Self-adaptive low-power-consumption data transmission method for circuit breaker of Internet of Things based on service awareness

Abstract

The invention discloses a service awareness-based self-adaptive low-power-consumption data transmission method for a circuit breaker of the Internet of Things, relates to the technical field of communication of the Internet of Things, and aims to solve the technical problems of large positioning delay and poor real-time performance due to the fact that fault positioning and analysis of a power distribution network depend on centralized processing of a master station in the prior art. Comprising the following steps: S1, when an Internet of Things circuit breaker trips due to a fault, generating a layered fault signature for representing fault characteristics based on fault electrical characteristics; s2, executing parallel transmission operation by the Internet of Things circuit breakers: sending a complete fault report containing detailed electrical parameters to the master station, and broadcasting collaborative request information carrying own identifiers and layered fault signatures to adjacent Internet of Things circuit breakers according to a preset topological relation, according to the invention, by introducing a fault rapid reporting mechanism based on hierarchical fault signature and distributed cooperative verification, the positioning and identification of the power distribution network fault are realized, and the problem of large positioning delay of a centralized analysis mode is solved.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) communication technology, and more specifically, to a service-aware IoT circuit breaker adaptive low-power data transmission method. Background Technology

[0002] With the deepening of smart grid construction, distribution networks are rapidly developing towards automation and digitalization. As a key node for intelligent sensing and execution in distribution networks, IoT circuit breakers are being deployed on an increasingly large scale, undertaking important tasks such as line status monitoring, fault protection, and control. These tasks place drastically different and often contradictory stringent requirements on the real-time performance and reliability of data transmission, as well as the endurance of terminal equipment. Especially in the event of short circuits, grounding faults, quickly and accurately locating the fault point and determining the nature of the fault is a core prerequisite for minimizing the power outage area and accelerating power restoration, and it is also a prominent challenge in current distribution network operation.

[0003] Traditional distribution network fault handling heavily relies on centralized analysis at the master station. After a fault occurs, monitoring terminals along the line (such as circuit breakers) typically upload complete electrical quantity data, including fault waveforms, to the master station according to fixed cycles or simple event triggering mechanisms. The master station needs to collect the data uploaded by each node, perform centralized comparison, calculation, and analysis, before it can finally determine the fault range and type. This "terminal acquisition-centralized upload-centralized analysis" model has inherent defects: First, fault location has a large delay. Because data transmission, queuing, and processing all take time, it usually takes several seconds or even tens of seconds from the occurrence of a fault to the master station completing the location, which cannot meet the timeliness requirements for rapid fault isolation. Second, data transmission is energy inefficient. In pursuit of analytical accuracy, terminals often report large amounts of raw waveform data, occupying valuable wireless channel resources and causing a surge in terminal power consumption. Finally, reliability is insufficient in modern distribution networks with a high proportion of distributed power sources. The fault current characteristics of inverter interface power supplies such as photovoltaic and wind power are significantly different from those of traditional power supplies, and the fault characteristics are complex and variable. Relying solely on the main station for post-event centralized analysis is difficult to meet the needs of multi-dimensional information fusion and rapid decision-making at the moment of a fault, which can easily lead to misjudgment or delay.

[0004] Therefore, designing a data transmission method that deeply understands the operational implications of distribution networks and intelligently schedules communication resources, enabling massive IoT circuit breakers to collaboratively achieve real-time fault detection, efficient aggregation, and intelligent preliminary judgment of fault characteristics with near-zero latency during critical events such as faults, while operating at extremely low power under normal conditions. This provides accurate and rapid decision-making support for upper-level systems and has become a critical technical issue that urgently needs to be addressed to promote the practical application of distribution network IoT and enhance its self-healing capabilities. In light of this, we propose a service-aware adaptive low-power data transmission method for IoT circuit breakers. Summary of the Invention

[0005] The purpose of this invention is to provide a business-aware IoT circuit breaker adaptive low-power data transmission method to solve the technical problems in the prior art where distribution network fault location and analysis rely on centralized processing by the master station, resulting in large location delays and poor real-time performance.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a service-aware adaptive low-power data transmission method for IoT circuit breakers, applied to a power distribution network communication system including a master station and multiple IoT circuit breakers, comprising the following steps: S1: When an IoT circuit breaker trips due to a fault, a hierarchical fault signature is generated based on the fault's electrical characteristics to characterize the fault. S2: The IoT circuit breaker performs parallel transmission operations: sending a complete fault report containing detailed electrical parameters to the master station, and broadcasting a collaborative request message carrying its own identifier and the hierarchical fault signature to adjacent IoT circuit breakers according to the preset topology. S3: The adjacent IoT circuit breaker that receives the collaborative request information matches and verifies the locally monitored electrical quantities with the hierarchical fault signature, and generates a corresponding structured simplified report based on the verification result and local status and sends it to the master station. S4: The main station aggregates and processes the complete fault report and the structured simplified report from at least one adjacent IoT circuit breaker, determines the location and type of the fault through collaborative analysis, and generates a corresponding adaptive recovery instruction based on the determined fault type and location, which is then sent to the relevant IoT circuit breaker via an optimized low-power path.

