Centralized acquisition and distribution method and system for multi-user environment of electric energy meter

Abstract

The invention discloses a centralized collection and distribution method and system for an electric energy meter multi-user environment, and relates to the technical field of electric energy data collection and distribution, and the method comprises the steps: a plurality of electric energy meter terminals send centralized collection request information through a communication network, receive the centralized collection request information, and generate a collection task queue; performing flow prediction on the plurality of electric energy meter terminals, and constructing a plurality of channel resource data; dynamically allocating the acquisition task queue, and constructing a time slot allocation matrix; and receiving a plurality of energy consumption data packets fed back by the plurality of electric energy meter terminals, performing data verification, and generating a distribution routing table. According to the invention, the technical problems of low system operation efficiency and poor stability caused by low electric energy data acquisition efficiency and unreasonable channel resource allocation in the multi-user environment in the prior art are solved, and efficient acquisition of electric energy data and reasonable allocation of channel resources in the multi-user environment are realized. And the operation efficiency and the stability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy data collection and distribution, and in particular to a centralized collection and distribution method and system for electric energy meters in a multi-user environment. Background Art

[0002] Traditional methods for collecting and distributing electricity meter data face numerous challenges in multi-user power data management scenarios. Due to the lack of an efficient mechanism for processing collection requests from multiple meter terminals and the inability to dynamically allocate channel resources based on data traffic characteristics, these methods lead to chaotic collection task queues, frequent channel conflicts, and inefficient and unreliable data transmission. This makes it difficult to meet the real-time and stability requirements for energy data collection when multiple users are online simultaneously.

[0003] The existing technology has technical problems such as low efficiency of power data collection in a multi-user environment and unreasonable allocation of channel resources, which leads to low system operation efficiency and poor stability. Summary of the Invention

[0004] The present application provides a centralized collection and distribution method and system for electric energy meters in a multi-user environment, which is used to solve the technical problems in the prior art of low efficiency in electric energy data collection in a multi-user environment and unreasonable allocation of channel resources, resulting in low system operation efficiency and poor stability.

[0005] In view of the above problems, the present application provides a centralized collection and distribution method and system for electric energy meters in a multi-user environment.

[0006] In a first aspect of the present application, a centralized data collection and distribution method for a multi-user environment of an electric energy meter is provided, the method comprising: Multiple electric energy meter terminals send centralized collection request information through a communication network, receive the centralized collection request information to generate a collection task queue, and the collection task queue contains multiple collection instructions; perform traffic prediction on the multiple electric energy meter terminals, extract data traffic characteristics, and construct multiple channel resource data according to the data traffic characteristics; dynamically allocate the collection task queue according to the multiple channel resource data, construct a time slot allocation matrix, and issue the multiple collection instructions to the multiple electric energy meter terminals based on the time slot allocation matrix; receive multiple energy consumption data packets fed back by the multiple electric 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.

[0007] In a possible implementation method, initial centralized collection request information sent by multiple electricity meter terminals through a communication network is received through a concentrator node; the initial centralized collection request information is deduplicated to obtain centralized collection request information; multiple collection instructions are set based on the centralized collection request information, the collection distance is calculated for the centralized collection request information, and the initial collection execution priority is set; the multiple collection instructions are sorted and integrated according to the initial collection execution priority to construct the collection task queue.

[0008] In a possible implementation method, historical transmission data streams of multiple electricity meter terminals are retrieved, and periodic distribution calculation is performed based on the historical transmission data streams to obtain periodic data traffic characteristics; high-frequency decomposition is performed based on the historical transmission data streams to obtain burst data traffic characteristics; the periodic data traffic characteristics and the burst data traffic characteristics are combined and predicted to construct data traffic characteristics; channel demand analysis is performed based on the data traffic characteristics to obtain channel demand prediction information, and resources are configured according to the channel demand prediction information to construct the multiple channel resource data.

[0009] In a possible implementation, multi-dimensional analysis and extraction are performed based on the data traffic characteristics to determine a four-dimensional vector; demand forecast calculation is performed based on the four-dimensional vector to generate channel demand forecast information; a resource allocation engine is started, and the channel demand forecast information is input into the resource allocation engine to perform resource division and obtain multi-dimensional resource configuration parameters; an allocation sequence is constructed, and channel resources are configured using the multi-dimensional resource configuration parameters according to the allocation sequence to construct multiple triplet data; and the multiple triplet data are integrated to construct the multiple channel resource data.

[0010] In a possible implementation, the multiple channel resource data are parsed to obtain time slot unit attributes and conflict constraints; channel division is performed based on the multiple channel resource data to determine multiple channel types; the time slot basic unit length 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 time slot basic unit length 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.

[0011] In a possible implementation, matrix partitioning is performed based on the multiple matrix elements to determine a fixed allocation area and a contention access area; the acquisition task queue is traversed to map the multiple acquisition instructions into matrix element triplets; time slot matching is performed based on the matrix element triplets, and when the acquisition instruction is a periodic type, it is allocated to the fixed allocation area; when the acquisition instruction is a burst type, 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.

[0012] In a possible implementation, an independent receiving buffer is configured for each channel of a plurality of electric energy meter terminals based on the plurality of channel resource data; the collection task queue is polled through the independent receiving buffer, a plurality of data packets are read, and a pre-processing queue is constructed; out-of-order reorganization is performed based on the pre-processing queue, a timestamp sequence is extracted, and the plurality of data packets are rearranged according to the timestamp sequence to generate an ordered data stream; feedback indexing is performed on the plurality of electric energy meter terminals according to the ordered data stream to obtain the plurality of energy consumption data packets.

[0013] In a possible implementation method, a three-level verification is performed on the multiple energy consumption data packets to generate a verification result label set; the real-time network topology structure of the multiple electricity meter terminals is retrieved, and weights are allocated according to the real-time network topology structure based on the verification result label set 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; screening is performed according to the minimum path cost to generate a target path, a target route is determined based on the target path, and the target route is added to the allocation routing table.

[0014] In a possible implementation, three-level verification is performed on the multiple energy-consuming data packets based on the physical layer, link layer, and application layer; when the multiple energy-consuming data packets fail the physical layer verification, the multiple energy-consuming data packets are marked as having abnormal signal quality, and physical layer verification label information is generated; when the multiple energy-consuming data packets fail the link layer verification, the data frame structure is marked as having abnormal transmission integrity, and link layer verification label information is generated; when the multiple energy-consuming data packets fail the application layer verification, the payloads of the multiple energy-consuming data packets are marked as abnormal, and application layer verification label information is generated; when the multiple energy-consuming data packets pass all three-level verifications, verification pass label information is generated.

