50 results about "Weighted network" patented technology

The invention discloses a multipath channel parameter reconstruction and beam coverage prediction method, and relates to the technical field of wireless communication. Aiming at the problems of low data utilization efficiency, limited environment modeling precision, strong coupling of measured data and antenna beam configuration and the like in the prior art, the method comprises the steps of configuring antenna beams and orientation parameters, collecting reference signal receiving strength, performing deterministic channel modeling simulation, establishing a mapping model, performing sparse recovery, constructing a channel propagation model, evaluating a channel map and the like. In combination with a coherence perception weighting network (CARE-Net) algorithm and ray tracing (RT) physical prior guidance, accurate reconstruction from a low-dimensional reference signal receiving strength (RSRP) measurement value to a high-dimensional multipath parameter is realized. The method has the advantages that the reconstructed multi-path parameters are decoupled from the antenna configuration, the method has the cross-beam / cross-configuration generalization prediction capability, the channel coverage performance under different antenna configurations can be accurately predicted, a reliable basis is provided for wireless network optimization, and the method is low in cost, high in precision and strong in generalization.

The invention discloses a lightweight optical fiber vibration intrusion event identification method and system for perimeter security and protection, and belongs to the technical field of optical fiber sensing technology and mode identification, and the method comprises the steps: collecting an original vibration signal of a perimeter monitoring region through a distributed optical fiber vibration sensing system; performing wavelet threshold de-noising preprocessing on the original vibration signal to obtain a de-noised signal; performing feature extraction on the denoised signal, and constructing a high-dimensional feature vector; performing dimension reduction processing on the high-dimensional feature vector by using a linear discriminant analysis method to obtain a low-dimensional classification feature vector; and inputting the low-dimensional classification feature vector into a pre-trained lightweight convolutional neural network model for classification and identification, and outputting a corresponding intrusion event category. According to the method, through cooperation of front-end LDA dimension reduction and a rear-end lightweight network, the model parameter quantity and calculation overhead are greatly reduced while high recognition precision is guaranteed, efficient real-time deployment on edge equipment is achieved, and the method is suitable for intrusion detection in perimeter security and protection.

The invention provides a product full production cycle carbon footprint tracing evaluation method and system, and relates to the technical field of carbon footprint tracing, and the method comprises the steps: obtaining production activity data of each link of a product full life cycle, constructing a directed weighted network model, and executing path traversal, extracting a traversal path of which the path carbon intensity meets a preset path carbon intensity condition as a first carbon flow path, obtaining the node net carbon emission of each node, and determining the apportionment amount of each node to the third-range indirect emission; and matching the production activity data of the link corresponding to each node with the emission parameters in the carbon emission database to obtain the link carbon emission intensity of each link, and determining the link carbon emission of each link based on the link carbon emission intensity and the node net carbon emission. According to the method and the device, a small-flow high-carbon emission intensity path can be identified, and the range three-emission accounting accuracy is improved to the level of tracing to a specific supply chain node through a double-layer mapping relation of link level microscopic accounting and node level macroscopic tracing.

The embodiment of the invention provides an external risk factor cause quantitative analysis method and system, and belongs to the technical field of risk assessment. The method comprises the steps that historical accident information is collected, accident cause analysis is performed on the historical accident information, and an external risk factor set is constructed and obtained; obtaining a corresponding relation matrix; constructing a directed weighted network based on the external risk factor set and the relation matrix, and constructing an evaluation system corresponding to the directed weighted network; constructing a corresponding external factor cause quantitative evaluation model; and evaluating the current operation state information of the target chemical enterprise based on the external factor cause quantitative evaluation model to obtain a safety risk evaluation result. According to the scheme, the problem that the external risk factor causes cannot be quantified in the safety risk assessment process of the chemical production process at present is solved, and the accuracy of the safety risk assessment result of the chemical production process is improved.

This application discloses a low-altitude airspace planning method and system based on micro-terminal areas, relating to the field of low-altitude airspace planning. The method includes: generating corresponding take-off and landing areas for low-altitude take-off and landing points or fields, and generating take-off and landing hub areas for adjacent take-off and landing points or fields to construct micro-terminal areas of the low-altitude airspace; constructing risk areas, which, together with control areas and no-fly zones, constitute the restricted areas of the low-altitude flight path network; generating alternative intermediate points connecting each take-off and landing point or field based on the restricted areas; constructing an initial low-altitude flight path network connecting all points based on the data of the alternative intermediate points and the data of the micro-terminal areas; calculating the corresponding weights of each edge line in the initial low-altitude flight path network, and optimizing the initial low-altitude flight path network based on the weighted network; and removing redundant intermediate points based on the optimized network, constructing the flight path network and the entry and exit points of each micro-terminal area to generate the final low-altitude flight path network. This application provides a more reliable technical basis for low-altitude flight path network planning.

