Power profile compliance checking method based on target detection

Abstract

This invention relates to the field of digital management technology for power archives, and particularly to a method for verifying the compliance of power archive data based on target detection. The method includes: receiving power archive image data into a visual recognition network model; extracting power node identifiers, issuance timestamps, and candidate character tensor sets; and migrating the storage media of historical archive data slices when the active power value of a distribution network node exceeds a preset load threshold. The system retrieves historical power grid physical topology subgraphs, substitutes candidate load values ​​into power flow calculations to extract over-limit trial calculation residuals; converts the over-limit trial calculation residuals into penalty gradients to correct character probability distribution parameters, and outputs structured text information corresponding to the highest probability score. The system compares and verifies the structured text information with preset business review specification text, generates a compliance judgment result, and stores it in a distributed file system. This invention improves verification accuracy and processing efficiency.

Description

Technical Field

[0001] This invention relates to the field of digital management technology for power archives, and in particular to a method for verifying the compliance of power archive data based on target detection. Background Technology

[0002] In the application scenario of digital management technology for power records, business systems need to verify the compliance of users' records. These records include various business form files in different formats, and the types of business records are extremely diverse. Auditors need to conduct completeness comparisons and consistency audits of the multi-dimensional key information recorded in the records, according to specific business review standards.

[0003] Currently, the power system primarily relies on manual, item-by-item reading to verify archival materials. Due to the diverse types of power system archival materials and the highly complex corresponding review standards and related verification logic, manual verification consumes significant manpower and time. Furthermore, this manual verification model heavily depends on the individual experience of the reviewers, and the prolonged and repetitive reading and comparison work easily leads to physical fatigue. Physical fatigue directly results in omissions in text information extraction and can cause deviations in compliance judgment standards. The inherent limitations of the manual verification model lead to very slow business verification processing and a high risk of errors in judgment.

[0004] Existing technologies lack automated information extraction mechanisms and intelligent rule engines, and the conventional approach of relying solely on manual comparison faces significant technical obstacles. Conventional methods cannot address the technical pain points that make it difficult to guarantee the accuracy and efficiency of compliance verification of power archive materials due to the diverse types of materials, complex verification rules, and low efficiency of manual item-by-item checking. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for verifying the compliance of power archive materials based on target detection. This invention solves the technical problem that the accuracy and efficiency of verifying the compliance of power archive materials are difficult to guarantee due to the large variety of power archive materials, the complexity of verification rules, and the low efficiency of manual item-by-item verification.

[0006] To solve the above-mentioned technical problems, the specific contents of the present invention are as follows: The present invention provides a method for verifying the compliance of power archive data based on target detection, comprising: Receive power archive image data, input the power archive image data into a pre-set visual recognition network model to extract the power consumption node identifier and the issuance timestamp, and output a candidate character tensor set including character probability distribution parameters and candidate load values; The real-time telemetry data sequence of the distribution network node corresponding to the power consumption node identifier is obtained, wherein the real-time telemetry data sequence includes active power value; when it is determined that the active power value is greater than a preset load threshold, the network address parameter of the distribution network node is extracted to generate a storage scheduling instruction, and the historical archive data slice associated with the distribution network node is migrated from the first access medium to the second access medium. The power consumption node identifier and the issuance timestamp are concatenated and input into a preset spatiotemporal topology map to retrieve the corresponding historical power grid physical topology sub-graph. The candidate load values ​​included in the candidate character tensor set are extracted and substituted into the historical power grid physical topology subgraph to calculate the power flow, thereby obtaining the over-limit trial residual. The excess trial residual is converted into a penalty gradient. The penalty gradient is subtracted from the character probability distribution parameters included in the candidate character tensor set to obtain the updated probability score. The character sequence corresponding to the highest probability score in the updated probability score is extracted as the structured text information output. The structured text information is compared with the preset business review specification text, and the resulting compliance judgment is written into the preset distributed file system.

[0007] Furthermore, the power archive data compliance verification method based on target detection described in this invention, wherein the step of acquiring the real-time telemetry data sequence of the distribution network node corresponding to the power consumption node identifier, wherein the real-time telemetry data sequence includes active power values; when it is determined that the active power value is greater than a preset load threshold, extracting the network address parameters of the distribution network node to generate a storage scheduling instruction, and migrating the historical archive data slice associated with the distribution network node from the first access medium to the second access medium, includes: Divide the active power value by the rated capacity value of the distribution network node to obtain the real-time load rate parameter of the distribution network node. When it is determined that the real-time load rate parameter is greater than the preset load threshold within a preset continuous sampling period, the network address parameter of the distribution network node is used as the physical network identifier, and an overload alarm tensor with the physical network identifier is generated. Within the preset topology association database, a downward traversal is performed starting from the physical network identifier to extract the set of user identity identifiers located within the power supply range of the distribution network node; The user identity identifier set is mapped to an object identifier within the distributed file system, wherein the object identifier is bound to a data sharding placement strategy parameter; Modify the read priority value in the data fragment placement strategy parameters, obtain the modified read priority value and the object identifier to generate the storage scheduling instruction, transmit it to the distributed file system, and migrate the historical archive data fragment from the first access medium to the second access medium.

[0008] Furthermore, the power archive data compliance verification method based on target detection described in this invention involves receiving power archive image data, inputting the power archive image data into a pre-set visual recognition network model to extract the power consumption node identifier and issuance timestamp, and outputting a candidate character tensor set including character probability distribution parameters and candidate load values, including: Convert the preset business review specification text into a rule feature vector sequence; Visual feature vectors are extracted from the power archive image data, and the sequence of regular feature vectors and the visual feature vectors are input into the cross-modal attention computing network included in the visual recognition network model; Perform matrix multiplication on the regular feature vector sequence and the visual feature vector, and output the attention heatmap matrix tensor between the regular feature vector sequence and the visual feature vector; Extract the center coordinates of pixel value distribution within the attention heatmap matrix tensor, generate a coordinate detection box based on the center coordinates of pixel value distribution, and extract local feature region images from the power archive image data; The local feature region image is input into the recurrent convolutional recognition network included in the visual recognition network model to perform character decoding calculation, extract the electricity node identifier and the issuance timestamp, and output the candidate character tensor set carrying the character probability distribution parameters and the candidate load value.

[0009] Furthermore, the power archive compliance verification method based on target detection described in this invention, after performing matrix multiplication calculation on the regular feature vector sequence and the visual feature vector, and outputting the attention heatmap matrix tensor between the regular feature vector sequence and the visual feature vector, includes: Extract the coordinates of pixel regions whose values ​​are greater than a preset attention threshold within the attention heatmap matrix tensor; The power archive image data is cropped using the pixel region coordinates to extract high-weight image slice data, wherein the distributed file system is configured with a copy redundancy parameter for the high-weight image slice data; Modify the replica redundancy parameter to a preset maximum number limit value; The high-weight image slice data, after modifying the copy redundancy parameter, is stored in a high-speed cache memory address segment.

[0010] Furthermore, the power archive data compliance verification method based on target detection described in this invention involves concatenating the power consumption node identifier with the issuance timestamp and inputting it into a preset spatiotemporal topology map to retrieve the corresponding historical power grid physical topology subgraph; extracting the candidate load values ​​included in the candidate character tensor set and substituting them into the historical power grid physical topology subgraph for power flow calculation to obtain the over-limit trial calculation residual, including: The issuance timestamp and the electricity consumption node identifier are combined according to a set number of bits to generate a composite query key-value sequence; The composite query key-value sequence is input into the spatiotemporal topology map that records the power grid time-series evolution data to perform a matching query, and the historical power grid physical topology sub-map and the impedance operating parameters of the attached conductors corresponding to the successful match are extracted. The candidate load values ​​are substituted into the power flow operation equations included in the historical power grid physical topology subgraph containing the conductor impedance operation parameters to perform load trial operation calculations, and the output includes verification calculation results including node simulated voltage values. Calculate the numerical difference between the simulated node voltage value and the preset node voltage limit threshold, and extract the numerical difference exceeding the preset node voltage limit threshold as the over-limit trial residual.

