Power grid load shedding service dynamic channel allocation method based on multi-carrier aggregation

Abstract

The invention relates to the technical field of power wireless communication, and particularly discloses a power grid load shedding service dynamic channel allocation method based on multi-carrier aggregation, which comprises the following steps: firstly analyzing a control instruction to determine a terminal group, and anchoring a high-stability main carrier as a synchronization reference; and then, an evaluation strategy based on channel quality distribution balance is introduced, the average performance and internal consistency of the group are considered when the slave carrier waves are screened, and the wooden barrel effect caused by channel deterioration of individual terminals is effectively avoided. On the basis, a control instruction is decoupled into a core layer and an enhancement layer, high-redundancy cross-carrier transmission is carried out on core data, and self-adaptive fragmentation bearing is carried out on enhanced data. By means of physical layer synchronous broadcast of master and slave carriers and joint decoding of a terminal side, stable and synchronous issuing of key control signals to massive heterogeneous terminals is achieved, and execution of a precise load shedding task of a power grid is guaranteed.

Description

Technical Field

[0001] This application relates to the field of power wireless communication technology, and more specifically, to a dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation. Background Technology

[0002] The large-scale integration of a high proportion of distributed renewable energy sources has altered the characteristics of power grid operation, making the coordinated interaction of power generation, grid, load, and storage a core requirement of the new power system. To ensure the safe and stable operation of the power grid in emergency situations, precise load shedding services have emerged. This service requires communication systems to accurately send control commands to a massive number of distributed terminal devices within millisecond delays, enabling rapid load shedding for specific areas or equipment. Although power wireless private networks based on the 230MHz band have become an important means of carrying such services due to their dedicated spectrum, wide coverage, and high security, the limitations of single narrowband carriers in terms of bandwidth capacity and transmission reliability are gradually becoming apparent in the face of increasing concurrent control demands.

[0003] However, most existing dynamic channel allocation mechanisms are designed based on single-user or homogeneous multi-user models, which have significant limitations when handling group-oriented broadcast or multicast services. Specifically, existing technologies typically use statistical indicators such as average signal-to-interference-plus-noise ratio (SIR / NNR) to measure the quality of carriers when evaluating channel quality. This average-based evaluation mechanism ignores the differences in channel quality distribution among terminals within a group, i.e., it is insensitive to the "weakest link" effect in the communication system. For example, although some carriers may have a high overall average SIR / NNR, their channel quality distribution within the group may be extremely uneven, with some terminals experiencing very poor channel quality. If the system selects such carriers with large dispersion based solely on the average value, terminals in the weakest position will be unable to correctly decode control commands, leading to the failure of the entire regional load shedding task. Furthermore, when aggregating and transmitting multiple discrete carriers, existing technologies lack effective synchronization mechanisms tailored to group characteristics, making it difficult to guarantee the time synchronization of multiple physical carrier signals at terminals in different geographical locations, and failing to provide differentiated data protection strategies for terminals with different channel conditions, thus failing to meet the stringent requirements of high reliability and high synchronization for power grid load shedding services.

[0004] Therefore, an optimized dynamic channel allocation scheme for power grid load shedding services is desired. Summary of the Invention

[0005] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation, comprising: Obtain power grid control commands; The power grid control commands are parsed and the primary carrier is selected to obtain the terminal group and the primary carrier ID; Based on the available carrier pool, the terminal group is subjected to carrier selection and channel quality detection to obtain the carrier set and group channel status profile. Based on the group channel state profile, the primary carrier ID, and the secondary carrier set, the power grid control commands are hierarchically encoded and aggregated to obtain an aggregated data packet set, which includes one primary carrier data packet and N secondary carrier data packets; Based on the master carrier ID and slave carrier set, the aggregated data packet set is broadcast synchronously on the master and slave carriers.

[0006] Compared with existing technologies, this invention addresses the communication bottlenecks caused by insufficient synchronization of multi-carrier transmission and differences in group channel quality in power grid load shedding services by constructing a master-slave carrier collaborative architecture and a hierarchical instruction encoding mechanism. First, it parses control instructions to establish terminal groups and anchors a highly stable master carrier as the synchronization benchmark. Then, it introduces an evaluation strategy based on the uniformity of channel quality distribution, considering both the average performance of the group and internal consistency when selecting slave carriers, effectively avoiding the "weakest link" effect caused by channel degradation in individual terminals. Based on this, the control instructions are decoupled into a core layer and an enhancement layer. Core data is transmitted with high redundancy across carriers, while enhancement data is adaptively fragmented and carried out. By utilizing the physical layer synchronous broadcasting of master and slave carriers and joint decoding on the terminal side, stable and synchronous delivery of critical control signals to a massive number of heterogeneous terminals is achieved, ensuring the execution efficiency of precise load shedding tasks in the power grid. Attached Figure Description

[0007] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0008] Figure 1 This is a flowchart of a dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation, according to an embodiment of this application. Figure 2 This is a data flow diagram of a dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation, according to an embodiment of this application. Figure 3 This is a flowchart illustrating the process of parsing power grid control commands and selecting the primary carrier to obtain the terminal group and primary carrier ID in a dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation, according to an embodiment of this application. Figure 4This is a flowchart illustrating the process of selecting a carrier from a terminal group and probing the channel quality of the terminal group based on the available carrier pool in the dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation, according to an embodiment of this application, to obtain a carrier set and a group channel status profile. Figure 5 This is a flowchart illustrating the process of evaluating the group utility of a candidate carrier list and optimizing the sub-carrier set to obtain a sub-carrier set and a group channel state profile based on a channel state report set in a dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation, according to an embodiment of this application. Figure 6 This is a flowchart illustrating the process of hierarchically encoding and aggregating packets of power grid control commands to obtain an aggregated data packet set, based on a group channel state profile, master carrier ID, and slave carrier set, according to the dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation in this application embodiment. Detailed Implementation

[0009] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0010] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0011] While this application makes various references to certain modules of the systems according to embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The modules described are merely illustrative, and different aspects of the systems and methods may use different modules.

[0012] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0013] Most existing dynamic channel allocation mechanisms are designed based on a single-user model. When handling broadcast services for power grid groups, they typically only use statistical indicators such as average signal-to-interference-plus-noise ratio (SINR) to evaluate carrier quality. This evaluation method, which ignores the differences in channel quality distribution within the group, makes the system insensitive to the "weakest link" effect in communication and lacks an effective synchronization mechanism for discrete multi-carriers, making it difficult to meet the stringent requirements of high reliability and high synchronization for power grid load shedding services. Therefore, this application proposes a dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation. By constructing a master-slave carrier cooperative architecture and a hierarchical instruction coding mechanism, it achieves stable and synchronous transmission of key control signals to a massive number of heterogeneous terminals. Specifically, this method first parses the power grid control commands to establish the target terminal group and anchors a highly stable master carrier as the synchronization reference. Then, it introduces an evaluation strategy based on the uniformity of channel quality distribution, which punishes carriers with uneven quality distribution by quantifying the channel dispersion of the group. This ensures that the average performance of the group and internal consistency are considered when selecting slave carriers, effectively avoiding the bottleneck problem caused by the deterioration of individual terminal channels. On this basis, the control commands are further decoupled into a core layer and an enhancement layer. High-redundancy cross-carrier transmission is used for core data, and adaptive fragmentation is implemented for enhancement data. Finally, the physical layer synchronous broadcast of master and slave carriers and joint decoding on the terminal side are used to ensure the execution efficiency of the power grid's precise load shedding task.

