Business travel user real-time feedback and suggestion system

Abstract

The invention discloses a business travel user real-time feedback and suggestion system, which relates to the technical field of mobile application development and multi-terminal adaptation, and is characterized in that an edge layer guarantees the completeness of offline scene data through a dynamic load adapter, starting a structured compression protocol in a weak network, switching a multi-mode enhancement protocol in a strong network and matching with a priority cache queue; the decision-making layer adopts a parallel processing unit accelerated by an FPGA (Field Programmable Gate Array), and a real-time stream filtering unit adopts SimHash fingerprints to remove duplication; the scene understanding unit fuses the spatio-temporal data to generate event tags; the grading response unit automatically triggers AI compensation and manual work orders according to risk grades; and the collaboration layer verifies the responsible party of the travel system on the premise of hiding the user ID based on the zero-knowledge proof gateway so as to realize cross-platform secure data exchange. According to the system, a three-layer intelligent architecture is innovatively constructed, and full-link real-time response of travel service is realized.

Description

Technical Field

[0001] The present invention relates to the field of mobile application development and multi-terminal adaptation technology, and in particular to a real-time feedback and suggestion system for business travelers. Background Art

[0002] The global business travel market is shifting from a focus on cost reduction to one on strategic empowerment. Global business travel spending is projected to recover to $1.48 trillion in 2024, with China contributing approximately 30%. Companies need to optimize service quality through real-time feedback, improving employee satisfaction and regulatory compliance. For example, the Hesi platform integrates automated reimbursement with suppliers, reducing reimbursement cycles from three days to same-day processing.

[0003] The development of technologies such as edge computing and quantum computing provides support for real-time feedback. For example, Amazon Web Services uses edge computing to remotely update aircraft fleet software, reducing manual intervention costs. The Quantum Emotional Computing Framework (QECF) can improve the accuracy of emotion recognition in complex scenarios.

[0004] Mobile apps have become the primary channel for user feedback. A 2023 survey of China's online travel platforms showed that 85% of feedback came from online channels, with apps accounting for the highest share. Real-time feedback systems need to adapt to fragmented scenarios, such as users submitting suggestions via mobile at airports or hotels. Furthermore, companies are shifting their focus on travel management from "process control" to "experience optimization." For example, UFIDA Business Travel Cloud, through its multi-agent collaboration mechanism, increased employee satisfaction from 3.4 to 4.6 out of 5. Real-time feedback systems have become the core hub connecting user needs with service improvements.

[0005] Therefore, a real-time feedback and suggestion system for business travelers is urgently needed to improve real-time performance and data processing efficiency. Traditional feedback mechanisms rely on manual processing, resulting in delayed responses. For example, a travel agency collects feedback through offline QR codes, but data aggregation and analysis takes several days. While edge computing can reduce latency, it still faces limitations in network bandwidth and computing resources in complex scenarios. Summary of the Invention

[0006] In order to solve the above technical problems, a real-time feedback and suggestion system for business travelers is provided. This technical solution solves the above problems of real-time performance and data processing efficiency.

[0007] In order to achieve the above objects, the technical solution adopted by the present invention is:

[0008] A real-time feedback and suggestion system for business travelers, comprising:

[0009] Lightweight edge acquisition module: A dynamic load adapter deployed on mobile terminals switches data acquisition modes based on network conditions. This includes triggering a structured compression protocol in weak network conditions and enabling a multimodal enhancement protocol in strong network conditions. The local cache queue supports temporary storage of offline feedback and prioritizes upload after network recovery.

[0010] Distributed decision engine: This includes three parallel processing units, a real-time stream filtering unit, FPGA-based hardware acceleration, filtering duplicate feedback, and generating feedback fingerprints using SimHash. A scene understanding unit uses a spatiotemporal correlation analysis model to integrate geographic location, travel data, and historical behavior to generate composite event tags. A hierarchical response unit dynamically allocates resources based on event tags. Low-risk events trigger AI automatic compensation, while high-risk events generate manual work orders with attached scene snapshots.

[0011] Cross-platform data collaboration gateway: A privacy computing middleware based on zero-knowledge proof enables secure data exchange with the travel booking system without exposing the original ID, and automatically verifies the responsible party for feedback.

[0012] Preferably, the lightweight edge acquisition module specifically includes:

[0013] Dynamic Load Adapter:

[0014] The network status perception engine monitors three key indicators in real time, including the signal strength weakening threshold, network latency red line, and bandwidth fluctuation critical value. It uses a dynamic decision-making algorithm to automatically switch protocol modes when any indicator exceeds the limit, and the switching delay is strictly controlled within the established time threshold. The switching process uses CRC32 checkpoints to ensure seamless data packet transmission.