[0011] Furthermore, the power archive data compliance verification method based on target detection described in this invention, wherein the step of converting the excess trial calculation residual into a penalty gradient, subtracting the penalty gradient from the character probability distribution parameters included in the candidate character tensor set to obtain an updated probability score, and extracting the character sequence corresponding to the highest probability score from the updated probability scores as structured text information output, includes: The product of the over-limit trial residual and the preset rule penalty weight parameter is performed to calculate the negative deviation decay value as the penalty gradient; Extract the character probability distribution parameters carried by the candidate character tensor set as the initial character decoding score data, subtract the negative deviation attenuation value from the initial character decoding score data, and calculate the target decoding path score as the updated probability score; Based on the descending order of the target decoding path scores, the characters in the candidate character tensor set are reordered by position rearrangement. The character sequence that is first and second after recombination and sorting is taken as the character sequence corresponding to the highest probability score, and the character sequence corresponding to the highest probability score is extracted as the structured text information output.

[0012] Furthermore, the power archive data compliance verification method based on target detection described in this invention further includes, before comparing the structured text information with the preset business review specification text and generating a compliance judgment result which is then written into the preset distributed file system: Locate the electrical wiring image region within the power archive image data, and input the electrical wiring image region into a preset convolutional network model to extract electrical symbol node features and connecting line segment edge features; The adjacency matrix association mapping calculation is performed on the electrical symbol node features using the edge features of the connecting line segments, and the resulting graph is spliced ​​to generate a local topology map within the archive. Extract the entity correspondences recorded in the structured text information, and generate a text logic topology graph based on the entity correspondences; The local topology graph inside the archive, the text logical topology graph, and the historical power grid physical topology subgraph are synchronously input into a preset graph isomorphic computing network, and graph feature aggregation and dimensionality reduction calculations are performed to output the corresponding three sets of graph-level representation vectors respectively. Perform dot product calculations on each pair of the three sets of graph-level representation vectors to obtain the cosine similarity numerical parameters; When it is determined that any of the cosine similarity parameters is lower than the preset isomorphism threshold, a structural conflict warning mark is generated, and the structural conflict warning mark is combined into the compliance determination result.

[0013] Furthermore, the power archive compliance verification method based on target detection described in this invention, after receiving power archive image data, inputting the power archive image data into a pre-set visual recognition network model to extract the power consumption node identifier and issuance timestamp, and outputting a candidate character tensor set including character probability distribution parameters and candidate load values, includes: The signature sub-image is extracted from the power archive image data and input into the preset anti-counterfeiting network model. The classification confidence parameters output by the anti-counterfeiting network model are then extracted. When the classification confidence parameter is determined to be lower than the preset authenticity verification threshold, the first feature tensor output by the hidden layer of the authentication network model is extracted as the current latent variable to be verified. The power consumption node identifier is used to query and extract historical seal image slice data within the historical archive time period in the distributed file system. The historical seal image slice data is input into the anti-counterfeiting network model, the second feature tensor output by the hidden layer is extracted, and mean pooling dimensionality reduction calculation is performed on the extracted second feature tensors to calculate the benchmark comparison latent variable. The current latent variable to be tested and the benchmark latent variable are matrix-concatenated according to the data channel dimension to generate fused tensor data; The fused tensor data is input into a preset multilayer perceptron to perform classification boundary function mapping calculation, outputting a classification judgment result indicating the authenticity attribute of the signature sub-image, and the classification judgment result is combined and written into the compliance judgment result.

[0014] Furthermore, the power archive data compliance verification method based on target detection described in this invention, wherein comparing the structured text information with a preset business review specification text and generating a compliance judgment result which is then written into a preset distributed file system, includes: Extract the real-time telemetry data sequence, the over-limit trial calculation residual, the map version number information of the historical power grid physical topology sub-graph, and the compliance judgment result that are attached to the process of generating the compliance judgment result, and splice them together to form a block metadata set; The text information in the block metadata set is extracted as the leaf node of the hash binary tree. The hash algorithm is calculated according to the hierarchical order of ascending level, and the task root hash string associated with the compliance judgment result is generated. The task root hash string with the issuance timestamp and the compliance determination result are stored in the hash storage space sequence within the distributed file system via the append write interface configured in the distributed file system.

[0015] Furthermore, the power archive data compliance verification method based on target detection described in this invention also includes: According to a preset time interval, the compliance judgment result with error flags and the corresponding historical verification record block data are read from the hash storage space sequence. The cumulative number of errors reported by the distribution network node to which the compliance judgment result belongs is counted, and the network address parameters of the distribution network node whose cumulative number of errors exceeds a preset threshold are extracted. The historical verification record block data associated with the network address parameters is input into a preset clustering analysis model to perform density clustering partitioning and extract the abnormal document type codes that exceed a preset proportion threshold. The network address parameter and the abnormal document type code are concatenated and merged according to a preset control protocol format to generate a cache control instruction carrying the abnormal document type code and the network address parameter, which is then transmitted to the scheduling node of the distributed file system. When the scheduling node receives an overload alarm message including the network address parameter, it parses the cache control instruction to obtain the abnormal document type code and increases the read queue priority parameter of the file data bound to the abnormal document type code in the first access medium.

[0016] Beneficial effects of this invention: This invention addresses the acquisition of power archive image data. The image data is input into a pre-defined visual recognition network model for feature analysis and calculation, extracting the power consumption node identifier and issuance timestamp, and simultaneously outputting a candidate character tensor set including character probability distribution parameters and candidate load values. Simultaneously, real-time telemetry data sequences of the distribution network nodes corresponding to the power consumption node identifiers are acquired, and the active power values ​​included in the real-time telemetry data sequences are extracted. When the active power value is determined to be greater than a preset load threshold, the network address parameters of the distribution network node are extracted to generate a storage scheduling instruction, migrating the historical archive data slices associated with the distribution network node from a low-speed read / write first access medium to a high-speed response second access medium. Conventional manual verification methods rely entirely on auditors reading and extracting archive information line by line. Due to the complexity of power archive materials and the lack of a physical verification mechanism for real-time distribution network operation status, manual experience alone cannot quickly locate historical data of nodes with heavy load risks, resulting in severe delays and high omission rates in archive information extraction. The aforementioned steps automatically extract key identifier parameters through a visual recognition network model, establishing a mapping between physical power consumption nodes and archive text information. By combining the active power values ​​extracted from real-time telemetry data sequences to trigger the data hot migration mechanism of hierarchical storage media, historical archive data slices of distribution network nodes in high-load risk state are preloaded in advance, eliminating the physical time delay caused by manual volume-by-volume retrieval, breaking through the bottleneck of business processing progress from the underlying data scheduling level, and realizing agile retrieval of historical ledger data of high-risk nodes.