[0014] Figure 1 This is a flowchart of a dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation, according to an embodiment of this application. Figure 2 This is a schematic diagram of data flow in a dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation, according to an embodiment of this application. Figure 1 and Figure 2 As shown, the dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation according to an embodiment of this application includes: S100, obtaining power grid control commands; S200, parsing and selecting a primary carrier from the power grid control commands to obtain a terminal group and a primary carrier ID; S300, selecting secondary carriers and performing channel quality detection on the terminal group based on an available carrier pool to obtain a set of secondary carriers and a group channel status profile; S400, performing hierarchical encoding and aggregation packetization on the power grid control commands based on the group channel status profile, the primary carrier ID, and the set of secondary carriers to obtain an aggregated data packet set, wherein the aggregated data packet set includes one primary carrier data packet and N secondary carrier data packets; S500, performing primary and secondary carrier synchronous broadcasting on the aggregated data packet set based on the primary carrier ID and the set of secondary carriers.

[0015] Specifically, in step S100, a power grid control command is acquired. It is understood that due to the highly time-sensitive and sudden nature of sudden power grid failures or regional supply-demand imbalances, the communication processing side must be able to respond in real-time to authoritative commands from the dispatch control center to initiate subsequent precise load shedding processes. Otherwise, it will lead to invalid occupation of wireless resources or delays in load shedding actions, thereby posing a risk to power grid stability. Therefore, in the technical solution of this application, a power grid control command is acquired in real-time as the trigger source for initiating the entire multi-carrier aggregation transmission and dynamic channel allocation process. This ensures that subsequent channel resource scheduling, group identification, and data hierarchical coding are based on the latest and legitimate power grid operation requirements, guaranteeing the validity and timeliness of commands for source-grid-load-storage interaction services, and laying the foundation for achieving millisecond-level load response.

[0016] More specifically, in a specific example of this application, the acquisition process of power grid control commands follows a standardized data interaction process based on a dedicated power communication protocol. First, a dedicated communication link is established with the power grid dispatch master station or load management master station to maintain real-time monitoring of the downlink control port. This link, built on the power dispatch data network or a dedicated fiber optic backbone network, is configured to carry high-priority control messages. When the dispatching side detects abnormal conditions requiring load intervention, such as frequency drops or line overloads, a digital message containing the load shedding action type, target area identifier, and execution level is pushed to the communication interface. Next, the receiving end reads the data stream from the buffer via a socket communication mechanism and, according to a preset communication protocol, such as the IEC 104 protocol or the MQTT protocol, performs frame header identification, length verification, and cyclic redundancy check on the received data packets to verify the integrity and legality of the data, eliminating incomplete or illegal packets caused by transmission link interference. Finally, the verified raw binary command data is stored in a high-speed waiting queue for subsequent command parsing and primary carrier selection steps. This process rigorously ensures the determinism and security of instruction transmission from the business source to the communication system, providing reliable data input for subsequent multi-carrier resource allocation for specific terminal groups.

[0017] Specifically, in step S200, the power grid control command is parsed and a primary carrier is selected to obtain the terminal group and primary carrier ID. It is understood that since the original power grid control command typically encapsulates specific business logic and target objects, without deep semantic parsing, it is impossible to pinpoint the physical terminal range requiring load shedding. Furthermore, in a multi-carrier aggregation communication architecture, without a reference channel with high stability and wide coverage to carry the synchronization beacon, subsequent aggregation transmission will struggle to achieve cross-carrier symbol alignment due to the lack of a unified timing reference. Therefore, in the technical solution of this application, the received power grid control command is further parsed to extract the target terminal group, and a primary carrier ID is simultaneously selected based on the carrier attributes in the system configuration data. This clarifies the physical execution boundary of the control service and establishes the synchronization reference anchor point for wireless transmission. This ensures that the load shedding command is accurately routed to the designated set of controlled terminals, avoiding false triggering or failure to trigger. Simultaneously, the physical robustness of the primary carrier ensures reliable delivery of core synchronization information, building a solid physical layer foundation for subsequent multi-carrier collaborative work and layered data transmission.

[0018] Figure 3 This is a flowchart illustrating the process of parsing power grid control commands and selecting the primary carrier to obtain the terminal group and primary carrier ID, according to the dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation, as described in an embodiment of this application. Figure 3 As shown, step S200 includes: S210, parsing the power grid control command into a target group based on the group membership table in the system configuration data to obtain the terminal group; S220, extracting a candidate primary carrier set from the carrier attribute table in the system configuration data; S230, calculating the primary carrier adaptability score of each candidate primary carrier in the candidate primary carrier set; S240, selecting the ID of the candidate primary carrier corresponding to the largest primary carrier adaptability score as the primary carrier ID.

[0019] In step S210, based on the group membership table in the system configuration data, the power grid control command is parsed to obtain the terminal group. It is understood that since power grid dispatch commands are typically issued to logical service sets such as substations, distribution areas, or specific load levels, rather than directly targeting thousands of dispersed terminals with different physical addresses, if the communication system lacks the ability to map the logical service view to the physical communication view, it will be unable to translate the control intent into underlying wireless addressing operations. Therefore, in the technical solution of this application, the power grid control command is further parsed to obtain the terminal group based on the group membership table in the system configuration data, thereby accurately translating the abstract service target identifier into a specific set of physical terminal identifiers. This shields the dispatching side from the complexity of managing underlying communication addresses, while ensuring that load shedding commands can cover every execution unit within the target area without omission, achieving precise integration between business logic and network transmission.

[0020] More specifically, in a concrete example of this application, the parsing and generation process of the target group follows a rigorous logic from message extraction to database mapping. First, the received power grid control command message is parsed using the application layer protocol. The target type field, which identifies the target attribute, and the business logic ID field, which identifies the specific object, are extracted according to a predefined frame format. This business logic ID uniquely corresponds to a specific management unit in the power grid topology. Then, using the extracted business logic ID as the index key, the system configuration data pre-stored in the core network storage unit is retrieved, particularly the group member relationship table. This table maintains a one-to-many mapping relationship between logical groups and physical terminal IDs (such as IMSI, MAC address, or dedicated tunnel identifier) ​​in a relational structure or key-value pair format. Finally, all associated records in the query are traversed, and the corresponding terminal physical identifier in each record is extracted. The extraction results are deduplicated and validated. All validated terminal identifiers are aggregated to generate the final terminal group list. This list will serve as the physical addressing basis for initiating channel quality probing and multicarrier multicast in subsequent steps.