[0007] Preferably, in step S1, generating a hierarchical fault signature based on the fault electrical characteristics specifically includes: Extract the current waveform features within one power frequency cycle in the early stage of the fault occurrence to generate the first-layer core signature for rapid identification and direction determination. At the same time or after the generation of the first-layer core signature, a short-time analysis lasting multiple power frequency cycles is initiated to calculate at least one of the zero-sequence component, high-frequency harmonic distribution, or voltage sag characteristics, and to generate a second-layer auxiliary signature for fine identification of fault types. The layered fault signature includes at least the first-layer core signature; The first-layer core signature extracts key feature vectors by standardizing and segmenting the current signal in the initial stage of the fault, and generates a fixed-length signature code through a preset feature hash compression function.

[0008] This invention introduces a fault rapid reporting mechanism based on hierarchical fault signatures and distributed collaborative verification, achieving second-level accurate fault location and type identification in distribution networks. This fundamentally solves the core problem of large location delays in traditional centralized analysis modes. When an IoT circuit breaker trips due to a fault, it does not only report data to the master station but immediately broadcasts a highly compressed digital signature representing the core electrical characteristics of the fault to neighboring nodes, simultaneously triggering a matching and verification process between the local and neighboring nodes. The neighboring nodes provide feedback on the verification results in a concise report format, allowing the master station to quickly integrate multiple pieces of evidence and rapidly locate the faulty section and determine the nature of the fault using a weighted decision algorithm. This new paradigm, which decentralizes some computation and analysis tasks to the network edge and replaces traditional sequential reporting and centralized processing with real-time collaboration between nodes, reduces fault location and identification time from the traditional second level to sub-second levels, significantly improving the speed and accuracy of distribution network fault response and laying a solid foundation for rapid fault isolation.

[0009] Preferably, the first-layer core signature The generation of satisfies the following algorithm formula: ; In the formula, This indicates the first half-wave of the power frequency after the fault occurred. arrive (phase) current signal A set of features extracted from; Indicates the DC component attenuation coefficient; A sign function indicating the direction of a sudden change in current. It represents the change in current at the instant of the fault.

[0010] Preferably, in step S2, before performing the parallel transmission operation and broadcasting the coordination request information to adjacent IoT circuit breakers, a channel sensing step is also included: Monitor the status of the preset broadcast channel; Based on the channel's busy / idle status, adaptively select the transmission timing or switch to a backup channel for broadcasting; The adaptive decision-making process for channel selection is based on the dynamic adjustment of the channel quality index CQI_k, which is calculated by evaluating channel energy and collision history. The calculation formula is as follows: ,choose The channel with the highest value is used for broadcasting; where, Indicates the first Quality index of each selectable channel; Indicates the first time during the current listening period Average noise energy measured on each channel; This indicates the preset system energy limit reference value; This indicates the first result obtained based on historical statistical data. Estimated probability of packet collisions occurring on each channel; and These are weighting coefficients, and .

[0011] Preferably, in step S3, matching and verifying the locally monitored electrical quantities with the hierarchical fault signature specifically includes: The feature matching degree M between the locally monitored electrical quantity and the features represented by the received first-layer core signature is calculated. The calculation process satisfies the following algorithm formula: ; In the formula, This represents the calculated feature matching degree, with a value range of [value range missing]. ; This refers to the local feature vector calculated by the adjacent IoT circuit breaker based on its own monitoring data, according to the same feature extraction rules as the fault node. This indicates that the neighboring node receives the first-level core signature. In this context, the standard feature vector is parsed or restored according to the agreed rules.

[0012] Preferably, in step S3, generating a corresponding structured and simplified report based on the verification results and local state specifically includes: Combined with the calculated feature matching degree A credibility weight value is generated by combining the status information of whether the local circuit breaker has tripped and the historical reliability assessment of this node. ; Generate a list containing the node identifier and the matching degree. Action status and the aforementioned credibility weight value The simplified data package serves as the structured simplified report.