[0017] The process involves extracting the issuance timestamp and power node identifier, performing data concatenation, generating a retrieval command, inputting it into a preset spatiotemporal topology map, and retrieving and locking historical power grid physical topology sub-graphs recording actual physical connection patterns. Subsequently, candidate load values ​​are extracted from the candidate character tensor set, and these values ​​are substituted into the power flow operation equations within the historical power grid physical topology sub-graph to perform load trial calculations, extracting the over-limit trial calculation residuals exceeding the limit boundaries. These residuals are converted into a mathematical penalty gradient, which is subtracted from the character probability distribution parameters in the candidate character tensor set to obtain an updated probability score. The character sequences are sorted and truncated according to the updated probability scores, and the character sequence corresponding to the highest probability score is output as structured text information. Conventional document review methods only compare the surface logic of the text symbols themselves, detached from the constraints of electrical engineering and physical laws in the real power grid environment. When the document image is damaged, causing ambiguity in optical character recognition, conventional methods cannot identify erroneously extracted abnormally large installed capacity parameters, easily classifying text sequences with logical paradoxes as compliant results. The aforementioned logic control closed loop innovatively introduces a historical physical topology model of the power grid and a power flow calculation mechanism based on Kirchhoff's current law. By converting text recognition results into physical load parameters and inputting them into a real topology subgraph for inversion calculation, it captures the over-limit calculation residuals caused by violations of objective physical laws. These over-limit calculation residuals are then converted into probability scores to penalize gradients and reduce the likelihood of incorrectly identified paths. This feedback correction mechanism integrates objective physical evolution laws as mandatory constraints into the image character recognition algorithm. By introducing cross-domain physical verification calculations, it eliminates statistical blind spots caused by single image pattern recognition, intercepts erroneous text outputs that do not conform to the actual power grid operating state, and solves the problem of human judgment bias caused by long-term fatigue.

[0018] The system extracts the updated structured text information and sends it to the compliance judgment state machine. It then performs text-level comparison verification against pre-stored preset business review specification text, generating a compliance judgment result indicating the review status and writing it to a preset distributed file system. To address the vulnerability of multimodal verification information to tampering, the system extracts a set of block metadata, including real-time telemetry data sequences, over-limit trial calculation residuals, historical power grid physical topology sub-graph version numbers, and compliance judgment results. A hash algorithm is used to calculate the associated task root hash string. This task root hash string, with its issuance timestamp, is appended and stored within the distributed file system. Furthermore, the system continuously monitors the hash storage space sequence and counts the cumulative number of errors associated with distribution network nodes in the compliance judgment results, including error markers. For distribution network nodes with a cumulative error count exceeding a preset threshold, the system extracts the associated historical verification record block data and inputs it into a clustering analysis model for density clustering, separating out abnormal document type codes whose cluster proportion exceeds a preset threshold. The abnormal document type code and network address parameters are merged into a cache control command and sent to the scheduling node, increasing the read queue priority parameter of the file bound to the specific abnormal document type code in the first access medium. This overall business loop, based on compliance verification, uses cryptographic hash calculations to solidify the data traceability defense of the verification results. Combining the distribution patterns of abnormal file types mined by cluster analysis models, the queue scheduling strategy of the distributed underlying storage medium is dynamically adjusted. Access permissions are preemptively increased for high-risk document categories that are prone to triggering violation errors, establishing a data linkage feedback link between the physical power grid operation characteristics and the underlying storage queue response level, achieving adaptive positive optimization of the business system's workflow efficiency based on file verification results. Attached Figure Description

[0019] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on the accompanying drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the compliance verification method for power archive data based on target detection according to the present invention. Detailed Implementation

[0021] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. The various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0022] Please see Figure 1 The present invention provides a method for verifying the compliance of power archive data based on target detection, comprising: Step 1: Receive power archive image data, input the power archive image data into a pre-set visual recognition network model to extract the power consumption node identifier and the issuance timestamp, and output a candidate character tensor set including character probability distribution parameters and candidate load values; Step 2: Obtain the real-time telemetry data sequence of the distribution network node corresponding to the power consumption node identifier, wherein the real-time telemetry data sequence includes active power values; when it is determined that the active power value is greater than a preset load threshold, extract the network address parameters of the distribution network node to generate a storage scheduling instruction, and migrate the historical archive data slice associated with the distribution network node from the first access medium to the second access medium. Step 3: Concatenate the power consumption node identifier with the issuance timestamp, and input the data into the preset spatiotemporal topology map to retrieve the corresponding historical power grid physical topology sub-map; Step 4: Extract the candidate load values ​​included in the candidate character tensor set and substitute them into the historical power grid physical topology subgraph to calculate the power flow and obtain the over-limit trial residual. Step 5: Convert the over-limit trial residual into a penalty gradient, subtract the penalty gradient from the character probability distribution parameters included in the candidate character tensor set to obtain the updated probability score, and extract the character sequence corresponding to the highest probability score in the updated probability score as the structured text information output. Step 6: Compare the structured text information with the preset business review specification text, generate a compliance judgment result, and write it into the preset distributed file system.

[0023] To more clearly demonstrate the core technical logic of this invention, the main process of the method is first described in detail: The business system receives power archive image data, which includes scanned images of low-voltage business expansion capacity confirmation forms, electronic documents of photovoltaic grid connection agreements, and draft user distribution wiring diagrams. The system converts the preset business review specification text into a rule feature vector sequence. The preset business review specification text includes the clauses of the business expansion application process and the load access restriction conditions of the power grid nodes.

[0024] The system extracts visual feature vectors from power archive image data and inputs the regular feature vector sequence and visual feature vectors into the cross-modal attention computation network included in the visual recognition network model. The cross-modal attention computation network performs matrix multiplication on the regular feature vector sequence and visual feature vectors, and outputs an attention heatmap matrix tensor between the regular feature vector sequence and visual feature vectors.

[0025] This document outlines the underlying network architecture and parameter training process of a visual recognition network model. The model primarily consists of a visual feature extraction backbone network, a cross-modal attention computation network, and a recurrent convolutional recognition network. The visual feature extraction backbone network employs a convolutional neural network architecture with multi-layer residual connections. Before input, the pre-defined business review specification text is transformed into a fixed-dimensional sequence of regular feature vectors using a natural language word embedding mapping algorithm. The visual feature extraction backbone network performs multi-scale two-dimensional convolution operations on the power archive image data, outputting visual feature vectors with spatial physical coordinate dimensions. The cross-modal attention computation network internally includes multiple independent and parallel query matrix mapping layers, key matrix mapping layers, and value matrix mapping layers. During the parameter training phase of the visual recognition network model, the business system constructs a historical labeled supervised dataset, including manually verified power archive image samples marked as compliant and those marked as non-compliant. The training computation unit uses the cross-entropy loss function to calculate the error gradient between the predicted classification results output by the visual recognition network model and the actual labeled parameters. The training computation unit updates the neuron weight parameters within the cross-modal attention computation network and the visual feature extraction backbone network layer by layer through the backpropagation algorithm. The visual recognition network model iteratively runs through the backpropagation algorithm until the cross-entropy loss function reaches the lower bound of convergence, thereby completing the model construction and feature parameter solidification in the physical scenario of power archive compliance review.

[0026] The system extracts the coordinates of pixel regions whose values ​​within the attention heatmap matrix tensor exceed a preset attention threshold. These pixel coordinates are then used to crop power archive image data, extracting high-weight image slices. The distributed file system is configured with a replication redundancy parameter for these high-weight image slices. The system modifies the replication redundancy parameter to a preset maximum value and stores the modified high-weight image slice data in a high-speed cache memory address range.

[0027] The business system synchronously extracts the center coordinates of pixel value distribution within the attention heatmap matrix tensor. The center coordinates of the pixel value distribution are defined as weighted centroid coordinates based on attention numerical weights. The arithmetic logic unit performs a weighted average calculation on each coordinate point within the local pixel matrix according to the corresponding attention score. The specific centroid coordinate extraction equation is as follows:

[0028]

[0029] In the formula, The x-coordinate represents the center coordinates of the extracted pixel value distribution. The vertical coordinate represents the center coordinates of the extracted pixel value distribution. This represents the total number of pixel coordinates included within the local pixel matrix. Represents the first pixel in the local pixel matrix The x-coordinate value of each pixel coordinate point Represents the first pixel in the local pixel matrix The y-coordinate value of each pixel coordinate point Represents the first pixel in the local pixel matrix The attention score weight value corresponds to each pixel coordinate point. The microprocessor unit parses the calculation result and locks the center coordinates of the pixel value distribution in the video memory. The business system uses the center coordinates of the pixel value distribution as the reference point in two-dimensional space to generate a coordinate detection box. The cropping range of the local feature region image is determined by the physical boundary of the generated coordinate detection box.

