Electronic fence control method and system based on multi-mode identity authentication

Abstract

The invention discloses an electronic fence control method and system based on multi-mode identity authentication. The method comprises the following steps: arranging a plurality of identity information acquisition devices in an electronic fence coverage area, and acquiring multi-mode identity characteristic data such as fingerprints, faces, irises, intelligent card information and walking gaits of a user; and the collected data are fused to generate a comprehensive identity feature vector, and the comprehensive identity feature vector is input into the identity authentication module to be compared with a legal user template library to generate an authentication result. And according to the authentication result and a preset strategy, the electronic fence control module dynamically adjusts the fence state, such as opening, closing or alarming. The system also records the operation process and feeds back the operation process to the user. According to the invention, the accuracy and safety of identity recognition are improved through multi-mode identity authentication, and the flexibility and adaptability of the electronic fence are enhanced in combination with a dynamic control strategy.

Description

Technical Field

[0001] This application relates to the field of identity authentication technology, and in particular to an electronic fence control method and system based on multimodal identity authentication. Background Technology

[0002] With the rapid development of information technology, identity authentication technology plays a vital role in ensuring information security and preventing unauthorized access.

[0003] Traditional authentication methods primarily rely on single modalities, such as passwords, fingerprints, and facial recognition. However, single-modal authentication has numerous limitations. For example, passwords are easily forgotten or cracked; fingerprint recognition may fail when fingers are dirty, injured, or in extreme environments; and facial recognition can be affected by lighting conditions, makeup, and other factors. Furthermore, as an important means of protecting physical or virtual boundaries, the reliability and security of the control methods for electronic fences are crucial. Existing electronic fence control methods often rely on simple triggering mechanisms, lacking accurate identification and multi-dimensional verification of entrants. They are easily triggered by false alarms or maliciously breached, failing to effectively distinguish between legitimate users and unauthorized intruders, leading to security vulnerabilities and wasted resources. Summary of the Invention

[0004] Based on this, the embodiments of this application provide an electronic fence control method and system based on multimodal identity authentication. Through multimodal identity authentication, the accuracy of identity recognition can be effectively improved. At the same time, combined with the dynamic control strategy of the electronic fence, the opening, closing, alarm and other states of the electronic fence can be flexibly adjusted according to different identity authentication results, thereby achieving precise control of specific areas.

[0005] Firstly, an electronic fence control method based on multimodal identity authentication is provided, the method comprising:

[0006] Multiple identity information collection devices are set up within or around the electronic fence coverage area to collect users' multimodal identity feature data; wherein, the multimodal identity feature data includes at least fingerprint information, facial image, iris image, smart card information, and walking gait data;

[0007] The collected multimodal identity information is formatted and standardized, and a data fusion algorithm is used to fuse the identity information of multiple modalities to generate a comprehensive identity feature vector.

[0008] The fused identity feature vector is compared and matched with a pre-stored library of identity feature templates for legitimate users to generate identity authentication result information.

[0009] According to the identity authentication result information, a decision is made according to a preset control strategy, and the opening, closing or triggering of an alarm device of the electronic fence device is controlled;

[0010] The entire identity authentication and control process is recorded, and corresponding information is fed back to the user according to the user's authorization level.

[0011] Optionally, the multi-modal identity information collection device includes a fingerprint sensor, a camera, a smart card reader, and a behavior feature monitoring device, and each device preprocesses data during the collection process, and the collected data is preliminarily packaged, with a timestamp and device identification information attached.

[0012] The fingerprint sensor adopts optical or capacitive fingerprint recognition technology and can adapt to different humidity and temperature conditions; the camera has high resolution and low-light image acquisition capability and supports real-time face and iris recognition; the smart card reader supports various non-contact and contact smart cards and can quickly read user identity information; the behavior feature monitoring device collects user gait data through accelerometer and gyroscope sensors and extracts feature vectors combined with machine learning algorithms.

[0013] Optionally, in the identity information fusion and preprocessing, a weighted average method or a Bayesian fusion method is used to fuse and process the identity information of multiple modalities, and different weights are assigned according to the reliability and importance of each modal data.

