A graphical timeline-based net point business hour intelligent synchronization system

Abstract

This invention relates to an intelligent synchronization system for branch operating hours based on a graphical timeline. The system includes: a data acquisition unit, a data processing unit, an access verification unit, and an encrypted archiving unit; it acquires escort process data, uses an adaptive feature extraction algorithm to obtain corresponding features; it calls a preset whitelist feature library to perform full-dimensional feature comparison calculations to obtain feature matching quantification results; it automatically determines the risk level of the escort scenario based on preset risk thresholds, and simultaneously generates differentiated control instructions; it dynamically manages handover behavior over time, obtaining corresponding control actions; it assigns unique identifiers to operation process nodes, generating a traceable verification chain; it generates encrypted electronic handover files and performs tamper-proofing and reinforcement processing and hierarchical binding with job permissions; and it uses a distributed evidence storage mode to synchronously store the files on a local server and a cloud backup node. This achieves an intelligent synchronization system for branch operating hours based on a graphical timeline.

Description

Technical Field

[0001] This invention belongs to the field of intelligent branch operation and management technology, specifically relating to an intelligent synchronization system for branch business hours based on a graphical timeline. Background Technology

[0002] Currently, the management of bank branch operating hours and the coordinated control of security escort services still have the following areas for improvement: In the process of digital transformation of bank branches, unified management of business hours, on-site operational collaboration, and the security of cash box transport have become core operational aspects. Currently, most banks still use traditional manual management models, with business hour configuration mainly relying on text entry and spreadsheet editing. They lack graphical timeline interaction tools and cannot intuitively, precisely, or batch-set regular business days, weekends, holidays, and temporary adjustments. Configuring business hours for multiple branches, multiple rules, and multiple time periods relies on manual, line-by-line modification, which is cumbersome, prone to data entry errors and time conflicts, and fails to meet the needs of unified management and flexible scheduling for large-scale bank branches. This results in low efficiency and accuracy in business hour management.

[0003] Meanwhile, the dissemination of branch operating hours information suffers from severe channel fragmentation and synchronization lag. Multiple display channels, including offline branch displays, mobile banking, official WeChat accounts, and third-party map platforms, operate independently. Adjustments to operating hours require manual updates to different systems, making one-click publishing and real-time synchronization impossible. This situation leads to inconsistencies between online and offline information and delayed external presentations. Customers are prone to making unnecessary visits due to information errors, reducing customer service experience and impacting the bank's brand image and service reputation, thus failing to achieve the management goals of open, transparent, and consistent operating information.

[0004] More significantly, business hours management is completely disconnected from core operations such as branch security duties, cash box escort handover, access control, and equipment maintenance, failing to form a unified and collaborative timeline management system. The time windows for escort missions, the on-duty status of security personnel, and the start and end times of branch operations lack automatic correlation and intelligent verification, easily leading to problems such as conflicts between escort times and business hours, handover delays, and mismatches in security status. Traditional escort handover relies on manual visual verification of personnel identity, vehicle information, and cash box numbers, lacking AI recognition, whitelist comparison, and liveness detection mechanisms, resulting in security risks such as fraudulent claims, incorrect claims, and missed verifications. The handover process lacks full recording, node identification, and a traceable chain, resulting in weak risk control capabilities.

[0005] From a system architecture and data management perspective, existing branch operations suffer from prominent issues such as fragmented platforms, isolated functions, and lack of data interoperability. Business hours, security attendance, escort handover, and access control recording are handled by different systems, lacking an integrated intelligent terminal for unified overview and one-click operation. This results in numerous repetitive manual operations and high management costs. Escort handover files are still primarily paper-based or fragmented electronic documents, lacking encryption, tamper-proofing, hierarchical access control, and distributed storage. Data security, integrity, and compliance are difficult to guarantee, failing to meet the regulatory requirements of the financial industry and the need for refined operational decision-making. This severely restricts the upgrading of bank branches towards intelligence, lightweight operations, and digitalization. Summary of the Invention

[0006] To address the aforementioned problems in the existing technology, this invention provides an intelligent synchronization system for branch business hours based on a graphical timeline; The objective of this invention can be achieved through the following technical solutions: Data acquisition unit, data processing unit, access verification unit, and encrypted archiving unit; The data acquisition unit acquires escort process data based on a dual-trust acquisition mode deployed at network points; The data processing unit uses an adaptive feature extraction algorithm based on the escort process data to obtain corresponding features, and at the same time constructs a five-dimensional feature set of people-vehicle-goods-time-space; it calls a preset whitelist feature library to perform full-dimensional feature comparison calculation to obtain feature matching quantification results; The permission verification unit introduces a dynamic risk decision-making model based on the quantification results of the five-dimensional feature set, automatically determines the risk level of the escort scenario according to the preset risk threshold, and generates differentiated control instructions; it dynamically manages the handover behavior in the time dimension and obtains corresponding control actions; it assigns a unique identifier to each node in the operation process and generates a traceable verification chain. The encrypted archiving unit integrates the escort process data based on the traceable verification chain, generates encrypted electronic handover files through encryption processing, and simultaneously performs anti-tampering reinforcement processing and hierarchical binding of job permissions; the encrypted electronic handover files are synchronously stored on the local server and cloud backup nodes, and the escort process information is automatically updated synchronously.

[0007] As a preferred embodiment of the present invention, the specific process of obtaining the escort process data includes: Obtain relevant data for the escort mission, and simultaneously perform lightweight hash encryption and blockchain micro-node encryption processing through edge nodes to obtain whitelist data; The vehicle license plate, vehicle characteristics, and arrival image data of the armored truck are obtained by a single carrier identification terminal, and feature comparison is performed in combination with the whitelist data to obtain vehicle arrival identification data. The 3D live face recognition terminal acquires the live facial features of the escort personnel and real-time video stream data of the handover area; The data is edge-securely encapsulated and embedded with timestamps and unique device identifiers to obtain the escort process data.

[0008] Specifically, the process of obtaining the corresponding features includes: An adaptive feature extraction algorithm is started based on the escort process data to perform deduplication, noise reduction and outlier removal preprocessing on the escort process data; Based on the preprocessed data, preset information in the escort process is extracted, including: escort task time window and branch business hours, vehicle identification and identity verification information.

