Centralized data acquisition and distribution method and system for multi-user environments of electricity meters

Abstract

This invention discloses a centralized data acquisition and allocation method and system for multi-user environments of electricity meters, relating to the field of electricity data acquisition and allocation technology. The method includes: multiple electricity meter terminals sending centralized acquisition request information through a communication network; receiving centralized acquisition request information to generate an acquisition task queue; performing traffic prediction on multiple electricity meter terminals to construct multiple channel resource data; dynamically allocating the acquisition task queue to construct a time slot allocation matrix; receiving multiple energy consumption data packets fed back by multiple electricity meter terminals, performing data verification, and generating an allocation routing table. This invention solves the technical problems of low electricity data acquisition efficiency and unreasonable channel resource allocation in existing technologies under multi-user environments, leading to low system operating efficiency and poor stability. It achieves efficient acquisition of electricity data and reasonable allocation of channel resources under multi-user environments, improving operating efficiency and stability.

Description

Technical Field

[0001] This invention relates to the field of power data acquisition and distribution technology, specifically to a centralized acquisition and distribution method and system for multi-user environments of power meters. Background Technology

[0002] In multi-user environments for electricity data management, traditional methods for collecting and allocating electricity meter data face numerous challenges. The lack of an efficient mechanism for processing collection requests from multiple electricity meter terminals and the inability to dynamically allocate channel resources based on data traffic characteristics lead to chaotic task queues, frequent channel conflicts, low data transmission efficiency, and insufficient reliability. This makes it difficult to meet the real-time and stability requirements for electricity data collection when multiple users are online simultaneously.

[0003] Existing technologies suffer from low efficiency in acquiring power data in multi-user environments and poor system operation efficiency and stability due to unreasonable channel resource allocation. Summary of the Invention

[0004] This application provides a centralized data acquisition and distribution method and system for multi-user environments of electricity meters, which is used to address the technical problems of low efficiency in electricity data acquisition and unreasonable channel resource allocation in multi-user environments, resulting in low system operating efficiency and poor stability.

[0005] In view of the above problems, this application provides a centralized data collection and distribution method and system for multi-user environments of electricity meters.

[0006] The first aspect of this application provides a centralized data collection and distribution method for multi-user environments of electricity meters, the method comprising:

[0007] Multiple energy meter terminals send centralized data collection request information through a communication network. Upon receiving the centralized data collection request information, a data collection task queue is generated, which contains multiple data collection instructions. Traffic flow prediction is performed on the multiple energy meter terminals to extract data flow characteristics. Multiple channel resource data are constructed based on the data flow characteristics. The data collection task queue is dynamically allocated according to the multiple channel resource data to construct a time slot allocation matrix. Based on the time slot allocation matrix, the multiple data collection instructions are issued to the multiple energy meter terminals. Multiple energy consumption data packets are received from the multiple energy meter terminals. Data verification is performed on the multiple energy consumption data packets. Centralized allocation is performed according to the verification results to generate an allocation routing table.

[0008] In one possible implementation, a concentrator node receives initial centralized data collection request information sent by multiple energy meter terminals through a communication network; the initial centralized data collection request information is deduplicated to obtain centralized data collection request information; multiple data collection instructions are set based on the centralized data collection request information, the data collection distance is calculated, and an initial data collection execution priority is set; the multiple data collection instructions are sorted and integrated according to the initial data collection execution priority to construct the data collection task queue.

[0009] In one possible implementation, historical transmission data streams from multiple electricity meter terminals are retrieved, and periodic distribution calculations are performed based on the historical transmission data streams to obtain periodic data flow characteristics; high-frequency decomposition is performed based on the historical transmission data streams to obtain bursty data flow characteristics; the periodic data flow characteristics and the bursty data flow characteristics are combined and predicted to construct data flow characteristics; channel demand analysis is performed based on the data flow characteristics to obtain channel demand prediction information; and resource allocation is performed according to the channel demand prediction information to construct the multiple channel resource data.

[0010] In one possible implementation, multi-dimensional parsing and extraction are performed based on the data traffic characteristics to determine a four-dimensional vector; demand prediction calculation is performed based on the four-dimensional vector to generate channel demand prediction information; a resource allocation engine is started, and the channel demand prediction information is input into the resource allocation engine for resource allocation to obtain multi-dimensional resource configuration parameters; an allocation sequence is constructed, and channel resources are configured according to the multi-dimensional resource configuration parameters according to the allocation sequence to construct multiple triplet data; the multiple triplet data are integrated to construct the multiple channel resource data.

[0011] In one possible implementation, the multiple channel resource data are parsed to obtain time slot unit attributes and conflict constraints; channels are partitioned based on the multiple channel resource data to determine multiple channel types; the length of the time slot basic unit is defined based on the time slot unit attributes, and multiple matrix columns are divided according to the multiple channel types; the multiple matrix columns are initialized according to the conflict constraints and the length of the time slot basic unit to obtain multiple matrix elements; the multiple matrix elements are injected into the acquisition task queue for dynamic allocation to construct the time slot allocation matrix.

[0012] In one possible implementation, the matrix is ​​partitioned based on the multiple matrix elements to determine a fixed allocation area and a contention access area; the acquisition task queue is traversed, and the multiple acquisition instructions are mapped to matrix element triples; time slot matching is performed based on the matrix element triples, and when the acquisition instruction is periodic, it is allocated to the fixed allocation area; when the acquisition instruction is bursty, it is allocated to the contention access area; conflict optimization is performed on the fixed allocation area and the contention access area to construct the time slot allocation matrix.

[0013] In one possible implementation, an independent receive buffer is configured for each channel of multiple energy meter terminals based on the multiple channel resource data; the acquisition task queue is polled through the independent receive buffer to read multiple data packets and construct a preprocessing queue; out-of-order reordering is performed based on the preprocessing queue to extract the timestamp sequence, and the multiple data packets are rearranged according to the timestamp sequence to generate an ordered data stream; feedback indexing is performed on the multiple energy meter terminals according to the ordered data stream to obtain the multiple energy consumption data packets.

[0014] In one possible implementation, a three-level verification is performed on the multiple energy consumption data packets to generate a verification result tag set; the real-time network topology of multiple energy meter terminals is retrieved, and weights are allocated according to the verification result tag set based on the real-time network topology to construct a multi-dimensional allocation weight matrix; the path cost is calculated based on the multi-dimensional allocation weight matrix to determine the minimum path cost; the path cost is filtered according to the minimum path cost to generate a target path; the target route is determined based on the target path, and the target route is added to the allocation routing table.

