Frame sequence intelligent queuing processing system and method based on dl / t698 protocol

Abstract

The application discloses a frame sequence intelligent queuing processing system and method based on a DL / T698 protocol, and the system comprises a DL / T698 frame information acquisition module, a dynamic frame sequence parameter analysis module, a quantum annealing disaggregation operation module, a non-cooperative game scheduling decision module, a dynamic self-adaptive ant colony optimization sorting module and a frame sequence output execution module, and each module cooperatively works. The method acquires frame data conforming to the DL / T698 protocol in a communication link, analyzes parameters such as frame length and priority, separates redundant information and effective information through disaggregation processing, generates a scheduling strategy in combination with a scheduling model, dynamically queues frame data through an optimized sorting algorithm, and finally controls frame data sending according to a sorting result. The system and method can accurately process frame data, dynamically adjust frame sequences, adapt to complex communication environments, guarantee stable and efficient communication of a power utilization information acquisition system, and meet power dispatching, user power utilization monitoring and system operation and maintenance requirements.

Description

Technical Field

[0001] This invention relates to the field of electricity user information collection technology, and in particular to a frame order intelligent queuing processing system and method based on the DL / T698 protocol. Background Technology

[0002] With the accelerated digitalization of the power industry and the continuous expansion of the user base, the frequency and volume of electricity data collection have increased significantly during the collection of electricity consumption information. This places higher demands on the communication transmission efficiency and stability of the electricity consumption information collection system. As a key component supporting system communication, frame order queuing processing needs to cope with the link pressure brought by massive data transmission, ensuring the accurate and timely transmission of various types of electricity consumption information to meet the needs of power dispatching, user electricity consumption monitoring, and system operation and maintenance. Currently, traditional frame order processing methods are no longer suitable for dynamic transmission scenarios with surging data volumes, and cannot effectively balance transmission efficiency and data reliability. A more efficient processing solution is urgently needed to address the bottlenecks in the existing transmission process.

[0003] Existing technologies have significant drawbacks in processing electricity consumption information collected from power users under the DL / T698 protocol. On the one hand, existing technologies lack an effective mechanism for processing the inherent information within frame data, failing to accurately distinguish between key information and redundant content. Furthermore, they struggle to dynamically adjust frame order based on actual transmission conditions, leading to data accumulation and congestion during transmission and reduced overall transmission efficiency. On the other hand, existing scheduling strategies fail to adequately consider the mutual influence of multiple frames during transmission, neglecting to construct reasonable scheduling logic that combines the characteristics of frame data with transmission requirements. This results in an inability to provide targeted transmission arrangements for different types of frame data, making it difficult to adapt to complex and ever-changing communication environments. Consequently, the reliability of frame data transmission is insufficient, failing to meet the high-quality requirements of electricity consumption information collection systems for frame order queuing processing. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of the existing technology, the present invention provides a frame order intelligent queuing processing system and method based on the DL / T698 protocol.

[0005] The frame order intelligent queuing processing system based on the DL / T698 protocol includes: a DL / T698 frame information acquisition module, a dynamic frame order parameter parsing module, a quantum annealing de-aggregation operation module, a non-cooperative game scheduling decision module, a dynamic adaptive ant colony optimization sorting module, and a frame order output execution module. The DL / T698 frame information acquisition module is connected to the dynamic frame order parameter parsing module. The dynamic frame order parameter parsing module is connected to both the quantum annealing de-aggregation operation module and the non-cooperative game scheduling decision module. Both the quantum annealing de-aggregation operation module and the non-cooperative game scheduling decision module are connected to the dynamic adaptive ant colony optimization sorting module. The dynamic adaptive ant colony optimization sorting module is connected to the frame order output execution module.

[0006] The DL / T698 frame information acquisition module extracts frame data conforming to the DL / T698 protocol from the communication link of the power user electricity consumption information acquisition system. The dynamic frame order parameter parsing module parses the frame length, frame priority, frame type, frame transmission timestamp, frame target address, and frame source address parameters contained in the extracted frame data. The quantum annealing de-aggregation operation module performs de-aggregation processing on the parsed frame parameters according to the quantum annealing de-aggregation optimization algorithm to separate redundant information from valid information in the frame parameters. The non-cooperative game scheduling decision module constructs a non-cooperative game scheduling model based on the parsed frame parameters and calculates the scheduling strategy. The dynamic adaptive ant colony optimization sorting module combines the valid information output by the quantum annealing de-aggregation operation module and the scheduling strategy output by the non-cooperative game scheduling decision module, and performs dynamic frame order queuing calculation on the frame data through the dynamic adaptive ant colony optimization algorithm. The frame order output execution module controls the transmission order of frame data in the communication link according to the frame order result output by the dynamic adaptive ant colony optimization sorting module.

[0007] Furthermore, the dynamic adaptive ant colony optimization algorithm used in the dynamic adaptive ant colony optimization sorting module satisfies the formula: ,in, This represents the pheromone concentration from frame i to frame j at time t. Indicates the pheromone evaporation coefficient. This represents the set of ant colonies that have completed frame sorting at time t. This represents the pheromone increment left by the k-th ant on the path from frame i to frame j; it also satisfies the formula: ,in, Let represent the probability that the k-th ant selects frame j from frame i as the next sorting object at time t. Factors indicating the importance of pheromones This represents the importance factor of the heuristic function. This represents the heuristic function value from frame i to frame j at time t. This represents the set of next sorted frames that frame i can select.

[0008] Furthermore, the non-cooperative game scheduling model constructed by the non-cooperative game scheduling decision module satisfies the following formula: ,in, Indicates frame i in the policy Other frame strategies The profit function under the following conditions Let i represent the set of feasible strategies for frame i. and These represent the frame delay weight coefficient and the frame priority weight coefficient, respectively. Indicates frame i in the policy Other frame strategies The transmission delay below, Indicates frame i in the policy Other frame strategies The priority benefits are as follows; and the formula is also satisfied: ,in, This represents the Nash equalization strategy for frame i. This represents the set of Nash equalization strategies for other frames.

[0009] Furthermore, the quantum annealing depolymerization optimization algorithm used in the quantum annealing depolymerization operation module satisfies the formula: ,in, Indicates the annealing parameters The changing Hamiltonian. Indicates the annealing progress parameter and This represents the initial Hamiltonian. Represents the target Hamiltonian. This indicates the frame parameter de-aggregation state; and simultaneously satisfies the formula: ,in, Indicates the annealing parameters The next frame parameters are in a de-aggregation state. The probability, Indicates the annealing parameters The changing inverse temperature parameter, This represents the set of all possible frame parameter solution aggregate states.

[0010] Furthermore, the DL / T698 frame information acquisition module and the DL / T698 spatiotemporal big data analysis platform work together to satisfy the formula: ,in, This indicates the total amount of frame data collected. Indicates the start time of data collection. Indicates the end time of data collection. This represents the set of communication links monitored by the DL / T698 spatiotemporal big data analysis platform. This represents the frame transmission rate of link m at time t. This represents the frame acquisition enable signal for link m at time t; the parameters resolved by the dynamic frame sequence parameter parsing module satisfy the formula: ,in, This represents the set of parsed parameters for the i-th frame. Indicates the length of the i-th frame. This indicates the priority of the i-th frame. Indicates the type of the i-th frame. This represents the timestamp of the transmission of the i-th frame. This represents the target address of the i-th frame. This represents the source address of the i-th frame.

[0011] Furthermore, the frame order output execution module controls the frame transmission order to satisfy the formula: ,in, Indicates the final output frame order. This indicates the initial frame order output by the dynamic adaptive ant colony optimization sorting module. This represents the set of frame transmission constraints under the DL / T698 protocol; simultaneously satisfying the formula: ,in, This indicates the maximum frame length limit of the communication link. This represents the lowest priority threshold for frame transmission. Indicates the current system timestamp. This indicates the maximum permissible deviation of the frame transmission timestamp.