[0009] Specifically, the process of obtaining the feature matching quantization result includes: The corresponding feature data is standardized to construct a five-dimensional feature set of people-vehicle-object-time-space. The five-dimensional feature set is embedded into the step code and blockchain traceability identifier, and is also associated and bound with the whitelist information and escort task plan pre-set in the escort process. A mapping relationship between feature data, task information, and process steps is established through a feature association mechanism. A feature comparison model is constructed by calling a preset whitelist feature library. Vehicle features in the five-dimensional feature set are extracted and compared with the whitelist vehicle registration features dimension by dimension to obtain vehicle measurement indicators. Adjust the weight of feature comparison and extract the facial features of the escort personnel. Cross-compare the facial features with those of the whitelisted personnel and combine them with the escort personnel’s identification information to assist in verification and obtain quantitative indicators of personnel credibility. The unique RFID code of the cash box is matched with the whitelist registration code to obtain the quantitative indicators of the cash box; The graphical timeline features are compared with preset time windows and business hours to calculate the time deviation and obtain the output time window matching degree. The spatial coordinate characteristics of the handover area are compared with the preset range to calculate the spatial deviation and obtain the validity of the spatial position. Standardized feature matching quantitative results are generated by integrating relevant quantitative indicators.

[0010] Specifically, the process of automatically determining the risk level of a security escort scenario includes: Based on the feature matching and quantification results, a dynamic risk classification decision model is introduced. At the same time, the safety thresholds of the corresponding quantification indicators are preset, and the two indicators of vehicle compliance and personnel credibility are compared with the corresponding thresholds to obtain the comparison results for judgment.

[0011] Specifically, the process of generating differentiated control instructions includes: Based on the risk level of the escort scenario, differentiated control instructions are generated through key segmentation and encapsulation and process traceability watermarking technology. The differentiated control instructions include: automatic verification instructions, manual review instructions, and abnormal locking instructions. The automatic verification command, manual review command, and abnormal lockout command are encrypted and encapsulated using a photonic encryption transmission protocol, and the command generation timestamp, permission identifier, and verification code are embedded in the code.

[0012] Specifically, the key segmentation and encapsulation and process traceability watermarking technology is as follows: Key segmentation and encapsulation technology splits the encryption key that generates differentiated control instructions into segments according to the instruction type, and binds it to the unique ID of the escort mission and updates it with a one-time key; Process traceability watermarking technology embeds the corresponding traceability information of the escort mission ID and process step code into the core of the instruction in the form of an invisible watermark. Based on the differentiated control commands, five-dimensional feature set, and real-time location data of the armored vehicle, time control is initiated after atomic clock calibration and verification. The spatiotemporal reference anchoring and dynamic time window elastic scaling algorithm are used, combined with the corresponding control actions output by the command, to generate a time control traceability chain for evidence storage.

[0013] Specifically, the process of dynamically controlling the handover process over time includes: The handover time is synchronized with the system time through the atomic clock time calibration module. At the same time, the dynamic time window elastic scaling algorithm is combined to automatically adjust the time window range according to the real-time location of the armored truck and abnormal scenarios on site. By linking and binding time management results, control action records, and time calibration data, a time management traceability chain is generated, which is then embedded in blockchain nodes for evidence storage, thereby controlling the time management and escort process.

[0014] Specifically, the process of generating the traceable verification chain includes: Based on the dynamic control of the time dimension, combined with node holographic mapping and blockchain sidechain traceability mechanism, the operation nodes of the escort process are holographically mapped to generate node holographic data packets. The node holographic data packet is uniquely identified by the escort process steps, operating entity and equipment identifier through node association hash encoding; The node holographic data packets are integrated, and holographic compressed data of control action records, abnormal events and key video segments are added. A verification link is formed by using lossless encryption technology for holographic data. The verification link is encapsulated using a blockchain sidechain, and the link data is synchronized to the traceability sidechain. The sidechain adopts a Byzantine fault-tolerant consensus mechanism and embeds a unique link identifier, process step code and operation entity fingerprint identifier to generate a traceable verification chain.

[0015] Specifically, the process of generating encrypted electronic handover files through encryption includes: Based on the traceable verification chain, holographic data extraction and DNA digital watermark embedding are used to extract escort process data, which includes: escort task plan, whitelist pre-set information, vehicle identification results, and personnel verification results. Electronic handover documents are generated using holographic data analysis algorithms; The core information of the electronic handover file is filtered, which includes four categories: time, location, personnel, and verification results required by the escort process. At the same time, corresponding data is added to form the original file data. The original archive data is divided into encrypted fragments using data fragmentation encryption technology. Each encrypted fragment is assigned a corresponding decryption key, and the decryption key is bound to the permissions of the operating entity. The archive fragments are encrypted using a dual encryption technology of SHA-384 hash encryption and DNA digital watermarking, with the DNA digital watermark embedded in the core data fragments of the archive.

[0016] Specifically, the process of performing anti-tampering reinforcement and binding of job-related permissions includes: Based on the encrypted electronic handover file, anti-tampering technology is used to trace and locate the location and subject of the tampering; A multi-dimensional permission matrix is ​​constructed based on the corresponding job responsibilities in the escort process. The permission matrix includes four dimensions: operating subject, operating permission, operating time period, and operating equipment. The four dimensions are assigned corresponding permission codes, and the file fragments after permission binding are subjected to secondary encryption with a key, embedding permission identifiers and operation subject fingerprints.

[0017] Specifically, the process of synchronously storing the encrypted electronic handover file to both the local server and the cloud backup node includes: The star-shaped distributed evidence storage model is used, with the local server at the branch as the core node, and the cloud backup node and the blockchain side chain node as auxiliary nodes. The encrypted fragments of the archives are synchronously stored to the three nodes, and a synchronous link is established between the core node and the auxiliary nodes. The encrypted electronic handover file and tamper-proof report are pushed to the cloud backup node; Establish a three-dimensional verification and synchronization mechanism to compare the file fragment hash values ​​and permission identifiers of core nodes and auxiliary nodes in real time; A distributed evidence log is generated, which embeds node synchronization records, transmission details, and permission operation records. The log and the archive are simultaneously stored on the blockchain sidechain.

