Efficient dual-mode communication method and system for HPLC (High Performance Liquid Chromatography)

Abstract

The invention discloses an efficient HPLC dual-mode communication method and system, and relates to the technical field of communication analysis, and the method comprises the steps: obtaining service request data and channel state data in a dual-mode network; performing service priority classification based on the service request data so as to obtain service priority data, and performing channel quality evaluation based on the channel state data so as to obtain channel quality data; performing service and channel dynamic matching analysis according to the service priority data and the channel quality data to obtain dynamic matching strategy data, and performing scheduling queue optimization based on the dynamic matching strategy data to obtain optimized scheduling queue data; and performing a dual-mode channel switching decision according to the optimized scheduling queue data, generating channel switching decision data, performing service data transmission control through the channel switching decision data, obtaining service data transmission control data, and further realizing dual-mode communication according to the service data transmission control data. The dual-mode communication method has the effect of improving the dual-mode communication efficiency.

Description

Technical Field

[0001] This application relates to the field of communication analysis technology, and in particular to a highly efficient dual-mode communication method and system for HPLC. Background Technology

[0002] Against the backdrop of the deep integration of smart distribution networks and the Internet of Things, high-speed power line carrier (HPLC) and high-speed wireless (HRF) dual-mode communication technologies are becoming core solutions for building highly reliable local communication networks due to their complementary characteristics. The services carried by this system are becoming increasingly diverse and complex, expanding from traditional automated meter reading and rate control to numerous in-depth applications with varying real-time and reliability requirements, including proactive power outage reporting, precise phase and transformer area identification, distributed photovoltaic monitoring, and orderly management of electric vehicle charging stations. These service data streams are transmitted concurrently on the network, constituting a typical heterogeneous service hybrid carrying environment. However, existing dual-mode communication methods face a severe problem of priority conflicts among multiple service data streams when handling these services. The root cause lies in the fact that while current dual-mode communication protocols and network scheduling mechanisms logically set static priority identifiers for different services, they fail to achieve deep coordination and intelligent adaptation with the real-time, dynamically changing transmission quality of the underlying HPLC and HRF channels.

[0003] In related technologies, in the actual operation environment of the power grid, power line channels are susceptible to load fluctuations, switching power supplies, and inductive load switching, resulting in sudden noise and attenuation, leading to a sharp drop in communication performance. Wireless channels are susceptible to terrain obstruction, weather changes, and co-channel interference, causing signal fluctuations. When a critical service with high real-time requirements experiences a momentary degradation of its preferred channel under a preset "high priority" logic, existing static scheduling strategies cannot sensitively detect this sudden change in channel state and dynamically and seamlessly switch the critical service flow to a backup channel with better quality, or effectively preempt resources within the channel. Instead, these critical service data packets may experience retransmission or congestion on the degraded channel, being blocked by a large backlog of regular services with lower priority but still acceptable channel conditions. This "priority inversion" phenomenon, caused by the failure of dynamic matching between channel state and service priority, severely restricts the realization of the potential of dual-mode networks, leading to increased transmission delays and decreased success rates for critical services. It directly affects the accuracy of state perception, fault handling speed, and reliability of advanced applications in the power distribution network, thereby reducing the efficiency of dual-mode communication and indicating areas for improvement. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application provides a highly efficient dual-mode communication method and system for HPLC.

[0005] In a first aspect, this application provides a highly efficient two-mode communication method for HPLC, comprising the following steps: Step S1: Obtain service request data and channel state data in the dual-mode network; classify service priorities based on service request data to obtain service priority data, and evaluate channel quality based on channel state data to obtain channel quality data; Step S2: Perform dynamic matching analysis between services and channels based on service priority data and channel quality data to obtain dynamic matching strategy data. Optimize the scheduling queue based on the dynamic matching strategy data to obtain optimized scheduling queue data. Step S3: Make dual-mode channel switching decisions based on the optimized scheduling queue data, generate channel switching decision data, control service data transmission through the channel switching decision data, obtain service data transmission control data, and then realize dual-mode communication based on the service data transmission control data.

[0006] Preferably, step S1 includes the following steps: Step S11: Obtain service request data and channel state data in the dual-mode network; Step S12: Parse the business request data for business type and generate business type data; Step S13: Identify business priorities based on business type data to obtain business priority data; Step S14: Extract channel quality indicators from the channel state data to obtain channel quality data.

[0007] Preferably, step S2 includes the following steps: Step S21: Calculate service scheduling weights based on service priority data and channel quality data to obtain service scheduling weight data; Step S22: Sort the channel selection priority based on the service scheduling weight data and the channel quality data to obtain the channel selection priority data; Step S23: Generate a dynamic matching strategy based on the channel selection priority data to obtain dynamic matching strategy data; Step S24: Optimize the scheduling queue based on the dynamic matching strategy data to obtain optimized scheduling queue data.

[0008] Preferably, step S22 includes the following steps: Step S221: Sort service scheduling priorities based on service scheduling weight data to obtain service scheduling priority order data; Step S222: Based on the service scheduling priority order data and channel quality data, perform channel adaptability evaluation to obtain channel adaptability data; Step S223: Sort the channel selection priority according to the channel adaptability data to obtain the channel selection priority data.

[0009] Preferably, step S23 includes the following steps: Step S231: Parse the channel selection priority data for service priority channel selection to obtain service priority channel selection data; Step S232: Perform channel conflict detection based on service priority channel selection data to obtain channel conflict detection data; Step S233: Adjust the dynamic matching strategy based on the channel conflict detection data to obtain dynamic matching strategy data.

[0010] Preferably, step S232 includes the following steps: Step S2321: Perform channel allocation parsing on the service priority channel selection data to obtain channel allocation data; Step S2322: Perform multi-service channel occupancy analysis based on channel allocation data to obtain multi-service channel occupancy data; Step S2323: Based on the multi-service channel occupancy data, assess the severity of the conflict to obtain channel conflict detection data.