[0012] Furthermore, the quantum annealing de-aggregation operation module includes a frame parameter redundancy identification unit, a quantum state initialization unit, an annealing iteration operation unit, and an effective information extraction unit. The frame parameter redundancy identification unit compares the frame length, frame priority, frame type, frame transmission timestamp, frame target address, and frame source address parameters output by the dynamic frame order parameter parsing module parameter by parameter, and filters out parameter values ​​and combinations that appear repeatedly between different frames. The quantum state initialization unit constructs a quantum bit sequence corresponding to the frame parameters based on the number of frame parameters and the redundancy identification results, and randomly initializes the quantum bit sequence to generate an initial quantum state. The annealing iteration operation unit gradually adjusts the weight ratio of the initial Hamiltonian and the target Hamiltonian in the Hamiltonian according to the annealing progress parameter change curve set by the quantum annealing de-aggregation optimization algorithm, and performs iterative evolution calculations on the quantum state. The effective information extraction unit measures the final quantum state output by the annealing iteration operation unit, separates the redundant information and effective information in the frame parameters based on the measurement results, and transmits the effective information to the dynamic adaptive ant colony optimization sorting module.

[0013] Furthermore, the non-cooperative game scheduling decision-making module includes a frame strategy set construction unit, a payoff function calculation unit, a Nash equilibrium solution unit, and a scheduling strategy output unit. The frame strategy set construction unit, based on historical frame transmission data provided by the DL / T698 spatiotemporal big data analysis platform and considering current communication link bandwidth, link load, and frame transmission delay requirements, constructs a set of feasible strategies for each frame, including sending timing selection, retransmission count setting, and priority adjustment magnitude. The payoff function calculation unit, based on the feasible strategies output by the frame strategy set construction unit and considering frame priority, transmission delay, and link resource occupancy, calculates the payoff value for each frame under different strategy combinations. The Nash equilibrium solution unit uses an iterative approximation method to solve the payoff function for all frames, seeking a Nash equilibrium strategy combination that optimizes the payoff for each frame and cannot be improved by individually changing the strategy. The scheduling strategy output unit converts the strategy combinations obtained by the Nash equilibrium solution unit into specific scheduling instructions and transmits them to the dynamic adaptive ant colony optimization sorting module.

[0014] Furthermore, the dynamic adaptive ant colony optimization sorting module includes a pheromone matrix initialization unit, an ant path search unit, a pheromone update unit, and a frame order sorting unit. The pheromone matrix initialization unit constructs a pheromone concentration matrix between frames based on the number of frame data, and assigns values ​​to all elements in the matrix according to preset initial pheromone concentration values. The ant path search unit sets the number of ants, allowing each ant to start from a randomly selected initial frame and, based on the transition probability calculated from the pheromone concentration matrix and heuristic function values, sequentially select the next frame to be sorted until all frames have been selected once to form a complete path. After all ants have completed one path search, the pheromone update unit updates the elements in the pheromone concentration matrix according to the pheromone evaporation coefficient and pheromone increment calculation method set by the dynamic adaptive ant colony optimization algorithm. The frame order sorting unit evaluates the frame order corresponding to the paths obtained by all ants, selects the frame order with the optimal path evaluation value as the initial sorting result, and adjusts the initial sorting result based on the scheduling instructions output by the non-cooperative game scheduling decision module to obtain the final frame order.

[0015] The intelligent queuing method for frame order processing based on the DL / T698 protocol includes the following steps:

[0016] The first step is to extract frame data conforming to the DL / T698 protocol from the communication link of the power user electricity consumption information acquisition system through the DL / T698 frame information acquisition module, and then transmit the extracted frame data to the dynamic frame sequence parameter parsing module.

[0017] The second step is for the dynamic frame order parameter parsing module to parse the received frame data and extract parameters such as frame length, frame priority, frame type, frame sending timestamp, frame target address, and frame source address. The parsed parameters are then transmitted to the quantum annealing de-aggregation operation module and the non-cooperative game scheduling decision module, respectively.

[0018] The third step is that the quantum annealing de-aggregation operation module receives the parsed frame parameters, performs de-aggregation processing according to the process set by the quantum annealing de-aggregation optimization algorithm, separates the redundant information and valid information in the frame parameters, and transmits the valid information to the dynamic adaptive ant colony optimization sorting module.

[0019] The fourth step is that the non-cooperative game scheduling decision module receives the parsed frame parameters, combines the link status data provided by the DL / T698 spatiotemporal big data analysis platform to construct a non-cooperative game scheduling model, solves the model to obtain the Nash equilibrium strategy combination and converts it into scheduling instructions, and transmits the scheduling instructions to the dynamic adaptive ant colony optimization sorting module.

[0020] The fifth step is that the dynamic adaptive ant colony optimization and sorting module receives valid information and scheduling instructions, performs frame order queuing calculation through the dynamic adaptive ant colony optimization algorithm, first initializes the pheromone concentration matrix, then controls the ants to search for paths, updates the pheromone concentration matrix, filters and adjusts to obtain the final frame order, and transmits the final frame order to the frame order output execution module.

[0021] The sixth step is for the frame sequence output execution module to receive the final frame sequence, verify the final frame sequence in accordance with the frame transmission constraints under the DL / T698 protocol, and control the transmission order of frame data in the communication link of the power user electricity consumption information collection system according to the verified frame sequence.

[0022] Beneficial effects:

[0023] This invention proposes a frame order intelligent queuing processing system and method based on the DL / T698 protocol. By constructing a multi-module collaborative architecture, it first comprehensively collects and analyzes frame data in the communication link of the power user electricity consumption information collection system. Then, through de-aggregation processing, it accurately separates redundant and valid information in the frame data, avoiding transmission burden caused by information mixing. Simultaneously, combined with an optimized sorting algorithm, it dynamically adjusts the frame order based on the frame's own attributes and the actual link situation, solving the problems of frame data accumulation and congestion in existing technologies and significantly improving transmission efficiency. Regarding scheduling strategies, by constructing a scientific scheduling model, it fully considers the mutual influence during multi-frame transmission and formulates targeted transmission arrangements based on frame priority, transmission requirements, and other characteristics. This provides adaptive transmission strategies for different types of frame data, effectively coping with complex and ever-changing communication environments and improving the insufficient transmission reliability caused by unreasonable scheduling logic in existing technologies. Ultimately, it achieves accurate and timely frame data transmission, meeting the needs of power dispatching, user electricity consumption monitoring, and system operation and maintenance, adapting to dynamic transmission scenarios under surges in data volume, and ensuring the stable and efficient operation of the electricity consumption information collection system. Attached Figure Description

[0024] Figure 1 This is a diagram showing the system module composition of the present invention;

[0025] Figure 2 This is a flowchart of the method steps of the present invention. Detailed Implementation

[0026] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] like Figure 1 As shown, the frame order intelligent queuing processing system based on the DL / T698 protocol includes: a DL / T698 frame information acquisition module, a dynamic frame order parameter parsing module, a quantum annealing de-aggregation operation module, a non-cooperative game scheduling decision module, a dynamic adaptive ant colony optimization sorting module, and a frame order output execution module. The DL / T698 frame information acquisition module is connected to the dynamic frame order parameter parsing module. The dynamic frame order parameter parsing module is connected to both the quantum annealing de-aggregation operation module and the non-cooperative game scheduling decision module. Both the quantum annealing de-aggregation operation module and the non-cooperative game scheduling decision module are connected to the dynamic adaptive ant colony optimization sorting module. The dynamic adaptive ant colony optimization sorting module is connected to the frame order output execution module.

