Dynamic face identity recognition and key point track positioning method and system

Abstract

The invention discloses a dynamic face identity recognition and key point track positioning method and system, belongs to the technical field of artificial intelligence, and aims to solve the technical problem of how to realize safety standardized supervision of hydraulic engineering construction and quality safety supervision and inspection. Comprising a dynamic face recognition integrated development module which develops a dynamic living body face detection and face login module and configures an interface for the dynamic living body face detection and face login module; the GPS trajectory tracking module is used for providing trajectory collection, trajectory playback and anomaly recognition based on map rickiness; the block chain document cochain module uploads the document hash to the alliance chain, establishes a cochain record index for supervising the document, and supports query and verification of the document hash; the electronic signature system docking module is used for providing multi-party signature and verification for the supervision document; and the test and integration module is used for performing function, performance and safety tests to form a test report.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method and system for dynamic face identity recognition and key point trajectory positioning. Background Art

[0002] Traditional water conservancy project construction quality and safety supervision and inspection lack unified supervision standards and processes, personnel organization and coordination are difficult, supervision work becomes perfunctory, personnel are not in place, identity information is difficult to confirm, and paper-based office work is inefficient; when project problems arise, responsibilities are difficult to implement and people pass the buck to each other.

[0003] How to achieve safe and standardized supervision of water conservancy project construction and quality and safety supervision and inspection is a technical problem that needs to be solved. Summary of the Invention

[0004] The technical task of the present invention is to address the above shortcomings and provide a dynamic face identity recognition and key point trajectory positioning method and system to solve the technical problem of how to achieve safe and standardized supervision of water conservancy project construction and quality and safety supervision and inspection.

[0005] In a first aspect, the present invention provides a dynamic face identification and key point trajectory positioning system, including a dynamic face recognition integrated development module, a GPS trajectory tracking module, a blockchain document uploading module, an electronic signature system docking module, and a testing and integration module;

[0006] The dynamic face recognition integrated development module is used to develop a dynamic live face detection and face login module and configure an interface for the dynamic live face detection and face login module. The dynamic live face detection and face login module is configured with a face recognition model for providing face recognition services and face login services based on the face recognition model;

[0007] The GPS track tracking module is integrated with a map engine for providing track collection, track playback and anomaly identification based on the map. The track is formed by collecting location information, the track is visualized through track playback, and whether the track deviates abnormally is determined through anomaly identification, and an early warning prompt is issued based on the detection results.

[0008] The blockchain document on-chain module is used to build a consortium chain, perform hash calculation on the supervision document, generate the document hash, upload the document hash to the consortium chain and establish an on-chain record index for the supervision document, and support query and verification of the document hash;

[0009] The electronic signature system docking module is used to connect to the electronic signature service provider to provide multi-party signature and verification for the supervision of documents;

[0010] The testing and integration module is used to perform functional, performance, and security testing on the dynamic face recognition integrated development module, GPS trajectory tracking module, blockchain document uploading module, and electronic signature system docking module, and generate a test report.

[0011] Preferably, the dynamic face recognition integrated development module is used to perform the following operations:

[0012] Select an appropriate deep convolutional neural network-related algorithm as the face recognition algorithm, and select an interface that is compatible with the face recognition algorithm;

[0013] Constructing a face recognition model based on the selected face recognition algorithm and performing model training on the constructed face recognition model;

[0014] Build a dynamic liveness detection and face login module. The dynamic liveness detection and face login module calls the face recognition model through the configured interface to provide face recognition service, and provides face login service based on the face recognition service.