[0011] Preferably, step S24 includes the following steps: Step S241: Parse the dynamic matching strategy data for channel resource allocation to obtain channel resource allocation data; Step S242: Perform scheduling queue conflict detection based on channel resource allocation data to obtain scheduling queue conflict data; Step S243: Optimize and adjust the queues based on the scheduling queue conflict data to obtain queue optimization and adjustment data; Step S244: Generate optimized scheduling queue data based on queue optimization adjustment data.

[0012] Preferably, step S3 includes the following steps: Step S31: Perform high-priority service identification on the optimized scheduling queue data to obtain high-priority service identification data; Step S32: Determine the channel handover triggering condition based on the high-priority service identification data to obtain the channel handover triggering condition data; Step S33: Generate channel handover decision data based on channel handover triggering condition data; Step S34: Perform service data transmission control through channel switching decision data to obtain service data transmission control data.

[0013] Preferably, step S32 includes the following steps: Step S321: Perform service channel state parsing on the high-priority service identification data to obtain service channel state data; Step S322: Compare channel quality thresholds based on service channel state data to obtain channel quality comparison data; Step S323: Determine the triggering conditions based on the channel quality comparison data to obtain the channel handover triggering condition data.

[0014] Secondly, this application provides a high-efficiency dual-mode communication system for HPLC, comprising: The data acquisition module is used to acquire service request data and channel status data in the dual-mode network; it classifies service priorities based on service request data to obtain service priority data, and performs channel quality assessment based on channel status data to obtain channel quality data. The optimization analysis module is used to perform dynamic matching analysis between services and channels based on service priority data and channel quality data to obtain dynamic matching strategy data. Based on the dynamic matching strategy data, the scheduling queue is optimized to obtain optimized scheduling queue data. The control module is used to make dual-mode channel switching decisions based on optimized scheduling queue data, generate channel switching decision data, control service data transmission through the channel switching decision data, obtain service data transmission control data, and then realize dual-mode communication based on the service data transmission control data.

[0015] In summary, this application includes at least one of the following beneficial technical effects: 1. This application provides a highly efficient dual-mode communication method for HPLC. Through dynamic matching analysis of service priority data and channel quality data, it achieves accurate adaptation between service requirements and channel status. When a high-priority service experiences a degradation in the current channel quality, the system can quickly switch it to a backup channel with better quality based on the channel switching trigger condition. This effectively avoids service delays caused by channel congestion in traditional static scheduling and significantly improves the transmission success rate and real-time performance of critical services. 2. By calculating service scheduling weights and prioritizing channel selection, a dynamic mapping relationship between services and channels is established. Based on the channel adaptability evaluation results, the system allocates appropriate channel resources to different services, avoiding channel congestion and resource contention. At the same time, through conflict detection and queue optimization mechanisms, resource conflicts during parallel transmission of multiple services are eliminated, achieving efficient utilization of dual-mode channel resources. 3. By constructing a full-process processing mechanism from service priority classification and channel quality assessment to dynamic matching strategy generation, the shortcomings of the traditional method of decoupling service priority and channel selection logic are overcome. It can ensure that high-priority services are scheduled to high-quality channels in the event of network congestion or channel degradation, fundamentally avoiding the priority inversion phenomenon and ensuring the service quality of critical services. 4. Through the synergistic effect of scheduling queue optimization and channel switching decision, efficient data transmission can still be maintained even when there are concurrent service conflicts or sudden changes in channel state. The optimized scheduling queue eliminates service blocking and channel contention, while the intelligent switching mechanism reduces unnecessary channel switching times and transmission interruption time, thereby significantly improving the overall throughput and communication efficiency of the dual-mode network. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for efficient two-mode communication in HPLC according to an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of a dual-mode communication system for high-efficiency HPLC according to an embodiment of this application. Detailed Implementation

[0019] The following is in conjunction with the appendix Figure 1-2 This application will be described in further detail.

[0020] Example 1 This application discloses a highly efficient two-mode communication method for HPLC.

[0021] Reference Figure 1 A highly efficient two-mode communication method for HPLC includes the following steps: Step S1: Obtain service request data and channel state data in the dual-mode network; classify service priorities based on service request data to obtain service priority data, and evaluate channel quality based on channel state data to obtain channel quality data; For example, the service request data includes service type, service priority identifier, and service data volume; the channel status data includes power line carrier channel quality indicators and wireless channel quality indicators. For example, step S1 includes the following steps: Step S11: Obtain service request data and channel state data in the dual-mode network; Step S12: Parse the business request data for business type and generate business type data; Step S13: Identify business priorities based on business type data to obtain business priority data; Step S14: Extract channel quality indicators from the channel state data to obtain channel quality data.

[0022] Specifically, in this embodiment of the invention, service request data and channel status data are first collected in real time through a dual-mode network interface. The service request data includes service type, service priority identifier, and service data volume, while the channel status data includes power line carrier channel quality indicators and wireless channel quality indicators. After acquiring the data, when parsing the service request data for service type, a method based on protocol field extraction and feature classification is used to parse the service type identifier from the service request data packet. The identifier is then mapped to an internal service type code by querying a preset service type mapping table, thereby generating service type data. Next, when identifying service priority based on the service type data, the service category (e.g., real-time service, batch processing service, or event-driven service) in the service type data is combined with a preset... The priority strategy determines priority levels by weighting the ratio of service criticality factors to latency tolerance, and encodes these priority levels as service priority identifiers, ultimately yielding service priority data. Simultaneously, when extracting channel quality indicators from channel state data, a multi-dimensional indicator fusion method is employed. This method separates power line carrier channel quality indicators and wireless channel quality indicators from the channel state data. It calculates the average value, variance, and historical trend slope of raw indicators such as signal-to-noise ratio, bit error rate, and signal strength, and generates a comprehensive channel quality score based on threshold comparison and normalization, ultimately outputting channel quality data. The entire processing link achieves seamless connection between each step through a data flow pipeline, ensuring that service priority data and channel quality data provide accurate input for subsequent dynamic matching analysis.

[0023] For example, step S12 includes the following steps: Step S121: Extract the business type field from the business request data to obtain business type field data; the extraction of the business type field includes parsing the business type identifier from the business request data; Step S122: Perform business type encoding mapping based on business type field data to obtain business type encoding data; the business type encoding mapping includes mapping business type identifiers to internal business type codes according to preset encoding rules; Step S123: Classify business types according to business type coding data to generate business type data; the business type classification includes determining the business type category based on the internal business type coding.