[0015] In one possible implementation, the multiple power-consuming data packets undergo three levels of verification: physical layer, link layer, and application layer. If any of the multiple power-consuming data packets fails physical layer verification, a signal quality anomaly is identified, and physical layer verification tag information is generated. If any of the multiple power-consuming data packets fails link layer verification, a transmission integrity anomaly is identified in the data frame structure, and link layer verification tag information is generated. If any of the multiple power-consuming data packets fails application layer verification, an anomaly is identified in the payload of the multiple power-consuming data packets, and application layer verification tag information is generated. If any of the multiple power-consuming data packets pass all three levels of verification, a verification pass tag information is generated.

[0016] A second aspect of this application provides a centralized data acquisition and distribution system for multi-user environments of electricity meters, the system comprising:

[0017] The system includes a task queue generation module, used for multiple energy meter terminals to send centralized collection request information through a communication network, receiving the centralized collection request information to generate a collection task queue, which contains multiple collection instructions; a channel resource data construction module, used for traffic prediction of multiple energy meter terminals, extracting data traffic characteristics, and constructing multiple channel resource data based on the data traffic characteristics; a dynamic allocation module, used for dynamically allocating the collection task queue according to the multiple channel resource data, constructing a time slot allocation matrix, and issuing the multiple collection instructions to the multiple energy meter terminals based on the time slot allocation matrix; and a centralized allocation module, used for receiving multiple energy consumption data packets fed back by the multiple energy meter terminals, performing data verification based on the multiple energy consumption data packets, performing centralized allocation according to the verification results, and generating an allocation routing table.

[0018] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0019] Multiple energy meter terminals send centralized data collection request information through a communication network, and the system receives these requests to generate a data collection task queue. Traffic flow prediction is performed on the multiple energy meter terminals, data flow characteristics are extracted, and multiple channel resource data are constructed based on these characteristics. The data collection task queue is dynamically allocated according to the multiple channel resource data, a time slot allocation matrix is ​​constructed, and multiple data collection commands are issued to the multiple energy meter terminals. Multiple energy consumption data packets are received from the multiple energy meter terminals, data verification is performed based on these data packets, and centralized allocation is performed according to the verification results to generate an allocation routing table. This achieves the technical effect of efficient energy data collection and reasonable allocation of channel resources in a multi-user environment, improving operational efficiency and stability. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A schematic flowchart of a centralized data collection and distribution method for a multi-user environment of electricity meters provided in this application embodiment;

[0022] Figure 2 This is a schematic diagram of a centralized data acquisition and distribution system for a multi-user environment of electricity meters, provided in an embodiment of this application.

[0023] Figure labeling: Acquisition task queue generation module 10, channel resource data construction module 20, dynamic allocation module 30, centralized allocation module 40. Detailed Implementation

[0024] This application provides a centralized data acquisition and distribution method and system for multi-user environments of electricity meters, which addresses the technical problems of low efficiency in electricity data acquisition and unreasonable channel resource allocation in existing technologies, resulting in low system operating efficiency and poor stability in multi-user environments.

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0026] Example 1, as Figure 1 As shown, this application provides a centralized data collection and distribution method for multi-user environments of electricity meters, the method comprising:

[0027] Step S100: Multiple electricity meter terminals send centralized data collection request information through the communication network, and generate a data collection task queue upon receiving the centralized data collection request information. The data collection task queue contains multiple data collection instructions.

[0028] Specifically, multiple electricity meter terminals send initial centralized data collection request information to the concentrator node through the communication network. The information is deduplicated to obtain valid centralized data collection request information. Then, multiple data collection instructions are set based on the valid request information. At the same time, the data collection distance is calculated for the centralized data collection request information to set the initial data collection execution priority. Finally, the multiple data collection instructions are sorted and integrated according to the initial data collection execution priority to construct a data collection task queue containing multiple data collection instructions.

[0029] Step S200: Perform traffic prediction on multiple electricity meter terminals, extract data traffic characteristics, and construct multiple channel resource data based on the data traffic characteristics.

[0030] Specifically, the process begins by retrieving historical data streams from multiple electricity meter terminals and performing periodic distribution calculations based on these data streams to obtain periodic data flow characteristics. Next, high-frequency decomposition is performed on the historical data streams to obtain bursty data flow characteristics. These periodic and bursty data flow characteristics are then combined for prediction to construct data flow features. Following this, multi-dimensional analysis is performed on the data flow characteristics to extract and determine a four-dimensional vector, which is then used for demand prediction calculations to generate channel demand prediction information. A resource allocation engine is then activated, and the channel demand prediction information is input to perform resource allocation, obtaining multi-dimensional resource configuration parameters. An allocation sequence is constructed, and the multi-dimensional resource configuration parameters are used to configure channel resources according to this sequence, constructing multiple triplet data sets. Finally, these triplet data sets are integrated to form multiple channel resource data sets.

[0031] Step S300: Dynamically allocate the acquisition task queue according to the multiple channel resource data, construct a time slot allocation matrix, and issue the multiple acquisition commands to multiple energy meter terminals based on the time slot allocation matrix.

[0032] Specifically, multiple channel resource data are parsed to obtain time slot unit attributes and conflict constraints. Based on the channel resource data, channel partitioning is performed to determine multiple channel types. Subsequently, the length of the time slot basic unit is defined according to the time slot unit attributes, and matrix columns are divided according to the channel type. The matrix columns are then initialized based on the conflict constraints and the length of the time slot basic unit to obtain multiple matrix elements. Next, the matrix elements are divided into fixed allocation areas and contention access areas. The acquisition task queue is traversed, and acquisition instructions are mapped to matrix element triples. If the instruction is periodic, it is allocated to the fixed allocation area; if it is bursty, it is allocated to the contention access area. After conflict optimization, a time slot allocation matrix is ​​constructed. Finally, acquisition instructions are issued to multiple energy meter terminals based on this matrix.

[0033] Step S400: Receive multiple energy consumption data packets fed back by the multiple energy meter terminals, perform data verification based on the multiple energy consumption data packets, centrally allocate them according to the verification results, and generate an allocation routing table.

[0034] Specifically, after issuing collection commands to multiple energy meter terminals based on the time slot allocation matrix, an independent receiving buffer is configured for each channel based on multiple channel resource data. This buffer is used to poll the collection task queue to read multiple data packets, constructing a preprocessing queue. The preprocessing queue is then reordered, timestamp sequences are extracted, and the data packets are rearranged accordingly to generate an ordered data stream. This ordered data stream is then used to perform feedback indexing on the energy meter terminals, obtaining multiple energy consumption data packets. Subsequently, three levels of verification are performed on the energy consumption data packets: physical layer (signal quality verification), link layer (data frame integrity verification), and application layer (payload content verification), generating a verification result tag set containing verification tags for each layer. Simultaneously, the real-time network topology of multiple energy meter terminals is retrieved, and weights are allocated based on the verification result tag set and the real-time topology, constructing a multi-dimensional allocation weight matrix. The minimum path is determined by calculating the path cost, the target path is selected, and the target route is determined. Finally, the target route is added to the allocation routing table, completing the centralized data allocation and route generation.