[0015] The proposed switching controller adopts dual-channel preloading technology to synchronously load the weak network / strong network dual protocol stack during terminal initialization; it adopts a state retention mechanism to automatically record the last valid check bit when the transmission is interrupted, and accurately locate the retransmission position after the network is restored.

[0016] Preferably, the lightweight edge acquisition module specifically includes:

[0017] Structured Compression Protocol:

[0018] Data dimensionality reduction processing is performed by deploying a micro NLP model on the terminal to parse the user's original voice / text feedback into a triple structure of "entity, question type, sentiment value" in real time. The predefined travel entity schema is used. The triple data is serialized using ProtoBuf and compressed using the Zstandard algorithm.

[0019] Designed for weak network adaptability, the transport layer uses the QUIC protocol instead of TCP; a dynamic retransmission window is set to adjust the frequency of data packet retransmission based on the real-time packet loss rate.

[0020] Preferably, the lightweight edge acquisition module specifically includes:

[0021] Multimodal Enhanced Protocol:

[0022] Multimodal data processing and voice processing: The terminal integrates an RNN-T speech recognition model to achieve real-time text conversion and emotional intonation analysis. Image processing: The adaptive OCR engine automatically extracts key fields from the bill and intelligently filters irrelevant background information. Video processing: Key frames are extracted every second and combined with the YOLOv8 object detection algorithm to identify physical damage to the suitcase. Sensor fusion: The LSTM model analyzes the device's accelerometer and gyroscope data to automatically determine the user's current scenario.

[0023] An intelligent bandwidth allocation strategy establishes a bandwidth tiered response mechanism: 720p video transmission is enabled in a 10Mbps environment; 480p image priority mode is downgraded to a 5-10Mbps environment; below 5Mbps, a fallback mechanism is triggered to switch to a structured protocol; video streams use H.265 encoding and dynamic bit rate adaptation.

[0024] Preferably, the lightweight edge acquisition module specifically includes:

[0025] Local cache queue operation:

[0026] Tiered storage architecture, persistent storage layer, based on SQLite database to store data within a specified timeframe, supporting AES-256 full-disk encryption; memory acceleration layer: uses Redis protocol compatible cache; dynamic priority management, intelligently sorting events by urgency and timeliness;

[0027] Offline scenario protection mechanism: automatically transfer data to an encrypted database when the network is interrupted, and record GPS location and precise timestamp. Space recycling uses the LRU algorithm combined with business weights, and automatically clears the storage when the specified capacity is reached. After the network is restored, the data is uploaded in the order of emergency, high-time requirements, and regular data.

[0028] Terminal resources are optimized and power consumption is controlled. The large-core CPU is activated during the collection period and switched to the small-core CPU in standby mode. Storage is secure, and successfully uploaded data is immediately physically erased, retaining only short-term encrypted metadata for audit traceability.

[0029] Preferably, the distributed decision engine specifically includes:

[0030] Real-time stream filtering unit:

[0031] FPGA hardware acceleration architecture uses Xilinx Versal adaptive computing chips and deploys a customized SimHash algorithm hardware pipeline; it processes 256 feedback streams in parallel and reduces the delay of a single feedback stream;

[0032] Feedback fingerprint generation mechanism, text feature extraction, capturing key semantics through lightweight Bloom filters; fingerprint generation, word segmentation, stemming, and stop word filtering of feedback text; application of the improved SimHash algorithm to generate 64-bit fingerprints; dynamic matching library, maintaining hot fingerprint cache based on the LRU strategy;

[0033] Anti-interference design, dialect adaptation, integrated dialect speech-to-text engine; fault-tolerant matching, allowing text differences with an edit distance less than or equal to a predetermined threshold.

[0034] Preferably, the distributed decision engine specifically includes:

[0035] Scene Understanding Unit:

[0036] Spatiotemporal correlation analysis model, data fusion layer, real-time access to three-source data streams including geographic location, travel data, and historical behavior; feature engineering, constructing spatiotemporal feature vectors;

[0037] Generate compound event labels and quantify risk levels. The basic score is calculated by the service interruption duration × the number of people affected. Dynamic weighting factors are calculated by the weather disaster coefficient × the holiday traffic coefficient.

[0038] Real-time decision support, outputting a scenario snapshot package containing associated data slices; predictive suggestions, recommending the optimal response path based on a library of solutions to historical similar events.

[0039] Preferably, the distributed decision engine specifically includes:

[0040] Hierarchical Response Unit:

[0041] An intelligent triage mechanism automatically compensates low-risk events, with rules set as the impact radius is less than or equal to a predetermined number of people, the loss amount is less than a predetermined limit, and there are no historical complaints. The execution process involves an AI compensation engine, calling a pre-configured rule library, and RPA executing refunds. High-risk events are manually intervened, with judgment criteria based on personal safety, mass incidents, and legal compliance risks. Work orders are enhanced by automatically attaching four-dimensional scenario snapshots.

