Electric energy meter data adaptive fragmentation transmission communication method and system based on multi-dimensional perception

Abstract

The invention discloses an electric energy meter data adaptive fragmentation transmission communication method and system based on multi-dimensional perception, and the method comprises the steps: enabling an intelligent electric meter to extract the fingerprint stability characteristics of a power supply voltage waveform, detecting a load switching edge event, generating perception data, and uploading the perception data; the concentrator generates a channel quality map reflecting a physical channel state of a power grid according to sensing data of each electric meter, and generates and issues a dynamic fragmentation strategy parameter defining a corresponding relation between a channel quality grade, a network load grade and a recommended fragmentation size and an error correction coding scheme in combination with a real-time network load state; the intelligent electric meter generates a final transmission strategy according to the voltage fingerprint characteristics, the load event and the strategy parameters in combination with the key level of the to-be-transmitted data; processing to-be-transmitted data into fragments according to the strategy, and sending the fragments; the concentrator receives and decodes the data fragments to obtain reading data. The method can be adaptive to channel and network changes, and the copying and reading reliability and efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to an adaptive segmented transmission communication method and system for electricity meter data based on multi-dimensional sensing. Background Technology

[0002] Remote automatic meter reading systems are the foundation of advanced metering systems in smart grids. Traditional remote meter reading methods mainly rely on fixed communication scheduling strategies and data encapsulation formats. For example, they employ predetermined time division multiple access (TDMA) polling or carrier sense multiple access (CSMA) mechanisms to establish a communication link between the concentrator and the smart meter, and then package the electricity data as a whole and transmit it using a fixed error correction coding scheme.

[0003] However, power line carrier (PLC) or wireless (such as low-power wireless, NB-IoT) communication environments are highly complex and time-varying. The starting and stopping of nonlinear loads in the power grid and the switching of large equipment generate strong burst noise, leading to drastic fluctuations in channel quality. Simultaneously, the massive number of meters in the network reporting concurrently at fixed time intervals easily triggers data packet collisions and network congestion. Existing technical solutions exhibit inherent shortcomings when facing these dynamic challenges: First, rigid communication strategies fail to perceive and adapt to fundamental changes in the physical channels of the power grid, resulting in a sharp drop in communication success rate when interference intensifies. Second, data transmission lacks differentiation, treating high-value settlement data and low-value monitoring data interchangeably, failing to guarantee the reliability of core services when channel resources are scarce. Third, fixed data encapsulation methods cannot flexibly adjust data packet size according to real-time network load, exacerbating collisions and retransmissions during network congestion, reducing overall system efficiency.

[0004] Therefore, there is a core problem in the existing technology that urgently needs to be solved: how to realize an intelligent reading method that can adapt to channel quality and network load, distinguish the importance of data services, and thus significantly improve the reliability and efficiency of remote reading in a complex and time-varying power grid communication environment. Summary of the Invention

[0005] In view of the aforementioned deficiencies of the prior art, the technical problem to be solved by the present invention is to provide a method and system for adaptive segmented transmission of electricity meter data based on multi-dimensional sensing.

[0006] To achieve the above objectives, the first aspect of this invention discloses an adaptive segmented transmission communication method for electricity meter data based on multi-dimensional sensing, applied to a concentrator and its subordinate smart meters, the method comprising: Step S1: The smart meter samples and analyzes the power supply voltage waveform to extract voltage waveform fingerprint stability features that characterize the intrinsic quality of the power grid channel; the smart meter samples and analyzes the load current waveform to detect and identify load switching edge events; the smart meter generates sensing data and uploads it to the concentrator based on the voltage waveform fingerprint stability features and the load switching edge events. Step S2: The concentrator generates a channel quality map reflecting the physical channel status of the power grid based on the sensing data uploaded by each of the subordinate smart meters; the concentrator generates dynamic sharding strategy parameters based on the channel quality map and the real-time network load status and sends them to the corresponding smart meters; wherein, the dynamic sharding strategy parameters define the correspondence between channel quality level, network load level, recommended sharding size, and error correction coding scheme; Step S3: The smart meter generates a final transmission strategy based on the voltage waveform fingerprint stability characteristics, the load switching edge events, and the dynamic fragmentation strategy parameters, combined with the criticality level of the data to be transmitted; wherein, the final transmission strategy includes at least the data fragment content, the data fragment size, the transmission priority and timing corresponding to the data fragment, and the error correction coding scheme corresponding to the data fragment. Step S4: The smart meter processes the data to be transmitted into one or more data fragments and sends them according to the final transmission strategy; Step S5: The concentrator receives the data fragments and decodes the data fragments to obtain the corresponding data to be copied.

