A method for identity authentication of a patrol intercom terminal

Abstract

The present application relates to the field of network communication and information security technology, in particular to a kind of patrol intercom terminal identity authentication method;Contain context initialization, environment perception collection, token iteration generation and server reconstruction verification process;Terminal receives initial parameter to establish the first context, in offline patrol, radio frequency, motion characteristics and biological information are fused to generate authentication token, and the current token is used as the context element of next node, and the authentication token chain of ordered delivery is cyclically constructed;Its core is to use iteration protocol to convert discrete patrol behavior into a chain of cryptographic hash, and the server performs reverse check after network recovery;The present application ensures the time domain correlation of data, effectively prevents replay attack and trajectory tampering in offline environment, and protects the security of communication network.

Description

Technical Field

[0001] This invention relates to the field of network communication and information security technology, specifically to a method for authenticating the identity of a patrol intercom terminal. Background Technology

[0002] In current security patrol operations, patrol intercom terminals are tasked with ensuring that the holder arrives at the designated location at a specific time according to the prescribed route, and often need to operate in environments with weak or no network, such as basements and high-rise buildings.

[0003] To monitor patrol activities, existing solutions generally adopt a check-in authentication architecture based on discrete nodes. This involves using terminals to collect geographic coordinates, NFC tags, or biometrics at a single moment, and uploading them to a server for independent comparison in real-time or asynchronously. While this solution can complete basic recording in typical online scenarios, its authentication data is isolated in the time domain, lacking cryptographic connections to the preceding and following contexts. Furthermore, its perception of physical environmental characteristics is limited, making the system unable to effectively defend against fraudulent attacks using location simulators, data replay, or non-human vibration devices. Simultaneously, facing complex fluctuations in the wireless radio frequency environment, traditional rigid comparison mechanisms struggle to address false identification while ensuring security, failing to meet the stringent requirements of high-security parks for the continuity and authenticity of patrol trajectories.

[0004] Therefore, how to construct a continuous spatiotemporal evidence chain with tamper-proof characteristics to improve the robustness and anti-counterfeiting capabilities of identity authentication in weak network environments has become an urgent technical problem to be solved. Summary of the Invention

[0005] The purpose of this invention is to provide a method for authenticating the identity of a patrol intercom terminal, so as to solve the problems mentioned in the background art. Specifically, the technical solution of this invention is as follows:

[0006] The authentication communication protocol between the terminal and the server is as follows:

[0007] Step 1: The terminal receives the initial authentication parameters sent by the server through the first communication connection and stores them as the first authentication context.

[0008] Step 2: During the offline patrol process, the terminal collects on-site environmental perception data in real time, including radio frequency signal characteristics and terminal motion characteristics, according to the protocol encapsulation rules.

[0009] Step 3: The terminal organizes the first authentication context, the on-site environmental perception data, and the user biometric data into a data unit to be authenticated according to the authentication communication protocol, and generates a first authentication token corresponding to the current patrol node according to the cryptographic processing flow specified in the protocol.

[0010] Step 4: The terminal updates the first authentication token to the next authentication context and iteratively executes steps 2 and 3, thereby generating a chain of authentication tokens based on context-ordered transmission in the communication network.

[0011] Step 5: When the terminal restores the second communication connection, it uploads the authentication token chain as an authentication credential data packet to the server. The server performs server-side reconstruction verification on the authentication credential data packet according to the authentication communication protocol and the pre-stored environment feature library, and returns the identity authentication result and network risk control strategy to the terminal or management terminal through the communication network based on the verification result.

[0012] Preferably, in step two, data collection according to the protocol encapsulation rules includes:

[0013] S21. Activate the radio frequency receiving module of the terminal to scan spatial wireless beacons and obtain the radio frequency signal vector composed of beacon physical address, signal strength and channel characteristics;

[0014] S22. Activate the motion sensor of the terminal to collect terminal movement data to construct a motion feature vector; at the same time, activate the biometric acquisition module of the terminal, which includes a fingerprint recognition sensor or an image acquisition unit for acquiring user biometric data.

[0015] S23. Align and encapsulate the radio frequency signal vector and the motion feature vector according to the timestamp format specified in the protocol to form the field environment perception data.

[0016] Preferably, in step three, organizing the data unit to be authenticated and generating the first authentication token includes:

[0017] S31. According to the feature extraction rules specified in the protocol, the radio frequency signal vector is dimensionality reduced and encoded to generate a standard environmental fingerprint code;

[0018] S32, Read the currently stored authentication context;

[0019] S33. According to the data fusion format specified in the protocol, the authentication context, the standard environment fingerprint code, the motion feature vector and the collected user biometric data are spliced ​​and obfuscated to generate a hybrid authentication primitive;

[0020] S34. Perform a one-way hash function operation as specified in the protocol on the hybrid authentication primitive, and output a fixed-length hash value as the first authentication token.

[0021] Preferably, in step five, the server-side reconstruction verification includes:

[0022] S51. The server receives the authentication credential data packet and parses out the authentication token chain;

[0023] S52. Based on the pre-stored standard feature library of patrol points, reconstruct the theoretical environment fingerprint code sequence;

[0024] S53. Starting with the initial authentication parameters, and combining the theoretical environment fingerprint code sequence, perform forward calculations according to the same authentication communication protocol as the terminal to generate a theoretical token chain.

[0025] S54. The received authentication token chain is compared with the theoretical token chain node by node to calculate the chain consistency matching degree.

[0026] S55. Generate the identity authentication result based on the chain consistency matching degree, and trigger the corresponding network risk control strategy.

