Battery replacement cabinet battery anti-theft system based on artificial intelligence

Abstract

The invention discloses a battery replacement cabinet battery anti-theft system based on artificial intelligence, and relates to the technical field of theft prevention. According to the battery replacement cabinet battery anti-theft system based on artificial intelligence, the user identity is verified through a face recognition module, the operation legality is dynamically evaluated in combination with a behavior analysis technology, a unique user operation identifier is generated, unauthorized use is completely eradicated from the source, and when abnormal recognition is achieved, the user is not authorized; the system instantly limits user authority or triggers multi-factor verification, safety protection is further strengthened, the sensor module collects vibration, displacement and ambient light data in the battery taking and placing process in real time, and the data are transmitted to the central control unit through the Internet of Things module. And the central control unit performs conjoint analysis on sensor data and surrounding environment characteristics acquired by the camera by using an artificial intelligence model, extracts dynamic characteristics of multi-modal time sequence data through a bidirectional recurrent neural network, accurately evaluates behavior legality in combination with a classification algorithm, and triggers an emergency response according to a threat level if it is confirmed that an abnormal behavior exists.

Description

Technical Field

[0001] The present invention relates to the field of anti-theft technology, and in particular to an artificial intelligence-based battery anti-theft system for a battery swap cabinet. Background Art

[0002] In high-density urban areas, in order to cover more electric vehicles, the distribution of user battery swap cabinets is relatively compact; the flow of people in high-density urban areas is large and complex, and the frequency of use of battery swap cabinets is high. Operators need to deal with high-intensity equipment requirements and potential security threats at the same time. Since battery swap cabinets are mostly deployed in open environments, such as community entrances and exits, business districts or street locations, the anti-theft pressure is particularly prominent.

[0003] The battery anti-theft methods of traditional battery swap cabinets mainly rely on mechanical locks, electronic locks and RFID identity recognition systems. The original intention of such methods is to restrict unauthorized operations and to ensure battery safety as much as possible. However, in high-density area environments, mechanical locks and electronic locks are vulnerable to violent damage, and RFID technology also has risks such as forgery and cracking. In addition, high-frequency equipment operation will lead to increased maintenance workload. When the equipment is not repaired in time due to failure, it is more likely to become a security vulnerability.

[0004] Some traditional solutions address these problems by strengthening lock materials, increasing the complexity of electronic locks, and increasing the frequency of inspections. However, these countermeasures are essentially "passive defense" and lack specificity and initiative. Inspections require a lot of human resources and cannot achieve real-time response. Increasing the complexity of locks still has limitations when facing malicious damage. Therefore, there is an urgent need for an artificial intelligence-based battery anti-theft solution for battery swap cabinets to solve such problems. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides an artificial intelligence-based battery anti-theft system for battery swap cabinets to solve the problem that in high-density urban areas, the battery swap cabinets are compactly distributed but have a high risk of theft, and in densely populated urban areas, there are a large number of battery swap cabinets and a complex mobile population, which may lead to rapid battery theft or malicious destruction.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: The embodiment of the present invention provides a battery anti-theft system for a battery swap cabinet based on artificial intelligence, which includes: Facial recognition module, with built-in camera, is used to verify the user's identity and dynamically evaluate the legitimacy of operations in combination with behavioral analysis technology to generate a user operation identifier; The sensor module is used to collect vibration, displacement and ambient light data during battery placement in real time; The IoT module is used to transmit the data collected by the sensors to the central control unit. The central control unit receives sensor data and uses artificial intelligence models to perform multi-dimensional analysis, identifying abnormal behavior by combining surrounding environmental characteristics and triggering emergency response; The management center is used to receive abnormal event data, coordinate with the security department to respond, and store all battery replacement operation records and abnormal events through blockchain technology.

