A packet-oriented interactive service system

Abstract

The application discloses a kind of interactive service systems for data packet, it is related to computer network communication technical field, in the system operation, including data packet perception unit, semantic feature extraction unit, interactive strategy scheduling unit, dynamic path optimization unit and abnormal recovery coordination unit;Through real-time analysis data packet element information and load semantics, dynamically allocate processing resource and forwarding priority, and combine link state prediction to optimize transmission path, when detecting packet loss or out of order, trigger selective repeat and path self-healing mechanism.The application realizes the deep understanding of service intention and closed-loop autonomous optimization by the above technical solution, significantly improves the transmission efficiency in high concurrency scenario, low latency guarantee capability and the strong consistency and high availability of key business flow.

Description

Technical Field

[0001] This invention relates to the field of computer network communication technology, specifically to a data packet-oriented interactive service system. Background Technology

[0002] With the rapid evolution of internet communication and distributed computing technologies, data packets, as the core carrier of information exchange and resource scheduling, have become key indicators for measuring the service quality of modern network systems, particularly in terms of transmission efficiency and processing accuracy. Traditional network interaction modes primarily rely on standard protocol stacks for physical layer encapsulation and static routing forwarding, aiming to achieve basic data connectivity and transparent transmission. Against the backdrop of deepening digital transformation, various industries are placing higher demands on large-scale real-time collaboration, cross-platform data sharing, and dynamic distribution of complex business logic, making highly flexible and responsive interaction mechanisms the core focus of network architecture optimization.

[0003] Among them, packet-oriented interactive service systems aim to achieve efficient information flow in heterogeneous network environments by finely decomposing and strategically managing the underlying data stream. These systems not only involve rapid packet reassembly and multi-path distribution, but also dynamically adjust interaction priorities according to the needs of real-time business scenarios to ensure strong consistency and low latency of critical business flows. Especially when facing the challenges of high-frequency concurrent requests and massive fragmented data packets, the system needs to achieve microsecond-level perception and accurate mapping of interactive requests while ensuring data integrity. This places higher demands on the system's data throughput, task scheduling algorithms, and protocol adaptation mechanisms.

[0004] However, existing technologies often face severe performance bottlenecks when processing large-scale interactive data packets. Traditional serial processing models struggle to adapt to the complex interactive needs of multi-source concurrency, leading to data packet accumulation or chaotic interactive logic during peak traffic periods. Furthermore, due to the lack of deep feature extraction and semantic parsing of data packet content, the system cannot implement differentiated scheduling for services with varying sensitivities, making it difficult to accurately match network bandwidth resources with the interactive load. In addition, existing solutions typically rely on fixed relay logic in cross-network segment interaction scenarios, failing to effectively cope with link fluctuations caused by dynamic topology changes. This results in redundant data packet transmission paths and lengthy anomaly recovery processes, severely impacting the stability and real-time performance of interactive services. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a data packet-oriented interactive service system, which solves the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a data packet-oriented interactive service system, comprising a data packet awareness unit, an interactive policy scheduling unit, a dynamic path optimization unit, a semantic feature extraction unit, and an anomaly recovery coordination unit, wherein: The packet sensing unit is configured to capture raw data packets transmitted in the network in real time, and perform preliminary parsing of the source address, destination address, protocol type and payload length of the data packets to generate a data packet description structure containing basic metadata. The semantic feature extraction unit is connected to the data packet perception unit and is configured to perform deep analysis on the payload content in the data packet description structure, identify the embedded business type identifier, interaction priority label and context association information, thereby constructing a data packet feature vector with semantic dimension. The interaction strategy scheduling unit is connected to the semantic feature extraction unit and is configured to dynamically allocate processing resources and set forwarding priorities based on the service sensitivity and real-time interaction requirements represented by the data packet feature vector, so as to ensure that high-sensitivity service flows receive priority scheduling and low-latency channels. The dynamic path optimization unit is connected to the interactive strategy scheduling unit and is configured to calculate the optimal transmission path for each data packet to be forwarded based on the current network topology status, link load status and historical transmission quality indicators, and switch to the backup path in real time when link fluctuations or congestion are detected. The anomaly recovery coordination unit is connected to the dynamic path optimization unit and the data packet sensing unit. It is configured to monitor the integrity and order consistency of data packets during transmission. When packet loss, out-of-order delivery, or timeout anomalies are detected, a retransmission mechanism or path rollback strategy is triggered, and the path optimization model is updated synchronously to improve the robustness of subsequent transmissions.

[0007] Preferably, the data packet sensing unit adopts a multi-channel parallel listening architecture, which can simultaneously process data streams from different physical interfaces or virtual network ports, and sort concurrent data packets at the microsecond level through a timestamp marking mechanism to support accurate event sequence reconstruction in high-throughput scenarios.

[0008] The semantic feature extraction unit has a built-in scalable rule engine that supports dynamically loading business semantic recognition templates through configuration files, thereby adapting to the differentiated definitions of data packet content structure in different fields such as financial transactions, industrial control, and telemedicine.

[0009] The interactive strategy scheduling unit maintains a hierarchical resource pool, which is divided into high-priority queues, ordinary queues and latency-tolerant queues according to a preset threshold. The interactive strategy scheduling unit maps data packets to the corresponding queues based on the priority labels in the data packet feature vectors and dynamically adjusts the bandwidth quotas of each queue to respond to overall load changes.

[0010] The dynamic path optimization unit integrates a link state awareness module and a predictive routing algorithm. The link state awareness module periodically collects the round-trip delay, packet loss rate, and jitter indicators of each available path. The predictive routing algorithm predicts the path stability within a predetermined time period based on historical indicator trends and pre-selects a set of candidate paths accordingly.

[0011] The anomaly recovery coordination unit is equipped with a sliding window verification mechanism. This mechanism verifies the integrity of continuously received data packet sequences. Once a missing sequence number is found within the window, a selective retransmission request is immediately initiated to the source end, instead of waiting for a global timeout, thereby significantly shortening the anomaly recovery time.

[0012] The anomaly recovery coordination unit also works in conjunction with the dynamic path optimization unit. After multiple retransmission failures, it automatically switches the subsequent transmission path of the data stream to a historically stable alternative link and marks the original path as temporarily unavailable until its performance indicators recover to a specific range.