[0028] The DL / T698 frame information acquisition module extracts frame data conforming to the DL / T698 protocol from the communication link of the power user electricity consumption information acquisition system;

[0029] Specifically, the DL / T698 frame information acquisition module extracts frame data conforming to the DL / T698 protocol from the communication link of the power user's electricity consumption information acquisition system. The process begins by real-time monitoring of the communication link's transmission rate, adapting to a common range of 1200bps to 115200bps. The built-in link signal detection unit captures the start and end identifiers of frames conforming to the DL / T698 protocol structure. The start identifier is a fixed byte sequence, and the end identifier includes a check field and a frame tail flag. The module's acquisition frequency can be adjusted from 1 to 10 times per second according to actual needs. During acquisition, the integrity of the frame data is initially assessed. When the start and end identifiers of the frame data are complete and the check field passes CRC16 verification, the frame data is temporarily stored in a buffer unit with a capacity of 1MB to 8MB. Simultaneously, the acquisition timestamp of the frame data is recorded, with timestamp accuracy controlled at the millisecond level. This module provides complete and accurate raw frame data for subsequent processing, avoiding deviations in subsequent parameter parsing and frame ordering due to missing or incorrect data. The frame data it collects covers various types, including real-time power consumption data frames from power users and equipment status feedback frames, laying the data foundation for subsequent processing stages of the entire system.

[0030] The dynamic frame sequence parameter parsing module parses the frame length, frame priority, frame type, frame transmission timestamp, frame destination address, and frame source address parameters contained in the extracted frame data;

[0031] Specifically, the dynamic frame sequence parameter parsing module receives frame data output from the DL / T698 frame information acquisition module. During implementation, it first calls the DL / T698 protocol parsing library to split the frame data into fields according to the frame structure format defined in the protocol, extracting core parameters such as frame length, frame priority, frame type, frame transmission timestamp, frame destination address, and frame source address. Specifically, the frame length parameter parsing reads the 2-byte length field from the frame header, with a parsing result ranging from 8 bytes to 2048 bytes, corresponding to the standard length range for different types of frame data. The frame priority parameter is extracted from the 3-bit priority identifier in the frame control field, divided into 8 levels from 0 to 7, with 0 being the highest priority and 7 the lowest. The frame type parameter is determined by the 4-bit type identifier in the frame control field, including 16 common types such as data transmission frames, control command frames, and status query frames. The frame transmission timestamp parameter parsing converts the 4-byte time field in the frame data into a standard time format with second-level precision. The frame destination address and frame source address parameters respectively parse the 6-byte address field in the frame header, corresponding to the unique identifier code of the power user acquisition terminal. After the module completes parsing, it transmits the parameters to subsequent modules in a structured data format, transforming the original frame data into key parameters that can be used for computation and decision-making. This provides accurate parameter input for quantum annealing de-aggregation operations and non-cooperative game scheduling decisions, ensuring that subsequent algorithm processing is based on accurate frame attribute information.

[0032] The quantum annealing de-aggregation operation module performs de-aggregation processing on the parsed frame parameters according to the quantum annealing de-aggregation optimization algorithm to separate redundant information from valid information in the frame parameters;

[0033] Specifically, the quantum annealing de-aggregation module receives frame parameters output from the dynamic frame sequence parameter parsing module. The process begins by classifying and organizing the input parameters, dividing them into multiple parameter groups based on frame type. Each group contains at least 10 to 100 frames of the same type of frame parameters. Then, the quantum annealing de-aggregation operation is initiated, setting the initial annealing temperature to 1000K and the annealing cooling rate to decrease by 50K every 100 iterations until the temperature drops to 10K, at which point iteration stops. During iteration, redundant information in the frame parameters is identified and separated by adjusting parameter weight factors. Redundant information includes repeated frame target addresses between different frames and consecutively identical frame type identifiers, while valid information includes unique frame transmission timestamps and differentiated frame length values ​​for each frame. This module sets a redundancy information identification threshold; when a parameter appears more than 80% of the total number of frames in the same parameter group, it is considered redundant. After de-aggregation processing, the valid information is stored in association with the frame sequence number, forming a list of valid parameters. Redundant content in frame parameters is removed to reduce the amount of data processed by subsequent algorithms, avoid interference from redundant information with frame order sorting results, and ensure that the parameters transmitted to the dynamic adaptive ant colony optimization sorting module are unique and valid, thereby improving the computational efficiency and accuracy of the sorting algorithm.

[0034] The non-cooperative game scheduling decision module constructs a non-cooperative game scheduling model and calculates scheduling strategies based on the parsed frame parameters.

[0035] Specifically, the non-cooperative game scheduling decision module constructs a scheduling model based on the frame parameters of the dynamic frame sequence parameter parsing module. During implementation, it first collects historical frame parameter data from the past hour, statistically analyzing the transmission frequency and link occupancy duration of different priorities and types of frame data to determine the participants in the game model as the "players" corresponding to each frame. Then, it defines a game payoff function, using frame transmission delay and link resource occupancy as payoff evaluation indicators. The target value for frame transmission delay is set to no more than 500 milliseconds, and the link resource occupancy rate is controlled within a reasonable range of 60% to 80%. Through iterative calculation, the strategy space for each player is determined, including three strategies: "priority transmission," "delayed transmission," and "segmented transmission," each corresponding to a different payoff coefficient. The model solution uses a stepwise approximation method, updating the payoff value of each player every 50 iterations until the payoff values ​​of all players no longer change. The resulting strategy combination is the Nash equilibrium strategy. This module translates the balancing strategy into specific scheduling instructions, such as "priority level 0 frames are transmitted first" and "frames with a length exceeding 1024 bytes are transmitted in segments". Through a game theory model, it achieves reasonable resource allocation among multiple frames of data, avoids excessive occupation of link resources by a single frame of data, provides a scientific scheduling basis for dynamic adaptive ant colony optimization sorting, and ensures that frame ordering takes into account both efficiency and fairness.

[0036] The dynamic adaptive ant colony optimization sorting module combines the effective information output by the quantum annealing de-aggregation operation module with the scheduling strategy output by the non-cooperative game scheduling decision module, and performs dynamic frame order queuing calculation on frame data through the dynamic adaptive ant colony optimization algorithm.

[0037] Specifically, the dynamic adaptive ant colony optimization sorting module integrates the effective information from the quantum annealing de-aggregation operation module and the scheduling instructions from the non-cooperative game scheduling decision module. During implementation, the pheromone matrix is ​​first initialized, with dimensions equal to the number of frames multiplied by the number of frames. The initial pheromone concentration is set to 0.5, and the pheromone evaporation coefficient is set to 0.1. Then, the ant colony size is set to 1.5 times the number of frames. Each ant starts from a randomly selected initial frame and calculates the transition probability based on the pheromone concentration and the heuristic function value. The heuristic function value is determined based on the frame priority and the frame transmission timestamp from the effective information; higher priority frames and newer timestamps have larger heuristic function values. After each ant completes a full frame sort, the path fitness value is calculated. The fitness value aims to minimize the total frame transmission delay. When the fitness value of a path is better than the current optimal value, the globally optimal path is updated. Simultaneously, the pheromone matrix is ​​updated according to the ant colony algorithm rules, increasing the pheromone concentration of the optimal path by an increment of 0.2, while decreasing the pheromone concentration of other paths according to the evaporation coefficient. The module iterates 200 times. After the iteration, it outputs the frame order corresponding to the globally optimal path. Through an adaptive ant colony optimization algorithm, combined with effective information and scheduling instructions, the frame order is dynamically adjusted to ensure that frame data is transmitted in the optimal order, reduce the probability of link congestion, and improve the overall transmission efficiency.

[0038] The frame order output execution module controls the transmission order of frame data in the communication link based on the frame order results output by the dynamic adaptive ant colony optimization sorting module.