[0021] In step S220, a set of candidate primary carriers is extracted from the carrier attribute table in the system configuration data. It is understood that, since the primary carrier in a multi-carrier aggregation system bears the critical responsibility of transmitting synchronization beacons and core control signaling, its physical robustness and coverage capability are prerequisites for the success of the entire communication task. If carriers experiencing hardware failures, limited transmission power, or those defined solely for data transmission are hastily included in the selection range, the synchronization stability of the entire group will be directly threatened. Therefore, in the technical solution of this application, a set of candidate primary carriers is further extracted from the carrier attribute table in the system configuration data to conduct preliminary validity screening and qualification access control of the entire network's physical resources. This eliminates carriers that do not meet the basic physical conditions or logical definitions of the primary carrier role from the source, ensuring that subsequent comprehensive evaluation focuses only on a high-quality subset of resources capable of supporting the system's synchronization benchmark, thereby improving decision-making efficiency and system security.

[0022] More specifically, in a concrete example of this application, the extraction process of the candidate primary carrier set is implemented through database query and multi-dimensional attribute matching mechanism. First, the core network processing unit accesses the carrier attribute table stored in the system configuration database. This table maintains detailed static parameters such as the center frequency, bandwidth, and maximum transmit power of each physical carrier, as well as its current real-time operating status information. Next, a conditional filtering operation is performed on all records in the table, checking the operating status field of each carrier one by one to verify whether it is in a normal or idle state, thereby excluding carriers currently marked as faulty, locked, or under maintenance. Simultaneously, the carrier type or function definition field is checked to confirm whether the carrier has been pre-configured by the system with the authority or capability to act as a primary carrier, such as whether it supports high-power broadcasting and synchronization sequence transmission. Finally, unique identifiers of all carriers that simultaneously meet the health status and function definition conditions are extracted to construct a candidate primary carrier set, which serves as the data input for subsequent primary carrier adaptability score calculations. Specifically, the carrier attribute table in the configuration data can be stored using the following data structure: {Carrier_ID:001,Freq:232.5MHz,Max_Power:43dBm,Function_Tag:"Broadcast_Capable",Status:"Idle"}. By parsing the Function_Tag field, the frequency points that the physical layer supports for synchronization beacon transmission can be quickly filtered out.

[0023] In step S230, the carrier adaptability score of each candidate carrier in the candidate carrier set is calculated. It is understood that different physical carriers differ in long-term operational stability, wireless signal coverage, current electromagnetic interference levels, and service load. A single available status is insufficient to identify the carrier best suited for network-wide synchronization and core signaling transmission. Selecting a carrier with numerous coverage blind spots or severe interference as the primary anchor point will directly lead to a decrease in the reach rate of group control commands. Therefore, in this application's technical solution, the carrier adaptability score of each candidate carrier in the candidate carrier set is further calculated to establish a multi-dimensional quantitative evaluation model, accurately ranking the comprehensive performance of each carrier in ensuring critical control services. This ensures that the finally selected primary carrier possesses optimal robustness and wide coverage advantages in physical characteristics, while avoiding high-interference and high-load frequency points in terms of environmental characteristics, providing solid physical layer support for the highly reliable execution of load-switching services.

[0024] More specifically, in a concrete example of this application, the scoring calculation process is implemented using a mathematical model combining weighted summation and a penalty mechanism. First, the four key indicator data for each candidate carrier are read from the carrier attribute table and normalized to eliminate dimensional differences. Then, these are substituted into a preset primary carrier adaptability scoring formula for calculation. This formula is specifically constructed as follows: in, The primary carrier adaptability score, representing the candidate carriers, is the basis for the final decision. This long-term reliability score is derived from statistics on the carrier's historical fault-free operating time and mean time between failures, reflecting the inherent stability of the physical hardware and spectrum environment at that frequency. Coverage performance scoring characterizes the signal penetration capability and coverage breadth of a frequency point within a target geographical area, and is typically obtained from drive test data or propagation model simulations. The background interference level, obtained through real-time spectrum scanning, represents the current noise floor and external interference intensity at that frequency point. The current load rate indicates the current service usage on this carrier. , , and These are all preset weighting coefficients used to adjust the weight of each indicator on the total score. For example, urban areas can take... =0.4、 =0.3、 =0.2、 =0.1; High-interference industrial parks can take =0.3、 =0.2、 =0.4、 =0.1. This calculation logic uses reliability and coverage performance as positive gain terms and interference and load as negative penalty terms to achieve optimal performance. For example, in actual operation scenarios, if a certain frequency point has historically had excellent signal coverage, i.e., coverage performance score... The level is high, but at the current moment, the background interference level is high due to the presence of nearby co-channel interference sources. A sudden increase, calculated using the above formula, will cause the penalty value of high interference to offset the score advantage brought by coverage, thus leading to a decrease in its final adaptability score. This method will then automatically avoid this risky frequency point and instead select another carrier with slightly weaker coverage but a cleaner spectrum environment as the main carrier, thereby effectively preventing the load shedding command from encountering severe channel congestion in the early stage of issuance.

[0025] In step S240, the ID of the candidate primary carrier corresponding to the primary carrier with the highest primary carrier adaptability score is selected as the primary carrier ID. It is understood that since the adaptability score calculated in the preceding steps is a comprehensive quantitative representation of each candidate carrier in four dimensions—long-term reliability, coverage, anti-interference capability, and load idleness—a higher score means a greater probability of successful transmission of critical signaling under the current power grid operating environment. If the principle of selecting the best among the best is not strictly followed, the synchronization beacon may be lost in a harsh electromagnetic environment due to the selection of a suboptimal carrier. Therefore, in the technical solution of this application, the ID of the candidate primary carrier corresponding to the primary carrier with the highest primary carrier adaptability score is further selected as the primary carrier ID, thereby locking the physical anchor point of the entire multi-carrier aggregation system onto the spectrum resource with the best overall performance at the current moment. This ensures that the synchronization header sequence and core layer instruction data in the subsequent aggregation frame structure are carried on the most robust transmission channel within the system, minimizing the risk of group synchronization failure caused by channel quality fluctuations, and providing the strongest first-packet arrival guarantee for millisecond-level load shedding services.

[0026] More specifically, in a concrete example of this application, the selection process is executed by the core network's resource scheduling unit through numerical comparison logic. First, all candidate carriers and their corresponding calculated scores are constructed into a key-value pair list or index array. Then, an efficient traversal search algorithm or sorting algorithm, such as quicksort, is used to perform a full scan and comparison of the score values ​​in the list. During the traversal, a pointer variable pointing to the current maximum score is maintained in real time. Once it is found that the score of the currently traversed object is higher than the value recorded by the pointer, the pointer and its corresponding carrier identifier are updated. After the traversal process is completed, the unique frequency identifier of the candidate carrier locked by the final pointer is directly extracted, officially marked as the primary carrier ID for this load shedding task, and an instruction is immediately issued to the base station radio frequency control module, commanding it to tune the primary transmission channel to this frequency, preparing the physical layer for the upcoming synchronous broadcast. If there is a tie for the highest score, a carrier with a smaller frequency index or fewer historical selections is selected according to preset secondary rules to ensure the uniqueness and determinism of the decision.