[0008] As a preferred solution of the battery anti-theft system for battery swap cabinet based on artificial intelligence described in the present invention, the battery swapping method of the battery anti-theft system for battery swap cabinet based on artificial intelligence is: Step S1, obtain user facial data through the facial recognition module embedded in the battery swap cabinet, use the convolutional neural network CNN to perform real-time facial feature extraction and comparison based on the user facial data, verify the user identity, and authorize after successful verification, and generate a unique user operation identifier; Step S2: a sensor module is integrated inside the battery swap cabinet, including a vibration sensor, a displacement sensor and a light sensor, to collect battery access process data, including vibration, displacement and ambient light data, and the collected access process data is transmitted to the central control unit through the Internet of Things module; Step S3, using the camera to obtain the surrounding environment characteristics, and using the recurrent neural network (RNN) to jointly analyze the pick-and-place process data and the surrounding environment characteristics to identify abnormal behaviors, including illegal tool operation, multi-person gathering, and unauthorized operation; Step S4, after confirming the existence of abnormal behavior, trigger the emergency response mechanism, and classify the abnormal behavior at the same time, and different threat levels trigger different loudness alarms.

[0009] As a preferred solution of the artificial intelligence-based battery anti-theft system for battery swap cabinets described in the present invention, in which: in step S1, when facial recognition detects an identity mismatch, user permissions are immediately restricted or additional multi-factor verification is required.

[0010] As a preferred solution of the battery anti-theft system for battery swap cabinets based on artificial intelligence described in the present invention, the steps of extracting and comparing facial features in real time using a convolutional neural network CNN based on user facial data and verifying the user's identity are as follows: Get the user's real-time facial image through the built-in camera, assuming the image is: , in, represents the original image collected. represents the field of real numbers, Indicates the image height, Indicates the image width, Indicates the number of image channels; right Perform grayscale processing to generate a grayscale image , and normalize it. The normalization formula is: , in, represents a grayscale image, represents the pixel mean of the grayscale image, represents the pixel standard deviation of the grayscale image, Represents the normalized grayscale image; The normalized grayscale image Input convolutional neural network CNN, the CNN used consists of three convolutional layers and two fully connected layers, where the output feature map of each convolution layer for: , in, Indicates The feature map after layer convolution, Indicates The convolution kernel of the layer, Indicates The bias of the layer, represents the convolution operation, represents the activation function; The convolution output features are reduced in dimension through the maximum pooling operation, and the pooling window is set to , the pooling operation formula is: , in, represents the features after pooling, represents the last layer of convolutional feature map, represents the pooling window size, The pooling feature Input to the fully connected layer for classification processing to generate feature vector : , in, represents the weight matrix of the fully connected layer, represents the bias of the fully connected layer, represents the final extracted feature vector; Extracted feature vectors The feature vector of the registered users in the database The cosine similarity calculation is performed using the following formula: , in, represents the similarity between the extracted features and the reference features, represents the reference user feature vector, and are the modulus lengths of the eigenvectors respectively; If the similarity Exceeding the set threshold , the verification is successful and a unique user identifier is generated : , in, Indicates the user's unique operation identifier. Represents a hash generation function.

[0011] As a preferred solution of the artificial intelligence-based battery anti-theft system for battery swap cabinets described in the present invention, the surrounding environment characteristics include crowd density and tool usage.

[0012] As a preferred solution of the artificial intelligence-based battery theft prevention system for battery swap cabinets described in the present invention, all battery swap operations and abnormal event data are encrypted and stored through blockchain technology.

[0013] As a preferred solution of the battery anti-theft system for battery swap cabinets based on artificial intelligence described in the present invention, the steps of using a recurrent neural network RNN ​​to jointly analyze the data of the picking and placing process and the characteristics of the surrounding environment to identify abnormal behavior are as follows: Get real-time data of the pick-and-place process, including vibration data, through the sensor module , displacement data , ambient light data , the surrounding environment characteristics collected by the camera, including the density of people and tool usage , combining all data into a joint time series input : , in, Indicates that at time step The vibration data value, Indicates that at time step The displacement data value, Indicates that at time step The ambient light data value, Indicates that at time step The crowd density characteristics, Indicates that at time step The tool usage characteristics, represents the total length of the time series, Bidirectional recurrent neural network Bi-RNN is used to extract features from sequence data. The hidden state of the forward RNN is , the hidden state of the backward RNN is , the combined output is: , in, Represents the forward RNN at time step The hidden state of Represents the backward RNN at time step The hidden state of Represents the joint hidden state, which contains the forward and backward feature information. The update formula for the single-step hidden state is: , in, Represents the forward RNN at time step The hidden state of Indicates that at time step The combined input features of represents the weight matrix of the forward hidden state, represents the weight matrix of the forward input features, represents the forward bias term, Represents the backward RNN at time step The hidden state of represents the weight matrix of the backward hidden state, represents the weight matrix of the backward input features, represents the backward bias term, represents the tanh activation function; The hidden state for all time steps Perform average aggregation to obtain global feature representation : , in, The global representation vector representing the joint features, represents the total length of the time series, Represents the time step The joint hidden state of The global feature representation Enter the fully connected layer and use the Softmax function for classification: , in, represents the probability distribution of abnormal behavior classification results, represents the weight matrix of the fully connected layer, represents the bias term of the fully connected layer; According to the classification results , the highest probability category is selected as the prediction result, and if the probability exceeds the preset threshold, it is determined to be an abnormal behavior.