The invention discloses a material creep behavior prediction method and device based on a physical mechanism weighted network, and aims to solve the problems of low prediction precision and poor physical interpretability of an existing data-driven model under a small sample condition. The method comprises the following steps: constructing a physical mechanism weighting network which comprises a plurality of weighting mechanism units; each weighting mechanism unit is formed by coupling a weighting layer and a physical mechanism layer, the physical mechanism layer is packaged with a preset creep physical mechanism model, and the weighting layer is configured to represent a competition and conversion relationship of different physical mechanisms under an external load condition through a learnable weighting function; training the physical mechanism weighting network by using creep experiment data of a material so as to determine parameters of the weighting function; and predicting the creep behavior of the target material under a given stress-temperature condition by using the trained physical mechanism weighting network. According to the method, high-precision and physically interpretable creep prediction under small samples and a wide load domain is realized.

The application discloses a physical information guided SAR airplane target detection method, comprising the following steps: constructing an airplane target slice data set and extracting scattering key points; based on a Gaussian mixture model, generating a probability density image corresponding to the airplane target slice as a true value heat map by using the scattering key points; constructing a target scattering key point prediction network and training the same, wherein the true value heat map corresponding to the airplane target slice and a predicted heat map predicted by the prediction network are used to calculate a network loss during the training process; constructing a feature reweighting network; combining the feature reweighting network and the trained target scattering key point prediction network to form a joint model, and connecting the joint model to a deep learning backbone network and a target detection network, so as to construct a physical information guided SAR airplane target detection network; and using a SAR airplane detection image data set to train the SAR airplane target detection network, and saving the trained network model for use in detecting and identifying unknown target categories of SAR airplane detection images.

The application discloses a traditional Chinese medicine compound analysis method based on a multi-dimensional data and information weighted network, and belongs to the field of traditional Chinese medicine compound analysis. The method comprises the following steps: constructing a multi-dimensional disease gene set; acquiring a traditional Chinese medicine compound set and a target set; determining disease gene nodes, compound nodes and target nodes respectively, and constructing a heterogeneous correlation network; performing propagation node screening on the heterogeneous correlation network, and weighting the edge set of the heterogeneous correlation network; running three algorithms on the updated heterogeneous correlation network respectively, and performing equal-weight average fusion on the propagation results of the three algorithms to obtain a final weighted network correlation degree score; sorting the final weighted network correlation degree score from high to low to obtain a key target candidate list; and selecting the first N candidate targets in the key target candidate list for enrichment analysis. The application solves the problems of single data source, lack of functional semantic support in network construction, static node evaluation index, and disconnection between enrichment analysis and core algorithm in the prior art.

The invention provides an intelligent grading and judging method for wooden furniture plates, which comprises the following steps of: constructing a high-quality image database which is acquired by an industrial camera at multiple angles and is bound with material labels, performing multi-stage denoising, enhancement and normalization preprocessing, extracting a key texture feature channel through a lightweight convolutional neural network and a channel attention mechanism, and performing classification and judgment on the key texture feature channel; according to the method, weight distribution and online optimization are carried out on feature channels in combination with a dynamic feature reweighting network, intelligent compensation of feature expression ability after lightweight pruning and quantification of the model is realized, in addition, a weight drift monitoring and local recalibration mechanism is arranged, the discrimination robustness of the model in detection of different batches of plates is guaranteed, and the detection accuracy is improved. According to the method, the edge deployment efficiency is improved, and meanwhile, the accuracy and adaptability of automatic judgment of the plate grade are enhanced.