[0015] A second aspect of the present application provides a centralized data collection and distribution system for a multi-user environment of electric energy meters, the system comprising: A collection task queue generation module is used for multiple electric energy meter terminals to send centralized collection request information through a communication network, receive the centralized collection request information to generate a collection task queue, and the collection task queue contains multiple collection instructions; a channel resource data construction module is used to perform traffic prediction on multiple electric energy meter terminals, extract data traffic characteristics, and construct multiple channel resource data based on the data traffic characteristics; a dynamic allocation module is used to dynamically allocate the collection task queue according to the multiple channel resource data, construct a time slot allocation matrix, and issue the multiple collection instructions to multiple electric energy meter terminals based on the time slot allocation matrix; a centralized allocation module is used to receive multiple energy consumption data packets fed back by the multiple electric 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.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: Multiple electricity meter terminals send centralized collection request information via a communication network, receive the centralized collection request information, and generate a collection task queue. Traffic prediction is performed on the multiple electricity meter terminals, data traffic characteristics are extracted, and multiple channel resource data are constructed based on the data traffic characteristics. The collection task queue is dynamically allocated according to the multiple channel resource data, a time slot allocation matrix is constructed, and the multiple collection instructions are issued to the multiple electricity meter terminals. Multiple energy usage data packets fed back by the multiple electricity meter terminals are received, data verification is performed on the multiple energy usage data packets, and centralized allocation is performed according to the verification results to generate a distribution routing table. This achieves the technical effect of achieving efficient collection of electricity data and reasonable allocation of channel resources in a multi-user environment, improving operational efficiency and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flow chart of a centralized data collection and distribution method for a multi-user environment of electric energy meters provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a centralized collection and distribution system for a multi-user environment of electric energy meters provided in an embodiment of the present application.

[0019] Description of the accompanying drawings: collection task queue generation module 10, channel resource data construction module 20, dynamic allocation module 30, centralized allocation module 40. DETAILED DESCRIPTION

[0020] The present application provides a centralized collection and distribution method and system for electric energy meters in a multi-user environment, aiming to solve the technical problems in the prior art of low efficiency in electric energy data collection in a multi-user environment and unreasonable allocation of channel resources, which lead to low system operation efficiency and poor stability.

[0021] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of this application.

[0022] Example 1, as Figure 1 As shown, the present application provides a centralized collection and distribution method for an electric energy meter in a multi-user environment, the method comprising: Step S100: a plurality of electric energy meter terminals send centralized collection request information via a communication network, and a collection task queue is generated upon receiving the centralized collection request information. The collection task queue includes a plurality of collection instructions.

[0023] Specifically, multiple electricity meter terminals send initial centralized collection request information to the concentrator node through the communication network, deduplicate the information to obtain valid centralized collection request information, and then set multiple collection instructions based on the valid request information. At the same time, the collection distance of the centralized collection request information is calculated to set the initial collection execution priority. Finally, the multiple collection instructions are sorted and integrated according to the initial collection execution priority to construct a collection task queue containing multiple collection instructions.

[0024] Step S200: performing flow prediction on a plurality of electric energy meter terminals, extracting data flow characteristics, and constructing a plurality of channel resource data according to the data flow characteristics.

[0025] Specifically, the historical transmission data streams of multiple electricity meter terminals are first retrieved, and the periodic distribution calculation is performed based on the data stream to obtain the periodic data traffic characteristics; the historical transmission data streams are then decomposed at high frequency to obtain the burst data traffic characteristics; the periodic and burst data traffic characteristics are combined and predicted to construct the data traffic characteristics; then, multi-dimensional analysis and extraction are performed based on the data traffic characteristics to determine the four-dimensional vector, which is used to perform demand forecasting calculations and generate channel demand forecast information; the resource allocation engine is started, and the channel demand forecast information is input into it for resource division to obtain 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 to construct multiple triplet data, and finally these triplet data are integrated to form multiple channel resource data.

[0026] Step S300: dynamically allocating the collection task queue according to the plurality of channel resource data, constructing a time slot allocation matrix, and issuing the plurality of collection instructions to a plurality of electric energy meter terminals based on the time slot allocation matrix.

[0027] Specifically, multiple channel resource data are parsed to obtain time slot unit attributes and conflict constraints, and channel division is completed based on the channel resource data to determine multiple channel types; then, the time slot basic unit length is defined according to the time slot unit attributes, and the matrix columns are divided according to the channel type. The matrix columns are initialized based on the conflict constraints and the time slot basic unit length to obtain multiple matrix elements; then, the matrix elements are divided into fixed allocation areas and contention access areas, the acquisition task queue is traversed and the acquisition instructions are mapped to matrix element triplets. If the instruction is periodic, it is allocated to the fixed allocation area, and if it is burst type, it is allocated to the contention access area. After completing the conflict optimization, the time slot allocation matrix is constructed; finally, based on the matrix, acquisition instructions are issued to multiple electricity meter terminals.

[0028] Step S400: receiving a plurality of energy usage data packets fed back by the plurality of electric energy meter terminals, performing data verification according to the plurality of energy usage data packets, performing centralized distribution according to the verification results, and generating a distribution routing table.

[0029] Specifically, after sending collection instructions to multiple energy meter terminals based on the time slot allocation matrix, an independent receive 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 and build a preprocessing queue. The preprocessing queue is then reorganized out of order, and the timestamp sequence is extracted and rearranged accordingly to generate an ordered data stream. The energy meter terminals are then indexed based on the ordered data stream to obtain multiple energy consumption data packets. Subsequently, the energy consumption data packets are subjected to three levels of verification: 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 assigned based on the verification result tag set and the real-time topology. A multi-dimensional distribution weight matrix is constructed. The minimum path is determined by calculating the path cost, and the target path is screened and the target route is determined. Finally, the target route is added to the distribution routing table, completing the centralized data distribution and route generation.

[0030] In one possible implementation, step S100 further includes: Step S110: receiving, via a concentrator node, initial centralized collection request information sent by a plurality of electric energy meter terminals via a communication network.

[0031] Step S120: Deduplication is performed on the initial centralized collection request information to obtain centralized collection request information.