[0024] Specifically, in this embodiment of the invention, the business type field is first extracted from the business request data. By parsing the protocol structure and data format of the business request data packet, the business type identifier is separated from the data stream using a method based on field location and delimiter recognition. Specific operations include extracting the original value of the business type field according to a predefined business request message format, and using string matching or regular expression techniques to remove redundant information, thereby obtaining clean business type field data. Next, business type encoding mapping is performed based on the business type field data. By querying a preset encoding mapping table (which defines the correspondence between external business type identifiers and internal business type codes), a hash lookup or binary matching algorithm is used to extract the business type field data... Identifiers are quickly mapped to unique internal business type codes. This mapping process includes calculating the hash value of the identifier to reduce lookup time, handling potential coding conflicts, and ensuring mapping accuracy through priority weight adjustments, thereby generating business type code data. Finally, business types are classified based on this code data. Using classification rules based on coding ranges (such as dividing codes into continuous intervals corresponding to different business categories), the business type category is determined by comparing the internal business type codes with preset classification thresholds, employing decision trees or conditional branching logic. Simultaneously, business data volume and other contextual information are used for auxiliary verification to ensure the classification results are consistent with actual business needs, ultimately generating structured business type data for subsequent processing. The entire process is seamlessly integrated through a data pipeline, ensuring that business type field extraction, code mapping, and classification operations are completed with low latency, providing accurate input for business priority identification.

[0025] For example, step S13 includes the following steps: Step S131: Perform business type criticality analysis based on business type data to obtain business type criticality data; the business type criticality analysis includes determining the criticality level of the business based on the business type data; Step S132: Perform priority level mapping based on the key data of the business type to obtain priority level mapping data; the priority level mapping includes querying a preset priority mapping table based on the keyness level of the business to generate the corresponding priority level; Step S133: Based on the priority level mapping data, perform business priority identification to obtain business priority data; the business priority identification includes converting the priority level mapping data into a business priority identifier.

[0026] Specifically, in this embodiment of the invention, firstly, business type criticality analysis is performed based on business type data. By analyzing the business categories and their attribute characteristics in the business type data, a multi-factor weighted evaluation method is used to calculate the business criticality level. This includes extracting business feature parameters from the business type data, performing weighted summation using preset weight coefficients, and then dynamically adjusting the data based on the business scenario context (such as network load status or historical performance data) to obtain quantified business type criticality data. Next, priority level mapping is performed based on the business type criticality data. By querying a preset priority mapping table (which defines the correspondence between business criticality levels and priority levels), binary search or hash index algorithms are used to quickly match the level values ​​in the critical data, and boundary cases are handled. At the same time, auxiliary corrections are made considering the business data volume and real-time network status to ensure that the mapping results meet the actual scheduling requirements, thereby generating priority level mapping data. Finally, business priority identification is performed based on the priority level mapping data. By converting the numerical level values ​​in the priority level mapping data into standardized business priority identifiers, an encoding conversion algorithm is used to ensure the uniqueness and readability of the identifiers, and verification and optimization are performed in conjunction with the metadata of the business request to prevent identifier conflicts and improve processing efficiency. Finally, business priority data is generated for subsequent dynamic matching analysis. The entire process, through continuous data transformation and optimization operations, ensures that the criticality analysis of service types, priority level mapping, and service priority identification are completed efficiently and with low latency, providing accurate priority input for dynamic matching of services and channels.

[0027] For example, step S14 includes the following steps: Step S141: Perform channel quality index analysis on the channel state data to obtain channel quality index data; the channel quality index analysis includes extracting power line carrier channel quality index and wireless channel quality index from the channel state data; Step S142: Perform channel index fluctuation analysis based on channel quality index data to obtain channel index fluctuation data; the channel index fluctuation analysis includes calculating the fluctuation range of power line carrier channel quality index and wireless channel quality index; Step S143: Map the channel quality level based on the channel index fluctuation data to obtain the channel quality data; the channel quality level mapping includes querying a preset quality level table based on the channel index fluctuation range to generate the channel quality level.

[0028] Specifically, in this embodiment of the invention, the channel state data is first analyzed for channel quality indicators. By analyzing the protocol structure and data fields of the channel state data packets, a multi-dimensional feature extraction method is used to separate the power line carrier channel quality indicators and the wireless channel quality indicators from the original data stream. Specific operations include identifying and extracting key quality parameters (such as signal-to-noise ratio, bit error rate, signal strength, channel bandwidth utilization, and latency jitter) according to a predefined channel state message format, and removing noise interference through data cleaning and outlier filtering techniques to ensure that the extracted indicator data accurately reflects the actual channel state, thereby obtaining structured channel quality indicator data. Next, channel indicator fluctuation analysis is performed based on the channel quality indicator data, and time series analysis is used to calculate the power line carrier channel quality indicators and the wireless channel quality indicators. The fluctuation range of the indicators specifically involves processing the extracted indicator data using a sliding window method, calculating the standard deviation and range of the indicator values ​​within each window, and using a weighted average algorithm to synthesize the changing trends of multiple indicators to determine the overall fluctuation amplitude. Simultaneously, historical fluctuation data is used for trend correction, thereby generating quantified channel indicator fluctuation data. Finally, channel quality level mapping is performed based on the channel indicator fluctuation data. By querying a preset quality level table (which defines the correspondence between fluctuation range and quality level, e.g., low fluctuation corresponds to high level, and high fluctuation corresponds to low level), an interval matching algorithm is used to map the values ​​in the fluctuation data to the preset level. Real-time adjustments are made considering dynamic factors of the network environment to ensure that the mapping results meet actual communication requirements. The final channel quality data is used for subsequent service and channel dynamic matching analysis. The entire process, through continuous data parsing, fluctuation analysis, and level mapping operations, ensures that channel state data is efficiently converted into reliable quality input, providing fundamental support for dual-mode communication scheduling.