[0018] The beneficial effects of this invention are as follows: By adopting a graphical timeline configuration and a multi-channel one-click synchronization mechanism, it is possible to intuitively complete the fine-grained setting of business hours for regular periods, weekends, holidays and special business days, and realize the real-time unified release of business information on branch display screens, mobile banking, official accounts and third-party platforms, effectively eliminating the information gap between online and offline, reducing unnecessary customer visits, and significantly improving the branch service experience and external image.

[0019] By linking business hours with escort time windows, security duties, and access control status, and introducing AI facial recognition, vehicle recognition, automatic whitelist comparison, and liveness detection technologies, the system can automatically complete the full-dimensional verification of escort personnel, armored vehicles, and cash boxes, effectively avoiding the risks of misjudgment, fraudulent claims, and wrong claims caused by manual verification, and significantly improving the security and efficiency of escort handover.

[0020] By constructing a traceable verification chain, encrypted electronic archives, and a distributed evidence storage mechanism, each node in the entire escort process is assigned a unique identifier and reinforced with anti-tampering measures and hierarchical access control. This ensures that the entire handover process is traceable, verifiable, and traceable. At the same time, relying on an integrated operation terminal, it enables a unified view and one-click operation, reducing management costs and meeting the security, compliance, and refined operation management needs of the financial industry. Attached Figure Description

[0021] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0022] Figure 1 This is a flowchart illustrating an intelligent synchronization system for branch business hours based on a graphical timeline, according to the present invention. Figure 2 This is a structural block diagram of the permission verification in this invention. Detailed Implementation

[0023] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0024] Please see Figure 1-2 A network of branches business hours intelligent synchronization system based on a graphical timeline, comprising: Data acquisition unit, data processing unit, access verification unit, and encrypted archiving unit; The data acquisition unit acquires escort process data based on a dual-trust acquisition mode deployed at network points; The data processing unit uses an adaptive feature extraction algorithm based on the escort process data to obtain corresponding features, and at the same time constructs a five-dimensional feature set of people-vehicle-goods-time-space; it calls a preset whitelist feature library to perform full-dimensional feature comparison calculation to obtain feature matching quantification results; The permission verification unit introduces a dynamic risk decision-making model based on the quantification results of the five-dimensional feature set, automatically determines the risk level of the escort scenario according to the preset risk threshold, and generates differentiated control instructions; it dynamically manages the handover behavior in the time dimension and obtains corresponding control actions; it assigns a unique identifier to each node in the operation process and generates a traceable verification chain. The encrypted archiving unit integrates the escort process data based on the traceable verification chain, generates encrypted electronic handover files through encryption processing, and simultaneously performs anti-tampering reinforcement processing and hierarchical binding of job permissions; the encrypted electronic handover files are synchronously stored on the local server and cloud backup nodes, and the escort process information is automatically updated synchronously.

[0025] As a preferred embodiment of the present invention, the specific process of obtaining the escort process data includes: Obtain relevant data for the escort mission, and simultaneously perform lightweight hash encryption and blockchain micro-node encryption processing through edge nodes to obtain whitelist data; The vehicle license plate, vehicle characteristics, and arrival image data of the armored truck are obtained by a single carrier identification terminal, and feature comparison is performed in combination with the whitelist data to obtain vehicle arrival identification data. The 3D live face recognition terminal acquires the live facial features of the escort personnel and real-time video stream data of the handover area; The data is edge-securely encapsulated and embedded with timestamps and unique device identifiers to obtain the escort process data.

[0026] In this embodiment, combining the dual-trust data collection design of the smart branch integrated management system with the escort process specifications, relevant data for the escort process is acquired through a dual-trust data collection mode. The specific implementation process strictly follows the requirements of the entire escort task process: basic information such as the escort task plan, registered escort personnel information (including facial features and identification information), armored vehicle information (including license plate and vehicle type characteristics), and cash box RFID code are processed by edge nodes using lightweight hash encryption and blockchain micro-node encryption to generate secure and controllable whitelist data, ensuring that the pre-set information is not tampered with or leaked; when the armored vehicle arrives at the branch as planned, the license plate of the armored vehicle is automatically collected through a single-carrier identification terminal. The system first analyzes vehicle characteristics and arrival video data, then performs preliminary feature comparison with a pre-set whitelist to quickly identify the vehicle's arrival. Only after a successful match can the next step be taken. Simultaneously, a 3D liveness detection terminal acquires real-time facial features of the escort personnel and real-time video stream data from the handover area, effectively preventing fraudulent activities such as face forgery and proxy signing, and ensuring the authenticity of personnel identity collection. Finally, all collected vehicle, personnel, and video data are edge-securely encapsulated and embedded with timestamps and unique device identifiers to form complete, reliable, and traceable escort process data, providing a solid and reliable data source for subsequent feature extraction, intelligent verification, and risk assessment.

[0027] Specifically, the process of obtaining the corresponding features includes: An adaptive feature extraction algorithm is started based on the escort process data to perform deduplication, noise reduction and outlier removal preprocessing on the escort process data; Based on the preprocessed data, preset information in the escort process is extracted, including: escort task time window and branch business hours, vehicle identification and identity verification information.

[0028] In this embodiment, combining the technical advantages of adaptive feature extraction algorithms with the core requirements of the escort process, the system performs full-process preprocessing on the collected escort process data after activating the adaptive feature extraction algorithm. This includes deduplication to remove redundant data such as duplicate vehicle images and personnel faces, noise reduction to eliminate interference in video streams and feature data, and outlier removal to eliminate invalid data caused by equipment malfunctions or environmental interference during the collection process, thus ensuring data purity and accuracy. Based on this, the system focuses on extracting various pre-set information from the escort process, including escort task time windows (combined with the start and end times in the escort records), branch operating hours (regular and holiday operating hours configured based on a graphical timeline), vehicle identification information (license plates, vehicle features), and personnel identity verification information (live faces, identification information), etc. This constructs a standardized and structured data foundation, effectively avoiding subsequent comparison deviations caused by data clutter, making subsequent five-dimensional feature comparisons of people, vehicles, objects, time, and space, and risk level determination more accurate and efficient, while also providing clear data support for process traceability.

