Emergency rescue and first aid management method based on multiple authentication and identity verification

Abstract

The application provides an emergency rescue and first aid management method based on multiple authentication and identity verification, relates to the technical field of emergency rescue, and comprises the following steps: constructing a request message through a time sequence session identifier, performing three types of authentication and live detection, generating a space-time binding record, granting a minimum permission in a scene, performing field-level encryption and authentication on sensitive loads by using a session key, and performing risk assessment based on a time-frequency anomaly identifier. The method can effectively prevent information leakage, improve the accuracy of emergency rescue response, ensure the safety and reliability of rescue instruction execution, and adapt to the information security requirements in complex emergency scenarios.

Description

Technical Field

[0001] This invention relates to emergency rescue technology, and more particularly to an emergency rescue and first aid management method based on multi-factor authentication and identity verification. Background Technology

[0002] With the increasing demand for emergency rescue and first aid management in modern society, information technology-based emergency rescue systems have become an important means to ensure public safety and improve emergency response efficiency. Emergency rescue and first aid management systems typically need to process large amounts of sensitive information, including patient location data, medical information, and treatment instructions. The security and accuracy of this information directly affect the effectiveness of rescue operations and the safety of patients' lives.

[0003] Existing emergency rescue and first aid management systems suffer from a number of problems in identity verification and information security. Traditional emergency systems often employ a single authentication mechanism, such as simple password verification or fingerprint recognition, making the system vulnerable to identity impersonation and fraud attacks. In emergency situations, these security vulnerabilities can lead to the execution of incorrect rescue instructions or the unauthorized acquisition of sensitive information, thereby endangering the lives of patients.

[0004] In existing technologies, the geolocation verification mechanism of emergency rescue systems is not robust enough to effectively combat location spoofing or deception. Accurate geolocation information is crucial for the timely arrival of rescue teams during rescue operations, but current systems lack the ability to cross-verify geolocation data from multiple sources, failing to ensure the authenticity and accuracy of location information.

[0005] Furthermore, the access control mechanisms of existing emergency rescue systems are often too static, failing to dynamically adjust access permissions based on actual circumstances. Systems typically assign fixed permission levels to users, lacking a dynamic adjustment mechanism based on specific scenarios and risk assessments. This makes it difficult for the system to maintain rescue efficiency while ensuring information security, especially in the face of abnormal operations or potential security threats, where it cannot make corresponding permission adjustments and security protection measures in real time. Summary of the Invention

[0006] This invention provides an emergency rescue and first aid management method based on multi-factor authentication and identity verification, which can solve the problems in the prior art.

[0007] A first aspect of this invention provides an emergency rescue and first aid management method based on multi-factor authentication and identity verification, comprising:

[0008] An emergency event request is received and a time-series session identifier is generated. Based on the time-series session identifier, a request message is constructed according to a fixed field order and an integrity verification digest is calculated. The geographic location data, medical information digest, and treatment instructions in the request message are combined and defined as sensitive session load.

[0009] Based on the time-series session identifier, the request message is located. Authentication and liveness detection are performed on the requester in parallel to obtain three types of authentication results. The three types of authentication results are jointly determined with the integrity verification digest to obtain a pass mark. A trusted timestamp is generated based on the pass mark, and multi-source verification is performed according to the geographical location data in the request message. When the verification passes, the trusted timestamp is bound to the time-series session identifier to generate a spatiotemporal binding record.

[0010] Based on the spatiotemporal binding record, the minimum permissions of the scenario are granted to the identity that has been marked. After successful authorization, a session key is negotiated based on the time-series session identifier. The session key is then used to perform field-level encryption and integrity authentication on the sensitive session payload.

[0011] Based on the results of the field-level encryption and the integrity authentication, a time-frequency anomaly identifier is generated according to the handling instructions in the sensitive session load. When the time-frequency anomaly identifier deviates from the preset evaluation threshold, a risk escalation is triggered, and the minimum permissions granted in the scenario are temporarily tightened accordingly, while requiring a re-completion of liveness detection.

[0012] Based on the time-series session identifier, a request message is constructed according to a fixed field order, and an integrity verification digest is calculated. The geographic location data, medical information digest, and treatment instructions in the request message are combined and defined as the sensitive session payload, including:

[0013] A field sequence table is constructed based on the time-series session identifier. The preset fixed field order in the field sequence table is mapped to geographic location data, medical information summary, and treatment instructions. The request message is organized and constructed according to the fixed field order. A segmented hash operation is performed on the request message to obtain an integrity verification digest. The geographic location data, medical information summary, and treatment instruction tags in the request message are merged and defined as sensitive session load.

[0014] The method further includes:

[0015] Based on the field sequence table, the field composition of the request message is parsed to verify the validity of the integrity verification digest; after the verification is passed, a field mapping matrix is ​​constructed for the sensitive session load, and hierarchical protection control is performed on the sensitive session load based on the field mapping matrix;

[0016] Based on the sensitivity levels recorded in the field mapping matrix, an encryption key and an authentication key are assigned to each field in the sensitive session payload; the encryption key is used to perform encryption processing on fields with sensitivity levels higher than a preset threshold, and the authentication key is used to calculate the message authentication code of the sensitive session payload;

[0017] The message authentication code is bound to the time-series session identifier to obtain a session security identifier. Based on the session security identifier, the integrity of the sensitive session load is verified according to a preset fixed period. When the verification fails, risk escalation is triggered and field-level protection control is re-executed.

[0018] Based on the generation of a trusted timestamp through tagging, and the performance of multi-source verification based on the geographic location data in the request message, the process of binding the trusted timestamp with the time-series session identifier to generate a spatiotemporal binding record upon successful verification includes:

[0019] Based on the above method of generating a trusted timestamp by calling a trusted time source through a marker, the trusted timestamp is decomposed into a time sequence number, a time verification value, and a random entropy value; the random entropy value is used to perturb and map the geographic location data in the request information to obtain a geographic location fingerprint; spatial feature vectors and time feature vectors are extracted from the geographic location fingerprint, and the spatial feature vectors and the time feature vectors are respectively used with multi-source fused positioning data to perform multi-source verification and calculate the location verification score.

[0020] The location verification score is compared with a preset location deviation threshold. When the location verification score meets the location deviation threshold, the timestamp sequence in the time verification value is extracted and the time verification code is calculated. The time sequence number in the trusted timestamp is irreversibly bound to the time sequence session identifier to generate an initial spatiotemporal binding record. The initial spatiotemporal binding record is encrypted using the time verification code to obtain the final spatiotemporal binding record.

[0021] Based on the spatiotemporal binding record, the minimum scene permissions are granted to the identity with the tagged identifier. After successful authorization, a session key is negotiated based on the time-series session identifier. The session key is then used to perform field-level encryption and integrity authentication on the sensitive session payload, including:

[0022] Based on the spatiotemporal binding record, a reliable time window and location activity features are extracted. The reliable time window and location activity features are mapped to a scene permission matrix. The minimum permission set for the scene is extracted from the scene permission matrix. Fine-grained decomposition is performed on the permission items in the minimum permission set to generate a permission sequence number. The permission sequence number is associated and bound with the tag to generate a time-sensitive authorization token.

[0023] Based on the time-series session identifier, a key negotiation process is performed to generate a session key seed and a key update vector; the session key seed is decomposed using the key update vector to obtain the message authentication key;

[0024] A sensitivity assessment is performed on the fields in the sensitive session payload, and the field encryption priority is determined based on the assessment results; the priority fields are encrypted using the message authentication key, and the integrity check code of the entire sensitive session payload is calculated using the authorization token;

[0025] The integrity verification code is bound to the authorization token to generate a session security identifier; the integrity of the sensitive session payload is verified based on the session security identifier at a preset fixed period; when the verification fails, a key update and permission re-granting process is triggered.

[0026] Fine-grained decomposition is performed on the permission items in the minimum permission set of the scenario to generate permission sequence numbers; the permission sequence numbers are then associated and bound with the markers to generate time-sensitive authorization tokens, including:

[0027] Extract the permission item identifier and permission item attribute from the minimum permission set of the scenario, perform hierarchical parsing on the permission item identifier and permission item attribute, map the permission item identifier to an opcode sequence, and map the permission item attribute to a constraint code sequence; generate a permission sequence number using the opcode sequence and the constraint code sequence;

[0028] A permission mapping table is constructed based on the permission serial number. The permission serial numbers in the permission mapping table are associated and bound with the tags to generate an authorization token. The validity of the permission serial number in the authorization token is verified according to a preset fixed period. When the permission serial number is detected to be about to expire, the authorization token is regenerated based on the permission execution conditions recorded in the permission mapping table.