[0032] Step S130: setting a plurality of collection instructions based on the centralized collection request information, performing collection distance calculation on the centralized collection request information, and setting an initial collection execution priority.

[0033] Step S140: sorting and integrating the multiple acquisition instructions according to the initial acquisition execution priority to construct the acquisition task queue.

[0034] Specifically, the concentrator node receives initial centralized collection request information sent by multiple electric energy meter terminals via the communication network. This process realizes centralized access of electric energy meter data collection requests in a multi-user environment.

[0035] 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 through a hash algorithm, and the encoded value is stored in a cache hash table. When a new request arrives, its hash value is calculated and compared with the records in the table. Requests corresponding to duplicate hash values are marked as redundant data and filtered, and only request information with unique hash values is retained, thereby obtaining deduplicated centralized collection request information to ensure the uniqueness and validity of subsequently processed request data.

[0036] 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 of the toll station and the communication network routing table, the physical distance corresponding to each request is calculated: the straight-line distance from the terminal to the concentrator and the network distance (e.g., the number of communication link hops). The two are weighted and summed according to preset weights (e.g., 60% for physical distance and 40% for network distance) to obtain the collection distance value. The initial collection execution priority is set based on the collection distance value, from small to large. The smaller the distance value, the higher the priority. At the same time, a collection instruction is generated for each request, including parameters such as the collection frequency and data type, to ensure that collection tasks requiring short distances and low latency are executed first.

[0037] A priority-based sorting algorithm is established, using the initial execution priority value of each acquisition instruction as the sorting key. Using a quick sorting method, multiple acquisition instructions are sorted from high to low priority. After sorting, the sorted acquisition instructions are sequentially stored in a first-in-first-out task queue data structure. Metadata such as a timestamp and task status identifier are added to each acquisition instruction to form an ordered acquisition task queue. During this process, if acquisition instructions with the same priority exist, they are re-sorted according to the order of their receipt time. This ensures the orderliness and executable nature of the task queue, providing a regular task sequence for subsequent task scheduling and processing.

[0038] In one possible implementation, step S200 further includes: Step S210: Retrieve historical transmission data streams of multiple electric energy meter terminals, perform periodic distribution calculation based on the historical transmission data streams, and obtain periodic data flow characteristics.

[0039] Step S220: performing high-frequency decomposition based on the historical transmission data stream to obtain bursty data traffic characteristics.

[0040] Step S230: combining and predicting the periodic data traffic characteristics and the bursty data traffic characteristics to construct data traffic characteristics.

[0041] Step S240: performing channel demand analysis according to the data traffic characteristics, obtaining channel demand prediction information, performing resource configuration according to the channel demand prediction information, and constructing the plurality of channel resource data.

[0042] Specifically, the historical transmission data streams of multiple electricity meter terminals within a preset time window (such as the past 30 days) are retrieved from the historical data storage module through the database query interface. The data stream is analyzed in the frequency domain using the Fourier transform algorithm, and the time domain signal is decomposed into a superposition of sinusoidal waves of different frequencies. The periodic fluctuation period of the data flow (such as the peak electricity consumption period of 8:00-10:00 every day and every Monday morning) is determined by identifying the frequency components with concentrated energy in the spectrum graph. The peak, valley and change trend of the data flow in each period are extracted by combining the sliding window mean calculation method to form a regular flow feature vector with the time period as the dimension, thereby obtaining the characteristic parameters that characterize the periodic changes of the data flow.

[0043] A wavelet transform algorithm is used to decompose the high-frequency components of historical transmission data streams. By selecting appropriate wavelet basis functions (such as the DB4 wavelet), the data stream is subjected to multi-layer decomposition, breaking the signal into approximate and detail components in different frequency bands. The detail components correspond to high-frequency burst signals. A threshold denoising method is used to process the detail components. An adaptive threshold is set to filter out normal traffic fluctuations, while retaining burst traffic spikes that exceed the threshold. By extracting parameters such as burst amplitude, duration, and occurrence time, a burst data traffic feature vector is constructed, thereby obtaining burst characteristics that can characterize abnormal data traffic fluctuations over a short period of time.

[0044] A fusion model of the ARIMA (Autoregressive Integrated Moving Average) and LSTM (Long Short-Term Memory) networks is used to combine and predict periodic and bursty data traffic characteristics. The periodic feature vector is first fed into the ARIMA model, leveraging its linear time series analysis capabilities to capture the periodic patterns of data traffic. The bursty feature vector is then fed into the LSTM neural network, which uses a gating mechanism to learn the nonlinear patterns of bursty traffic changes. A weighted fusion strategy is then used to assign dynamic weights to the periodic predictions output by the ARIMA and the bursty predictions output by the LSTM (the weights are adaptively adjusted based on historical prediction errors). The two results are then superimposed to generate a comprehensive forecast sequence. Finally, key parameters such as traffic peak interval, average transmission rate, and fluctuation variance are extracted from the comprehensive forecast sequence to construct a multidimensional data traffic feature vector encompassing time, frequency, and statistical features, accurately characterizing the overall trend of data traffic.

[0045] First, a multi-dimensional analysis of data traffic characteristics is performed to extract parameters such as peak traffic volume, average bandwidth, transmission delay, and bit error rate (BER), constructing a four-dimensional vector. This four-dimensional vector is then fed into a machine learning-based demand prediction model. The trained model then generates channel demand forecast 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 forecast information. Resources are then allocated according to a pre-defined resource allocation strategy, resulting in multi-dimensional resource configuration parameters such as channel bandwidth allocation ratio, transmission time slot length, and error correction coding scheme. An allocation sequence is then constructed, mapping the multi-dimensional resource configuration parameters into multiple triples containing channel identifiers, resource parameters, and time windows. Finally, these triples are integrated to construct multiple channel resource data sets to meet the data transmission needs of different electricity meter terminals.

[0046] In one possible implementation, step S240 further includes: Step S241: Perform multi-dimensional analysis and extraction based on the data flow characteristics to determine a four-dimensional vector.

[0047] Step S242: performing demand prediction calculation based on the four-dimensional vector to generate channel demand prediction information.

[0048] Step S243: starting a resource allocation engine, inputting the channel demand prediction information into the resource allocation engine to perform resource division and obtain multi-dimensional resource configuration parameters.