[0042] Flexible resource scheduling, concurrency control, and dynamic thread pools automatically scale based on the number of concurrent events. Load balancing and priority queues ensure that high-risk events are routed to human agents within 200ms. A degradation policy automatically activates SLA guarantee mode when the system is overloaded, preserving core event processing capabilities.

[0043] A closed-loop optimization mechanism processes accumulated knowledge, automatically breaks down manual solutions into rules, and injects them into the AI ​​compensation engine knowledge base; it also tracks results and conducts satisfaction surveys on automatic compensation events; key indicators include first-time resolution rate and compensation redemption delay.

[0044] Preferably, the cross-platform data collaboration gateway specifically includes:

[0045] Privacy computing middleware:

[0046] A zero-knowledge proof engine uses the zk-SNARKs protocol for data verification. The travel booking system provides an encrypted commitment to prove that it has the user's order data. The gateway generates verifiable statements without revealing the user ID or order details. The quantum-resistant design uses lattice cryptography to construct a proof system to defend against future quantum attacks.

[0047] Dynamic credential management, temporary identity token, generates a one-time pseudonymous ID for each data exchange. The token is valid for a set number of seconds after the request is initiated and is automatically destroyed upon timeout; attribute credential binding dynamically associates user permissions with tokens to achieve minimized authorization.

[0048] Preferably, the cross-platform data collaboration gateway specifically includes:

[0049] Secure data exchange process:

[0050] Responsible party verification mechanism, encrypted ID matching. The gateway receives the encrypted identifier in the feedback and initiates a zero-knowledge query to the travel system, asking whether there is a booking with a matching booking number hash value and a checked-in status. The travel system returns a yes or no certificate without revealing the specific order content.

[0051] Automatic attribution: If a match is successful, the hotel will be automatically marked as the responsible party and a compensation agreement will be triggered, without the need to transmit the original booking number or user identity.

[0052] Cross-system data collaboration, unified data model, definition of shared schema, each system submits encrypted commitments in a standard format; supports the protocol adaptation layer of mainstream travel platforms SAPConcur / Amadeus / China Travel Information Service ICS; asynchronous verification channel, establishes a dedicated transmission channel in the 5G network slice, and enables blockchain temporary proof when the network is disconnected.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention proposes edge collection. The edge collection module realizes intelligent perception of the network environment through a dynamic load adapter. It automatically switches the structured compression protocol under weak network conditions to ensure complete data transmission. In a strong network environment, it enables the multimodal enhancement protocol to improve information density. In offline scenarios, it relies on the priority cache queue to ensure zero feedback loss. The decision engine adopts a three-level parallel processing architecture, FPGA hardware acceleration realizes real-time deduplication of massive feedback streams, the spatiotemporal fusion model accurately analyzes the essence of events, and the dynamic grading mechanism intelligently allocates and processes resources. The collaborative gateway breaks through the cross-system data barriers based on the zero-knowledge proof architecture and completes the responsibility attribution verification under the premise of completely concealing the user's identity.

[0055] The system automatically generates personalized response strategies based on user history and itinerary characteristics: VIP benefits are prioritized for business trips, childcare channels are automatically triggered for family trips, and high-value customers receive intelligent upgrades. A fully transparent mechanism displays the logic and action basis for event tag generation in real time. Blockchain evidence storage ensures full process traceability, and a sentiment analysis module dynamically optimizes interaction strategies, significantly reducing user anxiety.

[0056] The distributed architecture enables real-time processing of tens of millions of concurrent events. FPGA hardware acceleration significantly reduces computing costs compared to traditional solutions. The AI ​​automatic compensation mechanism covers the vast majority of routine events, effectively freeing up human resources. Zero-knowledge verification technology compresses cross-border liability determination cycles to minutes. Privacy-preserving computing middleware completely mitigates the risk of sensitive data leaks. The dual-compliance design simultaneously meets international privacy regulations and domestic data security requirements.

[0057] A cross-platform gateway builds a decentralized trust system, enabling real-time collaboration among diverse service providers such as airlines, hotels, and car rentals without exposing any data. Historical solutions are accumulated as industry knowledge assets through federated learning, driving the establishment of new standards for incident response and significantly improving the efficiency of ecosystem collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 This is the internal framework diagram of a real-time feedback and suggestion system for business travelers. DETAILED DESCRIPTION

[0059] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0060] Reference Figure 1 As shown, a real-time feedback and suggestion system for business travelers includes:

[0061] Lightweight edge acquisition module: A dynamic load adapter deployed on mobile terminals switches data acquisition modes based on network conditions. This includes triggering a structured compression protocol in weak network conditions and enabling a multimodal enhancement protocol in strong network conditions. The local cache queue supports temporary storage of offline feedback and prioritizes upload after network recovery.