[0015] Preferably, the GPS trajectory tracking module is used to perform the following:

[0016] Select an appropriate map engine;

[0017] In response to the needs of trajectory display, geographic positioning and real-time data update, the map engine interface is configured, and the map engine is called through the interface to provide location marking, route drawing and area division;

[0018] Configure the GPS unit in the GPS trajectory tracking module, collect location information in real time through GPS, and store the location information of each collection point in a data packet format;

[0019] Configure the trajectory playback service of the GPS trajectory tracking module, realize the visualization of historical trajectories through the trajectory revisit service, and support the control of trajectories based on the timeline;

[0020] Configure the GPS trajectory anomaly recognition service. The trajectory anomaly recognition service sets preset driving trajectory templates and boundary conditions, compares real-time data to determine abnormal deviations, and triggers early warning prompts or records abnormal logs for subsequent analysis.

[0021] Preferably, the blockchain document upload module is used to perform the following operations:

[0022] Build a consortium chain based on Hyperledger Fabric, Corda or other enterprise-level blockchain platforms;

[0023] For alliance chains, establish member authentication and authority management mechanisms;

[0024] Plan the distribution of each node to achieve data synchronization, consensus mechanism and fault tolerance between nodes;

[0025] Develop a document hash chain and verification interface. For supervisory documents uploaded to the alliance chain, perform hash calculation on the supervisory file through the document hash chain and verification interface to generate document hashes, and build a batch chain mechanism. Based on the batch chain mechanism, upload the document hashes to the alliance chain in batches, and establish a chain record index for the supervisory documents. Provide query and verification of document hashes based on the chain record index and the configured verification interface.

[0026] In a second aspect, the present invention provides a dynamic face identity recognition and key point trajectory positioning method, which is used to implement face identity recognition and key point trajectory positioning using a dynamic face identity recognition and key point trajectory positioning system as described in any one of the first aspects, comprising the following steps:

[0027] Dynamic face recognition integrated development: Develop a dynamic live face detection and face login module and configure an interface for it. The module is configured with a face recognition model and provides face recognition and face login services based on the face recognition model.

[0028] GPS track tracking: Based on the map, it provides track collection, track playback and anomaly identification. It forms a track by collecting location information, visualizes the track through track playback, and determines whether the track deviates abnormally through anomaly identification, and issues early warning prompts based on the detection results.

[0029] Blockchain document upload: Build a consortium chain, perform hash calculation on the supervision document, generate the document hash, upload the document hash to the consortium chain and establish an on-chain record index for the supervision document, supporting query and verification of the document hash;

[0030] Electronic signature system connection: Connect with electronic signature service providers to provide multi-party signature and verification for document supervision.

[0031] As a preference, dynamic face recognition integrated development includes the following operations:

[0032] Select an appropriate deep convolutional neural network-related algorithm as the face recognition algorithm, and select an interface that is compatible with the face recognition algorithm;

[0033] Constructing a face recognition model based on the selected face recognition algorithm and performing model training on the constructed face recognition model;

[0034] Build a dynamic liveness detection and face login module. The dynamic liveness detection and face login module calls the face recognition model through the configured interface to provide face recognition service, and provides face login service based on the face recognition service.

[0035] Preferably, GPS track tracking includes the following operations:

[0036] Select an appropriate map engine;

[0037] In response to the needs of trajectory display, geographic positioning and real-time data update, the map engine interface is configured, and the map engine is called through the interface to provide location marking, route drawing and area division;

[0038] Configure the GPS unit in the GPS trajectory tracking module, collect location information in real time through GPS, and store the location information of each collection point in a data packet format;

[0039] Configure the trajectory playback service of the GPS trajectory tracking module, realize the visualization of historical trajectories through the trajectory revisit service, and support the control of trajectories based on the timeline;

[0040] Configure the GPS trajectory anomaly recognition service. The trajectory anomaly recognition service sets preset driving trajectory templates and boundary conditions, compares real-time data to determine abnormal deviations, and triggers early warning prompts or records abnormal logs for subsequent analysis.