[0029] Based on the results of the field-level encryption and the integrity authentication, a time-frequency anomaly identifier is generated according to the handling instructions in the sensitive session load. When the time-frequency anomaly identifier deviates from a preset evaluation threshold, a risk escalation is triggered, and the granted minimum scene permissions are temporarily tightened accordingly. At the same time, a liveness detection is required to be performed again, including:

[0030] Based on the results of the field-level encryption and the integrity authentication, the handling instructions are extracted from the sensitive session load; an operation sequence table is constructed based on the handling instructions, time-frequency analysis is performed on the operation sequence table, the time interval characteristics and frequency fluctuation characteristics of the instruction execution are extracted, and the time interval characteristics and frequency fluctuation characteristics are merged to generate a time-frequency anomaly identifier;

[0031] The deviation between the time-frequency anomaly identifier and the preset evaluation threshold is calculated. When the result of the deviation calculation exceeds the warning range, the risk level is upgraded. Based on the risk level, the minimum permissions granted to the scene are temporarily tightened, and a permission tightening record is generated.

[0032] According to the permission tightening record, a liveness detection request is sent to the user terminal, and the liveness detection data returned by the user terminal is received; the liveness detection data is subjected to feature extraction and authenticity verification, and a liveness detection result is generated.

[0033] A second aspect of the present invention provides an electronic device, comprising:

[0034] processor;

[0035] Memory used to store processor-executable instructions;

[0036] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0037] A third aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0038] The beneficial effects of this application are as follows:

[0039] This invention, by accessing emergency event requests and generating time-series session identifiers, performs multi-factor authentication and liveness detection on the requester, and combines integrity verification digests to generate pass tags, can ensure the authenticity of identity and the legitimacy of requests during emergency rescue processes, effectively prevent identity impersonation and unauthorized access, and improve the security of emergency rescue systems.

[0040] This invention employs a spatiotemporal binding record and a scenario-based least privilege authorization mechanism to perform field-level encryption and integrity authentication on sensitive session loads, thereby achieving refined protection of rescue information. This not only ensures the security of data transmission but also guarantees the visibility of critical rescue information within the authorized scope, thus improving the information security level of emergency rescue and first aid management.

[0041] This invention, through a time-frequency anomaly identification monitoring mechanism, can identify abnormal operation patterns in real time. When a risk is detected, it automatically triggers permission tightening and re-verification, forming a dynamic security response capability. This effectively prevents persistent security threats that may occur during emergency rescue and enhances the resilience and reliability of the entire rescue management system. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the emergency rescue and first aid management method based on multiple authentication and identity verification according to an embodiment of the present invention.

[0043] Figure 2 This is a flowchart illustrating the identity authentication and key management process according to an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0046] Figure 1 This is a flowchart illustrating an emergency rescue and first aid management method based on multi-factor authentication and identity verification, as described in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0047] An emergency event request is received and a time-series session identifier is generated. Based on the time-series session identifier, a request message is constructed according to a fixed field order and an integrity verification digest is calculated. The geographic location data, medical information digest, and treatment instructions in the request message are combined and defined as sensitive session load.

[0048] Based on the time-series session identifier, the request message is located. Authentication and liveness detection are performed on the requester in parallel to obtain three types of authentication results. The three types of authentication results are jointly determined with the integrity verification digest to obtain a pass mark. A trusted timestamp is generated based on the pass mark, and multi-source verification is performed according to the geographical location data in the request message. When the verification passes, the trusted timestamp is bound to the time-series session identifier to generate a spatiotemporal binding record.

[0049] Based on the spatiotemporal binding record, the minimum permissions of the scenario are granted to the identity that has been marked. After successful authorization, a session key is negotiated based on the time-series session identifier. The session key is then used to perform field-level encryption and integrity authentication on the sensitive session payload.

[0050] Based on the results of the field-level encryption and the integrity authentication, a time-frequency anomaly identifier is generated according to the handling instructions in the sensitive session load. When the time-frequency anomaly identifier deviates from the preset evaluation threshold, a risk escalation is triggered, and the minimum permissions granted in the scenario are temporarily tightened accordingly, while requiring a re-completion of liveness detection.

[0051] In one optional implementation, a request message is constructed based on the time-series session identifier in a fixed field order, and an integrity verification digest is calculated. The geographic location data, medical information digest, and treatment instructions in the request message are combined and defined as the sensitive session payload, including:

[0052] A field sequence table is constructed based on the time-series session identifier. The preset fixed field order in the field sequence table is mapped to geographic location data, medical information summary, and treatment instructions. The request message is organized and constructed according to the fixed field order. A segmented hash operation is performed on the request message to obtain an integrity verification digest. The geographic location data, medical information summary, and treatment instruction tags in the request message are merged and defined as sensitive session load.

[0053] Obtain the time-series session identifier, which uniquely identifies the current communication session. It can be a system-generated combination of a timestamp, a random number, and a session sequence number, such as "TS20230615-RND78945-SEQ001". The time-series session identifier ensures the uniqueness of each session and prevents replay attacks.

[0054] Based on the acquired time-series session identifier, a field sequence table is constructed. This field sequence table defines the order of each field in the request message, specifically including a preset fixed field order, such as "Field A-Field B-Field C-Field D-Field E-Field F". The system maps these fields to actual data content according to the mapping relationship. For example, field A corresponds to the session identifier "TS20230615-RND78945-SEQ001", field B corresponds to the geographical location data "37.7749°N, 122.4194°W", field C corresponds to the medical information summary "patient blood pressure 140 / 90mmHg, heart rate 88 beats / min, blood glucose 6.2mmol / L", field D corresponds to the treatment instruction "immediately administer intravenous infusion and monitor vital signs", field E corresponds to the operator ID "OP12345", and field F corresponds to the timestamp "20230615143022".

[0055] The request message is constructed by organizing the fields in a fixed order. The complete request message structure is: "TS20230615-RND78945-SEQ001|37.7749°N, 122.4194°W|Patient's blood pressure 140 / 90 mmHg, heart rate 88 bpm, blood glucose 6.2 mmol / L|Immediately administer intravenous fluids and monitor vital signs|OP12345|20230615143022". Here, "|" is used as the field separator. In practical applications, an appropriate separator can be selected according to system requirements.

[0056] The request message is subjected to segmented hashing to obtain an integrity verification digest. The segmented hashing process is as follows: the system divides the request message into several segments, performs a separate hash calculation on each segment, and then concatenates the hash values ​​of each segment for a second hash to obtain the final integrity verification digest. Specific steps include: dividing the request message into 6 fields using the field separator "|"; calculating the hash value for each field using the SHA-256 hash algorithm to obtain 6 hash values ​​H1, H2, H3, H4, H5, and H6; concatenating these 6 hash values ​​into a string H1||H2||H3||H4||H5||H6, where "||" represents the string concatenation operation; and then calculating the final integrity verification digest again using the SHA-256 algorithm on the concatenated string, such as "8f7d88e4c3b91232c99c7b2418b213e8c63c8b69d2330a75".

[0057] The geographic location data, medical information summary, and treatment instruction markers in the request message are combined and defined as the sensitive session payload. Fields B, C, and D are extracted from the request message and combined into an encrypted unit. The extracted sensitive session payload is: "37.7749°N, 122.4194°W | Patient's blood pressure 140 / 90 mmHg, heart rate 88 bpm, blood glucose 6.2 mmol / L | Immediately administer intravenous fluids and monitor vital signs."

[0058] Add tagging information to sensitive session payloads, including the sensitivity level tag "SL3" (indicating high sensitivity), the data type tag "MED" (indicating medical data), and the encryption requirement tag "ENC" (indicating encryption protection is required). The format of the tagged sensitive session payload is: "[SL3][MED][ENC]37.7749°N, 122.4194°W|Patient blood pressure 140 / 90 mmHg, heart rate 88 bpm, blood glucose 6.2 mmol / L|Immediately administer intravenous infusion and monitor vital signs".

[0059] Sensitive session payloads, once marked, undergo special processing, such as data anonymization and encryption. For example, the Advanced Encryption Standard (AES) can be used to encrypt sensitive session payloads, with the encryption key derived from the session identifier. The encrypted sensitive session payload is presented in ciphertext form, such as "AQs4Wp9r7UhTzX2bJKvEm3n8LqFcGdY6iHjNxO5...".

[0060] The encrypted sensitive session payload and integrity check digest are encapsulated together in the final transport data packet. The transport data packet structure is: Session Identifier + "*" + Encrypted Sensitive Session Payload + "*" + Integrity Check Digest + "*" + Operator ID + "*" + Timestamp. For example: "TS20230615-RND78945-SEQ001*AQs4Wp9r7UhTzX2bJKvEm3n8LqFcGdY6iHjNxO5...*8f7d88e4c3b91232c99c7b2418b213e8c63c8b69d2330a75*OP12345*20230615143022".