[0013] Preferably, the packet sensing unit adopts a multi-channel parallel listening architecture, which includes multiple independent listening sub-modules. Each listening sub-module is assigned to a specific physical interface or virtual network port to process heterogeneous data streams from different sources simultaneously. The packet sensing unit is equipped with a high-speed cache array and a multi-core parallel processing chip. The high-speed cache array adopts a circular buffer structure. The monitoring sub-modules communicate through shared memory and use lock-free queue technology to realize data flow. The packet sensing unit also has a built-in high-precision clock generator, which can generate microsecond-level time sequence pulses. When the original data packet enters the packet sensing unit, the packet sensing unit uses a timestamp marking mechanism to insert a nanosecond-level precision arrival timestamp into the data packet description structure, and sorts the concurrent data packets entering from different channels at the microsecond level according to the arrival timestamp to support accurate event sequence reconstruction in high-throughput scenarios.

[0014] Preferably, the data packet sensing unit further includes a pre-filtering module, which is configured with preliminary parsing logic, including a protocol whitelist verification sub-step. The protocol whitelist verification sub-step extracts the protocol type of the original data packet and matches it with the pre-stored security whitelist; If the protocol type is not on the security whitelist or the original data packet is determined to be a malformed packet with attack characteristics, the data packet sensing unit will directly discard the data packet and record the abnormal event in the log module, so as to reduce the computational burden of the semantic feature extraction unit through the pre-filtering module.

[0015] Preferably, the semantic feature extraction unit has a built-in extensible rule engine. The rule engine is configured with a soft logic framework that supports dynamically loading business semantic recognition templates through configuration files, in order to adapt to the differentiated definitions of data packet content structure in different domains. The semantic recognition template is written in extensible markup language format, which defines the offset, length and matching rules of business fields. When the rule engine loads a new semantic recognition template, its internal parsing state machine is automatically reconstructed to adapt to the new data packet structure. The deep parsing process includes a full scan of the application layer protocol data or pattern matching of key fields, and uses semantic recognition algorithms to identify interaction priority tags that reflect real-time requirements, as well as contextual association information that records the sequence number of the original data packet in its business flow and the logical dependency relationship between the preceding and following data packets.

[0016] Preferably, the semantic feature extraction unit is configured with feature dimensionality reduction and vectorization algorithms, and the data packet feature vector is constructed as a multi-dimensional numerical array, where each dimension corresponds to a specific business attribute weight; In the process of constructing the data packet feature vector, the semantic feature extraction unit introduces context-related weighting logic. If the current data packet belongs to a continuous business flow, its corresponding data packet feature vector contains the state features of the preceding data packets, so as to achieve a holistic semantic understanding of the interaction process. The semantic feature extraction unit also integrates a dedicated content processing processor. The content processing processor has a proprietary instruction set to accelerate regular expression matching and string search operations, and stores the semantic recognition template in a lookup table in a high-speed on-chip memory to complete the zero-latency parsing of key status words in industrial protocols with a microsecond-level latency.

[0017] Preferably, the interactive policy scheduling unit maintains a hierarchical resource pool, which is implemented based on virtualization isolation technology. It is logically divided into high-priority queues, ordinary queues and latency-tolerant queues in physical memory and computing cores. Each queue has an independent memory address space and processor cycle slice. Preferably, the interaction strategy scheduling unit is equipped with a sensitivity assessment model, which is used to calculate the comprehensive scheduling index of the data packet based on the business sensitivity component and the real-time interaction requirement component in the data packet feature vector; the interaction strategy scheduling unit maps the description structure of each data packet to the corresponding queue through a mapping algorithm based on the comprehensive scheduling index, and allocates a dedicated low-latency processing channel for data packets that are higher than the preset high sensitivity threshold. High-priority queues use an absolute priority scheduling algorithm, while ordinary queues and delay-tolerant queues use a weighted round-robin scheduling algorithm.

[0018] The interactive policy scheduling unit has the ability to dynamically adjust bandwidth quotas. Its configuration is used to monitor the overall load changes of the system and dynamically adjust the bandwidth quotas of each queue according to the feedback control logic. When high-priority service flows surge, the interaction strategy scheduling unit reduces the bandwidth ratio of the latency tolerance queue and allocates it to the high-priority queue. The adjustment step size in the dynamic bandwidth quota adjustment capability is negatively correlated with the packet loss rate of the current queue, and the interactive policy scheduling unit is configured with smooth transition logic to limit the fluctuation range of bandwidth quota within a predetermined time and prevent network flow instability. In addition, the interactive policy scheduling unit also has a built-in hard rate limiting module to block the massive data packets generated by abnormal nodes at the interface layer.

[0019] Preferably, the dynamic path optimization unit integrates a link status awareness module. The link status awareness module adopts a non-intrusive measurement method. By inserting metadata tags into the header field of the service packet and using in-band telemetry technology, it periodically collects the round-trip delay, packet loss rate and jitter indicators of each available path, and feeds the indicators back to the status database in real time. The dynamic path optimization unit is configured with a multi-constraint optimization algorithm. Its optimization objective is to minimize the overall cost of the transmission path. The calculation model of the overall cost includes the physical bandwidth of the link, the current queuing delay, the number of hops in the path, the stability coefficient of the link, and the total energy consumption of the path evaluation dimension. The dynamic path optimization unit interacts with the interactive strategy scheduling unit by sending path confirmation frames, which contain a sorted list of hops.

[0020] Preferably, the dynamic path optimization unit integrates a predictive routing algorithm. The predictive routing algorithm is based on a trend prediction model of time series analysis. It uses historical index data stored in the state database to predict the stability trend of each candidate path within a predetermined time period through regression analysis. If the jitter index of a certain path shows an upward trend within a predetermined period of time, the prediction model lowers the stability score of that path. Based on this, the dynamic path optimization unit pre-selects a candidate path set containing multiple backup paths, and when fluctuations or congestion are detected in the current primary link, it controls the forwarding engine to switch to the optimal backup path in the candidate path set in real time through instruction control. In the industrial IoT scenario, the dynamic path optimization unit adopts a mode that combines offline pre-computation and online fine-tuning to achieve sub-millisecond path switching at the hardware layer.