[0027] Preferably, in step S55, generating the identity authentication result includes:

[0028] When the chain consistency matching degree is 100%, the authentication is deemed successful;

[0029] When the chain consistency matching degree is lower than 100% but higher than the protocol fault tolerance threshold, the conditional authentication is deemed successful.

[0030] When the chain consistency matching degree is equal to or lower than the protocol fault tolerance threshold, the authentication is deemed to have failed.

[0031] Preferably, in step S55, the network risk control strategies triggered include:

[0032] For nodes that pass authentication, the server generates a feature database update instruction, marks the corresponding area as having a stable network environment, and extends the validity period of its features;

[0033] For nodes that pass conditional authentication, the server generates an environmental anomaly alarm and an enhanced sampling instruction, and sends them to the terminal through the communication network;

[0034] For nodes that fail authentication, the server generates a security alarm command, marks the authentication data as invalid, and sends a security event notification to the management terminal.

[0035] Preferably, this method also includes protocol extensions for non-sequential patrol communication scenarios:

[0036] Step A: When the terminal is set to random access mode, the authentication communication protocol supports multi-branch reference relationships of the authentication context;

[0037] Step B: When generating the authentication token for the current node, the terminal may use the authentication tokens of multiple preceding nodes as input according to the protocol.

[0038] Step C: During verification, the server enables the topology verification module specified in the protocol to parse and verify the authentication token chain with branch dependencies. The verification includes constructing a directed acyclic graph model of the authentication nodes, traversing and checking whether the in-degree references of the current node all point to the verified predecessor node tokens, and verifying the consistency of the hash combination of multiple branch references.

[0039] Preferably, in step S22, the protocol compliance determination of the motion feature vector includes:

[0040] S221. Calculate the statistical variance of acceleration in the motion feature vector;

[0041] S222. Compare the acceleration statistical variance with the static threshold and abnormal vibration threshold predefined in the protocol;

[0042] S223. If the acceleration statistical variance is less than the static threshold, the terminal is determined to be in a non-working state, and the current authentication data encapsulation is terminated.

[0043] S224. If the acceleration statistical variance is greater than the abnormal vibration threshold, the motion data is determined to be abnormal, and the current authentication data encapsulation is terminated.

[0044] S225. If the acceleration statistical variance is between the static threshold and the abnormal vibration threshold, it is determined to be a compliant holding motion characteristic, and step S23 is continued.

[0045] Compared with the prior art, the present invention has the following improvements and advantages:

[0046] 1. This invention transforms discrete patrol check-in behaviors into a cryptographically interconnected hash chain by executing a context-based iterative authentication protocol on the terminal side, using the authentication context of the previous patrol node as the input primitive for the current node to generate the authentication token. This mechanism ensures that patrol data is no longer isolated in the time domain. Any tampering with historical data or using false coordinates generated by a location simulator will result in drastic deviations in subsequent hash calculations due to the lack of correct preceding context states and accurate physical environment characteristics. Thus, even in offline or weak network environments, the continuity and authenticity of patrol trajectories can be ensured, eliminating the possibility of replay attacks using recorded data.

[0047] 2. This invention simultaneously collects radio frequency signal features and terminal motion features through protocol encapsulation rules, constructing a deep fingerprint that includes spatial and behavioral attributes. On the one hand, it utilizes the multipath effect of indoor wireless signals to lock the physical location, preventing simple coordinate simulation. On the other hand, by performing statistical variance analysis on the motion feature vectors, it can effectively identify whether the terminal is in a normal handheld micro-tremor state or in a state of violent vibration from a stationary device or a non-human-driven vibration device, such as being attached to a drone or a stepper. This implicit detection mechanism, which requires no additional human interaction, effectively solves the problem of security personnel handing over their terminals to others or machines for card swipes while ensuring the efficiency of patrol operations.

[0048] 3. This invention performs reverse deduction on the server side by reconstructing the fingerprint sequence of the theoretical environment and uses chain consistency matching degree for comprehensive judgment; by setting a protocol fault tolerance threshold, the system can tolerate a certain proportion of fingerprint mismatch caused by dynamic changes in the environment, and as long as the causal logic of the overall chain is complete, the authentication can be passed; this not only reduces false negatives caused by signal drift, but also ensures availability in scenarios with large fluctuations in wireless radio frequency environment such as shopping malls and parks.

[0049] 4. This invention utilizes authentication result feedback during the patrol process to achieve dynamic updates and self-healing of the environmental feature database. For nodes that pass authentication, the system smoothly updates the standard feature database using the latest environmental perception data, thereby adapting to the natural aging or changes of the environment over time. For nodes that pass or fail conditional authentication, the system automatically triggers enhanced sampling or security alarm commands. This mechanism transforms simple identity authentication into continuous monitoring of the network environment in the security area, which can not only promptly detect abnormal signal sources, but also continuously improve the accuracy of the feature database through crowdsourcing, thus extending the effective life cycle of the system. Attached Figure Description

[0050] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0051] Figure 1 This is a flowchart of the method of the present invention; Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0053] Example 1:

[0054] Please see Figure 1 A method for authenticating the identity of a patrol intercom terminal is provided, applied to a communication network containing patrol intercom terminals and an authentication server. The authentication communication protocol between the terminal and the server is as follows:

[0055] Step 1: The terminal receives the initial authentication parameters sent by the server through the first communication connection and stores them as the first authentication context.

[0056] Step 2: During the offline patrol process, the terminal collects on-site environmental perception data in real time, including radio frequency signal characteristics and terminal motion characteristics, according to the protocol encapsulation rules.