[0049] Step S244: constructing an allocation sequence, performing channel resource configuration on the multi-dimensional resource configuration parameters according to the allocation sequence, and constructing a plurality of triplet data.

[0050] Step S245: Integrate the multiple triplet data to construct the multiple channel resource data.

[0051] Specifically, a multi-dimensional analysis of data traffic characteristics is conducted, extracting four key dimensional parameters from historical transmission data streams: cyclic stability, burst intensity, average data volume, and data volume standard deviation. By analyzing the periodic fluctuation patterns of data traffic in time series, the fluctuation amplitude and repetition frequency within the cycle are calculated to determine the cyclic stability parameter. Based on the burst traffic characteristics obtained by high-frequency decomposition, the peak size and duration of the burst traffic are extracted to determine the burst intensity parameter. The average data volume parameter is obtained by calculating the average value of data traffic per unit time. The degree of dispersion of data traffic relative to the average value is calculated to determine the data volume standard deviation parameter. A four-dimensional vector containing the above four parameters is then constructed to comprehensively characterize the characteristics of data traffic and transmission requirements.

[0052] When a four-dimensional vector containing cyclic stability, burst intensity, average data volume, and data volume standard deviation is input into a trained LSTM model to generate channel demand forecast information, the specific implementation is as follows: the four-dimensional vector is first normalized to eliminate the dimensionality effects of different parameters (for example, cyclic stability is normalized to [0, 1] and burst intensity is converted to Mbps). It is then divided into input sequences according to 5-minute time windows and input into a three-layer LSTM network. The model uses a forget gate to filter irrelevant historical traffic information, an input gate to update current traffic characteristics, and an output gate to generate predictions. This captures the nonlinear mapping between the four-dimensional vector and channel resource requirements (for example, when the burst intensity increases from 50 Mbps to 60 Mbps, the model automatically predicts that a 5 MHz bandwidth increase is required). During training, the Adam optimizer is used with a learning rate of 0.001 to iteratively adjust weights based on the experimental data errors in the table (for example, the bandwidth prediction error in Experiment 1 was 2 MHz). Training is terminated when the error on the validation set decreases by less than 0.5% for 10 consecutive rounds. Finally, the model's fully connected layer outputs channel demand prediction information, including bandwidth requirements, time slot allocation, and QoS level. For example, when inputting a four-dimensional vector with a cycle stability of 0.85, a burst intensity of 60Mbps, an average data volume of 1200KB, and a data volume standard deviation of 220, the model is corrected to output a prediction result of 55MHz bandwidth, 12 time slots, and a high QoS level, with the error between the model and the actual channel demand controlled within 3%.

[0053] The comparison table of four-dimensional vector input and channel demand prediction experimental data is shown in Table 1: Table 1 Experimental data comparison table

[0054] Start the resource allocation engine and input the channel demand prediction information including bandwidth demand, time slot allocation, and QoS level into it. The engine uses the maximum-minimum fairness algorithm combined with the proportional fairness algorithm to divide resources. The channel bandwidth is dynamically allocated according to the bandwidth threshold in the channel demand prediction information. The transmission time slot is divided according to the buffer time slot length corresponding to the burst intensity. The error correction coding method and transmission priority are configured according to the QoS level. Finally, multi-dimensional resource configuration parameters including channel bandwidth allocation ratio, transmission time slot length, error correction coding type, and priority weight are obtained.

[0055] An allocation sequence is constructed based on the priority and time sequence of channel resource usage, prioritizing channels with high QoS requirements before allocating standard channels. Following this allocation sequence, the channel bandwidth in the multidimensional resource configuration parameters is mapped to frequency resources. The time slot mode (e.g., continuous or non-continuous) is determined based on the transmission time slot length. Spreading codes are assigned based on the error correction coding type and priority weight. This constructs multiple triples of frequency, time slot mode, and spreading code, such as (frequency 100 MHz, time slot mode continuous 5 ms, spreading code C1), enabling precise configuration and structured description of channel resources.

[0056] 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 identification, resource parameters, time attributes, etc. in each triplet data, eliminate format differences and contradictions between different triplet data, and then classify and aggregate them 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.

[0057] In one possible implementation, step S300 further includes: Step S310: parse the plurality of channel resource data to obtain time slot unit attributes and conflict constraint conditions.

[0058] Step S320: performing channel division based on the plurality of channel resource data to determine a plurality of channel types.

[0059] Step S330: defining a time slot basic unit length based on the time slot unit attributes, and dividing a plurality of matrix columns according to the plurality of channel types.

[0060] Step S340: Initializing the plurality of matrix columns according to the conflict constraint condition and the time slot basic unit length to obtain a plurality of matrix elements.

[0061] Step S350: injecting the plurality of matrix elements into the acquisition task queue for dynamic allocation to construct the time slot allocation matrix.

[0062] Specifically, by parsing the triplet data (frequency, time slot mode, spread spectrum code) in multiple channel resource data, the time slot unit attributes are extracted, including parameters such as time slot width, protection interval, and time slot period. At the same time, conflict constraints are obtained from the resource allocation rules. For example, the same frequency point cannot be occupied by different channels in the same time slot, and a guard interval must be retained between adjacent time slots. In this way, the time slot unit attributes and conflict constraints required to construct the time slot allocation matrix are obtained.

[0063] Channels are divided based on parameters such as frequency, bandwidth, and transmission requirements from multiple channel resource data, grouping channels with the same or similar characteristics into one category to determine multiple channel types. For example, channels can be divided into high-frequency and low-frequency channels based on frequency range, wideband and narrowband channels based on bandwidth, or real-time and non-real-time data channels based on the type of transmitted data, to meet the channel requirements of different acquisition instructions.

[0064] First, based on the parsed time slot unit attributes (such as slot width and guard interval), the basic time slot unit length is defined. For example, the basic time slot unit length is set to 5 milliseconds to ensure that it meets the basic time unit requirements for data transmission. Then, the matrix columns are divided according to the multiple determined channel types (such as high-frequency channels, low-frequency channels, and broadband channels). Each channel type corresponds to a column in the matrix. This ensures that the matrix column divisions correspond to the channel types, laying the foundation for the subsequent construction of the column dimensions of the time slot allocation matrix.