[0013] Preferably, in step S4, the master station determines the location of the fault through collaborative analysis, specifically including: The main site is based on the matching degree included in the filtered valid reports. Action state coefficient and credibility weight The information is used to locate the fault area using a weighted decision algorithm; The weighted decision algorithm calculates that the fault occurs at the first... Each line section probability Satisfy the following formula: ; In the formula, Indicates the relationship between the distribution network topology and the section to be evaluated. The set of all IoT circuit breaker nodes that are directly electrically connected; This represents the set of all nodes that have submitted structured, simplified reports; the main site selection probability. The highest section is considered the most likely location of the fault.

[0014] Preferably, under normal conditions where no faults occur, the method further includes a normal low-power inspection and predictive performance maintenance data transmission mechanism: The IoT circuit breaker reports lightweight status data to the master station at a first long period interval. The lightweight status data is an approximate entropy or sample entropy value that characterizes the complexity of the load current waveform. The main station or intelligent agent deployed at the network edge analyzes the temporal changes of the lightweight status data to identify potential sub-healthy IoT circuit breakers with abnormal status. Deep data acquisition commands are directed to the identified IoT circuit breakers with potential sub-health conditions. The potential sub-health IoT circuit breaker is activated by an instruction, collects high-resolution electrical data for a specified period, and reports it.

[0015] Preferably, the method further includes activating a progressive channel reservation and data preloading mechanism when the IoT circuit breaker senses an overload warning but has not reached the tripping threshold: Calculate the risk assessment value of the current overload state. ; When the risk assessment value When the threshold is below the first threshold, channel sounding signaling is sent at low power while maintaining normal periodic reporting; When the risk assessment value When the value is between the first and second thresholds, detailed warning data is pre-encapsulated and cached. When the risk assessment value When the second threshold is exceeded or a trip is triggered, the pre-encapsulated data is directly uploaded through a reserved or quickly established channel.

[0016] An Internet of Things (IoT) circuit breaker, comprising: The business awareness module is used to monitor the status of electrical circuits and identify various business states such as fault, early warning, and normal. A signature generation module is used to generate a hierarchical fault signature based on the output of the service awareness module when a faulty service is in operation. The adaptive communication module is used to switch data transmission strategies under different service states, including: performing parallel transmission to the master station and adjacent nodes in the fault state; performing progressive channel and data preparation in the early warning state; and performing periodic low-power reporting or responding to deep data acquisition commands in the normal state. The collaborative processing module is used to process collaborative request information from adjacent IoT circuit breakers, perform matching verification, and generate a structured and concise report.

[0017] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention introduces a fault rapid reporting mechanism based on hierarchical fault signatures and distributed collaborative verification, achieving second-level accurate fault location and type identification in distribution networks. This fundamentally solves the core problem of large location delays in traditional centralized analysis modes. When an IoT circuit breaker trips due to a fault, it does not only report data to the master station but immediately broadcasts a highly compressed digital signature representing the core electrical characteristics of the fault to neighboring nodes, simultaneously triggering a matching verification process between the local and neighboring nodes. The neighboring nodes provide feedback on the verification results in a concise report format, allowing the master station to quickly integrate multiple pieces of evidence and rapidly locate the faulty section and determine the nature of the fault through a weighted decision algorithm. This new paradigm, which decentralizes some computational and analytical tasks to the network edge and replaces traditional sequential reporting and centralized processing with real-time collaboration between nodes, reduces fault location and identification time from the traditional second level to sub-second level, significantly improving the speed and accuracy of distribution network fault response and laying a solid foundation for rapid fault isolation.

[0018] 2. This invention also achieves efficient and low-power transmission of critical fault information through a dual design of signature compression and simplified reporting information, effectively alleviating network congestion and energy consumption pressure caused by the concurrent uploading of massive amounts of terminal data. Complex fault waveform features are extracted and compressed into lightweight digital signatures for broadcasting and matching, replacing the direct transmission of the original waveform data. Simultaneously, neighboring nodes participating in the collaboration only need to upload micro-data packets containing key information such as matching degree and status, rather than complete monitoring data. This paradigm shift from transmitting raw data to transmitting features and conclusions significantly reduces the total data traffic in the network during the fault moment, the period with the most pressing demand for communication resources. It lowers the probability of competition and collisions in the wireless channel, ensuring the reliability of critical information transmission and significantly saving communication energy consumption of each IoT circuit breaker, achieving unified optimization of service performance and network energy efficiency.