[0030] The system inputs the local feature region image into the recurrent convolutional recognition network model, which performs character decoding calculations to extract the power consumption node identifier and issuance timestamp. The power consumption node identifier includes the distribution substation asset number and the user's meter logical address code, while the issuance timestamp includes the year, month, and day of the official effective date of the business document. The recurrent convolutional recognition network simultaneously outputs a candidate character tensor set containing character probability distribution parameters and candidate load values. The candidate character tensor set is represented as an array of installed power consumption capacity values ​​with different prediction probability weights.

[0031] During the synchronous process of document image parsing, the system acquires real-time telemetry data sequences of the distribution network nodes corresponding to the power consumption node identifiers. These real-time telemetry data sequences are transmitted back at high frequency by the data acquisition and monitoring control system, including active power values, reactive power values, and three-phase node voltage values ​​under the operating environment of the distribution transformer. The active power values ​​are measured and evaluated based on the international standard unit of kW. The system divides the active power values ​​by the rated capacity of the distribution network node to obtain the real-time load rate parameter of the distribution network node.

[0032] When the real-time load rate parameter is determined to be greater than the preset load threshold within a preset continuous sampling period, the system uses the network address parameters of the distribution network nodes as physical network identifiers and combines them to generate an overload alarm tensor with the physical network identifiers. The business system performs a downward traversal within the preset topology association database, starting from the physical network identifier. The downward traversal is defined as a hierarchical search path along the radial distribution network topology from the transformer side to the load side. The address generator reads the index corresponding to the physical network identifier and recursively retrieves the user identity identifier set of all associated child nodes. The storage controller locks the user physical address range affected by the overload to execute subsequent data hot migration scheduling instructions. The user identity identifier set within the power supply range of the distribution network node is composed of the results of the recursive retrieval.

[0033] The system maps user identity sets to object identifiers within the distributed file system. Each object identifier is bound to a data fragment placement strategy parameter. The system modifies the read priority value in the data fragment placement strategy parameter, obtains the modified read priority value and the object identifier, and generates a storage scheduling instruction. The system transmits the storage scheduling instruction to the distributed file system, migrating historical archive data fragments associated with distribution network nodes from the first access medium to the second access medium. The first access medium is represented by a low-frequency response mechanical hard disk storage array, and the second access medium is represented by a high-frequency response solid-state drive memory buffer pool.

[0034] The system combines the power consumption node identifier and the issuance timestamp according to a set bit concatenation format to generate a composite query key-value sequence. This sequence is then input into a spatiotemporal topology map containing power grid time-series evolution data for matching queries. The system extracts the historical power grid physical topology sub-map and the impedance operating parameters of the connected conductors corresponding to the successful matches. The historical power grid physical topology sub-map objectively records the actual physical connection network configuration of the distribution equipment at the time the issuance timestamp occurred. The conductor impedance operating parameters include the DC resistance and AC reactance values ​​of the lines.

[0035] The system substitutes candidate load values ​​into a historical power grid physical topology subgraph containing conductor impedance operating parameters. The floating-point unit retrieves the conductor impedance matrix parameters and uses the candidate load values ​​as the injected current vector. The power flow operating equations are solved using sparse matrix operations to complete the load trial operation calculation. The arithmetic logic unit performs iterative calculations to calculate the node simulation voltage values ​​for each bus node. The specific node power balance equations are as follows:

[0036] In the formula, This represents the injected node power data input from the floating-point arithmetic unit. Representing the Simulated voltage values ​​at each bus node. This represents the total number of bus nodes included in the historical power grid physical topology subgraph. The parameter representing the impedance matrix of the conductor is the first one. Line 1 The conductivity values ​​of the column, Representing the The simulated voltage values ​​of each bus node are calculated. When the feedback conductor resistance is 0.1 ohms and the injected current is 40 amperes, the floating-point unit calculates the voltage drop at the terminal node as 4 volts using a multiply-accumulate unit. The microprocessor unit compares the calculated simulated node voltage value of 216 volts with the preset node voltage limit threshold of 220 volts stored in the register. The arithmetic logic unit executes a subtraction instruction, subtracting the simulated node voltage value from the preset node voltage limit threshold, and extracts an over-limit trial residual of 4 volts. The simulated node voltage value is calculated based on the international standard unit of measurement, V. The above process extracts the parameter exceeding the preset node voltage limit threshold as an over-limit trial residual by calculating the numerical difference, completing the feature verification logic based on the physical evolution law of the power system.

[0037] The system multiplies the over-limit trial residual with a preset rule penalty weight parameter to calculate the negative bias attenuation value as the penalty gradient. The system extracts the character probability distribution parameters carried by the candidate character tensor set as the initial character decoding score data. The negative bias attenuation value is subtracted from the initial character decoding score data to calculate the target decoding path score, which serves as the updated probability score. Incorrectly identified abnormally high load capacity values ​​can cause voltage drops exceeding limits in the power distribution network simulation environment, resulting in increased over-limit trial residuals.

[0038] The system applies a severe penalty to the decoding branch that triggers physical violations, lowering the score parameters of recognition results that do not conform to objective physical evolution laws. The generation process of compliance judgment results is based on hard constraints of the transient power flow physical characteristics of the power system. The compliance judgment step is not simply text semantic matching. By feeding back the residuals of the over-limit trial calculation to the visual feature decoding space, the system achieves cross-modal physical logical alignment. The output of compliance judgment results is controlled by the underlying physical computing power, eliminating the risk of network topology distortion caused by falsified installed parameters.

[0039] The system reorders the characters in the candidate character tensor set according to their descending order of target decoding path scores. The system then extracts the character sequence at the first position after reordering as the character sequence corresponding to the highest probability score, and outputs this sequence as structured text information.

[0040] The system locates the electrical wiring image area within the power archive image data, inputs the electrical wiring image area into a preset convolutional network model to extract the electrical symbol node features and connecting line segment edge features.

[0041] The convolutional network model is built upon a fast convolutional neural network architecture based on region proposal. Internally, the model includes a feature pyramid feature extraction layer, a candidate region generation network, and a region of interest (ROI) pooling layer. The business system inputs the electrical wiring image region into the feature pyramid feature extraction layer, which performs bottom-up forward convolutional mapping and top-down lateral connections to fuse the output, resulting in a multi-scale electrical visual feature map. The candidate region generation network slides across the multi-scale electrical visual feature map to generate anchor boxes, outputting a matrix of candidate bounding boxes including electrical symbols and connecting line segments. The ROI pooling layer maps the candidate bounding box matrix into a fixed-dimensional region feature tensor. The classification and regression fully connected layer performs classification mapping calculations on the region feature tensor, ultimately outputting the electrical symbol node features and connecting line segment edge features.

[0042] Electrical symbol node features include circuit breaker symbols, instrument transformer symbols, and transformer graphic features. Connecting line segment edge features include the routing characteristics of connecting wires between various devices. The system uses the connecting line segment edge features to perform adjacency matrix association mapping calculations on the electrical symbol node features, and splices them to generate a local topology map within the file.