[0057] Step 3: The terminal organizes the first authentication context, the on-site environmental perception data, and the user's biometric data into a data unit to be authenticated according to the authentication communication protocol, and generates the first authentication token corresponding to the current patrol node according to the cryptographic processing procedure specified in the protocol.

[0058] Step 4: The terminal updates the first authentication token to the next authentication context and iteratively executes steps 2 and 3, thereby generating a chain of authentication tokens based on context-ordered transmission in the communication network.

[0059] Step 5: When the terminal restores the second communication connection, it uploads the authentication token chain as the authentication credential data packet to the server. The server performs server-side reconstruction verification of the authentication credential data packet according to the authentication communication protocol and the pre-stored environment feature library, and returns the identity authentication result and network risk control strategy to the terminal or management terminal through the communication network based on the verification result.

[0060] This embodiment discloses a patrol intercom terminal authentication method based on spatiotemporal entropy flow evolution. This method solves the dual authentication problem of verifying the authenticity of the terminal's physical location and the legitimacy of the holder's identity in weak network environments by establishing a dynamic sliding window spatiotemporal entanglement protocol. The terminal performs an initial entropy introduction step, handshaking with the authentication server and receiving initial authentication parameters through a first communication connection, i.e., Wi-Fi or 4G / 5G online status. This parameter Originating from a server-side true random number generator, its physical meaning is the random number of the genesis block of the entire authentication chain, designed to prevent replay attacks targeting the entire dataset; due to initial authentication parameters Randomly generated by the server and unique for each task, combined with the avalanche effect of the hash chain, ensures that even if an attacker records historical radio frequency and motion data at the same location, they will fail verification due to the lack of the correct context primitives for the current task; the terminal will then... Stored as the first authentication context ;

[0061] When the terminal enters the offline patrol mode that disconnects the first communication connection, the system starts the multi-dimensional entropy flow acquisition process and collects the on-site environmental perception data in real time according to the protocol encapsulation rules. This data integrates the spatial entropy that flows with time, i.e. the radio frequency signal characteristics, and the temporal entropy, i.e. the terminal motion characteristics.

[0062] Based on this, the terminal performs spatiotemporal entanglement and token generation steps, and stores the current authentication context. Collected on-site environmental perception data and user biometric data The data is organized into units to be authenticated, and a corresponding unit for the current patrol node is generated according to the cryptographic processing flow. First authentication token The terminal executes context iteration and chained construction steps, which will... The authentication context is updated to the next node, and the collection and generation steps are executed iteratively to generate an authentication token chain locally based on context-ordered transmission. ;

[0063] When the terminal restores the second communication connection, the system performs a full credential synchronization step, synchronizing the authentication token chain. And the dynamic parameter set necessary for generating the tokens of each node in the chain, including the motion feature vectors corresponding to each node at any given time. User biometric data The data is packaged and encapsulated into authentication credentials and uploaded to ensure that the server has all the dynamic input primitives required for reconstructing the hash operation; the server uses a pre-stored environment feature library to simulate the patrol path to perform server-side reconstruction verification, and returns the identity authentication result and issues network risk control policies.

[0064] This embodiment transforms discrete security patrol check-in behaviors into a continuous cryptographic hash chain, constructing an evidence chain with an avalanche effect. In actual park or building security patrol scenarios, even if an attacker attempts to forge check-in data in non-designated locations, due to the lack of the correct residual state of the previous moment and the precise physical environment characteristics of the current moment, any slight forgery will cause the subsequent calculation results to deviate drastically from the theoretical value, thereby greatly increasing the cost of forgery and effectively preventing false patrol behaviors using simulators or location spoofing software.

[0065] Example 2:

[0066] In step two, data collection according to the protocol encapsulation rules includes: S21, activating the terminal's radio frequency receiving module to scan spatial wireless beacons and obtain radio frequency signal vectors composed of beacon physical addresses, signal strength, and channel characteristics; S22, activating the terminal's motion sensor to collect terminal movement data to construct motion feature vectors; simultaneously activating the terminal's biometric acquisition module, which includes a fingerprint recognition sensor or image acquisition unit, to acquire user biometric data; S23, aligning and encapsulating the radio frequency signal vectors and motion feature vectors according to the timestamp format specified in the protocol to form on-site environmental perception data.

[0067] This embodiment further specifies the data acquisition steps according to the protocol encapsulation rules in Embodiment 1, focusing on constructing a high-dimensional physical environment feature vector to lock in physical space attributes; the system executes the radio frequency panoramic scanning step, activating the terminal's radio frequency receiving module to perform a full-band scan of wireless beacons in the space and acquire radio frequency signal vectors. This vector contains the beacon's physical address, a signal strength indication derived from the RF receiver gain reading, and channel characteristics derived from the physical layer demodulation parameters.

[0068] The system performs a motion attitude capture step, activating the terminal's built-in motion sensors, namely a three-axis accelerometer and a gyroscope, to collect micro-motion data of the terminal within the authentication action window at a preset high sampling rate, and construct a motion feature vector. The system performs a biometric extraction step, activating the terminal's built-in fingerprint sensor or image acquisition unit to collect the current operator's biometric information as user biometric data. The source;

[0069] The system performs a time-domain aligned packaging step, taking into account the time difference between radio frequency scanning and motion acquisition, using the time of biometric input as the reference point. Extract a specific time window, with specific parameters defined as follows: Data within the sampling interval of 500ms before and 500ms after the specified time, i.e., a total duration of 1000ms, will be... and Encapsulated as on-site environmental perception data;

[0070] This embodiment achieves deep fingerprinting of the patrol environment by introducing multi-dimensional physical features; in complex indoor patrol scenarios, the uniqueness of the signal fingerprint caused by the indoor multipath effect is used to prevent simple GPS coordinate simulation attacks; at the same time, by recording the unique micro-vibrations when the device is held, implicit liveness and device holding detection is achieved, effectively preventing security personnel from performing non-human operation fraud by binding the terminal to a drone or stepper.