[0019] 3. This invention also provides real-time and reliable decision input for adaptive recovery control of the distribution network through rapid and accurate fault perception and location capabilities, thereby opening up the key technical link from fault perception to autonomous recovery and improving the overall self-healing level of the distribution network. Traditional recovery control often struggles to execute quickly and accurately due to incomplete or delayed fault information. This invention enables the master station to not only know about the occurrence of a fault but also accurately grasp its location and type within a very short time after the fault occurs. Based on this, the master station can intelligently generate and issue differentiated recovery commands. For example, for transient faults, it can control the upstream circuit breaker to reclose with a delay, while for permanent faults, it can perform fault section isolation and power restoration to non-fault sections. This closed loop of accurate perception-intelligent decision-making-rapid control enables the distribution network to shift from passively responding to faults to actively managing them, significantly shortening the average power outage time for users and enhancing power supply reliability. Attached Figure Description

[0020] Figure 1 This is a flowchart of the fault handling process of the present invention; Figure 2 This is a flowchart of the routine inspection process of the present invention; Figure 3 This is a flowchart of the early warning processing of the present invention. Detailed Implementation

[0021] like Figures 1 to 3 As shown, the present invention relates to a service-aware IoT circuit breaker adaptive low-power data transmission method, applied to a distribution network communication system including a master station and multiple IoT circuit breakers. The method includes: S1: When an IoT circuit breaker trips due to a fault, a hierarchical fault signature is generated based on the fault's electrical characteristics to characterize the fault. In an embodiment of the present invention, step S1, generating a hierarchical fault signature based on fault electrical characteristics, specifically includes: Extract the current waveform features within one power frequency cycle in the early stage of the fault occurrence to generate the first-layer core signature for rapid identification and direction determination. At the same time or after the generation of the first-layer core signature, a short-time analysis lasting multiple power frequency cycles is initiated to calculate at least one of the zero-sequence component, high-frequency harmonic distribution, or voltage sag characteristics, and to generate a second-layer auxiliary signature for fine identification of fault types. The layered fault signature includes at least the first-layer core signature; The method for generating the first-layer core signature is as follows: The current signal in the initial stage of the fault is standardized and segmented, key feature vectors are extracted, and a fixed-length signature code is generated through a feature hash function.

[0022] The specific generation process satisfies the following algorithm formula: ; In the formula: The generated first-layer core signature is a fixed-length digital sequence used to quickly characterize the core electrical features of a fault in subsequent collaborations. For the preset feature hash compression function, The input must be for... , and The dimensionless feature vector after standardization is a deterministic algorithm that maps input data of arbitrary length to output of fixed length. This indicates the first half-wave of the power frequency after the fault occurred. arrive (phase) current signal The set of features extracted from it can typically include fundamental amplitude, phase angle, and specific subharmonic content, etc. It represents the DC component attenuation coefficient, which quantifies the magnitude or attenuation rate of the DC component in the fault current, and is one of the key features for distinguishing the nature of a fault (such as an inductive circuit fault). A sign function indicating the direction of a sudden change in current. This represents the change in current at the instant of the fault. The function outputs its sign (positive, negative, or zero) to help determine whether the fault occurred upstream or downstream of the circuit breaker.

[0023] Operational logic: This formula describes the first-level core signature. The generation process. Its core logic is to compress the high-dimensional, complex original fault current waveform characteristics using a preset feature hashing function. This is mapped to a fixed-length, condensed digital signature. The input parameters include a set of phase and harmonic features extracted from the first half-wave current. DC component attenuation coefficient reflecting fault transient characteristics And the sign function indicating the direction of the fault current abrupt change. Hash function This approach ensures the uniqueness and collision resistance of the output while achieving significant data compression. Through this hash compression algorithm, the crucial, multi-dimensional electrical transient characteristics in the early stages of a fault are successfully transformed into an extremely concise digital fingerprint (core signature). This lays the data foundation for subsequent fast, low-overhead broadcasting and collaborative matching in resource-constrained wireless channels. It is a primary key technology for achieving efficient collaborative fault information sensing, fundamentally avoiding the enormous communication burden caused by transmitting raw waveform data.

[0024] S2: The IoT circuit breaker performs parallel transmission operations: sending a complete fault report containing detailed electrical parameters to the master station, and broadcasting a collaborative request message carrying its own identifier and the hierarchical fault signature to adjacent IoT circuit breakers according to the preset topology. In an embodiment of the present invention, the signature carried in the collaborative request information includes the first-layer core signature, or simultaneously includes index information of the first-layer core signature and the second-layer auxiliary signature; In an embodiment of the present invention, before broadcasting the coordination request information to adjacent IoT circuit breakers during the parallel transmission operation in step S2, a channel sensing step is also included: Monitor the status of the preset broadcast channel; Based on the channel's busy / idle status, adaptively select the transmission timing or switch to a backup channel for broadcasting; Among them, the adaptive decision-making for channel selection is based on the dynamic adjustment of the channel quality index, which is calculated by evaluating channel energy and collision history.