[0043] The system extracts entity correspondences from structured text information and generates a text logical topology graph based on these correspondences. The system simultaneously inputs the local topology graph within the archive, the text logical topology graph, and the historical power grid physical topology subgraph into a pre-defined graph isomorphism computing network, performs graph feature aggregation and dimensionality reduction calculations, and outputs three sets of corresponding graph-level representation vectors.

[0044] The graph isomorphic computing network is constructed using a deep graph convolutional neural network architecture. Before inputting the model, the local topology graph within the archive, the textual logical topology graph, and the historical power grid physical topology subgraph are formatted into graph data mapping structures composed of node feature matrices and edge adjacency matrices. The graph isomorphic computing network internally employs vertically cascaded multi-layer graph convolutional feature extraction hidden layers. Utilizing the connectivity relationships recorded by the edge adjacency matrices, it performs aggregation operations on the node feature matrices along the topological edge paths. These aggregation operations apply linear weight matrix transformations and nonlinear activation function mappings to the input feature parameters of adjacent nodes, thereby completing the transfer of global spatial information within the topology. After performing multiple local neighborhood aggregation mapping operations, the graph isomorphic computing network, through a globally readout pooling layer physically connected to the output terminal, performs mean pooling dimensionality reduction mapping calculations on the hidden high-dimensional representations of all nodes in the entire graph. The globally readout pooling layer uniformly converts the graph data mapping structure with irregular node sizes into three sets of graph-level representation vectors with preset fixed dimensions. Graph isomorphic computing networks transform discrete physical topology states of power networks and logical topology states of business texts into underlying mathematical representations within a low-dimensional continuous vector space, ultimately realizing an inherent one-to-one mapping relationship between cross-modal graph structure data in compliance verification business scenarios.

[0045] The system performs dot product calculations on each pair of the three sets of graph-level representation vectors to obtain cosine similarity parameters. When it is determined that any cosine similarity parameter is lower than a preset isomorphism threshold, the system generates a structural conflict warning flag and incorporates the structural conflict warning flag into the compliance determination result.

[0046] The system extracts the signature sub-image from the power archive image data and inputs it into the preset anti-counterfeiting network model, then extracts the classification confidence parameters output by the anti-counterfeiting network model.

[0047] The counterfeit detection network model is constructed using a deep metric learning network with a Siamese network architecture. The model comprises two deep residual feature extraction branches sharing weight parameters and a contrastive loss calculation metric layer. The business system inputs the signature sub-image and historical seal image slices into the two independent deep residual feature extraction branches. These branches utilize multi-layered two-dimensional convolutional kernels with skip connections to perform spatial texture mapping calculations, extracting the first and second feature tensors mapped into a high-dimensional Euclidean space. The contrastive loss calculation metric layer calculates the feature spatial distance distribution between the first and second feature tensors, and outputs a classification confidence parameter with a value between zero and one via a fully connected classifier.

[0048] When the classification confidence parameter is lower than the preset authenticity verification threshold, the system extracts the first feature tensor output by the hidden layer of the authentication network model as the current latent variable to be verified.

[0049] The system uses electricity node identifiers to query and retrieve historical seal image slice data within a distributed file system for the historical archive period. The system inputs this historical seal image slice data into a counterfeit detection network model to extract the second feature tensor output from the hidden layer. The system performs mean pooling dimensionality reduction on the extracted second feature tensors to calculate the baseline comparison latent variable. The system then performs matrix concatenation between the current latent variable to be verified and the baseline comparison latent variable according to the data channel dimension, generating fused tensor data. The system inputs this fused tensor data into a pre-defined multilayer perceptron to perform classification boundary function mapping calculations, outputting a classification judgment result indicating the authenticity of the seal sub-image, and then combines the classification judgment results into the compliance judgment result.

[0050] The multilayer perceptron (MLP) is a feedforward fully connected neural network, comprising an input layer, more than two fully connected hidden layers, and a classification output layer at the physical logic level. After the fused tensor data is input to the MLP's input layer, the computation nodes within the fully connected hidden layers perform inner product operations on the local physical texture features included in the fused tensor data and the global baseline seal comparison features. Simultaneously, the fully connected hidden layers introduce a linear rectified nonlinear activation function for activation mapping, thereby achieving a high-dimensional nonlinear cross-combination of the latent variable to be tested and the baseline comparison latent variable in the deep feature space. Subsequently, the classification output layer, deployed with nodes containing normalized exponential function nodes, receives the highest-dimensional feature data stream output by the fully connected hidden layers and compresses and maps this high-dimensional feature data stream into probability judgment values ​​distributed between zero and one. Finally, the business system constructs a multi-dimensional closed-loop mechanism for physical seal authenticity verification data flow based on the probability judgment values ​​and the classification confidence parameters output by the authentication network model, effectively reducing the false negative rate of seal forgery caused by relying solely on shallow image pixel comparison.

[0051] The system compares structured text information with preset business review specification text, generates a compliance judgment result, and writes it into a preset distributed file system. The system extracts real-time telemetry data sequences, over-limit trial residuals, historical power grid physical topology sub-graph version information, and the compliance judgment result, which are then concatenated into a block metadata set. The system extracts text information from the block metadata set as leaf nodes of a hash binary tree, performs hash algorithm calculations according to a hierarchical order, and aggregates the task root hash string associated with the compliance judgment result. The system stores the task root hash string with the issuance timestamp and the compliance judgment result in a hash storage space sequence within the distributed file system via the append-write interface configured in the distributed file system.

[0052] The business system reads compliance judgment results with error markers and corresponding historical verification record blocks from the hash storage space sequence at preset time intervals. The system counts the cumulative number of errors for the distribution network nodes to which the read compliance judgment results belong, and extracts the network address parameters of distribution network nodes whose cumulative error count exceeds a preset threshold. The system inputs the historical verification record blocks associated with the network address parameters into a preset clustering analysis model to perform density clustering, extracting abnormal document type codes for those whose cluster proportion exceeds a preset ratio threshold. These abnormal document type codes correspond to either photovoltaic equipment access protocol classification codes or high-voltage power supply contract document classification codes.

[0053] The clustering analysis model is built upon a density-based noise-based spatial clustering algorithm. The business system transforms historical verification record blocks, including network address parameters, into a multi-dimensional data scatter feature set and inputs it into the clustering analysis model. The model calculates the Euclidean distance between each multi-dimensional data scatter point based on a set neighborhood search radius. It then counts the number of adjacent data scatter points within the neighborhood search radius. When the number of adjacent data scatter points exceeds a set threshold for the number of core points, the model marks the corresponding data scatter point as a core cluster node and continuously expands outward along density-reachable paths, merging high-density connected data regions. Finally, it outputs an abnormal document type code indicating that the proportion of clusters exceeds a set range.

[0054] The system concatenates the network address parameter and the abnormal document type code according to a preset control protocol format to generate a cache control instruction carrying the abnormal document type code and network address parameter, which is then transmitted to the scheduling node of the distributed file system. When the scheduling node receives a reload alarm message including the network address parameter, the system parses the cache control instruction to obtain the abnormal document type code and increases the read queue priority parameter of the file data bound to the abnormal document type code in the first access medium.

[0055] The business system configures a preset attention threshold in the feature parsing pipeline, setting the specific value of the preset attention threshold to 0.85. When the calculated value at the pixel coordinates within the attention heatmap matrix tensor is greater than 0.85, the business system extracts the corresponding pixel region coordinates as high-weight image slice data. The distributed file system also has a preset maximum quantity limit, set to 5. The business system forcibly modifies the redundancy parameter of the high-weight image slice data within the distributed file system to a value of 5, improving the parallel read throughput bandwidth of the high-weight image slice data. In the seal anti-counterfeiting verification stage, the business system configures a preset authenticity verification threshold, setting the specific value of the preset authenticity verification threshold to 0.90. When the classification confidence parameter output by the anti-counterfeiting network model is lower than 0.90, the business system determines that the seal sub-image has a forgery risk and triggers a deep feature tensor comparison extraction instruction.