[0035] In one possible implementation, step S100 further includes:

[0036] Step S110: Receive initial centralized data collection request information sent by multiple electricity meter terminals through the communication network via the concentrator node.

[0037] Step S120: Deduplicate the initial centralized collection request information to obtain centralized collection request information.

[0038] Step S130: Based on the centralized collection request information, set multiple collection instructions, calculate the collection distance of the centralized collection request information, and set the initial collection execution priority.

[0039] Step S140: Sort and integrate the multiple acquisition instructions according to the initial acquisition execution priority to construct the acquisition task queue.

[0040] Specifically, the process of receiving initial centralized data collection request information from multiple electricity meter terminals via a communication network through a concentrator node enables centralized access to electricity meter data collection requests in a multi-user environment.

[0041] The key features of the initial centralized collection request information (such as the request terminal identifier, request timestamp, and request content verification code) are uniquely encoded using a hash algorithm. The encoded values ​​are stored in a cache hash table. When a new request arrives, its hash value is calculated and compared with the record in the table. Requests with duplicate hash values ​​are marked as redundant data and filtered out. Only request information with unique hash values ​​is retained, thereby obtaining deduplicated centralized collection request information and ensuring that the request data processed subsequently is unique and valid.

[0042] The physical address and communication network address of the electricity meter terminal are extracted from the centralized collection request information. The physical area and network level of the terminal are determined by parsing the address encoding rules. Combined with the pre-stored physical topology map of the toll station and the communication network routing table, the physical distance corresponding to each request is calculated, i.e., the straight-line distance from the terminal to the concentrator and the network distance (e.g., the number of hops in the communication link). These two distances are then weighted and summed according to preset weights (e.g., physical distance accounts for 60%, network distance accounts for 40%) to obtain the collection distance value. The initial collection execution priority is set according to the collection distance value from smallest to largest, with smaller distance values ​​having higher priority. Simultaneously, a collection instruction containing parameters such as collection frequency and data type is generated for each request to ensure that collection tasks requiring short distances and low latency are executed first.

[0043] A priority-based sorting algorithm is established, using the initial execution priority value of each acquisition command as the sorting key. A quicksort algorithm is employed to sort the acquisition commands from highest to lowest priority. After sorting, the sorted acquisition commands are sequentially stored in a first-in, first-out (FIFO) task queue data structure. Metadata such as timestamps and task status identifiers are added to each acquisition command, forming an ordered acquisition task queue. During this process, if acquisition commands with the same priority exist, they are re-sorted according to their reception time to ensure the orderliness and executability of the task queue, providing a regular task sequence for subsequent task scheduling and processing.

[0044] In one possible implementation, step S200 further includes:

[0045] Step S210: Retrieve historical transmission data streams from multiple electricity meter terminals, perform periodic distribution calculations based on the historical transmission data streams, and obtain periodic data flow characteristics.

[0046] Step S220: Perform high-frequency decomposition based on the historical transmission data stream to obtain bursty data traffic characteristics.

[0047] Step S230: Combine the periodic data flow characteristics and the burst data flow characteristics for prediction to construct data flow characteristics.

[0048] Step S240: Perform channel demand analysis based on the data traffic characteristics to obtain channel demand prediction information, configure resources according to the channel demand prediction information, and construct the multiple channel resource data.

[0049] Specifically, the system retrieves historical data streams from multiple electricity meter terminals within a preset time window (e.g., the last 30 days) through a database query interface. It then uses a Fourier transform algorithm to perform frequency domain analysis on the data streams, decomposing the time-domain signal into a superposition of sine waves of different frequencies. By identifying the frequency components with concentrated energy in the spectrum, the system determines the periodic fluctuation cycle of the data flow (e.g., peak electricity consumption cycles from 8:00-10:00 daily or Monday mornings). Combined with the sliding window mean calculation method, the system extracts the peak, valley, and trend of the data flow within each cycle, forming a regular flow feature vector with time period as the dimension. This yields characteristic parameters representing the periodic changes in data flow.

[0050] A wavelet transform algorithm is used to decompose the historical transmitted data stream into high-frequency components. By selecting an appropriate wavelet basis function (such as the db4 wavelet), the data stream is decomposed into multiple layers, breaking down the signal into approximate and detail components in different frequency bands. The detail components correspond to high-frequency burst signals. Threshold denoising is used to process the detail components. An adaptive threshold is set to filter normal traffic fluctuations and retain burst traffic spikes exceeding the threshold. By extracting parameters such as the amplitude, duration, and occurrence time of the burst traffic, a burst data traffic feature vector is constructed, thereby obtaining burst characteristics that can characterize abnormal fluctuations in data traffic within a short period of time.

[0051] A combined ARIMA (Autoregressive Integral Moving Average) and LSTM (Long Short-Term Memory) model is employed to predict both periodic and bursty data traffic characteristics. First, the periodic feature vector is input into the ARIMA model, leveraging its linear time-series analysis capabilities to capture the periodic patterns of data traffic. Then, the bursty feature vector is input into the LSTM neural network, which learns the nonlinear patterns of sudden traffic changes through a gating mechanism. Next, a weighted fusion strategy is used to dynamically assign weights (adaptively adjusted based on historical prediction errors) to the periodic and bursty prediction values ​​output by the ARIMA and LSTM, respectively. The results are then superimposed to generate a comprehensive prediction sequence. Finally, key parameters such as peak traffic range, average transmission rate, and fluctuation variance are extracted from the comprehensive prediction sequence to construct a multidimensional data traffic feature vector encompassing time, frequency, and statistical characteristics, achieving an accurate representation of the overall trend of data traffic changes.

[0052] First, the data traffic characteristics are analyzed in multiple dimensions to extract parameters such as peak traffic, average bandwidth, transmission delay, and bit error rate, constructing a four-dimensional vector. This four-dimensional vector is then input into a machine learning-based demand prediction model. The trained model calculates and generates channel demand prediction information, including required channel bandwidth, transmission time slots, and Quality of Service (QoS) levels. The resource allocation engine is then activated, inputting the channel demand prediction information. Based on a preset resource allocation strategy, resources are allocated to obtain multi-dimensional resource configuration parameters such as channel bandwidth allocation ratios, transmission time slot lengths, and error correction coding methods. An allocation sequence is constructed, mapping the multi-dimensional resource configuration parameters into multiple triplet data sets containing channel identifiers, resource parameters, and time windows. Finally, these triplet data sets are integrated to construct multiple channel resource data sets to meet the data transmission needs of different electricity meter terminals.

[0053] In one possible implementation, step S240 further includes:

[0054] Step S241: Perform multi-dimensional parsing and extraction based on the data traffic characteristics to determine the four-dimensional vector.