[0061] After receiving the transmitted data packet, the receiver first verifies whether the integrity check digest matches. If the verification is successful, the receiver uses the same key to decrypt the encrypted sensitive session payload, recover the original geographic location data, medical information digest, and treatment instructions, and then performs the corresponding operation according to the treatment instructions.

[0062] The above methods not only ensure the security and integrity of sensitive medical data during transmission but also achieve structured management and precise processing of the data. This method is particularly suitable for scenarios requiring high security, such as emergency medical systems and telemedicine consultation systems, effectively preventing data tampering, theft, or misuse, and protecting patient privacy and medical data security.

[0063] In one optional implementation, the method further includes:

[0064] Based on the field sequence table, the field composition of the request message is parsed to verify the validity of the integrity verification digest; after the verification is passed, a field mapping matrix is ​​constructed for the sensitive session load, and hierarchical protection control is performed on the sensitive session load based on the field mapping matrix;

[0065] Based on the sensitivity levels recorded in the field mapping matrix, an encryption key and an authentication key are assigned to each field in the sensitive session payload; the encryption key is used to perform encryption processing on fields with sensitivity levels higher than a preset threshold, and the authentication key is used to calculate the message authentication code of the sensitive session payload;

[0066] The message authentication code is bound to the time-series session identifier to obtain a session security identifier. Based on the session security identifier, the integrity of the sensitive session load is verified according to a preset fixed period. When the verification fails, risk escalation is triggered and field-level protection control is re-executed.

[0067] In a typical application scenario, when a request message carrying sensitive session payload is received, the system retrieves the field sequence list contained in the request message. The field sequence list typically contains information such as the name, type, and order of each field in the request message. Based on this field sequence list, the system identifies and extracts each field from the request message one by one according to predefined parsing rules. For example, for a request message containing three fields: user identity, transaction amount, and transaction time, the system will accurately extract the values ​​of each field from the binary data stream of the request message based on the order and data type of these three fields recorded in the field sequence list.

[0068] After extracting each field, the system recalculates the integrity check digest of the request message. Specifically, the system combines the extracted fields in the order specified in the field sequence table, calculates a digest value using a pre-agreed hash algorithm (such as SHA-256), and then compares this digest value with the integrity check digest carried in the request message. If they match, the request message has not been tampered with during transmission, and the verification passes; otherwise, the verification fails, and the system refuses to process the request message. For example, for the aforementioned request message containing three fields, assuming the extracted user identity is "USER12345", the transaction amount is "1000.00", and the transaction time is "2023-05-20 15:30:00", the system combines these three values ​​in the order specified in the field sequence table, calculating a digest value of "7e9f1d7a5e3b2c1d". If this value is the same as the integrity check digest carried in the request message, the verification passes.

[0069] After successful verification, a field mapping matrix is ​​constructed for the sensitive session load, and hierarchical protection control is implemented based on this matrix. The field mapping matrix is ​​a two-dimensional table structure, where each row corresponds to a field in the sensitive session load, and each column contains various attributes of that field, such as field name, data type, sensitivity level, and protection policy. The system determines the sensitivity level of each field in the sensitive session load according to pre-configured rules. For example, in a financial transaction scenario, the account field is marked as sensitivity level 3, the transaction amount field is marked as sensitivity level 4, and the transaction description field is marked as sensitivity level 1.

[0070] Based on the constructed field mapping matrix, and according to the sensitivity levels recorded in the matrix, corresponding encryption and authentication keys are assigned to each field in the sensitive session payload. Specifically, the system maintains a key pool containing encryption and authentication keys of different security strengths. For fields with higher sensitivity levels, the system selects a key with higher security strength from the key pool; for fields with lower sensitivity levels, it selects a key with relatively lower security strength. This is done to reduce computational resource consumption while ensuring security. For example, for the transaction amount field with a sensitivity level of 4, the system assigns a 256-bit encryption key and a 192-bit authentication key; while for the transaction description field with a sensitivity level of 1, only a 128-bit authentication key is assigned, without encryption.

[0071] The assigned encryption key is used to encrypt fields with a sensitivity level higher than a preset threshold. In a practical application, the preset threshold is set to 2, meaning that fields with sensitivity levels of 3 and 4 (such as the account and transaction amount in the previous example) need to be encrypted, while fields with a sensitivity level of 1 (such as the transaction description) do not require encryption. Encryption typically uses a symmetric encryption algorithm, such as AES. The specific encryption process includes: converting the field value to binary form, encrypting the binary data using the assigned encryption key and the preset encryption algorithm, resulting in ciphertext. For example, the transaction amount "1000.00" becomes a seemingly random string of characters after encryption, such as "X7Y2Z9A4B6C8D1E3".

[0072] The system calculates a message authentication code (MAC) for the sensitive session payload using the assigned authentication key. A MAC is a cryptographic tool used to verify message integrity and authenticity. The system combines all fields (both encrypted and unencrypted) in the sensitive session payload in a specific order and uses the assigned authentication key and a preset MAC algorithm (such as HMAC) to calculate a fixed-length value, the MAC. For example, for a sensitive session payload containing an encrypted account number, an encrypted transaction amount, and an unencrypted transaction description, the system would calculate a MAC value of "5a4b3c2d1e".

[0073] After calculating the message authentication code, it is bound to the time-series session identifier to obtain the session security identifier. The binding process can be a simple string concatenation or a more complex cryptographic operation. For example, if the time-series session identifier is "SESSION20230520153000" and the message authentication code is "5a4b3c2d1e", then the bound session security identifier is "SESSION20230520153000-5a4b3c2d1e".

[0074] Based on the session security identifier, the system verifies the integrity of sensitive session payloads at a preset fixed period. Specifically, the system periodically (e.g., every 5 minutes) recalculates the message authentication code of the sensitive session payload and compares the calculated value with the message authentication code stored in the session security identifier. If they match, it indicates that the sensitive session payload has not been tampered with during that period; otherwise, it indicates that the sensitive session payload has been maliciously modified. When verification fails, the system triggers a risk escalation and re-executes field-level protection controls. Risk escalation includes raising the preset level threshold, requiring more fields to be encrypted; replacing encryption and authentication algorithms with more secure ones; or increasing the verification frequency. For example, when the system detects a failure in the integrity verification of a sensitive session payload, it lowers the preset level threshold from 2 to 1, so that fields with a sensitivity level of 2 also need to be encrypted, thereby improving overall security.

[0075] In one optional implementation, based on the generation of a trusted timestamp through the marker, and performing multi-source verification according to the geographic location data in the request message, the process of binding the trusted timestamp with the time-series session identifier to generate a spatiotemporal binding record upon successful verification includes:

[0076] Based on the above method of generating a trusted timestamp by calling a trusted time source through a marker, the trusted timestamp is decomposed into a time sequence number, a time verification value, and a random entropy value; the random entropy value is used to perturb and map the geographic location data in the request information to obtain a geographic location fingerprint; spatial feature vectors and time feature vectors are extracted from the geographic location fingerprint, and the spatial feature vectors and the time feature vectors are respectively used with multi-source fused positioning data to perform multi-source verification and calculate the location verification score.

[0077] The location verification score is compared with a preset location deviation threshold. When the location verification score meets the location deviation threshold, the timestamp sequence in the time verification value is extracted and the time verification code is calculated. The time sequence number in the trusted timestamp is irreversibly bound to the time sequence session identifier to generate an initial spatiotemporal binding record. The initial spatiotemporal binding record is encrypted using the time verification code to obtain the final spatiotemporal binding record.

[0078] The generation of trusted timestamps through a distributed timestamp authorization mechanism, using tokens as identity credentials, ensures the accuracy and immutability of time. The timestamp generator receives requests sent to multiple independent trusted time source servers. The trusted time sources use atomic clocks or GPS clocks as the base time and synchronize via the NTP network time protocol. The timestamp request process includes requester authentication, timestamp format negotiation, and accuracy requirement confirmation. Timestamp generation follows the RFC3161 standard format and includes millisecond-accurate time information, time source identifiers, and digital signatures. Timestamps returned from multiple time sources undergo consistency verification using a Byzantine fault-tolerant algorithm, ensuring the reliability and tamper-proof nature of the timestamps.