[0056] The business system configures a preset error threshold during the status monitoring process, setting the specific value of the preset error threshold to 3. When the business system calculates that the cumulative number of error reports for a specific distribution network node exceeds 3, it triggers a density-based clustering analysis task. After performing density partitioning, the clustering analysis model outputs various abnormal data clusters. The business system simultaneously configures a preset proportion threshold in the clustering analysis pipeline, setting the specific value of the preset proportion threshold to 0.60. When the clustering analysis model determines that the proportion of the number of clusters corresponding to a certain abnormal document type exceeds 0.60 of the total number of error reports, the business system determines that the specific abnormal document type has a risk of structured batch errors and sends a targeted cache control instruction to the underlying storage scheduling node.

[0057] To further fully disclose the underlying computational rules of this invention, the following supplementary explanation of the key data processing path in the above steps, combined with specific mathematical derivation equations and numerical verification processes, is provided: The system extracts visual feature vectors from power archive image data and inputs the regular feature vector sequence and the visual feature vector into the cross-modal attention computation network included in the visual recognition network model. The tensor processing unit inside the cross-modal attention computation network performs matrix multiplication calculations on the regular feature vector sequence and the visual feature vector, outputting an attention heatmap matrix tensor between the regular feature vector sequence and the visual feature vector. The specific matrix multiplication calculation depends on the underlying hardware computation logic, and the corresponding calculation equation is:

[0058] In the formula, The attention heatmap matrix tensor represents the output of the cross-modal attention computation network. The sequence of regular feature vectors generated by the system transformation. This represents the visual feature vector matrix after mathematical transpose. Within one computation cycle executed by the tensor processing unit, if the extracted regular feature vector sequence is... The visual feature vector matrix after mathematical transpose is: The tensor processing unit calculates the numerical values ​​through a multiply-accumulate hardware path. The value 0.68 is used as the calculation result for the corresponding pixel coordinate position in the attention heatmap matrix tensor. The system extracts the pixel region coordinates within the attention heatmap matrix tensor where the value is greater than the preset attention threshold. These pixel region coordinates are then used to crop the power archive image data, extracting high-weight image slices. During the synchronous process of document image parsing, the system acquires the real-time telemetry data sequence of the distribution network node corresponding to the power consumption node identifier. This real-time telemetry data sequence is transmitted back at high frequency by the data acquisition and monitoring control system, including active power values, reactive power values, and three-phase node voltage values ​​under the operating environment of the distribution transformer. The active power value is measured and evaluated based on the international standard unit of kW. The system's internal arithmetic logic unit divides the active power value by the rated capacity value of the distribution network node to obtain the real-time load rate parameter of the distribution network node. The underlying hardware calculation equation is:

[0059] In the formula, The real-time load rate parameter represents the output of the arithmetic logic unit. This represents the active power values ​​included in the real-time telemetry data sequence. This represents the rated capacity of the distribution network node. After calculating the real-time load rate parameter, the microprocessor unit inputs the real-time load rate parameter into a hardware comparator and compares it with a preset load threshold stored in a register. For example, if the active power value returned by the data acquisition and monitoring control system is 800kW, and the rated capacity value of the distribution network node retrieved by the storage controller is 1000kVA, the arithmetic logic unit executes a division instruction to calculate the real-time load rate parameter as 0.80. The preset load threshold is set to 0.75. The hardware comparator determines that the value 0.80 is greater than the value 0.75, triggering a state machine transition. When the real-time load rate parameter is determined to be greater than the preset load threshold within a preset continuous sampling period, the system uses the network address parameter of the distribution network node as the physical network identifier and combines it to generate an overload alarm tensor with the physical network identifier.

[0060] The system substitutes candidate load values ​​into the power flow equations included in the historical power grid physical topology subgraph containing conductor impedance operating parameters to perform load trial operation calculations, outputting verification calculation results including simulated node voltage values. The power flow equations follow Kirchhoff's current law, extrapolating the theoretical operating voltage state of each physical node in the distribution substation based on the input predicted installed capacity. Simulated node voltage values ​​are calculated using the international standard unit of measurement, V. The floating-point unit calculates the numerical difference between the simulated node voltage value and the preset node voltage limit threshold, extracting the numerical difference exceeding the preset node voltage limit threshold as an over-limit trial calculation residual. The corresponding over-limit trial calculation residual extraction equation is:

[0061] In the formula, This represents the over-limit trial residual extracted by the floating-point arithmetic unit. This represents the preset node voltage limit threshold. This represents the node simulated voltage value output from the load test calculation. A preset node voltage limit threshold is set to 209V. The node simulated voltage value obtained from the load test calculation using the power flow operation equations is 205V. The floating-point unit subtracts 205V from 209V, resulting in a difference of 4V. The system extracts this 4V difference as the over-limit calculation residual and transmits it to the downstream calculation pipeline. The system inputs the over-limit calculation residual and the preset rule penalty weight parameters into the multiplier to perform a product calculation, calculating the negative deviation decay value as the penalty gradient. The corresponding penalty gradient mapping equation is:

[0062] In the formula, This represents the negative bias attenuation value calculated by the multiplier. This represents the excess trial residual from the input of the pre-module. This represents the preset rule penalty weight parameters stored in memory. The system extracts the character probability distribution parameters carried by the candidate character tensor set as the initial character decoding score data. The subtraction logic unit subtracts the negative bias decay value from the initial character decoding score data to calculate the target decoding path score, which is the updated probability score. The corresponding target decoding path score correction equation is:

[0063] In the formula, The target decoding path score represents the output of the subtraction logic unit. This represents the initial character decoding score data extracted by the system. This represents the negative bias attenuation value calculated by the multiplier. Based on the data from the aforementioned embodiment, the over-limit trial residual output by the floating-point unit is 4, and the preset rule penalty weight parameter is 0.05. The multiplier executes the product instruction to obtain a negative bias attenuation value of 0.20. The initial character decoding score data carried by the candidate character tensor set is 0.95. The subtraction logic unit subtracts 0.20 from 0.95 to obtain a target decoding path score of 0.75. The system applies a heavy penalty attenuation value to the decoding branch that causes physical violations, lowering the recognition result score parameter that does not conform to the objective physical evolution law. The system synchronously inputs the local topology graph within the file, the text logical topology graph, and the historical power grid physical topology subgraph into a preset graph isomorphic computing network, performs graph feature aggregation and dimensionality reduction calculations, and outputs the corresponding three sets of graph-level representation vectors. The system calls the graphics processing unit to perform dot product calculations between each pair of the three sets of graph-level representation vectors to obtain the cosine similarity parameter. The vector space association matching equation is:

[0064] In the formula, The cosine similarity parameter represents the output of the graphics processing unit. This represents the first set of graph-level representation vectors that are compared among the three sets of graph-level representation vectors. This represents the second set of graph-level representation vectors that are compared among the three sets of graph-level representation vectors. The vector magnitude representing the first group of graph-level vectors. This represents the magnitude of the vector representing the second group of graph-level representation vectors. Let the first group of graph-level representation vectors be... The corresponding vector magnitude is 0.5; the second set of graph-level representation vectors is set as follows. The corresponding vector magnitude is 1.0. The graphics processing unit calculates the dot product, yielding a value of 0.50. Dividing this dot product value by the magnitude product 0.50 gives a cosine similarity parameter of 1.0. The multi-channel comparator hardware compares this cosine similarity parameter with a preset isomorphism threshold. The preset isomorphism threshold is set to 0.85. If the multi-channel comparator hardware determines that the value 1.0 is greater than 0.85, no alarm signal is generated. When any cosine similarity parameter is determined to be below the preset isomorphism threshold, the system generates a structural conflict warning flag, which is then incorporated into the compliance determination result.