[0014] The data fusion algorithm also includes an anomaly detection mechanism that can identify and eliminate abnormal data caused by device failure or environmental interference, ensuring the accuracy of the fusion result.

[0015] Optionally, the fused identity feature vector is compared and matched with a pre-stored legal user identity feature template library to generate identity authentication result information, including:

[0016] Support vector machines or deep learning algorithms are used for comparison and matching, and a threshold is set to determine whether the identity authentication is passed, and the generated identity authentication result information includes the user's identity, authentication status, and authentication confidence.

[0017] The support vector machine uses a radial basis kernel function, and the optimization parameters are determined by a grid search method; the deep learning algorithm uses a convolutional neural network, which includes multiple convolutional layers, pooling layers, and fully connected layers, and the training data set includes a large number of labeled multi-modal identity feature samples.

[0018] The identity feature template library also includes an online learning mechanism that can dynamically update the identity feature template library according to new authentication data.

[0019] Optionally, according to the identity authentication result information, a decision is made according to a preset control strategy to control the opening, closing or triggering of an alarm device of the electronic fence, including:

[0020] According to the identity type of the user, the entering time and the current state combination of the electronic fence area, the control decision of the electronic fence is set;

[0021] The identity type includes ordinary users, administrators and temporary visitors, and different identity types correspond to different access permissions and control strategies;

[0022] The entering time is divided into working hours, non-working hours and emergency state time, and the control strategies of different time periods are different, specifically including allowing ordinary users to normally enter during working hours, and only allowing administrators to enter during non-working hours;

[0023] The current state of the electronic fence area includes normal state, alert state and emergency state, the alert state is specifically triggered by abnormal behavior or system warning, the emergency state is specifically triggered by external threat or internal failure, and the control strategies in different states include limiting entry, triggering alarm and notifying security personnel.

[0024] Optionally, in the log recording and feedback, the log recording content includes the identity information of the user, the collected multi-modal identity feature data, the identity authentication result, the control decision result of the electronic fence and the operation timestamp, and the feedback information is informed to the user through mobile phone short message or application program push and the like;

[0025] The database used for recording the entire identity authentication and control process adopts a distributed storage architecture, supports high-concurrency writing and fast querying, and the log data is regularly backed up and encrypted to ensure the security and integrity of the data;

[0026] The feedback information also includes user operation guide and safety prompt, including prompting the user to re-collect identity information or contact the system administrator when the identity authentication fails, and reminding the user to pay attention to safety and take corresponding measures when the electronic fence triggers the alarm.

[0027] In a second aspect, an electronic fence control system based on multi-modal identity authentication is provided, which includes:

[0028] The acquisition module is used for setting multiple identity information acquisition devices in the electronic fence coverage area or its periphery to acquire multi-modal identity feature data of the user; wherein the multi-modal identity feature data at least includes fingerprint information, face image, iris image, smart card information and walking gait data;

[0029] The processing module is configured to unify and standardize the collected multi-modal identity information, and to generate a comprehensive identity feature vector by using a data fusion algorithm to fuse the identity information of various modalities.

[0030] The comparison module is configured to compare and match the fused identity feature vector with a pre-stored identity feature template library of legitimate users to generate identity authentication result information.

[0031] The control module is configured to make decisions according to the identity authentication result information and a preset control strategy, and to control the opening, closing or triggering of an alarm device of the electronic fence device.

[0032] The recording module is configured to record the entire identity authentication and control process, and to feed back corresponding information to the user according to the user's authorization level.

[0033] In a third aspect, an electronic device is provided, including a memory and a processor, the memory stores a computer program, and the processor implements the electronic fence control method of any one of the first aspect when executing the computer program.

[0034] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the electronic fence control method of any one of the first aspect.

[0035] In a fifth aspect, a computer program product is provided, which stores a computer program, and the computer program is executed by a processor to implement the electronic fence control method of any one of the first aspect.

[0036] The technical scheme provided by the embodiments of the present application has at least the following beneficial effects:

[0037] (1) By fusing multiple identity authentication modalities (such as fingerprint, face, iris, smart card and behavior characteristics), the limitations of single modality authentication can be effectively avoided, and the accuracy and robustness of identity authentication can be significantly improved.