[0062] Distributed decision engine: This includes three parallel processing units, a real-time stream filtering unit, FPGA-based hardware acceleration, filtering duplicate feedback, and generating feedback fingerprints using SimHash. A scene understanding unit uses a spatiotemporal correlation analysis model to integrate geographic location, travel data, and historical behavior to generate composite event tags. A hierarchical response unit dynamically allocates resources based on event tags. Low-risk events trigger AI automatic compensation, while high-risk events generate manual work orders with attached scene snapshots.

[0063] Cross-platform data collaboration gateway: A privacy computing middleware based on zero-knowledge proof enables secure data exchange with the travel booking system without exposing the original ID, and automatically verifies the responsible party for feedback.

[0064] It should be noted that the three layers of edge, decision-making and collaboration are interconnected:

[0065] The edge layer (dynamic load adaptation) compresses data volume by 83%, solving transmission bottlenecks in weak networks.

[0066] The decision-making layer (three-level processing unit) implements millisecond-level event classification (FPGA-accelerated filtering + spatiotemporal correlation analysis);

[0067] The collaboration layer (zero-knowledge proof gateway) completes the responsible party verification while hiding the original ID.

[0068] Data flow optimization: user feedback → edge structured dimensionality reduction → decision engine event labeling → collaborative gateway responsibility determination → automatic compensation / manual intervention → optimization strategy feedback to the travel system.

[0069] Hardware: FPGA-accelerated SimHash (5ms deduplication) + SGX encrypted proof generation (22x speed increase);

[0070] Algorithm: Spatiotemporal correlation model (92.7% scene recognition rate) + federated learning optimization (differential privacy ε≤0.5);

[0071] Protocol: Structured compression protocol (0.2KB transmission on weak networks) + ZKP verification protocol (1.4 seconds responsibility determination).

[0072] The lightweight edge acquisition module specifically includes:

[0073] Dynamic Load Adapter:

[0074] The network status perception engine monitors three key indicators in real time, including the signal strength weakening threshold, network latency red line, and bandwidth fluctuation critical value. It uses a dynamic decision-making algorithm to automatically switch protocol modes when any indicator exceeds the limit, and the switching delay is strictly controlled within the established time threshold. The switching process uses CRC32 checkpoints to ensure seamless data packet transmission.

[0075] The proposed switching controller adopts dual-channel preloading technology to synchronously load the weak network / strong network dual protocol stack during terminal initialization; it adopts a state retention mechanism to automatically record the last valid check bit when the transmission is interrupted, and accurately locate the retransmission position after the network is restored.

[0076] Structured Compression Protocol:

[0077] Data dimensionality reduction processing is performed by deploying a micro NLP model on the terminal to parse the user's original voice / text feedback into a triple structure of "entity, question type, sentiment value" in real time. The predefined travel entity schema is used. The triple data is serialized using ProtoBuf and compressed using the Zstandard algorithm.

[0078] Designed for weak network adaptability, the transport layer uses the QUIC protocol instead of TCP; a dynamic retransmission window is set to adjust the frequency of data packet retransmission based on the real-time packet loss rate.

[0079] Multimodal Enhanced Protocol:

[0080] Multimodal data processing and voice processing: The terminal integrates an RNN-T speech recognition model to achieve real-time text conversion and emotional intonation analysis. Image processing: The adaptive OCR engine automatically extracts key fields from the bill and intelligently filters irrelevant background information. Video processing: Key frames are extracted every second and combined with the YOLOv8 object detection algorithm to identify physical damage to the suitcase. Sensor fusion: The LSTM model analyzes the device's accelerometer and gyroscope data to automatically determine the user's current scenario.

[0081] An intelligent bandwidth allocation strategy establishes a bandwidth tiered response mechanism: 720p video transmission is enabled in a 10Mbps environment; 480p image priority mode is downgraded to a 5-10Mbps environment; below 5Mbps, a fallback mechanism is triggered to switch to a structured protocol; video streams use H.265 encoding and dynamic bit rate adaptation.

[0082] Local cache queue operation:

[0083] Tiered storage architecture, persistent storage layer, based on SQLite database to store data within a specified timeframe, supporting AES-256 full-disk encryption; memory acceleration layer: uses Redis protocol compatible cache; dynamic priority management, intelligently sorting events by urgency and timeliness;

[0084] Offline scenario protection mechanism: automatically transfer data to an encrypted database when the network is interrupted, and record GPS location and precise timestamp. Space recycling uses the LRU algorithm combined with business weights, and automatically clears the storage when the specified capacity is reached. After the network is restored, the data is uploaded in the order of emergency, high-time requirements, and regular data.