[0007] Optionally, the final transmission strategy in step S3 follows the following mapping relationship: Under the same channel quality level and network load level, the higher the criticality level of the data to be transmitted, the smaller the determined fragment size, and the stronger the error correction capability of the selected error correction coding scheme. For the data to be transmitted at the same criticality level, when the channel quality level decreases and / or the network load level increases, the determined fragment size decreases accordingly, and the error correction capability of the selected error correction coding scheme is enhanced accordingly. When the load switching edge event exists in the data to be transmitted, the load switching edge event is independently encapsulated and marked as the first transmission priority, and the error correction coding scheme with an error correction capability level higher than the threshold is adopted.

[0008] Optionally, step S1 includes: The smart meter samples and analyzes the power supply voltage waveform, and obtains the voltage waveform fingerprint stability characteristics by calculating the statistical variance of a preset duration for a specific harmonic content and / or by calculating the phase jitter variance of the voltage zero-crossing time series. The smart meter detects transient steps in the load current and obtains the load switching edge event based on the waveform corresponding to the transient step.

[0009] Optionally, the method further includes: Step S6: In response to the incomplete reception of the retransmitted data fragment, the concentrator initiates network-assisted collaborative reassembly and uses the successful data fragments from other associated smart meters to speculatively recover the incompletely received data fragment.

[0010] Optionally, the network-assisted cooperative reorganization in step S6 specifically includes: The concentrator acquires concurrently successful data fragments from one or more neighboring smart meters associated with the target smart meter based on preset electricity consumption behavior correlations; wherein, the target smart meter is the smart meter to which the data fragments cannot be fully received. By combining historical load event maps, analyze whether there are common events within the target time period; Based on the synchronous successful data fragments and common events of the neighboring smart meters, the optimal estimate of the missing data of the target smart meter is inferred through an algorithm.

[0011] Optionally, the criticality level of the data to be transmitted is predefined according to business rules, wherein: frozen electricity data used for electricity bill settlement is defined as the first criticality level; data generated by the load switching edge event is defined as the second criticality level; and periodic energy increment data is defined as the third criticality level.

[0012] The second aspect of this invention discloses an adaptive data fragmentation transmission communication system for electricity meters based on multi-dimensional perception, applied to a concentrator and its subordinate smart meters. The system includes: a data acquisition and analysis module, a transmission strategy generation module, and a data fragmentation transmission module applied to the smart meters; and a dynamic fragmentation strategy parameter generation module and a data fragmentation receiving module applied to the concentrator. The data acquisition and analysis module is used to sample and analyze the power supply voltage waveform, extract voltage waveform fingerprint stability features that characterize the intrinsic quality of the power grid channel; sample and analyze the load current waveform, detect and identify load switching edge events; and generate sensing data and upload it to the concentrator based on the voltage waveform fingerprint stability features and the load switching edge events. The dynamic fragmentation strategy parameter generation module is used to generate a channel quality map reflecting the physical channel status of the power grid based on the sensing data uploaded by each of the subordinate smart meters; and to generate dynamic fragmentation strategy parameters based on the channel quality map and the real-time network load status and send them to the corresponding smart meters; wherein, the dynamic fragmentation strategy parameters define the correspondence between the channel quality level, the network load level, the recommended fragmentation size, and the error correction coding scheme; The transmission strategy generation module is used to generate a final transmission strategy based on the voltage waveform fingerprint stability characteristics, the load switching edge event, and the dynamic fragmentation strategy parameters, combined with the criticality level of the data to be transmitted; wherein, the final transmission strategy includes at least the data fragment content, the data fragment size, the transmission priority and timing corresponding to the data fragment, and the error correction coding scheme corresponding to the data fragment. The data fragmentation sending module is used to process the data to be transmitted into one or more data fragments and send them according to the final transmission strategy; The data fragment receiving module is used to receive the data fragments and decode the data fragments to obtain the corresponding copied data.

[0013] Optionally, the final transmission strategy follows the following mapping relationship: Under the same channel quality level and network load level, the higher the criticality level of the data to be transmitted, the smaller the determined fragment size, and the stronger the error correction capability of the selected error correction coding scheme. For the data to be transmitted at the same criticality level, when the channel quality level decreases and / or the network load level increases, the determined fragment size decreases accordingly, and the error correction capability of the selected error correction coding scheme is enhanced accordingly. When the load switching edge event exists in the data to be transmitted, the load switching edge event is independently encapsulated and marked as the first transmission priority, and the error correction coding scheme with an error correction capability level higher than the threshold is adopted.

[0014] Optionally, the data acquisition and analysis module is specifically used for: The data acquisition and analysis module samples and analyzes the power supply voltage waveform, and obtains the voltage waveform fingerprint stability characteristics by calculating the preset duration statistical variance of specific harmonic content and / or by calculating the phase jitter variance of the voltage zero-crossing time series. The data acquisition and analysis module detects the transient step of the load current and obtains the load switching edge event based on the waveform corresponding to the transient step.