[0027] Specifically, in step S300, based on the available carrier pool, carrier selection and channel quality detection are performed on the terminal group to obtain a set of carriers and a group channel state profile. It is understood that, due to the time-varying and frequency-selective fading characteristics of wireless channels, and the fact that power grid load shedding services involve terminal groups located in different geographical locations, the channel reception quality of these terminals on the same physical carrier at the same time often exhibits significant spatial differences. If real-time channel state information for a specific target group is lacking and carriers are blindly assigned, the selected carriers are likely to experience deep fading or high interference for some critical terminals within the group, thereby undermining the reliability gains brought by multi-carrier aggregation. Therefore, in the technical solution of this application, carrier selection and channel quality detection are further performed on the terminal group based on the available carrier pool to obtain a set of carriers and a group channel state profile. This proactively acquires real-time communication quality data of the group at each candidate frequency point and establishes a digital profile reflecting the overall channel conditions and distribution characteristics of the group. This ensures that the final carrier set is a combination of physical resources that has been tested and verified and has the best overall adaptability to the current group. At the same time, it provides accurate data support for subsequent hierarchical coding and adaptive transmission strategies, thereby minimizing the shortcomings of group communication caused by improper channel selection at the physical resource level.

[0028] Figure 4 This is a flowchart illustrating the process of selecting slave carriers and probing channel quality for a terminal group to obtain a slave carrier set and a group channel state profile, based on an available carrier pool, according to an embodiment of this application for a dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation. Figure 4As shown, step S300 includes: S310, based on the available carrier pool, performing carrier selection and channel quality detection on the terminal group to obtain a candidate carrier list and a channel state report set; S320, based on the channel state report set, performing group utility evaluation and carrier set optimization on the candidate carrier list to obtain a carrier set and a group channel state profile.

[0029] In step S310, based on the available carrier pool, carrier selection and channel quality probing are performed on the terminal group to obtain a candidate carrier list and a channel state report set. It is understood that due to the complexity and variability of the wireless spectrum environment, static configuration or unilateral monitoring at the base station cannot accurately determine the true downlink reception quality at the terminal side during power grid load shedding. Furthermore, full probing of all frequencies in the available carrier pool would incur significant signaling overhead and latency. Therefore, in this application's technical solution, carrier selection and channel quality probing are further performed on the terminal group based on the available carrier pool to obtain a candidate carrier list and a channel state report set. This delineates high-potential detection ranges before large-scale measurements and triggers on-site measurement and feedback mechanisms at the terminal side. This allows for the acquisition of accurate channel state information for each terminal in the group at the target frequency with minimal signaling cost, providing objective and detailed data input for subsequent carrier selection decisions.

[0030] More specifically, in a specific example of this application, the detection process is implemented through a combination of broadcast triggering on the base station side and distributed measurement feedback on the terminal side. First, based on the current spectrum resource occupancy of the system, the base station selects several idle carriers not heavily occupied from the available carrier pool, constructing a candidate carrier list. This list, along with a pre-calculated terminal reporting time slot scheduling table, is encapsulated into a detection request protocol data unit. Then, using the established high-reliability broadcast channel of the main carrier, the detection request is broadcast omnidirectionally to the target terminal group. Upon hearing the request on the main carrier, each unloaded terminal in the group immediately parses the candidate frequency information to be measured and initiates a fast frequency hopping scanning mode on the RF front-end, sequentially tuning to each candidate carrier frequency. It then performs millisecond-level rapid sampling and calculation of the downlink reference signal's received signal strength and signal-to-interference-plus-noise ratio. After completing the measurement, each terminal packages the measurement results of all candidate carriers to generate a channel state report and, strictly following the dedicated reporting time slot specified in the scheduling table, transmits the report back to the base station via the uplink control channel, thus completing a closed-loop channel quality detection process.

[0031] In step S320, based on the channel state report set, the candidate carrier list is evaluated for group utility and a subset of carriers is optimized to obtain the subset of carriers and a group channel state profile. It is understandable that existing methods have a technical problem with their group utility evaluation mechanisms: they are insensitive to the "weakest link" effect in channel quality distribution within a group. In critical control scenarios such as precise load shedding in power grids, the success of a group task highly depends on the communication assurance of the weakest member within the group. For example, in a centralized load shedding task in a power distribution area, evaluating carrier quality solely by calculating the average signal-to-interference-plus-noise ratio (SNR) masks individual differences. This results in an average value inflated due to a few terminals having extremely good channel quality, potentially concealing the risk of some terminals experiencing near-interruption of channel quality, leading to the selected carriers failing to provide reliable coverage for all members of the group. Especially in areas with complex terrain, terminals in low-lying or peripheral areas may have weak signals due to obstruction, yet the overall average value appears normal. This method ignores the strong coupling between the fairness of channel allocation and the reliability of the entire group task in critical control tasks oriented towards groups. In essence, it optimizes the total throughput potential of the group rather than the minimum service guarantee of group communication. Therefore, it may choose a carrier with a huge gap in channel quality, which does not match the underlying requirements of high reliability and high synchronization for power grid load shedding services.

[0032] Therefore, the technical solution of this application further evaluates the group utility of the candidate carrier list and optimizes the slave carrier set based on the channel state report set to obtain the slave carrier set and group channel state profile. This overcomes the information loss and decision risk caused by relying solely on average values ​​in existing methods. By deeply mining the group channel state report, it quantifies and penalizes the consistency of channel quality among its members while evaluating the overall performance of the carrier. In this way, more robust and reliable communication carriers can be selected, ensuring that the finally selected slave carriers not only meet the overall signal strength requirements but also have a more uniform signal distribution within the group. This effectively avoids the risk that the entire load shedding command cannot be executed synchronously due to individual terminals becoming communication bottlenecks, and ensures that the power grid control commands are delivered without discrimination throughout the entire domain.

[0033] Figure 5 This is a flowchart illustrating the process of evaluating the group utility of a candidate carrier list and optimizing the slave carrier set to obtain a slave carrier set and a group channel state profile, based on a channel state report set, according to an embodiment of this application for a dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation. Figure 5As shown, step S320 includes: S321, extracting the channel state matrix and the number of effective terminals from the channel state report set; S322, performing group channel discretization on the channel state matrix and the number of effective terminals to obtain a discreteness vector and an average signal-to-interference-plus-noise ratio vector; S323, performing group utility evaluation based on equalization enhancement on the discreteness vector and the average signal-to-interference-plus-noise ratio vector to obtain a utility component vector; S324, performing robust carrier set decision on the candidate carrier list based on the utility component vector and the channel state matrix to obtain a channel state profile from the carrier set and the group.

[0034] In step S321, the channel state matrix and the number of effective terminals are extracted from the channel state report set. It is understood that since the channel state reports uploaded by each terminal are discrete, asynchronously arriving data packets, and may contain invalid data due to transmission errors, directly performing complex group equilibrium calculations based on these raw messages would be extremely difficult and inefficient. Therefore, in the technical solution of this application, the channel state matrix and the number of effective terminals are further extracted from the channel state report set to clean and reorganize the unstructured discrete reported data into a structured mathematical matrix form, and to determine the effective sample size actually involved in the calculation. This provides standardized data input for subsequent calculations of the group average signal-to-interference-plus-noise ratio and channel dispersion, ensuring that the group utility evaluation algorithm can run on a complete and validated dataset, thereby improving the mathematical accuracy of the decision.