[0038] (2) According to the identity authentication result and various real-time factors (such as user identity type, entering time, device performance, power state, etc.), the control strategy of the electronic fence is dynamically adjusted. It can meet the safety needs in different scenes, and also can optimize the use of resources and improve the overall efficiency of the system.

[0039] (3) By recording and feeding back the identity authentication and control process in detail, the user can know the operation state and system response in real time, and enhance the trust of the system. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required by the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and for those skilled in the art, other drawings can also be obtained from the provided drawings without creative labor.

[0041] Figure 1 A step flow chart of an electronic fence control method based on multi-modal identity authentication provided by an embodiment of the present application;

[0042] Figure 2 A block diagram of an electronic fence control system based on multi-modal identity authentication provided by an embodiment of the present application;

[0043] Figure 3 A schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0044] In order to make the objects, technical solutions and advantages of the present application clearer, the following will further describe the present application in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0045] In the description of the present application, the terms "comprising", "having" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units explicitly listed, but can also include other steps or units inherent to these processes, methods, products or devices, or steps or units added based on further optimization of the concept of the present application.

[0046] With the rapid development of information technology, identity authentication technology plays a crucial role in ensuring information security and preventing unauthorized access.

[0047] Traditional identity authentication methods mainly rely on a single modality, such as password, fingerprint, facial recognition, etc. However, single-modal identity authentication has many limitations. For example, passwords are easy to forget or crack; fingerprint recognition may fail when the finger is stained, injured or in extreme environments; facial recognition may be affected by factors such as lighting conditions, makeup, etc. In addition, as an important physical or virtual boundary protection means, the reliability and security of the control method of electronic fence is also crucial. Existing electronic fence control methods mostly rely on simple trigger mechanisms, lack precise identification and multi-dimensional verification of the identity of the entrant, are easy to be triggered by mistake or broken by maliciousness, cannot effectively distinguish between legitimate users and illegal intruders, and thus lead to security vulnerabilities and resource waste.

[0048] Please refer to Figure 1 , which shows a flowchart of an electronic fence control method based on multi-modal identity authentication provided by an embodiment of the present application. The method can include the following steps:

[0049] S1, multiple identity information collection devices are arranged in the electronic fence coverage area or its periphery to collect multi-modal identity feature data of the user.

[0050] The multi-modal identity feature data at least includes fingerprint information, facial image, iris image, smart card information, and walking gait data.

[0051] In the electronic fence coverage area or its periphery, multiple identity information collection devices are arranged to collect multi-modal identity feature data of the user. These collection devices include:

[0052] Fingerprint sensor: using optical or capacitive fingerprint recognition technology, which can adapt to different humidity and temperature conditions. Optical fingerprint sensor captures fingerprint image through optical imaging technology, while capacitive fingerprint sensor detects fingerprint ridge and valley through capacitive change. These sensors can quickly and accurately collect user's fingerprint information and convert it into digital signals.

[0053] Camera: with high resolution and low light image acquisition capability, supporting real-time face and iris recognition. The camera uses advanced image processing technology to clearly capture the user's face and iris image under different light conditions. Through deep learning algorithm, the camera can extract the feature vector of face and iris in real time for subsequent identity authentication.

[0054] Smart card reader: supporting multiple non-contact and contact smart cards, which can quickly read user identity information. Smart card reader reads data in smart card through radio frequency identification (RFID) technology or contact interface, ensuring fast and accurate reading of user identity information.

[0055] Behavior characteristic monitoring device: through accelerometer and gyroscope sensors to collect user's walking gait data. These sensors can monitor the user's motion state in real time and extract gait feature vector through machine learning algorithm. Behavior characteristic monitoring device can identify the unique walking pattern of the user as an auxiliary means of identity authentication.

[0056] During the collection process, each device preprocesses the data and preliminarily encapsulates the collected data, with timestamps and device identification information attached. For example, the fingerprint images collected by the fingerprint sensor are subjected to denoising and normalization processing, the facial images collected by the camera are subjected to illumination correction and feature extraction, the smart card information read by the smart card reader is subjected to formatting processing, and the gait data collected by the behavior characteristic monitoring device are subjected to feature extraction and normalization processing. The processed data are encapsulated into a data packet, containing timestamps and device identification information, for subsequent processing.