[0079] A trusted timestamp is decomposed into a time sequence number, a time checksum, and a random entropy value. Structured parsing techniques are used to extract these components from the timestamp. The timestamp parser performs field segmentation and content extraction according to a predefined data structure. The time sequence number extraction process retrieves the year, month, day, hour, minute, second, and millisecond from the timestamp's time field and converts it into a 64-bit integer sequence using a time encoding algorithm. The time checksum extraction includes verification information such as the digital signature, hash digest, and checksum from the timestamp, used for subsequent time integrity verification. The random entropy value is obtained by processing uncertainties such as microsecond-level time precision, time source noise, and random signature numbers in the timestamp using an entropy extraction algorithm. The entropy value is 256 bits long and exhibits high randomness and unpredictability.

[0080] The random entropy value perturbation mapping of geographic location data in the request information employs cryptographic obfuscation techniques to generate a geographic location fingerprint. The location fingerprint generator uses random entropy values ​​as perturbation seeds to perform numerical transformation processing on geographic location data such as GPS coordinates, base station positioning, and WiFi access points. The perturbation mapping process converts the original location coordinates into perturbed coordinate values ​​through a pseudo-random function. The perturbation amplitude is controlled within a range that does not affect the identification of the location area, typically with an accuracy range of tens to hundreds of meters. The location data perturbation uses a reversible transformation algorithm to ensure that the original location information can be restored during the verification process. The generation of the geographic location fingerprint also includes metadata information such as location accuracy identifiers, acquisition timestamps, and positioning method markers, forming a complete location identity. The perturbation processing process uses the SHA-256 hash algorithm to calculate a digest of the perturbed location data, generating a fixed-length geographic location fingerprint.

[0081] The spatial feature vector extraction of geographic location fingerprints employs geographic information analysis (GIS) techniques to perform multi-dimensional feature calculations on location data. The spatial feature extractor analyzes spatial parameters such as latitude and longitude coordinates, altitude, and azimuth angle in the geographic location fingerprint to calculate the spatial distribution characteristics of the location. Feature calculation includes spatial attributes such as geographic grid encoding, geographic region identification, and relative positional relationships. Geographic grid encoding uses GeoHash encoding technology to convert two-dimensional geographic coordinates into one-dimensional string representations, supporting spatial indexing at different precision levels. Relative positional relationships determine the spatial context of the location by calculating parameters such as distance, direction, and relative altitude from known reference points. The spatial feature vector also includes geographic environmental features of the location, such as geographic type identifiers like urban areas, suburbs, mountains, and water bodies.

[0082] The extraction of time feature vectors is based on time series analysis techniques to model the features of time information acquired at a location. The time feature extractor analyzes time attributes such as acquisition time, update frequency, and time intervals of geographic location data to construct temporal behavior patterns. Time feature calculation includes dimensions such as periodicity analysis, trend analysis, and anomaly detection. Periodicity analysis identifies regular patterns in location data acquisition, such as daily, weekly, and monthly cycles. Trend analysis uses time series regression methods to identify the direction and rate of change in location trends. The time feature vector also includes statistical characteristics of time, such as descriptive statistics like mean, variance, skewness, and kurtosis.

[0083] Multi-source verification of spatial feature vectors and multi-source fused positioning data employs a feature matching algorithm to verify the consistency and accuracy of location data. The multi-source verifier acquires reference location data from multiple positioning sources, including GPS, base station, WiFi, and Bluetooth beacons, to construct a multi-source positioning database. The verification process compares the matching degree between spatial feature vectors and multi-source positioning data using a feature similarity calculation algorithm. Similarity calculation uses a cosine similarity algorithm to measure the angle between feature vectors and an Euclidean distance algorithm to measure distances in the feature space. The verification algorithm also employs a weighted fusion technique, assigning weight coefficients based on the accuracy and reliability of different positioning sources to calculate a comprehensive location matching score.

[0084] The time verification of time feature vectors and multi-source fused positioning data verifies the temporal rationality of location data through time consistency analysis. The time verifier compares the time feature vector with the time information recorded in the multi-source positioning data to detect the consistency and rationality of timestamps. The verification process includes multiple dimensions such as time synchronization verification, time continuity verification, and time logic verification. Time synchronization verification verifies whether the timestamps of different positioning sources are within a reasonable time window and detects whether the time deviation exceeds the preset synchronization threshold. Time continuity verification analyzes whether there are abnormal jumps or interruptions in the time series of location data, verifying the integrity of the time series. Time logic verification verifies the logical consistency between time and location changes by analyzing the rationality of the speed and trajectory of location changes.

[0085] The location verification score is calculated using a multi-index comprehensive evaluation algorithm that fuses the results of spatial and temporal verification. The verification score calculator performs a weighted summation of multiple evaluation indicators, including spatial feature matching score, temporal consistency score, positioning accuracy score, and data integrity score. Weight allocation is determined based on the importance and reliability of different indicators: spatial matching score accounts for 40% of the weight, temporal consistency score accounts for 30%, positioning accuracy score accounts for 20%, and data integrity score accounts for 10%. The scoring process also includes anomaly detection and score normalization to ensure the score remains within the standard range of 0 to 100. The verification score also considers the impact of historical verification records, smoothing out fluctuations in the current score through a moving average algorithm of historical scores.

[0086] The comparison between the location verification score and the location deviation threshold employs a dynamic threshold adjustment mechanism to adapt to the verification requirements of different scenarios. The threshold comparator maintains multi-level location deviation thresholds, including strict thresholds, standard thresholds, and lenient thresholds with varying degrees of stringency. Threshold selection is determined based on the security requirements and accuracy needs of the application scenario. Strict thresholds are used in emergency medical rescue scenarios to ensure location accuracy, while standard thresholds are used in general business scenarios to balance accuracy and availability. The comparison process determines the verification result by comparing the score with the threshold value. When the verification score is higher than the set threshold, the verification is considered passed; when it is lower than the threshold, the verification is considered failed. The comparison result also includes a confidence assessment, calculating the credibility of the verification result based on the difference between the score and the threshold.

[0087] The extraction of timestamp sequences from time verification values ​​employs time parsing technology to separate the time components from the verification value. The timestamp sequence extractor performs structured parsing on the time verification value, identifying its components such as timestamp information, signature information, and hash information. The timestamp sequence contains multi-level time precision information, including year, month, day, hour, minute, second, millisecond, and microsecond, and is numerically encoded using big-endian byte order. The extraction process also includes time format conversion, unifying the time formats used by different time sources into the standard UTC time format. The integrity of the timestamp sequence is verified through a checksum to ensure that no errors occurred during the data extraction process.

[0088] The timestamp checksum calculation employs Message Authentication Code (MAC) technology to protect the integrity of the timestamp sequence. The checksum generator uses the HMAC-SHA256 algorithm to calculate the MAC from the extracted timestamp sequence. The calculation process uses the private key of the time source as the HMAC key to perform digest operations on the timestamp sequence, time source identifier, precision identifier, and other data. The generated checksum is 256 bits long, exhibiting strong collision resistance and unforgeability. The checksum calculation also includes digital signature verification of the timestamp, using the public key of the time source to verify the authenticity and integrity of the timestamp.

[0089] The irreversible binding operation between the time sequence number and the time-series session identifier employs cryptographic commitment techniques to generate a binding proof. The binding operator uses the Pedersen commitment scheme to perform commitment calculations on the time sequence number and session identifier. The commitment process selects random numbers as a blinding factor and generates the commitment value through elliptic curve group operations. The binding operation also includes the generation of zero-knowledge proofs of the commitments, proving the correctness of the binding without revealing the original data. Irreversibility is guaranteed by the computational irreversibility of the commitments and the discrete logarithm difficulty problem, ensuring that the binding relationship cannot be cracked or forged. The binding result includes the commitment value, zero-knowledge proof, binding timestamp, and other information, forming the core data of the initial spatiotemporal binding record.

[0090] The encryption process for the initial spatiotemporal binding record employs symmetric encryption to protect the confidentiality of the binding record. The encryption processor uses the time checksum as the encryption key and encrypts the initial binding record using the AES-256-CBC algorithm. The encryption process generates a unique initialization vector for each encryption operation, ensuring that the same plaintext produces different ciphertexts. The encryption scope includes sensitive fields in the binding record, such as the time sequence number, session identifier, and commitment value. The encrypted data is securely transmitted and stored using Base64 encoding. The final spatiotemporal binding record contains complete information, including the encrypted binding data, initialization vector, encryption algorithm identifier, and integrity checksum, ensuring the security and verifiability of the binding record.