[0014] As a preferred solution of the artificial intelligence-based battery anti-theft system for battery swap cabinets described in the present invention, the emergency response mechanism includes remotely locking the battery compartment door and activating a high-pitched alarm, while pushing abnormal event data to the management center in real time.

[0015] As a preferred solution of the battery anti-theft system for battery swap cabinets based on artificial intelligence described in the present invention, the steps of triggering the emergency response mechanism after confirming the existence of abnormal behavior and grading the abnormal behavior are as follows: According to the classification result in step S3 The probability value and threat level corresponding to the category define the threat value of abnormal behavior : , in, Indicates the threat value of abnormal behavior, Indicates the classification result The probability value of class behavior, Indicates Predefined threat weights for class behaviors, Set the threat level threshold ,Will Mapping to different threat levels : like ,but , like ,but , … like ,but , in, Indicates the threat level, A sequence of thresholds representing threat classification; Based on threat level , select the corresponding emergency response measures, and set the emergency response matrix as , among which The row indicates the threat level is The emergency action set: , like , then execute the response action Specific measures include the following operations: Remotely lock the battery door, Activate the high-pitched alarm, And push abnormal event data to the management center and store it encrypted through blockchain.

[0016] As a preferred solution of the battery anti-theft system for battery swap cabinets based on artificial intelligence described in the present invention, the encryption method through blockchain is: Organize abnormal event data into transactions , including timestamp , Threat Level , User Operation Identifier , Description of abnormal behavior : , in, Indicates abnormal event data transaction, The timestamp indicating when the event occurred. Indicates the threat level, A unique identifier for the user. Descriptive information indicating abnormal behavior, Will Broadcast to the blockchain network and generate new blocks : , in, Represents a new block, represents a hash function, Indicates the current transaction data. Indicates the hash value of the previous block; According to the abnormal event data received by the management center, the security department will be linked to perform corresponding operations, and the system will monitor in real time changes and add stronger emergency responses as the threat level increases.

[0017] The beneficial effects of the present invention are as follows: the present invention verifies the identity of the user through a facial recognition module, dynamically evaluates the legality of the operation in combination with behavioral analysis technology, and generates a unique user operation identifier, thereby eliminating unauthorized use at the source. When an abnormality is identified, the system immediately restricts user permissions or triggers multi-factor verification to further strengthen security protection. The sensor module collects vibration, displacement and ambient light data during battery placement in real time, and transmits the data to the central control unit through the Internet of Things module. The central control unit uses an artificial intelligence model to jointly analyze the sensor data and the surrounding environment characteristics acquired by the camera to identify abnormal behaviors such as illegal tool operations, multi-person gatherings and unauthorized operations. The system extracts dynamic features of multimodal time series data through a bidirectional recurrent neural network, and accurately evaluates the legality of the behavior in combination with a classification algorithm. If abnormal behavior is confirmed, an emergency response is triggered according to the threat level, including remotely locking the battery compartment door, activating an alarm, and pushing event data to the management center. At the same time, all battery replacement operation records and abnormal events are encrypted and stored through blockchain technology to ensure that the data cannot be tampered with and provide a basis for subsequent tracking. Furthermore, the management center can work with the security department to respond to threats in real time and dynamically adjust protection measures.