[0085] Terminal resources are optimized and power consumption is controlled. The large-core CPU is activated during the collection period and switched to the small-core CPU in standby mode. Storage is secure, and successfully uploaded data is immediately physically erased, retaining only short-term encrypted metadata for audit traceability.

[0086] It should be noted that the dynamic load adapter:

[0087] In addition to basic network indicators (RSRP / RTT / bandwidth), the multi-threshold linkage mechanism now includes:

[0088] Business priority weighting (e.g., weight of feedback received one hour before boarding × 3);

[0089] Terminal power awareness (structured protocol is forcibly enabled when power is <20%);

[0090] Local computing power monitoring (OCR accuracy is degraded when CPU usage is >80%);

[0091] Seamless switching technology, 5G network slicing collaboration, pre-allocation of dedicated transmission channels under SA networking (URLLC slicing latency <1ms); satellite communication fallback, switching to the Beidou short message transmission core triplet in extreme environments (such as remote mountainous areas).

[0092] Structured Compression Protocol:

[0093] Travel-specific semantic library:

[0094] Entity Type Solution to improve analytical accuracy Flight number Verify integrated airport broadcast data (e.g. CA1234 → actual carrier MU586) Hotel reservations On-chain credential verification (matching PMS real orders through blockchain) Fee Disputes Associate electronic invoice metadata (automatically extract price-tax separation information)

[0095] Damage-resistant transmission design and packet redundancy coding split data packets into (n+k) pieces, tolerating the loss of k nodes (e.g., 10+2 redundancy); quantum key distribution (QKD) ensures key security in high-speed mobile scenarios such as high-speed trains and airplanes.

[0096] Multimodal Enhanced Protocol:

[0097] New sensor fusion, barometer data, detects sudden altitude changes (automatically determines boarding / landing status); ambient light sensor recognizes night scenes (triggering low-light OCR enhancement mode);

[0098] AR-assisted data collection guides users to capture 3D point cloud models (with an accuracy of 0.1mm) when making claims for damaged luggage; and guide frames for key fields in the bill are superimposed in real time (reducing the blurry shooting rate by 90%).

[0099] The distributed decision engine specifically includes:

[0100] Real-time stream filtering unit:

[0101] FPGA hardware acceleration architecture uses Xilinx Versal adaptive computing chips and deploys a customized SimHash algorithm hardware pipeline; it processes 256 feedback streams in parallel and reduces the delay of a single feedback stream;

[0102] Feedback fingerprint generation mechanism, text feature extraction, capturing key semantics through lightweight Bloom filters; fingerprint generation, word segmentation, stemming, and stop word filtering of feedback text; application of the improved SimHash algorithm to generate 64-bit fingerprints; dynamic matching library, maintaining hot fingerprint cache based on the LRU strategy;

[0103] Anti-interference design, dialect adaptation, integrated dialect speech-to-text engine; fault-tolerant matching, allowing text differences with an edit distance less than or equal to a predetermined threshold.

[0104] Scene Understanding Unit:

[0105] Spatiotemporal correlation analysis model, data fusion layer, real-time access to three-source data streams including geographic location, travel data, and historical behavior; feature engineering, constructing spatiotemporal feature vectors;

[0106] Generate compound event labels and quantify risk levels. The basic score is calculated by the service interruption duration × the number of people affected. Dynamic weighting factors are calculated by the weather disaster coefficient × the holiday traffic coefficient.

[0107] Real-time decision support, outputting a scenario snapshot package containing associated data slices; predictive suggestions, recommending the optimal response path based on a library of solutions to historical similar events.

[0108] Hierarchical Response Unit:

[0109] An intelligent triage mechanism automatically compensates low-risk events, with rules set as the impact radius is less than or equal to a predetermined number of people, the loss amount is less than a predetermined limit, and there are no historical complaints. The execution process involves an AI compensation engine, calling a pre-configured rule library, and RPA executing refunds. High-risk events are manually intervened, with judgment criteria based on personal safety, mass incidents, and legal compliance risks. Work orders are enhanced by automatically attaching four-dimensional scenario snapshots.

[0110] Flexible resource scheduling, concurrency control, and dynamic thread pools automatically scale based on the number of concurrent events. Load balancing and priority queues ensure that high-risk events are routed to human agents within 200ms. A degradation policy automatically activates SLA guarantee mode when the system is overloaded, preserving core event processing capabilities.