[0055] Step S242: Perform demand prediction calculation based on the four-dimensional vector to generate channel demand prediction information.

[0056] Step S243: Start the resource allocation engine, input the channel demand prediction information into the resource allocation engine to perform resource allocation, and obtain multi-dimensional resource configuration parameters.

[0057] Step S244: Construct an allocation sequence, configure channel resources according to the multidimensional resource configuration parameters according to the allocation sequence, and construct multiple triplet data.

[0058] Step S245: Integrate the multiple triplet data to construct the multiple channel resource data.

[0059] Specifically, a multi-dimensional analysis of data traffic characteristics is performed, extracting four key parameters from historical data streams: periodic stability, burst intensity, average data volume, and standard deviation of data volume. By analyzing the periodic fluctuations of data traffic over time, the amplitude and frequency of fluctuations within the period are calculated to determine the periodic stability parameter. Based on the burst traffic characteristics obtained from high-frequency decomposition, the peak size and duration of burst traffic are extracted to determine the burst intensity parameter. The average data volume parameter is obtained by statistically analyzing the average data traffic per unit time. The standard deviation parameter of data volume is determined by calculating the dispersion of data traffic relative to the average value. Finally, a four-dimensional vector containing these four parameters is constructed to comprehensively characterize the features and transmission requirements of data traffic.

[0060] When a four-dimensional vector containing periodic stability, burst strength, average data volume, and standard deviation of data volume is input into a trained LSTM model to generate channel demand prediction information, the specific implementation is as follows: First, the four-dimensional vector is standardized to eliminate the influence of different parameters' dimensions (e.g., periodic stability is normalized to [0, 1], and burst strength is converted to Mbps). Then, it is divided into input sequences according to a 5-minute time window and input into a three-layer LSTM network. The model filters irrelevant historical traffic information through a forget gate, updates the current traffic characteristics through an input gate, and generates predicted values ​​through an output gate, capturing the nonlinear mapping relationship between the four-dimensional vector and channel resource demand (e.g., when the burst strength increases from 50 Mbps to 60 Mbps, the model automatically predicts that an additional 5 MHz of bandwidth is needed). During training, based on the error of the experimental data in the table (e.g., a bandwidth prediction error of 2 MHz in Experiment 1), the Adam optimizer is used to iteratively adjust the weights with a learning rate of 0.001. Training stops when the error on the validation set decreases by less than 0.5% for 10 consecutive rounds. Finally, the fully connected layer of the model outputs channel demand prediction information including bandwidth requirements, time slot allocation, and QoS level. For example, when the input is a four-dimensional vector with periodic stability of 0.85, burst strength of 60Mbps, average data size of 1200KB, and data size standard deviation of 220, the model outputs a prediction result with 55MHz bandwidth, 12 time slots, and high QoS level after correction, and the error with the actual channel demand is controlled within 3%.

[0061] Table 1 shows a comparison of experimental data on four-dimensional vector input and channel demand prediction.

[0062] Table 1 Comparison of Experimental Data

[0063]

[0064] The resource allocation engine is started by inputting channel demand prediction information, including bandwidth requirements, time slot allocation, and QoS levels. The engine uses a combination of the maximum-minimum fairness algorithm and the proportional fairness algorithm to allocate resources. It dynamically allocates channel bandwidth based on the bandwidth threshold in the channel demand prediction information, divides transmission time slots according to the buffer time slot length corresponding to the burst intensity, and configures error correction coding methods and transmission priorities according to QoS levels. Finally, it obtains multi-dimensional resource configuration parameters including channel bandwidth allocation ratio, transmission time slot length, error correction coding type, and priority weight.

[0065] An allocation sequence is constructed based on the usage priority and time order of channel resources, following the principle of prioritizing high-QoS channels before allocating ordinary channels. According to the allocation sequence, the channel bandwidth in the multi-dimensional resource configuration parameters is mapped to frequency resources. The time slot mode (e.g., continuous or discontinuous time slots) is determined based on the transmission time slot length. Spreading codes are allocated based on the error correction coding type and priority weight, thereby constructing multiple triplets of data containing frequency, time slot mode, and spreading code, for example (frequency 100MHz, continuous time slot mode 5ms, spreading code C1), achieving precise configuration and structured description of channel resources.

[0066] When integrating multiple triplet data to construct multiple channel resource data, a unified data structure can be established to standardize key information such as channel identifiers, resource parameters, and time attributes in each triplet data, eliminating format differences and contradictions between different triplet data. Then, the data can be classified and aggregated according to dimensions such as channel type and usage priority, ultimately forming multiple channel resource data sets containing complete channel resource information, providing an accurate and unified data source for subsequent channel resource management and allocation.

[0067] In one possible implementation, step S300 further includes:

[0068] Step S310: Parse the multiple channel resource data to obtain time slot unit attributes and conflict constraints.

[0069] Step S320: Based on the multiple channel resource data, perform channel partitioning to determine multiple channel types.

[0070] Step S330: Define the length of the time slot basic unit based on the time slot unit attributes, and divide the matrix into multiple columns according to the multiple channel types.

[0071] Step S340: Initialize the multiple matrix columns according to the conflict constraint conditions and the length of the time slot basic unit to obtain multiple matrix elements.

[0072] Step S350: Dynamically allocate the multiple matrix elements into the acquisition task queue to construct the time slot allocation matrix.

[0073] Specifically, by parsing the triplet data (frequency point, time slot mode, spreading code) in multiple channel resource data, the time slot unit attributes are extracted, including parameters such as time slot width, guard interval, and time slot period. At the same time, conflict constraints are obtained from the resource allocation rules, such as the same frequency point cannot be occupied by different channels in the same time slot, and a guard interval must be maintained between adjacent time slots. Thus, the time slot unit attributes and conflict constraints required to construct the time slot allocation matrix are obtained.

[0074] Channels are classified based on parameters such as frequency, bandwidth, and transmission requirements from multiple channel resource data. Channels with the same or similar characteristics are grouped together to determine multiple channel types. For example, they can be classified into high-frequency channels and low-frequency channels based on frequency range, wideband channels and narrowband channels based on bandwidth, or real-time data channels and non-real-time data channels based on the type of transmitted data, to meet the channel requirements of different acquisition commands.

[0075] First, the basic unit length of the time slot is defined based on the parsed time slot unit attributes (such as time slot width, guard interval, etc.). For example, the basic unit length of the time slot is set to 5 milliseconds to ensure that the basic time unit requirement for data transmission is met. Then, the matrix columns are divided according to the determined channel types (such as high-frequency channels, low-frequency channels, wideband channels, etc.), with each channel type corresponding to one column in the matrix. This ensures that the matrix column division corresponds one-to-one with the channel type, laying the foundation for the subsequent construction of the column dimensions of the time slot allocation matrix.