[0091] In one optional implementation, based on the spatiotemporal binding record, the minimum scene permissions are granted to the identity with the tagged identifier. After successful authorization, a session key is negotiated based on the time-series session identifier. The session key is then used to perform field-level encryption and integrity authentication on the sensitive session payload, including:

[0092] Based on the spatiotemporal binding record, a reliable time window and location activity features are extracted. The reliable time window and location activity features are mapped to a scene permission matrix. The minimum permission set for the scene is extracted from the scene permission matrix. Fine-grained decomposition is performed on the permission items in the minimum permission set to generate a permission sequence number. The permission sequence number is associated and bound with the tag to generate a time-sensitive authorization token.

[0093] Based on the time-series session identifier, a key negotiation process is performed to generate a session key seed and a key update vector; the session key seed is decomposed using the key update vector to obtain the message authentication key;

[0094] A sensitivity assessment is performed on the fields in the sensitive session payload, and the field encryption priority is determined based on the assessment results; the priority fields are encrypted using the message authentication key, and the integrity check code of the entire sensitive session payload is calculated using the authorization token;

[0095] The integrity verification code is bound to the authorization token to generate a session security identifier; the integrity of the sensitive session payload is verified based on the session security identifier at a preset fixed period; when the verification fails, a key update and permission re-granting process is triggered.

[0096] like Figure 2 As shown, the method includes:

[0097] The extraction of reliable time windows from spatiotemporal binding records employs time series analysis techniques to verify the validity of timestamp data. The reliable time extractor reads the timestamp field from the binding records and checks the accuracy of the timestamps using a clock synchronization verification algorithm. The time window segmentation process is based on the temporal characteristics of emergency events, dividing continuous time periods into stages such as event occurrence, response initiation, and handling execution. The reliability assessment of time windows calculates a reliability score by analyzing indicators such as timestamp dispersion and time jump anomalies; only time windows that reach a preset threshold are marked as reliable time windows.

[0098] Location activity feature extraction employs geographic information processing (GIS) technology to perform feature analysis on spatial coordinate data. The location feature extractor obtains multi-source location data, including GPS coordinates and base station positioning, from spatiotemporally bound records, and calculates the optimal location estimate using a location fusion algorithm. Activity feature analysis includes spatial behavior parameters such as location change rate, movement direction, and dwell time. Location clustering algorithms identify the user's main activity areas, and movement trajectory analysis identifies different activity patterns such as walking, driving, and stationary activities.

[0099] A reliable time window and location activity features are mapped to a scene permission matrix using multi-dimensional indexing technology for fast lookup. The scene permission matrix adopts a three-dimensional array structure: the time dimension represents different time periods, the spatial dimension represents different geographical regions, and the permission dimension represents different permission types. The mapping algorithm locates the time index in the time dimension using the time window and the region index in the spatial dimension using location activity features.

[0100] The extraction of the minimum permission set from the scene permission matrix employs a permission convergence algorithm to identify the minimum permission combination that satisfies the requirements of the current scene. The permission convergent processor obtains a list of candidate permissions from the permission matrix query results and constructs a dependency graph between permissions using a permission dependency analysis algorithm. The minimum permission calculation process uses the minimum spanning tree algorithm from graph theory to compute the minimum permission subset covering all necessary functions.

[0101] The fine-grained decomposition of permission items employs a recursive decomposition algorithm to break down composite permissions into atomic-level permission units. The permission decomposer analyzes the internal structure of each permission item, identifying the specific operation type and resource scope included in the permission. The decomposition process matches predefined decomposition rules with a permission template library, segmenting composite permissions according to dimensions such as operation type and resource object.

[0102] The permission serial number is generated using hash encoding technology to convert permission information into a fixed-length unique identifier. The serial number generator uses the SHA-256 hash algorithm to calculate a digest of the permission item's identification information, attribute parameters, and other data. The generated 256-bit hash value is then converted into a readable string format using Base58 encoding to form the permission serial number.

[0103] The association between the access sequence number and the pass token employs cryptographic binding technology to ensure the security of the binding relationship. The binding processor uses the ECDSA elliptic curve digital signature algorithm to sign the combined data of the access sequence number and the pass token. The signature input includes data such as the access sequence number, the pass token, and the binding timestamp, and a digest value is calculated using the SHA-256 hash algorithm.

[0104] The authorization token is generated using the JSON Web Token standard format to encapsulate permission information and binding relationships. The token generator creates a JWT structure containing three parts: header, payload, and signature. The payload includes standard declarations such as the permission sequence number, pass flag, issuance time, and expiration time. The signature part uses the HMAC-SHA256 algorithm to calculate the message authentication code from the header and payload.

[0105] The key negotiation process for the time-series session identifier employs the Diffie-Hellman key exchange protocol to achieve secure key generation. The key negotiator uses the session identifier as the seed parameter for the negotiation process and generates the random numbers required for negotiation using a pseudo-random number generator. During the negotiation process, both parties generate their own private random numbers and calculate and exchange their corresponding public values.

[0106] The session key seed and key update vector are generated using key derivation function technology to derive multiple subkeys from the shared key. The key deriver uses the PBKDF2 key derivation function, taking the shared key as the input key and the session identifier as the salt value, and performs multiple rounds of iterative calculations. The derivation process generates a sufficiently long key material, which is then divided into two parts—the session key seed and the key update vector—using a key segmentation algorithm.

[0107] The key update vector is used to decompose the session key seed using key segmentation techniques to generate multiple function-specific subkeys. The key decomposer uses the update vector as the decomposition parameter and transforms the key seed through an XOR operation. The decomposition process generates subkeys for different purposes, such as encryption keys, authentication keys, and integrity keys.

[0108] Sensitivity assessment of fields in sensitive session loads employs a rule-based classification algorithm to categorize data fields by sensitivity level. The sensitivity assessor maintains a sensitivity assessment rule base, and the assessment process analyzes attributes such as data type, content characteristics, and business importance of fields to calculate their sensitivity scores. Medical data and identity information are marked as high-sensitivity fields, while status information is marked as medium-sensitivity fields.

[0109] The determination of field encryption priority is based on a comprehensive consideration of sensitivity assessment results and encryption cost analysis. The priority calculator calculates an encryption priority score based on factors such as field sensitivity, data size, and access frequency. High-sensitivity fields automatically receive the highest encryption priority, while the priority of medium-sensitivity fields is adjusted based on other factors.

[0110] The message authentication key uses the AES-GCM authentication encryption algorithm to achieve integrated encryption and integrity protection for the priority field. The encryption processor performs encryption operations sequentially according to the field priority order, with high-priority fields using the AES-256-GCM algorithm and medium-priority fields using the AES-128-GCM algorithm.

[0111] The process of calculating the integrity check code for the sensitive session payload using the authorization token employs the HMAC message authentication code algorithm to ensure the overall integrity of the payload. The integrity calculator uses the key information contained in the authorization token as the HMAC key to perform a digest calculation on the entire sensitive session payload. The HMAC-SHA256 algorithm generates a 256-bit integrity check code.

[0112] The binding of the integrity checksum to the authorization token employs token extension technology to embed the checksum into the payload portion of the JWT token. The binding processor adds an integrity checksum field to the custom declaration of the authorization token. The session security identifier is generated by hashing the updated authorization token to produce a unique session security identifier.

[0113] The periodic integrity verification of session security identifiers employs scheduled task technology to automate security checks. The integrity verifier initiates verification tasks at preset fixed intervals. During the verification process, the integrity checksum of the sensitive session payload is recalculated and compared with the original checksum stored in the authorization token. If verification fails, a trigger immediately initiates a key update and permission re-granting process.

[0114] In one optional implementation, fine-grained decomposition is performed on the permission items in the minimum permission set of the scenario to generate permission serial numbers; the permission serial numbers are then associated and bound with the markers to generate time-sensitive authorization tokens, including:

[0115] Extract the permission item identifier and permission item attribute from the minimum permission set of the scenario, perform hierarchical parsing on the permission item identifier and permission item attribute, map the permission item identifier to an opcode sequence, and map the permission item attribute to a constraint code sequence; generate a permission sequence number using the opcode sequence and the constraint code sequence;

[0116] A permission mapping table is constructed based on the permission serial number. The permission serial numbers in the permission mapping table are associated and bound with the tags to generate an authorization token. The validity of the permission serial number in the authorization token is verified according to a preset fixed period. When the permission serial number is detected to be about to expire, the authorization token is regenerated based on the permission execution conditions recorded in the permission mapping table.

[0117] The fine-grained decomposition process of permission items in the minimum permission set of the scenario employs recursive hierarchical analysis to progressively break down the permission structure. The permission decomposer performs deep analysis on each permission item, splitting composite permissions into atomic permission units. The decomposition process identifies inclusion, mutual exclusion, and dependency relationships between permissions through a permission dependency graph, ensuring that the decomposed permission items maintain semantic integrity and logical consistency. The decomposition algorithm uses a breadth-first traversal strategy, expanding the permission tree structure layer by layer from the root permission node until all permission items are decomposed into the smallest indivisible unit. Each atomic permission unit contains basic attribute information such as a unique permission identifier, permission description, permission level, and permission scope.