[0015] Optionally, the system also includes a data recovery module. The data recovery module is used to initiate network-assisted collaborative reassembly in response to the incomplete reception of the retransmitted data fragment, and to perform speculative recovery of the incompletely received data fragment using the successful data fragments from other associated smart meters.

[0016] Optionally, the network-assisted cooperative reorganization specifically includes: The data recovery module obtains concurrent successful data fragments from one or more neighboring smart meters associated with the target smart meter based on preset electricity consumption behavior correlations; wherein, the target smart meter is the smart meter to which the data fragments cannot be completely received belong. By combining historical load event maps, analyze whether there are common events within the target time period; Based on the synchronous successful data fragments and common events of the neighboring smart meters, the optimal estimate of the missing data of the target smart meter is inferred through an algorithm.

[0017] Optionally, the criticality level of the data to be transmitted is predefined according to business rules, wherein: frozen electricity data used for electricity bill settlement is defined as the first criticality level; data generated by the load switching edge event is defined as the second criticality level; and periodic energy increment data is defined as the third criticality level.

[0018] The beneficial effects of this invention are as follows: 1. This invention utilizes the real-time sensing of voltage waveform fingerprint stability by smart meters, enabling the system to diagnose the inherent quality of the channel at the physical level and pre-identify interference. Combined with dynamic strategies issued by the concentrator based on channel quality maps and network load status, the meter can automatically switch to smaller fragment sizes and stronger error correction coding when the channel deteriorates. This mechanism of sensing first and then adapting transforms the passive response of traditional solutions into proactive optimization, greatly improving the success rate of first-read and anti-interference capability in power lines or wireless channels with varying noise and interference. 2. This invention uses the criticality level of the data to be transmitted (such as settlement data, event data, and incremental data) as the core decision-making dimension. The system follows the principle of "the higher the data value, the greater the protection strength," allocating higher priority transmission times and more robust coding schemes to critical data. At the same time, through dynamic fragmentation strategies, the data packet size is automatically reduced to decrease the probability of collisions when the network is congested. This dual optimization based on business value and network status ensures the absolute reliability of critical services (such as billing) and significantly improves overall channel utilization and system throughput, while reducing invalid retransmissions and communication delays. 3. This invention constructs a closed loop of "perception-decision-execution-optimization". The concentrator integrates global information generation strategies, and the electricity meters make final decisions based on local real-time conditions, achieving a balance between central planning and edge intelligence. Furthermore, the network-assisted collaborative reconfiguration mechanism can intelligently predict data integrity by utilizing the spatiotemporal correlation of adjacent meter data when traditional retransmission fails. The entire system possesses the ability to continuously learn and adapt to environmental changes, breaking free from the rigid mode of fixed parameter configuration. 4. This invention's detection and high-priority reporting of load switching edge events enables the system to capture transient characteristics of user electricity consumption. This not only achieves more accurate load monitoring but also provides a high-granularity data foundation for advanced applications such as non-intrusive load decomposition and demand-side response.

[0019] In summary, this invention proposes an adaptive, highly reliable, and highly efficient data transmission and communication technology for electricity meters by deeply integrating the physical characteristics of the power system, the status of the communication network, and the data service logic. This effectively solves the core problems of poor reliability, low efficiency, and rigid strategies in existing technologies under dynamic and complex environments. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating an adaptive fragmented transmission communication method for electricity meter data based on multi-dimensional perception, provided in a specific embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of an adaptive fragmented transmission communication system for electricity meter data based on multi-dimensional sensing, provided in a specific embodiment of the present invention; Figure 3This is a signaling diagram of an adaptive fragmented transmission communication method for electricity meter data based on multi-dimensional sensing, provided in a specific embodiment of the present invention. Detailed Implementation

[0021] This invention discloses an adaptive segmented transmission communication method and system for electricity meter data based on multi-dimensional sensing. Those skilled in the art can refer to the content of this document and appropriately modify the technical details for implementation. It should be particularly noted that all similar substitutions and modifications are obvious to those skilled in the art and are considered to be included in this invention. The methods and applications of this invention have been described through preferred embodiments. Those skilled in the art can obviously modify or appropriately change and combine the methods and applications described herein without departing from the content, spirit, and scope of this invention to implement and apply the technology of this invention.

[0022] This invention provides an adaptive data fragmentation and transmission communication method for electricity meters based on multi-dimensional sensing, applicable to concentrators and their subordinate smart meters, such as... Figure 1 As shown, the method includes: Step S1: The smart meter samples and analyzes the power supply voltage waveform to extract the voltage waveform fingerprint stability features that characterize the intrinsic quality of the power grid channel; the smart meter samples and analyzes the load current waveform to detect and identify load switching edge events; the smart meter generates sensing data and uploads it to the concentrator based on the voltage waveform fingerprint stability features and load switching edge events.