[0065] First, conflict constraints are defined, such as the inability to use different channels simultaneously within the same time slot and the need to maintain a guard interval between adjacent time slots. Then, based on the defined time slot unit length (e.g., 5 milliseconds), multiple matrix columns are initialized. Specifically, based on the conflict constraints, time slots are divided within each matrix column according to the time slot unit length. Channel usage within each time slot is checked to see if it complies with the conflict constraints. If so, the time slot is marked as available; otherwise, it is marked as unavailable. This results in multiple matrix elements, each of which records the usage status and related parameters of the corresponding channel in a specific time slot.

[0066] Each acquisition instruction in the acquisition task queue is sequentially injected into multiple matrix elements, and dynamically allocated based on the acquisition instruction type and requirements. Periodic acquisition instructions are preferentially allocated to the corresponding matrix elements in the fixed allocation zone to ensure their periodic transmission requirements are met. Burst-type acquisition instructions are allocated to matrix elements in the contention access zone, where time slot resources are acquired through a competitive mechanism. During the allocation process, the allocation is checked in real time to ensure compliance with conflict constraints. If conflicts arise, the allocation plan is adjusted. Ultimately, a complete time slot allocation matrix is constructed, achieving optimal time slot allocation for acquisition tasks.

[0067] In one possible implementation, step S350 further includes: Step S351: performing matrix partitioning based on the plurality of matrix elements to determine a fixed allocation area and a contention access area.

[0068] Step S352: traverse the acquisition task queue and map the multiple acquisition instructions into matrix element triplets.

[0069] Step S353: performing time slot matching based on the matrix element triples, and when the acquisition instruction is a periodic type, allocating it to the fixed allocation area.

[0070] Step S354: When the acquisition instruction is a burst type, it is allocated to the contention access zone.

[0071] Step S355: Conflict optimization is performed on the fixed allocation area and the contention access area to construct the time slot allocation matrix.

[0072] Specifically, the matrix is divided into a fixed allocation area and a contention access area based on the time slot occupancy status, channel type, and conflict constraints of multiple matrix elements. The fixed allocation area stores the time slot allocations for periodic acquisition instructions. Its position in the matrix is relatively fixed and repeats periodically to meet the requirements of periodic tasks for time slot resource stability. The contention access area is used to handle time slot requests for bursty acquisition instructions. Time slot resources in this area are not pre-allocated, but are obtained by bursty tasks based on real-time demand through competition. This partitioning method enables differentiated time slot resource management for different types of acquisition instructions.

[0073] Each acquisition instruction in the acquisition task queue is traversed sequentially, and key information is extracted from each acquisition instruction, such as the required time slot, corresponding channel identifier, and task priority parameters. These parameters are then combined into a matrix element triple. For example, for an acquisition instruction that requires the 10th time slot, uses channel A, and has a high priority, it is mapped to a matrix element triple such as (time slot 10, channel A, high priority). In this way, all acquisition instructions are converted into a structured data form suitable for distribution within the matrix.

[0074] For each matrix element triplet, the acquisition instruction corresponding to it is determined to be a periodic instruction. By analyzing parameters such as the acquisition instruction's transmission period and repetition frequency, if it is determined to be a periodic instruction, a time slot location matching the instruction's period is searched in the fixed allocation area. The fixed allocation area has been pre-divided into periodically repeating time slots based on the timing characteristics of periodic instructions. Once a matching time slot segment is found, the periodic acquisition instruction is assigned to the corresponding location in the fixed allocation area, ensuring that it occupies time slot resources according to a fixed periodic pattern, thus meeting the periodic instruction's requirements for time slot stability and regularity.

[0075] Burst-type acquisition instructions, due to their non-periodic and random nature, are assigned to the contention access zone. Time slots in this zone are not pre-assigned, requiring burst-type acquisition instructions to compete for available slots. The time slot status in this zone is monitored in real time. When available slots are available, appropriate slot resources are allocated based on the priority and real-time needs of the burst-type instruction. This ensures that burst-type acquisition instructions can flexibly obtain the required slots in the contention access zone to meet their random data transmission needs.

[0076] To address the spatial conflicts (the same channel being occupied by different acquisition instructions) and temporal overlap (violation of guard intervals in the same or adjacent time slots) in the fixed allocation zone and the contention access zone, a conflict detection and resolution algorithm based on graph theory is employed for optimization. First, the periodic time slot allocations in the fixed allocation zone are traversed. By adjusting the cycle start point and slot offset, long-term temporal overlap caused by periodic repetition is eliminated. For the contention access zone, a dynamic priority scheduling mechanism is introduced. When bursty instructions 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 instructions. The occupancy status of matrix elements is updated in real time during the optimization process. Through iterative detection and adjustment, a time slot allocation matrix is ultimately constructed that is free of spatial conflicts and temporal overlap, ensuring that all acquisition instructions in both the fixed allocation zone and the contention access zone can be transmitted within the legal time slots.

[0077] In one possible implementation, step S400 further includes: Step S410: configuring an independent receiving buffer for each channel of a plurality of electric energy meter terminals based on the plurality of channel resource data.

[0078] Step S420: polling the collection task queue through the independent receiving buffer, reading multiple data packets, and building a pre-processing queue.

[0079] Step S430: performing out-of-order reorganization based on the pre-processing queue, extracting a timestamp sequence, and rearranging the multiple data packets according to the timestamp sequence to generate an ordered data stream.

[0080] Step S440: performing feedback indexing on a plurality of electric energy meter terminals according to the ordered data stream to obtain the plurality of energy consumption data packets.

[0081] Specifically, based on the bandwidth, transmission protocol, and other parameters of each channel in the multiple channel resource data, an independent receive buffer is configured for each channel of the multiple energy meter terminals. Each independent receive buffer corresponds to a specific channel and has a cache capacity that adapts to the data transmission rate and packet size of the channel. It is used to temporarily store energy usage packets received by the corresponding channel, preventing data from different channels from interfering with each other and providing an orderly cache space for subsequent data processing.

[0082] Each channel's independent receive buffer is polled in the order of the acquisition task queue, sequentially reading the cached data packets in each buffer. During the polling process, multiple packets are temporarily stored in the order they were received and constructed into a preprocessing queue. This allows for unified preprocessing of these packets, providing an ordered data source for subsequent data reorganization and indexing.

[0083] For out-of-order packets in the preprocessing queue, the timestamp information carried by each packet is first extracted to form a timestamp sequence. Then, based on the timestamp sequence, multiple packets are reordered in chronological order, reorganizing the originally disordered packets into an ordered data stream arranged in chronological order, ensuring that the data subsequently processed has the correct temporal logical order.