[0039] Specifically, the frame order output execution module receives the frame order results from the dynamic adaptive ant colony optimization sorting module. The implementation process first verifies the compliance of the frame order results. Verification includes checking whether the frame order contains all frames to be transmitted, whether high-priority frames precede low-priority frames, and whether segments of transmitted frames are arranged consecutively according to their segment numbers. If the verification fails, the process is returned to the sorting module for reprocessing, achieving a 100% pass rate. After successful verification, the module establishes a connection with the communication link control unit of the power user's electricity consumption information collection system and sends frame data sequentially according to the frame order results. The sending interval is dynamically adjusted based on the link bandwidth: 10 milliseconds for a link bandwidth greater than 10 Mbps, 50 milliseconds for a link bandwidth between 1 Mbps and 10 Mbps, and 100 milliseconds for a link bandwidth less than 1 Mbps. During transmission, link feedback signals are monitored in real time. If frame transmission failure feedback is received, the frame is retransmitted according to the frame order priority, with a maximum of three retransmissions. After all frame data has been sent, a frame order execution report is generated, recording the transmission time, transmission status, and number of retransmissions for each frame. The sorted frame order is transformed into actual link transmission actions. By dynamically adjusting the transmission interval and retransmission mechanism, the frame data is ensured to be transmitted stably in the optimal order, ultimately achieving efficient frame queuing processing of the communication protocol of the power user electricity consumption information collection system.

[0040] Preferably, the dynamic adaptive ant colony optimization algorithm used in the dynamic adaptive ant colony optimization sorting module satisfies the formula: ,in, This represents the pheromone concentration from frame i to frame j at time t. Indicates the pheromone evaporation coefficient. This represents the set of ant colonies that have completed frame sorting at time t. This represents the pheromone increment left by the k-th ant on the path from frame i to frame j; it also satisfies the formula: ,in, Let represent the probability that the k-th ant selects frame j from frame i as the next sorting object at time t. Factors indicating the importance of pheromones This represents the importance factor of the heuristic function. This represents the heuristic function value from frame i to frame j at time t. This represents the set of next sorted frames that frame i can select.

[0041] Specifically, the algorithm used in the dynamic adaptive ant colony optimization sorting module first sets a specific value for the pheromone evaporation coefficient, typically controlled between 0.05 and 0.2. This value is determined by the update frequency of frame data in the communication link; a larger value is used when frame data updates are frequent to accelerate pheromone updates, while a smaller value is used to maintain pheromone stability. When calculating pheromone concentration updates, the set of ants that have completed frame order sorting at each time point is statistically analyzed. The number of ants in the set is determined based on the number of frames to be sorted, generally 1 to 2 times the number of frames. The pheromone increment left by each ant on its path is adjusted according to the quality of its sorting path; ants with better sorting results leave larger increments, typically between 0.1 and 0.5. When calculating the probability of an ant choosing the next sorting target, both the pheromone importance factor and the heuristic function importance factor range from 1 to 5. When there are significant differences in frame priorities in the link, the heuristic function importance factor is increased to highlight the impact of frame priority on sorting. When high frame transmission path stability is required, the pheromone importance factor is increased to rely on historical sorting experience. By clarifying the value range and adjustment rules of key parameters in the algorithm, the dynamic adaptive ant colony optimization algorithm can be flexibly adapted to the actual communication situation of the power user electricity information collection system, improve the accuracy and real-time performance of frame order sorting, and provide a reasonable order basis for subsequent frame data transmission.

[0042] Preferably, the non-cooperative game scheduling model constructed by the non-cooperative game scheduling decision module satisfies the formula: ,in, Indicates frame i in the policy Other frame strategies The profit function under the following conditions Let i represent the set of feasible strategies for frame i. and These represent the frame delay weight coefficient and the frame priority weight coefficient, respectively. Indicates frame i in the policy Other frame strategies The transmission delay below, Indicates frame i in the policy Other frame strategies The priority benefits are as follows; and the formula is also satisfied: ,in, This represents the Nash equalization strategy for frame i. This represents the set of Nash equalization strategies for other frames.

[0043] Specifically, in the non-cooperative game scheduling model, the specific values ​​of the frame delay weight coefficient and the frame priority weight coefficient in the payoff function are first determined, and their sum is 1. When the power user's electricity consumption information collection system has high requirements for the real-time performance of frame transmission, the frame delay weight coefficient is taken as 0.6 to 0.8, and the frame priority weight coefficient is taken as 0.2 to 0.4. When the system has high requirements for the reliability of high-priority frame transmission, the proportions of the two are adjusted to make the frame priority weight coefficient larger. When calculating the transmission delay of frames under different strategy combinations, historical transmission data provided by the DL / T698 spatiotemporal big data analysis platform is used, and the time interval for delay calculation is set to the most recent 10 minutes. The average transmission delay of the same type of frame under different strategies within this interval is statistically analyzed as the basis for calculation. The set of feasible strategies for frames is dynamically adjusted according to the communication link bandwidth and load conditions. When the link bandwidth is sufficient (greater than 20Mbps), the feasible strategies only include "priority transmission" and "normal transmission"; when the link bandwidth is tight (less than 5Mbps), "delayed transmission" and "segmented transmission" strategies are added. When solving for the Nash equilibrium strategy, iterative calculations are performed until the change in payoff value for all frames in five consecutive iterations is less than 0.01, ensuring a stable strategy combination. By quantifying the payoff function parameters and clarifying the strategy adjustment rules, the non-cooperative game scheduling model can accurately reflect the matching relationship between frame transmission demand and link status, providing scientific scheduling strategy support for frame ordering.

[0044] Preferably, the quantum annealing depolymerization optimization algorithm used in the quantum annealing depolymerization operation module satisfies the formula: ,in, Indicates the annealing parameters The changing Hamiltonian. Indicates the annealing progress parameter and This represents the initial Hamiltonian. Represents the target Hamiltonian. This indicates the frame parameter de-aggregation state; and simultaneously satisfies the formula: ,in, Indicates the annealing parameters The next frame parameters are in a de-aggregation state. The probability, Indicates the annealing parameters The changing inverse temperature parameter, This represents the set of all possible frame parameter solution aggregate states.

[0045] Specifically, the quantum annealing de-aggregation optimization algorithm follows a linear change in the annealing progress parameter, gradually increasing from 0 to 1 over 1000 iterations, with each iteration incrementing the progress parameter by 0.001 to ensure a smooth annealing process. The initial and target Hamiltonians are set based on the frame parameter characteristics. The initial Hamiltonian emphasizes the random distribution of the frame parameters, while the target Hamiltonian emphasizes the separation of effective and redundant information within the frame parameters. Their weights change synchronously with the annealing progress parameter. The inverse temperature parameter increases with the annealing progress parameter; the initial inverse temperature parameter is set to 1, and when the annealing progress parameter reaches 1, the inverse temperature parameter increases to 100 to enhance the algorithm's ability to identify effective information. When calculating the probability of the frame parameters being in different de-aggregation states, the energy values ​​of all possible states are statistically analyzed. The energy value calculation is based on the redundancy of the frame parameters; states with higher redundancy have larger energy values ​​and lower probabilities. The number of iterations in this algorithm is adjusted based on the number of frame parameters. When the number of frame parameters is less than 100, the number of iterations is set to 500; when the number of frame parameters is between 100 and 500, the number of iterations is set to 1000; and when the number of frame parameters is greater than 500, the number of iterations is set to 2000. By clearly defining the rules for parameter changes during the annealing process and the standard for setting the number of iterations, the quantum annealing de-aggregation optimization algorithm can effectively separate redundant and valid information in the frame parameters, reduce the amount of data processed subsequently, and improve the overall processing efficiency of the system.

[0046] Preferably, the DL / T698 frame information acquisition module and the DL / T698 spatiotemporal big data analysis platform work together to satisfy the formula: ,in, This indicates the total amount of frame data collected. Indicates the start time of data collection. Indicates the end time of data collection. This represents the set of communication links monitored by the DL / T698 spatiotemporal big data analysis platform. This represents the frame transmission rate of link m at time t. This represents the frame acquisition enable signal for link m at time t; the parameters resolved by the dynamic frame sequence parameter parsing module satisfy the formula: ,in, This represents the set of parsed parameters for the i-th frame. Indicates the length of the i-th frame. This indicates the priority of the i-th frame. Indicates the type of the i-th frame. This represents the timestamp of the transmission of the i-th frame. This represents the target address of the i-th frame. This represents the source address of the i-th frame.