[0023] It should be noted that step S1 is the source of environmental information acquired by the system. The smart meter utilizes its inherent high-precision metering unit to perform two core sensing tasks: Voltage waveform fingerprint stability feature extraction: Electricity meters perform high-frequency sampling of the supply voltage, not only for metering but also for communication diagnostics. By analyzing the voltage waveform, the short-term variance of specific harmonic content (e.g., 3rd, 5th) or the variance of sequence jitter at voltage zero-crossing points is calculated to quantify the "intrinsic quality" of the channel environment. Stable characteristics indicate a clean power grid, while drastic fluctuations suggest the presence of intermittent interference sources. Essentially, this reuses the electricity meter from a "metering sensor" into a "channel condition diagnostic instrument."

[0024] Load switching edge event detection and identification: The electricity meter synchronously monitors the load current waveform and captures transient current steps through an edge detection algorithm. Features of the transient waveform (such as rising edge slope and oscillation frequency) are extracted and an "event signature" is generated. This signature is compared with a local database to identify the switching of specific appliances (such as air conditioners and charging stations). This captures key event information related to the user's electricity consumption.

[0025] Sensing data reporting: The meter encapsulates the above characteristics (such as "harmonic distortion index +0.05%" and "air conditioner turned on at 13:05") into a lightweight sensing data package and uploads it to the concentrator. This data forms the basis for subsequent centralized decision-making.

[0026] It is worth mentioning that the voltage waveform fingerprint stability characteristics and load switching edge events are less affected by channel interference when uploaded due to the small fragment size.

[0027] In this specific embodiment, step S1 includes: The smart meter samples and analyzes the power supply voltage waveform, and obtains the voltage waveform fingerprint stability characteristics by calculating the statistical variance of a preset duration for a specific harmonic content and / or by calculating the phase jitter variance of the voltage zero-crossing time series. The smart meter detects the transient step of the load current and obtains the load switching edge event based on the waveform corresponding to the transient step.

[0028] It should be noted that this embodiment further defines the specific technical means for acquiring the two types of sensing data in step S1. For voltage waveform fingerprints, it is explicitly stated that they are obtained by calculating the statistical variance of specific harmonic content or the variance of voltage zero-crossing phase jitter, providing two operable and measurable mathematical methods for quantifying the "inherent quality of the channel." For load switching events, it is explicitly stated that they are obtained by analyzing the waveform of the current transient step, establishing the basis for the conversion from physical signals to event information. This claim concretizes the sensing action from a concept into an implementable detection algorithm, enhancing the feasibility of the solution.

[0029] Step S2: The concentrator generates a channel quality map reflecting the physical channel status of the power grid based on the sensing data uploaded by each of its subordinate smart meters; the concentrator generates dynamic sharding strategy parameters based on the channel quality map and the real-time network load status and sends them to the corresponding smart meters.

[0030] The dynamic fragmentation strategy parameters define the correspondence between channel quality level, network load level, recommended fragmentation size, and error correction coding scheme.

[0031] It should be noted that the channel quality map is generated as follows: The concentrator receives sensing data (especially voltage fingerprints) reported by all meters within its jurisdiction and performs spatiotemporal fusion analysis. For example, it may find that harmonic distortion is generally aggravated on a certain power supply line in the afternoon, thus creating a dynamic channel quality map. This map reflects the inherent channel quality distribution and temporal patterns of different areas and phases of the power grid at the system level. Dynamic fragmentation strategy parameters are generated and distributed: The concentrator combines the aforementioned channel quality map with real-time monitored network load status (such as channel collision rate and packet delay) and generates a set of "dynamic fragmentation strategy parameters" through a decision algorithm. This set of parameters is essentially an adaptive lookup table, which explicitly defines the recommended fragmentation size range and error correction coding scheme options for transmitting different critical data when a meter is in "channel quality level A" and "network load level B" (e.g., under poor channel conditions, it is recommended to use small fragments of less than 512 bytes combined with RS(255,223) strong error correction codes). The concentrator broadcasts or distributes these strategy parameters to each smart meter.

[0032] Step S3: The smart meter generates a final transmission strategy based on the voltage waveform fingerprint stability characteristics, load switching edge events, and dynamic fragmentation strategy parameters, combined with the criticality level of the data to be transmitted. The final transmission strategy includes at least the data fragment content, data fragment size, the transmission priority and timing of the data fragment, and the error correction coding scheme corresponding to the data fragment.

[0033] It should be noted that information fusion involves the smart meter's decision engine simultaneously acquiring four inputs when preparing to report data: (a) the local real-time measured voltage waveform fingerprint (latest channel status), (b) recent load switching events, (c) dynamic sharding strategy parameters issued by the concentrator, and (d) the criticality level of the data to be transmitted (e.g., defined as: frozen electricity as the highest level, event data as the second highest level, and regular data as the ordinary level). The final transmission strategy is then generated based on these four dimensions. The meter uses built-in rules or lightweight algorithms (e.g., querying the strategy parameter table and fine-tuning based on local characteristics) to make a final decision and output an executable "final transmission strategy." This strategy specifically specifies: which shards the data to be transmitted will be divided into, the size of each shard, the error correction coding scheme to be used for each shard, and the transmission priority and recommended transmission time for each shard. For example, it might decide: "Encapsulate the settled electricity amount into a small fragment, use the strongest LDPC encoding, and send it immediately in the next high-priority window; merge the incremental data from the past 15 minutes into a large fragment, use ordinary CRC encoding, and send it during network idle periods."

