi-source data acquisition involves obtaining electronic fence status data, including intrusion behavior data, environmental interference factors, and equipment health status; and performing spatiotemporal benchmark unification and data standardization on the acquired data.

6. The electronic fence status monitoring method based on digital twin mapping according to claim 1, characterized in that: In step S2, the construction of the digital twin model includes: Geometric space mapping defines the geometric topology and spatial coordinate system of the fence; The data interface layer is designed to encapsulate the access protocol and standardized interface for electronic fence status data in S1; The logic rule engine is designed with an embedded weight calculation matrix and dynamic threshold rule library to support S3's adaptive weighting mechanism. The state evolution operator, with the time decay function and comprehensive score calculation preset in S5, enables dynamic projection of risk over time.

7. The electronic fence status monitoring method based on digital twin mapping according to claim 6, characterized in that: In step S6, the electronic fence security status monitoring is based on the comprehensive situation score output by the digital twin model, and performs risk score-alarm level mapping of the virtual-real mapping layer, thereby realizing predictive monitoring of the security status of the physical entity.

8. An electronic fence status monitoring system based on digital twin mapping, used to implement the electronic fence status monitoring method based on digital twin mapping as described in any one of claims 1-7, characterized in that: It includes a multi-source data acquisition module, a digital twin model construction module, an adaptive weight construction module, a monitoring indicator quantification module, a comprehensive situation score calculation module, and an electronic fence security status monitoring module; The multi-source data acquisition module acquires electronic fence status data and performs preprocessing. The digital twin model construction module constructs a digital twin of the electronic fence, defining geometric space mapping, data interface layer, logical rule engine and state evolution operator; The adaptive weight construction module calculates weights based on information dispersion and data fluctuation, and introduces a redundancy correction coefficient to determine the dynamic weights of monitoring indicators in the digital twin model. The monitoring indicator quantification module sets dynamic thresholds according to the risk orientation of the monitoring indicators, constructs a virtual-real state mapping function, and transforms physical monitoring values ​​into standardized risk scores. The comprehensive situation score calculation module fits the risk time decay rate, designs a risk time decay model, and calculates the digital twin comprehensive situation score by combining the weights of monitoring indicators. The electronic fence security status monitoring module monitors the security status of the electronic fence based on a digital twin comprehensive situational assessment.

Images