[0021] Preferably, the anomaly recovery coordination unit is configured with a sliding window verification mechanism. The sliding window verification mechanism defines a window range that slides in the sequence number space. By comparing the received data packet sequence number with the expected sequence number in real time, the integrity of the continuously received data packet sequence is verified. Once a missing sequence number is detected within the window, the anomaly recovery coordination unit uses the selective retransmission request logic to initiate a selective retransmission request for the missing sequence number to the source end. The anomaly recovery coordination unit also works in conjunction with the dynamic path optimization unit. After multiple retransmission failures, it determines that there is a fault in the transmission path, automatically triggers the path rollback strategy, forces the subsequent transmission path to switch to the historically stable alternative link, and marks the faulty original path as temporarily unavailable until its performance indicators recover to a specific range. The retransmission mechanism also includes exponential backoff logic, which gradually extends the interval between the next retransmission when consecutive retransmissions fail.

[0022] This invention provides a data packet-oriented interactive service system, which has the following advantages: (1) During system operation, the data packet-oriented interactive service system provided by this invention breaks through the limitation of traditional network systems that rely solely on protocol header information for static forwarding by introducing semantic feature extraction and dynamic policy scheduling mechanisms, and realizes a deep understanding and differentiated processing of data packet service intent; the system adopts a multi-unit collaborative architecture, decoupling and organically integrating the functions of perception, parsing, scheduling, optimization and recovery, effectively improving the data packet processing efficiency and interactive service quality in high-concurrency, multi-source heterogeneous environments; the dynamic path optimization unit, combined with real-time link status and prediction models, significantly reduces transmission delay and path redundancy caused by topology fluctuations; the anomaly recovery coordination unit, through refined retransmission control and path self-healing capability, ensures strong consistency and high availability of critical business flows in complex network environments; the overall system can complete closed-loop optimization without relying on an external controller, has good autonomous adaptability and deployment flexibility, and is suitable for modern distributed interactive scenarios with stringent requirements for real-time performance and reliability.

[0023] (2) This system upgrades the traditional shallow identification method based solely on 5-tuples or port numbers to a semantic-level processing mechanism with capabilities for business type identification, interaction priority parsing, and context association modeling through the collaborative work of the data packet perception unit and the semantic feature extraction unit. Especially in industrial IoT, remote control, or high-frequency interactive business scenarios, it can accurately identify business flows sensitive to latency and continuity, avoiding congestion or delay drift caused by the mixed transmission of critical control commands and ordinary data flows. At the same time, by introducing pre-filtering and a dedicated content processing processor, it effectively reduces the computational burden on the system by invalid or malformed data, reducing the computational load from the source and achieving stable operation in high-throughput environments. Overall, it enhances the business identification and real-time response capabilities of the interactive system in complex network environments.

[0024] (3) The dynamic path optimization unit performs comprehensive cost calculations under a multi-constraint model considering link bandwidth, queuing delay, path hop count, stability coefficient, and energy consumption, avoiding path oscillation problems caused by single-indicator decisions. Through in-band telemetry and non-intrusive indicator collection mechanisms, it can monitor network topology changes in real time and, combined with a time series trend prediction model, proactively avoid links with potential jitter increases. Compared to traditional static routing or passive switching mechanisms, this system can complete backup path pre-selection and rapid switching before link quality deteriorates significantly, achieving sub-millisecond path adjustment in industrial IoT or critical infrastructure scenarios, significantly reducing the impact of sudden congestion or link fluctuations on business continuity, and improving overall network resilience and service availability.

[0025] (4) The anomaly recovery coordination unit adopts a sliding window verification and selective retransmission mechanism to achieve precise control over the integrity and order consistency of data packet sequences. Combined with exponential backoff logic and path rollback strategy, it can proactively determine path failures and complete link replacement after multiple retransmission failures, avoiding resource waste caused by repeated invalid retransmissions. At the same time, the interactive strategy scheduling unit uses a hierarchical resource pool and dynamic bandwidth quota adjustment mechanism to adaptively adjust the queue bandwidth ratio according to packet loss rate and load feedback, and sets smooth transition constraints to prevent system oscillation caused by drastic bandwidth fluctuations. This collaborative closed-loop control of resource allocation and anomaly recovery not only improves the guarantee capability of high-priority services, but also improves the overall bandwidth and computing power resource utilization efficiency, enabling the system to maintain stable, controllable and efficient operation under high concurrency and sudden traffic scenarios. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the overall technical architecture of a data packet-oriented interactive service system according to an embodiment of this application; Figure 2This is a schematic diagram of the core principle framework for constructing feature vectors based on semantic feature extraction and performing hierarchical resource scheduling in a data packet-oriented interactive service system according to an embodiment of this application; Figure 3 This is a schematic diagram of the multi-level interaction relationship and data flow in a data packet-oriented interactive service system according to an embodiment of this application, involving dynamic path calculation and anomaly recovery and self-healing. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Example 1 This invention provides a data packet-oriented interactive service system. Please refer to [link / reference]. Figure 1 The system includes a data packet sensing unit, a semantic feature extraction unit, an interaction strategy scheduling unit, a dynamic path optimization unit, and an anomaly recovery coordination unit. The data packet sensing unit is connected to the external network environment and is used to capture raw data packets transmitted in the network in real time. The semantic feature extraction unit is connected to the data packet sensing unit and is used to perform deep semantic parsing of the data packet content and construct feature vectors. The interaction strategy scheduling unit is connected to the semantic feature extraction unit and is used to allocate resources and set priorities based on the feature vectors. The dynamic path optimization unit is connected to the interaction strategy scheduling unit and is used to calculate and switch the optimal transmission path for data packets. The anomaly recovery coordination unit is connected to both the dynamic path optimization unit and the data packet sensing unit and is used to monitor the transmission status and execute retransmission or rollback strategies.

[0029] The packet sensing unit, serving as the system's traffic entry point, is configured to connect to external communication links via a high-bandwidth network physical interface. This network physical interface supports multiple standard protocols, including but not limited to 10 Gigabit Ethernet interfaces, fiber optic distributed data interfaces, and asynchronous transmission mode interfaces, ensuring compatibility with different physical link layer protocols. Internally, the packet sensing unit incorporates a high-speed cache array and a multi-core parallel processing chip to receive high-concurrency raw data packet streams without packet loss. Upon capturing raw data packets, the packet sensing unit initiates preliminary parsing logic. This preliminary parsing logic extracts the source Internet Protocol address, destination Internet Protocol address, transport layer protocol type, and total payload length of the data packet by reading the link layer and network layer header information. Based on this extracted basic metadata, the packet sensing unit generates a packet description structure containing this basic metadata. This description structure exists in memory as a structure with a specific format, serving as a unified data carrier for subsequent processing by various units.