[0065] As a further extension of the technical solution of this invention, the following is a specific embodiment focusing on the collaborative verification of instructions executed by the underlying hardware system: A high-speed optical scanning peripheral captures power archive image data according to a preset sampling clock cycle. After responding to the hardware interrupt request signal of the peripheral, the direct memory access controller continuously writes discrete pixel data into the designated physical address space of the dynamic random access memory through a 64-bit wide system high-speed bus. The visual recognition network model is pre-frozen in the on-chip static random access memory of the tensor processing unit; when the main state machine of the microprocessor unit jumps to the inference instruction cycle, the tensor processing unit prefetches the pixel array from the physical address space and loads it into the internal register set. Subsequently, the multiply-accumulate hardware array inside the tensor processing unit performs parallel matrix multiplication operations and outputs a data frame carrying the power node identifier and issuance timestamp to the bus. Synchronously, the hardware logic unit writes the candidate character tensor set, including character probability distribution parameters and candidate load values, into the external high-speed cache memory.

[0066] The industrial Ethernet interface module receives real-time telemetry data sequences from the power distribution network nodes corresponding to the identified power nodes. The network controller parses the active power values ​​included in the real-time telemetry data sequences and stores them in a 64-bit data register. The arithmetic logic unit of the microprocessor retrieves the active power values ​​and executes a division micro-instruction to calculate the real-time load rate parameter of the distribution network node. Next, the microprocessor sends the real-time load rate parameter to a multi-stage comparator hardware array and compares it with a preset load threshold stored in read-only memory. When the comparison logic gate outputs a high-level valid signal representing a greater than threshold, the hardware interrupt controller triggers the system exception handling program. The microprocessor extracts the network address parameters of the distribution network node and sends them to the storage controller. The storage controller generates bus-level storage scheduling instructions based on the network address parameters. The serial connection system interface controller responds to the storage scheduling instructions and takes over the underlying physical disk drive, directly and physically moving the historical archive data slices associated with the identified distribution network node from the low-speed track sector of the first access medium to the high-frequency solid-state flash memory address segment of the second access medium.

[0067] After the memory management unit extracts the power node identifier and issuance timestamp, the data concatenation logic gates perform binary concatenation according to the set bit width rules and output a physical addressing pointer. The memory controller uses the physical addressing pointer to match and address the corresponding continuous memory blocks in the preset spatiotemporal topology map, locking the base address of the historical power grid physical topology sub-graph for the microprocessor unit to read the topology matrix and conductor impedance operating parameters. Subsequently, the floating-point coprocessor takes over the candidate load values ​​included in the candidate character tensor set, calls the internal hardware multiplier and adder network, and substitutes the candidate load values ​​into the historical power grid physical topology sub-graph to run the hardware-level power flow calculation instruction sequence. The floating-point coprocessor outputs the node analog voltage value and stores it in the floating-point result register; the arithmetic logic unit executes the subtraction microinstruction, subtracts the node analog voltage value from the preset node voltage limit threshold, and extracts the over-limit trial calculation residual by truncating the over-limit data bits.

[0068] The arithmetic logic unit inputs the over-limit trial residuals into a fixed-point multiplier, which converts the residuals into a numerical penalty gradient based on an external clock signal. A hardware subtractor array extracts the character probability distribution parameters from the candidate character tensor set in the external cache memory and performs parallel subtraction operations to subtract the penalty gradient. The hardware subtractor array outputs the updated probability scores and refreshes the cache block. A multi-path parallel sorting network hardware receives all updated probability scores and, through internal comparison and exchange logic, pushes the character sequence corresponding to the highest probability score to the top of the output queue. The microprocessor unit extracts the character sequence at the top of the output queue as structured text information and outputs it to the system main memory via the external bus.

[0069] After the business comparison state machine is activated, the microprocessor unit loads the structured text information and the pre-stored preset business review specification text into the matching register. The XOR logic gate compares the consistency state of the character encoding byte by byte, and the state machine generates a compliance judgment result data packet based on the comparison level result. The network interface controller performs network layer protocol encapsulation on the compliance judgment result data packet and, through the remote direct memory access protocol, directly bypasses the operating system kernel and writes the protocol-encapsulated compliance judgment result data stream to the physical storage node cluster corresponding to the preset distributed file system, ultimately completing the data closure and state locking of the underlying hardware link.

Claims

1. A method for verifying the compliance of power sector archive data based on target detection, characterized in that, include: Receive power archive image data, input the power archive image data into a pre-set visual recognition network model to extract the power consumption node identifier and the issuance timestamp, and output a candidate character tensor set including character probability distribution parameters and candidate load values; Obtain the real-time telemetry data sequence of the distribution network node corresponding to the power consumption node identifier, wherein the real-time telemetry data sequence includes active power values; When it is determined that the active power value is greater than the preset load threshold, the network address parameters of the distribution network node are extracted to generate a storage scheduling instruction, and the historical archive data slice associated with the distribution network node is migrated from the first access medium to the second access medium. The power consumption node identifier and the issuance timestamp are concatenated and input into a preset spatiotemporal topology map to retrieve the corresponding historical power grid physical topology sub-graph. Extract the candidate load values ​​included in the candidate character tensor set and substitute them into the historical power grid physical topology subgraph to calculate the power flow and obtain the over-limit trial residual; The excess trial residual is converted into a penalty gradient. The penalty gradient is subtracted from the character probability distribution parameters included in the candidate character tensor set to obtain the updated probability score. The character sequence corresponding to the highest probability score in the updated probability score is extracted as the structured text information output. The structured text information is compared with the preset business review specification text, and the resulting compliance judgment is written into the preset distributed file system.

2. The method for verifying the compliance of power archive data based on target detection according to claim 1, characterized in that, The step of obtaining the real-time telemetry data sequence of the distribution network node corresponding to the power consumption node identifier, wherein the real-time telemetry data sequence includes active power values; when it is determined that the active power value is greater than a preset load threshold, extracting the network address parameters of the distribution network node to generate a storage scheduling instruction, and migrating the historical archive data slice associated with the distribution network node from the first access medium to the second access medium, includes: Divide the active power value by the rated capacity value of the distribution network node to obtain the real-time load rate parameter of the distribution network node. When it is determined that the real-time load rate parameter is greater than the preset load threshold within a preset continuous sampling period, the network address parameter of the distribution network node is used as the physical network identifier, and an overload alarm tensor with the physical network identifier is generated. Within the preset topology association database, a downward traversal is performed starting from the physical network identifier to extract the set of user identity identifiers located within the power supply range of the distribution network node; The user identity identifier set is mapped to an object identifier within the distributed file system, wherein the object identifier is bound to a data sharding placement strategy parameter; Modify the read priority value in the data fragment placement strategy parameters, obtain the modified read priority value and the object identifier to generate the storage scheduling instruction, transmit it to the distributed file system, and migrate the historical archive data fragment from the first access medium to the second access medium.