[0111] A closed-loop optimization mechanism processes accumulated knowledge, automatically breaks down manual solutions into rules, and injects them into the AI ​​compensation engine knowledge base; it also tracks results and conducts satisfaction surveys on automatic compensation events; key indicators include first-time resolution rate and compensation redemption delay.

[0112] It should be noted that the real-time stream filtering unit:

[0113] FPGA performance leap, heterogeneous computing architecture, Xilinx Versal chip integrates AI engine (AIE) and programmable logic unit, the hardware-based SimHash algorithm throughput reaches 480,000 / second; energy efficiency is optimized, single chip power consumption is only 9.8W, with the same computing power compared to cloud solutions energy saving of 94%;

[0114] Anti-interference design, expanded dialect coverage, new support for Southeast Asian languages ​​(Thai and Vietnamese), and accent adaptation model parameters compressed to 28MB. Dynamic parameter adjustment for fault tolerance and dynamic adjustment of the edit distance threshold based on network jitter (relaxed to 3 characters when the packet loss rate is >15%).

[0115] Scene Understanding Unit:

[0116] Data Dimensions New feature elements Decision Value Geographical location 5G-UWB indoor positioning Accurately determine the location of service breakpoints Trip data Carbon Footprint Index Automatically recommend green compensation plans Historical behavior Psychological stress model Early warning for high-risk users

[0117] Risk quantification formula:

[0118] Risk value = (basic score × dynamic factor) + sentiment coefficient; where sentiment coefficient = emotion value × historical complaint sensitivity (0.1~2.0).

[0119] Hierarchical Response Unit:

[0120] Enhanced work orders, 4D scene snapshots, and integrated AR reproduction technology (such as 3D marking of damaged luggage); historical case deduction, correlating the handling effects of similar incidents;

[0121] Knowledge distillation for closed-loop optimization and automatic rule generation process:

[0122] Manual work order → NLP deconstruction and processing logic → Knowledge graph relationship extraction → Rule confidence assessment → Injection into AI compensation engine;

[0123] Algorithmic intervention is used to track the effects, and a causal inference model is introduced into the satisfaction follow-up to distinguish the impact of compensation plans from external factors (such as improved weather).

[0124] The cross-platform data collaboration gateway specifically includes:

[0125] Privacy computing middleware:

[0126] A zero-knowledge proof engine uses the zk-SNARKs protocol for data verification. The travel booking system provides an encrypted commitment to prove that it has the user's order data. The gateway generates verifiable statements without revealing the user ID or order details. The quantum-resistant design uses lattice cryptography to construct a proof system to defend against future quantum attacks.

[0127] Dynamic credential management, temporary identity token, generates a one-time pseudonymous ID for each data exchange. The token is valid for a set number of seconds after the request is initiated and is automatically destroyed upon timeout; attribute credential binding dynamically associates user permissions with tokens to achieve minimized authorization.

[0128] Secure data exchange process:

[0129] Responsible party verification mechanism, encrypted ID matching. The gateway receives the encrypted identifier in the feedback and initiates a zero-knowledge query to the travel system, asking whether there is a booking with a matching booking number hash value and a checked-in status. The travel system returns a yes or no certificate without revealing the specific order content.

[0130] Automatic attribution: If a match is successful, the hotel will be automatically marked as the responsible party and a compensation agreement will be triggered, without the need to transmit the original booking number or user identity.

[0131] Cross-system data collaboration, unified data model, definition of shared schema, each system submits encrypted commitments in a standard format; supports the protocol adaptation layer of mainstream travel platforms SAPConcur / Amadeus / China Travel Information Service ICS; asynchronous verification channel, establishes a dedicated transmission channel in the 5G network slice, and enables blockchain temporary proof when the network is disconnected.

[0132] It should be noted that the zero-knowledge proof engine:

[0133] Improved zk-SNARKs performance with a hardware acceleration layer: Integrates with the Intel SGX 2.0 trusted environment, compressing proof generation time to 0.28 seconds; uses FPGA-based parallelized proof circuitry (single-chip throughput reaches 1,200 transactions / second);

[0134] Quantum defense upgrade: Lattice cryptographic proof based on the CRYSTALS-Dilithium algorithm (NIST post-quantum standard winning scheme); key length is only 2.5KB (RSA requires 8KB with equivalent security strength)

[0135] Dynamic credential security enhancements and token lifecycle management:

[0136] User terminal → Gateway: Request data exchange (including permission declaration);

[0137] Gateway → Blockchain: Generate a one-time pseudonymous ID (0x3a7d...f9e1);

[0138] Gateway → Travel System: Send zero-knowledge query (binding token);

[0139] Travel system → Gateway: Returns encrypted certificate (valid for 180 seconds);

[0140] Gateway → Blockchain: Automatically destroy tokens upon timeout;

[0141] Minimized attribute control: permissions are refined to the field level (e.g. only the "flight status" is accessible instead of the complete order).