[0084] By using the terminal identification, channel number and other information carried by each data packet in the ordered data stream, a corresponding relationship with multiple electricity meter terminals is established. The data packets in the ordered data stream are accurately mapped to the corresponding electricity meter terminals through the feedback index mechanism, thereby identifying and extracting the energy consumption data packets fed back by each electricity meter terminal from the ordered data stream, providing an accurate data source for subsequent data verification and centralized distribution.

[0085] In one possible implementation, step S400 further includes: Step S450: performing three-level verification on the plurality of energy consumption data packets to generate a verification result label set.

[0086] Step S460: Retrieve the real-time network topology of multiple electric energy meter terminals, perform weight distribution according to the verification result label set and the real-time network topology, and construct a multi-dimensional distribution weight matrix.

[0087] Step S470: Calculate the path cost based on the multi-dimensional allocation weight matrix and determine the minimum value of the path cost.

[0088] Step S480: Screening is performed according to the minimum path cost to generate a target path, determining a target route according to the target path, and adding the target route to the distribution routing table.

[0089] Specifically, the signal strength, spectrum distribution, signal-to-noise ratio and other physical parameters of the energy-consuming data packet are first detected by a signal detector at the physical layer and compared with the preset threshold. If the standard is not met, the signal quality is marked as abnormal and a physical layer verification tag is generated; then the CRC verification algorithm and frame structure verification mechanism are used at the link layer to check the integrity and correctness of the data frame. If problems such as frame header errors and check code mismatches are found, the data frame structure is marked as a transmission integrity abnormality and a link layer verification tag is generated; then, at the application layer, according to the application protocol specifications, the format, content and semantics of the data payload are parsed. If a format error or content abnormality occurs, the payload is marked as abnormal and an application layer verification tag is generated; finally, the verification results of each layer are summarized to generate a verification result tag set containing the verification status of the physical layer, link layer and application layer.

[0090] The network topology discovery protocol is used to obtain information such as the connection relationship, node status, and link quality of multiple electricity meter terminals in real time, and a real-time network topology is constructed. 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 result, the weight value of the corresponding node is increased; for links that fail the link layer verification, the transmission cost weight of the link is increased. Next, a multi-dimensional weight distribution matrix is constructed by 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 of each element in the network topology, providing a comprehensive weight basis for subsequent path cost calculations.

[0091] Based on the constructed multi-dimensional weight distribution matrix, the node weights and link weights of each possible path in the network topology are accumulated to obtain the total cost of the path. For example, if a path passes through nodes A, B, and C and the corresponding links, the weight added to node A due to physical layer verification failure, the normal weight of node B, and the weights set for each link due to the link layer verification status must be added. By traversing all possible paths and comparing the total costs of each path, the path with the lowest cost is determined. This minimum cost is the path cost minimum, providing a key basis for subsequent screening of the optimal transmission path.

[0092] Based on the determined minimum path cost, all paths with a total cost equal to this minimum are selected from the multi-dimensional allocation weight matrix. These paths are designated as target paths. For each target path, the node connectivity and link status are further analyzed to determine the specific transmission path from the source node to the target node, known as the target route. Finally, the determined target route is added to the allocation routing table, allowing for subsequent centralized allocation of energy-consuming data packets based on this route, ensuring data transmission along the path with the lowest cost, thereby improving transmission efficiency and reliability.

[0093] In one possible implementation, step S450 further includes: Step S451: performing three-level verification on the multiple energy consumption data packets based on the physical layer, link layer, and application layer.

[0094] Step S452: When the physical layer verification of the multiple energy-consuming data packets fails, the multiple energy-consuming data packets are marked as having abnormal signal quality, and physical layer verification tag information is generated.

[0095] Step S453: When link layer verification fails in the plurality of energy-consuming data packets, a transmission integrity abnormality mark is performed on the data frame structure, and link layer verification tag information is generated.

[0096] Step S454: When the multiple energy-consuming data packets fail application layer verification, abnormality marks are performed on the payloads of the multiple energy-consuming data packets, and application layer verification tag information is generated.

[0097] Step S455: When the plurality of energy consumption data packets pass all three levels of verification, verification pass label information is generated.

[0098] Specifically, multiple energy data packets are sequentially verified at the physical layer, link layer, and application layer. The physical layer ensures the quality of the physical medium used for data transmission by detecting physical parameters such as signal strength, noise level, and frequency offset. The link layer verifies the integrity of data frames using algorithms such as cyclic redundancy checks (CRCs), checking the correctness of frame headers, frame trailers, and checksums. The application layer verifies the format and content semantics of the data payload according to specific protocol specifications to ensure that the data complies with the application layer protocol requirements. These three levels of verification are implemented layer by layer to fully guarantee the transmission quality of energy data packets.

[0099] When multiple energy-consuming data packets fail the physical layer verification, these packets are marked as having abnormal signal quality. Specifically, problems such as insufficient signal strength, too low signal-to-noise ratio, or severe interference that cause the physical layer transmission quality to not meet the standards are marked. Physical layer verification label information containing information such as the abnormality type and signal parameters is generated to clearly indicate the problems with the data packet in the physical layer transmission.

[0100] When multiple energy-consuming data packets fail the link layer checksum, this indicates an integrity issue with the data frame structure during transmission, such as header / footer errors, CRC checksum mismatches, or data frame loss. The data frame structure of these packets is flagged as an integrity anomaly and a link layer checksum tag is generated. This tag contains the error type (e.g., frame structure error, checksum error), error location, and related frame parameters to clearly indicate integrity issues in link layer transmission.

[0101] When multiple energy-consuming data packets fail application-layer validation, this indicates that the payload content of the packets does not comply with the application protocol. For example, there may be issues such as data format errors, semantic parsing failures, or missing key fields. The payloads of these packets are then flagged as abnormal, and application-layer validation tags are generated. These tags detail the payload anomaly type (e.g., format error, abnormal content), anomaly location, and related payload parameters, clearly indicating any issues with application-layer data processing.

[0102] When multiple energy-consuming data packets pass all checks at the physical layer, link layer, and application layer in sequence, that is, the signal quality of the physical layer meets the standards, the data frame structure of the link layer is complete and correct, and the payload content of the application layer meets the protocol requirements, a check pass label information will be generated to indicate that no abnormalities were found in the transmission and processing of the data packet, and it can be used normally for subsequent operations such as data concentration distribution.