[0071] Example 3:

[0072] Step three, organizing the data unit to be authenticated and generating the first authentication token, includes:

[0073] S31. According to the feature extraction rules specified in the protocol, the radio frequency signal vector is dimensionality reduced and encoded to generate a standard environmental fingerprint code;

[0074] S32, Read the currently stored authentication context;

[0075] S33. According to the data fusion format specified in the protocol, the authentication context, standard environmental fingerprint code, motion feature vector and collected user biometric data are spliced ​​and obfuscated to generate a hybrid authentication primitive;

[0076] S34. Perform the one-way hash function operation specified in the protocol on the hybrid authentication primitive, and output the fixed-length hash value as the first authentication token.

[0077] This embodiment further specifies the step of generating the first authentication token in Embodiment 1, and elaborates on the specific implementation logic of the rolling hash evolution machine; the system performs an environmental feature dimensionality reduction step, targeting the original radio frequency vector. Due to the high dimensionality and fluctuations, a variant of the Locality Sensitive Hash algorithm, SimHash, is used to address this issue. Dimensionality reduction encoding is performed, specifically setting the projection dimension to 64 bits, to generate a stable standard environmental fingerprint code. During this process, the system executes explicit input mapping rules: the discrete data of the beacon physical address in the radio frequency signal vector is used as a feature term for hash mapping;

[0078] For channel characteristics, specifically the signal-to-noise ratio (SNR) in the channel state information (CSI), its normalized value is fused with the normalized signal strength value at a 1:1 weight to generate a comprehensive weight value for this feature. The specific normalization calculation uses a Min-Max scaling formula. For signal strength, the following settings are applied:

[0079]

[0080] in, , set according to channel characteristics:

[0081]

[0082] It should be noted that the above and The boundary values ​​are typical empirical values ​​under standard indoor Wi-Fi communication environments. In practical applications, these boundary parameters can be used as configuration items and adaptively adjusted according to the transmission power of the base station and the background noise floor at the deployment site, rather than being limited to the specific values ​​mentioned above; final weight:

[0083]

[0084] Furthermore, values ​​outside the range are truncated to ensure... ;

[0085] The system executes the weighted accumulation and dimensionality reduction logic of the SimHash variant: initializing a 64-dimensional feature vector. The result is all zeros; the system executes a feature aggregation loop, iterating through the features contained in the RF scan results. One independent wireless beacon, of which The value of must satisfy To ensure that the collected environmental fingerprints possess sufficient spatial distinguishability and uniqueness in a statistically significant manner; among them, ; Regarding the first Each beacon, extract its physical address hash value. and comprehensive weight ;

[0086] Nested execution bit update logic: For arrive ,like The If the bit is 1, then If it is 0, then Traverse all beacons ( ) and all bits ( After that, the accumulated vector is obtained. ;right Generate a 64-bit fingerprint by executing a dimensionality reduction decision function. :for arrive ,like ,but The If the bit is set to 1, then the bit is set to 0;

[0087] The final fingerprint is generated through the above weighted superposition and dimensionality reduction. This solves the problem of converting structured data to SimHash input; [This is in response to...] To address the mismatch in bit width between the SHA-256 operation and subsequent operations, the system performs bit width alignment expansion, employing a cyclic padding algorithm to extend the 64-bit data. By concatenating the fingerprints four times, a 256-bit extended fingerprint code is constructed. ;

[0088] The system reads the previous node's authentication context stored in memory. That is, the preorder hash residual, for the first node, the initial parameters defined in Implementation Example 1 are read. ,Right now The system executes the hybrid authentication primitive construction steps, constructing the hybrid authentication primitive through bit-level concatenation and obfuscation operations according to the data fusion format specified in the protocol. To ensure data dimension consistency in XOR operations, the system pre-processes multidimensional vectors. and biological characteristics Execute serialization and hash mapping functions Specifically, the The function is defined as: first, input data... that is, vector or biometric data The data is first serialized into binary data, and then a SHA-256 hash operation is performed on the serialized data to generate a 256-bit hash digest, ensuring that its bit width matches the extended environment fingerprint. and context Maintain consistency; convert it to be consistent with For bit strings of equal length, the corrected calculation formula is as follows:

[0089]

[0090] in, : Hybrid authentication primitive, originating from the result of an XOR operation, and physically representing a snapshot of the current spacetime state; Bitwise XOR operator; : Represents the sequence number of the current patrol node, with a value range of 100. ,in, The total number of nodes preset for the patrol route; The previous moment, i.e., the first moment The hash residual of a node originates from local storage;

[0091] In particular, when hour, The value is the initial authentication parameter. This is to ensure consistency with the parameter meanings and indexing logic in Example 1; Extended environmental fingerprint, source is The cyclic splicing, that is This is intended to align the bit width of the XOR operation; : A fixed-length motion feature bit string after serialization and mapping to SHA-256; : A fixed-length user biometric bit string after serialization and SHA-256 mapping;

[0092] The system performs a one-way hash operation, on The one-way hash function operation specified in the execution protocol is performed. Output a fixed-length hash value as the first authentication token. ;

[0093] This embodiment achieves rigorous spatiotemporal entanglement logic by using the output of the previous node as the input parameter of the current node. In continuous patrol tasks, the generated authentication token not only proves that the security personnel are at the designated location at the current moment, but also proves through mathematical correlation that they were indeed at the previous location at the previous moment, thereby constructing an unforgeable patrol trajectory evidence chain, enabling post-event audits to accurately verify the continuity and integrity of the patrol route.