[0057] S2, the collected multi-modal identity information is subjected to format unification and standardization processing, and a data fusion algorithm is used to fuse the identity information of multiple modalities to generate a comprehensive identity feature vector.

[0058] Specifically, in this step, the data of different modalities are converted into a unified format, such as converting fingerprint images, facial images, etc. into grayscale images or feature vector forms. Standardization processing includes data normalization, denoising, etc. to ensure the consistency and comparability of different modal data.

[0059] A weighted average method or a Bayesian fusion method is used to fuse the identity information of multiple modalities. Different weights are assigned according to the reliability and importance of each modality data. For example, the weight of the biometric modalities (fingerprint, face, iris) ranges from 0.4 to 0.6, the weight of the smart card information ranges from 0.2 to 0.3, and the weight of the behavior characteristic modalities (gait) ranges from 0.1 to 0.2. The data fusion algorithm also includes an anomaly detection mechanism that can identify and eliminate abnormal data caused by device failure or environmental interference, ensuring the accuracy of the fusion results.

[0060] S3, the fused identity feature vector is compared and matched with a pre-stored identity feature template library of legitimate users to generate identity authentication result information.

[0061] Specifically, in this step, a support vector machine (SVM) or a deep learning algorithm (such as a convolutional neural network, CNN) is used for comparison and matching. The support vector machine uses a radial basis kernel function, and the optimization parameters are determined by a grid search method. The deep learning algorithm uses a convolutional neural network, which includes multiple convolutional layers, pooling layers and fully connected layers, and the training data set includes a large number of labeled multi-modal identity feature samples.

[0062] The generated identity authentication result information includes the user's identity, authentication status (pass or fail) and authentication confidence. The authentication status is determined by setting a threshold value, and when the similarity between the fused identity feature vector and a certain template in the template library is higher than the threshold value, the identity authentication is determined to be passed, otherwise the identity authentication is determined to be failed.

[0063] The identity feature template library also includes an online learning mechanism that can dynamically update the identity feature template library according to new authentication data. The online learning mechanism updates the feature vectors in the template library in real time through an incremental learning algorithm, improving the adaptability and robustness of the system.

[0064] S4, according to the identity authentication result information, making a decision according to the preset control strategy, controlling the opening, closing or triggering of the alarm device of the electronic fence device.

[0065] Specifically, in this step, the control decision of the electronic fence is set according to the user's identity type, entry time and current state of the electronic fence area. The identity type includes ordinary users, administrators and temporary visitors, and different identity types correspond to different access permissions and control strategies. The entry time is divided into working hours, non-working hours and emergency state time, and the control strategy is different in different time periods. For example, ordinary users are allowed to enter normally during working hours, while only administrators are allowed to enter during non-working hours. The current state of the electronic fence area includes normal state, alert state and emergency state, the alert state is triggered by abnormal behavior or system warning, and the emergency state is triggered by external threat or internal failure. The control strategy in different states includes limiting entry, triggering alarm and notifying security personnel.

[0066] According to the device performance and real-time scene, the resource loading strategy is dynamically adjusted. For example, in low power mode, the weight of P0 level resources is increased, critical resources are preferentially loaded, and unnecessary resources are reduced; in WiFi environment, the loading limit of P1 level resources is relaxed, allowing more resources to be preloaded in the background; in high temperature state, it is forced to enter compression mode, reduce the amount of resource loading, and reduce the load of the device; in the background state, all non-P0 level resource loading is suspended, only critical resources are retained, in order to save system resources.

[0067] S5, record the whole identity authentication and control process, and feedback the corresponding information to the user according to the user's authorization level.

[0068] Specifically, in this step, the log record content includes the user's identity information, the collected multi-modal identity feature data, the identity authentication result, the control decision result of the electronic fence and the operation timestamp. The record module adopts a distributed storage architecture, supports high-concurrency writing and fast querying, and the log data is regularly backed up and encrypted to ensure the security and integrity of the data.

[0069] The feedback information is informed to the user through mobile phone SMS or application program push, etc. The feedback information also includes user operation guide and safety prompt. For example, when the identity authentication fails, the user is prompted to re-collect identity information or contact the system administrator, and when the electronic fence triggers the alarm, the user is reminded to pay attention to safety and take corresponding measures.