[0025] The specific decision-making process satisfies the following algorithmic formula: ; choose The channel with the highest value will be used for broadcasting; In the formula: Indicates the first The quality index of each selectable channel is a dimensionless scalar value; the higher the value, the better the channel quality. This indicates that during the current listening period, in the [number]th [period]... Average noise energy measured on each channel; This represents a preset system energy upper limit reference value, used for... Perform normalization processing; This indicates the first result obtained based on historical statistical data. The probability estimate of a data packet collision occurring on a channel, i.e., the historical collision probability; and These are weighting coefficients, representing the degree of importance attached to the current noise energy and the probability of historical conflicts, respectively. This is used to adjust the evaluation strategy (such as whether to focus more on real-time performance or stability).

[0026] Operational logic: This formula defines the channel quality index. The calculation method is used to select the best among multiple potential broadcast channels. Its logic is to comprehensively evaluate the instantaneous interference level (noise energy) of the channel. ) and historical reliability (conflict probability) ), and then normalize both and assign weights ( , A comprehensive evaluation index is synthesized by selecting... The highest-performing channel is designed to achieve the highest broadcast success rate with minimal transmission cost. This channel awareness and selection algorithm enables faulty circuit breakers to intelligently avoid highly interfered or congested channels before sending critical cooperative request broadcasts. This significantly improves the initial transmission success rate of broadcast frames, reduces the number of retransmissions and delays caused by collisions or failures, ensures the rapid and reliable propagation of fault signatures in the cooperative network, buys valuable time for subsequent multi-node cooperative verification, and indirectly reduces overall communication energy consumption.

[0027] S3: The adjacent IoT circuit breaker that receives the collaborative request information matches and verifies the locally monitored electrical quantities with the hierarchical fault signature, and generates a corresponding structured simplified report based on the verification result and local status and sends it to the master station. In an embodiment of the present invention, step S3 involves matching and verifying the locally monitored electrical quantities with the hierarchical fault signature, and generating a corresponding structured simplified report based on the verification results and the local status. Specifically, this includes: Calculate the feature matching degree between the locally monitored electrical quantities and the received hierarchical fault signatures; By combining the feature matching degree, the status information of whether the local circuit breaker has been activated, and the historical reliability assessment of this node, a credibility weight value is generated. Generate a simplified data packet containing the node identifier, the matching degree, the action status, and the credibility weight value, as the structured simplified report; The feature matching degree is calculated by comparing the consistency between the locally extracted feature vector and the features represented by the signature.

[0028] The specific calculation process follows the following algorithm formula: ; In the formula: This represents the calculated feature matching degree. The result is a scalar with a value range of [value range missing]. A higher value indicates a higher degree of matching. This indicates that the adjacent IoT circuit breaker calculates its local feature vector based on real-time monitoring data from its own current / voltage sensors, following the same feature extraction rules as the fault node. This indicates the fault signature received by the adjacent node ( In this context, the standard feature vector parsed or reconstructed according to agreed-upon rules is the feature of the fault node. Standardized representation of etc.; Calculation logic: This formula calculates the matching degree between the local monitoring features of neighboring nodes and the received fault signatures. Its essence is to calculate two eigenvectors. and The cosine similarity between two vectors is calculated by taking the dot product of the two vectors and dividing by the product of their magnitudes. Its range is... In this application, through feature design, its effective value range is... The closer this value is to 1, the more consistent the directions of the two vectors, meaning the more similar the local electrical characteristics are to the broadcast fault characteristics. Matching Degree The calculation transforms qualitative "feature similarity" judgments into quantitative numerical measures, providing precise and comparable input for subsequent weighted decision-making. It enables each adjacent node to not only make a binary "yes / no" judgment but also contribute a continuous value reflecting its degree of confidence. This refined information representation is the foundation for the master station's ability to perform high-precision probabilistic positioning and diagnosis, significantly improving the system's resolution and robustness in complex fault and noisy environments compared to simple Boolean logic voting.

[0029] S4: The main station aggregates and processes the complete fault report and the structured simplified report from at least one adjacent IoT circuit breaker, determines the location and type of the fault through collaborative analysis, and generates a corresponding adaptive recovery instruction based on the determined fault type and location, which is then sent to the relevant IoT circuit breaker via an optimized low-power path.

[0030] When a fault occurs, the faulty IoT circuit breaker (end) and its adjacent IoT circuit breakers (ends) interact directly via wireless broadcast and verification without going through the main station (cloud), and work together to complete the initial perception and confirmation of the fault characteristics. Steps S2 (broadcast) and S3 (adjacent nodes verify and generate a simplified report) are typical end-to-end collaborative processes.