[0094] Example 4:

[0095] Step five, the server-side reconstruction verification includes:

[0096] S51. The server receives the authentication credential data packet and parses out the authentication token chain.

[0097] S52. Based on the pre-stored standard feature library of patrol points, reconstruct the theoretical environment fingerprint code sequence;

[0098] S53. Starting with the initial authentication parameters, combined with the theoretical environment fingerprint code sequence, perform forward calculations according to the same authentication communication protocol as the terminal to generate a theoretical token chain.

[0099] S54. Compare the received authentication token chain with the theoretical token chain node by node and calculate the chain consistency matching degree.

[0100] S55. Generate identity authentication results based on chain consistency matching degree and trigger corresponding network risk control strategies.

[0101] This embodiment further specifies the server-side reconstruction verification steps in Embodiment 1, describing the server-side verification mechanism as a security defense; the server executes the chain parsing step, receiving and parsing the authentication credential data packet, and extracting the authentication token chain to be verified. And the synchronously uploaded terminal motion feature vector sequence and user biometric data sequences ;

[0102] The system executes the theoretical environment reconstruction step. According to the patrol task plan, it retrieves the theoretical radio frequency characteristics of each patrol point in the corresponding time period from the pre-stored standard feature library of patrol points and reconstructs the theoretical environment fingerprint code sequence.

[0103] The system executes the theoretical chain forward derivation step, using the initial parameters issued by the server. Starting from, combined with the extracted , The reconstructed fingerprint is recursively calculated using a standard hash chain according to the same authentication communication protocol as the terminal.

[0104] Specifically, in step S54, given that the cryptographic hash function used in step S34 has an avalanche effect, i.e., a tiny bit flip in the input environment fingerprint will cause the output token to be affected. Dramatic changes occurred, making it directly related to When Hamming distance comparison fails, the server employs a fingerprint fault-tolerant traversal verification mechanism; specifically, in calculating the... When verifying the node's results, the server uses the reconstructed theoretical environment fingerprint. This generates a candidate fingerprint set containing all possible variations. This set covers This refers to the fault tolerance threshold, such as all cases of 3 bits; the server uses these candidate fingerprints in combination with the context. Calculate the candidate theoretical token set;

[0105] If the terminal uploads the actual token If a node's fingerprint variant matches any token in the candidate theoretical token set, the node is considered a successful match, and a context synchronization correction mechanism is executed: the system updates the matched candidate fingerprint variant. rather than the original reconstructed theoretical environment fingerprint As a given environmental input, the hash evolution formula specified in the protocol is substituted to calculate the corrected hash value. Node context and take it as the first The input baseline for node forward inference; this mechanism aims to eliminate the hash avalanche effect caused by minor environmental perturbations, ensuring that the verification chain can continue to evolve correctly after single-point fault tolerance; matching function Return 1 at this point;

[0106] Based on this, the system performs comparison and result generation steps to calculate the chain consistency matching degree. The formula is as follows:

[0107]

[0108] in, Chain consistency matching degree, which is derived from statistical calculations, and its physical meaning is the degree of agreement between the patrol trajectory and the theoretical expectation; The total number of nodes in the chain indicates the total number of checkpoints included in this patrol mission. : The summation traversal variable, representing the sequence index of the patrol node;

[0109] The first authentication token in the chain uploaded by the terminal The actual authentication token of each node; : No. The node generates a set of candidate theoretical tokens based on a fault tolerance threshold; : Matching function, if The system returns 1, or a weighted score based on the matching fingerprint distance, or 0 otherwise. This embodiment explicitly employs the following binary decision logic to eliminate ambiguity: Calculate... With sets Minimum Hamming distance between elements ,like That is, the aforementioned fault tolerance threshold, such as 3 bits, is used to determine a successful match. Return 1; if If the match fails, the function will be considered unsuitable. Returns 0;

[0110] The system according to Generate identity authentication results and trigger risk control strategies;

[0111] This embodiment resolves the contradiction between cryptographic hash avalanche effect and environmental fuzzy matching by reconstructing the theoretical hash chain on the server side and introducing fault-tolerant traversal verification. By combining the actual dynamic features uploaded by the terminal with the pre-stored theoretical environmental features, the system can logically reproduce the legitimate patrol path. Only when the environment actually perceived by the terminal is highly consistent with the theoretical environment and the context evolution logic is not destroyed will the two chains show a high degree of matching, thereby effectively identifying location fraud or replay attacks.

[0112] Example 5:

[0113] In step S55, the generation of identity authentication results includes: when the chain consistency matching degree is 100%, it is determined that the authentication is successful; when the chain consistency matching degree is less than 100% but higher than the protocol fault tolerance threshold, it is determined that the conditional authentication is successful; when the chain consistency matching degree is equal to or lower than the protocol fault tolerance threshold, it is determined that the authentication is unsuccessful.

[0114] This embodiment further specifies the steps for generating identity authentication results in Embodiment 4, introducing hierarchical quantification processing logic; the system determines the full authentication condition and responds to the chain consistency matching degree. A value of 100% indicates successful authentication, meaning that the physical environment of all nodes is completely consistent with theoretical expectations.