The disclosure provides a traditional Chinese medicine efficacy evaluation method and device based on a node-weighted network, equipment and a storage medium. The method determines target disease protein targets and corresponding target weights based on the comprehensive scoring results of candidate disease protein targets by a public database and a large language model. When determining the protein targets of medicinal materials and the corresponding protein target weights of medicinal materials, the prescription ratio, chemical component information, and the interaction probability between the chemical components and the protein targets of each medicinal material in the traditional Chinese medicine prescription to be evaluated are comprehensively considered. The target disease protein targets and the corresponding disease protein target weights, as well as the protein targets of medicinal materials and the corresponding protein target weights of medicinal materials, are added to a pre-constructed protein interaction network. The obtained node-weighted network focuses on the real pharmacological basis, effectively improves the network biological interpretation, and the multi-dimensional network index determined accordingly is used to evaluate the regulation effect of the traditional Chinese medicine prescription to be evaluated on the target disease, which is more accurate.

The application is suitable for the technical field of charging station site selection, and provides a method for electric vehicle charging station site selection and capacity optimization based on structural entropy, comprising: constructing an initial feature vector of a candidate parking lot based on business information and geographic location information of the candidate parking lot; constructing a weighted network graph and initializing the weighted network graph as a coding tree; calculating the structural entropy of the candidate parking lot based on the coding tree, generating an enhanced feature vector based on the structural entropy and the initial feature vector; clustering the candidate parking lot based on the enhanced feature vector to obtain multiple clusters, and defining each cluster as a demand area; constructing an energy utilization rate function based on the demand area, taking the energy utilization rate function as a fitness function of a genetic algorithm; and optimizing the electric vehicle charging station construction scheme by using the genetic algorithm to obtain an optimal electric vehicle charging station construction scheme which maximizes the fitness function and meets the construction constraint condition. The application can improve the energy utilization rate so that the energy can be fully utilized.

The application discloses a subway network resilience evaluation method and device, and relates to the technical field of traffic resilience evaluation. The method comprises the following steps: constructing a directed weighted network model of a subway network according to subway stations and running routes; determining subway network variable indexes according to the directed weighted network model, and performing initial load distribution of nodes of the directed weighted network model according to the subway network variable indexes; performing iterative attacks on the directed weighted network model with the initial load distributed, re-distributing node loads after each attack, and calculating subway network efficiency after each attack according to re-distribution results; and evaluating subway network resilience according to the subway network efficiency after each attack. The application can more accurately identify key nodes in the network, the evaluation result of the network resilience is more comprehensive and reliable, and the operation efficiency and management level of the subway network are improved.

The invention provides a false data injection attack identification method based on an adaptive residual weighted PINN. The method comprises the following steps: constructing a physical equation describing a system operation state; a self-adaptive residual weighting network is constructed, and the self-adaptive residual weighting network takes sensor measurement data with noise or attack as input and takes the reconstructed system state as output; training the adaptive residual weighting network by using a gradient descent algorithm; mapping sensor data into a system state meeting physical consistency in real time by using an adaptive residual weighting network; and according to a weight coefficient and physical residual information which are calculated in real time by the self-adaptive residual weighting network, identifying a false data injection attack through a dual-criterion mechanism.

This invention discloses a method and system for measuring the sound insulation of in-ear hearing protectors, relating to the field of sound insulation measurement technology. The method includes: calibrating a test microphone and an external microphone to obtain sensitivity at different frequencies; pre-selecting sound source points for noise simulation; dividing the pre-processed sound signal into several short time periods, obtaining frequency domain signals through Fourier transform, dividing into harmonics according to the fundamental frequency and obtaining voltage characteristics; calculating the sound pressure amplitude and sound pressure level at each harmonic based on sensitivity, and calculating the sound insulation and sound insulation rate for each harmonic; combining the sound insulation of each harmonic within all short time periods to obtain the sound insulation of each harmonic within the complete time period, and then calculating the comprehensive sound insulation based on an A-weighted network; turning on the test sound source at each sound source point, calculating the sound insulation rate of each harmonic at each sound source point, extracting the high, medium, and low frequency sound insulation rates, and fitting a function of the distance variation based on the straight-line distance between the sound source point and the test microphone; thus achieving accurate measurement and analysis of the sound insulation of in-ear hearing protectors.

The invention discloses an atmospheric pollution transmission modeling and pollution driving factor identification method based on multiple networks, and the method comprises the steps: taking an atmospheric monitoring station as a common node set, and constructing a corresponding directed weighted network layer according to pollutant transmission, meteorological driving, geographical constraint, pollution source contribution and urban activity influence, the directed weighted network layers share the same node set, are not fused, and are superposed to form a directed weighted multiple network; performing label propagation community detection on the directed weighted multiple network to obtain a detection result; determining a site set corresponding to each community in the detection result as a pollution associated area; and calculating layer correlation according to the internal directed weighted edge connection cumulative strength of the pollution correlation area on each network layer, and determining the network layer with the maximum layer correlation as a dominant pollution driving factor of the corresponding pollution correlation area.