[0041] Preferably, blockchain document uploading includes the following operations:

[0042] Build a consortium chain based on Hyperledger Fabric, Corda or other enterprise-level blockchain platforms;

[0043] For alliance chains, establish member authentication and authority management mechanisms;

[0044] Plan the distribution of each node to achieve data synchronization, consensus mechanism and fault tolerance between nodes;

[0045] Develop a document hash chain and verification interface. For supervisory documents uploaded to the alliance chain, perform hash calculation on the supervisory file through the document hash chain and verification interface to generate document hashes, and build a batch chain mechanism. Based on the batch chain mechanism, upload the document hashes to the alliance chain in batches, and establish a chain record index for the supervisory documents. Provide query and verification of document hashes based on the chain record index and the configured verification interface.

[0046] The dynamic face identification and key point trajectory positioning method and system of the present invention have the following advantages:

[0047] 1. Applying blockchain technology to the storage of water conservancy supervision archives solves the problems of traditional supervision archives, such as easy data tampering, difficult process traceability, and decentralized storage. It builds a "verifiable, tamper-proof, and traceable" supervision archive evidence mechanism to ensure the authenticity, credibility, and compliance of supervision documents. Through methods such as document summary on-chain, timestamp evidence storage, and on-chain verification interfaces, it meets the high standards of supervision and audit for archive credibility.

[0048] 2. Realize the integration of dynamic facial recognition and GPS trajectory backtracking verification: To address risks such as supervisor identity fraud and false reporting of work attendance, we innovatively integrate dynamic facial recognition with real-time GPS trajectory management to establish a "person-post-task" linkage verification mechanism. Supervisors are required to log in through facial recognition and complete supervision tasks at the location point. The entire process is recorded and traced, enhancing the controllability and accountability of the supervision process.

[0049] 3. Build a collaborative process engine for multi-departmental joint supervision: To address issues such as overlapping responsibilities, information barriers, and duplicate supervision among multiple departments, a joint supervision process engine has been developed. This engine supports collaborative task assignment, multi-role concurrent recording, and data mutual recognition and verification. It upgrades the supervisory coordination mechanism from "decentralized control" to "integrated collaboration," effectively improving the efficiency of joint supervision and resource utilization.

[0050] 4. Achieve paperless supervision records with electronic signatures throughout the entire process: Based on the cumbersome and difficult-to-archive signing process of traditional paper documents, this system fully integrates electronic signature technology to build a paperless signing mechanism covering multiple links such as inspection task orders, problem rectification notices, and acceptance records. Signatures are traceable in real time, improving the security and compliance of supervisory documents and significantly reducing the cost of manual paper circulation.

[0051] 5. A multi-dimensional data cockpit enables intelligent analysis of supervisory trends: The project builds a supervisory cockpit based on big data and a visualization engine. This cockpit integrates multiple dimensions, including supervisory frequency, problem type, regional distribution, and rectification status, to present a comprehensive, real-time supervisory overview. Combined with data models, it supports intelligent early warning, trend forecasting, and performance evaluation, assisting managers in making informed decisions and achieving a new model of intelligent supervision that "visualizes trends, manages risks, and identifies accountability." BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] The present invention will be further described below with reference to the accompanying drawings.

[0054] Figure 1 This is a flowchart of a method for dynamic face identification and key point trajectory positioning in Example 2. DETAILED DESCRIPTION

[0055] The present invention will be further described below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments given are not intended to limit the present invention. Unless there is a conflict, the embodiments of the present invention and the technical features in the embodiments may be combined with each other.

[0056] The embodiments of the present invention provide a method and system for dynamic face identification and key point trajectory positioning, which are used to solve the technical problem of how to achieve safe and standardized supervision of water conservancy project construction and quality and safety supervision and inspection.

[0057] Example 1:

[0058] The present invention provides a dynamic face identity recognition and key point trajectory positioning system, which includes a dynamic face recognition integrated development module, a GPS trajectory tracking module, a blockchain document uploading module, an electronic signature system docking module, and a testing and integration module.