[0115] The threshold The specific value can be set to a fixed value, such as 0.85, or dynamically set according to the measured signal-to-noise ratio of the deployment environment. When using dynamic setting, for the current node... Its dynamic fault tolerance threshold The calculation formula can be designed as follows:

[0116]

[0117] in, This is the minimum threshold to ensure the basic robustness of the system, for example, 0.5. It is an adjustment factor less than 1, for example, 0.2. It is the first The linear power ratio of the signal-to-noise ratio of each patrol node;

[0118] in, Defined as the environmental fluctuation adjustment coefficient, its physical meaning is to characterize the average disturbance degree of the current wireless environment relative to the ideal static environment. Based on measured data from typical office parks, its optimal value range is [missing value]. The more unstable the environment, The smaller the value, the lower the threshold requirement; Clearly defined as the terminal in the first The arithmetic mean of the measured signal-to-noise ratios uploaded during the node's acquisition period, rather than the theoretical value pre-stored on the server, is used to accurately reflect the channel quality at the current moment. To prevent the formula from failing in extremely low signal-to-noise ratio environments, the following settings can be configured:

[0119]

[0120] This formula ensures Always Within this range, and with a higher signal-to-noise ratio, the fault tolerance threshold is closer to 1, the allowable mismatch ratio is smaller, and the requirements are more stringent;

[0121] Meanwhile, to prevent the circuit breaker from tripping due to the denominator approaching 0 in extremely low signal-to-noise ratio environments, such as no signal or pure noise environments, the system is configured as follows:

[0122]

[0123] That is, the minimum forced signal-to-noise ratio threshold is approximately 3dB, where the linearity value is... This is an empirical protection lower limit set based on the theoretical demodulation failure critical point of communication chips, aiming to prevent failure due to pure noise environments. Approaching This leads to the calculated threshold This can lead to divergence or numerical instability, thereby ensuring the system's robustness under extremely harsh signal environments.

[0124] Ensure that the threshold adapts to environmental quality; this threshold This refers to the percentage of fingerprint mismatches allowed by the system due to dynamic environmental changes. It aims to adapt to environmental fluctuations such as the increase or decrease of Wi-Fi hotspots. In this case, although some nodes may have mismatched fingerprints, the overall causal relationship of the chain remains intact. The system determines authentication failure conditions and responds accordingly. equal to or lower than If the authentication fails, the system considers the chain to have serious breaks or signs of forgery.

[0125] This embodiment solves the false negative problem caused by the dynamic time-varying radio frequency environment by introducing a conditional authentication mechanism. In complex commercial complexes or open parks with complex radio environments, small fluctuations in environmental signals often lead to hard matching failures for legitimate users. This solution, while ensuring security, significantly improves the availability and robustness of the patrol system by setting a fault tolerance threshold, and reduces false alarms caused by environmental interference.

[0126] Example 6:

[0127] In step S55, the triggered network risk control strategies include: for nodes that pass authentication, the server generates a feature database update instruction, marks the corresponding regional network environment as stable, and extends the validity period of its features; for nodes that pass conditional authentication, the server generates an environment anomaly alarm and an enhanced sampling instruction, and sends it to the terminal through the communication network; for nodes that fail authentication, the server generates a security alarm instruction, marks the authentication data as invalid, and sends a security event notification to the management terminal.

[0128] This embodiment further specifies the steps for triggering the network risk control strategy in Embodiment 4, achieving adaptive evolution of the system. For nodes that pass authentication, the server executes a positive feedback strategy, generates a feature database update instruction, and utilizes the latest radio frequency signal vector uploaded by the terminal. Update the standard feature library on the server side; the specific update logic for handling different data types in the vectors is as follows:

[0129]

[0130] in, A numerical value representing signal strength or channel characteristics. The historical memory factor, with a value of 0.9, determines the system's sensitivity to environmental changes. Its value is not fixed and can be adjusted based on the dynamic stability of the application scenario. Adjustments can be made within the range; for example, in high-dynamic areas with frequent personnel movement, the value can be appropriately reduced to speed up feature updates, while in relatively static nighttime patrol scenarios, the value can be increased to filter out occasional noise.

[0131] This smooths out random fluctuations in a single sample; simultaneously, it executes anchor point drift constraint logic to calculate the updated feature values. Compared with the reference feature values ​​initially acquired by the system Euclidean distance between ,like Exceeding the preset safe drift radius If the update fails, the update will be rejected and an environment reset alert will be triggered to prevent the signature database from being maliciously misled from its true physical location due to continuous minor fraud; and the network environment in the corresponding area will be marked as stable.

[0132] For nodes that pass conditional authentication, the server executes an anomaly warning policy, generates an environmental anomaly alarm and an enhanced sampling instruction, requiring the terminal to increase the sampling duration or frequency when patrolling the point again, in order to recalibrate the environmental fingerprint; for nodes that fail authentication, the server executes a blocking and alarm policy, generates a security alarm instruction, marks the data as invalid, and pushes a security report containing the location, time and hash deviation of the faulty node to the management terminal.

[0133] This embodiment transforms the simple authentication process into an environmental perception and system self-healing process. In long-term security operations, by continuously absorbing data uploaded by legitimate terminals, the system's environmental feature database becomes increasingly accurate, realizing crowdsourced updates of environmental radio frequency fingerprints, thereby reducing future false recognition rates and enabling timely detection of abnormal signal sources or illegal interference devices in the environment.

[0134] Example 7:

[0135] This method also includes protocol extensions for non-sequential patrol communication scenarios:

[0136] Step A: When the terminal is set to random access mode, the authentication communication protocol supports multi-branch reference relationships of the authentication context;

[0137] Step B: When generating the authentication token for the current node, the terminal may use the authentication tokens of multiple preceding nodes as input according to the protocol.