[0029] Specifically, the process of obtaining the feature matching quantization result includes: The corresponding feature data is standardized to construct a five-dimensional feature set of people-vehicle-object-time-space. The five-dimensional feature set is embedded into the step code and blockchain traceability identifier, and is also associated and bound with the whitelist information and escort task plan pre-set in the escort process. A mapping relationship between feature data, task information, and process steps is established through a feature association mechanism. A feature comparison model is constructed by calling a preset whitelist feature library. Vehicle features in the five-dimensional feature set are extracted and compared with the whitelist vehicle registration features dimension by dimension to obtain vehicle measurement indicators. Adjust the weight of feature comparison and extract the facial features of the escort personnel. Cross-compare the facial features with those of the whitelisted personnel and combine them with the escort personnel’s identification information to assist in verification and obtain quantitative indicators of personnel credibility. The unique RFID code of the cash box is matched with the whitelist registration code to obtain the quantitative indicators of the cash box; The graphical timeline features are compared with preset time windows and business hours to calculate the time deviation and obtain the output time window matching degree. The spatial coordinate characteristics of the handover area are compared with the preset range to calculate the spatial deviation and obtain the validity of the spatial position. Standardized feature matching quantitative results are generated by integrating relevant quantitative indicators.

[0030] In this embodiment, the five-dimensional feature set is as follows: Human dimension: The core revolves around the personnel involved in the escort, mainly including: the 3D live facial features, ID card information, job authority identifiers and other data of the escort personnel. This is used to accurately verify the legality of the escort personnel's identity and prevent risks such as identity fraud and intervention by unauthorized personnel. It is the core dimension to ensure the safety of escort personnel during handover. The corresponding data is mainly collected through 3D live facial recognition terminals and compared with whitelist information.

[0031] Vehicle dimension: Focusing on information related to armored trucks, specifically including: license plate number, vehicle appearance features, and on-site video data, etc., to quickly identify the identity of armored trucks, confirm whether the vehicle is a whitelisted vehicle, and prevent illegal vehicles from entering the handover area. The corresponding data is collected through a single-carrier identification terminal and combined with the whitelist to complete the initial matching and verification.

[0032] The item dimension focuses on the core item being transported (cash box), mainly covering: the unique RFID code of the cash box, the appearance characteristics of the cash box, quantity information, etc., to accurately match the identity of the cash box, prevent problems such as misclaiming, fraudulent claims, and missed verification, and ensure the safety and accuracy of the transported items. The corresponding data is collected through RFID identification equipment and compared with the whitelist registration code.

[0033] Time dimension: Focusing on time elements, it includes: branch business hours configured with a graphical timeline, preset time windows for escort tasks, specific time of handover (embedded with timestamps), and arrival time of armored trucks, etc., to verify whether the escort handover is carried out within a reasonable time range, avoid problems such as handover exceeding time limits and time conflicts, and provide data support for dynamic management of the time dimension.

[0034] Spatial dimension: Focusing on spatial location elements, mainly including: the preset spatial coordinate range of the handover area, the parking position of the armored truck after its arrival, and the specific spatial location where the handover occurs, etc., to determine whether the handover is carried out within the designated area, and to prevent risks such as deviation from the handover area and illegal handover. The corresponding data is collected by spatial positioning equipment and compared with the preset range.

[0035] The system establishes verification standards for the handover of AI-powered smart cash boxes, standardizes pre-processed feature data, unifies data formats and quantification standards, and constructs a five-dimensional feature set encompassing people, vehicles, goods, time, and space. This five-dimensional feature set is embedded into process step codes and blockchain traceability identifiers, deeply linking it to pre-defined whitelist information and escort task plans within the escort process. This ensures a one-to-one correspondence between feature data, task information, and process steps. A feature association mechanism establishes a mapping relationship between feature data, task information, and process steps. A professional feature comparison model is built by calling a pre-defined whitelist feature library. The core components of the whitelist feature library are all legal, valid, and compliant feature data from the network escort process. Based on the network escort handover scenario of this system, it specifically includes four core feature categories, each corresponding one-to-one with the five-dimensional feature set, comprehensively covering all elements of the escort handover and providing a precise benchmark for feature comparison. Personnel whitelist features: Corresponding to the human dimension of the five-dimensional feature set, it stores the legal feature data of all registered escort personnel, including: 3D live face baseline features, standard ID information, job authority identifiers, affiliated escort company and registration number, etc. All of these are legal personnel information verified by the branch and used to compare with the collected live face and ID information of escort personnel to accurately verify the legality of personnel identity and prevent unauthorized personnel from interfering with the handover.

[0036] Vehicle whitelist features: Corresponding to the vehicle dimension of the five-dimensional feature set, it includes the standard features of all registered armored vehicles, specifically the standard format of license plate number, basic features of vehicle appearance, vehicle registration number, information of the escort company to which it belongs, and on-site verification requirements, etc. It is reported in advance by the escort company and reviewed and entered by the branch. It is used to compare with the armored vehicle information collected by the single-carrier identification terminal to quickly confirm the legality of the vehicle and prevent illegal vehicles from entering the handover area.

[0037] Item whitelist features: corresponding to the item dimension in the five-dimensional feature set, the core of which is the legal feature data of all registered cash boxes, including: unique RFID base code of cash box, standard appearance features of cash box, base information on the quantity of cash box, and the identification of the affiliated outlet and task association, etc., which are used to compare with the cash box information collected by RFID identification equipment to ensure that the cash box is legal and the match is correct, and to prevent the problem of mis-claiming or fraudulent claims.

[0038] Spatiotemporal whitelist features: corresponding to the temporal and spatial dimensions of the five-dimensional feature set, including the network's preset legal handover time range, the business hours benchmark configured on the graphical timeline, the preset time window standard for escort tasks, as well as the preset spatial coordinate range of the handover area and the benchmark for legal parking areas, etc., are used to verify the temporal and spatial legality of handover behavior and avoid problems such as handover exceeding the time limit or handover in illegal areas.