[0030] The packet sensing unit employs a multi-channel parallel monitoring architecture. This architecture comprises multiple independent monitoring sub-modules, each assigned to a specific physical interface or virtual network port. This design enables simultaneous processing of heterogeneous data streams from different sources, effectively distributing the processing pressure across a single interface. To address potential timing discrepancies during parallel processing of concurrent packets, the packet sensing unit incorporates a high-precision clock generator capable of producing microsecond-level time-series pulses. Whenever a packet enters the sensing unit, a timestamp mechanism immediately inserts a nanosecond-level arrival timestamp into the packet description structure. This timestamp mechanism allows the system to precisely sort concurrent packets arriving from different channels at the microsecond level. This sorting capability is crucial for supporting accurate event sequence reconstruction in high-throughput scenarios, particularly during the interaction of financial trading instructions or industrial control signals, ensuring that packets are processed according to their actual physical order of occurrence.

[0031] The semantic feature extraction unit is connected to the data packet sensing unit, and its core function is to deeply strip and parse the actual business load carried within the data packet. Unlike traditional processing methods that only focus on the protocol header, the semantic feature extraction unit is configured to perform deep parsing of the load content in the data packet description structure. The deep parsing process involves a full scan of the application layer protocol data or pattern matching of key fields. Through a built-in semantic recognition algorithm, this unit can identify the business type identifier, interaction priority label, and context association information embedded in the data packet. The business type identifier is used to distinguish whether the current data packet belongs to instant messaging, video streaming, file transfer, or control commands; the interaction priority label is pre-labeled by the source application, reflecting the data packet's real-time requirements; and the context association information records the sequence number of the data packet in its business flow and its logical dependencies with preceding and following data packets.

[0032] Based on the multidimensional information identified above, the semantic feature extraction unit constructs a data packet feature vector with semantic dimensions through feature dimensionality reduction and vectorization algorithms. The data packet feature vector is a multidimensional numerical array, where each dimension corresponds to a specific business attribute weight. The semantic feature extraction unit incorporates a scalable rule engine. This rule engine is not fixed, hard-coded logic, but rather a soft logic framework that supports dynamically loading business semantic recognition templates through configuration files. By loading different templates, the system can quickly adapt to the differentiated definitions of data packet content structure in fields such as financial transactions, industrial control, and telemedicine. For example, in a financial transaction scenario, the rule engine will strengthen the extraction weight of fields such as transaction amount and transaction sequence number; while in a telemedicine scenario, it will focus on feature recognition of real-time ECG data packets and high-resolution image slice data.

[0033] The interaction strategy scheduling unit is connected to the semantic feature extraction unit and is responsible for formulating and executing differentiated scheduling strategies based on the business intent reflected in the feature vector. After obtaining the data packet feature vector, the interaction strategy scheduling unit inputs it into a preset sensitivity evaluation model. This evaluation model calculates the comprehensive scheduling index of the data packet based on the business sensitivity component and the real-time interaction requirement component in the feature vector. The comprehensive scheduling index determines the status of the data packet in the allocation of processing resources. For data packets with a scheduling index higher than a preset high sensitivity threshold, the interaction strategy scheduling unit marks them as the highest priority and allocates a dedicated low-latency processing channel to them.

[0034] In its implementation, the interactive policy scheduling unit maintains a hierarchical resource pool. This resource pool is logically divided into high-priority queues, ordinary queues, and latency-tolerant queues within physical memory and computing cores, based on preset resource allocation thresholds. The interactive policy scheduling unit uses a mapping algorithm to map the description structure of each pending data packet to its corresponding queue based on the priority label in the packet feature vector. Simultaneously, the interactive policy scheduling unit has the ability to dynamically adjust the bandwidth quotas of each queue. When the system detects a change in overall load, such as a sudden surge in high-priority service flows, the scheduling unit automatically reduces the bandwidth ratio of the latency-tolerant queue and allocates it to the high-priority queue to ensure that critical service flows still receive priority scheduling during peak traffic periods. This dynamic quota adjustment mechanism is based on feedback control logic, and its adjustment step size is negatively correlated with the current queue's packet loss rate.

[0035] The dynamic path optimization unit is connected to the interactive policy scheduling unit, and its main task is to find and establish the optimal forwarding path in the network for the scheduled data packets. The dynamic path optimization unit is configured to calculate the optimal transmission path for each data packet to be forwarded based on the current network topology, link load, and historical transmission quality indicators. The calculation process employs a multi-constraint optimization algorithm, whose optimization objective is to minimize the overall cost of the transmission path. The overall cost calculation model includes the link's physical bandwidth, current queuing delay, path hop count, and link stability coefficient.

[0036] To achieve real-time network status awareness, the dynamic path optimization unit integrates a link status awareness module and a predictive routing algorithm. The link status awareness module periodically sends active probe packets across the network to collect round-trip time, packet loss rate, and jitter metrics for each available path. These metrics are fed back to the dynamic path optimization unit's status database in real time. The predictive routing algorithm is a trend prediction model based on time series analysis. It uses stored historical metric data and regression analysis or a neural network prediction model to predict the stability trend of each candidate path over a predetermined future time period. For example, if the jitter metric of a path shows an upward trend in the past ten minutes, the prediction model will lower the stability score of that path. Based on this, the dynamic path optimization unit pre-selects a candidate path set containing multiple backup paths. When fluctuations or congestion are detected on the current primary link, the unit controls the forwarding engine to switch to the optimal backup path in the candidate set in real time.

[0037] The anomaly recovery coordination unit is connected to the dynamic path optimization unit and the data packet sensing unit, forming a closed-loop feedback loop in the system. Its core function is to monitor the integrity and sequential consistency of data packets during transmission. During data interaction, a state synchronization mechanism is maintained between the sending and receiving ends. The anomaly recovery coordination unit determines whether an anomaly exists by comparing the received data packet sequence number with the expected sequence number in real time. When packet loss, out-of-order delivery, or timeout anomalies are detected, this unit does not immediately initiate a global retransmission; instead, it triggers a targeted recovery mechanism based on the type of anomaly.