[0076] First, define the conflict constraints, such as different channels not being able to be used simultaneously within the same time slot and the need to maintain a guard interval between adjacent time slots. Then, combine this with the defined time slot basic unit length (e.g., 5 milliseconds) to initialize multiple matrix columns. Specifically, based on the conflict constraints, divide each matrix column into time slots according to the time slot basic unit length. Check whether the channel usage within each time slot meets the conflict constraints. If it does, mark the time slot as available; otherwise, mark it as unavailable. This yields multiple matrix elements, each recording the usage status and related parameters of the corresponding channel in a specific time slot.

[0077] Each acquisition command in the acquisition task queue is sequentially injected into multiple matrix elements, and dynamically allocated according to the type and requirements of the acquisition command. For periodic acquisition commands, they are preferentially allocated to the corresponding matrix elements in the fixed allocation area to ensure that their periodic transmission requirements are met; for bursty acquisition commands, they are allocated to matrix elements in the contention access area to obtain time slot resources through a contention mechanism. During the allocation process, it is checked in real time whether the allocation meets the conflict constraint conditions. If a conflict occurs, the allocation scheme is adjusted, and finally a complete time slot allocation matrix is ​​constructed to achieve reasonable time slot allocation for acquisition tasks.

[0078] In one possible implementation, step S350 further includes:

[0079] Step S351: Perform matrix partitioning based on the multiple matrix elements to determine the fixed allocation area and the contention access area.

[0080] Step S352: Traverse the acquisition task queue and map the multiple acquisition instructions into matrix element triples.

[0081] Step S353: Perform time slot matching based on the matrix element triplet. If the acquisition command is periodic, it is allocated to a fixed allocation area.

[0082] Step S354: When the acquisition command is bursty, it is allocated to the contention access area.

[0083] Step S355: Perform conflict optimization on the fixed allocation area and the contention access area to construct the time slot allocation matrix.

[0084] Specifically, based on the time slot occupancy status, channel type, and conflict constraints of multiple matrix elements, the matrix is ​​divided into a fixed allocation area and a contention access area. The fixed allocation area stores time slot allocations for periodic acquisition commands; its position within the matrix is ​​relatively fixed and repeats periodically to meet the stability requirements of periodic tasks for time slot resources. The contention access area, on the other hand, handles time slot requests for bursty acquisition commands. Time slot resources in this area are not pre-allocated but are acquired competitively by bursty tasks based on real-time needs. This partitioning method enables differentiated time slot resource management for different types of acquisition commands.

[0085] The process iterates through each acquisition command in the acquisition task queue, extracting key information from each command, such as the required time slot, the corresponding channel identifier, and the task priority. These parameters are then integrated into a matrix triplet. For example, for a given acquisition command that requires the 10th time slot, uses channel A, and has a high priority, it is mapped to a matrix triplet of (time slot 10, channel A, high priority). In this way, all acquisition commands are converted into a structured data format suitable for allocation within a matrix.

[0086] For each matrix element triplet corresponding to a data acquisition command, it is determined whether it is a periodic command. By analyzing parameters such as the transmission period and repetition frequency of the acquisition command, if it is determined to be a periodic command, a time slot position matching the command period is searched in the fixed allocation area. The fixed allocation area has been pre-divided into periodically repeating time slot segments according to the time characteristics of periodic commands. After finding a matching time slot segment, the periodic acquisition command is allocated to the corresponding position in the fixed allocation area, ensuring that it occupies time slot resources according to a fixed periodic pattern, thus meeting the requirements of periodic commands for time slot stability and regularity.

[0087] For acquisition commands identified as bursty, due to their non-periodic and random characteristics, they are allocated to the contention-based access zone. Time slot resources in the contention-based access zone are not pre-allocated; bursty acquisition commands must acquire available time slots through a contention mechanism. The time slot status of the contention-based access zone is monitored in real time. When available time slots are available, appropriate time slot resources are allocated to the bursty command based on its priority and real-time requirements, ensuring that bursty acquisition commands can flexibly acquire the required time slots in the contention-based access zone and meet their random data transmission needs.

[0088] To address the spatial conflicts (same channel occupied by different acquisition commands) and temporal overlaps (violation of guard intervals in the same or adjacent time slots) existing in the fixed allocation area and contention access area, a conflict detection and resolution algorithm from graph theory is employed for optimization. First, the periodic time slot allocation in the fixed allocation area is traversed, and long-term temporal overlaps caused by periodic repetitions are eliminated by adjusting the periodic start point and time slot offset. For the contention access area, a dynamic priority scheduling mechanism is introduced. When bursty commands occupy the same channel spatially or overlap temporally, the time slot allocation is dynamically adjusted based on task priority, channel status, and real-time traffic, prioritizing the resource needs of high-priority commands. During the optimization process, the occupancy status of matrix elements is updated in real time. Through iterative detection and adjustment, a spatially conflict-free and temporally overlap-free time slot allocation matrix is ​​finally constructed, ensuring that all acquisition commands in both the fixed allocation area and the contention access area can be transmitted within legal time slots.

[0089] In one possible implementation, step S400 further includes:

[0090] Step S410: Configure an independent receive buffer for each channel of the multiple energy meter terminals based on the multiple channel resource data.

[0091] Step S420: Poll the acquisition task queue through the independent receiving buffer, read multiple data packets, and build a preprocessing queue.

[0092] Step S430: Perform out-of-order reordering based on the preprocessing queue, extract the timestamp sequence, rearrange the multiple data packets according to the timestamp sequence, and generate an ordered data stream.

[0093] Step S440: Perform feedback indexing on multiple energy meter terminals according to the ordered data stream to obtain the multiple energy consumption data packets.

[0094] Specifically, based on parameters such as bandwidth and transmission protocol of each channel in multiple channel resource data, an independent receive buffer is configured for each channel of multiple energy meter terminals. Each independent receive buffer corresponds one-to-one with a specific channel and has the buffering capability to adapt to the data transmission rate and data packet size of that channel. It is used to temporarily store the energy consumption data packets received by the corresponding channel, avoid mutual interference between data from different channels, and provide an orderly buffer space for subsequent data processing.

[0095] Each channel's independent receive buffer is polled according to the acquisition task queue, sequentially reading the buffered data packets. During polling, the read data packets are temporarily stored in the order of reception, forming a preprocessing queue for unified preprocessing operations, providing an ordered data source for subsequent data reassembly and indexing.

[0096] For out-of-order data packets in the preprocessing queue, the timestamp information carried by each data packet is extracted to form a timestamp sequence. Then, based on the timestamp sequence, multiple data packets are rearranged according to their chronological order, transforming the originally disordered data packets into an ordered data stream arranged in chronological order, ensuring that the data processed subsequently has the correct logical chronological order.