[0047] Specifically, the DL / T698 frame information acquisition module works in conjunction with the DL / T698 spatiotemporal big data analysis platform. The start and end times of the acquisition are determined based on the platform's data analysis cycle, typically one hour, meaning one frame data acquisition and transmission is completed per hour. The monitored communication links cover all main links of the power user electricity consumption information acquisition system, usually ranging from 5 to 20, adjusted according to system scale. The frame transmission rate of each link is monitored in real time, with a sampling frequency set to 1 time / second to ensure accurate acquisition of link transmission status. The triggering condition for the frame acquisition enable signal is based on the link load. When the link load rate is less than 50%, the enable signal remains "on"; when the link load rate is between 50% and 80%, the enable signal switches between "on for 5 seconds, off for 2 seconds"; when the link load rate is greater than 80%, the enable signal is "off" to avoid excessive acquisition leading to link congestion. The dynamic frame sequence parameter parsing module parses frame length parameters accurate to the byte level, frame priority parameters are divided into 5 levels from 0 to 4 according to the DL / T698 specification, frame type parameters include 8 common types such as data frames, control frames, and response frames, frame transmission timestamps are accurate to the millisecond level, and frame destination and source addresses use a 6-byte binary encoding format. By clearly defining the time rules for data collection, the link monitoring range, and parameter parsing standards, it ensures that the collected frame data and parsed parameters accurately meet the needs of the DL / T698 spatiotemporal big data analysis platform, providing a high-quality data foundation for subsequent algorithm processing.

[0048] Preferably, the frame order output execution module controls the frame transmission order to satisfy the formula: ,in, Indicates the final output frame order. This indicates the initial frame order output by the dynamic adaptive ant colony optimization sorting module. This represents the set of frame transmission constraints under the DL / T698 protocol; simultaneously satisfying the formula: ,in, This indicates the maximum frame length limit of the communication link. This represents the lowest priority threshold for frame transmission. Indicates the current system timestamp. This indicates the maximum permissible deviation of the frame transmission timestamp.

[0049] Specifically, the frame order output execution module strictly follows the results of the dynamic adaptive ant colony optimization sorting module when finalizing the output frame order, while also adjusting for frame transmission constraints under the DL / T698 protocol. The maximum frame length limit for the communication link is determined by the link type: 1500 bytes for twisted-pair links and 9000 bytes for fiber optic links, ensuring normal transmission of frame data within the link. The minimum frame transmission priority threshold is set with reference to the frame priority distribution in the system, typically set to level 2. This means only frames with priorities of 0, 1, and 2 are allowed to be transmitted, while frames with priorities of 3 and 4 are temporarily stored to avoid low-priority frames consuming excessive link resources. The maximum allowable deviation for the frame transmission timestamp is set to 500 milliseconds. When the difference between the frame transmission timestamp and the current system timestamp exceeds this deviation, the validity of the frame data is re-verified to ensure that the transmitted frame data is the latest data. The frame order output execution module monitors the transmission status signals from the link in real time when controlling frame data transmission. Upon receiving a "transmission successful" signal, it continues sending the next frame; upon receiving a "transmission failed" signal, it records the reason for the failure and handles it according to the set retransmission mechanism. Simultaneously, it feeds back the failure information to the dynamic adaptive ant colony optimization and sorting module, providing a reference for subsequent frame order adjustments. By clearly defining the constraints on frame transmission and the anomaly handling mechanism, it ensures that frame data is transmitted stably and accurately in the communication link according to the sorting results, guaranteeing the normal operation of the power user electricity information collection system.

[0050] Preferably, the quantum annealing de-aggregation operation module includes a frame parameter redundancy identification unit, a quantum state initialization unit, an annealing iteration operation unit, and an effective information extraction unit. The frame parameter redundancy identification unit compares the frame length, frame priority, frame type, frame transmission timestamp, frame target address, and frame source address parameters output by the dynamic frame order parameter parsing module parameter by parameter, and filters out parameter values ​​and combinations that appear repeatedly between different frames. The quantum state initialization unit constructs a quantum bit sequence corresponding to the frame parameters based on the number of frame parameters and the redundancy identification results, and randomly initializes the quantum bit sequence to generate an initial quantum state. The annealing iteration operation unit gradually adjusts the weight ratio of the initial Hamiltonian and the target Hamiltonian in the Hamiltonian according to the annealing progress parameter change curve set by the quantum annealing de-aggregation optimization algorithm, and performs iterative evolution calculations on the quantum state. The effective information extraction unit measures the final quantum state output by the annealing iteration operation unit, separates the redundant information and effective information in the frame parameters based on the measurement results, and transmits the effective information to the dynamic adaptive ant colony optimization sorting module.

[0051] Specifically, the quantum annealing de-aggregation operation module comprises four units. During implementation, the frame parameter redundancy identification unit first receives parameters such as frame length and frame priority output by the dynamic frame order parameter parsing module. It employs a parameter-by-parameter comparison method, setting a parameter repetition threshold where three consecutive frames displaying the same parameter value or combination are considered redundant. The comparison frequency is consistent with the parameter parsing frequency, at 10 times per second, ensuring real-time capture of redundancy information. The quantum state initialization unit determines the qubit sequence length based on the number of frame parameters, with one qubit corresponding to every two frame parameters. When the number of frame parameters is even, the qubit sequence length is half the number of parameters; when it is odd, one qubit is added to fill the gap. Initialization uses a random phase assignment method. The phase value ranges from 0 to 2π. After assignment, an initial quantum state is generated and stored. The annealing iteration unit performs iterations according to a preset annealing progress parameter change curve. The curve is set so that the progress parameter increases by 0.2 every 200 iterations, and the weight ratio of the initial Hamiltonian and the target Hamiltonian is adjusted synchronously. During the iteration process, quantum state change data is recorded every 50 iterations until the progress parameter reaches 1. The effective information extraction unit measures the final quantum state, with the number of measurements set to 10. The state with the highest frequency in the measurement results is taken as the effective state. Redundant information and effective information are separated according to this state. The effective information is associated by frame number and transmitted to the dynamic adaptive ant colony optimization sorting module at a rate of 500 bytes per second. By clarifying the parameter settings and operation procedures of each unit, the de-aggregation processing is ensured to be accurate and efficient, providing high-quality effective information for subsequent sorting.

[0052] Preferably, the non-cooperative game scheduling decision module includes a frame strategy set construction unit, a payoff function calculation unit, a Nash equilibrium solution unit, and a scheduling strategy output unit. The frame strategy set construction unit, based on historical frame transmission data provided by the DL / T698 spatiotemporal big data analysis platform and considering current communication link bandwidth, link load, and frame transmission delay requirements, constructs a set of feasible strategies for each frame, including sending timing selection, retransmission count setting, and priority adjustment magnitude. The payoff function calculation unit, based on the feasible strategies output by the frame strategy set construction unit and considering frame priority, transmission delay, and link resource occupancy, calculates the payoff value for each frame under different strategy combinations. The Nash equilibrium solution unit uses an iterative approximation method to solve the payoff function for all frames, seeking a Nash equilibrium strategy combination that optimizes the payoff for each frame and cannot be improved by individually changing the strategy. The scheduling strategy output unit converts the strategy combination obtained by the Nash equilibrium solution unit into specific scheduling instructions and transmits them to the dynamic adaptive ant colony optimization sorting module.