[0118] The generation of the permission serial number employs hash encoding technology to convert the decomposed permission items into a fixed-length numeric sequence. The serial number generator uses the SHA-256 hash algorithm to calculate a digest of the permission item's identification information, attribute parameters, timestamp, and other data, generating a 256-bit binary hash value. This hash value is then Base58 encoded into a readable string format, forming a permission serial number containing uppercase and lowercase letters and numbers. The serial number generation process also includes checksum calculation, adding a 4-bit checksum to the end of the serial number using the CRC32 cyclic redundancy check algorithm to detect errors during transmission or storage. The generated permission serial number possesses security features such as uniqueness, unpredictability, and tamper-proof properties.

[0119] The process of extracting permission item identifiers and attributes from the minimum permission set for a scenario employs XML parsing technology to perform structured analysis of the permission configuration file. The permission parser reads the permission definition nodes in the permission configuration file, extracting basic information such as the identifier, name, description, and type fields for each permission item. Permission item identifiers use a hierarchical naming convention; for example, in a medical rescue scenario, the device control permission identifier is "medical.device.control", and the data access permission identifier is "medical.data.access". Permission item attributes include multi-dimensional parameters such as permission level, validity period, usage frequency, access scope, and operation type. The attribute extraction process uses XPath expressions to locate specific attribute nodes in the permission configuration file, ensuring the accuracy and completeness of the attribute information.

[0120] The hierarchical parsing of permission item identifiers and attributes employs semantic analysis technology to structure permission information. The hierarchical parser segments the permission item identifier using dot separators, forming a multi-level permission hierarchy. The parsing process identifies semantic components within the permission identifier, such as the domain name, module, function, and operation components, each corresponding to a different level of permission control. The hierarchical parsing of permission item attributes uses an attribute classifier to categorize attribute information into two main categories: static attributes and dynamic attributes. Static attributes include invariant information such as permission name and description, while dynamic attributes include variable information such as permission status and usage statistics. The parsing result forms a tree-like permission hierarchy, facilitating subsequent mapping and encoding processes.

[0121] The process of mapping permission item identifiers to opcode sequences employs an encoding table lookup technique to convert text identifiers into numerical codes. The opcode mapper maintains a predefined opcode mapping table, which maps common permission operation types such as read, write, execute, delete, and manage to fixed numerical codes. The mapping process uses a string matching algorithm to identify operation keywords in the permission item identifier and converts them into corresponding opcodes according to the mapping table. The opcode sequences use variable-length encoding, assigning shorter codes to frequently used operation types and longer codes to less common operation types to improve encoding efficiency. Compound operations are represented by combinations of opcodes; for example, the opcode sequence corresponding to read and write permissions is a concatenation of read and write codes.

[0122] The technical implementation of mapping permission attributes to constraint code sequences employs a multi-dimensional encoding strategy to numerically represent attribute parameters. The constraint code generator encodes constraints such as permission levels, time limits, frequency limits, and range limits into a fixed-format numerical sequence. Permission levels are represented by 4-bit binary codes, supporting 16 different permission levels. Time limits are encoded using Unix timestamp format, representing the start and end times of the permission's validity period. Frequency limits are encoded using a counter to represent the maximum number of operations allowed per unit time. Range limits use bitmap encoding technology, with each bit representing access permissions for a specific resource or functional module. The constraint code sequence uses fixed-length encoding to ensure the consistency and comparability of the encoding results.

[0123] The process of generating permission serial numbers from opcode and constraint code sequences employs sequence concatenation and hash compression techniques. The serial number generator concatenates the opcode and constraint code sequences according to a predefined format to form a complete permission description sequence. A separator is inserted between the two sequences during concatenation to ensure parsability. The concatenated sequence is then compressed using a hash function to generate a fixed-length permission serial number. The compression process uses the MD5 hash algorithm to calculate the sequence's digest value and converts it to a readable format using hexadecimal encoding. The permission serial number includes a version number prefix to identify the generation algorithm version, supporting backward compatibility and smooth upgrades.

[0124] The permission mapping table is constructed using a hash table data structure to achieve a fast mapping between permission serial numbers and permission information. The mapping table builder creates a collection of key-value pairs with permission serial numbers as keys and permission details as values. Permission details include complete data such as the original permission item identifier, permission item attributes, generation time, validity period, and usage statistics. The mapping table uses open addressing to handle hash collisions; when multiple permission serial numbers produce the same hash value, linear probing is used to find the next available storage location. The mapping table also includes an index structure, supporting fast query and filtering operations based on different dimensions such as permission type, permission level, and validity period.

[0125] The permission sequence number and pass token association in the permission mapping table employs digital signature technology to ensure the security and non-repudiation of the binding relationship. The binding processor uses the RSA digital signature algorithm to sign the combined data of the permission sequence number and pass token. The signing process uses a private key to encrypt the hash value of data such as the permission sequence number, pass token, timestamp, and random number to generate a digital signature. The binding record contains fields such as permission sequence number, pass token, digital signature, and binding time, forming a complete proof of the association relationship. The binding verification process decrypts the digital signature using the public key and compares it with the recalculated hash value to confirm the authenticity and integrity of the binding relationship.

[0126] The authorization token is generated using the JSON Web Token format, which encapsulates permission information and binding relationships. The token generator creates a JWT structure consisting of a header, payload, and signature. The header contains the token type and signature algorithm information, while the payload includes declarations such as the permission sequence number, pass flag, issuer, recipient, issuance time, and expiration time. The signature part uses the HMAC-SHA256 algorithm to sign the header and payload, ensuring the token's integrity and authenticity. The token is securely transmitted using Base64URL encoding and supports being carried in various ways, including HTTP headers, URL parameters, and message bodies. The generated authorization token is self-contained, allowing the recipient to independently verify its validity without querying an additional database.

[0127] The validity period verification of the authorization token's permission sequence number employs a scheduled task technology for periodic checks. The validity period verifier triggers a verification task via a timer, checking the relationship between the expiration time recorded in the token and the current time. The verification process parses the payload of the JWT token, extracts the expiration time declaration, and compares it with the current timestamp. The verification task uses batch processing, checking the validity period of multiple tokens at once to improve efficiency. Verification results are recorded in the token status table, including token identifier, verification time, validity period status, and remaining validity period.

[0128] The mechanism for detecting impending expiration of permission serial numbers employs threshold monitoring technology to provide early warnings. The expiration detector sets multiple warning thresholds, triggering warning notifications at different points in time before token expiration. Warning thresholds include emergency warning thresholds and normal warning thresholds. Emergency warnings are triggered 5 minutes before expiration, and normal warnings are triggered 30 minutes before expiration. The detection process determines the warning level by comparing the difference between the current time and the expiration time and sends a warning message to the token management service. The warning message includes information such as token identifier, remaining validity period, warning level, and suggested actions, supporting both automatic and manual renewal.

[0129] The process of regenerating authorization tokens based on permission execution conditions in the permission mapping table employs token refresh technology to achieve seamless permission continuation. The token refresher queries the permission mapping table based on the permission sequence number in the original token to obtain complete permission information and execution conditions. The refresh process verifies whether the permission execution conditions are still met, including multi-dimensional checks such as user identity validity, permission usage statistics, and security risk assessment. After the conditions are verified, the refresher generates a new authorization token using the same permission sequence number and update time parameters. The new token inherits the permission scope and constraints of the original token but has an extended validity period. The refresh process also includes incrementing the token version number to ensure differentiation and tracking between the old and new tokens. Refresh records are stored in a token history table for auditing and security analysis.

[0130] In one optional implementation, a time-frequency anomaly identifier is generated based on the results of the field-level encryption and the integrity authentication, according to the handling instructions in the sensitive session load. When the time-frequency anomaly identifier deviates from a preset evaluation threshold, a risk escalation is triggered, and the granted minimum scene permissions are temporarily tightened accordingly. Simultaneously, a liveness detection re-completion is required, including:

[0131] Based on the results of the field-level encryption and the integrity authentication, the handling instructions are extracted from the sensitive session load; an operation sequence table is constructed based on the handling instructions, time-frequency analysis is performed on the operation sequence table, the time interval characteristics and frequency fluctuation characteristics of the instruction execution are extracted, and the time interval characteristics and frequency fluctuation characteristics are merged to generate a time-frequency anomaly identifier;

[0132] The deviation between the time-frequency anomaly identifier and the preset evaluation threshold is calculated. When the result of the deviation calculation exceeds the warning range, the risk level is upgraded. Based on the risk level, the minimum permissions granted to the scene are temporarily tightened, and a permission tightening record is generated.