[0035] More specifically, in a specific example of this application, the extraction process is performed by the data processing unit on the base station side, employing a combination of data cleaning and matrix mapping. First, an empty matrix with dimensions equal to the preset group size multiplied by the number of candidate carriers is initialized, and the valid terminal counter is set to zero. Then, the received channel state report set is traversed, and each report undergoes integrity verification, discarding reports that fail verification or have incorrect formats. For reports that pass verification, the terminal identifier and the signal-to-interference-plus-noise ratio (SIR) values ​​of each carrier are parsed, mapped, and filled into the corresponding row and column positions in the matrix, while the counter is incremented. If a terminal fails to report within a specified window, the corresponding row in the matrix is ​​marked as invalid or filled with a minimum value for distinction, triggering a secondary report; during the decision-making stage, if the missing proportion exceeds a threshold... Carriers with a weighting factor of 10% (e.g.) are applied. Ultimately, the generated channel state matrix not only fully records the quality details of all responding terminals at all candidate frequencies, but also provides an accurate normalization factor for the denominator in subsequent formulas for the number of effective terminals output.

[0036] In step S322, the channel state matrix and the number of effective terminals are subjected to group channel discretization to obtain a discrete vector and an average signal-to-interference-plus-noise ratio (SIR / NOR) vector. It is understandable that since the original SIR / NOR data only reflects information in a single dimension, directly using it for decision-making often fails to identify weaker terminals at the communication edge of the group due to the masking effect of the average value. This is especially true in power grid load shedding scenarios, where if a carrier has a strong overall signal but extremely poor coverage of certain key nodes, this highly unbalanced channel distribution will lead to local failures of the group task, thus triggering the "weakest link" effect. Therefore, in the technical solution of this application, the channel state matrix and the number of effective terminals are further subjected to group channel discretization to obtain a discrete vector and an average SIR / NOR vector, thereby extracting higher-level features that better characterize the group performance and internal consistency through data preprocessing. In this way, this step can upgrade the original channel state data through a single data transformation, expanding it from a single performance indicator to a two-dimensional characterization of group performance and internal consistency. This provides a physically meaningful input for the subsequent construction of a utility function that can perceive and suppress the barrel effect, ensuring that subsequent resource allocation not only focuses on total capacity, but also on coverage fairness and overall task completion rate.

[0037] More specifically, in a particular example of this application, the quantization process is implemented by the base station's computing unit performing statistical analysis on each column vector of the channel state matrix. Specifically, for each candidate carrier... It not only calculates its average signal-to-interference-plus-noise ratio within the group. Furthermore, the standard deviation of the group signal-to-interference-plus-noise ratio distribution was calculated. This is used as an index of the dispersion of the carrier channel quality. The calculation process is based on a mathematical model constructed from statistical principles, and is expressed as: in, Candidate carrier Average signal-to-interference-plus-noise ratio within the group Candidate carrier The standard deviation of the signal-to-interference-plus-noise ratio distribution of the upper group is used as an index of dispersion. To successfully report the number of valid terminals in the CSI report, For the terminal In candidate carriers The signal-to-interference-plus-noise ratio (SIR) measured above. For example, in a mixed group scenario including indoor substation terminals and outdoor overhead line terminals, a candidate carrier may have excellent signal strength for outdoor terminals but severe attenuation for indoor terminals, which will affect its calculated standard deviation. The signal is very large; while the other carrier may have a mediocre overall signal but good penetration, making all terminals... The values ​​are not high but are very close, resulting in a very small standard deviation. The above calculations are performed by traversing all candidate carriers, ultimately generating a dataset containing all... The discrete vector and contains all The average signal-to-interference-plus-noise ratio vector accurately quantifies the performance of each frequency point in two dimensions: average performance and coverage balance.

[0038] In step S323, a group utility evaluation based on balance enhancement is performed on the discreteness vector and the average signal-to-interference-plus-noise ratio (SINNR) vector to obtain a utility component vector. It is understandable that after obtaining the key features characterizing group performance and internal consistency, continuing to use traditional algorithms that only focus on total throughput will fail to effectively utilize the advanced feature of discreteness to circumvent communication bottlenecks. That is, the original utility function has decision-making flaws and cannot meet the stringent requirements of accurate load balancing for network-wide synchronization success. Therefore, in the technical solution of this application, a new function is designed to comprehensively consider average performance, internal consistency, and system load by further performing a group utility evaluation based on balance enhancement on the discreteness vector and the average SINNR vector to obtain a utility component vector. This allows for the design of a novel function that comprehensively considers average performance, internal consistency, and system load. In this way, instead of simply searching for the carrier with the optimal average performance, the optimal balance is sought between high average performance and high internal consistency, thus solving the technical problems in existing methods and ensuring that the utility evaluation results are highly aligned with the actual requirements of load balancing services for group communication reliability.

[0039] More specifically, in a particular example of this application, the evaluation process is executed by the resource scheduling processor on the base station side, and its core lies in applying a weighted scoring model that includes a penalty mechanism. Specifically, for each candidate carrier... The score is calculated using a group utility function that enhances balance, expressed as: in, Candidate carrier The balance enhances the group utility score. These are the average signal-to-interference-plus-noise ratio, dispersion, and the weighting coefficient of the current load, respectively. Candidate carrier Normalized current load, This represents the average signal-to-interference-plus-noise ratio (SIR) obtained from the preceding steps. This is the dispersion index obtained in the previous step. It should be noted that in this step, the dispersion index is introduced. This is a crucial penalty factor. It means that the more uneven the distribution of a carrier's channel quality within the group, and the greater the dispersion (i.e., the existence of obvious communication bottlenecks), the greater the penalty will be on its final score. For example, in a load shedding task targeting an old urban area, candidate carrier A, although having an extremely high average signal-to-interference-plus-noise ratio (SIR), had its high SIR boosted by extremely strong signals from some near-end terminals, while the signals from far-end terminals were weak, resulting in high dispersion. The signal strength of candidate carrier B is very high; while candidate carrier B, although having a moderate average signal-to-interference-plus-noise ratio, has very similar signal strength across all terminals, resulting in high dispersion. The value is extremely small. Calculated using the above formula, carrier A receives a lower score due to its significant dispersion penalty, while carrier B scores higher due to its robust consistency. This is used to generate a utility component vector, ensuring that the selected carrier can guarantee the communication quality of the weakest node in the group.

[0040] In step S324, robust carrier set decisions are made on the candidate carrier list based on the utility component vector and the channel state matrix to obtain the carrier set and group channel state profile. It is understood that, due to the aforementioned enhanced balance in the utility evaluation, executing the robust carrier set decision step requires a clear rule to utilize the calculation results from the preceding steps and transform them into the final physical resource allocation decision. Without this decision transformation step, the calculated optimal score cannot truly drive the scheduling of underlying radio frequency resources, resulting in the theoretical optimization failing to materialize. Therefore, in the technical solution of this application, robust carrier set decisions are further made on the candidate carrier list based on the utility component vector and the channel state matrix to obtain the carrier set and group channel state profile. This transforms the abstract utility score into a concrete physical resource decision, serving as the final execution step of the entire optimization mechanism. This ensures that the output carrier set is a robust optimal combination that has undergone careful evaluation and is more likely to guarantee reliable communication for all members within the group, rather than merely an average optimal set.