[0029] Step S2: Perform dynamic matching analysis between services and channels based on service priority data and channel quality data to obtain dynamic matching strategy data. Optimize the scheduling queue based on the dynamic matching strategy data to obtain optimized scheduling queue data. For example, step S2 includes the following steps: Step S21: Calculate service scheduling weights based on service priority data and channel quality data to obtain service scheduling weight data; Step S22: Sort the channel selection priority based on the service scheduling weight data and the channel quality data to obtain the channel selection priority data; Step S23: Generate a dynamic matching strategy based on the channel selection priority data to obtain dynamic matching strategy data; Step S24: Optimize the scheduling queue based on the dynamic matching strategy data to obtain optimized scheduling queue data.

[0030] For example, step S21 includes the following steps: Step S211: Parse the business priority data for business urgency to obtain business urgency data; the business urgency parsing includes determining the business urgency level based on the priority identifier in the business priority data; Step S212: Perform a comprehensive calculation of weighting factors based on service urgency data and channel quality data to obtain weighting factor data; the comprehensive calculation of weighting factors includes calculating a comprehensive weight value based on the service urgency level and the channel quality level; Step S213: Calculate the service scheduling weight based on the weight factor data to obtain the service scheduling weight data; the service scheduling weight calculation includes sorting the scheduling priority of services and allocating weights based on the weight factor data.

[0031] Specifically, in this embodiment of the invention, the service priority data is first parsed to determine its urgency. By parsing the priority identifiers in the service priority data, a method based on a preset urgency mapping table is used to map the priority identifiers to service urgency levels. This includes querying the mapping table and determining the urgency level based on the identifier's numerical range or category (e.g., high, medium, low priority). Simultaneously, dynamic adjustments are made based on historical service data and real-time network conditions. The correlation between service criticality factors and priority identifiers is calculated using weighted calculations to obtain quantified service urgency data. Next, a comprehensive weighting factor calculation is performed based on the service urgency data and channel quality data. A weighted average algorithm is used to combine the urgency level and channel quality level to calculate a comprehensive weight value. The weighting coefficients are preset according to the service type and network conditions, for example, high... Urgent services are assigned higher weight coefficients. When channel quality is low, these weight coefficients are adjusted to balance performance requirements. Specifically, this involves calculating the product of the urgency level and the channel quality level, multiplying it by a preset scaling factor, and incorporating the service data volume as an auxiliary parameter for normalization, thus generating weight factor data. Finally, service scheduling weights are calculated based on this weight factor data. Sorting algorithms such as quicksort or priority queues are used to sort services in descending order according to their weight factors, and scheduling weights are assigned based on this order. For example, services with higher weight values ​​receive higher scheduling priority. Simultaneously, optimizations are made considering service data volume and system processing capacity. Weight values ​​are dynamically allocated by calculating the ratio of the weight factor to the service data volume, ensuring that high-priority services are still scheduled first even when resources are limited. The final service scheduling weight data is used for subsequent channel selection priority ranking. This entire process, through continuous data parsing, weight calculation, and sorting operations, ensures that service urgency analysis, weight factor comprehensive calculation, and service scheduling weight calculation are completed efficiently and with low latency, providing accurate scheduling input for dynamic matching of services and channels.

[0032] For example, step S22 includes the following steps: Step S221: Sort service scheduling priorities based on service scheduling weight data to obtain service scheduling priority order data; the service scheduling priority sorting includes arranging services in priority order according to the weight values ​​in the service scheduling weight data; Step S222: Perform channel adaptability evaluation based on service scheduling priority order data and channel quality data to obtain channel adaptability data; the channel adaptability evaluation includes calculating the degree of adaptability between the channel and the service based on service scheduling priority order data and channel quality data; Step S223: Sort the channel selection priority according to the channel adaptability data to obtain channel selection priority data; the channel selection priority sorting includes arranging the channels in priority order based on the channel adaptability data.

[0033] Specifically, in this embodiment of the invention, firstly, service scheduling priority is sorted based on service scheduling weight data. Using a quicksort algorithm or priority queue method, services are arranged in descending order according to their weight values ​​in the service scheduling weight data, with services having higher weight values ​​receiving higher scheduling priority. Simultaneously, the order is dynamically adjusted based on the amount of service data and the real-time system load. The ratio of weight value to service data volume is calculated to optimize the sorting order, ensuring that high-weight services are still processed first even when resources are limited, thus obtaining the service scheduling priority order data. Next, channel adaptability is evaluated based on the service scheduling priority order data and channel quality data. The matching degree between the service scheduling priority order and channel quality indicators is calculated, and a weighted scoring method is used to combine service priority scores and channel quality indicators. The process involves quantifying channel suitability (e.g., matching high-priority services to high-quality channels). Specifically, this includes calculating the product of the service priority score and the channel quality score, multiplying it by a preset adaptation coefficient, and then correcting for service type characteristics. Normalization eliminates dimensional differences, generating quantified channel suitability data. Finally, channel selection priorities are ranked based on the suitability data. Ranking algorithms such as bubble sort or selection sort are used to arrange channels in ascending or descending order of suitability values, prioritizing channels with higher suitability. Historical channel performance data and network topology are also considered for optimization. The ranking results are dynamically corrected by calculating a weighted sum of suitability values ​​and channel load factors to ensure the ranking meets actual communication needs. This ultimately generates channel selection priority data for subsequent dynamic matching strategy generation. The entire process, through continuous data sorting, suitability evaluation, and priority ranking operations, ensures that service scheduling weight data is efficiently converted into channel selection input, providing fundamental support for dynamic matching in dual-mode communication.

[0034] For example, step S23 includes the following steps: Step S231: Perform service-priority channel selection parsing on the channel selection priority data to obtain service-priority channel selection data; the service-priority channel selection parsing includes allocating the highest priority channel to each service according to the channel selection priority data; Step S232: Perform channel conflict detection based on service priority channel selection data to obtain channel conflict detection data; the channel conflict detection includes identifying the conflict situation when multiple services are allocated to the same channel; Step S233: Adjust the dynamic matching strategy based on the channel conflict detection data to obtain dynamic matching strategy data; the dynamic matching strategy adjustment includes reallocating service channels based on the channel conflict detection data to eliminate conflicts.