[0070] In conclusion, the application can realize electronic fence control based on multi-modal identity authentication, improve the accuracy and security of identity authentication, enhance the flexibility and adaptability of electronic fence control, and improve user experience and system maintainability.

[0071] As Figure 2 The application also provides an electronic fence control system based on multi-modal identity authentication. The system can include:

[0072] The acquisition module is configured to set multiple identity information acquisition devices in the electronic fence coverage area or its periphery, and acquire multi-modal identity feature data of a user. The multi-modal identity feature data at least includes fingerprint information, facial image, iris image, smart card information, and walking gait data.

[0073] The processing module is configured to uniformly format and standardize the acquired multi-modal identity information, and perform fusion processing on the identity information of multiple modalities by using a data fusion algorithm, to generate a comprehensive identity feature vector.

[0074] The comparison module is configured to compare and match the fused identity feature vector with a pre-stored identity feature template library of a legal user, to generate identity authentication result information.

[0075] The control module is configured to make a decision according to the identity authentication result information and a pre-set control strategy, to control the opening, closing, or triggering of an alarm device of the electronic fence device.

[0076] The recording module is configured to record the entire identity authentication and control process, and feed back corresponding information to the user according to the user's authorization level.

[0077] For specific limitations of the electronic fence control system based on multi-modal identity authentication, refer to the limitations of the electronic fence control method based on multi-modal identity authentication in the foregoing description, which will not be repeated here. Each module in the electronic fence control system based on multi-modal identity authentication can be realized by software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0078] In one embodiment, an electronic device, which can be a computer, is provided, and an internal structure diagram of the electronic device can be as shown in Figure 3As shown. The electronic device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the device is used to provide computing and control capabilities. The memory of the device includes a non-volatile storage medium, an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is used to control the data of the electronic fence based on the multi-modal identity authentication. The network interface of the computer device is used to communicate with the external terminal through the network connection. The computer program is executed by the processor to implement a multi-modal identity authentication-based electronic fence control method.

[0079] Those skilled in the art can understand that the structure shown in Figure 3 The skilled in the art can understand that the structure shown in

[0080] In an embodiment of the present application, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. The computer program is executed by the processor to implement the steps of the multi-modal identity authentication-based electronic fence control method.

[0081] In an embodiment of the present application, a computer program product is provided, and the computer program product includes computer programs / instructions. The computer program is executed by the processor to implement the steps of the multi-modal identity authentication-based electronic fence control method.

[0082] The computer readable storage medium and the computer program product provided by the embodiment have similar implementation principles and technical effects to the above method embodiments, and will not be described here.

[0083] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, storage, databases, or other media in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (SyMchliMk) DRAM (SLDRAM), memory bus (RaMbus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0084] The technical features of the above-mentioned embodiments can be combined in any way. In order to make the description simple, all possible combinations of the technical features in the above-mentioned embodiments are not described, but as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0085] The above-mentioned embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of the patent protection of the present application should be subject to the appended claims.

Claims

1. An electronic fence control method based on multimodal identity authentication, characterized in that, The method includes: Multiple identity information collection devices are set up within or around the electronic fence coverage area to collect users' multimodal identity feature data; wherein, the multimodal identity feature data includes at least fingerprint information, facial image, iris image, smart card information, and walking gait data; The collected multimodal identity information is formatted and standardized, and a data fusion algorithm is used to fuse the identity information of multiple modalities to generate a comprehensive identity feature vector. The fused identity feature vector is compared and matched with a pre-stored library of identity feature templates for legitimate users to generate identity authentication result information. Based on the identity authentication results, decisions are made according to the preset control strategy to control the opening and closing of the electronic fence equipment or trigger the alarm device. The entire identity authentication and control process is recorded, and corresponding information is fed back to the user based on the user's authorization level.