[0041] More specifically, in a particular example of this application, the decision-making process is implemented using a ranking and data persistence mechanism. During execution, all candidate carriers are... Sort the scores in descending order and select the highest score in the sorted results. Each carrier, along with its ID, forms the final set of slave carriers.

[0042] in, For the final output set of carriers, This indicates selecting the highest score. Each operation. This provides a list of candidate carriers. For example, consider a load shedding task covering a mixed-use commercial and residential area, which includes both rooftop distributed photovoltaic inverter terminals and electric vehicle charging stations in an underground parking garage. Calculate three candidate carriers. Scores: Carrier A, despite its extremely strong signal on the rooftop resulting in a high average signal-to-interference-plus-noise ratio, cannot penetrate underground, leading to a significant dispersion penalty, ultimately scoring 0.45; Carrier B, while having moderate overall signal strength, exhibits good penetration into underground parking garages and excellent signal consistency within the group, scoring 0.82; Carrier C performs poorly, scoring 0.60. If the system is configured to aggregate a single slave carrier (i.e.... Based on the aforementioned sorting rules, carrier A, which appears to have better average performance, is decisively abandoned, and carrier B, with the highest score, is selected for inclusion in the set. This decision ensures that subsequent load shedding commands can simultaneously reach the bottleneck node of the underground parking garage, avoiding the risk of insufficient overall load drop due to the failure to disconnect underground charging piles. Simultaneously, all column data corresponding to the preferred carriers are extracted from the original channel state matrix and encapsulated into a group channel state profile. This profile serves as the basis for calculating the average signal-to-interference-plus-noise ratio and selecting the MCS level in subsequent hierarchical coding steps, thus completing the closed loop from evaluation to decision-making to data preparation.

[0043] In summary, the technical objective of this optimization mechanism is to fundamentally address the technical deficiency of the original average value algorithm in its insensitivity to the bottleneck effect in group communication by introducing a quantitative assessment of the channel quality distribution balance. This technique constructs and applies a group utility function with enhanced balance, explicitly considering and weighting the internal consistency of channel quality in carrier selection decisions. This allows for the selection of a robust carrier set that possesses both high average performance and no significant communication bottlenecks. The ultimate technical effect is to improve the broadcast / multicast success rate of group control commands in precise load shedding scenarios in the power grid, ensuring high reliability and high synchronization of critical control tasks, and effectively avoiding the risk of entire regional control tasks failing due to communication failures of individual terminals.

[0044] Specifically, in step S400, based on the group channel state profile, the primary carrier ID, and the secondary carrier set, the power grid control command is hierarchically encoded and aggregated to obtain an aggregated data packet set. This aggregated data packet set includes one primary carrier data packet and N secondary carrier data packets. It is understandable that, because the information contained within the power grid load shedding command has different priorities, the core tripping action code is crucial for the safe and stable operation of the power grid and has extremely high reliability requirements. Auxiliary log reporting or parameter configuration information is sensitive to data volume but has a relatively high tolerance for latency. If a traditional unified encoding method is used to transmit indiscriminately on one or more carriers, not only will the bandwidth gain from multiple carriers be unutilized, but deep fading on individual carriers may also cause the entire critical command packet to fail to decode. Therefore, in the technical solution of this application, based on the group channel state profile, the primary carrier ID, and the set of secondary carriers, the power grid control commands are further hierarchically encoded and aggregated to obtain an aggregated data packet set. This aggregated data packet set includes one primary carrier data packet and N secondary carrier data packets. This logically decouples the service commands into a highly reliable core layer and a high-throughput enhancement layer, and physically maps and adapts them to the primary carrier with a synchronization reference and the secondary carriers with extended bandwidth. In this way, by providing high redundancy protection for core data and utilizing the synchronization header of the primary carrier, it ensures that the most critical load shedding commands can be reliably received and synchronously executed by all terminals in the group even under poor channel conditions. Simultaneously, the aggregated bandwidth of the secondary carriers is used to efficiently transmit auxiliary data, achieving a dual optimization of service reliability and transmission efficiency.

[0045] Figure 6 This is a flowchart illustrating the hierarchical encoding and aggregation packetization of power grid control commands to obtain an aggregated data packet set, based on group channel state profiles, primary carrier IDs, and secondary carrier sets, according to the dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation, as described in the embodiments of this application. Figure 6 As shown, step S400 includes: S410, performing instruction semantic parsing and layering on the power grid control instructions to obtain core layer data and enhancement layer data; S420, performing dual-layer adaptive channel coding on the core layer data and enhancement layer data based on the group channel state profile and the slave carrier set to obtain one primary carrier data packet and N slave carrier data packets; S430, aggregating and encapsulating the one primary carrier data packet and N slave carrier data packets to obtain an aggregated data packet set.

[0046] In step S410, the power grid control commands are semantically parsed and layered to obtain core layer data and enhancement layer data. It is understandable that the information fields contained within the power grid control commands exhibit heterogeneous characteristics in terms of business urgency and transmission reliability requirements. The instruction codes and synchronization timestamps involved in action execution are directly related to the security of power grid regulation and must be received with zero bit errors under any channel conditions. Meanwhile, the accompanying parameter configuration or log strategy information has a relatively large data volume but a certain tolerance for real-time packet loss. If both are bundled and transmitted together, the large volume of enhancement information is highly susceptible to deep channel fading, which could negatively impact the decoding success rate of the core commands. Therefore, in the technical solution of this application, the power grid control commands are further semantically parsed and layered to obtain core layer data and enhancement layer data. This constructs a differentiated data structure based on business semantic awareness, logically decoupling the high-priority, minimally sized instruction set from the low-priority, large-capacity dataset. This provides a structured data foundation for the subsequent implementation of asymmetric channel coding protection strategies at the physical layer, ensuring that even in extreme conditions where only a weak signal from the primary carrier is available, the most critical load shedding action can still be correctly parsed and executed by the terminal, thereby safeguarding the bottom line of power grid safety control.

[0047] More specifically, in a specific example of this application, the parsing and layering process is executed by the protocol processing unit on the core network side based on a predefined service semantic template library. The system first receives application layer protocol data units from the scheduling master station, and uses deep packet inspection technology to perform syntax specification checks and field decomposition on the binary code stream, identifying the service attributes of each data field. Next, according to preset field mapping rules, short-byte key fields such as action control codes (e.g., ASDU type identifiers 45 / 46 in the IEC60870-5-104 protocol), target group logical identifiers, globally unique transaction sequence numbers, and absolute execution timestamps are extracted from the instructions and reassembled into core layer data packets according to a compact encoding format. These data packets are not only extremely small in size but also have self-contained check bits. Simultaneously, long-byte descriptive fields such as the remaining load shedding power setting parameters, equipment status feedback log format requirements, and subsequent power restoration strategies are extracted and categorized into enhancement layer data packets. Finally, hierarchical identifiers and association indexes are attached to these two independent data packets, which are then stored in different transmission buffer queues, awaiting subsequent steps to be mapped to a highly reliable primary carrier and a high-bandwidth secondary carrier for transmission, thus achieving precise adaptation from business semantics to physical transmission resources.