[0035] Specifically, in this embodiment of the invention, the channel selection priority data is first parsed for service-priority channel selection. By parsing the channel priority ranking list in the channel selection priority data, the highest priority channel is assigned to each service using a highest priority selection algorithm. The specific operations include traversing the channel selection priority data of each service, sorting it in descending order according to the channel priority score (e.g., a comprehensive score based on channel quality, latency, and bandwidth), and selecting the highest-ranked channel as the priority channel for that service. Simultaneously, the channel identifier and service association are recorded, thus obtaining the service-priority channel selection data. Next, channel conflict detection is performed based on the service-priority channel selection data. By constructing a channel-service allocation mapping table, a hash-based conflict detection algorithm is used to identify conflicts arising when multiple services are allocated to the same channel. Specifically, this includes counting the number of services allocated to each channel, and when the same channel is used by multiple services... Each service occupancy is marked as a conflict, and the severity of the conflict is calculated based on the difference in service priority and the channel load factor. The conflict level is quantified by weighted summation of the reciprocal of the conflict service priority and the channel load over-limit ratio, thus generating channel conflict detection data. Finally, dynamic matching strategy adjustment is performed based on the channel conflict detection data. This involves reallocating conflicting services to second-highest priority channels and using an iterative adjustment algorithm based on priority inheritance and load balancing to eliminate conflicts. Specifically, affected services are sorted according to conflict severity, prioritizing the preservation of the original channel allocation for high-priority services, reallocating low-priority services to the second-highest priority available channel in their channel selection priority data, and incorporating historical channel stability data for decision support. The adjustment effect is verified by calculating the channel load balancing degree and service satisfaction after reallocation. Finally, dynamic matching strategy data is generated for subsequent scheduling queue optimization. The entire process, through continuous channel selection parsing, conflict detection, and strategy adjustment operations, ensures that channel selection priority data is efficiently converted into a conflict-free dynamic matching strategy, providing a reliable foundation for dual-mode communication scheduling.

[0036] For example, step S232 includes the following steps: Step S2321: Perform channel allocation parsing on the service priority channel selection data to obtain channel allocation data; the channel allocation parsing includes extracting the channel identifiers allocated to each service from the service priority channel selection data; Step S2322: Perform multi-service channel occupancy analysis based on channel allocation data to obtain multi-service channel occupancy data; the multi-service channel occupancy analysis includes identifying the situation where the same channel is occupied by multiple services based on the channel allocation data; Step S2323: Perform a collision severity assessment based on the multi-service channel occupancy data to obtain channel collision detection data; the collision severity assessment includes calculating the severity of channel collisions based on the multi-service channel occupancy data.

[0037] Specifically, in this embodiment of the invention, the service priority channel selection data is first parsed for channel allocation. By parsing the service-channel mapping relationship in the service priority channel selection data, a key-value pair extraction method is used to separate the channel identifier allocated to each service from the data. Specific operations include traversing the service records in the service priority channel selection data, extracting the channel identifier according to a predefined field structure, removing invalid or duplicate identifiers using data cleaning techniques, and verifying the completeness of the parsing by calculating the matching degree between the number of services and the channel identifiers, thereby obtaining structured channel allocation data. Next, multi-service channel occupancy analysis is performed based on the channel allocation data. By constructing a channel usage frequency statistics table, a hash counting algorithm is used to identify the situation where the same channel is occupied by multiple services. Specifically, this includes counting the number of times each channel identifier appears in the channel allocation data. When a channel is allocated to multiple services, it is marked as a conflicting channel. Channel occupancy is calculated based on the ratio of the number of services to the channel's maximum capacity. The impact of occupancy is quantified by weighted averaging of the service priority and data type of the conflicting channel, thus generating multi-service channel occupancy data. Finally, conflict severity is assessed based on this multi-service channel occupancy data. A multi-factor weighted scoring method is used to calculate the severity of channel conflicts by comprehensively considering the occupancy rate of the conflicting channel, the difference in service priority, and channel quality indicators. Specifically, this involves calculating the product of the conflicting channel's occupancy rate and the reciprocal of the service priority, multiplying this by a channel quality degradation coefficient, and then normalizing the score to map it to a preset severity level. Simultaneously, dynamic adjustments are made considering real-time network load conditions to ensure that the assessment results accurately reflect the impact of conflicts on system performance. Finally, channel conflict detection data is generated for subsequent dynamic matching strategy adjustments. The entire process, through continuous data parsing, occupancy analysis, and conflict assessment operations, ensures that service priority channel selection data is efficiently converted into quantified conflict detection results, providing crucial input for channel resource optimization.

[0038] For example, step S24 includes the following steps: Step S241: Perform channel resource allocation parsing on the dynamic matching strategy data to obtain channel resource allocation data; the channel resource allocation parsing includes determining the allocation scheme of each service in the dual-mode channel based on the dynamic matching strategy data; Step S242: Perform scheduling queue conflict detection based on channel resource allocation data to obtain scheduling queue conflict data; the scheduling queue conflict detection includes identifying service scheduling conflicts existing in the channel resource allocation data; Step S243: Perform queue optimization and adjustment based on the scheduling queue conflict data to obtain queue optimization and adjustment data; the queue optimization and adjustment includes rearranging the service scheduling order based on the scheduling queue conflict data; Step S244: Generate optimized scheduling queue data based on queue optimization adjustment data; the optimized scheduling queue data includes the business scheduling queue after eliminating conflicts.