The invention discloses a zero sample prediction and numerical phase diagram generation method and system for a creep behavior of a metal material, and belongs to the technical field of digital design and performance prediction of materials. The method comprises the following steps: firstly, constructing a cross-material generality rule mapping relation library, and establishing quantitative correlation among material macroscopic attributes, crystal structures, phase change temperatures, physical mechanism weight distribution and model parameters; for a to-be-predicted target metal material, a preset model and parameters can be called from the relation library only by inputting the crystal structure and the melting point of the to-be-predicted target metal material, a high-precision physical mechanism weighted network prediction model is automatically assembled, and accurate prediction of the creep behavior of the to-be-predicted target metal material in the full stress-temperature domain is achieved. Through continuous calculation and visualization, a quantitative and continuous creep numerical phase diagram with physical interpretability is generated, and competition and evolution of different creep mechanisms are visually revealed. According to the method, normal form transformation from experience fitting to physical prediction is realized, and a core tool is provided for rapid evaluation and digital design of new materials.

The present disclosure discloses a method for SFC deployment based on VNF-dependent software migration in a multi-domain network, and relates to the field of communications technologies. The method jointly optimizes the SFC deployment and the migration of VNF-dependent software to resolve issues concerning service provision in the multi-domain network. According to the method, information related to the multi-domain network is first initialized, and then a multi-layer network architecture is constructed to support SFC deployment. Then, a multi-layer weighted network is established by using an analytic hierarchy process (AHP), and an SFC deployment policy and a VNF-dependent software migration policy are executed on this basis. In the present disclosure, the impact between SFC deployment and VNF-dependent software migration decisions is comprehensively considered, achieving efficient SFC deployment and VNF-dependent software migration in the multi-domain network.

The invention relates to the field of power system operation and maintenance, and particularly provides a power grid state early warning and alarming method and system based on dynamic characteristic atlas and risk conduction path analysis, a terminal and a medium, and the method comprises the steps: synchronously collecting and preprocessing multi-dimensional data of power grid monitoring nodes, calculating the real-time abnormality of the nodes and the dynamic association strength between the nodes, and calculating the real-time abnormality of the nodes; constructing a weighted network map evolved along with time; an initial risk source is identified based on a map, risk conduction is simulated to calculate a whole network node influence score, a key fragile path is identified, a dynamic risk level is obtained, and finally graded early warning information is generated. According to the method, false alarms caused by normal fluctuation of working conditions are reduced, the early recognition capability of a hidden abnormal mode is improved, a risk conduction process is simulated, current node abnormality evaluation, risk diffusion range and degree pre-judgment and key fragile path tracing recognition are realized, and weak links of a system are locked in advance.

The invention provides a traditional Chinese medicine curative effect evaluation method and device based on a node weighted network, equipment and a storage medium. According to the method, a target disease protein target and a corresponding target weight are determined on the basis of a comprehensive scoring result of candidate disease protein targets by a public database and a large language model. When the medicinal material protein target and the corresponding medicinal material protein target weight are determined, the prescription proportion and the chemical component information of each medicinal material in the to-be-evaluated traditional Chinese medicine prescription and the interaction probability between the chemical component and the medicinal material target are comprehensively considered. A target disease protein target, a corresponding disease protein target weight, a medicinal material protein target and a corresponding medicinal material protein target weight are added to a pre-constructed protein-protein interaction network, and an obtained node weighted network focuses on a real pharmacological basis, so that the biological interpretation force of the network is effectively improved; the accuracy of evaluating the regulation effect of the to-be-evaluated traditional Chinese medicine prescription on the target disease by using the determined multi-dimensional network index is higher.

This invention discloses a ranking information recommendation method based on distribution entropy, belonging to the field of recommendation strategy technology, including the following steps: S1, obtaining ranking information: each ranking entity ranks all or a portion of the evaluated objects separately; S2, networking the ranking data: constructing an undirected weighted network; S3, the ranking information recommendation method based on distribution entropy: introducing global edge weight distribution entropy, quantifying the uniformity of edge weight distribution, and designing a recommendation scoring mechanism based on the quantification results. This invention, employing the above-mentioned ranking information recommendation method based on distribution entropy, not only provides an intuitive and scientific means to objectively evaluate the quality of ranking data, but also, through a low-computational-cost, high-generalization-ability mechanism, suppresses the Matthew effect in the evaluation system from the source, ensuring that the final obtained ranking set has extremely high coverage and fairness, providing solid data support for scientific decision-making in the field of computer and auxiliary equipment repair.