[0059] The dynamic face recognition integrated development module is used to develop a dynamic live face detection and face login module and configure an interface for the dynamic live face detection and face login module. The dynamic live face detection and face login module is configured with a face recognition model to provide face recognition services and face login services based on the face recognition model.

[0060] In this embodiment, the dynamic face recognition integrated development module is used to perform the following operations:

[0061] (1) Select an appropriate deep convolutional neural network-related algorithm as the face recognition algorithm and select an interface that is compatible with the face recognition algorithm;

[0062] (2) constructing a face recognition model based on the selected face recognition algorithm, and performing model training on the constructed face recognition model;

[0063] (3) Construct a dynamic liveness detection and face login module. The dynamic liveness detection and face login module calls the face recognition model through the configured interface to provide face recognition services, and provides face login services based on the face recognition service.

[0064] When selecting an algorithm, it's important to comprehensively consider accuracy, real-time performance, and computational overhead. Deep convolutional neural network (CNN) algorithms, such as FaceNet and ArcFace, are typically recommended. Furthermore, considering factors such as lighting, posture, and occlusion in the industry application scenario, appropriate pre-trained models should be selected or targeted algorithms should be developed.

[0065] The GPS trajectory tracking module is integrated with a map engine, which is used to provide trajectory collection, trajectory playback and anomaly identification based on the map. The trajectory is formed by collecting location information through the trajectory, and the trajectory is visualized through trajectory playback. The abnormal deviation of the trajectory is judged through anomaly identification, and early warning prompts are issued based on the detection results.

[0066] In this embodiment, the GPS trajectory tracking module is used to perform the following:

[0067] (1) Select an appropriate map engine;

[0068] (2) In response to the needs of trajectory display, geographic positioning, and real-time data update, the map engine interface is configured, and the map engine is called through the interface to provide location marking, route drawing, and area division;

[0069] (3) A GPS unit is configured in the GPS trajectory tracking module to collect location information in real time through GPS, and the location information of each collection point is stored in a data packet format;

[0070] (4) Configure the trajectory playback service of the GPS trajectory tracking module, realize the visualization of historical trajectories through the trajectory revisit service, and support the control of trajectories based on the timeline;

[0071] (5) Configure the GPS trajectory anomaly recognition service. The trajectory anomaly recognition service sets a preset driving trajectory template and boundary conditions, compares the real-time data to determine abnormal deviations, and triggers early warning prompts or records abnormal logs for subsequent analysis.

[0072] When selecting an engine in this embodiment, you can choose Amap, Tiandi Map or other mainstream map service platforms, and evaluate them based on regional data coverage, interface stability and call cost.

[0073] When integrating the interface, the map engine API needs to be called to meet the needs of trajectory display, geographic positioning and real-time data updates, to achieve location marking, route drawing and area division, and to ensure efficient caching of the interface and encrypted data transmission.

[0074] This operation in this embodiment realizes trajectory collection, playback and anomaly identification.

[0075] Track collection: The built-in GPS module collects location information in real time and uses a data packet formatting solution to store the collected points, maintaining data continuity and high accuracy.

[0076] Trajectory playback: Provides trajectory playback function to realize historical trajectory visualization and support timeline control, allowing users to reproduce the device movement path; at the same time, pay attention to data compression and batch loading performance optimization.

[0077] Abnormal trajectory identification: By setting preset driving trajectory templates and boundary conditions, the system compares real-time data to identify abnormal deviations (such as deviations from the normal working area, sudden interruptions, etc.), triggers early warning prompts, or records abnormal logs for subsequent analysis.

[0078] The blockchain document on-chain module is used to build a consortium chain, perform hash calculations on supervisory documents, generate document hashes, upload document hashes to the consortium chain, and establish an on-chain record index for supervisory documents, supporting query and verification of document hashes.