[0038] The anomaly recovery coordination unit is configured with a sliding window verification mechanism. This mechanism defines a sliding window range in the sequence number space; only data packets falling within the window are considered valid and acceptable. This mechanism verifies the integrity of continuously received data packet sequences. If a missing sequence number is found within the window, it indicates intermediate packet loss. In this case, the anomaly recovery coordination unit immediately initiates a selective retransmission request to the source for the missing sequence number, instead of waiting for a global timeout. This mechanism significantly shortens the anomaly recovery time and avoids unnecessary full retransmissions. Simultaneously, the anomaly recovery coordination unit also works in conjunction with the dynamic path optimization unit. After multiple retransmission failures for the same destination address, the coordination unit determines that the current transmission path may have persistent physical damage or logical faults. In this case, it automatically triggers a path rollback strategy, notifying the dynamic path optimization unit to forcibly switch the subsequent transmission path of the data stream to a historically stable alternative link, and marking the faulty original path as temporarily unavailable until its performance indicators recover to a specific range after multiple rounds of probing. By synchronously updating the path optimization model, the system can continuously improve the robustness of subsequent transmissions, enabling the system to self-repair and evolve.

[0039] Furthermore, within the packet sensing unit, the preliminary parsing logic also includes a protocol whitelist verification sub-step. This step matches the extracted protocol types against a pre-stored security whitelist. If the captured packet belongs to an illegal protocol or is a malformed packet with potential attack characteristics, the packet sensing unit is configured to directly discard the packet and record the anomaly in the log module. This pre-filtering mechanism significantly reduces the computational burden on the semantic feature extraction unit, protecting the core processing logic from malicious traffic. In addition, the packet sensing unit's cache array employs a circular buffer structure, capable of supporting packet processing speeds of tens of millions per second. When processing concurrent streams, the listening sub-modules communicate via shared memory, utilizing lock-free queue technology to achieve high-speed data flow, ensuring that the microsecond-level sorting process does not introduce additional computational bottlenecks.

[0040] In the semantic feature extraction unit, the dynamic loading mechanism of the rule engine is implemented through an independent management interface. Administrators or upper-level business control systems can inject new semantic recognition templates into the system through this interface. These templates are written in Extensible Markup Language (EXPLAIN) and define in detail the offsets, lengths, and matching rules of business fields. When the rule engine loads a new template, its internal parsing state machine is automatically reconstructed to adapt to the new data packet structure. This design gives the system extremely high scalability, enabling smooth support for emerging business protocols. Furthermore, the feature vector construction process incorporates context-based weighting. If the current data packet is part of a continuous business flow, its feature vector will include the state features of preceding data packets, thereby achieving a holistic semantic understanding of the interaction process, rather than analyzing individual data packets in isolation.

[0041] In the interactive policy scheduling unit, the hierarchical resource pool is divided based on virtualization isolation technology. Each priority queue has an independent memory address space and processor cycle slice. High-priority queues use an absolute priority scheduling algorithm; as long as there are packets to be processed in this queue, the processor will immediately suspend low-priority tasks for processing. A weighted round-robin scheduling algorithm is used between ordinary queues and latency-tolerant queues to ensure that after meeting the needs of high-priority services, other services can also obtain basic transmission guarantees. Furthermore, the bandwidth quota adjustment mechanism also includes a smooth transition logic to prevent drastic fluctuations in quotas within a short period from causing network flow instability.

[0042] In the dynamic path optimization unit, the calculation of the optimal transmission path not only considers the current state but also introduces an energy consumption assessment dimension. For some energy-sensitive deployment scenarios, the optimization algorithm tends to select forwarding paths with lower total energy consumption, balancing performance and operating costs. The link state awareness module uses a non-intrusive measurement method when collecting metrics. By inserting a small number of metadata tags into the header fields of service packets, it utilizes in-band telemetry technology to obtain the true transmission performance of the path, thereby avoiding the additional load caused by a large number of probe packets.

[0043] In the anomaly recovery coordination unit, the retransmission mechanism also includes an exponential backoff logic. In the event of consecutive retransmission failures, the system gradually extends the interval between the next retransmission to prevent further deterioration of the network condition due to frequent retransmissions during link congestion. Simultaneously, before triggering the path rollback strategy, a pre-judgment logic is executed, assessing the current carrying capacity of the backup path. If the backup path is also under high load, the anomaly recovery coordination unit will coordinate with the interaction policy scheduling unit to implement a rate-limiting strategy for the service flow, ensuring the overall stability of the system from the source.

[0044] Example 2 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically, the system's units are deployed in geographically dispersed edge computing nodes and core data center nodes, enabling larger-scale data packet interaction services through distributed collaboration.

[0045] In this embodiment, the packet sensing units are distributed across multiple network edge access points. Each edge access point's packet sensing unit is independently responsible for traffic capture and preliminary parsing within its coverage area. After generating a preliminary packet description structure locally, the raw packets captured by the sensing unit are not all transmitted to the central node; instead, they undergo preliminary filtering locally based on service type. For some interaction packets with high real-time requirements and relatively simple business logic, the edge node's sensing unit directly guides them to the local processing unit. This distributed sensing mechanism greatly alleviates the transmission pressure on the backbone network.

[0046] The semantic feature extraction unit adopts a hierarchical parsing mode in a distributed architecture. The first layer of parsing is completed by a lightweight parsing module deployed on edge nodes, mainly identifying basic business tags and priorities. The second layer of parsing is performed by a deep parsing server deployed in a regional data center, which is responsible for complex semantic association analysis and the construction of high-dimensional feature vectors. This hierarchical design achieves a balanced distribution of computing resources. The rule engine is managed through a unified configuration server in a distributed environment. The configuration server uses a publish-subscribe mechanism to synchronously distribute the latest semantic recognition templates to all edge nodes and central nodes, ensuring the consistency of the parsing logic across the entire network.

[0047] In Embodiment 2, the interaction strategy scheduling unit evolves into an architecture combining global and local scheduling. Each edge node has a built-in local scheduler responsible for handling short-range interactions that are extremely sensitive to latency. Cross-regional, long-distance data packet interactions are uniformly coordinated by the global scheduler. The global scheduler maintains a real-time view of the entire network resources and dynamically adjusts the task distribution ratio based on the processing load of each regional data center. The tiered resource pool is also logically integrated across the entire network, using virtual private network technology to build cross-regional priority forwarding channels for specific high-value customers.

[0048] The dynamic path optimization unit introduces multi-level topology awareness technology in distributed deployment. The optimization modules at edge nodes only perceive local topology information, while the optimization unit deployed in the core network has a global view of link status. The two levels of optimization modules synchronize their states through a topology information exchange protocol. In a distributed environment, the predictive routing algorithm leverages big data to analyze traffic migration patterns across the entire network, thereby making more accurate path pre-selection schemes. When an edge node detects a link anomaly, it not only switches paths locally but also disseminates the anomaly information to neighboring nodes, achieving collaborative disaster avoidance and forwarding.