[0138] Step C: During verification, the server enables the topology verification module specified in the protocol to parse and verify the authentication token chain with branch dependencies. The verification includes constructing a directed acyclic graph model of the authentication nodes, traversing and checking whether the in-degree references of the current node all point to the verified predecessor node tokens, and verifying the consistency of the hash combination of multi-branch references.

[0139] This embodiment is an extension of the authentication communication protocol in Embodiment 1, providing a solution for non-sequential random patrol scenarios; the system executes the multi-branch reference mode setting step, and when the terminal is set to random access mode, the authentication communication protocol switches to support a directed acyclic graph structure, allowing a node to have multiple predecessor nodes;

[0140] The system performs a multi-source hash fusion step, and the terminal generates the current node. When generating the authentication token, reference the set of all completed predecessor nodes. The token is used to generate the current context through a multi-branch hash fusion algorithm. This embodiment uses XOR stacking, but Merkle root hashing can also be used:

[0141]

[0142] in, The context of the current node, derived from the fusion of previous tokens, is used here. As a node index, it remains consistent with the aforementioned embodiments; Preorder node The authentication token originates from historical generation records; Fusion operator, physically meaning the aggregation of multi-path historical states;

[0143] The system executes a topology verification step. The server activates the topology verification module to parse and verify the authentication token chain with branch dependencies. The specific verification includes: constructing a directed acyclic graph model of the authentication nodes based on the node access order and reference relationships uploaded by the terminal; traversing each node in the graph and checking whether all predecessor node tokens referenced by its in-degree exist in the verified legitimate historical records; if the verification passes, then according to the formula:

[0144]

[0145] The theoretical context of the node is reconstructed and compared with the actual context of the node uploaded by the terminal to confirm the topological validity and hash consistency of the nonlinear traversal path;

[0146] This embodiment enhances the flexibility of the patrol strategy by supporting non-linear multi-branch references. In security scenarios that require surprise inspections or random checks, this extended scheme makes the system suitable not only for fixed-route patrols but also for security guards' random access behavior, while still maintaining the cryptographic locking characteristic of the hash chain to historical paths, ensuring the immutability of random patrol data.

[0147] Example 8:

[0148] In step S22, the protocol compliance determination of the motion feature vector includes:

[0149] S221. Calculate the statistical variance of acceleration in the motion feature vector;

[0150] S222. Compare the acceleration statistical variance with the static threshold and abnormal vibration threshold predefined by the protocol.

[0151] S223. If the acceleration statistical variance is less than the static threshold, the terminal is determined to be in a non-working state, and the current authentication data encapsulation is terminated.

[0152] S224. If the statistical variance of acceleration is greater than the abnormal vibration threshold, the motion data is determined to be abnormal, and the data encapsulation for this certification is terminated.

[0153] S225. If the acceleration statistical variance is between the static threshold and the abnormal vibration threshold, it is determined to be a compliant holding motion characteristic, and step S23 is continued.

[0154] This embodiment further specifies the steps for constructing motion feature vectors in Embodiment 2, detailing the logic for protocol compliance determination; the system executes the variance calculation step to calculate the statistical variance of acceleration data within the acquisition window. The formula is as follows:

[0155]

[0156] in, Acceleration statistical variance, derived from sensor data calculations, physically represents the dispersion of equipment vibration, and its physical dimension is the square of the unit of acceleration. ; The total number of acceleration data sampling points within the sampling window is determined by the product of the preset sampling frequency and the window time. To distinguish it from the total number of chain nodes; : Sampling point index variable, with a value range of ; : No. The acceleration magnitude of each sampling point; the specific calculation formula is as follows:

[0157]

[0158] in, The three-axis accelerometers are respectively in The component readings at time points on the X, Y, and Z axes are used, and the three-axis composite modulus is adopted to eliminate the influence of different terminal grip postures on motion detection. The average acceleration within the sampling window;

[0159] The system performs a threshold decision step, and... The comparison is performed against a predefined threshold; in this process, to adhere to the principle of physical dimension consistency, the protocol predefined static threshold... With abnormal vibration threshold All are set to have the dimension of square. The variance threshold, rather than the first power of acceleration threshold;

[0160] In response to Less than the static threshold For example, the value is The corresponding standard deviation of acceleration is approximately The system determines that the terminal is stationary and terminates the encapsulation process; in response to Greater than the abnormal vibration threshold For example, the value is , corresponding to about If the continuous vibration value is not valid, the motion data is judged to be abnormal, and the data encapsulation for this certification is terminated.

[0161] It should be understood that the above static threshold With abnormal vibration threshold These are calibration values ​​measured for handheld walkie-talkie terminals with specific mass and sensor sensitivity. For different models of terminal equipment, due to differences in their inertial mass and accelerometer noise floor, the above thresholds should be recalibrated based on actual measurement data before the equipment leaves the factory.

[0162] Corresponding to approximately 2.24 If the continuous vibration value is not detected, the system determines it as machine vibration and stops the packaging process; in response to... Between and If the system determines that the movement characteristics are compliant, it will continue to execute the subsequent data encapsulation steps.

[0163] This embodiment utilizes physical sensor data to achieve implicit liveness detection. Without requiring security personnel to perform additional interactive operations such as shaking their heads or blinking, the system can effectively distinguish between normal human handheld device status and abnormal status. It effectively defends against static attacks that simulate clocking in using software by placing the terminal on a desktop, as well as abnormal vibration attacks that are attached to drones or non-human vehicles, ensuring that patrol tasks are performed by real people holding the device in the real physical world.