[0079] In this embodiment, the blockchain document upload module is used to perform the following operations:

[0080] (1) Building a consortium chain based on Hyperledger Fabric, Corda or other enterprise-level blockchain platforms;

[0081] (2) For alliance chains, establish member authentication and authority management mechanisms;

[0082] (3) Plan the distribution of each node to achieve data synchronization, consensus mechanism and fault tolerance between nodes;

[0083] (4) Develop a document hash chain and verification interface. For supervisory documents uploaded to the alliance chain, perform hash calculations on the supervisory files through the document hash chain and verification interface to generate document hashes, and build a batch chain mechanism. Based on the batch chain mechanism, the document hashes are uploaded to the alliance chain in batches, and a chain record index for the supervisory documents is established. Based on the chain record index and the configured verification interface, document hash query and verification are provided.

[0084] In this embodiment, the alliance chain environment is first built, which involves technology selection, alliance member management, node deployment and topology design.

[0085] Technology selection: Based on Hyperledger Fabric, Corda, or other enterprise-level blockchain platforms, build a consortium chain suitable for water conservancy supervision scenarios to ensure the security, transparency, and traceability of on-chain data.

[0086] Alliance member management: Establish a complete member authentication and permission management mechanism to ensure that participating institutions can only query data related to themselves.

[0087] Node deployment and topology design: Rationally plan the distribution of nodes across regions to implement data synchronization, consensus mechanism, and fault-tolerant processing to ensure system stability.

[0088] Then, supervise the development of document hash chain and verification interface, involving document hash generation, chain process and verification interface.

[0089] Document hash generation: For uploaded supervisory documents, a unique hash value is generated using security algorithms such as SHA-256 to ensure that the document content is not tampered with.

[0090] On-chain process: Build a batch on-chain mechanism, or on-chain in real time as needed to ensure that document hash data is persisted on the blockchain; at the same time, design an on-chain record index for easy traceability.

[0091] Verification interface: Develop a docking interface to enable internal systems and external regulatory authorities to query document hash records on the blockchain in real time and compare document hash values ​​to verify the authenticity and integrity of the files.

[0092] The electronic signature system docking module is used to connect with electronic signature service providers to provide multi-party signature and verification for document supervision.

[0093] Connect to electronic signature service providers (such as CA organizations or government cloud platforms) to achieve multi-party signing and verification.

[0094] The testing and integration module is used to perform functional, performance, and security testing on the dynamic face recognition integrated development module, GPS trajectory tracking module, blockchain document uploading module, and electronic signature system docking module, and generate a test report.

[0095] The system of this embodiment solves the shortcomings of traditional supervision methods, eliminates the problems of personnel absence and personnel identity fraud, and improves the efficiency of water conservancy project quality supervision and inspection.

[0096] Example 2:

[0097] The present invention provides a dynamic face identity recognition and key point trajectory positioning method, which includes four steps: dynamic face recognition integrated development, GPS trajectory tracking, blockchain document uploading, and electronic signature system docking.

[0098] Step S100 dynamic face recognition integrated development: develop a dynamic live face detection and face login module and configure an interface for the dynamic live face detection and face login module, wherein the dynamic live face detection and face login module is configured with a face recognition model, and provides face recognition services and face login services based on the face recognition model.

[0099] In this embodiment, dynamic face recognition integrated development includes the following operations:

[0100] (1) Select an appropriate deep convolutional neural network-related algorithm as the face recognition algorithm and select an interface that is compatible with the face recognition algorithm;

[0101] (2) constructing a face recognition model based on the selected face recognition algorithm, and performing model training on the constructed face recognition model;

[0102] (3) Construct a dynamic liveness detection and face login module. The dynamic liveness detection and face login module calls the face recognition model through the configured interface to provide face recognition services, and provides face login services based on the face recognition service.

[0103] When selecting an algorithm, it's important to comprehensively consider accuracy, real-time performance, and computational overhead. Deep convolutional neural network (CNN) algorithms, such as FaceNet and ArcFace, are typically recommended. Furthermore, considering factors such as lighting, posture, and occlusion in the industry application scenario, appropriate pre-trained models should be selected or targeted algorithms should be developed.