[0048] In step S420, based on the group channel state profile and the set of slave carriers, dual-layer adaptive channel coding is performed on the core layer data and enhancement layer data to obtain one master carrier data packet and N slave carrier data packets. It is understandable that, since the core layer data in the power grid load shedding service carries absolute control commands concerning the safety of the power grid, it requires zero-error reception under any adverse channel conditions. The enhancement layer data, on the other hand, focuses more on information richness and transmission efficiency, and the channel conditions of each terminal within the group objectively exhibit dispersion. Therefore, in the technical solution of this application, dual-layer adaptive channel coding is further performed on the core layer data and enhancement layer data based on the group channel state profile and the set of slave carriers to obtain one master carrier data packet and N slave carrier data packets. This allows for the implementation of an asymmetric error control strategy for data streams with different security levels. In this way, by applying strong error correction protection at extremely low code rates to the core layer data and utilizing the high stability of the master carrier for anchoring, the absolute delivery of load shedding commands can be ensured. Simultaneously, the coding rate of the enhancement layer data is adaptively adjusted according to the overall group channel profile, maximizing the system's spectral efficiency while ensuring basic connectivity.

[0049] More specifically, in a specific example of this application, the encoding and packetization process follows the principles of reliability priority and resource adaptation. First, for core layer data, regardless of the current channel state profile, the physical layer processing unit forcibly adopts a preset extremely low code rate forward error correction coding scheme, such as a convolutional code with a coding rate of 1 / 3 or 1 / 4 (e.g., using a tail-biting convolutional code with a generator polynomial of [133,171,145]), and uses the lowest-order modulation scheme with the strongest anti-interference capability, such as BPSK or QPSK, to generate a pre-coded core data stream with extremely high redundancy to resist deep fading. Second, for enhancement layer data, the average signal-to-interference-plus-noise ratio and dispersion index in the group channel state profile are called, and a modulation and coding strategy that can cover the demodulation threshold of most terminals in the group is dynamically selected by looking up a table. The enhancement layer data is adaptively encoded to generate a pre-coded enhancement data stream, which is then divided into several data fragments equal to the number of carriers N. Specifically, the core layer data includes action codes, group IDs, transaction IDs, and execution timestamps; the enhancement layer data includes load shedding parameters, log reporting requirements, and recovery strategy information. Finally, an aggregation packet operation is performed to construct a unique primary carrier data packet, which is formed by concatenating a high-precision physical layer synchronization header sequence with the aforementioned encoded core layer data. Simultaneously, N secondary carrier data packets are constructed. Each secondary carrier data packet not only encapsulates the corresponding enhancement layer data fragments for high-bandwidth transmission but also redundantly encapsulates a complete copy of the encoded core layer data. This ensures, through cross-carrier frequency diversity technology, that even if the primary carrier is blocked, the terminal still has the opportunity to recover critical control commands from any secondary carrier with acceptable channel quality.

[0050] In step S430, a primary carrier data packet and N secondary carrier data packets are aggregated and encapsulated to obtain an aggregated data packet set. It is understandable that, because the physical channels in a discrete narrowband multi-carrier system inherently lack a unified reference in the time domain, and simple channel-coded data streams cannot be directly identified and demodulated by the physical layer, without a frame structure encapsulation containing precise timing information and logical relationships, the terminal will find it difficult to achieve synchronous reception and data splicing across carriers. Furthermore, if core control commands are transmitted only on a single carrier, the high reliability advantage brought by multi-carrier frequency diversity cannot be truly utilized; once the primary carrier suffers sudden interference, the entire control task will fail. Therefore, in the technical solution of this application, a primary carrier data packet and N secondary carrier data packets are further aggregated and encapsulated to obtain an aggregated data packet set, thereby constructing a multi-carrier frame structure with physical layer synchronization capabilities and high-order redundancy characteristics. This ensures that the terminal first locks onto the primary carrier to obtain accurate time synchronization and resource scheduling information, and by repeatedly carrying core data on all carriers, it guarantees that even in the extreme case where the primary carrier is blocked, critical load-cutting commands can still be successfully captured by any surviving secondary carrier, achieving ultimate reliability at the physical level.

[0051] More specifically, in a concrete example of this application, the aggregation packetization process is executed by the link layer control unit of the base station, aiming to generate a data frame format adapted to the physical transmitter. First, a master carrier data packet is constructed. A dedicated synchronization header is added to the front end of the encoded core layer data. This header not only encodes a high-precision frame start timestamp for group terminals to calibrate their local clocks, but also explicitly writes a list of frequency indices for the N slave carriers involved in this collaborative operation, thus guiding the terminals to receive the remaining data. Next, N slave carrier data packets are constructed. For each slave carrier, a simplified reference header is added before the data payload. This reference header contains an association identifier pointing to the master carrier to assist in rapid acquisition. Then, the same encoded core layer data is completely copied and padded to the front of the data packet, followed by fragments of the corresponding enhancement layer data padded to the back. This design allows the core commands to be transmitted N+1 times on the spectrum. Finally, the constructed master carrier data packet is logically associated with the N slave carrier data packets, outputting an aggregated data packet set, which is then tagged with a unified transmission timestamp, ready to be sent to the physical layer RF link for synchronized transmission.

[0052] Specifically, in step S500, the aggregated data packet set is synchronously broadcast to the master and slave carriers based on the master carrier ID and the slave carrier set. It is understood that the effectiveness of multi-carrier aggregation technology highly depends on the strict alignment of physical layer signals in the time domain. Especially in scenarios with extremely high synchronization requirements, such as power grid load shedding, if there are millisecond or even microsecond-level timing deviations in the transmission actions of different carriers, it will directly lead to symbol misalignment and phase loss when the terminal attempts to perform cross-carrier joint decoding or maximum ratio combining, thus completely rendering the frequency diversity gain, originally intended to improve reliability, ineffective. Therefore, in the technical solution of this application, the aggregated data packet set is further synchronously broadcast to the master and slave carriers based on the master carrier ID and the slave carrier set. This drives the multiple radio frequency transmission links on the base station side to work collaboratively with nanosecond-level time accuracy, transforming logically generated data packets into physically strictly synchronized electromagnetic wavefronts. This ensures that multiple radio frequency signals carrying critical load-cutting commands arrive at the target coverage area in a completely synchronized time sequence, enabling a large number of terminals within the group to effectively capture the primary carrier and seamlessly demodulate the secondary carrier data using a unified time reference. This guarantees the high-concurrency execution of large-scale load-cutting tasks and the absolute delivery of commands.