The application relates to the field of bioinformatics and clinical auxiliary decision-making technology, in particular to a high-dimensional weighted membranous nephropathy gene co-expression network construction method, which integrates biomolecular interaction, disease pathways and health baseline data to construct an initial topological matrix, uses a Riemann manifold optimization algorithm to inject high-dimensional transcription sequencing data into the matrix and update edge weights, generates a high-dimensional weighted network of a target biological sample, accurately locks an abnormal gene module by calculating the topological residual error of the network and the health baseline and the dynamic offset of the eigenvector centrality, compares module features with a target point database to screen candidate drug targets, and stores network weight eigenvalues into a vector database, so that gene co-expression network construction and drug target screening based on biological priori and data driving are realized; and the application accurately retains weak but key local nonlinear cascade signals.

The invention discloses a metabonomics data analysis method and system, and the method comprises the steps: obtaining metabonomics data of a biological sample under multiple conditions, and carrying out the filling of missing values through employing a data distribution characteristic fusion strategy; calculating a response intensity distribution difference value of each metabolite feature through sliding window local fluctuation analysis, and screening candidate difference metabolite features; sequencing the response values of the features under different conditions according to the size and fitting the response values into a feature response curve, and clustering by calculating the similarity of the curve to obtain a feature cluster; further, on the basis of the collaborative change intensity between the overall form and local synchronism fusion measurement features, a weighted network is constructed, community division is carried out, feature pairs with high collaborative intensity are screened from all sub-communities, and a potential biomarker combination is formed. According to the method, the whole-process analysis from data reconstruction, difference screening to collaborative marker identification is realized, and the accuracy, robustness and biological interpretability of single-sample metabonomics data mining are improved.

Selection of a variant of a media object for delivery from a service provider to a user system using a delivery utility of available variants. The variants may each use different amounts of network resources. The delivery utility may be based on a network resource usage (e.g., a weighted network resource usage such as a provisioning cost value). The network resource usage may represent a cost to the service provider to transmit the variant. The delivery utility calculation may also be based on a predicted quality of experience for a variant. This may include a weighted probability of the quality of experience related to an effect of an adverse experience resulting from delivery of the variant (e.g., a churn cost value). The use of the delivery utility calculation may efficiently use communication network infrastructure to improve user quality of experience, server more users using given networking resources, or both.

The invention provides a data-driven manufacturing quality key control point identification method, which comprises the following steps of: generating a fault weight of each fault node according to a quality loss function on the basis of historical data including fault nodes, severity and maintenance cost; based on the mapping relationship between the fault nodes and the manufacturing quality control points, collecting a mapping fault node set of each manufacturing quality control point, and calculating the weight of the manufacturing quality control points in a maximum pooling manner; taking the fault weight and the manufacturing quality control point weight as weight input, performing weighted association rule mining on the mapping relation, and generating an association rule set of the control point and the fault node; and constructing a double-layer directed weighted network based on the association rule set, calculating topological characteristics of nodes based on the double-layer directed weighted network, analyzing the topological characteristics, performing importance ranking, and determining a key control point set.

The invention provides a weighted hash text duplicate checking method based on semantic weight and template suppression. The method sequentially comprises the following steps: extracting lexical, syntactic and semantic features; calculating word / segment / structure three-level template scores in an online stage based on an offline constructed template dictionary, and carrying out weighted fusion on the word / segment / structure three-level template scores; outputting the feature importance degree by the semantic weighting network subjected to comparative learning training, and combining the feature importance degree with the smooth IDF and the template inhibition factor to form a final weight; according to the weight, generating a weighted SimHash fingerprint or a weighted MinHash fingerprint; the fingerprints are written into a GPU accelerated multi-table LSH index and retrieved, the Hamming distance of the bit fingerprints is calculated, an adaptive threshold value is adopted for judgment, and when and only when the distance is not larger than the threshold value, a near-repetition detection result is output, and a document identifier, the distance and the threshold value are recorded. According to the method, virtual high similarity caused by template content is effectively inhibited, the detection rate and accuracy of anti-plagiarism such as word rewriting are improved, and the method has high efficiency and expandability.