3. The method for verifying the compliance of power archive data based on target detection according to claim 2, characterized in that, The process involves receiving power archive image data, inputting the image data into a pre-set visual recognition network model to extract power consumption node identifiers and issuance timestamps, and outputting a candidate character tensor set including character probability distribution parameters and candidate load values, including: Convert the preset business review specification text into a rule feature vector sequence; Visual feature vectors are extracted from the power archive image data, and the sequence of regular feature vectors and the visual feature vectors are input into the cross-modal attention computing network included in the visual recognition network model; Perform matrix multiplication on the regular feature vector sequence and the visual feature vector, and output the attention heatmap matrix tensor between the regular feature vector sequence and the visual feature vector; Extract the center coordinates of pixel value distribution within the attention heatmap matrix tensor, generate a coordinate detection box based on the center coordinates of pixel value distribution, and extract local feature region images from the power archive image data; The local feature region image is input into the recurrent convolutional recognition network included in the visual recognition network model to perform character decoding calculation, extract the electricity node identifier and the issuance timestamp, and output the candidate character tensor set carrying the character probability distribution parameters and the candidate load value.

4. The method for verifying the compliance of power archive data based on target detection according to claim 3, characterized in that, After performing matrix multiplication on the regular feature vector sequence and the visual feature vector, and outputting the attention heatmap matrix tensor between the regular feature vector sequence and the visual feature vector, the process includes: Extract the coordinates of pixel regions whose values ​​are greater than a preset attention threshold within the attention heatmap matrix tensor; The power archive image data is cropped using the pixel region coordinates to extract high-weight image slice data, wherein the distributed file system is configured with a copy redundancy parameter for the high-weight image slice data; Modify the replica redundancy parameter to a preset maximum number limit value; The high-weight image slice data, after modifying the copy redundancy parameter, is stored in a high-speed cache memory address segment.

5. The method for verifying the compliance of power archive data based on target detection according to claim 4, characterized in that, The process involves concatenating the power consumption node identifier with the issuance timestamp and inputting it into a preset spatiotemporal topology map to retrieve the corresponding historical power grid physical topology subgraph; extracting the candidate load values ​​included in the candidate character tensor set and substituting them into the historical power grid physical topology subgraph for power flow calculation to obtain the over-limit trial residual, including: The issuance timestamp and the electricity consumption node identifier are combined according to a set number of bits to generate a composite query key-value sequence; The composite query key-value sequence is input into the spatiotemporal topology map that records the power grid time-series evolution data to perform a matching query, and the historical power grid physical topology sub-map and the impedance operating parameters of the attached conductors corresponding to the successful match are extracted. The candidate load values ​​are substituted into the power flow operation equations included in the historical power grid physical topology subgraph containing the conductor impedance operation parameters to perform load trial operation calculations, and the output includes verification calculation results including node simulated voltage values. Calculate the numerical difference between the simulated node voltage value and the preset node voltage limit threshold, and extract the numerical difference exceeding the preset node voltage limit threshold as the over-limit trial residual.

6. The method for verifying the compliance of power archive data based on target detection according to claim 5, characterized in that, The process of converting the out-of-limit trial residual into a penalty gradient, subtracting the penalty gradient from the character probability distribution parameters included in the candidate character tensor set to obtain an updated probability score, and extracting the character sequence corresponding to the highest probability score from the updated probability scores as structured text information output includes: The product of the over-limit trial residual and the preset rule penalty weight parameter is performed to calculate the negative deviation decay value as the penalty gradient; Extract the character probability distribution parameters carried by the candidate character tensor set as the initial character decoding score data, subtract the negative deviation attenuation value from the initial character decoding score data, and calculate the target decoding path score as the updated probability score; Based on the descending order of the target decoding path scores, the characters in the candidate character tensor set are reordered and rearranged according to their positions. The character sequence that is first and second after recombination and sorting is taken as the character sequence corresponding to the highest probability score, and the character sequence corresponding to the highest probability score is extracted as the structured text information output.

7. The method for verifying the compliance of power archive data based on target detection according to claim 6, characterized in that, Before comparing the structured text information with the preset business review specification text and generating a compliance determination result which is then written into the preset distributed file system, the method further includes: Locate the electrical wiring image region within the power archive image data, and input the electrical wiring image region into a preset convolutional network model to extract electrical symbol node features and connecting line segment edge features; The adjacency matrix association mapping calculation is performed on the electrical symbol node features using the edge features of the connecting line segments, and the resulting graph is spliced ​​together to generate a local topology map within the archive. Extract the entity correspondences recorded in the structured text information, and generate a text logic topology graph based on the entity correspondences; The local topology graph inside the archive, the text logical topology graph, and the historical power grid physical topology subgraph are synchronously input into a preset graph isomorphic computing network, and graph feature aggregation and dimensionality reduction calculations are performed to output the corresponding three sets of graph-level representation vectors respectively. Perform dot product calculations on each pair of the three sets of graph-level representation vectors to obtain the cosine similarity numerical parameters; When it is determined that any of the cosine similarity parameters is lower than the preset isomorphism threshold, a structural conflict warning mark is generated, and the structural conflict warning mark is combined into the compliance determination result.

8. The method for verifying the compliance of power archive data based on target detection according to claim 7, characterized in that, After receiving power archive image data, inputting the power archive image data into a pre-set visual recognition network model to extract the power consumption node identifier and issuance timestamp, and outputting a candidate character tensor set including character probability distribution parameters and candidate load values, the process includes: The signature sub-image is extracted from the power archive image data and input into the preset anti-counterfeiting network model. The classification confidence parameters output by the anti-counterfeiting network model are then extracted. When the classification confidence parameter is determined to be lower than the preset authenticity verification threshold, the first feature tensor output by the hidden layer of the authentication network model is extracted as the current latent variable to be verified. The power consumption node identifier is used to query and extract historical seal image slice data within the historical archive time period in the distributed file system. The historical seal image slice data is input into the anti-counterfeiting network model, the second feature tensor output by the hidden layer is extracted, and mean pooling dimensionality reduction calculation is performed on the extracted second feature tensors to calculate the benchmark comparison latent variable. The current latent variable to be tested and the benchmark latent variable are matrix-concatenated according to the data channel dimension to generate fused tensor data; The fused tensor data is input into a preset multilayer perceptron to perform classification boundary function mapping calculation, outputting a classification judgment result indicating the authenticity attribute of the signature sub-image, and the classification judgment result is combined and written into the compliance judgment result.

9. The method for verifying the compliance of power archive data based on target detection according to claim 8, characterized in that, The step of comparing the structured text information with a preset business review specification text and generating a compliance determination result, which is then written into a preset distributed file system, includes: Extract the real-time telemetry data sequence, the over-limit trial calculation residual, the map version number information of the historical power grid physical topology sub-graph, and the compliance judgment result that are attached to the process of generating the compliance judgment result, and splice them together to form a block metadata set; The text information in the block metadata set is extracted as the leaf node of the hash binary tree. The hash algorithm is calculated according to the hierarchical order of ascending level, and the task root hash string associated with the compliance judgment result is generated. The task root hash string with the issuance timestamp and the compliance determination result are stored in the hash storage space sequence within the distributed file system via the append write interface configured in the distributed file system.

10. The method for verifying the compliance of power archive data based on target detection according to claim 9, characterized in that, Also includes: According to a preset time interval, the compliance judgment result with error flags and the corresponding historical verification record block data are read from the hash storage space sequence. The cumulative number of errors reported by the distribution network node to which the compliance judgment result belongs is counted, and the network address parameters of the distribution network node whose cumulative number of errors exceeds a preset threshold are extracted. The historical verification record block data associated with the network address parameters is input into a preset clustering analysis model to perform density clustering partitioning and extract the abnormal document type codes that exceed a preset proportion threshold. The network address parameter and the abnormal document type code are concatenated and merged according to a preset control protocol format to generate a cache control instruction carrying the abnormal document type code and the network address parameter, which is then transmitted to the scheduling node of the distributed file system. When the scheduling node receives an overload alarm message including the network address parameter, it parses the cache control instruction to obtain the abnormal document type code and increases the read queue priority parameter of the file data bound to the abnormal document type code in the first access medium.

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