[0053] More specifically, in a particular example of this application, the synchronization broadcast process is implemented by the baseband processing unit on the base station side in conjunction with a high-precision clock distribution network. First, the base station performs a strict channel mapping operation based on the primary carrier ID and the physical frequency index in the secondary carrier set, directing the primary carrier data packets in the aggregated data packet set to the primary RF transmit channel buffer with high power amplification capability, while simultaneously routing each secondary carrier data packet to its corresponding auxiliary RF transmit channel buffer, completing the digital domain transmission preparation. Next, the base station uses a high-precision global clock signal generated by an internally locked BeiDou or GPS satellite timing module or IEEE 1588v2 PTP to generate a nanosecond-level precision frame synchronization trigger pulse. This pulse signal is hardwired in parallel to the trigger pins of the digital-to-analog converters and quadrature modulators of all selected RF links. Finally, at the same physical moment when the rising edge of the synchronization trigger pulse arrives, all activated RF channels simultaneously start the digital-to-analog conversion and RF modulation process, converting the digital baseband signal into an analog RF signal, and radiating it omnidirectionally to the power grid area through the antenna array. This forms a strictly parallel downlink control signal flow at the air interface, ensuring that the receiver has a consistent time reference when processing multipath signals.

[0054] In summary, the dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation, according to the embodiments of this application, is explained. It addresses the communication bottlenecks caused by insufficient synchronization of multi-carrier transmission and differences in group channel quality in power grid load shedding services by constructing a master-slave carrier collaborative architecture and a hierarchical instruction coding mechanism. Specifically, the method first parses control instructions to establish terminal groups and anchors a highly stable master carrier as the synchronization benchmark. Subsequently, an evaluation strategy based on the uniformity of channel quality distribution is introduced, taking into account both the average performance of the group and internal consistency when selecting slave carriers, effectively avoiding the "weakest link" effect caused by the deterioration of individual terminal channels. Based on this, the control instructions are decoupled into a core layer and an enhancement layer. High-redundancy cross-carrier transmission is used for core data, and adaptive fragmentation is implemented for enhancement data. By utilizing the physical layer synchronous broadcasting of master and slave carriers and joint decoding on the terminal side, stable and synchronous delivery of key control signals to a massive number of heterogeneous terminals is achieved, ensuring the execution efficiency of precise power grid load shedding tasks.

[0055] As described above, the dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation according to the embodiments of this application can be implemented in various power communication and control devices, such as new power load management systems, distribution automation master stations, power wireless private network core network controllers, or communication management units. In one possible implementation, the dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation according to the embodiments of this application can be integrated as a software module or hardware module into the power grid enterprise's precise load control system or wireless resource scheduling platform. For example, the dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation can be an independent spectrum resource dynamic scheduling application running on a wireless private network management server, or it can be a communication adaptation and multi-carrier coordination function plug-in module of an existing load control master station, or a low-level driver service deployed on a communication front-end and sending carrier aggregation strategies to the base station through an interface; of course, the core group channel profile construction, master-slave carrier selection, and hierarchical coding aggregation modules in this method can also be embedded in hardware such as base station controllers, wireless communication gateways, or dedicated communication processing boards, as one of the real-time channel allocation or high-reliability multicast control hardware modules of the power grid wireless communication system.

[0056] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A dynamic channel allocation method for power grid load shedding services based on multi-carrier aggregation, characterized in that, include: Obtain power grid control commands; The power grid control commands are parsed and the primary carrier is selected to obtain the terminal group and the primary carrier ID; Based on the available carrier pool, the terminal group is subjected to carrier selection and channel quality detection to obtain the carrier set and group channel status profile. Based on the group channel state profile, the primary carrier ID, and the secondary carrier set, the power grid control commands are hierarchically encoded and aggregated to obtain an aggregated data packet set, which includes one primary carrier data packet and N secondary carrier data packets; Based on the master carrier ID and slave carrier set, the aggregated data packet set is broadcast synchronously on the master and slave carriers.

2. The method for dynamic channel allocation of power grid load shedding services based on multi-carrier aggregation according to claim 1, characterized in that, The power grid control commands are parsed and primary carrier selected to obtain the terminal group and primary carrier ID, including: Based on the group membership table in the system configuration data, the target group is parsed for the power grid control command to obtain the terminal group; Extract the candidate primary carrier set from the carrier attribute table in the system configuration data; Calculate the primary carrier adaptability score for each candidate primary carrier in the candidate primary carrier set; The ID of the candidate primary carrier corresponding to the primary carrier with the largest primary carrier adaptability score is selected as the primary carrier ID.

3. The method for dynamic channel allocation of power grid load shedding services based on multi-carrier aggregation according to claim 2, characterized in that, Calculating the primary carrier adaptability score of each candidate primary carrier in the candidate primary carrier set includes: calculating the primary carrier adaptability score of each candidate primary carrier in the candidate primary carrier set using the following formula, wherein the formula is: in, For long-term reliability scoring, To cover performance scoring, For background interference level, The current load rate, , , and These are the weighting coefficients.

4. The method for dynamic channel allocation of power grid load shedding services based on multi-carrier aggregation according to claim 1, characterized in that, Based on the available carrier pool, carrier selection and channel quality probing are performed on the terminal group to obtain the carrier set and group channel state profile, including: Based on the available carrier pool, terminal groups are subjected to carrier selection and channel quality detection to obtain a candidate carrier list and a set of channel state reports. Based on the channel state report set, the candidate carrier list is evaluated for group utility and the carrier set is optimized to obtain the carrier set and group channel state profile.

5. The method for dynamic channel allocation of power grid load shedding services based on multi-carrier aggregation according to claim 4, characterized in that, Based on the channel state report set, group utility evaluation and carrier set optimization are performed on the candidate carrier list to obtain the carrier set and group channel state profile, including: Extract the channel state matrix and the number of valid terminals from the channel state report set; The channel state matrix and the number of effective terminals are subjected to group channel discretization to obtain the discreteness vector and the average signal-to-interference-plus-noise ratio vector. A group utility evaluation based on balance enhancement is performed on the discreteness vector and the average signal-to-interference-plus-noise ratio vector to obtain the utility subvector; Based on the utility vector and the channel state matrix, robust carrier set decision-making is performed on the candidate carrier list to obtain a channel state profile from the carrier set and group.

6. The method for dynamic channel allocation of power grid load shedding services based on multi-carrier aggregation according to claim 1, characterized in that, Based on the group channel state profile, primary carrier ID, and secondary carrier set, power grid control commands are hierarchically encoded and aggregated to obtain an aggregated data packet set, including: Perform semantic parsing and layering of power grid control commands to obtain core layer data and enhancement layer data; Based on the group channel state profile and the slave carrier set, two-layer adaptive channel coding is performed on the core layer data and the enhancement layer data to obtain one master carrier data packet and N slave carrier data packets; Aggregate and encapsulate one primary carrier data packet and N secondary carrier data packets to obtain an aggregated data packet set.

7. The method for dynamic channel allocation of power grid load shedding services based on multi-carrier aggregation according to claim 6, characterized in that, The core layer data includes action codes, group IDs, transaction IDs, and execution timestamps; the enhancement layer data includes load shedding parameters, log reporting requirements, and recovery strategy information.

Images