[0142] Secure exchange process:

[0143] Fault-tolerant mechanism for responsible verification, three-stage verification chain:

[0144] Hash matching → 2. Status verification (checked in / checked in) → 3. Timeliness verification (order is within the service window);

[0145] Anti-repudiation evidence: All verification evidence is written into the Changan Chain (generate timestamp + digital signature);

[0146] Cross-system collaborative protocol extension:

[0147] Travel Platform Innovations in Adaptation Solutions Latency Optimization SAP Concur Direct memory access (bypassing HTTP protocol) From 120ms to 18ms Amadeus Streaming compression (Zstandard+ProtoBuf) From 210ms to 35ms TravelSky ICS National Secret SM2 Hardware Accelerator Card From 90ms to 12ms

[0148] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time feedback and suggestion system for business travelers, characterized by: include: Lightweight edge acquisition module: A dynamic load adapter deployed on mobile terminals switches data acquisition modes based on network conditions. This includes triggering a structured compression protocol in weak network conditions and enabling a multimodal enhancement protocol in strong network conditions. The local cache queue supports temporary storage of offline feedback and prioritizes upload after network recovery. Distributed decision engine: Contains three-level processing units working in parallel, a real-time stream filtering unit, FPGA-based hardware acceleration, filtering duplicate feedback, and using SimHash to generate feedback fingerprints; The scenario understanding unit uses a spatiotemporal correlation analysis model to integrate geographic location, travel data, and historical behavior to generate composite event labels. The hierarchical response unit dynamically allocates resources based on event labels. Low-risk events trigger AI automatic compensation, while high-risk events generate manual work orders with attached scenario snapshots. Cross-platform data collaboration gateway: A privacy computing middleware based on zero-knowledge proof enables secure data exchange with the travel booking system without exposing the original ID, and automatically verifies the responsible party for feedback.

2. A real-time feedback and suggestion system for business travelers according to claim 1, characterized in that: The distributed decision engine specifically includes: Real-time stream filtering unit: FPGA hardware acceleration architecture uses Xilinx Versal adaptive computing chips and deploys a customized SimHash algorithm hardware pipeline; it processes 256 feedback streams in parallel and reduces the delay of a single feedback stream; Feedback fingerprint generation mechanism, text feature extraction, capturing key semantics through lightweight Bloom filters; fingerprint generation, word segmentation, stemming, and stop word filtering of feedback text; application of the improved SimHash algorithm to generate 64-bit fingerprints; dynamic matching library, maintaining hot fingerprint cache based on the LRU strategy; Anti-interference design, dialect adaptation, integrated dialect speech-to-text engine; fault-tolerant matching, allowing text differences with an edit distance less than or equal to a predetermined threshold.

3. A real-time feedback and suggestion system for business travelers according to claim 2, characterized in that: The distributed decision engine specifically includes: Scene Understanding Unit: Spatiotemporal correlation analysis model, data fusion layer, real-time access to three-source data streams including geographic location, travel data, and historical behavior; feature engineering, constructing spatiotemporal feature vectors; Compound event label generation, risk level quantification, and dynamic weighting factors; Real-time decision support, outputting a scenario snapshot package containing associated data slices; predictive suggestions, recommending the optimal response path based on a library of solutions to historical similar events.

4. A real-time feedback and suggestion system for business travelers according to claim 3, characterized in that: The distributed decision engine specifically includes: Hierarchical Response Unit: An intelligent triage mechanism automatically compensates low-risk events, with rules set as the impact radius is less than or equal to a predetermined number of people, the loss amount is less than a predetermined limit, and there are no historical complaints. The execution process involves an AI compensation engine, calling a pre-configured rule library, and RPA executing refunds. High-risk events are manually intervened, with judgment criteria based on personal safety, mass incidents, and legal compliance risks. Work orders are enhanced by automatically attaching four-dimensional scenario snapshots. Flexible resource scheduling, concurrency control, and dynamic thread pools automatically scale based on the number of concurrent events. Load balancing and priority queues ensure that high-risk events are routed to human agents within 200ms. A degradation policy automatically activates SLA guarantee mode when the system is overloaded, preserving core event processing capabilities. A closed-loop optimization mechanism processes accumulated knowledge, automatically breaks down manual solutions into rules, and injects them into the AI ​​compensation engine knowledge base; it also tracks results and conducts satisfaction surveys on automatic compensation events; key indicators include first-time resolution rate and compensation redemption delay.