[0039] First, vehicle features are extracted from the five-dimensional feature set and compared dimension-by-dimensionally with the features of vehicles registered in the whitelist to obtain quantifiable indicators of vehicle compliance, ensuring the legality of the armored truck's identity. Next, the weights of the feature comparisons are adjusted, focusing on extracting the facial features of the security personnel and cross-comparing them with the facial features of personnel registered in the whitelist. Simultaneously, the security personnel's identification information is used for auxiliary verification, outputting quantifiable indicators of personnel credibility to prevent unauthorized personnel from participating in the handover. Then, the unique RFID code of the cash box is precisely matched with the whitelist registration code to obtain quantifiable indicators of the cash box, preventing misdelivery or fraudulent receipt of cash. Simultaneously, the branch's operating hours displayed on a graphical timeline are compared with the time window of the security task to calculate the time deviation and obtain the output time window matching degree, ensuring that the security handover is carried out in an orderly manner within the branch's operating hours. The spatial coordinate features of the handover area are compared with the preset handover area range to calculate the spatial deviation and obtain the validity of the spatial location, preventing the handover behavior from deviating from the designated area. By integrating the above quantifiable indicators, standardized and quantifiable feature matching results are generated.

[0040] Specifically, the process of automatically determining the risk level of a security escort scenario includes: Based on the feature matching and quantification results, a dynamic risk classification decision model is introduced. At the same time, the safety thresholds of the corresponding quantification indicators are preset, and the two indicators of vehicle compliance and personnel credibility are compared with the corresponding thresholds to obtain the comparison results for judgment.

[0041] In this embodiment, combining the design concept of the dynamic risk grading decision model with the risk prevention and control requirements of the escort process, the dynamic risk grading decision model is introduced based on the feature matching quantification results. The dynamic risk grading decision model takes the standardized feature matching quantification results as input data, covering five major categories of core data: vehicle compliance measurement indicators, personnel credibility measurement indicators, cash box matching quantification indicators, time window matching degree, and spatial location effectiveness. At the same time, auxiliary data such as escort task plans, branch business hours, and handover area specifications are simultaneously accessed to ensure the comprehensiveness of risk assessment.

[0042] The model incorporates multi-dimensional risk thresholds, all set in accordance with network security regulations, financial industry regulatory requirements, and historical handover risk data, and supports dynamic adjustment. These primarily include vehicle compliance thresholds, personnel credibility thresholds, cash box matching thresholds, time deviation thresholds, and spatial deviation thresholds. Each threshold corresponds to a different risk level determination standard, providing a clear basis for risk classification.

[0043] The model employs a multi-dimensional weighted scoring mechanism, which calculates the weights of five major categories of quantitative indicators. It assigns weights based on the importance of each indicator (with personnel credibility and vehicle compliance having the highest weights, prioritizing personnel and vehicle safety) to arrive at a comprehensive risk score. The comprehensive risk score is then compared with preset risk thresholds to automatically determine the risk level, which is divided into three levels: low risk (meets all threshold requirements, no anomalies), medium risk (a single dimension indicator slightly deviates from the threshold, requiring vigilance), and high risk (any core indicator seriously deviates from the threshold, or multiple dimensions show anomalies).

[0044] Unlike fixed risk assessment models, this model has dynamic adaptability. It can automatically adjust the weight of risk thresholds in each dimension according to real-time changes in the escort scenario (such as delayed arrival of armored trucks, temporary adjustment of handover areas, temporary personnel changes, etc.). At the same time, it combines historical handover risk data and real-time abnormal events to optimize the risk classification logic, ensuring the flexibility and accuracy of risk assessment and adapting to the differentiated needs of different outlets and different escort tasks.

[0045] Simultaneously, in line with the security standards for escort services in the financial industry, the system presets security thresholds for two core indicators: vehicle compliance and personnel credibility. It then compares the real-time collected data on these two key indicators against their corresponding preset thresholds one by one. Based on the comparison results, it quickly and accurately determines the risk level of the current escort scenario: if both indicators are higher than or equal to the preset thresholds, it is classified as low risk; if one indicator is lower than the threshold, it is classified as medium risk; and if both indicators are lower than the thresholds, it is classified as high risk, thus achieving proactive risk identification and tiered handling.

[0046] Specifically, the process of generating differentiated control instructions includes: Based on the risk level of the escort scenario, differentiated control instructions are generated through key segmentation and encapsulation and process traceability watermarking technology. The differentiated control instructions include: automatic verification instructions, manual review instructions, and abnormal locking instructions. The automatic verification command, manual review command, and abnormal lockout command are encrypted and encapsulated using a photonic encryption transmission protocol, and the command generation timestamp, permission identifier, and verification code are embedded in the code.

[0047] In this embodiment, combining the core requirements of key segmentation encapsulation and process traceability watermarking technologies, and based on the determined risk level of the escort scenario, three types of differentiated control instructions are generated using key segmentation encapsulation and process traceability watermarking technologies, corresponding to different risk levels: Low-risk scenarios generate automatic verification instructions, requiring no manual intervention, and the system automatically completes the subsequent handover process; medium-risk scenarios generate manual review instructions, triggering manual intervention for review to ensure handover security; high-risk scenarios generate anomaly locking instructions, immediately terminating the handover process, triggering an alarm signal, and jumping to the anomaly handling process. Simultaneously, the aforementioned automatic verification instructions, manual review instructions, and anomaly locking instructions are comprehensively encrypted and encapsulated using a photonic encryption transmission protocol, synchronously embedding instruction generation timestamps, permission identifiers, and verification codes to ensure that the instructions are not tampered with or stolen during transmission, achieving secure, traceable, and verifiable instruction transmission. This enables precise and secure control of the escort process, aligning with the core value of intelligent risk control in the integrated smart branch management system.

[0048] Specifically, the key segmentation and encapsulation and process traceability watermarking technology is as follows: Key segmentation and encapsulation technology splits the encryption key that generates differentiated control instructions into segments according to the instruction type, and binds it to the unique ID of the escort mission and updates it with a one-time key; Process traceability watermarking technology embeds the corresponding traceability information of the escort mission ID and process step code into the core of the instruction in the form of an invisible watermark. Based on the differentiated control commands, five-dimensional feature set, and real-time location data of the armored vehicle, time control is initiated after atomic clock calibration and verification. The spatiotemporal reference anchoring and dynamic time window elastic scaling algorithm are used, combined with the corresponding control actions output by the command, to generate a time control traceability chain for evidence storage.