[0103] Embodiment 2 is based on the same inventive concept as the centralized collection and distribution method for a multi-user environment of an electric energy meter in the above embodiment. Figure 2 As shown, the present application provides a centralized collection and distribution system for a multi-user environment of electric energy meters. The system and method embodiments in the present application are based on the same inventive concept. The system includes: The collection task queue generation module 10 is used for multiple electric energy meter terminals to send centralized collection request information through a communication network, receive the centralized collection request information and generate a collection task queue, wherein the collection task queue includes multiple collection instructions.

[0104] The channel resource data construction module 20 is used to perform flow prediction on multiple electric energy meter terminals, extract data flow characteristics, and construct multiple channel resource data according to the data flow characteristics.

[0105] The dynamic allocation module 30 is configured to dynamically allocate the collection task queue according to the plurality of channel resource data, construct a time slot allocation matrix, and issue the plurality of collection instructions to the plurality of electric energy meter terminals based on the time slot allocation matrix.

[0106] The centralized distribution module 40 is configured to receive a plurality of energy usage data packets fed back by the plurality of electric energy meter terminals, perform data verification based on the plurality of energy usage data packets, perform centralized distribution according to the verification results, and generate a distribution routing table.

[0107] Furthermore, the system is also used to implement the following functions: Initial centralized collection request information sent by multiple electricity meter terminals through the communication network is received through a concentrator node; the initial centralized collection request information is deduplicated to obtain centralized collection request information; multiple collection instructions are set based on the centralized collection request information, the collection distance is calculated for the centralized collection request information, and the initial collection execution priority is set; the multiple collection instructions are sorted and integrated according to the initial collection execution priority to construct the collection task queue.

[0108] Furthermore, the system is also used to implement the following functions: Retrieve historical transmission data streams of multiple electricity meter terminals, perform periodic distribution calculation based on the historical transmission data streams, and obtain periodic data traffic characteristics; perform high-frequency decomposition based on the historical transmission data streams to obtain burst data traffic characteristics; combine and predict the periodic data traffic characteristics and the burst data traffic characteristics to construct data traffic characteristics; perform channel demand analysis based on the data traffic characteristics to obtain channel demand prediction information, perform resource allocation according to the channel demand prediction information, and construct the multiple channel resource data.

[0109] Furthermore, the system is also used to implement the following functions: Multi-dimensional analysis and extraction are performed based on the data traffic characteristics to determine a four-dimensional vector; demand forecast calculation is performed based on the four-dimensional vector to generate channel demand forecast information; a resource allocation engine is started, and the channel demand forecast information is input into the resource allocation engine to perform resource division and 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.

[0110] Furthermore, the system is also used to implement the following functions: The multiple channel resource data are parsed to obtain time slot unit attributes and conflict constraints; channels are divided based on the multiple channel resource data to determine multiple channel types; a time slot basic unit length 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 time slot basic unit length 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.

[0111] Furthermore, the system is also used to implement the following functions: Matrix partitioning is performed based on the multiple matrix elements to determine a fixed allocation area and a contention access area; the acquisition task queue is traversed to map the multiple acquisition instructions into matrix element triplets; time slot matching is performed based on the matrix element triplets, and when the acquisition instruction is a periodic type, it is allocated to the fixed allocation area; when the acquisition instruction is a burst type, 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.

[0112] Furthermore, the system is also used to implement the following functions: Based on the multiple channel resource data, an independent receiving buffer is configured for each channel of the multiple electricity meter terminals; the collection task queue is polled through the independent receiving buffer, multiple data packets are read, and a preprocessing queue is constructed; out-of-order reorganization is performed based on the preprocessing queue, a timestamp sequence is extracted, 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 electricity meter terminals according to the ordered data stream to obtain the multiple energy consumption data packets.

[0113] Furthermore, the system is also used to implement the following functions: Perform three-level verification on the multiple energy consumption data packets to generate a verification result label set; retrieve the real-time network topology structure of multiple electricity meter terminals, perform weight distribution according to the real-time network topology structure based on the verification result label set, and construct a multi-dimensional distribution weight matrix; calculate the path cost based on the multi-dimensional distribution weight matrix to determine the minimum path cost; filter according to the minimum path cost to generate a target path, determine a target route based on the target path, and add the target route to the distribution routing table.

[0114] Furthermore, the system is also used to implement the following functions: A three-level verification is performed on the multiple energy-consuming data packets based on the physical layer, link layer, and application layer; when the multiple energy-consuming data packets fail the physical layer verification, the multiple energy-consuming data packets are marked as having abnormal signal quality, and physical layer verification label information is generated; when the multiple energy-consuming data packets fail the link layer verification, the data frame structure is marked as having abnormal transmission integrity, and link layer verification label information is generated; when the multiple energy-consuming data packets fail the application layer verification, the payloads of the multiple energy-consuming data packets are marked as abnormal, and application layer verification label information is generated; when the multiple energy-consuming data packets pass all three-level verifications, verification pass label information is generated.

[0115] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0116] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

[0117] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A centralized data collection and distribution method for a multi-user environment of electric energy meters, characterized in that: The method comprises: Multiple electric energy meter terminals send centralized collection request information through a communication network, and receive the centralized collection request information to generate a collection task queue, wherein the collection task queue includes multiple collection instructions; Performing flow prediction on multiple electric energy meter terminals, extracting data flow characteristics, and constructing multiple channel resource data based on the data flow characteristics; Dynamically allocating the collection task queue according to the plurality of channel resource data, constructing a time slot allocation matrix, and issuing the plurality of collection instructions to the plurality of electric energy meter terminals based on the time slot allocation matrix; Receive multiple energy usage data packets fed back by the multiple energy meter terminals, perform data verification based on the multiple energy usage data packets, perform centralized distribution according to the verification results, and generate a distribution routing table.