[0031] In an embodiment of the present invention, in step S4, the main station aggregates and processes reports, and determines the location and type of the fault through collaborative analysis, specifically including: The main station performs timing and path consistency verification based on the timestamps and distribution network topology carried in the reports submitted by each IoT circuit breaker, and filters valid reports. Based on the matching degree, action status, and credibility weight information contained in the filtered valid reports, a weighted decision algorithm is used to locate the fault range. By integrating the detailed electrical parameters in the complete fault report and the electrical quantity description information from multiple adjacent IoT circuit breakers, a comprehensive diagnosis of the fault type is performed. The weighted decision algorithm is used for fault location, and a decision is formed by integrating trusted reports from multiple nodes.

[0032] The specific positioning process satisfies the following algorithm formula: ; In the formula: This indicates that the fault occurred in the first... Each line section Based on the probability, the main station will select the segment with the highest probability as the most likely fault location; In the distribution network topology, this refers to the section to be evaluated. The set of all IoT circuit breaker nodes that are directly electrically connected (e.g., upstream and downstream); This represents the set of all IoT circuit breaker nodes that have submitted structured, simplified reports to the main station; For from node The credibility weight value calculated or derived using the above method reflects the credibility level of the node's historical reports or the health status of the device. The calculation satisfies ,in For nodes Reliability rating coefficient based on historical communication success rate, This is a pre-defined weighted combination function; For from node The feature matching degree calculated according to the above formula; For nodes The coefficient of its own action state, if the node Since the circuit breaker has already tripped due to this fault, Give it the highest weight; if no action is taken, then Choosing a constant less than 1 indicates that the indirect evidence it provides has a lower weight.

[0033] Calculation logic: This formula describes how the main station uses a weighted decision algorithm to locate the faulty section. probability The logic involves a weighted vote among all nodes that submitted the simplified report. For each potentially faulty segment... Related nodes Its voting weight is determined by the node's credibility weight. Feature matching degree and its own action state coefficient The product of these three factors constitutes the final result. Ultimately, the fault occurred... The probability of is calculated as: all with The weighted sum of votes from relevant nodes accounts for the percentage of the total weighted votes from all nodes. This weighted fusion localization algorithm creatively integrates multi-dimensional, heterogeneous evidence (matching degree) provided by distributed nodes. Action state Credibility This involves mathematical unification and synthesis. It's not merely a simple aggregation of information, but rather the management of uncertainty and optimal fusion of evidence through probabilistic models. This makes the location results not only fast but also resistant to interference from false alarms or data anomalies from individual nodes, significantly improving the accuracy and reliability of fault location in complex power distribution network environments. It integrates the dispersed sensing capabilities of individual nodes into precise system-level judgment capabilities, which is the core decision-making manifestation of this solution's intelligent collaboration.

[0034] In an embodiment of the present invention, step S4, based on the determined fault type and location, generates a corresponding adaptive recovery instruction, specifically including: If the fault is diagnosed as a transient fault, a delayed reclosing command is generated for the nearest circuit breaker upstream of the fault point. If the fault is diagnosed as a permanent fault, a power restoration command is generated to isolate the faulty section and close the tie switch. The recovery instruction encapsulates the fault type identifier and operation parameters required to execute the instruction.

[0035] In an embodiment of the present invention, the step of sending data via an optimized low-power path to the relevant IoT circuit breaker includes: The master station selects a multi-hop routing path consisting of active nodes or nodes that can be quickly woken up, based on the current network topology and the dormant status of each node. The recovery command is encapsulated into a high-priority service data packet and reliably transmitted along the selected path.

[0036] Under normal conditions where no faults occur, the method also includes a normal low-power inspection and predictive performance maintenance data transmission mechanism: Under normal conditions, the IoT circuit breaker reports lightweight status data containing only key entropy features of the load waveform to the master station at the first long period interval. In an embodiment of the present invention, the lightweight state data is an approximate entropy or sample entropy value obtained by calculating the complexity of the load current waveform; The main station or intelligent agent deployed at the network edge analyzes the temporal changes of the lightweight state data to identify potential sub-healthy IoT circuit breakers with abnormal state change patterns. Deep data acquisition commands are directed to the identified IoT circuit breakers with potential sub-health conditions. The potential sub-health IoT circuit breaker is awakened from deep sleep according to the deep data acquisition instruction, collects high-resolution electrical data for a specified period of time, and reports it to the instruction initiator at a rate higher than the first long cycle.