[0049] In Embodiment 2, the anomaly recovery coordination unit adds multi-replica fault-tolerance logic. For data packets of critical business flows, the system generates and transmits multiple replicas on different physical paths during transmission. The anomaly recovery coordination unit is responsible for deduplicating and integrating these replicas at the receiving end. As long as one replica arrives successfully, the system can achieve seamless anomaly recovery. Although this multi-path redundant transmission mechanism increases bandwidth overhead, it provides extremely high reliability for financial-grade and military-grade interactive services. Simultaneously, the sliding window verification mechanism supports cross-node window synchronization in a distributed environment, ensuring that the continuity of data interaction is not affected during node switching.

[0050] Furthermore, in Embodiment 2, the units communicate with each other via an encrypted control bus, ensuring the security and immutability of interactive commands during transmission. The system's management plane provides a graphical monitoring interface, allowing administrators to observe in real time the operational status of units distributed across various locations, traffic distribution, and path optimization effects. This distributed architecture gives the system strong horizontal scalability, easily handling the ever-increasing demand for data packet interaction by increasing the number of edge nodes.

[0051] Example 3 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically, based on the above embodiments, this embodiment describes a hardware-accelerated data packet-oriented interactive service system applied to high-reliability scenarios in the Industrial Internet of Things (IIoT). In this scenario, data packets typically have strong real-time requirements and high frequency, and the system has extremely high requirements for deterministic processing.

[0052] In this embodiment, the packet sensing unit is implemented using a field-programmable gate array (FPGA). Compared to general-purpose processors, FPGAs offer deterministic parallel processing latency. The multi-channel parallel monitoring architecture is implemented within the FPGA as logic gates directly interfaced to the physical layer. The timestamp mechanism is directly integrated into the media access control layer, driven by a hardware clock, eliminating jitter caused by operating system scheduling. The packet throughput per second reaches the theoretical limit of the hardware link, and the sorting accuracy reaches the tens of nanoseconds level.

[0053] In this embodiment, the semantic feature extraction unit integrates a dedicated content processing processor. This processor has a proprietary instruction set specifically designed to accelerate regular expression matching and string search operations. The recognition template of the rule engine is compiled into an optimized lookup table and stored in high-speed on-chip memory. This hardware acceleration method compresses the latency of semantic feature extraction to below microseconds. When constructing feature vectors, this unit can also directly extract key status words from industrial protocols (such as Modbus or Profinet) through hardware circuitry, achieving zero-latency parsing of industrial control commands.

[0054] In this embodiment, the interactive strategy scheduling unit employs a resource tiering scheme based on a hardware queue manager. The hardware queue manager, through a fixed weighted algorithm, completes packet enqueueing and scheduling decisions within nanoseconds. The tiered resource pools correspond to different physical buffers in the hardware chip. To cope with sudden traffic surges in industrial settings, the interactive strategy scheduling unit also incorporates a hardware rate limiting module. When an abnormal node generates a massive amount of junk packets, the hardware rate limiting module can directly block the traffic at the interface layer without consuming system computing resources.

[0055] In this embodiment, the dynamic path optimization unit pre-configures a series of deterministic forwarding strategies. Considering the relatively fixed topology of industrial networks, the optimization unit employs a combination of offline pre-calculation and online fine-tuning. Upon system startup, multiple independent redundant paths are pre-calculated based on the factory's network topology. The link status awareness module monitors link integrity at extremely high frequency; if the physical layer feedback signal is lost, the dynamic path optimization unit implements sub-millisecond path switching at the hardware level. This deterministic switching time ensures the stability of the industrial closed-loop control process.

[0056] In this embodiment, the anomaly recovery coordination unit enhances its core verification capabilities. Its sliding window verification logic is implemented by dedicated logic circuitry, enabling real-time integrity monitoring of continuous industrial control sequences. Upon detecting the loss of critical instructions, the anomaly recovery coordination unit notifies the actuators to enter a safe state by triggering an emergency interrupt signal, while simultaneously initiating a high-speed retransmission request. The path rollback strategy is integrated with the factory's safety instrumented system, ensuring that production equipment can be shut down in an orderly manner under extreme network failures, thus preventing safety accidents.

[0057] The system in Example 3 is encapsulated within an electromagnetic compatibility-enhanced housing, enabling it to withstand harsh industrial environments characterized by high temperature, high humidity, and strong electromagnetic interference. The interconnections between units utilize a redundant backplane bus design, ensuring that a failure of any single bus will not affect the normal operation of the system. This hardware-accelerated system implementation provides robust technical support for the high real-time data interaction required in modern smart factories.

[0058] Example 4 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: When a data packet carrying remote medical surgery instructions arrives at the system, the data packet sensing unit first captures the bit stream using its high-speed network card interface. At the hardware level, the physical layer receiver of the sensing unit detects the start-of-frame delimiter, and then the link layer controller verifies the frame check sequence. After successful verification, the preliminary parsing logic begins to operate. It extracts the source address of the data packet as the Internet Protocol address of the remote expert's terminal and the destination address as the address of the surgical robot control terminal. The protocol type is identified as a custom real-time transmission protocol. The raw information generated in this process is filled into the data packet description structure and accurately timestamped.

[0059] Subsequently, the data packet description structure is pushed to the semantic feature extraction unit. Based on the pre-loaded medical business template, the rule engine locates byte 64 in the payload, identifying it as a control command regarding "mechanical arm angle fine-tuning," with its interaction priority label set to the highest level. To construct a feature vector, the semantic feature extraction unit calculates the command's latency sensitivity score as 98 out of 100 and its reliability requirement score as 99. These scores, along with the serial number information in the context, constitute a five-dimensional feature vector. This vector clearly defines the data packet's "VIP" status within the system.

[0060] Upon receiving the feature vector, the interactive strategy scheduling unit immediately identifies it as a high-sensitivity service flow. The scheduling unit checks the current resource distribution and finds that the high-priority queue is currently 30% full, so it directly places the data packet at the front of the high-priority queue. Simultaneously, the scheduling unit adjusts the bandwidth quota algorithm, reducing the bandwidth allocated to the backend data backup service from 50% to 10%, reserving sufficient resources for potential subsequent instruction surges.