[0039] Specifically, in this embodiment of the invention, the channel resource allocation is first analyzed on the dynamic matching strategy data. By analyzing the service-channel mapping relationship in the dynamic matching strategy data, a priority-based resource allocation algorithm is used to determine the allocation scheme of each service in the dual-mode channel. The specific operations include traversing the service records in the dynamic matching strategy data, constructing a weighted scoring model based on service priority scores and channel quality indicators, and determining the optimal channel allocation scheme by calculating the product of service priority and channel quality and multiplying it by a preset weight coefficient. Simultaneously, dynamic adjustments are made considering the size of service data and the real-time channel load. A sliding window mechanism is used to optimize the allocation scheme in real time, thereby obtaining the channel resource allocation data. Next, scheduling queue conflict detection is performed based on the channel resource allocation data. By constructing a time-resource two-dimensional conflict matrix, an algorithm based on time window overlap detection is used to identify service scheduling conflicts in the channel resource allocation data. Specifically, this includes counting the number of services allocated on each channel within each time slice. When the same channel is occupied by multiple services within the same time slice, it is marked as a conflict. The conflict frequency is quantified by calculating the ratio of the number of conflicting time slices to the total number of time slices. Simultaneously, the conflict severity is constructed by combining service priority differences and the degree of channel load exceeding limits. The evaluation model uses a multi-dimensional weighted scoring method to calculate the impact of each conflict, thereby generating scheduling queue conflict data. Then, based on this data, queue optimization is performed by rearranging the service scheduling order to eliminate conflicts. An iterative optimization algorithm based on priority inheritance and load balancing is employed. Specifically, affected services are sorted in descending order according to conflict severity, prioritizing the preservation of the original scheduling positions of high-priority services and adjusting low-priority services to their alternative time windows or channel resources. The optimization effect is verified by calculating the adjusted queue load balancing and service satisfaction. A service dependency graph and channel switching cost model are introduced to assist decision-making, ensuring that the adjustment scheme minimizes system performance loss while eliminating conflicts, thus obtaining queue optimization adjustment data. Finally, optimized scheduling queue data is generated based on this data. By integrating all adjusted service scheduling information, a time-slice round-robin-based queue construction method is used to rearrange services according to the optimized time order and channel allocation, while adding necessary scheduling control information and timing constraints. The queue quality is verified by calculating the overall queue throughput and latency performance indicators, ultimately generating conflict-free service scheduling queue data for subsequent dual-mode channel switching decisions. The entire process, through continuous resource allocation resolution, conflict detection, queue optimization, and queue generation operations, ensures that dynamic matching strategy data is efficiently converted into conflict-free optimized scheduling queues, providing a reliable scheduling foundation for dual-mode communication.

[0040] Step S3: Make dual-mode channel switching decisions based on the optimized scheduling queue data, generate channel switching decision data, control service data transmission through the channel switching decision data, obtain service data transmission control data, and then realize dual-mode communication based on the service data transmission control data.

[0041] For example, step S3 includes the following steps: Step S31: Perform high-priority service identification on the optimized scheduling queue data to obtain high-priority service identification data; the high-priority service identification includes extracting service identifiers with high priority from the optimized scheduling queue data; Step S32: Determine the channel handover triggering condition based on the high-priority service identification data to obtain the channel handover triggering condition data; the channel handover triggering condition determination includes determining whether the channel handover condition is met based on the high-priority service identification data. Step S33: Generate channel handover decision data based on channel handover triggering condition data; the channel handover decision generation includes determining a specific channel handover scheme based on the channel handover triggering condition data. Step S34: Perform service data transmission control through channel switching decision data to obtain service data transmission control data; the service data transmission control includes performing actual channel switching operations based on the channel switching decision data.

[0042] For example, step S31 includes the following steps: Step S311: Extract the service priority field from the optimized scheduling queue data to obtain service priority field data; the extraction of the service priority field includes parsing the priority identifier field of each service from the optimized scheduling queue data; Step S312: Parse the priority level based on the business priority field data to obtain priority level data; the priority level parsing includes determining the priority level corresponding to each business based on the business priority field data; Step S313: Identify high-priority services based on priority level data to obtain high-priority service identification data; the high-priority service identification includes filtering out high-priority services that are higher than a preset threshold based on priority level data.

[0043] Specifically, in this embodiment of the invention, the business priority field is first extracted from the optimized scheduling queue data. By parsing the business record structure in the optimized scheduling queue data, a method based on field separators and pattern matching is used to separate the priority identifier field of each business from the data stream. Specific operations include locating the priority identifier according to a predefined queue data format, removing format errors or outliers using data cleaning techniques, and verifying data quality by calculating the field extraction completeness rate, thereby obtaining the business priority field data. Next, priority level parsing is performed based on the business priority field data. This is done by querying a pre-defined priority level mapping table (which defines the correspondence between priority identifiers and numerical level values) using binary search or... The hash index algorithm quickly maps identifiers in the service priority field data to specific priority levels, while dynamically correcting for service type characteristics. Mapping accuracy is calculated to ensure parsing reliability, thus generating priority level data. Finally, high-priority services are identified based on this data. By setting priority level thresholds (e.g., services with levels greater than or equal to a preset threshold are considered high priority), a threshold-based filtering algorithm quickly identifies high-priority services from the priority level data. Service data volume and real-time network status are used for auxiliary verification, and the identification effect is evaluated by calculating the proportion of high-priority services. Ultimately, high-priority service identification data is generated for subsequent channel handover trigger condition determination. This entire process, through continuous field extraction, level parsing, and service identification operations, ensures that optimized scheduling queue data is efficiently converted into high-priority service information, providing crucial input for dual-mode channel handover decisions.

[0044] For example, step S32 includes the following steps: Step S321: Perform service channel state parsing on the high-priority service identification data to obtain service channel state data; the service channel state parsing includes extracting the current channel state information corresponding to each high-priority service from the high-priority service identification data; Step S322: Perform channel quality threshold comparison based on service channel status data to obtain channel quality comparison data; the channel quality threshold comparison includes comparing the channel quality indicators in the service channel status data with preset thresholds; Step S323: Determine the triggering conditions based on the channel quality comparison data to obtain channel handover triggering condition data; the triggering condition determination includes determining whether the channel handover triggering conditions are met based on the channel quality comparison data.