[0034] In this specific embodiment, the final transmission strategy in step S3 follows the following mapping relationship: Under the same channel quality level and network load level, the higher the criticality level of the data to be transmitted, the smaller the determined fragment size, and the stronger the error correction capability of the selected error correction coding scheme. For data to be transmitted at the same criticality level, when the channel quality level decreases and / or the network load level increases, the determined fragment size decreases accordingly, and the error correction capability of the selected error correction coding scheme is enhanced accordingly. When a load switching edge event exists in the data to be transmitted, the load switching edge event is encapsulated independently and marked as the first transmission priority, and an error correction coding scheme with an error correction capability level higher than the threshold is adopted.

[0035] It should be noted that this embodiment explicitly defines the core decision-making rules followed by the "final transmission strategy" in step S3. It establishes a dual mapping relationship: First, under the same channel and network conditions, the higher the data's criticality, the smaller the fragmentation and the stronger the error correction, ensuring that high-value data receives the highest level of protection. Second, for the same type of data, when the channel deteriorates or the network becomes congested, the fragmentation also becomes smaller and the error correction becomes stronger, achieving adaptive enhancement of the communication strategy to adverse environments. Furthermore, it specifically stipulates that data triggered by load events must be independently encapsulated, assigned the highest priority, and given the strongest encoding, thereby ensuring the real-time nature and reliability of critical event information at the business level.

[0036] In this specific embodiment, the criticality level of the data to be transmitted is predefined according to business rules, wherein: frozen electricity data used for electricity bill settlement is defined as the first criticality level; data generated by load switching edge events is defined as the second criticality level; and periodic energy increment data is defined as the third criticality level.

[0037] It should be noted that this embodiment explicitly divides the data to be transmitted into three categories: frozen electricity for settlement (Level 1), load event data (Level 2), and periodic incremental data (Level 3). This division is not arbitrary, but based on the non-renewable and time-sensitive value of data in electricity metering operations. This definition is the fundamental basis for differentiated strategy decisions in step S3, solidifying business rules into technical characteristics and serving as a bridge connecting domain knowledge and communication strategies.

[0038] Step S4: The smart meter processes the data to be transmitted into one or more data fragments according to the final transmission strategy and sends them.

[0039] It should be noted that the communication module of the smart meter strictly follows the "final transmission strategy" generated in step S3 to encapsulate, encode, and fragment the raw electrical energy data to be transmitted, and sends out the data fragments one by one through power line carrier or wireless channel at the time specified by the strategy.

[0040] Step S5: The concentrator receives data fragments and decodes them to obtain the corresponding data to be read.

[0041] It should be noted that the concentrator receives data fragments from each meter, decodes and verifies them according to the communication protocol and the corresponding error correction coding scheme. All successfully received fragments are reassembled in sequence to finally restore complete electricity metering data (such as electricity consumption, power, and event records), completing one remote meter reading.

[0042] In this specific embodiment, the method further includes: Step S6: In response to the incomplete reception of retransmitted data fragments, the concentrator initiates network-assisted collaborative reassembly, using successful data fragments from other associated smart meters to speculatively recover the incompletely received data fragments.

[0043] It's worth noting that this step includes a safeguard called "network-assisted collaborative reassembly." This stipulates that when the traditional retransmission mechanism fails, the concentrator will initiate this advanced recovery process, using successful data from other associated meters to infer and repair missing data. Essentially, this adds a "safety net" for data integrity protection to the system, extending data recovery from reliance on single-point communication to utilizing the correlation of group data, greatly improving system robustness in extreme situations.

[0044] Furthermore, the network-assisted cooperative reorganization in step S6 specifically involves: The concentrator acquires concurrently successful data fragments from one or more neighboring smart meters associated with the target smart meter based on preset electricity consumption behavior correlations; wherein, the target smart meter is the smart meter to which the data fragments cannot be completely received. By combining historical load event maps, analyze whether there are common events within the target time period; Based on the simultaneous successful data fragments and common events of neighboring smart meters, the optimal estimate of the missing data of the target smart meter is inferred through an algorithm.

[0045] It should be noted that this embodiment describes three steps of network-assisted collaborative reconfiguration: acquiring successful data from relevant neighbors, analyzing commonalities by combining historical event graphs, and inferring the optimal estimate through algorithms. This clarifies that collaborative reconfiguration is not a simple data replication, but an intelligent data reconstruction process based on the similarity of electricity consumption behavior and the correlation of power grid events, demonstrating its logical rigor.