[0018] The present invention overcomes the problems of traditional mechanical locks and electronic locks being easily damaged and the risk of counterfeiting in RFID technology. At the same time, it avoids the problem of inspection relying on manpower through active monitoring and real-time response, and reduces maintenance costs in high-frequency usage scenarios. It also uses the deep combination of artificial intelligence and blockchain technology to achieve efficient, accurate and dynamic battery anti-theft management. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 It is a schematic diagram of the framework of the battery anti-theft system for battery swap cabinets based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0022] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0023] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0024] Example 1, reference Figure 1 This embodiment provides an artificial intelligence-based battery anti-theft system for a battery swap cabinet, including: Facial recognition module, with built-in camera, is used to verify the user's identity and dynamically evaluate the legitimacy of operations in combination with behavioral analysis technology to generate a user operation identifier; The sensor module is used to collect vibration, displacement and ambient light data during battery placement in real time; The IoT module is used to transmit the data collected by the sensors to the central control unit. The central control unit receives sensor data and uses artificial intelligence models to perform multi-dimensional analysis, identifying abnormal behavior by combining surrounding environmental characteristics and triggering emergency response; The management center is used to receive abnormal event data, link up with the security department to respond, and store all battery swap operation records and abnormal events through blockchain technology; This embodiment also discloses a battery replacement method of a battery theft prevention system for a battery replacement cabinet based on artificial intelligence, including: Step S1, obtain user facial data through the facial recognition module embedded in the battery swap cabinet, use the convolutional neural network CNN to perform real-time facial feature extraction and comparison based on the user facial data, verify the user identity, and authorize after successful verification, and generate a unique user operation identifier; In step S1, when facial recognition detects identity mismatch, user permissions are immediately restricted or additional multi-factor authentication is required; Based on the user's facial data, a convolutional neural network (CNN) is used to extract and compare facial features in real time. The steps for verifying the user's identity are as follows: Get the user's real-time facial image through the built-in camera, assuming the image is: , in, represents the original image collected. represents the field of real numbers, Indicates the image height, Indicates the image width, Indicates the number of image channels; right Perform grayscale processing to generate a grayscale image , and normalize it. The normalization formula is: , in, represents a grayscale image, represents the pixel mean of the grayscale image, represents the pixel standard deviation of the grayscale image, Represents the normalized grayscale image; The normalized grayscale image Input convolutional neural network CNN, the CNN used consists of three convolutional layers and two fully connected layers, where the output feature map of each convolution layer for: , in, Indicates The feature map after layer convolution, Indicates The convolution kernel of the layer, Indicates The bias of the layer, represents the convolution operation, represents the activation function; The convolution output features are reduced in dimension through the maximum pooling operation, and the pooling window is set to , the pooling operation formula is: , in, represents the features after pooling, represents the last layer of convolutional feature map, represents the pooling window size, The pooling feature Input to the fully connected layer for classification processing to generate feature vector : , in, represents the weight matrix of the fully connected layer, represents the bias of the fully connected layer, represents the final extracted feature vector; Extracted feature vectors The feature vector of the registered users in the database The cosine similarity calculation is performed using the following formula: , in, represents the similarity between the extracted features and the reference features, represents the reference user feature vector, and are the modulus lengths of the eigenvectors respectively; If the similarity Exceeding the set threshold , the verification is successful and a unique user identifier is generated : , in, Indicates the user's unique operation identifier. represents a hash generation function; Specifically, this step uses a convolutional neural network to extract facial features in real time. In the specific operation, the convolution layer is used to extract local features of the image, and the fully connected layer is used to reduce the dimension and classify the global features. In the feature comparison stage, the legitimacy of the identity is determined by cosine similarity to ensure verification accuracy; and a unique identifier is generated through feature hashing, taking into account both security and efficiency.