[0045] Specifically, in this embodiment of the invention, firstly, the high-priority service identification data is parsed for service channel state. By parsing the service identifiers and associated channel indexes in the high-priority service identification data, a method based on database query and real-time status monitoring is used to extract the current channel state information corresponding to each high-priority service from the system channel state database. This includes key quality indicators such as signal-to-noise ratio, bit error rate, signal strength, and latency jitter. Data integrity verification and outlier filtering techniques are used to ensure the accuracy of the extracted information, thereby obtaining service channel state data. Next, channel quality threshold comparison is performed based on the service channel state data. By comparing the channel quality indicators in the service channel state data with preset thresholds, a multi-dimensional weighted scoring algorithm is used. The process calculates the overall quality score for each channel and uses difference analysis to determine the deviation of the indicators from the thresholds. Simultaneously, it dynamically adjusts the thresholds based on historical quality data to adapt to network changes, thereby generating channel quality comparison data. Finally, it determines the trigger conditions based on the channel quality comparison data. By analyzing the deviation results and service priority characteristics in the channel quality comparison data, a rule-based engine-based decision logic determines whether the channel handover trigger conditions are met. For example, handover is triggered when the overall quality score is below the threshold and a usable backup channel exists, or the handover process is immediately initiated when multiple indicators simultaneously exceed limits. The trigger timing is optimized by calculating handover priority and risk assessment factors, resulting in channel handover trigger condition data used for subsequent channel handover decision generation. The entire process, through continuous state parsing, threshold comparison, and condition judgment operations, ensures that high-priority service identification data is efficiently converted into reliable handover trigger input, providing crucial decision-making basis for dynamic dual-mode channel handover.

[0046] For example, step S33 includes the following steps: Step S331: Perform handover requirement parsing on the channel handover triggering condition data to obtain handover requirement data; the handover requirement parsing includes extracting the service identifier to be switched and the corresponding handover triggering condition from the channel handover triggering condition data; Step S332: Perform available channel status assessment based on handover requirement data to obtain available channel status assessment data; the available channel status assessment includes assessing the quality status of each available channel based on the service identifier and handover triggering conditions in the handover requirement data; Step S333: Generate a handover decision based on the available channel state assessment data to obtain channel handover decision data; the handover decision generation includes selecting the optimal channel for the service that needs to be switched based on the available channel state assessment data.

[0047] Specifically, in this embodiment of the invention, the channel handover triggering condition data is first parsed to determine handover requirements. By parsing the trigger event records and service association information in the data, a method based on event classification and service identifier extraction is used to separate the service identifiers to be switched and the corresponding handover triggering conditions from the data. Specific operations include traversing the event entries in the triggering condition data, identifying the services to be switched according to predefined trigger types, removing duplicate or invalid records using data filtering techniques, and verifying the parsing accuracy by calculating the service identifier extraction completeness rate, thereby obtaining the handover requirement data. Next, the available channel status is assessed based on the handover requirement data. By querying the system channel status database and real-time monitoring information, the quality status of each available channel is assessed according to the service identifiers and handover triggering conditions in the handover requirement data, using a multi-pointer method. A comprehensive scoring method (such as weighted calculation of channel signal-to-noise ratio, bandwidth utilization, latency stability, and historical reliability scores) is used to determine channel suitability by comparing the matching degree between service requirements and channel characteristics. Network topology and load balancing factors are also incorporated for dynamic adjustment, generating available channel status assessment data. Finally, handover decisions are generated based on this data. By analyzing channel quality scores and service priorities in the assessment data, an optimal matching algorithm-based decision logic selects the optimal channel for the service requiring handover. This includes calculating the comprehensive score of each available channel and selecting the channel with the highest score as the handover target, while also considering handover costs for optimization. The effectiveness of the handover is ensured by verifying the channel load and expected service performance after the decision. Ultimately, channel handover decision data is generated for subsequent service data transmission control. This entire process, through continuous requirements analysis, status assessment, and decision generation operations, ensures that channel handover triggering condition data is efficiently converted into a reliable handover strategy, providing crucial support for dynamic channel management in dual-mode communication.

[0048] For example, step S34 includes the following steps: Step S341: Perform handover parameter parsing on the channel handover decision data to obtain handover parameter data; the handover parameter parsing includes extracting the target channel identifier and handover timing parameters from the channel handover decision data; Step S342: Perform channel handover execution control based on handover parameter data to obtain handover execution data; the channel handover execution control includes performing specific channel handover operations according to the handover parameter data; Step S343: Monitor the service transmission status based on the switching execution data to obtain service transmission status data; the service transmission status monitoring includes monitoring the service data transmission status after the channel switching. Step S344: Generate service data transmission control data based on service transmission status data; the service data transmission control data includes service transmission control parameters after channel switching is completed.

[0049] Specifically, in this embodiment of the invention, the channel handover decision data is first parsed using handover parameters. By parsing the channel identifier field and timing control information in the channel handover decision data, a method based on field matching and timing extraction is used to separate the target channel identifier and handover timing parameters from the data. Specific operations include traversing the handover records in the decision data, extracting key parameters according to a predefined data structure, and using data verification technology to ensure the integrity and accuracy of the parameters. The parsing quality is verified by calculating the parameter extraction success rate, thereby obtaining the handover parameter data. Next, channel handover execution control is performed based on the handover parameter data. By converting the target channel identifier and timing parameters in the handover parameter data into actual operation instructions, a control algorithm based on state machine and timing synchronization is used to execute specific channel handover operations, including calculating the handover time point and resource reallocation. A real-time feedback mechanism is used to monitor the handover execution status, thereby generating a handover execution command. The process involves several steps: first, monitoring the handover execution data; then, monitoring the service transmission status by tracking the data transmission metrics after the handover; and finally, identifying transmission anomalies using a sliding window and threshold alarm-based monitoring method. This includes calculating the deviation between the current transmission metrics and historical benchmarks, dynamically adjusting the monitoring frequency based on service priorities, and quantifying the transmission status by weighted evaluation of multiple metrics (such as latency, packet loss, and jitter). Based on this service transmission status data, service data transmission control data is generated. This involves analyzing the transmission performance metrics and service requirements in the status data, and using an adaptive control algorithm-based parameter adjustment method to generate transmission control parameters (such as adjusting data transmission rate, retransmission mechanisms, or error correction strategies). Specifically, this includes calculating the optimal values ​​of the transmission control parameters and verifying the improved service performance after parameter adjustment. The final result is the generated service data transmission control data used for optimized dual-mode communication transmission. This entire process, through continuous parameter parsing, handover control, status monitoring, and control parameter generation, ensures that channel handover decision data is efficiently converted into reliable transmission control strategies, providing accurate assurance for service data transmission in dual-mode communication.