[0037] In embodiments of the present invention, when the IoT circuit breaker senses an overload warning but the service state has not reached the tripping threshold, a progressive channel reservation and data preloading mechanism is activated: Calculate the risk assessment value of the current overload state. ; When the risk assessment value When the threshold is below the first threshold, channel sounding signaling is sent at low power while maintaining normal periodic reporting; When the risk assessment value When the value is between the first and second thresholds, detailed early warning data is pre-encapsulated and cached locally or at the edge node. When the risk assessment value When the second threshold is exceeded or a trip is triggered, a trigger command is sent directly to trigger the pre-encapsulated warning data or fault data to be uploaded through a reserved or quickly established channel.

[0038] Example 2: An Internet of Things (IoT) circuit breaker, comprising: The business awareness module is used to monitor the status of electrical circuits and identify various business states such as fault, early warning, and normal. A signature generation module is used to generate a hierarchical fault signature based on the output of the service awareness module when a faulty service is in operation. The adaptive communication module is used to switch data transmission strategies under different service states, including: performing parallel transmission to the master station and adjacent nodes in the fault state; performing progressive channel and data preparation in the early warning state; and performing periodic low-power reporting or responding to deep data acquisition commands in the normal state. The collaborative processing module is used to process collaborative request information from adjacent IoT circuit breakers, perform matching verification, and generate a structured and concise report.

[0039] Example 3: A power distribution network communication system includes a master station and multiple IoT circuit breakers, wherein the IoT circuit breakers are connected through a wireless ad hoc network or a hybrid network; The main station includes: The report aggregation and analysis unit is used to receive and process various reports from IoT circuit breakers, and perform fault location, type diagnosis and sub-health node identification; The communication scheduling and management unit is used to generate and issue control signaling, including deep data acquisition instructions and adaptive recovery instructions, according to service requirements and network status, and to manage network resources.

[0040] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. A service-aware adaptive low-power data transmission method for IoT circuit breakers, applied to a power distribution network communication system including a master station and multiple IoT circuit breakers, characterized in that... Includes the following steps: S1: When an IoT circuit breaker trips due to a fault, a hierarchical fault signature is generated based on the fault's electrical characteristics to characterize the fault. S2: The IoT circuit breaker performs parallel transmission operations: sending a complete fault report containing detailed electrical parameters to the master station, and broadcasting a collaborative request message carrying its own identifier and the hierarchical fault signature to adjacent IoT circuit breakers according to the preset topology. S3: The adjacent IoT circuit breaker that receives the collaborative request information matches and verifies the locally monitored electrical quantities with the hierarchical fault signature, and generates a corresponding structured simplified report based on the verification result and local status and sends it to the master station. S4: The main station aggregates and processes the complete fault report and the structured simplified report from at least one adjacent IoT circuit breaker, determines the location and type of the fault through collaborative analysis, and generates a corresponding adaptive recovery instruction based on the determined fault type and location, which is then sent to the relevant IoT circuit breaker via an optimized low-power path.

2. The adaptive low-power data transmission method for IoT circuit breakers based on service awareness as described in claim 1, characterized in that, In step S1, generating a hierarchical fault signature based on the fault electrical characteristics specifically includes: Extract the current waveform features within one power frequency cycle in the early stage of the fault occurrence to generate the first-layer core signature for rapid identification and direction determination. At the same time or after the generation of the first-layer core signature, a short-time analysis lasting multiple power frequency cycles is initiated to calculate at least one of the zero-sequence component, high-frequency harmonic distribution, or voltage sag characteristics, and to generate a second-layer auxiliary signature for fine identification of fault types. The layered fault signature includes at least the first-layer core signature; The first-layer core signature extracts key feature vectors by standardizing and segmenting the current signal in the initial stage of the fault, and generates a fixed-length signature code through a preset feature hash compression function.

3. The adaptive low-power data transmission method for IoT circuit breakers based on service awareness as described in claim 2, characterized in that, First layer core signature The generation of satisfies the following algorithm formula: ； In the formula, This indicates the first half-wave of the power frequency after the fault occurred. arrive (phase) current signal A set of features extracted from; Indicates the DC component attenuation coefficient; A sign function indicating the direction of a sudden change in current. It represents the change in current at the instant of the fault.