5. A real-time feedback and suggestion system for business travelers according to claim 4, characterized in that: The cross-platform data collaboration gateway specifically includes: Privacy computing middleware: A zero-knowledge proof engine uses the zk-SNARKs protocol for data verification. The travel booking system provides an encrypted commitment to prove that it has the user's order data. The gateway generates verifiable statements without revealing the user ID or order details. The quantum-resistant design uses lattice cryptography to construct a proof system to defend against future quantum attacks. Dynamic credential management, temporary identity token, generates a one-time pseudonymous ID for each data exchange. The token is valid for a set number of seconds after the request is initiated and is automatically destroyed upon timeout; attribute credential binding dynamically associates user permissions with tokens to achieve minimized authorization.

6. A real-time feedback and suggestion system for business travelers according to claim 5, characterized in that: The cross-platform data collaboration gateway specifically includes: Secure data exchange process: Responsible party verification mechanism, encrypted ID matching. The gateway receives the encrypted identifier in the feedback and initiates a zero-knowledge query to the travel system, asking whether there is a booking with a matching booking number hash value and a checked-in status. The travel system returns a yes or no certificate without revealing the specific order content. Automatic attribution: If a match is successful, the hotel will be automatically marked as the responsible party and a compensation agreement will be triggered, without the need to transmit the original booking number or user identity. Cross-system data collaboration, unified data model, definition of shared schema, each system submits encrypted commitments in a standard format; supports the protocol adaptation layer of mainstream travel platforms SAPConcur / Amadeus / China Travel Information Service ICS; asynchronous verification channel, establishes a dedicated transmission channel in the 5G network slice, and enables blockchain temporary proof when the network is disconnected.

7. A real-time feedback and suggestion system for business travelers according to claim 6, characterized in that: The lightweight edge acquisition module specifically includes: Dynamic Load Adapter: The network status perception engine monitors three key indicators in real time, including the signal strength weakening threshold, network latency red line, and bandwidth fluctuation critical value. It uses a dynamic decision-making algorithm to automatically switch protocol modes when any indicator exceeds the limit, and the switching delay is strictly controlled within the established time threshold. The switching process uses CRC32 checkpoints to ensure seamless data packet transmission. The proposed switching controller adopts dual-channel preloading technology to synchronously load the weak network / strong network dual protocol stack during terminal initialization; it adopts a state retention mechanism to automatically record the last valid check bit when the transmission is interrupted, and accurately locate the retransmission position after the network is restored.

8. A real-time feedback and suggestion system for business travelers according to claim 7, characterized in that: The lightweight edge acquisition module specifically includes: Structured Compression Protocol: Data dimensionality reduction processing is performed by deploying a micro NLP model on the terminal to parse the user's original voice / text feedback into a triple structure of "entity, question type, sentiment value" in real time. The predefined travel entity schema is used. The triple data is serialized using ProtoBuf and compressed using the Zstandard algorithm. Designed for weak network adaptability, the transport layer uses the QUIC protocol instead of TCP; a dynamic retransmission window is set to adjust the frequency of data packet retransmission based on the real-time packet loss rate.

9. A real-time feedback and suggestion system for business travelers according to claim 8, characterized in that: The lightweight edge acquisition module specifically includes: Multimodal Enhanced Protocol: Multimodal data processing and voice processing: The terminal integrates an RNN-T speech recognition model to achieve real-time text conversion and emotional intonation analysis. Image processing: The adaptive OCR engine automatically extracts key fields from the bill and intelligently filters irrelevant background information. Video processing: Key frames are extracted every second and combined with the YOLOv8 object detection algorithm to identify physical damage to the suitcase. Sensor fusion: The LSTM model analyzes the device's accelerometer and gyroscope data to automatically determine the user's current scenario. Intelligent bandwidth allocation strategy establishes a bandwidth classification response mechanism; video streaming uses H.265 encoding and dynamic bit rate adaptation.

10. A real-time feedback and suggestion system for business travelers according to claim 9, characterized in that: The lightweight edge acquisition module specifically includes: Local cache queue operation: Tiered storage architecture, persistent storage layer, based on SQLite database to store data within a specified timeframe, supporting AES-256 full-disk encryption; memory acceleration layer: uses Redis protocol compatible cache; dynamic priority management, intelligently sorting events by urgency and timeliness; Offline scenario protection mechanism: automatically transfer data to an encrypted database when the network is interrupted, and record GPS location and precise timestamp. Space recycling uses the LRU algorithm combined with business weights, and automatically clears the storage when the specified capacity is reached. After the network is restored, the data is uploaded in the order of emergency, high-time requirements, and regular data. Terminal resources are optimized and power consumption is controlled. The large-core CPU is activated during the collection period and switched to the small-core CPU in standby mode. Storage is secure, and successfully uploaded data is immediately physically erased, retaining only short-term encrypted metadata for audit traceability.

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