[0104] Step S200 GPS track tracking: based on the map, track collection, track playback and anomaly identification are provided. Tracks are formed by collecting location information through tracks, and tracks are visualized through track playback. Anomaly identification is used to determine whether the track deviates abnormally, and early warning prompts are given based on the detection results.

[0105] In this embodiment, GPS trajectory tracking includes the following operations:

[0106] (1) Select an appropriate map engine;

[0107] (2) In response to the needs of trajectory display, geographic positioning, and real-time data update, the map engine interface is configured, and the map engine is called through the interface to provide location marking, route drawing, and area division;

[0108] (3) A GPS unit is configured in the GPS trajectory tracking module to collect location information in real time through GPS, and the location information of each collection point is stored in a data packet format;

[0109] (4) Configure the trajectory playback service of the GPS trajectory tracking module, realize the visualization of historical trajectories through the trajectory revisit service, and support the control of trajectories based on the timeline;

[0110] (5) Configure the GPS trajectory anomaly recognition service. The trajectory anomaly recognition service sets a preset driving trajectory template and boundary conditions, compares the real-time data to determine abnormal deviations, and triggers early warning prompts or records abnormal logs for subsequent analysis.

[0111] When selecting an engine in this embodiment, you can choose Amap, Tiandi Map or other mainstream map service platforms, and evaluate them based on regional data coverage, interface stability and call cost.

[0112] When integrating the interface, the map engine API needs to be called to meet the needs of trajectory display, geographic positioning and real-time data updates, to achieve location marking, route drawing and area division, and to ensure efficient caching of the interface and encrypted data transmission.

[0113] This operation in this embodiment realizes trajectory collection, playback and anomaly identification.

[0114] Track collection: The built-in GPS module collects location information in real time and uses a data packet formatting solution to store the collected points, maintaining data continuity and high accuracy.

[0115] Trajectory playback: Provides trajectory playback function to realize historical trajectory visualization and support timeline control, allowing users to reproduce the device movement path; at the same time, pay attention to data compression and batch loading performance optimization.

[0116] Abnormal trajectory identification: By setting preset driving trajectory templates and boundary conditions, the system compares real-time data to identify abnormal deviations (such as deviations from the normal working area, sudden interruptions, etc.), triggers early warning prompts, or records abnormal logs for subsequent analysis.

[0117] Step S300: Blockchain document upload: Build a consortium chain, perform hash calculation on the supervision document, generate a document hash, upload the document hash to the consortium chain and establish a chain record index for the supervision document, supporting query and verification of the document hash.

[0118] In this embodiment, blockchain document uploading includes the following operations:

[0119] (1) Building a consortium chain based on Hyperledger Fabric, Corda or other enterprise-level blockchain platforms;

[0120] (2) For alliance chains, establish member authentication and authority management mechanisms;

[0121] (3) Plan the distribution of each node to achieve data synchronization, consensus mechanism and fault tolerance between nodes;

[0122] (4) Develop a document hash chain and verification interface. For supervisory documents uploaded to the alliance chain, perform hash calculations on the supervisory files through the document hash chain and verification interface to generate document hashes, and build a batch chain mechanism. Based on the batch chain mechanism, the document hashes are uploaded to the alliance chain in batches, and a chain record index for the supervisory documents is established. Based on the chain record index and the configured verification interface, document hash query and verification are provided.

[0123] In this embodiment, the alliance chain environment is first built, which involves technology selection, alliance member management, node deployment and topology design.

[0124] Technology selection: Based on Hyperledger Fabric, Corda, or other enterprise-level blockchain platforms, build a consortium chain suitable for water conservancy supervision scenarios to ensure the security, transparency, and traceability of on-chain data.

[0125] Alliance member management: Establish a complete member authentication and permission management mechanism to ensure that participating institutions can only query data related to themselves.