2. The electronic fence control method according to claim 1, characterized in that, The multimodal identity information collection device includes a fingerprint sensor, a camera, a smart card reader, and a behavior feature monitoring device. During the collection process, each device preprocesses the data and initially encapsulates the collected data, attaching a timestamp and device identification information. The fingerprint sensor employs optical or capacitive fingerprint recognition technology, adaptable to varying humidity and temperature conditions; the camera possesses high-resolution and low-light image acquisition capabilities, supporting real-time facial and iris recognition; the smart card reader supports various contactless and contact smart cards, enabling rapid reading of user identity information; the behavioral feature monitoring device collects user gait data using accelerometer and gyroscope sensors, and extracts feature vectors using machine learning algorithms.

3. The electronic fence control method according to claim 1, characterized in that, In the identity information fusion and preprocessing, a weighted average method or a Bayesian fusion method is used to fuse identity information of multiple modalities, and different weights are assigned according to the reliability and importance of each modal data. The data fusion algorithm also includes an anomaly detection mechanism, which can identify and remove abnormal data caused by equipment failure or environmental interference, ensuring the accuracy of the fusion results.

4. The electronic fence control method according to claim 1, characterized in that, The fused identity feature vector is compared and matched with a pre-stored library of identity feature templates for legitimate users to generate identity authentication result information, including: Support vector machine or deep learning algorithm is used for comparison and matching, and a threshold is set to determine whether the identity authentication is successful. The generated identity authentication result information includes the user's identity identifier, authentication status and authentication confidence level. The support vector machine uses a radial basis function kernel, and the optimization parameters are determined by a grid search method; the deep learning algorithm uses a convolutional neural network, which includes multiple convolutional layers, pooling layers and fully connected layers, and the training dataset includes a large number of labeled multimodal identity feature samples. The identity feature template library also includes an online learning mechanism that can dynamically update the identity feature template library based on new authentication data.

5. The electronic fence control method according to claim 1, characterized in that, Based on the identity authentication result information, decisions are made according to the preset control strategy to control the opening and closing of the electronic fence equipment or trigger the alarm device, including: The control decisions for the electronic fence are set based on a combination of the user's identity type, entry time, and the current status of the electronic fence area. The identity types include regular users, administrators, and temporary visitors, with different identity types corresponding to different access permissions and control policies; The access time is divided into working hours, non-working hours, and emergency periods. The control strategies for different time periods are different. Specifically, during working hours, ordinary users are allowed to enter normally, while during non-working hours, only administrators are allowed to enter. The current state of the electronic fence area includes normal state, alert state and emergency state. The alert state is specifically triggered by abnormal behavior or system warning, and the emergency state is specifically triggered by external threats or internal failures. The control strategies for different states include restricting access, triggering alarms and notifying security personnel.

6. The electronic fence control method according to claim 1, characterized in that, In the logging and feedback process, the logging content includes the user's identity information, the collected multimodal identity feature data, the identity authentication results, the control decision results of the electronic fence, and the operation timestamp. Feedback information is provided to the user via SMS or application push notifications. The database used to record the entire identity authentication and control process adopts a distributed storage architecture, supports high-concurrency writing and fast querying, and the log data is regularly backed up and encrypted to ensure data security and integrity. The feedback information also includes user operation guides and security tips, such as prompting users to re-collect identity information or contact the system administrator when identity authentication fails, and reminding users to pay attention to safety and take appropriate measures when the electronic fence triggers an alarm.

7. An electronic fence control system based on multimodal identity authentication, characterized in that, The system includes: The data acquisition module is used to set up various identity information acquisition devices within or around the coverage area of ​​the electronic fence to collect multimodal identity feature data of users; wherein, the multimodal identity feature data includes at least fingerprint information, facial image, iris image, smart card information, and walking gait data; The processing module is used to unify and standardize the format of the collected multimodal identity information, and to use a data fusion algorithm to fuse the identity information of multiple modalities to generate a comprehensive identity feature vector. The comparison module is used to compare and match the fused identity feature vector with a pre-stored library of identity feature templates for legitimate users to generate identity authentication result information. The control module is used to make decisions based on the identity authentication result information and according to the preset control strategy to control the opening and closing of the electronic fence equipment or trigger the alarm device. The recording module is used to record the entire identity authentication and control process and provide corresponding information to the user based on the user's authorization level.

8. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program that, when executed by the processor, implements the electronic fence control method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the electronic fence control method as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the electronic fence control method according to any one of claims 1 to 6.

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