[0133] According to the permission tightening record, a liveness detection request is sent to the user terminal, and the liveness detection data returned by the user terminal is received; the liveness detection data is subjected to feature extraction and authenticity verification, and a liveness detection result is generated.

[0134] Based on the results of field-level encryption and integrity authentication, the process of extracting treatment instructions from sensitive session payloads includes parsing each encrypted field and structurally identifying the instruction content. The decryption engine uses the AES-256-GCM algorithm to decrypt the treatment instruction fields in the sensitive payload. During decryption, the integrity authentication tag of each field is verified simultaneously to ensure that the instruction content has not been tampered with during transmission and storage. The extraction process of treatment instructions is performed by matching predefined instruction templates, which contain core elements such as medical treatment type, urgency level, resource allocation requirements, and execution time limit. The instruction parser performs semantic analysis on the treatment instructions based on the JSON format data structure, extracting specific operation types such as cardiopulmonary resuscitation, drug injection, and equipment mobilization, and identifying the execution priority and time constraints of each operation.

[0135] The technical implementation of constructing an operation sequence list based on extracted treatment instructions employs a directed acyclic graph (DAG) data structure to represent the dependencies and execution order between instructions. The operation sequence list records attribute information for each treatment instruction, including execution timestamp, operation type identifier, execution duration, and prerequisite conditions. The sequence list construction process automatically generates execution paths by analyzing the logical relationships between instructions. For example, in a cardiac arrest scenario, the operation sequence list would include the temporal arrangement of steps such as checking consciousness, opening the airway, chest compressions, and artificial respiration. Each operation node includes fields such as start time, end time, operation identifier, and execution status, forming a complete operation tracking chain.

[0136] The time-frequency analysis of the operation sequence list employs a sliding window technique to extract features from the time-series data. The time-frequency analysis engine divides the operation sequence into fixed time windows, with the length of each window dynamically adjusted according to the urgency of the emergency scenario, typically ranging from 30 seconds to 5 minutes. The analysis engine calculates statistical characteristics such as the frequency density of operations executed within each time window, the standard deviation of the operation interval, and the distribution entropy of the operation type. Frequency domain analysis uses Fast Fourier Transform (FFT) technology to convert the time-domain signal into a frequency-domain representation, identifying periodic features and abnormal fluctuations in the operation execution patterns.

[0137] The time interval feature extraction method employs statistical analysis to model the time difference between adjacent operations. The time interval feature extractor calculates descriptive statistics such as mean, variance, skewness, and kurtosis of the operation intervals and smooths random noise in the time series using a moving average technique. The feature extraction process also includes analysis of the operation execution rhythm, identifying time patterns such as rapid continuous operations, long pauses, and periods of intensive operation. The interval feature vector contains dimensional information such as minimum interval time, maximum interval time, quartiles of the interval time, and interval variation trends.

[0138] Frequency fluctuation feature extraction identifies anomalous patterns by analyzing the time-varying characteristics of operation execution frequency. A frequency calculator counts the number of operations executed per unit time, generating a frequency time series. The fluctuation analysis algorithm calculates the first and second differences of the frequency series to identify the velocity and acceleration characteristics of frequency changes. The frequency fluctuation feature vector includes key parameters such as the frequency baseline value, fluctuation amplitude, fluctuation period, and frequency abrupt change points. The feature extraction process also considers the weighting of different operation types, assigning higher weight coefficients to important operations such as life support operations.

[0139] The process of merging time interval features and frequency fluctuation features to generate time-frequency anomaly identifiers employs feature fusion technology. The feature fusion unit standardizes the time interval feature vector and the frequency fluctuation feature vector, eliminating dimensional differences between different feature dimensions. The fusion algorithm uses a weighted linear combination to merge the two types of features into a unified anomaly identifier vector, with the weighting coefficients determined based on statistical analysis of historical data. The time-frequency anomaly identifier includes fields such as a comprehensive anomaly index, time anomaly component, frequency anomaly component, and anomaly direction indication, forming a multi-dimensional anomaly description.

[0140] The deviation calculation between the time-frequency anomaly indicator and the preset assessment threshold employs a multi-level threshold comparison mechanism. The threshold manager maintains the threshold ranges corresponding to normal operation mode, minor anomaly mode, medium risk mode, and high risk mode. The deviation calculator uses Euclidean distance to measure the distance between the time-frequency anomaly indicator and the center point of each threshold level to determine the risk level of the current operation mode. The calculation process considers the differences in importance of various dimensions of the anomaly indicator and uses a weighted distance calculation method to improve assessment accuracy.

[0141] The risk level escalation mechanism when the deviation calculation result exceeds the warning range is managed using a state machine model. The risk level manager defines four states: low risk, medium risk, high risk, and extremely high risk. The transition conditions between states are determined based on the deviation calculation result and the duration. The risk escalation trigger immediately initiates the escalation process when an abnormal indicator is detected to exceed the threshold, recording information such as the escalation time, reason, and magnitude. The escalation process includes adjusting the risk level upwards and activating the corresponding safety policies.

[0142] Based on risk levels, the system employs a dynamic permission matrix adjustment technique to temporarily tighten permissions for scenarios with minimal privileges. The permission manager maintains a mapping table between user permissions and risk levels, defining the permitted operation scope and resource access permissions under different risk levels. The permission tightening executor automatically adjusts user operation permissions according to the current risk level. For example, in a high-risk state, it restricts a user's control over critical medical equipment, retaining only basic information viewing permissions. Tightening operations include measures such as reducing the scope of permissions, limiting operation frequency, and disabling sensitive functions.

[0143] The process of generating permission tightening records includes detailed operation logging and audit trail functionality. The record generator creates structured records containing information such as tightening time, tightening reason, tightening scope, tightening level, and recovery conditions. Permission tightening records are stored in an immutable log format to ensure traceability and audit integrity. The record content also includes user identifiers, session identifiers, permission status before tightening, and a comparison of permission status after tightening.

[0144] The communication mechanism for sending liveness detection requests to user terminals based on access control records employs secure push messaging technology. The request sender locates the target terminal device based on user information in the access control record and generates a liveness detection request message containing parameters such as detection requirements, detection type, and timeout. Message transmission uses an end-to-end encryption protocol to protect communication security, ensuring that the detection request is not intercepted or tampered with during transmission. The request message includes control parameters such as a random challenge code, detection mode selection, and response time limit.

[0145] The system receives liveness detection data returned by the user terminal using multimodal data processing technology. The data receiver supports the reception and preliminary verification of various biometric data, including facial images, fingerprint information, voice samples, and motion videos. The receiving process includes preprocessing steps such as data integrity verification, format compliance checks, and data volume and quality assessment. The received liveness detection data undergoes preprocessing operations such as denoising, enhancement, and standardization to prepare for subsequent feature extraction and verification.

[0146] The liveness detection data feature extraction and authenticity verification employs a combined approach of deep learning and traditional machine learning. The feature extractor uses specialized algorithms for different types of biometric data; facial feature extraction uses convolutional neural networks to extract deep feature representations, while fingerprint feature extraction uses minutiae detection algorithms to identify ridge structures. Authenticity verification uses liveness detection algorithms to determine the authenticity of biometric features, including anti-spoofing techniques such as blink detection, micro-expression analysis, and depth information verification. The verification process also includes similarity comparison with historical biometric templates to ensure the consistency between the current detected object and the authorized user's identity. The generated liveness detection results include comprehensive evaluation information such as authenticity score, matching confidence, and detection quality rating, providing a reliable basis for authorization restoration decisions.