[0046] This invention utilizes smart meters to perceive the stability of voltage waveform fingerprints in real time, enabling the system to diagnose the inherent quality of the channel at a physical level and pre-identify interference. Combined with dynamic strategies issued by the concentrator based on channel quality maps and network load status, the meter can automatically switch to smaller segment sizes and stronger error correction coding when the channel deteriorates. This mechanism of sensing first and then adapting transforms the passive response of traditional solutions into proactive optimization, significantly improving the first-read success rate and anti-interference capability in power line or wireless channels with varying noise and interference.

[0047] This invention uses the criticality level of the data to be transmitted (such as settlement data, event data, and incremental data) as the core decision-making dimension. Following the principle of "the higher the data value, the stronger the protection," the system allocates higher priority transmission times and more robust encoding schemes to critical data. Simultaneously, through a dynamic fragmentation strategy, the system automatically reduces packet size to lower the probability of collisions during network congestion. This dual optimization based on business value and network status ensures the absolute reliability of critical services (such as billing) and significantly improves overall channel utilization and system throughput, while reducing invalid retransmissions and communication delays.

[0048] This invention constructs a closed loop of "perception-decision-execution-optimization". The concentrator integrates global information generation strategies, and the meters make final decisions based on local real-time conditions, achieving a balance between central planning and edge intelligence. Furthermore, the network-assisted collaborative reassembly mechanism can intelligently predict data integrity by utilizing the spatiotemporal correlation of adjacent meter data when traditional retransmission fails, providing ultimate assurance for data integrity. The entire system possesses the ability to continuously learn and adapt to environmental changes, breaking free from the rigid mode of fixed parameter configuration.

[0049] The present invention's embodiments enable the system to capture transient characteristics of user power consumption by detecting and prioritizing load switching edge events. This not only achieves more accurate load monitoring but also provides a high-granularity data foundation for advanced applications such as non-intrusive load decomposition and demand-side response.

[0050] In summary, this invention proposes an adaptive, highly reliable, and efficient data transmission communication method for electricity meters by deeply integrating the physical characteristics of the power system, the status of the communication network, and the data service logic. This effectively solves the core problems of poor reliability, low efficiency, and rigid strategies in existing technologies under dynamic and complex environments.

[0051] The signaling diagrams corresponding to steps S1-S5 in this embodiment of the invention can be as follows: Figure 3 As shown. Figure 3 The communication process between the concentrator and its subordinate smart meters when performing remote meter reading tasks is demonstrated.

[0052] This invention also provides an adaptive data sharding transmission communication system for electricity meters based on multi-dimensional sensing, applicable to concentrators and their subordinate smart meters, such as... Figure 2 As shown, the system includes: a data acquisition and analysis module 11, a transmission strategy generation module 12, and a data fragmentation transmission module 13 applied to the smart meter 1 end; and a dynamic fragmentation strategy parameter generation module 21 and a data fragmentation receiving module 22 applied to the concentrator 2 end. The data acquisition and analysis module 11 is used to sample and analyze the power supply voltage waveform, extract the voltage waveform fingerprint stability features that characterize the intrinsic quality of the power grid channel; sample and analyze the load current waveform, detect and identify load switching edge events; and generate sensing data and upload it to the concentrator 2 based on the voltage waveform fingerprint stability features and load switching edge events. The dynamic fragmentation strategy parameter generation module 21 is used to generate a channel quality map reflecting the physical channel status of the power grid based on the sensing data uploaded by each of the subordinate smart meters 1; and to generate dynamic fragmentation strategy parameters based on the channel quality map and the real-time network load status and send them to the corresponding smart meters 1; wherein, the dynamic fragmentation strategy parameters define the correspondence between the channel quality level, the network load level, the recommended fragmentation size, and the error correction coding scheme. The transmission strategy generation module 12 is used to generate a final transmission strategy based on the voltage waveform fingerprint stability characteristics, load switching edge events, and dynamic fragmentation strategy parameters, combined with the criticality level of the data to be transmitted. The final transmission strategy includes at least the data fragment content, data fragment size, the transmission priority and timing of the data fragment, and the error correction coding scheme corresponding to the data fragment. The data fragmentation sending module 13 is used to process the data to be transmitted into one or more data fragments and send them according to the final transmission strategy; The data fragment receiving module 22 is used to receive data fragments and decode the data fragments to obtain the corresponding copy data.

[0053] Optionally, the final transmission strategy follows the mapping relationship below: Under the same channel quality level and network load level, the higher the criticality level of the data to be transmitted, the smaller the determined fragment size, and the stronger the error correction capability of the selected error correction coding scheme. For data to be transmitted at the same criticality level, when the channel quality level decreases and / or the network load level increases, the determined fragment size decreases accordingly, and the error correction capability of the selected error correction coding scheme is enhanced accordingly. When a load switching edge event exists in the data to be transmitted, the load switching edge event is encapsulated independently and marked as the first transmission priority, and an error correction coding scheme with an error correction capability level higher than the threshold is adopted.