[0049] In this embodiment, the specific implementation methods of key segmentation encapsulation and process traceability watermarking technologies are strictly implemented in accordance with the system technical specifications: The key segmentation encapsulation technology specifically involves segmenting the encryption key for generating differentiated control instructions according to instruction type (automatic verification, manual review, and anomaly locking). Each key segment is bound to a unique escort task ID, and a one-time key update mechanism is used to ensure the uniqueness and security of the key, avoiding process risks caused by key leakage. The process traceability watermarking technology specifically involves encapsulating the escort task ID, process step codes, and other relevant traceability information in a way that... The invisible watermark is embedded in the core of the instruction without affecting its normal execution. It can also be quickly extracted during subsequent traceability, enabling full traceability of the instruction process. Based on this, combined with differentiated control instructions, a five-dimensional feature set of people-vehicle-object-time-space, and real-time location data of the armored vehicle, the time control function is activated after precise calibration and verification by the atomic clock time calibration module. Using spatiotemporal benchmark anchoring and dynamic time window elastic scaling algorithm, combined with the corresponding control actions of the instruction output (such as allowing handover, terminating handover, and manual review), a time control traceability chain is generated and uploaded to the blockchain node for evidence storage.

[0050] Specifically, the process of dynamically controlling the handover process over time includes: The handover time is synchronized with the system time through the atomic clock time calibration module. At the same time, the dynamic time window elastic scaling algorithm is combined to automatically adjust the time window range according to the real-time location of the armored truck and abnormal scenarios on site. By linking and binding time management results, control action records, and time calibration data, a time management traceability chain is generated, which is then embedded in blockchain nodes for evidence storage, thereby controlling the time management and escort process.

[0051] In this embodiment, combining the core requirement of dynamic time-dimensional control with the time management specifications of the escort process, the atomic clock time calibration module accurately synchronizes the escort handover time with the system time, ensuring the uniformity and accuracy of time records and avoiding process chaos caused by time deviations. Simultaneously, combined with a dynamic time window elastic scaling algorithm, the handover time window range is automatically adjusted based on the real-time location of the armored vehicle (e.g., delays en route) and abnormal scenarios on-site (e.g., personnel verification anomalies, equipment malfunctions), flexibly adapting to unexpected situations in the escort process and avoiding handover timeouts or process delays caused by fixed time windows. Subsequently, the time control results, corresponding control action records, and time calibration data are linked and bound to generate a complete time control traceability chain, which is simultaneously embedded into blockchain nodes for evidence storage, achieving deep integration of time control and the escort process.

[0052] Specifically, the process of generating the traceable verification chain includes: Based on the dynamic control of the time dimension, combined with node holographic mapping and blockchain sidechain traceability mechanism, the operation nodes of the escort process are holographically mapped to generate node holographic data packets. The node holographic data packet is uniquely identified by the escort process steps, operating entity and equipment identifier through node association hash encoding; The node holographic data packets are integrated, and holographic compressed data of control action records, abnormal events and key video segments are added. A verification link is formed by using lossless encryption technology for holographic data. The verification link is encapsulated using a blockchain sidechain, and the link data is synchronized to the traceability sidechain. The sidechain adopts a Byzantine fault-tolerant consensus mechanism and embeds a unique link identifier, process step code and operation entity fingerprint identifier to generate a traceable verification chain.

[0053] In this embodiment, combining the construction standards of the traceable verification chain with the blockchain sidechain traceability mechanism, and based on the dynamic control results in the time dimension, a node holographic mapping and blockchain sidechain traceability mechanism are adopted to holographically map each operation node in the escort process (such as vehicle identification, personnel verification, cash box handover, and electronic archiving), comprehensively collect node-related data, and generate a complete node holographic data package. Through node association hash encoding technology, unique identifiers are assigned to the node holographic data package, escort process steps, operating entities (escort personnel, operations supervisors), and equipment identifiers (single-carrier identification terminal, face recognition terminal), ensuring that each link can be accurately located and traced. Subsequently, the node holographic data package is integrated, supplemented with holographic compressed data of control action records, abnormal events (such as verification failure, timeout handover), and key video segments, and encrypted using holographic data lossless encryption technology to form a complete and secure verification chain. This verification chain is then encapsulated using a blockchain sidechain, synchronizing the chain data to the traceability sidechain. The sidechain adopts a Byzantine fault-tolerant consensus mechanism, with the following security conditions: , N: Total number of system nodes; f: Maximum number of malicious / faulty nodes; It effectively prevents data tampering, while embedding unique identifiers for the link, process step codes, and fingerprint identifiers of the operating entity, ultimately generating an immutable and fully traceable verification chain.

[0054] Specifically, the process of generating encrypted electronic handover files through encryption includes: Based on the traceable verification chain, holographic data extraction and DNA digital watermark embedding are used to extract escort process data, which includes: escort task plan, whitelist pre-set information, vehicle identification results, and personnel verification results. Electronic handover documents are generated using holographic data analysis algorithms; The core information of the electronic handover file is filtered, which includes four categories: time, location, personnel, and verification results required by the escort process. At the same time, corresponding data is added to form the original file data. The original archive data is divided into encrypted fragments using data fragmentation encryption technology. Each encrypted fragment is assigned a corresponding decryption key, and the decryption key is bound to the permissions of the operating entity. The archive fragments are encrypted using a dual encryption technology of SHA-384 hash encryption and DNA digital watermarking, with the DNA digital watermark embedded in the core data fragments of the archive.

[0055] In this embodiment, combining the generation specifications of encrypted electronic handover files with the archiving requirements of the escort process, and based on a traceable verification chain, holographic data extraction and DNA digital watermark embedding technology are used to comprehensively extract core data from the escort process. This includes key content such as escort task plans, whitelist pre-set information (personnel, vehicles, cash boxes), vehicle identification results, and personnel verification results. The extracted data is then organized and analyzed using a holographic data parsing algorithm to generate standardized electronic handover files. Subsequently, the core information in the electronic handover files is selected, with a focus on retaining four categories: time (handover start and end times), location (branch handover area), personnel (escort personnel, operations supervisor), and verification results (vehicle, personnel, and cash box verification results). The content is supplemented with key video clips, anomaly handling records, and other relevant data to form complete original archive data. Data fragmentation encryption technology is used to split the original archive data into multiple encrypted fragments, assigning a corresponding decryption key to each fragment. These decryption keys are bound to the operator's job permissions, enabling hierarchical decryption and access control. Each archive fragment is encrypted using a dual encryption technology of SHA-384 hash encryption and DNA digital watermarking. The DNA digital watermark is embedded into the core data fragments of the archive, and the watermark information is bound to the unique ID of the escort mission and the fingerprint information of the operator, ensuring the security, uniqueness, and immutability of the electronically transferred archives, aligning with the electronic archiving functional requirements of the integrated smart branch management system.