[0050] Example 2 This application also discloses a high-efficiency dual-mode communication system for HPLC.

[0051] Reference Figure 2 A high-efficiency dual-mode communication system for HPLC, comprising: The data acquisition module is used to acquire service request data and channel status data in the dual-mode network; it classifies service priorities based on service request data to obtain service priority data, and performs channel quality assessment based on channel status data to obtain channel quality data. The optimization analysis module is used to perform dynamic matching analysis between services and channels based on service priority data and channel quality data to obtain dynamic matching strategy data. Based on the dynamic matching strategy data, the scheduling queue is optimized to obtain optimized scheduling queue data. The control module is used to make dual-mode channel switching decisions based on optimized scheduling queue data, generate channel switching decision data, control service data transmission through the channel switching decision data, obtain service data transmission control data, and then realize dual-mode communication based on the service data transmission control data.

[0052] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention, they should all fall within the protection scope of the present invention.

[0053] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0054] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A highly efficient dual-mode communication method for HPLC, characterized in that, Includes the following steps: Step S1: Obtain service request data and channel state data in the dual-mode network; Service priority data is obtained by classifying service request data into service priority categories, and channel quality data is obtained by evaluating channel quality based on channel state data. Step S2: Perform dynamic matching analysis between services and channels based on service priority data and channel quality data to obtain dynamic matching strategy data. Optimize the scheduling queue based on the dynamic matching strategy data to obtain optimized scheduling queue data. Step S3: Make dual-mode channel switching decisions based on the optimized scheduling queue data, generate channel switching decision data, control service data transmission through the channel switching decision data, obtain service data transmission control data, and then realize dual-mode communication based on the service data transmission control data.

2. The efficient dual-mode communication method for HPLC according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain service request data and channel state data in the dual-mode network; Step S12: Parse the business request data for business type and generate business type data; Step S13: Identify business priorities based on business type data to obtain business priority data; Step S14: Extract channel quality indicators from the channel state data to obtain channel quality data.

3. The efficient dual-mode communication method for HPLC according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Calculate service scheduling weights based on service priority data and channel quality data to obtain service scheduling weight data; Step S22: Sort the channel selection priority based on the service scheduling weight data and channel quality data to obtain the channel selection priority data; Step S23: Generate a dynamic matching strategy based on the channel selection priority data to obtain dynamic matching strategy data; Step S24: Optimize the scheduling queue based on the dynamic matching strategy data to obtain optimized scheduling queue data.

4. The efficient dual-mode communication method for HPLC according to claim 3, characterized in that, Step S22 includes the following steps: Step S221: Sort service scheduling priorities based on service scheduling weight data to obtain service scheduling priority order data; Step S222: Based on the service scheduling priority order data and channel quality data, perform channel adaptability evaluation to obtain channel adaptability data; Step S223: Sort the channel selection priority according to the channel adaptability data to obtain the channel selection priority data.

5. The efficient dual-mode communication method for HPLC according to claim 3, characterized in that, Step S23 includes the following steps: Step S231: Parse the channel selection priority data for service priority channel selection to obtain service priority channel selection data; Step S232: Perform channel conflict detection based on service priority channel selection data to obtain channel conflict detection data; Step S233: Adjust the dynamic matching strategy based on the channel conflict detection data to obtain dynamic matching strategy data.

6. The efficient dual-mode communication method for HPLC according to claim 5, characterized in that, Step S232 includes the following steps: Step S2321: Perform channel allocation parsing on the service priority channel selection data to obtain channel allocation data; Step S2322: Perform multi-service channel occupancy analysis based on channel allocation data to obtain multi-service channel occupancy data; Step S2323: Based on the multi-service channel occupancy data, assess the severity of the conflict to obtain channel conflict detection data.

7. The efficient dual-mode communication method for HPLC according to claim 3, characterized in that, Step S24 includes the following steps: Step S241: Parse the dynamic matching strategy data for channel resource allocation to obtain channel resource allocation data; Step S242: Perform scheduling queue conflict detection based on channel resource allocation data to obtain scheduling queue conflict data; Step S243: Optimize and adjust the queues based on the scheduling queue conflict data to obtain queue optimization and adjustment data; Step S244: Generate optimized scheduling queue data based on queue optimization adjustment data.

8. The efficient dual-mode communication method for HPLC according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Perform high-priority service identification on the optimized scheduling queue data to obtain high-priority service identification data; Step S32: Determine the channel handover triggering condition based on the high-priority service identification data to obtain the channel handover triggering condition data; Step S33: Generate channel handover decision data based on channel handover triggering condition data; Step S34: Perform service data transmission control through channel switching decision data to obtain service data transmission control data.

9. The efficient dual-mode communication method for HPLC according to claim 8, characterized in that, Step S32 includes the following steps: Step S321: Perform service channel state parsing on the high-priority service identification data to obtain service channel state data; Step S322: Compare channel quality thresholds based on service channel state data to obtain channel quality comparison data; Step S323: Determine the triggering conditions based on the channel quality comparison data to obtain the channel handover triggering condition data.

10. A high-efficiency dual-mode communication system for HPLC, applied to the high-efficiency dual-mode communication method for HPLC as described in any one of claims 1-9, characterized in that, include: The data acquisition module is used to acquire service request data and channel status data in the dual-mode network; Service priority data is obtained by classifying service request data into service priority categories, and channel quality data is obtained by evaluating channel quality based on channel state data. The optimization analysis module is used to perform dynamic matching analysis between services and channels based on service priority data and channel quality data to obtain dynamic matching strategy data. Based on the dynamic matching strategy data, the scheduling queue is optimized to obtain optimized scheduling queue data. The control module is used to make dual-mode channel switching decisions based on optimized scheduling queue data, generate channel switching decision data, control service data transmission through the channel switching decision data, obtain service data transmission control data, and then realize dual-mode communication based on the service data transmission control data.

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