[0054] Optional, data acquisition and analysis module 11, specifically used for: The data acquisition and analysis module 11 samples and analyzes the power supply voltage waveform, and obtains the voltage waveform fingerprint stability characteristics by calculating the preset duration statistical variance of specific harmonic content and / or by calculating the phase jitter variance of the voltage zero-crossing time series. The data acquisition and analysis module 11 detects the transient step of the load current and obtains the load switching edge event based on the waveform corresponding to the transient step.

[0055] Optionally, the system also includes a data recovery module. The data recovery module is used to initiate network-assisted collaborative reassembly in response to the incomplete reception of retransmitted data fragments, and to perform speculative recovery of the incompletely received data fragments using successful data fragments from other associated smart meters 1.

[0056] Optionally, network-assisted collaborative reorganization specifically includes: The data recovery module obtains the synchronous successful data fragments of one or more neighboring smart meters 1 associated with the target smart meter 1 based on the preset correlation of electricity consumption behavior; wherein, the target smart meter 1 is the smart meter 1 to which the data fragments cannot be completely received belong; By combining historical load event maps, analyze whether there are common events within the target time period; Based on the simultaneous successful data fragments and common events of neighboring smart meter 1, the optimal estimated value of the missing data of target smart meter 1 is inferred through an algorithm.

[0057] Optionally, the criticality level of the data to be transmitted is predefined according to business rules, wherein: frozen electricity data used for electricity bill settlement is defined as the first criticality level; data generated by load switching edge events is defined as the second criticality level; and periodic energy increment data is defined as the third criticality level.

[0058] This invention proposes an adaptive, highly reliable, and highly efficient data transmission communication system for electricity meters by deeply integrating the physical characteristics of the power system, the status of the communication network, and the data service logic. This effectively solves the core problems of poor reliability, low efficiency, and rigid strategies in existing technologies under dynamic and complex environments.

[0059] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0060] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0061] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for adaptive fragmented transmission communication of electricity meter data based on multi-dimensional sensing, characterized in that, The method, applied to a concentrator and its subordinate smart meters, includes: Step S1: The smart meter samples and analyzes the power supply voltage waveform to extract voltage waveform fingerprint stability features that characterize the intrinsic quality of the power grid channel; the smart meter samples and analyzes the load current waveform to detect and identify load switching edge events; the smart meter generates sensing data and uploads it to the concentrator based on the voltage waveform fingerprint stability features and the load switching edge events. Step S2: The concentrator generates a channel quality map reflecting the physical channel status of the power grid based on the sensing data uploaded by each of the subordinate smart meters; the concentrator generates dynamic sharding strategy parameters based on the channel quality map and the real-time network load status and sends them to the corresponding smart meters; wherein, the dynamic sharding strategy parameters define the correspondence between channel quality level, network load level, recommended sharding size, and error correction coding scheme; Step S3: The smart meter generates a final transmission strategy based on the voltage waveform fingerprint stability characteristics, the load switching edge events, and the dynamic fragmentation strategy parameters, combined with the criticality level of the data to be transmitted; wherein, the final transmission strategy includes at least the data fragment content, the data fragment size, the transmission priority and timing corresponding to the data fragment, and the error correction coding scheme corresponding to the data fragment. Step S4: The smart meter processes the data to be transmitted into one or more data fragments and sends them according to the final transmission strategy; Step S5: The concentrator receives the data fragments and decodes the data fragments to obtain the corresponding data to be copied.

2. The adaptive fragmented transmission communication method for electricity meter data based on multi-dimensional sensing according to claim 1, characterized in that, The final transmission strategy in step S3 follows the following mapping relationship: Under the same channel quality level and network load level, the higher the criticality level of the data to be transmitted, the smaller the determined fragment size, and the stronger the error correction capability of the selected error correction coding scheme. For the data to be transmitted at the same criticality level, when the channel quality level decreases and / or the network load level increases, the determined fragment size decreases accordingly, and the error correction capability of the selected error correction coding scheme is enhanced accordingly. When the load switching edge event exists in the data to be transmitted, the load switching edge event is independently encapsulated and marked as the first transmission priority, and the error correction coding scheme with an error correction capability level higher than the threshold is adopted.

3. The adaptive fragmented transmission communication method for electricity meter data based on multi-dimensional sensing according to claim 1, characterized in that, Step S1 includes: The smart meter samples and analyzes the power supply voltage waveform, and obtains the voltage waveform fingerprint stability characteristics by calculating the statistical variance of a preset duration for a specific harmonic content and / or by calculating the phase jitter variance of the voltage zero-crossing time series. The smart meter detects transient steps in the load current and obtains the load switching edge event based on the waveform corresponding to the transient step.