[0097] By utilizing the terminal identifier, channel number, and other information carried in each data packet of the ordered data stream, a correspondence with multiple energy meter terminals is established. Through a feedback indexing mechanism, the data packets in the ordered data stream are accurately mapped to the corresponding energy meter terminals. This allows for the identification and extraction of energy consumption data packets fed back by each energy meter terminal from the ordered data stream, providing an accurate data source for subsequent data verification and centralized allocation.

[0098] In one possible implementation, step S400 further includes:

[0099] Step S450: Perform three-level verification on the multiple power data packets and generate a set of verification result tags.

[0100] Step S460: Retrieve the real-time network topology of multiple electricity meter terminals, and allocate weights according to the real-time network topology based on the verification result tag set, thereby constructing a multi-dimensional weight allocation matrix.

[0101] Step S470: Calculate the path cost based on the multi-dimensional weight allocation matrix and determine the minimum path cost.

[0102] Step S480: Filter according to the minimum path cost to generate a target path, determine the target route according to the target path, and add the target route to the allocation route table.

[0103] Specifically, at the physical layer, a signal detector first checks the physical parameters of the data packets, such as signal strength, spectral distribution, and signal-to-noise ratio, and compares them with preset thresholds. If the parameters are not met, a signal quality anomaly is marked and a physical layer verification tag is generated. Next, at the link layer, a CRC check algorithm and frame structure verification mechanism are used to check the integrity and correctness of the data frames. If problems such as frame header errors or checksum mismatches are found, a transmission integrity anomaly is marked on the data frame structure and a link layer verification tag is generated. Then, at the application layer, the format, content, and semantics of the data payload are parsed according to the application protocol specifications. If format errors or content anomalies occur, the payload is anomaly marked and an application layer verification tag is generated. Finally, the verification results from each layer are summarized to generate a verification result tag set containing the verification status of the physical layer, link layer, and application layer.

[0104] A real-time network topology is constructed by acquiring information such as the connection relationships, node status, and link quality of multiple electricity meter terminals in real time through a network topology discovery protocol. Then, based on the generated verification result label set, weights are assigned to each node and link in the topology. For example, for electricity meter terminal nodes with a physical layer verification failure label in the verification results, the weight value of the corresponding node is increased; for links with link layer verification failures, the transmission cost weight of that link is increased. Next, a multi-dimensional weight allocation matrix is ​​constructed, comprehensively considering multiple dimensions such as node weight, link weight, verification status weight, and network load. This matrix covers the weight information related to the verification results for each element in the network topology, providing a comprehensive weight basis for subsequent path cost calculations.

[0105] Based on the constructed multi-dimensional weighting matrix, the weights of each node and link in each possible path in the network topology are summed to obtain the total cost of the path. For example, if a path passes through nodes A, B, and C and their corresponding links, the weights added to node A due to physical layer verification failure, the normal weights of node B, and the weights set by each link due to link layer verification status need to be added together. By traversing all possible paths and comparing the total costs of each path, the path with the minimum cost is determined. This minimum cost is the path cost minimization value, providing a key basis for subsequent selection of the optimal transmission path.

[0106] Based on the determined minimum path cost, all paths with a total cost equal to this minimum value are selected from the multi-dimensional weighting matrix; these paths are the target paths. For each target path, its node connectivity and link status are further analyzed to determine the specific transmission path from the source node to the target node, i.e., the target route. Finally, the determined target routes are added to the allocation routing table so that subsequent centralized allocation of power data packets can be achieved based on these routes, ensuring that data is transmitted along the path with the minimum cost, thereby improving transmission efficiency and reliability.

[0107] In one possible implementation, step S450 further includes:

[0108] Step S451: Perform three-level verification on the multiple power-consuming data packets based on the physical layer, link layer, and application layer.

[0109] Step S452: When the multiple power data packets fail physical layer verification, the multiple power data packets are marked with signal quality abnormalities, and physical layer verification tag information is generated.

[0110] Step S453: When the multiple data packets fail the link layer verification, the data frame structure is marked with a transmission integrity error and a link layer verification tag is generated.

[0111] Step S454: When the multiple power data packets fail application layer verification, the payload of the multiple power data packets is marked as abnormal, and application layer verification tag information is generated.

[0112] Step S455: When the multiple power data packets pass all three levels of verification, a verification pass tag is generated.

[0113] Specifically, multiple power-consuming data packets undergo a three-level verification process: physical layer, data link layer, and application layer. The physical layer ensures the quality of the physical medium for data transmission by detecting physical parameters such as signal strength, noise level, and frequency offset. The data link layer uses algorithms such as Cyclic Redundancy Check (CRC) to verify the integrity of data frames, checking the frame header, frame trailer, and checksum for correctness. The application layer verifies the format and semantics of the data payload according to specific protocol specifications, ensuring that the data conforms to application layer protocol requirements. This three-level verification process is progressive, comprehensively guaranteeing the transmission quality of power-consuming data packets.

[0114] When multiple data packets fail the physical layer verification, these data packets are marked with signal quality anomalies. Specifically, this means that problems such as insufficient signal strength, low signal-to-noise ratio, or serious interference are marked, which lead to substandard physical layer transmission quality. Physical layer verification tag information containing anomaly type and signal parameters is generated to clearly indicate the problems in the data packets during physical layer transmission.

[0115] When multiple data packets fail in the link layer check, it indicates that there is an integrity problem in the data frame structure during transmission, such as frame header / tail errors, CRC checksum mismatches, or data frame loss. In this case, the data frame structure of these packets is marked as having transmission integrity anomalies, and link layer checksum label information is generated. This label includes the error type (such as frame structure error, checksum error, etc.), error location, and relevant frame parameters to clearly indicate the integrity problem existing in the link layer transmission.

[0116] When multiple data packets fail during application layer verification, it indicates that the payload content of the data packets does not conform to the application protocol specifications, such as data format errors, semantic parsing failures, or missing key fields. In this case, the payloads of these data packets are marked as abnormal, and application layer verification tag information is generated. This tag details the type of payload anomaly (e.g., format error, content anomaly), the location of the anomaly, and related payload parameters to clearly indicate problems in application layer data processing.

[0117] When multiple data packets pass all checks at the physical layer, link layer, and application layer in sequence—that is, when the physical layer signal quality meets the standard, the link layer data frame structure is complete and correct, and the application layer payload content meets the protocol requirements—a check pass tag will be generated to indicate that no abnormalities were found in the data packet during transmission and processing, and it can be used normally for subsequent data centralization and allocation operations.

[0118] Example 2 is based on the same inventive concept as the centralized data collection and distribution method for multi-user environments of electricity meters in the foregoing examples, such as... Figure 2 As shown, this application provides a centralized data acquisition and distribution system for multi-user environments of electricity meters. The system and method embodiments in this application are based on the same inventive concept. The system includes:

[0119] The data acquisition task queue generation module 10 is used to send centralized data acquisition request information through a communication network by multiple energy meter terminals, and to receive the centralized data acquisition request information to generate a data acquisition task queue, which contains multiple data acquisition instructions.