The invention discloses a generation method of a directed weighted network model based on a triple reciprocity mechanism. The method comprises the following steps: constructing a directed weighted network model containing interaction between nodes, and initializing parameters of the directed weighted network model; obtaining original interaction data from an actual network, estimating potential structure information of each node, and performing iterative optimization on parameters of the directed weighted network model by adopting an EM algorithm; according to the parameters of the directed weighted network model after iteration optimization, combining the community attribution relation and shared neighbor information between the nodes, and calculating the expected weight of the connection between the nodes by using conditional Poisson distribution with ternary reciprocity items; and constructing an adjacent matrix of the target network based on the expected weight, sampling edges, generating corresponding edge weights, and realizing generation of the directed weighted network model. According to the method, the local reciprocity mode and the global ternary attribute can be effectively captured, so that the method is excellent in the aspect of keeping the authenticity of the network structure.

The application discloses a change segmentation method and system based on a learnable DCT and frequency band division and a medium, relates to the technical field of image processing, takes an improved AFB_DCT_FEM model as a core, connects a learnable two-dimensional DCT transformation network, an adaptive frequency band division AFB network and a Band Router dynamic weighting network into an overall framework that can be end-to-end back propagated, while maintaining the DCT energy concentration characteristics, endows the network with three abilities of frequency basis on-demand tuning, dynamic frequency band selection and automatic weight estimation, replaces the substructure of an existing image change region segmentation network in an end-to-end manner, realizes efficient extraction of double-time-phase image difference features, and significantly improves the fineness and robustness of image change region segmentation.

The application discloses a light-weighted optical fiber vibration intrusion event identification method and system for perimeter security, belongs to the field of optical fiber sensing technology and pattern recognition technology, and comprises the following steps: collecting original vibration signals of a perimeter monitoring area through a distributed optical fiber vibration sensing system; performing wavelet threshold denoising pretreatment on the original vibration signals to obtain denoised signals; performing feature extraction on the denoised signals to construct a high-dimensional feature vector; performing dimension reduction processing on the high-dimensional feature vector by using a linear discriminant analysis method to obtain a low-dimensional classification feature vector; inputting the low-dimensional classification feature vector into a pre-trained light-weighted convolutional neural network model for classification and recognition, and outputting corresponding intrusion event categories. The application cooperates the front-end LDA dimension reduction with the rear-end light-weighted network, greatly reduces the model parameter quantity and the calculation cost while ensuring high recognition accuracy, realizes efficient and real-time deployment on an edge device, and is suitable for intrusion detection in perimeter security.

The invention relates to the technical field of photovoltaic power generation, discloses a cable collection line planning optimization method based on a long and narrow photovoltaic field area, and aims to solve the technical problem that a traditional scheme is poor in economical efficiency and electrical performance in long and narrow photovoltaic field area cable collection line planning. The method comprises the following steps: constructing a weighted network model fusing terrain and electrical constraints; dividing electrical sub-regions based on power weight clustering and determining a virtual confluence point; and by applying a genetic algorithm, through electrical and economic coupling calculation, optimizing the trunk line by taking the total cost minimization of the whole life cycle as a target. According to the technical scheme, the power station investment return rate can be improved, the cable collection line considering the electrical performance and the economic benefit can be quickly generated, and the cable collection line is particularly suitable for a long and narrow photovoltaic field area.

This invention relates to the field of power system technology, specifically disclosing a comprehensive online monitoring and operation and maintenance management system for the entire lifecycle of power distribution and electrical equipment. The system includes a full lifecycle data collection module, a relationship graph construction module, a health status assessment module, a risk fusion assessment module, an operation and maintenance scheduling matching module, an operation and maintenance path planning module, and a closed-loop traceability and archiving module. It associates and stores full lifecycle data of power distribution and electrical equipment to construct its full lifecycle dataset; based on equipment relationships, a weighted network is constructed to obtain an equipment relationship graph; the dataset is analyzed to determine the equipment health status score based on risk level and location importance, matching resources to generate operation and maintenance work orders; combining work orders and a geographic information database, routes are planned to form a scheduling scheme; maintenance is completed, recorded, and archived, constructing a full lifecycle traceability link; this invention can improve the accuracy of health assessment of power distribution and electrical equipment, the scientific nature of operation and maintenance scheduling, and the completeness of traceability.