4. The adaptive fragmentation transmission communication method for electricity meter data based on multi-dimensional sensing according to claim 1, characterized in that, The method further includes: Step S6: In response to the incomplete reception of the retransmitted data fragment, the concentrator initiates network-assisted collaborative reassembly and uses the successful data fragments from other associated smart meters to speculatively recover the incompletely received data fragment.

5. The adaptive fragmentation transmission communication method for electricity meter data based on multi-dimensional sensing according to claim 4, characterized in that, The network-assisted cooperative reorganization in step S6 specifically refers to: The concentrator acquires concurrently successful data fragments from one or more neighboring smart meters associated with the target smart meter based on preset electricity consumption behavior correlations; wherein, the target smart meter is the smart meter to which the data fragments cannot be fully received. By combining historical load event maps, analyze whether there are common events within the target time period; Based on the synchronous successful data fragments and common events of the neighboring smart meters, the optimal estimate of the missing data of the target smart meter is inferred through an algorithm.

6. The adaptive fragmented transmission communication method for electricity meter data based on multi-dimensional sensing according to claim 1, characterized in that, The criticality level of the data to be transmitted is predefined according to business rules, wherein: frozen electricity data used for electricity bill settlement is defined as the first criticality level; data generated by the load switching edge event is defined as the second criticality level; and periodic energy increment data is defined as the third criticality level.

7. A multi-dimensional sensing-based adaptive segmented transmission communication system for electricity meter data, characterized in that, The system, applied to a concentrator and its subordinate smart meters, includes: a data acquisition and analysis module, a transmission strategy generation module, and a data fragmentation and transmission module for the smart meters; and a dynamic fragmentation strategy parameter generation module and a data fragmentation and reception module for the concentrator. The data acquisition and analysis module is used to sample and analyze the power supply voltage waveform, extract voltage waveform fingerprint stability features that characterize the intrinsic quality of the power grid channel; sample and analyze the load current waveform, detect and identify load switching edge events; and generate sensing data and upload it to the concentrator based on the voltage waveform fingerprint stability features and the load switching edge events. The dynamic fragmentation strategy parameter generation module is used to generate a channel quality map reflecting the physical channel status of the power grid based on the sensing data uploaded by each of the subordinate smart meters; and to generate dynamic fragmentation strategy parameters based on the channel quality map and the real-time network load status and send them to the corresponding smart meters; wherein, the dynamic fragmentation strategy parameters define the correspondence between the channel quality level, the network load level, the recommended fragmentation size, and the error correction coding scheme; The transmission strategy generation module is used to generate a final transmission strategy based on the voltage waveform fingerprint stability characteristics, the load switching edge event, and the dynamic fragmentation strategy parameters, combined with the criticality level of the data to be transmitted; wherein, the final transmission strategy includes at least the data fragment content, the data fragment size, the transmission priority and timing corresponding to the data fragment, and the error correction coding scheme corresponding to the data fragment. The data fragmentation sending module is used to process the data to be transmitted into one or more data fragments and send them according to the final transmission strategy; The data fragment receiving module is used to receive the data fragments and decode the data fragments to obtain the corresponding copied data.

8. The adaptive segmented transmission communication system for electricity meter data based on multi-dimensional sensing according to claim 7, characterized in that, The final transmission strategy follows the following mapping relationship: Under the same channel quality level and network load level, the higher the criticality level of the data to be transmitted, the smaller the determined fragment size, and the stronger the error correction capability of the selected error correction coding scheme. For the data to be transmitted at the same criticality level, when the channel quality level decreases and / or the network load level increases, the determined fragment size decreases accordingly, and the error correction capability of the selected error correction coding scheme is enhanced accordingly. When the load switching edge event exists in the data to be transmitted, the load switching edge event is independently encapsulated and marked as the first transmission priority, and the error correction coding scheme with an error correction capability level higher than the threshold is adopted.

9. The adaptive segmented transmission communication system for electricity meter data based on multi-dimensional sensing according to claim 7, characterized in that, The data acquisition and analysis module is specifically used for: The data acquisition and analysis module samples and analyzes the power supply voltage waveform, and obtains the voltage waveform fingerprint stability characteristics by calculating the preset duration statistical variance of specific harmonic content and / or by calculating the phase jitter variance of the voltage zero-crossing time series. The data acquisition and analysis module detects the transient step of the load current and obtains the load switching edge event based on the waveform corresponding to the transient step.

10. The adaptive segmented transmission communication system for electricity meter data based on multi-dimensional sensing according to claim 7, characterized in that, The system also includes a data recovery module. The data recovery module is used to initiate network-assisted collaborative reassembly in response to the incomplete reception of the retransmitted data fragment, and to perform speculative recovery of the incompletely received data fragment using the successful data fragments from other associated smart meters.

Images