[0025] Step S2: a sensor module is integrated inside the battery swap cabinet, including a vibration sensor, a displacement sensor and a light sensor, to collect battery access process data, including vibration, displacement and ambient light data, and the collected access process data is transmitted to the central control unit through the Internet of Things module; Step S3, using the camera to obtain the surrounding environment characteristics, and using the recurrent neural network (RNN) to jointly analyze the pick-and-place process data and the surrounding environment characteristics to identify abnormal behaviors, including illegal tool operation, multi-person gathering, and unauthorized operation; Surrounding environment characteristics include human density and tool use; All battery swap operations and abnormal event data are encrypted and stored using blockchain technology; The recurrent neural network (RNN) is used to jointly analyze the pick-and-place process data and the surrounding environment characteristics. The steps to identify abnormal behavior are as follows: Get real-time data of the pick-and-place process, including vibration data, through the sensor module , displacement data , ambient light data , the surrounding environment characteristics collected by the camera, including the density of people and tool usage , combining all data into a joint time series input : , in, Indicates that at time step The vibration data value, Indicates that at time step The displacement data value, Indicates that at time step The ambient light data value, Indicates that at time step The crowd density characteristics, Indicates that at time step The tool usage characteristics, represents the total length of the time series, Bidirectional recurrent neural network Bi-RNN is used to extract features from sequence data. The hidden state of the forward RNN is , the hidden state of the backward RNN is , the combined output is: , in, Represents the forward RNN at time step The hidden state of Represents the backward RNN at time step The hidden state of Represents the joint hidden state, which contains the forward and backward feature information. The update formula for the single-step hidden state is: , in, Represents the forward RNN at time step The hidden state of Indicates that at time step The combined input features of represents the weight matrix of the forward hidden state, represents the weight matrix of the forward input features, represents the forward bias term, Represents the backward RNN at time step The hidden state of represents the weight matrix of the backward hidden state, represents the weight matrix of the backward input features, represents the backward bias term, represents the tanh activation function; The hidden state for all time steps Perform average aggregation to obtain global feature representation : , in, The global representation vector representing the joint features, represents the total length of the time series, Represents the time step The joint hidden state of The global feature representation Enter the fully connected layer and use the Softmax function for classification: , in, represents the probability distribution of abnormal behavior classification results, represents the weight matrix of the fully connected layer, represents the bias term of the fully connected layer; According to the classification results , select the highest probability category as the prediction result, and if the probability exceeds the preset threshold, it is determined to be abnormal behavior; Specifically, the multimodal time series data is analyzed through a bidirectional recurrent neural network to effectively capture the dynamic relationship between the pick-and-place process and environmental characteristics; the single-step hidden state update formula integrates the forward and backward dependencies of the time series, generates a comprehensive representation vector H through global aggregation, and combines it with the Softmax classification function for anomaly detection, which is highly robust.

[0026] Step S4, after confirming the existence of abnormal behavior, triggering the emergency response mechanism, and grading the abnormal behavior at the same time, different threat levels trigger different loudness alarms; Emergency response mechanisms include remotely locking the battery compartment door and activating a high-pitched alarm, while pushing abnormal event data to the management center in real time; After confirming the existence of abnormal behavior, the emergency response mechanism is triggered, and the steps for grading the abnormal behavior are as follows: According to the classification result in step S3 The probability value and threat level corresponding to the category define the threat value of abnormal behavior : , in, Indicates the threat value of abnormal behavior, Indicates the classification result The probability value of class behavior, Indicates Predefined threat weights for class behaviors, Set the threat level threshold ,Will Mapping to different threat levels : like ,but , like ,but , … like ,but , in, Indicates the threat level, A sequence of thresholds representing threat classification; Based on threat level , select the corresponding emergency response measures, and set the emergency response matrix as , among which The row indicates the threat level is The emergency action set: , like , then execute the response action Specific measures include the following operations: Remotely lock the battery door, Activate the high-pitched alarm, And push abnormal event data to the management center and store it encrypted through blockchain.

[0027] The encryption method through blockchain is: Organize abnormal event data into transactions , including timestamp , Threat Level , User Operation Identifier , Description of abnormal behavior : , in, Indicates abnormal event data transaction, The timestamp indicating when the event occurred. Indicates the threat level, A unique identifier for the user. Descriptive information indicating abnormal behavior, Will Broadcast to the blockchain network and generate new blocks : , in, Represents a new block, represents a hash function, Indicates the current transaction data. Indicates the hash value of the previous block; According to the abnormal event data received by the management center, the security department will be linked to perform corresponding operations, and the system will monitor in real time changes and provide stronger emergency response when the threat level increases; Specifically, abnormal behaviors are quantitatively evaluated by calculating threat values ​​and grading mechanisms, and emergency response strategies are triggered according to the threat levels. Blockchain technology is used to encrypt and store abnormal event data to ensure data integrity. The real-time linkage response mechanism further enhances the system's dynamic protection capabilities.