[0056] Specifically, the process of performing anti-tampering reinforcement and binding of job-related permissions includes: Based on the encrypted electronic handover file, anti-tampering technology is used to trace and locate the location and subject of the tampering; A multi-dimensional permission matrix is ​​constructed based on the corresponding job responsibilities in the escort process. The permission matrix includes four dimensions: operating subject, operating permission, operating time period, and operating equipment. The four dimensions are assigned corresponding permission codes, and the file fragments after permission binding are subjected to secondary encryption with a key, embedding permission identifiers and operation subject fingerprints.

[0057] In this embodiment, combining the technical requirements of anti-tampering reinforcement and hierarchical binding of job permissions, based on encrypted electronic handover files, professional anti-tampering technology is used to provide comprehensive protection for encrypted file fragments. Once file data is detected to have been tampered with, the location of the tampering and the tampering entity can be quickly traced and located, triggering alarms in a timely manner and preserving evidence of tampering to ensure the integrity of the files. At the same time, based on the corresponding job responsibilities in the escort process, a multi-dimensional permission matrix is ​​constructed, including four dimensions: operating entity, operating permission, operating time period, and operating equipment. The operating permissions of different personnel (escort personnel, operations supervisors, and system administrators) are clearly defined—escort personnel can only view the handover files they are involved in, operations supervisors can view all handover files in their branch, and system administrators are responsible for permission allocation and file management. A unique permission code is assigned to each of the four dimensions of the permission matrix to achieve refined control of permissions. The file fragments after permission binding are subjected to secondary encryption processing with a key, embedding permission identifiers and operating entity fingerprints to further enhance file security and ensure that only personnel with corresponding permissions can view and operate the relevant files, meeting the security compliance and refined management needs of the financial industry.

[0058] Specifically, the process of synchronously storing the encrypted electronic handover file to both the local server and the cloud backup node includes: The star-shaped distributed evidence storage model is used, with the local server at the branch as the core node, and the cloud backup node and the blockchain side chain node as auxiliary nodes. The encrypted fragments of the archives are synchronously stored to the three nodes, and a synchronous link is established between the core node and the auxiliary nodes. The encrypted electronic handover file and tamper-proof report are pushed to the cloud backup node; Establish a three-dimensional verification and synchronization mechanism to compare the file fragment hash values ​​and permission identifiers of core nodes and auxiliary nodes in real time; A distributed evidence log is generated, which embeds node synchronization records, transmission details, and permission operation records. The log and the archive are simultaneously stored on the blockchain sidechain.

[0059] In this embodiment, combining the deployment requirements of the distributed evidence storage mode with data security specifications, a star-shaped distributed evidence storage mode is adopted. The local server at the branch is the core node, and cloud backup nodes and blockchain sidechain nodes are auxiliary nodes. Fragments of the encrypted electronic handover file are synchronously stored on the three nodes. A high-speed, secure synchronization link is established between the core node and the auxiliary nodes to ensure real-time data synchronization. Through an invisible transfer protocol, the encrypted electronic handover file and its corresponding anti-tampering report are synchronously pushed to the cloud backup node, achieving off-site data backup and preventing data loss due to local server failure. A three-dimensional verification synchronization mechanism is established to compare the hash values, entanglement status, and permission identifiers of the file fragments between the core node and the auxiliary nodes in real time. Once data inconsistency is detected, synchronization repair is immediately triggered to ensure complete data consistency across the three nodes. Simultaneously, a detailed distributed evidence storage log is generated, embedding key information such as node synchronization records, data transmission details, and permission operation records. The log and the encrypted electronic handover file are synchronously stored on the blockchain sidechain, achieving full traceability and immutability of data storage. This ensures the security and integrity of the escort handover data, complies with the security and compliance requirements for data storage in the financial industry, and supports subsequent auditing, verification, and dispute investigation.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A network of business hours intelligent synchronization system based on a graphical timeline, characterized in that, include: Data acquisition unit, data processing unit, access verification unit, and encrypted archiving unit; The data acquisition unit acquires escort process data based on a dual-trust acquisition mode deployed at network points; The data processing unit uses an adaptive feature extraction algorithm based on the escort process data to obtain corresponding features, and at the same time constructs a five-dimensional feature set of people-vehicle-goods-time-space; The preset whitelist feature library is called to perform full-dimensional feature comparison calculation to obtain the feature matching quantification result; The permission verification unit introduces a dynamic risk decision-making model based on the quantification results of the five-dimensional feature set, automatically determines the risk level of the escort scenario according to the preset risk threshold, and generates differentiated control instructions; it dynamically manages the handover behavior in the time dimension and obtains corresponding control actions; it assigns a unique identifier to each node in the operation process and generates a traceable verification chain. The encrypted archiving unit integrates the escort process data based on the traceable verification chain, generates encrypted electronic handover files through encryption processing, and simultaneously performs anti-tampering reinforcement processing and hierarchical binding of job permissions; The encrypted electronic handover file is synchronously stored on the local server and cloud backup node, and the escort process information is automatically updated synchronously.

2. The system according to claim 1, characterized in that, The specific process for obtaining escort process data includes: Obtain relevant data for the escort mission, and simultaneously perform lightweight hash encryption and blockchain micro-node encryption processing through edge nodes to obtain whitelist data; The vehicle license plate, vehicle characteristics, and arrival image data of the armored truck are obtained by a single carrier identification terminal, and feature comparison is performed in combination with the whitelist data to obtain vehicle arrival identification data. The 3D live face recognition terminal acquires the live facial features of the escort personnel and real-time video stream data of the handover area; The data is edge-securely encapsulated and embedded with timestamps and unique device identifiers to obtain the escort process data.

3. The system according to claim 1, characterized in that, The specific process for obtaining the corresponding features includes: An adaptive feature extraction algorithm is started based on the escort process data to perform deduplication, noise reduction and outlier removal preprocessing on the escort process data; Based on the preprocessed data, preset information in the escort process is extracted, including: escort task time window and branch business hours, vehicle identification and identity verification information.