[0164] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for authenticating the identity of a patrol intercom terminal, applied to a communication network including patrol intercom terminals and an authentication server, characterized in that, The terminal and the server communicate using the following authentication protocol: Step 1: The terminal receives the initial authentication parameters sent by the server through the first communication connection and stores them as the first authentication context. Step 2: During the offline patrol process, the terminal collects on-site environmental perception data in real time, including radio frequency signal characteristics and terminal motion characteristics, according to the protocol encapsulation rules. Step 3: The terminal organizes the first authentication context, the on-site environmental perception data, and the user biometric data into a data unit to be authenticated according to the authentication communication protocol, and generates a first authentication token corresponding to the current patrol node according to the cryptographic processing flow specified in the protocol. Step 4: The terminal updates the first authentication token to the next authentication context and iteratively executes steps 2 and 3, thereby generating a chain of authentication tokens based on context-ordered transmission in the communication network. Step 5: When the terminal restores the second communication connection, it uploads the authentication token chain as an authentication credential data packet to the server; The server performs server-side reconstruction verification of the authentication credential data packet based on the authentication communication protocol and the pre-stored environment feature library, and returns the identity authentication result and network risk control strategy to the terminal or management terminal through the communication network based on the verification result. Step two, which involves collecting data according to the protocol encapsulation rules, includes: S21. Activate the radio frequency receiving module of the terminal to scan spatial wireless beacons and obtain the radio frequency signal vector composed of beacon physical address, signal strength and channel characteristics; S22. Activate the motion sensor of the terminal to collect terminal movement data to construct a motion feature vector; at the same time, activate the biometric acquisition module of the terminal, which includes a fingerprint recognition sensor or an image acquisition unit for acquiring user biometric data. S23. Align and encapsulate the radio frequency signal vector and the motion feature vector according to the timestamp format specified in the protocol to form the field environment perception data; Step three, organizing the data unit to be authenticated and generating the first authentication token, includes: S31. According to the feature extraction rules specified in the protocol, the radio frequency signal vector is dimensionality reduced and encoded to generate a standard environmental fingerprint code; S32, Read the currently stored authentication context; S33. According to the data fusion format specified in the protocol, the authentication context, the standard environment fingerprint code, the motion feature vector and the collected user biometric data are spliced ​​and obfuscated to generate a hybrid authentication primitive; S34. Perform a one-way hash function operation as specified in the protocol on the hybrid authentication primitive, and output a fixed-length hash value as the first authentication token.

2. The authentication method for a patrol intercom terminal according to claim 1, characterized in that: Step five, server-side reconstruction verification, includes: S51. The server receives the authentication credential data packet and parses out the authentication token chain; S52. Based on the pre-stored standard feature library of patrol points, reconstruct the theoretical environment fingerprint code sequence; S53. Starting with the initial authentication parameters, and combining the theoretical environment fingerprint code sequence, perform forward calculations according to the same authentication communication protocol as the terminal to generate a theoretical token chain. S54. The received authentication token chain is compared with the theoretical token chain node by node to calculate the chain consistency matching degree. S55. Generate the identity authentication result based on the chain consistency matching degree, and trigger the corresponding network risk control strategy.

3. The authentication method for a patrol intercom terminal according to claim 2, characterized in that: In step S55, generating the identity authentication result includes: When the chain consistency matching degree is 100%, the authentication is deemed successful; When the chain consistency matching degree is lower than 100% but higher than the protocol fault tolerance threshold, the conditional authentication is deemed successful. When the chain consistency matching degree is equal to or lower than the protocol fault tolerance threshold, the authentication is deemed to have failed.

4. The authentication method for a patrol intercom terminal according to claim 3, characterized in that: In step S55, the network risk control strategies triggered include: For nodes that pass authentication, the server generates a feature database update instruction, marks the corresponding area as having a stable network environment, and extends the validity period of its features; For nodes that pass conditional authentication, the server generates an environmental anomaly alarm and an enhanced sampling instruction, and sends them to the terminal through the communication network; For nodes that fail authentication, the server generates a security alarm command, marks the authentication data as invalid, and sends a security event notification to the management terminal.

5. The authentication method for a patrol intercom terminal according to claim 1, characterized in that: The method also includes protocol extensions for non-sequential patrol communication scenarios: Step A: When the terminal is set to random access mode, the authentication communication protocol supports multi-branch reference relationships of the authentication context; Step B: When generating the authentication token for the current node, the terminal may use the authentication tokens of multiple preceding nodes as input according to the protocol. Step C: During verification, the server enables the topology verification module specified in the protocol to parse and verify the authentication token chain with branch dependencies. The verification includes constructing a directed acyclic graph model of the authentication nodes, traversing and checking whether the in-degree references of the current node all point to the verified predecessor node tokens, and verifying the consistency of the hash combination of multiple branch references.

6. The authentication method for a patrol intercom terminal according to claim 1, characterized in that: In step S22, the protocol compliance determination of the motion feature vector includes: S221. Calculate the statistical variance of acceleration in the motion feature vector; S222. Compare the acceleration statistical variance with the static threshold and abnormal vibration threshold predefined in the protocol; S223. If the acceleration statistical variance is less than the static threshold, the terminal is determined to be in a non-working state, and the current authentication data encapsulation is terminated. S224. If the acceleration statistical variance is greater than the abnormal vibration threshold, the motion data is determined to be abnormal, and the current authentication data encapsulation is terminated. S225. If the acceleration statistical variance is between the static threshold and the abnormal vibration threshold, it is determined to be a compliant holding motion characteristic, and step S23 is continued.

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