[0028] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An artificial intelligence-based battery anti-theft system for battery swap cabinets, characterized in that: include, Facial recognition module, with built-in camera, is used to verify the user's identity and dynamically evaluate the legitimacy of operations in combination with behavioral analysis technology to generate a user operation identifier; The sensor module is used to collect vibration, displacement and ambient light data during battery placement in real time; The IoT module is used to transmit the data collected by the sensors to the central control unit. The central control unit receives sensor data and uses artificial intelligence models to perform multi-dimensional analysis, identifying abnormal behavior by combining surrounding environmental characteristics and triggering emergency response; The management center is used to receive abnormal event data, coordinate with the security department to respond, and store all battery replacement operation records and abnormal events through blockchain technology.

2. The battery anti-theft system for battery swap cabinet based on artificial intelligence as claimed in claim 1 is characterized in that: The battery swapping method of the battery anti-theft system for battery swapping cabinet based on artificial intelligence is: Step S1, obtain user facial data through the facial recognition module embedded in the battery swap cabinet, use the convolutional neural network CNN to perform real-time facial feature extraction and comparison based on the user facial data, verify the user identity, and authorize after successful verification, and generate a unique user operation identifier; Step S2: a sensor module is integrated inside the battery swap cabinet, including a vibration sensor, a displacement sensor and a light sensor, to collect battery access process data, including vibration, displacement and ambient light data, and the collected access process data is transmitted to the central control unit through the Internet of Things module; Step S3, using the camera to obtain the surrounding environment characteristics, and using the recurrent neural network (RNN) to jointly analyze the pick-and-place process data and the surrounding environment characteristics to identify abnormal behaviors, including illegal tool operation, multi-person gathering, and unauthorized operation; Step S4, after confirming the existence of abnormal behavior, trigger the emergency response mechanism, and classify the abnormal behavior at the same time, and different threat levels trigger different loudness alarms.

3. The battery anti-theft system for battery swap cabinet based on artificial intelligence as claimed in claim 2 is characterized by: In step S1, when facial recognition detects an identity mismatch, user permissions are immediately restricted or additional multi-factor verification is required.

4. The battery anti-theft system for battery swap cabinet based on artificial intelligence as claimed in claim 3 is characterized by: The steps of extracting and comparing facial features in real time using a convolutional neural network (CNN) based on user facial data and verifying the user's identity are as follows: Get the user's real-time facial image through the built-in camera, assuming the image is: ， in, represents the original image collected. represents the field of real numbers, Indicates the image height, Indicates the image width, Indicates the number of image channels; right Perform grayscale processing to generate a grayscale image , and normalize it. The normalization formula is: ， in, represents a grayscale image, represents the pixel mean of the grayscale image, represents the pixel standard deviation of the grayscale image, Represents the normalized grayscale image; The normalized grayscale image Input convolutional neural network CNN, the CNN used consists of three convolutional layers and two fully connected layers, where the output feature map of each convolution layer for: ， in, Indicates The feature map after layer convolution, Indicates The convolution kernel of the layer, Indicates The bias of the layer, represents the convolution operation, represents the activation function; The convolution output features are reduced in dimension through the maximum pooling operation, and the pooling window is set to , the pooling operation formula is: ， in, represents the features after pooling, represents the last layer of convolutional feature map, represents the pooling window size, The pooling feature Input to the fully connected layer for classification processing to generate feature vector : ， in, represents the weight matrix of the fully connected layer, represents the bias of the fully connected layer, represents the final extracted feature vector; Extracted feature vectors The feature vector of the registered users in the database The cosine similarity calculation is performed using the following formula: ， in, Indicates the similarity between the extracted features and the reference features, represents the reference user feature vector, and are the modulus lengths of the eigenvectors respectively; If the similarity Exceeding the set threshold , the verification is successful and a unique user identifier is generated : ， in, Indicates the user's unique operation identifier. Represents a hash generation function.