[0061] Meanwhile, the dynamic path optimization unit begins searching for the optimal path for the instruction packet. The link state awareness module reports that the current primary path round-trip latency is 5 milliseconds, and the packet loss rate is 0.01%. However, the predictive routing algorithm, by analyzing the link jitter trend over the past minute, discovers that due to traffic fluctuations in the surrounding area, this path may experience congestion within the next three seconds. Therefore, the dynamic path optimization unit decisively selects a dedicated fiber optic link, although it has one more hop, but with extremely high bandwidth redundancy, as the current primary path and quickly updates the forwarding table entries.

[0062] During data packet transmission, the anomaly recovery coordination unit monitors its status in real time. Due to brief pulse interference in the network environment, the surgical instruction packet experienced a bit flip during transmission, causing the receiver's verification to fail. The receiver's anomaly recovery coordination unit detected the missing sequence number through a sliding window and immediately sent a selective retransmission request back to the sender. Because selective retransmission was used, the system only retransmitted this single damaged packet, and the entire recovery process was completed within 2 milliseconds. Simultaneously, the coordination unit recorded this anomaly and fed it back to the dynamic path optimization unit, suggesting a reduction in the credit score of the interfering route.

[0063] Finally, the surgical command packet is delivered to the surgical robot controller accurately, completely, and with low latency. The entire interaction process demonstrates the system's deep understanding and dynamic response capabilities regarding data packets, proving its application value in extremely demanding scenarios.

[0064] Example 5 A data packet-oriented interactive service system, please refer to... Figure 2 Specifically, the communication protocols and data exchange formats between the various units of the system are further standardized and described. To achieve efficient internal communication, each unit adopts a lightweight "inter-unit interaction protocol".

[0065] The data packet description structure, as the core payload of the protocol, is defined as a fixed-length frame structure. The frame header contains a version number and a frame type identifier to distinguish whether it is a raw data frame, a control signaling frame, or a status report frame. Next is the metadata area generated by each unit, such as timestamps written by the sensing unit and feature vectors written by the semantic unit. The frame trailer contains a cyclic redundancy check (CRC) code to ensure the accuracy of information transmitted between units.

[0066] When interacting with the dynamic path optimization unit, the interactive strategy scheduling unit triggers path calculation logic by sending a "path request frame." This request frame carries the target priority requirement and the expected latency limit. After completing the calculation, the dynamic path optimization unit sends back a "path confirmation frame," which contains a sorted list of hops.

[0067] The anomaly recovery coordination unit uses "state synchronization frames" to maintain coordination with other units. For example, when the coordination unit performs a path rollback, it sends a "resource freeze command" to the scheduling unit, requesting the scheduling unit to temporarily suspend the transmission of relevant service flows until the new path is fully established and verified. This rigorous protocol ensures that the system can maintain consistency among units when facing complex network fluctuations, avoiding logical race conditions.

[0068] In addition, to facilitate system debugging and maintenance, the system also includes an independent audit management unit. This unit connects to the monitoring interfaces of all other units and asynchronously records the flow trajectory of each data packet within the system. By collecting logs generated by each unit, the audit management unit can generate a network-wide interaction efficiency report, helping operators identify performance bottlenecks in the network. For example, if the audit report shows a significant increase in latency for the semantic feature extraction unit during a specific time period, it may indicate that the current recognition template is too complex and needs optimization.

[0069] In summary, the data packet-oriented interactive service system provided by this invention achieves refined management of the entire lifecycle of data packets, from perception and parsing to scheduling, optimization, and recovery, through the organic collaboration of multiple units. Its unique semantic feature extraction capability endows the system with the soul of "business perception," while dynamic path optimization and anomaly recovery mechanisms construct a robust "transmission assurance" system. This system not only solves the performance bottlenecks and rigid strategies in traditional network interactions but also provides a highly reliable and self-adaptive communication foundation for future cutting-edge application scenarios such as large-scale distributed collaboration, the Industrial Internet, and telemedicine. Its flexible deployment architecture and scalable rule engine ensure that the technical solution can continuously evolve with the development of business needs, possessing extremely high engineering practical value and broad market application prospects.

[0070] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A data packet-oriented interactive service system, characterized in that: It includes a data packet awareness unit, a semantic feature extraction unit, an interaction strategy scheduling unit, a dynamic path optimization unit, and an anomaly recovery coordination unit; The packet sensing unit is configured to capture raw data packets transmitted in the network in real time, and perform preliminary parsing of the source address, destination address, protocol type and payload length of the raw data packets to generate a packet description structure containing basic metadata. The semantic feature extraction unit is connected to the data packet perception unit and is configured to perform deep analysis of the payload content in the data packet description structure, identify the embedded business type identifier, interaction priority label and context association information, thereby constructing a data packet feature vector with semantic dimension. The interaction strategy scheduling unit is connected to the semantic feature extraction unit and is configured to dynamically allocate processing resources and set forwarding priorities based on the service sensitivity and real-time interaction requirements represented by the data packet feature vector. The dynamic path optimization unit is connected to the interactive policy scheduling unit and is configured to calculate the optimal transmission path for each data packet to be forwarded based on the current network topology status, link load status and historical transmission quality indicators. The anomaly recovery coordination unit is connected to the dynamic path optimization unit and the data packet awareness unit. It is configured to monitor the integrity and order consistency of data packets during transmission. When an anomaly is detected, it triggers a retransmission mechanism or a path rollback strategy.

2. The data packet-oriented interactive service system according to claim 1, characterized in that: The packet sensing unit adopts a multi-channel parallel listening architecture, which contains multiple independent listening sub-modules. Each listening sub-module is assigned to a specific physical interface or virtual network port to process heterogeneous data streams from different sources simultaneously. The packet sensing unit is equipped with a high-speed cache array and a multi-core parallel processing chip. The high-speed cache array adopts a circular buffer structure. The monitoring sub-modules communicate through shared memory and use lock-free queue technology to realize data flow. The packet sensing unit also has a built-in high-precision clock generator that can generate microsecond-level time sequence pulses. When the original data packet enters the packet sensing unit, the packet sensing unit uses a timestamp marking mechanism to insert a nanosecond-level precision arrival timestamp into the data packet description structure, and sorts the concurrent data packets entering from different channels at the microsecond level according to the arrival timestamp to support accurate event sequence reconstruction in high-throughput scenarios.