[0120] The channel resource data construction module 20 is used to predict the flow of multiple electricity meter terminals, extract data flow characteristics, and construct multiple channel resource data based on the data flow characteristics.

[0121] The dynamic allocation module 30 is used to dynamically allocate the acquisition task queue according to the multiple channel resource data, construct a time slot allocation matrix, and issue the multiple acquisition commands to multiple energy meter terminals based on the time slot allocation matrix.

[0122] The centralized allocation module 40 is used to receive multiple energy consumption data packets fed back by the multiple energy meter terminals, perform data verification based on the multiple energy consumption data packets, perform centralized allocation according to the verification results, and generate an allocation routing table.

[0123] Furthermore, the system is also used to implement the following functions:

[0124] The concentrator node receives initial centralized data collection request information sent by multiple energy meter terminals through the communication network; deduplicates the initial centralized data collection request information to obtain centralized data collection request information; sets multiple data collection instructions based on the centralized data collection request information, calculates the data collection distance based on the centralized data collection request information, and sets the initial data collection execution priority; sorts and integrates the multiple data collection instructions according to the initial data collection execution priority to construct the data collection task queue.

[0125] Furthermore, the system is also used to implement the following functions:

[0126] Historical data streams from multiple electricity meter terminals are retrieved, and periodic distribution calculations are performed based on these historical data streams to obtain periodic data flow characteristics. High-frequency decomposition is then performed on the historical data streams to obtain bursty data flow characteristics. The periodic and bursty data flow characteristics are combined and predicted to construct data flow characteristics. Channel demand analysis is performed based on these data flow characteristics to obtain channel demand prediction information. Resource allocation is then performed according to the channel demand prediction information to construct the multiple channel resource data sets.

[0127] Furthermore, the system is also used to implement the following functions:

[0128] Based on the data traffic characteristics, multidimensional parsing and extraction are performed to determine a four-dimensional vector; demand prediction calculation is performed based on the four-dimensional vector to generate channel demand prediction information; the resource allocation engine is started, and the channel demand prediction information is input into the resource allocation engine for resource allocation to obtain multidimensional resource configuration parameters; an allocation sequence is constructed, and channel resources are configured according to the multidimensional resource configuration parameters according to the allocation sequence to construct multiple triplet data; the multiple triplet data are integrated to construct the multiple channel resource data.

[0129] Furthermore, the system is also used to implement the following functions:

[0130] The multiple channel resource data are parsed to obtain time slot unit attributes and conflict constraints; channel partitioning is performed based on the multiple channel resource data to determine multiple channel types; the length of the time slot basic unit is defined based on the time slot unit attributes, and multiple matrix columns are divided according to the multiple channel types; the multiple matrix columns are initialized according to the conflict constraints and the length of the time slot basic unit to obtain multiple matrix elements; the multiple matrix elements are injected into the acquisition task queue for dynamic allocation to construct the time slot allocation matrix.

[0131] Furthermore, the system is also used to implement the following functions:

[0132] The matrix is ​​partitioned based on the multiple matrix elements to determine the fixed allocation area and the contention access area; the acquisition task queue is traversed to map the multiple acquisition instructions to matrix element triples; time slot matching is performed based on the matrix element triples, and when the acquisition instruction is periodic, it is allocated to the fixed allocation area; when the acquisition instruction is bursty, it is allocated to the contention access area; conflict optimization is performed on the fixed allocation area and the contention access area to construct the time slot allocation matrix.

[0133] Furthermore, the system is also used to implement the following functions:

[0134] Based on the multiple channel resource data, an independent receiving buffer is configured for each channel of multiple energy meter terminals; the acquisition task queue is polled through the independent receiving buffer to read multiple data packets and construct a preprocessing queue; based on the preprocessing queue, out-of-order reordering is performed to extract the timestamp sequence, and the multiple data packets are rearranged according to the timestamp sequence to generate an ordered data stream; the multiple energy meter terminals are fed back with the ordered data stream to obtain the multiple energy consumption data packets.

[0135] Furthermore, the system is also used to implement the following functions:

[0136] Perform three-level verification on the multiple energy consumption data packets to generate a verification result tag set; retrieve the real-time network topology of multiple energy meter terminals, and allocate weights according to the verification result tag set and the real-time network topology to construct a multi-dimensional allocation weight matrix; calculate the path cost based on the multi-dimensional allocation weight matrix to determine the minimum path cost; filter according to the minimum path cost to generate a target path, determine the target route based on the target path, and add the target route to the allocation route table.

[0137] Furthermore, the system is also used to implement the following functions:

[0138] The multiple energy-consuming data packets undergo three-level verification based on the physical layer, data link layer, and application layer. If any of the multiple energy-consuming data packets fails physical layer verification, a signal quality anomaly is identified, and physical layer verification tag information is generated. If any of the multiple energy-consuming data packets fails data link layer verification, a transmission integrity anomaly is identified in the data frame structure, and data link layer verification tag information is generated. If any of the multiple energy-consuming data packets fails application layer verification, an anomaly is identified in the payload of the multiple energy-consuming data packets, and application layer verification tag information is generated. If the multiple energy-consuming data packets pass all three levels of verification, a verification pass tag information is generated.

[0139] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0140] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0141] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A centralized data collection and distribution method for multi-user environments using electricity meters, characterized in that, The method includes: Multiple electricity meter terminals send centralized data collection request information through a communication network, and receive the centralized data collection request information to generate a data collection task queue, which contains multiple data collection instructions. Traffic flow prediction is performed on multiple electricity meter terminals, data flow characteristics are extracted, and multiple channel resource data are constructed based on the data flow characteristics. The acquisition task queue is dynamically allocated according to the multiple channel resource data to construct a time slot allocation matrix, and the multiple acquisition instructions are issued to multiple energy meter terminals based on the time slot allocation matrix. Receive multiple energy consumption data packets fed back by the multiple energy meter terminals, perform data verification based on the multiple energy consumption data packets, centrally allocate them according to the verification results, and generate an allocation routing table; This involves predicting traffic flow for multiple electricity meter terminals, extracting data flow characteristics, and constructing multiple channel resource data based on these characteristics, including: Historical data streams from multiple electricity meter terminals are retrieved, and periodic distribution calculations are performed based on these historical data streams to obtain periodic data flow characteristics. High-frequency decomposition is performed on the historical data streams to obtain the characteristics of bursty data traffic; The periodic data flow characteristics and the burst data flow characteristics are combined and predicted to construct data flow characteristics; Channel demand analysis is performed based on the data traffic characteristics to obtain channel demand prediction information. Resource allocation is then performed according to the channel demand prediction information to construct the multiple channel resource data.