[0053] Specifically, the non-cooperative game scheduling decision module comprises four units. The first unit, the frame strategy set construction unit, first obtains historical frame transmission data from the DL / T698 spatiotemporal big data analysis platform for the past 24 hours, statistically analyzing data such as link bandwidth fluctuation range and average load rate. When the link bandwidth fluctuation is less than 10% and the average load rate is less than 40%, it constructs two strategies for each frame: "priority transmission" and "normal transmission." When the bandwidth fluctuation is greater than 10% or the average load rate is greater than 40%, it adds "delayed transmission" and "segmented transmission" strategies. In the strategy details, the sending timing is selected with a time window divided at 10-millisecond intervals, the number of retransmissions is set to 1 to 3, and the priority adjustment range is ±1 level. The second unit, the revenue function calculation unit, calculates the revenue based on frame priority, transmission delay, and... Link resource occupancy is weighted, with priority accounting for 40%, transmission delay for 35%, and resource occupancy for 25%. The benefit value of each frame under different strategy combinations is calculated according to these weights, with the calculation precision retained to three decimal places. The Nash equilibrium solution unit uses an iterative approximation method, with an initial iteration step size set to 0.1. When the difference in benefit values ​​between two adjacent iterations is less than 0.001, the step size is adjusted to 0.01 until the benefit values ​​of all frames no longer change, resulting in the Nash equilibrium strategy combination. The scheduling strategy output unit converts the equilibrium strategy into scheduling instructions. The instruction format includes frame identifier, strategy type, and execution parameters. After conversion, it is transmitted to the dynamic adaptive ant colony optimization and sorting module at a rate of 20 messages per second via a dedicated communication interface. By refining the operation standards and parameters of each unit, it is ensured that the scheduling strategy scientifically adapts to the link status and frame requirements.

[0054] Preferably, the dynamic adaptive ant colony optimization sorting module includes a pheromone matrix initialization unit, an ant path search unit, a pheromone update unit, and a frame order sorting unit. The pheromone matrix initialization unit constructs a pheromone concentration matrix between frames based on the number of frame data, and assigns values ​​to all elements in the matrix according to preset initial pheromone concentration values. The ant path search unit sets the number of ants, allowing each ant to start from a randomly selected initial frame and, based on the transition probability calculated from the pheromone concentration matrix and heuristic function values, sequentially select the next frame to be sorted until all frames have been selected once to form a complete path. After all ants have completed one path search, the pheromone update unit updates the elements in the pheromone concentration matrix according to the pheromone evaporation coefficient and pheromone increment calculation method set by the dynamic adaptive ant colony optimization algorithm. The frame order sorting unit evaluates the frame order corresponding to the paths obtained by all ants, selects the frame order with the optimal path evaluation value as the initial sorting result, and adjusts the initial sorting result based on the scheduling instructions output by the non-cooperative game scheduling decision module to obtain the final frame order.

[0055] Specifically, the dynamic adaptive ant colony optimization sorting module comprises four units. During implementation, the pheromone matrix initialization unit constructs a matrix based on the number of frames to be sorted. When the number of frames is N, the matrix dimension is N×N. The initial pheromone concentration is set to 0.3, and the concentration value is in decimal format with two decimal places. The matrix is ​​stored in a two-dimensional array, with memory usage controlled within 100KB. The ant path search unit sets the number of ants to 1.2 times the number of frames. After each ant randomly selects an initial frame, it calculates the transition probability based on the pheromone concentration and the heuristic function value. The heuristic function value is assigned according to the frame priority: priority level 0 is assigned 1.0, level 1 0.8, level 2 0.6, level 3 0.4, and level 4 0.2. After selecting a subsequent frame, the frame is marked as selected. The process continues until all frames are marked. The pheromone update unit, after all ants have completed path searching, first reduces the pheromone concentration of all paths by a pheromone evaporation coefficient of 0.15, then increases the pheromone concentration of the optimal path by an increment of 0.25. The optimal path is determined by minimizing the total frame transmission delay, calculated based on the sum of the transmission times of each frame. The frame order sorting unit evaluates the frame order corresponding to all ant paths, calculates the fitness value for each frame order (fitness value = 1 / (1 + total delay)), selects the frame order with the highest fitness value as the initial result, and adjusts it in conjunction with the scheduling instructions from the non-cooperative game scheduling decision module. During adjustment, priority is given to meeting the strategy requirements of high-priority frames, and the adjustment range does not exceed 10% of the total number of frames. The final frame order is then temporarily stored in the buffer. By clearly defining the parameter settings and operation procedures of each unit, efficient frame order sorting and compliance with scheduling requirements are ensured.

[0056] The dynamic adaptive ant colony optimization algorithm in this invention is an algorithm that combines the dynamic frame order queuing requirements of the communication protocol of the power user electricity consumption information collection system and achieves frame order optimization by simulating the foraging path selection mechanism of ant colonies. The implementation process begins with a pheromone matrix initialization unit constructing an N×N matrix based on the number of frames to be sorted, setting the initial pheromone concentration to 0.3 (rounded to two decimal places), storing it in a two-dimensional array with memory controlled within 100KB. Next, an ant path search unit sets the number of ants to 1.2 times the number of frames. Each ant randomly selects an initial frame and calculates the transition probability based on the pheromone concentration and heuristic function value (assigned values ​​of 1.0, 0.8, 0.6, 0.4, and 0.2 for frame priorities 0-4 respectively), marking selected frames until all frames are selected. Subsequently, a pheromone update unit reduces the pheromone concentration of all paths with a volatility coefficient of 0.15, increasing the pheromone increment by 0.25 for the optimal path with the minimum total frame transmission delay. Finally, a frame order sorting unit calculates the fitness value of each frame order (formula: 1 / (1+total delay)), selects the optimal initial frame order, and adjusts it in conjunction with scheduling instructions (the magnitude not exceeding 10% of the total number of frames). The algorithm's function is to dynamically adjust the frame data sorting order, ensuring that the frame order meets the requirements of the communication link status and frame priority. This invention addresses the problem that traditional sorting algorithms are difficult to adapt to dynamic frame transmission scenarios, improves frame data transmission efficiency, reduces link congestion, and provides sorting support for the stable operation of the communication protocol of the power user electricity consumption information collection system.

[0057] The non-cooperative game-theoretic scheduling model in this invention is a model that constructs a "game" scenario between frame data based on the relationship between multi-frame data transmission requirements and link resource allocation to determine the optimal scheduling strategy. Its implementation begins with a frame strategy set construction unit acquiring nearly 24 hours of historical data from the DL / T698 spatiotemporal big data analysis platform. Based on link bandwidth fluctuations (two strategies are set when the fluctuation is less than 10% and the load rate is below 40%, and four strategies are set otherwise), a strategy set is constructed for each frame, including transmission timing (10-millisecond interval window), retransmission count (1-3 times), and priority adjustment (±1 level). Next, a payoff function calculation unit calculates the payoff value (retaining three decimal places) according to the weights of priority (40%), transmission delay (35%), and resource usage (25%). Subsequently, a Nash equilibrium solution unit uses an iterative approximation method with an initial step size of 0.1, adjusting to 0.01 when the difference is less than 0.001, until the payoff value stabilizes and an equilibrium strategy is obtained. Finally, a scheduling strategy output unit converts the equilibrium strategy into an instruction containing frame identifier, strategy type, and execution parameters, transmitting it at a rate of 20 frames per second. The model's role is to allocate appropriate transmission strategies to different frames of data, balancing link resource consumption with frame transmission requirements. It avoids excessive resource consumption by a single frame, improves the scientific rigor and adaptability of the scheduling strategy, and ensures the orderly transmission of multiple frames of data in complex link environments.

[0058] The quantum annealing de-aggregation optimization algorithm in this invention is an algorithm that draws on the physical process of quantum annealing to separate redundant information from valid information in the frame parameters of the communication protocol of a power user electricity information collection system. The implementation process is as follows: First, a frame parameter redundancy identification unit receives the parsed frame parameters and compares them parameter by parameter at a frequency of 10 times per second. If three consecutive frames contain the same parameter value or combination, it is determined to be redundant. Next, a quantum state initialization unit determines the length of the qubit sequence based on the number of frame parameters (one bit for every two parameters, with padding for odd numbers), and generates an initial quantum state using a 0-2π random phase assignment. Then, an annealing iteration operation unit adjusts the Hamiltonian weights according to a curve that increases the progress parameter by 0.2 every 200 iterations, recording the quantum state changes every 50 iterations until the progress parameter reaches 1. Finally, a valid information extraction unit measures the final quantum state 10 times, extracts the information from the state with the highest frequency, associates it with the frame sequence number, and transmits the valid information at a rate of 500 bytes per second. The algorithm's function is to accurately remove redundant content from the frame parameters and retain valid information. This reduces the amount of data processed by subsequent algorithms, avoids redundant information interfering with the sorting results, improves the overall system's processing efficiency and data quality, and provides a high-quality data foundation for frame order optimization.