4. The adaptive low-power data transmission method for IoT circuit breakers based on service awareness as described in claim 1, characterized in that, In step S2, before performing the parallel transmission operation and broadcasting the coordination request information to adjacent IoT circuit breakers, a channel sensing step is also included: Monitor the status of the preset broadcast channel; Based on the channel's busy / idle status, adaptively select the transmission timing or switch to a backup channel for broadcasting; The adaptive decision-making process for channel selection is based on the dynamic adjustment of the channel quality index CQI_k, which is calculated by evaluating channel energy and collision history. The calculation formula is as follows: ,choose The channel with the highest value is used for broadcasting; where, Indicates the first Quality index of each selectable channel; Indicates the first time during the current listening period Average noise energy measured on each channel; This indicates the preset system energy limit reference value; This indicates the first result obtained based on historical statistical data. Estimated probability of packet collisions occurring on each channel; and These are weighting coefficients, and .

5. The adaptive low-power data transmission method for IoT circuit breakers based on service awareness according to claim 3, characterized in that, In step S3, the locally monitored electrical quantities are matched and verified with the hierarchical fault signature, specifically including: The feature matching degree M between the locally monitored electrical quantity and the features represented by the received first-layer core signature is calculated. The calculation process satisfies the following algorithm formula: ； In the formula, This represents the calculated feature matching degree, with a value range of [value range missing]. ; This refers to the local feature vector calculated by the adjacent IoT circuit breaker based on its own monitoring data, according to the same feature extraction rules as the fault node. This indicates that the neighboring node receives the first-level core signature. In this context, the standard feature vector is parsed or restored according to the agreed rules.

6. The adaptive low-power data transmission method for IoT circuit breakers based on service awareness, as described in claim 5, is characterized in that... In step S3, a corresponding structured simplified report is generated based on the verification results and the local state, specifically including: Combined with the calculated feature matching degree A credibility weight value is generated by combining the status information of whether the local circuit breaker has tripped and the historical reliability assessment of this node. ; Generate a list containing the node identifier and the matching degree. Action status and the aforementioned credibility weight value The simplified data package serves as the structured simplified report.

7. The adaptive low-power data transmission method for IoT circuit breakers based on service awareness according to claim 6, characterized in that, In step S4, the master station determines the location of the fault through collaborative analysis, specifically including: The main site is based on the matching degree included in the filtered valid reports. Action state coefficient and credibility weight The information is used to locate the fault area using a weighted decision algorithm; The weighted decision algorithm calculates that the fault occurs at the first... Each line section probability Satisfy the following formula: ； In the formula, Indicates the relationship between the distribution network topology and the section to be evaluated. The set of all IoT circuit breaker nodes that are directly electrically connected; This represents the set of all nodes that have submitted structured, simplified reports; the main site selection probability. The highest section is considered the most likely location of the fault.

8. The adaptive low-power data transmission method for IoT circuit breakers based on service awareness according to claim 1, characterized in that, Under normal conditions where no faults occur, the method also includes a normal low-power inspection and predictive performance maintenance data transmission mechanism: The IoT circuit breaker reports lightweight status data to the master station at a first long period interval. The lightweight status data is an approximate entropy or sample entropy value that characterizes the complexity of the load current waveform. The main station or intelligent agent deployed at the network edge analyzes the temporal changes of the lightweight status data to identify potential sub-healthy IoT circuit breakers with abnormal status. Deep data acquisition commands are directed to the identified IoT circuit breakers with potential sub-health conditions. The potential sub-health IoT circuit breaker is activated by an instruction, collects high-resolution electrical data for a specified period, and reports it.

9. The adaptive low-power data transmission method for IoT circuit breakers based on service awareness according to claim 1, characterized in that, The method also includes activating a progressive channel reservation and data preloading mechanism when the IoT circuit breaker senses an overload warning but has not reached the tripping threshold: Calculate the risk assessment value of the current overload state. ; When the risk assessment value When the threshold is below the first threshold, channel sounding signaling is sent at low power while maintaining normal periodic reporting; When the risk assessment value When the value is between the first and second thresholds, detailed warning data is pre-encapsulated and cached. When the risk assessment value When the second threshold is exceeded or a trip is triggered, the pre-encapsulated data is directly uploaded through a reserved or quickly established channel.

10. An Internet of Things (IoT) circuit breaker, wherein the method of using the service-aware adaptive low-power data transmission method for IoT circuit breakers according to any one of claims 1-9 is characterized in that, include: The business awareness module is used to monitor the status of electrical circuits and identify various business states such as fault, early warning, and normal. A signature generation module is used to generate a hierarchical fault signature based on the output of the service awareness module when a faulty service is in operation. The adaptive communication module is used to switch data transmission strategies under different service states, including: performing parallel transmission to the master station and adjacent nodes in the fault state; performing progressive channel and data preparation in the early warning state; and performing periodic low-power reporting or responding to deep data acquisition commands in the normal state. The collaborative processing module is used to process collaborative request information from adjacent IoT circuit breakers, perform matching verification, and generate a structured and concise report.

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