2. The centralized data collection and distribution method for a multi-user environment of electric energy meters according to claim 1, characterized in that: Multiple electric energy meter terminals send centralized collection request information through a communication network, and receive the centralized collection request information to generate a collection task queue. The method includes: receiving, through a concentrator node, initial centralized collection request information sent by a plurality of electric energy meter terminals through a communication network; Deduplication of the initial centralized collection request information to obtain centralized collection request information; Setting a plurality of collection instructions based on the centralized collection request information, calculating a collection distance for the centralized collection request information, and setting an initial collection execution priority; 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 a multi-user environment of electric energy meters according to claim 1, characterized in that: Flow prediction is performed on multiple electric energy meter terminals, data flow characteristics are extracted, and multiple channel resource data are constructed based on the data flow characteristics. The method includes: Retrieving historical transmission data streams of multiple electric energy meter terminals, performing periodic distribution calculation based on the historical transmission data streams, and obtaining periodic data flow characteristics; Performing high-frequency decomposition based on the historical transmission data stream to obtain bursty data traffic characteristics; Combining and predicting the periodic data traffic characteristics and the bursty data traffic characteristics to construct a data traffic characteristic; Channel demand analysis is performed according to the data traffic characteristics to obtain channel demand prediction information, resource configuration is performed according to the channel demand prediction information, and the plurality of channel resource data are constructed.

4. The centralized data collection and distribution method for a multi-user environment of electric energy meters according to claim 3, characterized in that: Performing a channel demand analysis based on the data traffic characteristics to obtain channel demand prediction information, performing resource allocation according to the channel demand prediction information, and constructing the plurality of channel resource data, the method comprising: Perform multi-dimensional analysis and extraction based on the data traffic characteristics to determine a four-dimensional vector; Perform demand forecast calculation based on the four-dimensional vector to generate channel demand forecast information; Starting a resource allocation engine, inputting the channel demand prediction information into the resource allocation engine to perform resource division, and obtaining multi-dimensional resource configuration parameters; Constructing an allocation sequence, performing channel resource configuration on the multi-dimensional resource configuration parameters according to the allocation sequence, and constructing a plurality of triplet data; The multiple triplet data are integrated to construct the multiple channel resource data.

5. The centralized data collection and distribution method for a multi-user environment of electric energy meters according to claim 1, characterized in that: Dynamically allocating the acquisition task queue according to the plurality of channel resource data to construct a time slot allocation matrix, the method comprising: Parsing the plurality of channel resource data to obtain time slot unit attributes and conflict constraint conditions; Perform channel division based on the plurality of channel resource data to determine a plurality of channel types; defining a time slot basic unit length based on the time slot unit attribute, and dividing a plurality of matrix columns according to the plurality of channel types; Initializing the plurality of matrix columns according to the conflict constraint condition and the time slot basic unit length to obtain a plurality of matrix elements; The multiple matrix elements are injected into the acquisition task queue for dynamic allocation to construct the time slot allocation matrix.

6. The centralized data collection and distribution method for a multi-user environment of electric energy meters according to claim 5, characterized in that: Injecting the plurality of matrix elements into the acquisition task queue for dynamic allocation to construct the time slot allocation matrix, the method comprising: Performing matrix partitioning based on the plurality of matrix elements to determine a fixed allocation area and a contention access area; Traversing the acquisition task queue, mapping the multiple acquisition instructions into matrix element triplets; Perform time slot matching based on the matrix element triples, and when the acquisition instruction is a periodic type, allocate it to a fixed allocation area; When the acquisition instruction is of burst type, 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.

7. The centralized data collection and distribution method for a multi-user environment of electric energy meters according to claim 1, characterized in that: Receiving multiple energy usage data packets fed back by the multiple electric energy meter terminals, the method includes: configuring an independent receiving buffer for each channel of a plurality of electric energy meter terminals based on the plurality of channel resource data; Polling the collection task queue through the independent receiving buffer, reading multiple data packets, and building a pre-processing queue; Performing out-of-order reorganization based on the pre-processing queue, extracting a timestamp sequence, and rearranging the plurality of data packets according to the timestamp sequence to generate an ordered data stream; Feedback indexing is performed on multiple electric energy meter terminals according to the ordered data stream to obtain the multiple energy consumption data packets.

8. The centralized data collection and distribution method for a multi-user environment of electric energy meters according to claim 1, characterized in that: Performing data verification on the plurality of energy consumption data packets, performing centralized allocation according to the verification results, and generating an allocation routing table, the method comprising: Performing three-level verification on the plurality of energy consumption data packets to generate a verification result label set; Retrieving a real-time network topology structure of a plurality of electric energy meter terminals, performing weight distribution according to the real-time network topology structure based on the verification result label set, and constructing a multi-dimensional distribution weight matrix; Calculating the path cost based on the multi-dimensional allocation weight matrix and determining the minimum value of the path cost; Screening is performed according to the minimum path cost to generate a target path, a target route is determined according to the target path, and the target route is added to the distribution routing table.

9. The centralized data collection and distribution method for a multi-user environment of electric energy meters according to claim 8, characterized in that: Performing three-level verification on the plurality of energy consumption data packets, the method comprising: Performing three-level verification on the multiple energy consumption data packets based on the physical layer, link layer, and application layer; When the physical layer verification of the multiple energy consumption data packets fails, marking the signal quality of the multiple energy consumption data packets as abnormal and generating physical layer verification tag information; When the link layer verification of the plurality of energy-consuming data packets fails, a transmission integrity abnormality mark is performed on the data frame structure, and link layer verification tag information is generated; When the multiple energy consumption data packets fail application layer verification, abnormality marking is performed on the payloads of the multiple energy consumption data packets, and application layer verification tag information is generated; When the plurality of energy consumption data packets pass all three levels of verification, verification pass label information is generated.

10. A centralized data collection and distribution system for a multi-user environment of electric energy meters, characterized in that: The system is used to implement the centralized collection and distribution method for electric energy meters in a multi-user environment according to any one of claims 1 to 9, and the system includes: A collection task queue generation module is used for multiple electric energy meter terminals to send centralized collection request information through a communication network, receive the centralized collection request information and generate a collection task queue, wherein the collection task queue contains multiple collection instructions; A channel resource data construction module is used to perform flow prediction on multiple electric energy meter terminals, extract data flow characteristics, and construct multiple channel resource data according to the data flow characteristics; a dynamic allocation module, configured to dynamically allocate the collection task queue according to the plurality of channel resource data, construct a time slot allocation matrix, and issue the plurality of collection instructions to the plurality of electric energy meter terminals based on the time slot allocation matrix; The centralized distribution module is used to receive multiple energy usage data packets fed back by the multiple electric energy meter terminals, perform data verification based on the multiple energy usage data packets, perform centralized distribution according to the verification results, and generate a distribution routing table.

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