[0059] The DL / T698 spatiotemporal big data analysis platform of this invention is a platform based on the DL / T698 protocol for storing and analyzing spatiotemporal data related to frame transmission in the communication links of a power user electricity consumption information collection system. In reality, it receives frame transmission data from the communication links, including frame transmission rates (sampling frequency 1 time / second), load rates, and historical frame transmission records (data from the last 24 hours) for different links (typically 5-20 links). This data is stored categorized by spatiotemporal dimension, supports data querying and statistical analysis, and can output key indicators such as link bandwidth fluctuation range, average load rate, and frame transmission delay distribution. The platform's role is to provide data support for other core technology modules, such as providing historical data references for constructing frame strategy sets for non-cooperative game scheduling models, and providing link status information for triggering the frame information acquisition module's acquisition enable signal (activating when load rate is below 50%, periodically switching between 50%-80%, and deactivating above 80%). It builds a bridge between data and algorithms, enabling the dynamic frame order queuing system to adjust its strategy based on actual link data, avoiding blind calculations that are detached from the actual scenario, and ensuring the scientific nature and adaptability of the entire system's processing logic. It is an important data foundation platform for achieving frame order optimization processing.

[0060] like Figure 2As shown, the frame order intelligent queuing processing method based on the DL / T698 protocol includes the following steps: First, the DL / T698 frame information acquisition module extracts frame data conforming to the DL / T698 protocol from the communication link of the power user electricity consumption information acquisition system, and transmits the extracted frame data to the dynamic frame order parameter parsing module; Second, the dynamic frame order parameter parsing module parses the received frame data, extracting frame length, frame priority, frame type, frame transmission timestamp, frame target address, and frame source address parameters, and transmits the parsed parameters to the quantum annealing de-aggregation operation module and the non-cooperative game scheduling decision module respectively; Third, the quantum annealing de-aggregation operation module receives the parsed frame parameters, performs de-aggregation processing according to the process set by the quantum annealing de-aggregation optimization algorithm, separates redundant information and valid information in the frame parameters, and transmits the valid information to the dynamic adaptive ant colony optimization sorting module; Fourth, the non-cooperative game scheduling decision module... The cooperative game scheduling decision module receives the parsed frame parameters and constructs a non-cooperative game scheduling model based on the link status data provided by the DL / T698 spatiotemporal big data analysis platform. It solves the model to obtain a Nash equilibrium strategy combination and converts it into scheduling instructions, which are then transmitted to the dynamic adaptive ant colony optimization sorting module. In the fifth step, the dynamic adaptive ant colony optimization sorting module receives valid information and scheduling instructions, performs frame order queuing calculations using the dynamic adaptive ant colony optimization algorithm, first initializing the pheromone concentration matrix, then controlling the ants to perform path searching, updating the pheromone concentration matrix, filtering and adjusting to obtain the final frame order, and transmitting the final frame order to the frame order output execution module. In the sixth step, the frame order output execution module receives the final frame order, verifies it against the frame transmission constraints under the DL / T698 protocol, and controls the transmission order of frame data in the power user electricity consumption information collection system communication link according to the verified frame order.

[0061] The intelligent queuing system and method for frame order processing based on the DL / T698 protocol, through the collaborative work of multiple modules, first comprehensively collects and parses the frame data in the communication link to ensure the acquisition of complete attribute information of the frame. Then, through de-aggregation processing, it accurately separates redundant information and valid information in the frame data, avoiding the link burden caused by redundant content occupying transmission resources and solving the problem of information mixing in traditional technologies. At the same time, relying on the optimized sorting algorithm, it dynamically adjusts the frame order based on the frame's own attributes and the real-time status of the link, avoiding the accumulation and congestion caused by the disordered transmission of frame data, greatly improving the frame data transmission efficiency, and making up for the lack of dynamic adaptability in frame order processing of traditional technologies.

[0062] This system and method have significant advantages in scheduling strategies and system adaptability. By constructing a scientific scheduling model, it fully considers the mutual influences in the multi-frame transmission process. Unlike traditional technologies that ignore inter-frame correlations, it formulates targeted transmission arrangements based on frame priority, transmission requirements, and other characteristics. It provides adaptive transmission strategies for different types of frame data, ensuring the stable transmission of high-priority, high-demand frame data. At the same time, it can adapt to dynamic transmission scenarios with a surge in data volume, cope with complex and ever-changing communication environments, and avoid the problem of traditional technologies being unable to adapt to environmental changes due to fixed scheduling logic. Ultimately, it ensures accurate and timely transmission of frame data, meeting the needs of power dispatching, user electricity consumption monitoring, and system operation and maintenance, and ensuring the stable operation of the electricity information collection system.

[0063] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0064] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent 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 frame order intelligent queuing processing system based on the DL / T698 protocol, characterized in that, include: The system comprises a DL / T698 frame information acquisition module, a dynamic frame order parameter parsing module, a quantum annealing de-aggregation operation module, a non-cooperative game scheduling decision module, a dynamic adaptive ant colony optimization sorting module, and a frame order output execution module. The DL / T698 frame information acquisition module is connected to the dynamic frame order parameter parsing module. The dynamic frame order parameter parsing module is connected to both the quantum annealing de-aggregation operation module and the non-cooperative game scheduling decision module. Both the quantum annealing de-aggregation operation module and the non-cooperative game scheduling decision module are connected to the dynamic adaptive ant colony optimization sorting module. The dynamic adaptive ant colony optimization sorting module is connected to the frame order output execution module. The DL / T698 frame information acquisition module extracts frame data conforming to the DL / T698 protocol from the communication link of the power user electricity consumption information acquisition system. The dynamic frame order parameter parsing module parses the frame length, frame priority, frame type, frame transmission timestamp, frame target address, and frame source address parameters contained in the extracted frame data. The quantum annealing de-aggregation operation module performs de-aggregation processing on the parsed frame parameters according to the quantum annealing de-aggregation optimization algorithm to separate redundant information and effective information in the frame parameters. The non-cooperative game scheduling decision module constructs a non-cooperative game scheduling model based on the parsed frame parameters and calculates the scheduling strategy. The dynamic adaptive ant colony optimization sorting module combines the effective information output by the quantum annealing de-aggregation operation module and the scheduling strategy output by the non-cooperative game scheduling decision module, and performs dynamic frame order queuing calculation on the frame data through the dynamic adaptive ant colony optimization algorithm. The frame order output execution module controls the transmission order of frame data in the communication link according to the frame order result output by the dynamic adaptive ant colony optimization sorting module. The quantum annealing de-aggregation operation module includes a frame parameter redundancy identification unit, a quantum state initialization unit, an annealing iteration operation unit, and an effective information extraction unit. The frame parameter redundancy identification unit compares the frame length, frame priority, frame type, frame transmission timestamp, frame target address, and frame source address parameters output by the dynamic frame order parameter parsing module, filtering out repeated parameter values ​​and combinations between different frames. The quantum state initialization unit constructs a quantum bit sequence corresponding to the frame parameters based on the number of frame parameters and the redundancy identification results, and randomly initializes the quantum bit sequence to generate an initial quantum state. The annealing iteration operation unit gradually adjusts the weight ratio of the initial Hamiltonian and the target Hamiltonian in the Hamiltonian according to the annealing progress parameter change curve set by the quantum annealing de-aggregation optimization algorithm, performing iterative evolution calculations on the quantum state. The effective information extraction unit measures the final quantum state output by the annealing iteration operation unit, separates the redundant information and effective information in the frame parameters based on the measurement results, and transmits the effective information to the dynamic adaptive ant colony optimization sorting module. The non-cooperative game scheduling decision module includes a frame strategy set construction unit, a payoff function calculation unit, a Nash equilibrium solution unit, and a scheduling strategy output unit. The frame strategy set construction unit constructs a set of feasible strategies for each frame of data based on historical frame transmission data provided by the DL / T698 spatiotemporal big data analysis platform, combined with the current communication link bandwidth, link load, and frame transmission delay requirements. These strategies include the selection of transmission timing, the setting of retransmission count, and the adjustment range of priority. The revenue function calculation unit constructs feasible strategies based on the frame strategy set output by the unit, and calculates the revenue value of each frame under different strategy combinations by considering frame priority, transmission delay, and link resource occupancy. The Nash equilibrium solution unit uses an iterative approximation method to solve the revenue function of all frames, and finds the Nash equilibrium strategy combination that maximizes the revenue of each frame and cannot be improved by changing the strategy alone. The scheduling strategy output unit converts the strategy combination obtained by the Nash equilibrium solution unit into specific scheduling instructions and transmits them to the dynamic adaptive ant colony optimization sorting module. The dynamic adaptive ant colony optimization sorting module includes a pheromone matrix initialization unit, an ant path search unit, a pheromone update unit, and a frame order sorting unit. The pheromone matrix initialization unit constructs a pheromone concentration matrix between frames based on the number of frame data, and assigns values ​​to all elements in the matrix according to preset initial pheromone concentration values. The ant path search unit sets the number of ants, allowing each ant to start from a randomly selected initial frame, and select the next frame to be sorted in sequence based on the transition probability calculated by the pheromone concentration matrix and heuristic function value, until all frames have been selected once to form a complete path. After all ants have completed a path search, the pheromone update unit updates the elements in the pheromone concentration matrix according to the pheromone evaporation coefficient and pheromone increment calculation method set by the dynamic adaptive ant colony optimization algorithm. The frame order sorting unit evaluates the frame order corresponding to the paths searched by all ants, selects the frame order with the best path evaluation value as the initial sorting result, and adjusts the initial sorting result in combination with the scheduling instructions output by the non-cooperative game scheduling decision module to obtain the final frame order.