[0126] Node deployment and topology design: Rationally plan the distribution of nodes across regions to implement data synchronization, consensus mechanism, and fault-tolerant processing to ensure system stability.

[0127] Then, supervise the development of document hash chain and verification interface, involving document hash generation, chain process and verification interface.

[0128] Document hash generation: For uploaded supervisory documents, a unique hash value is generated using security algorithms such as SHA-256 to ensure that the document content is not tampered with.

[0129] On-chain process: Build a batch on-chain mechanism, or on-chain in real time as needed to ensure that document hash data is persisted on the blockchain; at the same time, design an on-chain record index for easy traceability.

[0130] Verification interface: Develop a docking interface to enable internal systems and external regulatory authorities to query document hash records on the blockchain in real time and compare document hash values ​​to verify the authenticity and integrity of the files.

[0131] Step S400: Electronic signature system connection: connection with the electronic signature service provider to provide multi-party signature and verification for the supervision of documents.

[0132] The method of this embodiment can realize dynamic face identity recognition and key point trajectory positioning through the system provided in Example 1.

[0133] The above is a detailed introduction to the dynamic face identity recognition and key point trajectory positioning system and method provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A dynamic face identification and key point trajectory positioning system, characterized in that: Including dynamic face recognition integrated development module, GPS track tracking module, blockchain document uploading module, electronic signature system docking module and testing and integration module; The dynamic face recognition integrated development module is used to develop a dynamic live face detection and face login module and configure an interface for the dynamic live face detection and face login module. The dynamic live face detection and face login module is configured with a face recognition model for providing face recognition services and face login services based on the face recognition model; The GPS track tracking module is integrated with a map engine for providing track collection, track playback and anomaly identification based on the map. The track is formed by collecting location information, the track is visualized through track playback, and whether the track deviates abnormally is determined through anomaly identification, and an early warning prompt is issued based on the detection results. The blockchain document on-chain module is used to build a consortium chain, perform hash calculation on the supervision document, generate the document hash, upload the document hash to the consortium chain and establish an on-chain record index for the supervision document, and support query and verification of the document hash; The electronic signature system docking module is used to connect to the electronic signature service provider to provide multi-party signature and verification for the supervision of documents; The testing and integration module is used to perform functional, performance, and security testing on the dynamic face recognition integrated development module, GPS trajectory tracking module, blockchain document uploading module, and electronic signature system docking module, and generate a test report.

2. The dynamic face identification and key point trajectory positioning system according to claim 1, characterized in that: The dynamic face recognition integrated development module is used to perform the following operations: Select an appropriate deep convolutional neural network-related algorithm as the face recognition algorithm, and select an interface that is compatible with the face recognition algorithm; Constructing a face recognition model based on the selected face recognition algorithm and performing model training on the constructed face recognition model; Build a dynamic liveness detection and face login module. The dynamic liveness detection and face login module calls the face recognition model through the configured interface to provide face recognition service, and provides face login service based on the face recognition service.

3. The dynamic face identification and key point trajectory positioning system according to claim 1, characterized in that: The GPS trajectory tracking module is used to perform the following: Select an appropriate map engine; In response to the needs of trajectory display, geographic positioning and real-time data update, the map engine interface is configured, and the map engine is called through the interface to provide location marking, route drawing and area division; Configure the GPS unit in the GPS trajectory tracking module, collect location information in real time through GPS, and store the location information of each collection point in a data packet format; Configure the trajectory playback service of the GPS trajectory tracking module, realize the visualization of historical trajectories through the trajectory revisit service, and support the control of trajectories based on the timeline; Configure the GPS trajectory anomaly recognition service. The trajectory anomaly recognition service sets preset driving trajectory templates and boundary conditions, compares real-time data to determine abnormal deviations, and triggers early warning prompts or records abnormal logs for subsequent analysis.