2. The centralized data collection and distribution method for multi-user environments of electricity meters as described in claim 1, characterized in that, Multiple electricity meter terminals send centralized data collection request information through a communication network, and the method for receiving the centralized data collection request information to generate a data collection task queue includes: The concentrator node receives initial centralized data collection request information sent by multiple energy meter terminals through the communication network. The initial centralized collection request information is deduplicated to obtain the centralized collection request information; Based on the centralized collection request information, multiple collection instructions are set, the collection distance is calculated from the centralized collection request information, and the initial collection execution priority is set; The multiple acquisition instructions are sorted and integrated according to the initial acquisition execution priority to construct the acquisition task queue.

3. The centralized data collection and distribution method for multi-user environments of electricity meters as described in claim 1, characterized in that, The method includes performing channel demand analysis based on the data traffic characteristics to obtain channel demand prediction information, configuring resources according to the channel demand prediction information, and constructing the multiple channel resource data. Based on the data traffic characteristics, multi-dimensional parsing and extraction are performed to determine a four-dimensional vector; Demand prediction calculations are performed based on the four-dimensional vector to generate channel demand prediction information. Start the resource allocation engine, input the channel demand prediction information into the resource allocation engine to perform resource allocation, and obtain multi-dimensional resource configuration parameters; Construct an allocation sequence, and configure channel resources according to the multidimensional resource configuration parameters according to the allocation sequence to construct multiple triplet data; The multiple triplet data are integrated to construct the multiple channel resource data.

4. The centralized data collection and distribution method for multi-user environments of electricity meters as described in claim 1, characterized in that, The method for dynamically allocating the acquisition task queue according to the multiple channel resource data and constructing a time slot allocation matrix includes: The multiple channel resource data are parsed to obtain time slot unit attributes and conflict constraints. Based on the aforementioned multiple channel resource data, channel partitioning is performed to determine multiple channel types; The length of the basic time slot unit is defined based on the time slot unit attributes, and multiple matrix columns are divided according to the multiple channel types; According to the conflict constraint conditions and the time slot basic unit length, the multiple matrix columns are initialized to obtain multiple matrix elements; The multiple matrix elements are injected into the acquisition task queue for dynamic allocation to construct the time slot allocation matrix.

5. The centralized data collection and distribution method for multi-user environments of electricity meters as described in claim 4, characterized in that, The method for dynamically allocating the multiple matrix elements into the acquisition task queue to construct the time slot allocation matrix includes: Based on the multiple matrix elements, matrix partitioning is performed to determine the fixed allocation area and the contention access area; Traverse the collection task queue and map the multiple collection instructions into matrix triplet elements; Time slot matching is performed based on the matrix element triplet. When the acquisition command is periodic, it is assigned to a fixed allocation area. When the acquisition command is bursty, it is allocated to the contention access area; Conflict optimization is performed on the fixed allocation area and the contention access area to construct the time slot allocation matrix.

6. The centralized data collection and distribution method for multi-user environments of electricity meters as described in claim 1, characterized in that, The method for receiving multiple energy consumption data packets fed back by the multiple energy meter terminals includes: Based on the aforementioned channel resource data, an independent receive buffer is configured for each channel of multiple energy meter terminals; The acquisition task queue is polled through the independent receive buffer to read multiple data packets and build a preprocessing queue. Based on the preprocessing queue, perform out-of-order reordering, extract the timestamp sequence, and rearrange the multiple data packets according to the timestamp sequence to generate an ordered data stream; The multiple energy meter terminals are indexed based on the ordered data stream to obtain the multiple energy consumption data packets.

7. The centralized data collection and distribution method for multi-user environments of electricity meters as described in claim 1, characterized in that, The method includes: performing data verification based on the multiple energy-consuming data packets, centrally allocating data according to the verification results, and generating an allocation routing table. Perform three-level verification on the multiple energy-consuming data packets and generate a set of verification result tags; The real-time network topology of multiple energy meter terminals is retrieved, and weights are allocated according to the real-time network topology based on the verification result tag set, thereby constructing a multi-dimensional weight allocation matrix. The path cost is calculated based on the multi-dimensional weighting matrix, and the minimum path cost is determined. Filter according to the minimum path cost to generate a target path, determine the target route based on the target path, and add the target route to the allocation route table.

8. The centralized data collection and distribution method for multi-user environments of electricity meters as described in claim 7, characterized in that, Perform three-level verification on the multiple power consumption data packets, the method including: The multiple energy-consuming data packets are verified at three levels: physical layer, data link layer, and application layer. If the multiple power data packets fail physical layer verification, then the multiple power data packets are marked with signal quality anomalies, and physical layer verification tag information is generated. If the multiple data packets fail the link layer verification, the data frame structure is marked with a transmission integrity error and a link layer verification tag is generated. If the application layer verification of the multiple energy data packets fails, the payload of the multiple energy data packets is marked as abnormal, and application layer verification tag information is generated. When the multiple power data packets pass all three levels of verification, a verification pass tag is generated.

9. A centralized data acquisition and distribution system for multi-user environments of electricity meters, characterized in that, The system is used to implement the centralized data collection and distribution method for multi-user environments of electricity meters as described in any one of claims 1-8, and the system comprises: The data acquisition task queue generation module is used to send centralized data acquisition request information through a communication network by multiple energy meter terminals, and to receive the centralized data acquisition request information to generate a data acquisition task queue, wherein the data acquisition task queue contains multiple data acquisition instructions. The channel resource data construction module is used to predict the traffic flow of multiple energy meter terminals, extract data traffic characteristics, and construct multiple channel resource data based on the data traffic characteristics. The dynamic allocation module is used to dynamically allocate the acquisition task queue according to the multiple channel resource data, construct a time slot allocation matrix, and issue the multiple acquisition commands to multiple energy meter terminals based on the time slot allocation matrix. The centralized allocation module is used to receive multiple energy consumption data packets fed back by the multiple energy meter terminals, perform data verification based on the multiple energy consumption data packets, perform centralized allocation according to the verification results, and generate an allocation routing table. The channel resource data construction module includes: The periodic distribution calculation module is used to retrieve the historical transmission data streams of multiple electricity meter terminals, perform periodic distribution calculations based on the historical transmission data streams, and obtain periodic data flow characteristics. The high-frequency decomposition module is used to perform high-frequency decomposition based on the historical transmitted data stream to obtain the characteristics of bursty data traffic. The combined prediction module is used to combine and predict the periodic data flow characteristics and the burst data flow characteristics to construct data flow characteristics; The resource configuration module is used to perform channel demand analysis based on the data traffic characteristics, obtain channel demand prediction information, configure resources according to the channel demand prediction information, and construct the multiple channel resource data.

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