4. The system according to claim 1, characterized in that, The specific process for obtaining the feature matching quantization result includes: The corresponding feature data is standardized to construct a five-dimensional feature set of people-vehicle-object-time-space. The five-dimensional feature set is embedded into the step code and blockchain traceability identifier, and is also associated and bound with the whitelist information and escort task plan pre-set in the escort process. A mapping relationship between feature data, task information, and process steps is established through a feature association mechanism. A feature comparison model is constructed by calling a preset whitelist feature library. Vehicle features in the five-dimensional feature set are extracted and compared with the whitelist vehicle registration features dimension by dimension to obtain vehicle measurement indicators. Adjust the weight of feature comparison and extract the facial features of the escort personnel. Cross-compare the facial features with those of the whitelisted personnel and combine them with the escort personnel’s identification information to assist in verification and obtain quantitative indicators of personnel credibility. The unique RFID code of the cash box is matched with the whitelist registration code to obtain the quantitative indicators of the cash box; The graphical timeline features are compared with preset time windows and business hours to calculate the time deviation and obtain the output time window matching degree. The spatial coordinate characteristics of the handover area are compared with the preset range to calculate the spatial deviation and obtain the validity of the spatial position. Standardized feature matching quantitative results are generated by integrating relevant quantitative indicators.

5. The system according to claim 1, characterized in that, The specific process for automatically determining the risk level of a security escort scenario includes: Based on the feature matching and quantification results, a dynamic risk classification decision model is introduced. At the same time, the safety thresholds of the corresponding quantification indicators are preset, and the two indicators of vehicle compliance and personnel credibility are compared with the corresponding thresholds to obtain the comparison results for judgment.

6. The system according to claim 1, characterized in that, The specific process for generating differentiated control instructions includes: Based on the risk level of the escort scenario, differentiated control instructions are generated through key segmentation and encapsulation and process traceability watermarking technology. The differentiated control instructions include: automatic verification instructions, manual review instructions, and abnormal locking instructions. The automatic verification command, manual review command, and abnormal lockout command are encrypted and encapsulated using a photonic encryption transmission protocol, and the command generation timestamp, permission identifier, and verification code are embedded in the code.

7. The system according to claim 1, characterized in that, The key segmentation encapsulation and process traceability watermarking technology is as follows: Key segmentation and encapsulation technology splits the encryption key that generates differentiated control instructions into segments according to the instruction type, and binds it to the unique ID of the escort mission and updates it with a one-time key; Process traceability watermarking technology embeds the corresponding traceability information of the escort mission ID and process step code into the core of the instruction in the form of an invisible watermark. Based on the differentiated control commands, five-dimensional feature set, and real-time location data of the armored vehicle, time control is initiated after atomic clock calibration and verification. The spatiotemporal reference anchoring and dynamic time window elastic scaling algorithm are used, combined with the corresponding control actions output by the command, to generate a time control traceability chain for evidence storage.

8. The system according to claim 1, characterized in that, The specific process of dynamically controlling the handover process over time includes: The handover time is synchronized with the system time through the atomic clock time calibration module. At the same time, the dynamic time window elastic scaling algorithm is combined to automatically adjust the time window range according to the real-time location of the armored truck and abnormal scenarios on site. By linking and binding time management results, control action records, and time calibration data, a time management traceability chain is generated, which is then embedded in blockchain nodes for evidence storage, thereby controlling the time management and escort process.

9. The system according to claim 1, characterized in that, The specific process for generating the traceable verification chain includes: Based on the dynamic control of the time dimension, combined with node holographic mapping and blockchain sidechain traceability mechanism, the operation nodes of the escort process are holographically mapped to generate node holographic data packets. The node holographic data packet is uniquely identified by the escort process steps, operating entity and equipment identifier through node association hash encoding; The node holographic data packets are integrated, and holographic compressed data of control action records, abnormal events and key video segments are added. A verification link is formed by using lossless encryption technology for holographic data. The verification link is encapsulated using a blockchain sidechain, and the link data is synchronized to the traceability sidechain. The sidechain adopts a Byzantine fault-tolerant consensus mechanism and embeds a unique link identifier, process step code and operation entity fingerprint identifier to generate a traceable verification chain.

10. The system according to claim 9, characterized in that, The specific process of generating encrypted electronic handover files through encryption includes: Based on the traceable verification chain, holographic data extraction and DNA digital watermark embedding are used to extract escort process data, which includes: escort task plan, whitelist pre-set information, vehicle identification results, and personnel verification results. Electronic handover documents are generated using holographic data analysis algorithms; The core information of the electronic handover file is filtered, which includes four categories: time, location, personnel, and verification results required by the escort process. At the same time, corresponding data is added to form the original file data. The original archive data is divided into encrypted fragments using data fragmentation encryption technology. Each encrypted fragment is assigned a corresponding decryption key, and the decryption key is bound to the permissions of the operating entity. The archive fragments are encrypted using a dual encryption technology of SHA-384 hash encryption and DNA digital watermarking, with the DNA digital watermark embedded in the core data fragments of the archive.

11. The system according to claim 1, characterized in that, The specific process of performing anti-tampering reinforcement and hierarchical binding of job permissions includes: Based on the encrypted electronic handover file, anti-tampering technology is used to trace and locate the location and subject of the tampering; A multi-dimensional permission matrix is ​​constructed based on the corresponding job responsibilities in the escort process. The permission matrix includes four dimensions: operating subject, operating permission, operating time period, and operating equipment. The four dimensions are assigned corresponding permission codes, and the file fragments after permission binding are subjected to secondary encryption with a key, embedding permission identifiers and operation subject fingerprints.

12. The system according to claim 1, characterized in that, The specific process of synchronously storing the encrypted electronic handover file to the local server and the cloud backup node includes: The star-shaped distributed evidence storage model is used, with the local server at the branch as the core node, and the cloud backup node and the blockchain side chain node as auxiliary nodes. The encrypted fragments of the archives are synchronously stored to the three nodes, and a synchronous link is established between the core node and the auxiliary nodes. The encrypted electronic handover file and tamper-proof report are pushed to the cloud backup node; Establish a three-dimensional verification and synchronization mechanism to compare the file fragment hash values ​​and permission identifiers of core nodes and auxiliary nodes in real time; A distributed evidence log is generated, which embeds node synchronization records, transmission details, and permission operation records. The log and the archive are simultaneously stored on the blockchain sidechain.

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