4. The dynamic face identification and key point trajectory positioning system according to claim 1, characterized in that: The blockchain document upload module is used to perform the following operations: Build a consortium chain based on Hyperledger Fabric, Corda or other enterprise-level blockchain platforms; For alliance chains, establish member authentication and authority management mechanisms; Plan the distribution of each node to achieve data synchronization, consensus mechanism and fault tolerance between nodes; Develop a document hash chain and verification interface. For supervisory documents uploaded to the alliance chain, perform hash calculation on the supervisory file through the document hash chain and verification interface to generate document hashes, and build a batch chain mechanism. Based on the batch chain mechanism, upload the document hashes to the alliance chain in batches, and establish a chain record index for the supervisory documents. Provide query and verification of document hashes based on the chain record index and the configured verification interface.

5. A method for dynamic face identification and key point trajectory positioning, characterized in that: The method for realizing face identification and key point trajectory positioning by using a dynamic face identification and key point trajectory positioning system according to any one of claims 1 to 4 comprises the following steps: Dynamic face recognition integrated development: Develop a dynamic live face detection and face login module and configure an interface for it. The module is configured with a face recognition model and provides face recognition and face login services based on the face recognition model. GPS track tracking: Based on the map, it provides track collection, track playback and anomaly identification. It forms a track by collecting location information, visualizes the track through track playback, and determines whether the track deviates abnormally through anomaly identification, and issues early warning prompts based on the detection results. Blockchain document upload: Build a consortium chain, perform hash calculation on the supervision document, generate the document hash, upload the document hash to the consortium chain and establish an on-chain record index for the supervision document, supporting query and verification of the document hash; Electronic signature system connection: Connect with electronic signature service providers to provide multi-party signature and verification for document supervision.

6. The method for dynamic face identification and key point trajectory positioning according to claim 5, characterized in that: Dynamic face recognition integrated development includes the following operations: Select an appropriate deep convolutional neural network-related algorithm as the face recognition algorithm, and select an interface that is compatible with the face recognition algorithm; Constructing a face recognition model based on the selected face recognition algorithm and performing model training on the constructed face recognition model; Build a dynamic liveness detection and face login module. The dynamic liveness detection and face login module calls the face recognition model through the configured interface to provide face recognition service, and provides face login service based on the face recognition service.

7. The method for dynamic face identification and key point trajectory positioning according to claim 5, characterized in that: GPS track tracking includes the following operations: Select an appropriate map engine; In response to the needs of trajectory display, geographic positioning and real-time data update, the map engine interface is configured, and the map engine is called through the interface to provide location marking, route drawing and area division; Configure the GPS unit in the GPS trajectory tracking module, collect location information in real time through GPS, and store the location information of each collection point in a data packet format; Configure the trajectory playback service of the GPS trajectory tracking module, realize the visualization of historical trajectories through the trajectory revisit service, and support the control of trajectories based on the timeline; Configure the GPS trajectory anomaly recognition service. The trajectory anomaly recognition service sets preset driving trajectory templates and boundary conditions, compares real-time data to determine abnormal deviations, and triggers early warning prompts or records abnormal logs for subsequent analysis.

8. The method for dynamic face identification and key point trajectory positioning according to claim 5, characterized in that: Uploading a document to the blockchain involves the following operations: Build a consortium chain based on Hyperledger Fabric, Corda or other enterprise-level blockchain platforms; For alliance chains, establish member authentication and authority management mechanisms; Plan the distribution of each node to achieve data synchronization, consensus mechanism and fault tolerance between nodes; Develop a document hash chain and verification interface. For supervisory documents uploaded to the alliance chain, perform hash calculation on the supervisory file through the document hash chain and verification interface to generate document hashes, and build a batch chain mechanism. Based on the batch chain mechanism, upload the document hashes to the alliance chain in batches, and establish a chain record index for the supervisory documents. Provide query and verification of document hashes based on the chain record index and the configured verification interface.

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