5. The battery anti-theft system for battery swap cabinet based on artificial intelligence as claimed in claim 4 is characterized by: The surrounding environment characteristics include human density and tool usage.

6. The battery anti-theft system for battery swap cabinet based on artificial intelligence as claimed in claim 5 is characterized by: All battery replacement operations and abnormal event data are encrypted and stored using blockchain technology.

7. The artificial intelligence-based battery anti-theft system for battery swap cabinets according to claim 6 is characterized in that: The steps of using a recurrent neural network (RNN) to jointly analyze the pick-and-place process data and the surrounding environment characteristics and identify abnormal behaviors are as follows: Get real-time data of the pick-and-place process, including vibration data, through the sensor module , displacement data , ambient light data , the surrounding environment characteristics collected by the camera, including the density of people and tool usage , combining all data into a joint time series input : ， in, Indicates that at time step The vibration data value, Indicates that at time step The displacement data value, Indicates that at time step The ambient light data value, Indicates that at time step The crowd density characteristics, Indicates that at time step Tool usage characteristics, represents the total length of the time series, Bidirectional recurrent neural network Bi-RNN is used to extract features from sequence data. The hidden state of the forward RNN is , the hidden state of the backward RNN is , the combined output is: ， in, Represents the forward RNN at time step The hidden state of Represents the backward RNN at time step The hidden state of Represents the joint hidden state, which contains the forward and backward feature information. The update formula for the single-step hidden state is: ， in, Represents the forward RNN at time step The hidden state of Indicates that at time step The combined input features of represents the weight matrix of the forward hidden state, represents the weight matrix of the forward input features, represents the forward bias term, Represents the backward RNN at time step The hidden state of represents the weight matrix of the backward hidden state, represents the weight matrix of the backward input features, represents the backward bias term, represents the tanh activation function; The hidden state for all time steps Perform average aggregation to obtain global feature representation : ， in, The global representation vector representing the joint features, represents the total length of the time series, Represents the time step The joint hidden state of The global feature representation Enter the fully connected layer and use the Softmax function for classification: ， in, represents the probability distribution of abnormal behavior classification results, represents the weight matrix of the fully connected layer, represents the bias term of the fully connected layer; According to the classification results , the highest probability category is selected as the prediction result, and if the probability exceeds the preset threshold, it is determined to be an abnormal behavior.

8. The battery anti-theft system for battery swap cabinet based on artificial intelligence as claimed in claim 7 is characterized by: The emergency response mechanism includes remotely locking the battery compartment door and activating a high-pitched alarm, while pushing abnormal event data to the management center in real time.

9. The artificial intelligence-based battery anti-theft system for battery swap cabinets according to claim 8 is characterized in that: After confirming the existence of abnormal behavior, the emergency response mechanism is triggered, and the steps of grading the abnormal behavior are: According to the classification result in step S3 The probability value and threat level corresponding to the category define the threat value of abnormal behavior : ， in, Indicates the threat value of abnormal behavior, Indicates the classification result The probability value of class behavior, Indicates Predefined threat weights for class behaviors, Set the threat level threshold ,Will Mapping to different threat levels : like ,but , like ,but , … like ,but , in, Indicates the threat level, A sequence of thresholds representing threat classification; Based on threat level , select the corresponding emergency response measures, and set the emergency response matrix as , among which The row indicates the threat level is The emergency action set: ， like , then execute the response action Specific measures include the following operations: Remotely lock the battery door, Activate the high-pitched alarm, And push abnormal event data to the management center and store it encrypted through blockchain.

10. The battery anti-theft system for battery swap cabinet based on artificial intelligence as claimed in claim 9 is characterized in that: The encryption method through blockchain is: Organize abnormal event data into transactions , including timestamp , Threat Level , User Operation Identifier , Description of abnormal behavior : ， in, Indicates abnormal event data transaction, The timestamp indicating when the event occurred. Indicates the threat level, A unique identifier for the user. Descriptive information indicating abnormal behavior, Will Broadcast to the blockchain network and generate new blocks : ， in, Represents a new block, represents a hash function, Indicates the current transaction data. Indicates the hash value of the previous block.

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