2. The intelligent queuing system for frame order processing based on the DL / T698 protocol according to claim 1, characterized in that, The dynamic adaptive ant colony optimization algorithm used in the dynamic adaptive ant colony optimization sorting module satisfies the formula: ,in, This represents the pheromone concentration from frame i to frame j at time t. Indicates the pheromone evaporation coefficient. This represents the set of ant colonies that have completed frame sorting at time t. This represents the pheromone increment left by the k-th ant on the path from frame i to frame j; it also satisfies the formula: ,in, Let represent the probability that the k-th ant selects frame j from frame i as the next sorting object at time t. Factors indicating the importance of pheromones This represents the importance factor of the heuristic function. This represents the heuristic function value from frame i to frame j at time t. This represents the set of next sorted frames that frame i can select.

3. The intelligent queuing system for frame order processing based on the DL / T698 protocol according to claim 1, characterized in that, The non-cooperative game scheduling model constructed by the non-cooperative game scheduling decision module satisfies the following formula: ,in, Indicates frame i in the policy Other frame strategies The profit function under the following conditions Let i represent the set of feasible strategies for frame i. and These represent the frame delay weight coefficient and the frame priority weight coefficient, respectively. Indicates frame i in the policy Other frame strategies The transmission delay below, Indicates frame i in the policy Other frame strategies The priority benefits are as follows; and the formula is also satisfied: ,in, This represents the Nash equalization strategy for frame i. This represents the set of Nash equalization strategies for other frames.

4. The intelligent queuing system for frame order processing based on the DL / T698 protocol according to claim 1, characterized in that, The quantum annealing depolymerization operation module uses a quantum annealing depolymerization optimization algorithm that satisfies the following formula: ,in, Indicates the annealing parameters The changing Hamiltonian. Indicates the annealing progress parameter and This represents the initial Hamiltonian. Represents the target Hamiltonian. This indicates the frame parameter de-aggregation state; and simultaneously satisfies the formula: ,in, Indicates the annealing parameters The next frame parameters are in a de-aggregation state. The probability, Indicates the annealing parameters The changing inverse temperature parameter, This represents the set of all possible frame parameter solution aggregate states.

5. The intelligent queuing system for frame order processing based on the DL / T698 protocol according to claim 1, characterized in that, The DL / T698 frame information acquisition module and the DL / T698 spatiotemporal big data analysis platform work together to satisfy the following formula: ,in, This indicates the total amount of frame data collected. Indicates the start time of data collection. Indicates the end time of data collection. This represents the set of communication links monitored by the DL / T698 spatiotemporal big data analysis platform. This represents the frame transmission rate of link m at time t. This represents the frame acquisition enable signal for link m at time t; the parameters resolved by the dynamic frame sequence parameter parsing module satisfy the formula: ,in, This represents the set of parsed parameters for the i-th frame. Indicates the length of the i-th frame. This indicates the priority of the i-th frame. Indicates the type of the i-th frame. This represents the timestamp of the transmission of the i-th frame. This represents the target address of the i-th frame. This represents the source address of the i-th frame.

6. The intelligent queuing system for frame order processing based on the DL / T698 protocol according to claim 1, characterized in that, The frame order output execution module controls the frame transmission order to satisfy the formula: ,in, Indicates the final output frame order. This indicates the initial frame order output by the dynamic adaptive ant colony optimization sorting module. This represents the set of frame transmission constraints under the DL / T698 protocol; simultaneously satisfying the formula: ,in, This indicates the maximum frame length limit of the communication link. This represents the lowest priority threshold for frame transmission. Indicates the current system timestamp. This indicates the maximum permissible deviation of the frame transmission timestamp.

7. The method corresponding to the frame order intelligent queuing processing system based on the DL / T698 protocol as described in any one of claims 1-6, characterized in that, Includes the following steps: The first step is to extract frame data conforming to the DL / T698 protocol from the communication link of the power user electricity consumption information acquisition system through the DL / T698 frame information acquisition module, and then transmit the extracted frame data to the dynamic frame sequence parameter parsing module. The second step is for the dynamic frame order parameter parsing module to parse the received frame data and extract parameters such as frame length, frame priority, frame type, frame sending timestamp, frame target address, and frame source address. The parsed parameters are then transmitted to the quantum annealing de-aggregation operation module and the non-cooperative game scheduling decision module, respectively. The third step is that the quantum annealing de-aggregation operation module receives the parsed frame parameters, performs de-aggregation processing according to the process set by the quantum annealing de-aggregation optimization algorithm, separates the redundant information and valid information in the frame parameters, and transmits the valid information to the dynamic adaptive ant colony optimization sorting module. The fourth step is that the non-cooperative game scheduling decision module receives the parsed frame parameters, combines the link status data provided by the DL / T698 spatiotemporal big data analysis platform to construct a non-cooperative game scheduling model, solves the model to obtain the Nash equilibrium strategy combination and converts it into scheduling instructions, and transmits the scheduling instructions to the dynamic adaptive ant colony optimization sorting module. The fifth step is that the dynamic adaptive ant colony optimization and sorting module receives valid information and scheduling instructions, performs frame order queuing calculation through the dynamic adaptive ant colony optimization algorithm, first initializes the pheromone concentration matrix, then controls the ants to search for paths, updates the pheromone concentration matrix, filters and adjusts to obtain the final frame order, and transmits the final frame order to the frame order output execution module. The sixth step is for the frame sequence output execution module to receive the final frame sequence, verify the final frame sequence in accordance with the frame transmission constraints under the DL / T698 protocol, and control the transmission order of frame data in the communication link of the power user electricity consumption information collection system according to the verified frame sequence.

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