[0147] A second aspect of the present invention provides an electronic device, comprising:

[0148] processor;

[0149] Memory used to store processor-executable instructions;

[0150] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0151] A third aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0152] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An emergency rescue and first aid management method based on multi-factor authentication and identity verification, characterized in that, include: An emergency event request is received and a time-series session identifier is generated. Based on the time-series session identifier, a request message is constructed according to a fixed field order and an integrity verification digest is calculated. The geographic location data, medical information digest, and treatment instructions in the request message are combined and defined as sensitive session load. Based on the time-series session identifier, the request message is located, and authentication and liveness detection are performed on the requester in parallel to obtain three types of authentication results. The three types of authentication results are then combined with the integrity verification digest to obtain a pass flag. Based on the generation of a trusted timestamp through tagging, and the performance of multi-source verification based on the geographic location data in the request message, upon successful verification, the trusted timestamp is bound to the time-series session identifier to generate a spatiotemporal binding record, including: Based on the above method of generating a trusted timestamp by calling a trusted time source through a marker, the trusted timestamp is decomposed into a time sequence number, a time verification value, and a random entropy value; the random entropy value is used to perturb and map the geographic location data in the request message to obtain a geographic location fingerprint; spatial feature vectors and time feature vectors are extracted from the geographic location fingerprint, and the spatial feature vectors and the time feature vectors are respectively used with multi-source fused positioning data to perform multi-source verification and calculate the location verification score. The location verification score is compared with a preset location deviation threshold. When the location verification score meets the location deviation threshold, the timestamp sequence in the time verification value is extracted and the time verification code is calculated. The time sequence number in the trusted timestamp is irreversibly bound to the time sequence session identifier to generate an initial spatiotemporal binding record. The initial spatiotemporal binding record is encrypted using the time verification code to obtain the final spatiotemporal binding record. Based on the spatiotemporal binding record, the minimum permissions of the scenario are granted to the identity that has been marked. After successful authorization, a session key is negotiated based on the time-series session identifier. The session key is then used to perform field-level encryption and integrity authentication on the sensitive session payload. Based on the results of the field-level encryption and the integrity authentication, a time-frequency anomaly identifier is generated according to the handling instructions in the sensitive session load. When the time-frequency anomaly identifier deviates from the preset evaluation threshold, a risk escalation is triggered, and the minimum permissions granted in the scenario are temporarily tightened accordingly, while requiring a re-completion of liveness detection.

2. The method according to claim 1, characterized in that, Based on the time-series session identifier, a request message is constructed according to a fixed field order, and an integrity verification digest is calculated. The geographic location data, medical information digest, and treatment instructions in the request message are combined and defined as the sensitive session payload, including: A field sequence table is constructed based on the time-series session identifier. The preset fixed field order in the field sequence table is mapped to geographic location data, medical information summary, and treatment instructions. The request message is organized and constructed according to the fixed field order. A segmented hash operation is performed on the request message to obtain an integrity verification digest. The geographic location data, medical information summary, and treatment instruction tags in the request message are merged and defined as sensitive session load.

3. The method according to claim 2, characterized in that, The method further includes: Based on the field sequence table, the field composition of the request message is parsed to verify the validity of the integrity verification digest; after the verification is passed, a field mapping matrix is ​​constructed for the sensitive session load, and hierarchical protection control is performed on the sensitive session load based on the field mapping matrix; Based on the sensitivity levels recorded in the field mapping matrix, an encryption key and an authentication key are assigned to each field in the sensitive session payload; the encryption key is used to perform encryption processing on fields with sensitivity levels higher than a preset threshold, and the authentication key is used to calculate the message authentication code of the sensitive session payload; The message authentication code is bound to the time-series session identifier to obtain a session security identifier. Based on the session security identifier, the integrity of the sensitive session load is verified according to a preset fixed period. When the verification fails, risk escalation is triggered and field-level protection control is re-executed.

4. The method according to claim 1, characterized in that, Based on the spatiotemporal binding record, the minimum scene permissions are granted to the identity with the tagged identifier. After successful authorization, a session key is negotiated based on the time-series session identifier. The session key is then used to perform field-level encryption and integrity authentication on the sensitive session payload, including: Based on the spatiotemporal binding record, a reliable time window and location activity features are extracted. The reliable time window and location activity features are mapped to a scene permission matrix. The minimum permission set for the scene is extracted from the scene permission matrix. Fine-grained decomposition is performed on the permission items in the minimum permission set to generate a permission sequence number. The permission sequence number is associated and bound with the tag to generate a time-sensitive authorization token. Based on the time-series session identifier, a key negotiation process is performed to generate a session key seed and a key update vector; the session key seed is decomposed using the key update vector to obtain the message authentication key; A sensitivity assessment is performed on the fields in the sensitive session payload, and the field encryption priority is determined based on the assessment results; the priority fields are encrypted using the message authentication key, and the integrity check code of the entire sensitive session payload is calculated using the authorization token; The integrity verification code is bound to the authorization token to generate a session security identifier; the integrity of the sensitive session payload is verified based on the session security identifier at a preset fixed period; when the verification fails, a key update and permission re-granting process is triggered.

5. The method according to claim 4, characterized in that, Fine-grained decomposition is performed on the permission items in the minimum permission set of the scenario to generate permission sequence numbers; Associating the permission serial number with the tag to generate a time-sensitive authorization token includes: Extract the permission item identifier and permission item attribute from the minimum permission set of the scenario, perform hierarchical parsing on the permission item identifier and permission item attribute, map the permission item identifier to an opcode sequence, and map the permission item attribute to a constraint code sequence; generate a permission sequence number using the opcode sequence and the constraint code sequence; A permission mapping table is constructed based on the permission serial number. The permission serial numbers in the permission mapping table are associated and bound with the tags to generate an authorization token. The validity of the permission serial number in the authorization token is verified according to a preset fixed period. When the permission serial number is detected to be about to expire, the authorization token is regenerated based on the permission execution conditions recorded in the permission mapping table.

6. The method according to claim 1, characterized in that, Based on the results of the field-level encryption and the integrity authentication, a time-frequency anomaly identifier is generated according to the handling instructions in the sensitive session load. When the time-frequency anomaly identifier deviates from a preset evaluation threshold, a risk escalation is triggered, and the granted minimum scene permissions are temporarily tightened accordingly. At the same time, a liveness detection is required to be performed again, including: Based on the results of the field-level encryption and the integrity authentication, the handling instructions are extracted from the sensitive session load; an operation sequence table is constructed based on the handling instructions, time-frequency analysis is performed on the operation sequence table, the time interval characteristics and frequency fluctuation characteristics of the instruction execution are extracted, and the time interval characteristics and frequency fluctuation characteristics are merged to generate a time-frequency anomaly identifier; The deviation between the time-frequency anomaly identifier and the preset evaluation threshold is calculated. When the result of the deviation calculation exceeds the warning range, the risk level is upgraded. Based on the risk level, the minimum permissions granted to the scene are temporarily tightened, and a permission tightening record is generated. According to the permission tightening record, a liveness detection request is sent to the user terminal, and the liveness detection data returned by the user terminal is received; the liveness detection data is subjected to feature extraction and authenticity verification, and a liveness detection result is generated.

7. An emergency rescue and first aid management system based on multi-factor authentication and identity verification, used to implement the method of any one of claims 1-6, characterized in that, include: The first unit is used to receive emergency event requests and generate time-series session identifiers. Based on the time-series session identifiers, a request message is constructed according to a fixed field order and an integrity verification digest is calculated. The geographic location data, medical information digest, and treatment instructions in the request message are combined and defined as sensitive session load. The second unit is used to locate the request message based on the time-series session identifier, perform authentication and liveness detection on the requester in parallel to obtain three types of authentication results, and jointly determine the three types of authentication results with the integrity verification digest to obtain a pass mark; Based on the generation of a trusted timestamp through tagging, and the performance of multi-source verification based on the geographic location data in the request message, upon successful verification, the trusted timestamp is bound to the time-series session identifier to generate a spatiotemporal binding record, including: Based on the above method of generating a trusted timestamp by calling a trusted time source through a marker, the trusted timestamp is decomposed into a time sequence number, a time verification value, and a random entropy value; the random entropy value is used to perturb and map the geographic location data in the request message to obtain a geographic location fingerprint; spatial feature vectors and time feature vectors are extracted from the geographic location fingerprint, and the spatial feature vectors and the time feature vectors are respectively used with multi-source fused positioning data to perform multi-source verification and calculate the location verification score. The location verification score is compared with a preset location deviation threshold. When the location verification score meets the location deviation threshold, the timestamp sequence in the time verification value is extracted and the time verification code is calculated. The time sequence number in the trusted timestamp is irreversibly bound to the time sequence session identifier to generate an initial spatiotemporal binding record. The initial spatiotemporal binding record is encrypted using the time verification code to obtain the final spatiotemporal binding record. The third unit is used to grant the minimum permission of the scenario to the identity with the mark based on the spatiotemporal binding record. After successful authorization, a session key is negotiated based on the time-series session identifier. The session key is then used to perform field-level encryption and integrity authentication on the sensitive session payload. The fourth unit is used to generate a time-frequency anomaly identifier based on the results of the field-level encryption and the integrity authentication, according to the processing instructions in the sensitive session load. When the time-frequency anomaly identifier deviates from the preset evaluation threshold, a risk escalation is triggered, and the minimum permissions granted in the scenario are temporarily tightened accordingly, while requiring a re-completion of liveness detection.

8. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 6.

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