3. The data packet-oriented interactive service system according to claim 2, characterized in that: The data packet sensing unit also includes a pre-filtering module, which is configured with preliminary parsing logic, including a protocol whitelist verification sub-step. The protocol whitelist verification sub-step extracts the protocol type of the original data packet and matches it with the pre-stored security whitelist; If the protocol type is not on the security whitelist or the original data packet is determined to be a malformed packet with attack characteristics, the data packet sensing unit will directly discard the data packet and record the abnormal event in the log module, so as to reduce the computational burden of the semantic feature extraction unit through the pre-filtering module.

4. The data packet-oriented interactive service system according to claim 1, characterized in that: The semantic feature extraction unit has a built-in extensible rule engine. The rule engine is configured with a soft logic framework that supports dynamically loading business semantic recognition templates through configuration files to adapt to the differentiated definitions of data packet content structure in different domains. The semantic recognition template is written in extensible markup language format and defines the offset, length and matching rules of business fields. When the rule engine loads a new semantic recognition template, its internal parsing state machine is automatically reconstructed to adapt to the new data packet structure. The deep parsing process includes a full scan of the application layer protocol data or pattern matching of key fields, and uses semantic recognition algorithms to identify interaction priority tags that reflect real-time requirements, as well as contextual association information that records the sequence number of the original data packet in its business flow and the logical dependency relationship between the preceding and following data packets.

5. The data packet-oriented interactive service system according to claim 1, characterized in that: The semantic feature extraction unit is equipped with feature dimensionality reduction and vectorization algorithms. The data packet feature vector is constructed as a multi-dimensional numerical array, where each dimension corresponds to a specific business attribute weight. In the process of constructing the data packet feature vector, the semantic feature extraction unit introduces context-related weighting logic. If the current data packet belongs to a continuous business flow, its corresponding data packet feature vector contains the state features of the preceding data packets, so as to achieve a holistic semantic understanding of the interaction process. The semantic feature extraction unit also integrates a dedicated content processing processor. The content processing processor has a proprietary instruction set to accelerate regular expression matching and string search operations, and stores the semantic recognition template in a lookup table in a high-speed on-chip memory to complete the zero-latency parsing of key status words in industrial protocols with a microsecond-level latency.

6. The data packet-oriented interactive service system according to claim 1, characterized in that: The interactive policy scheduling unit maintains a hierarchical resource pool, which is implemented based on virtualization isolation technology. It is logically divided into high-priority queues, ordinary queues and latency-tolerant queues in physical memory and computing cores. Each queue has an independent memory address space and processor cycle slice. The interaction strategy scheduling unit is equipped with a sensitivity evaluation model, which is used to calculate the comprehensive scheduling index of the data packet based on the business sensitivity component and the real-time interaction requirement component in the data packet feature vector. The interactive policy scheduling unit maps the description structure of each data packet to the corresponding queue through a mapping algorithm based on the comprehensive scheduling index, and allocates a dedicated low-latency processing channel for data packets that are higher than the preset high sensitivity threshold. High-priority queues use an absolute priority scheduling algorithm, while ordinary queues and delay-tolerant queues use a weighted round-robin scheduling algorithm.

7. The data packet-oriented interactive service system according to claim 1, characterized in that: The interactive policy scheduling unit has the ability to dynamically adjust bandwidth quotas. Its configuration is used to monitor the overall load changes of the system and dynamically adjust the bandwidth quotas of each queue according to the feedback control logic. When high-priority service flows surge, the interaction strategy scheduling unit compresses the bandwidth ratio of the latency tolerance queue and allocates it to the high-priority queue. The adjustment step size in the dynamic bandwidth quota adjustment capability is negatively correlated with the packet loss rate of the current queue, and the interactive policy scheduling unit is configured with smooth transition logic to limit the fluctuation range of bandwidth quota within a predetermined time and prevent network flow instability. In addition, the interactive policy scheduling unit also has a built-in hard rate limiting module to block the massive data packets generated by abnormal nodes at the interface layer.

8. The data packet-oriented interactive service system according to claim 1, characterized in that: The dynamic path optimization unit integrates a link status awareness module. The link status awareness module adopts a non-intrusive measurement method. By inserting metadata tags into the header field of the service packet and using in-band telemetry technology, it periodically collects the round-trip delay, packet loss rate and jitter indicators of each available path, and feeds the indicators back to the status database in real time. The dynamic path optimization unit is configured with a multi-constraint optimization algorithm. Its optimization objective is to minimize the overall cost of the transmission path. The calculation model of the overall cost includes the physical bandwidth of the link, the current queuing delay, the number of hops in the path, the stability coefficient of the link, and the total energy consumption of the path evaluation dimension. The dynamic path optimization unit interacts with the interactive strategy scheduling unit by sending path confirmation frames, which contain a sorted list of hops.

9. A data packet-oriented interactive service system according to claim 1, characterized in that: The dynamic path optimization unit integrates a predictive routing algorithm, which is based on a trend prediction model of time series analysis. It uses historical index data stored in the state database to predict the stability trend of each candidate path within a predetermined time period through regression analysis. If the jitter index of a certain path shows an upward trend over a predetermined period of time, the prediction model will lower the stability score of that path. Based on this, the dynamic path optimization unit pre-selects a candidate path set containing multiple backup paths, and when it detects fluctuations or congestion in the current primary link, it controls the forwarding engine to switch to the optimal backup path in the candidate path set in real time through instructions. In the industrial IoT scenario, the dynamic path optimization unit adopts a mode that combines offline pre-computation and online fine-tuning to achieve sub-millisecond path switching at the hardware layer.

10. A data packet-oriented interactive service system according to claim 1, characterized in that: The anomaly recovery coordination unit is equipped with a sliding window verification mechanism. The sliding window verification mechanism defines a window range that slides in the sequence number space. By comparing the received data packet sequence number with the expected sequence number in real time, the integrity of the continuously received data packet sequence is verified. Once a missing sequence number is detected within the window, the anomaly recovery coordination unit uses the selective retransmission request logic to initiate a selective retransmission request for the missing sequence number to the source end. The anomaly recovery coordination unit also works in conjunction with the dynamic path optimization unit. After multiple retransmission failures, it determines that there is a fault in the transmission path, automatically triggers the path rollback strategy, forces the subsequent transmission path to switch to the historically stable alternative link, and marks the faulty original path as temporarily unavailable until its performance indicators recover to a specific range. The retransmission mechanism also includes exponential backoff logic, which gradually extends the interval between the next retransmission when consecutive retransmissions fail.

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