2110 results about "Statistical analysis" patented technology

An apparatus and method for real-time assessment and mapping of quality of life indicators includes: selecting a set of quality of life dimensions, receiving objective quality of life indicators based on the quality of life dimensions, receiving subjective quality of life indicators based on the quality of life dimensions, performing a statistical analysis on the objective quality of life indicators and the subjective quality of life indicators, storing the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis in a database, and outputting the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis.

A system and method for real-time time series forecasting using a compound large codeword model with integrated supervisory neurons. The system processes diverse inputs through adaptive codebook generation and codeword allocation. A projection network fuses different data types, creating unified representations for a latent transformer-based machine learning core. The core contains local neural network regions of interconnected operational neurons, monitored by supervisory neurons. These supervisory neurons receive activation data from operational neurons, perform real-time statistical analysis, determine necessary structural modifications, and initiate their implementation during operation. This architecture enables efficient handling of multi-modal data, capturing complex relationships between different input types. The combination of adaptive codebook generation and the supervisory neuron system ensures responsiveness to evolving data patterns and task requirements. This approach provides more accurate and timely forecasts by leveraging diverse data types in a sophisticated, integrated manner, while continuously adapting its structure to maintain optimal performance.

The invention relates to an LLM-based power distribution network knowledge graph construction and fault intelligent diagnosis method. The method comprises the steps of constructing a knowledge system, constructing a normal operation state model and an anomaly detection threshold value, performing fault diagnosis through an LSTM-BP cascade network, and performing fault classification. The abnormal parameters and the fault classification result are input into an LLM model, the fault type is judged, corresponding diagnosis information is obtained, and the system generates a scheduling instruction and an emergency decision suggestion according to the diagnosis information; and generating a detailed diagnosis report. According to the system, statistical analysis and deep learning algorithms are adopted for operation parameters such as voltage and current after preprocessing, and automatic detection and fault classification of abnormal parameters are realized in combination with a CNN recognition module; a preset rule base in the knowledge system is combined to accurately determine a fault type, generate a scheduling instruction and an emergency decision suggestion, and assist operation and maintenance personnel in rapid response and dynamic management; finally, a detailed diagnosis report is generated, thereby improving the fault handling efficiency and the guarantee capability of safe and stable operation of a power grid.

The invention relates to the technical field of charging control, in particular to a new energy automobile charging safety regulation and control method and system, and the method comprises the steps: collecting a multi-dimensional feature parameter set; acquiring battery health state and charge state information, and collecting multi-point temperature information on the surface of the battery; constructing the multi-dimensional feature parameter set into a multi-dimensional time sequence, performing statistical analysis on the multi-dimensional time sequence, and extracting dynamic feature indexes; performing structured fusion on the dynamic characteristic indexes, thermal field gradient parameters calculated according to multi-point temperature information and a multi-dimensional characteristic parameter set, and projecting the dynamic characteristic indexes, thermal field gradient parameters and the multi-dimensional characteristic parameter set to a key characteristic subspace through principal component analysis to obtain a charging response vector; generating a multi-dimensional security hyperplane through the dynamic security boundary model; continuously comparing the charging response vector with the multi-dimensional safety hyperplane, and calculating a safety margin scalar in real time; and inputting the safety margin scalar into the self-adaptive regulation and control model, and adjusting the charging current output. According to the invention, active predictive charging safety regulation and control are realized through multi-dimensional perception.

The invention relates to the field of mold cooling optimization, and discloses an automobile mold cooling optimization system and method based on reinforcement learning, and the method comprises the steps: collecting the real-time temperature and flow data of each cooling loop, carrying out the preprocessing and abnormity filtering of the data, and constructing a multi-dimensional basic cooling state database through combining historical operation records and environmental parameters; performing spatial mapping and statistical analysis on the temperature and flow data in the basic database, identifying a high-temperature region with heat concentration and a part with remarkable temperature gradient, and forming a local heat feature model; establishing an independent control sub-model, and optimizing cooling water flow and temperature regulation parameters of each loop by adopting a simulation and reinforcement learning combined method; a local adjustment strategy and an overall cooling target are fused, and main loop parameters are optimized through a global control algorithm; in the mold cooling process, temperature and flow fluctuation are continuously monitored, and real-time data are input into an optimization system. The method has the advantage that the cooling precision and efficiency are improved.

The invention discloses a metabonomics data batch correction method based on multi-kernel learning, and belongs to the cross technical field of bioinformatics and analytical chemistry. According to the method, the multi-kernel learning technology is utilized, the advantages of different kernel functions are fused in a self-adaptive mode, a model conforming to data reality is constructed, complex drift characteristics of metabolite signals are accurately captured, and efficient and accurate normalization processing of metabonomics data is achieved. Compared with traditional data standardization methods such as SVR and LOESS, the method has the advantages that the performance is excellent in the aspect of reducing the metabolite peak intensity variability, and the data stability is remarkably improved. In the subsequent multivariate statistical analysis, the classification accuracy is greatly improved, the comparability among different batches of data is also remarkably enhanced, reliable data support can be provided for discovery of disease biomarkers, and the method plays a key role in large-scale metabonomics research.

The invention relates to the technical field of water quality data analysis, and discloses a river water quality pollution assessment method and system based on big data analysis, and the method comprises the steps: building a water quality parameter set; obtaining a preprocessed water quality data set; obtaining a global statistical analysis result through descriptive statistical analysis; carrying out hierarchical multi-time evaluation analysis to obtain a hierarchical evaluation result; obtaining a deviation value between the current water quality data and standard water quality data of a laboratory; reliable water quality characteristic data are obtained; a difference trend analysis result is obtained; performing relevance verification on the river water quality distribution map and the time change map of the target area according to the difference trend analysis result to form a comprehensive analysis result; and finally generating a comprehensive water quality pollution assessment report containing water quality traceability information and a comprehensive analysis result. According to the invention, a multi-parameter sensing technology and intelligent data processing are organically combined, so that the water quality of the river is efficiently, accurately and carefully analyzed, and the pollution degree is evaluated.

The present technology pertains to systems and methods for one-way authentication of quantum channels. A transmitter generates an entangled quantum state comprising a first state and a second state, modulates the second state according to a clock-synchronized pattern, and transmits it through a quantum channel to a receiver. The first state is retained and measured at the transmitter to extract quantum-state information. Authentication is performed based on this information, without requiring feedback from the receiver. The quantum-state information may be derived using quadrature measurements, Gaussian tomography, or other statistical analyses to detect whether the second state underwent irreversible interactions such as eavesdropping or decoherence. The system enables secure unidirectional quantum authentication, reduces protocol complexity, and supports real-time anomaly detection. It is compatible with continuous-variable quantum states, quantum key distribution (QKD), and scalable communication architectures.

The invention discloses a structured data quality evaluation and optimization system using a large language model, which comprises a data access module used for formatting and preprocessing data, a data quality detection module used for establishing data quality evaluation indexes, performing statistical analysis and rule verification to detect the data and calculating the overall quality grade, and a big language model, the semantic analysis and SQL generation module is used for analyzing a database structure through a large language model, converting a natural language instruction into an executable SQL statement and is based on the large language model, and the anomaly detection and optimization suggestion module is used for positioning anomaly by utilizing a mixed detection algorithm and generating natural language interpretation and optimization suggestions in combination with the large language model. And the user feedback and dynamic adjustment module is used for absorbing adjustment suggestions through a feedback loop mechanism, and optimizing a prompt project and an SQL generation strategy. According to the method, semantic analysis is carried out on a database structure by adopting a large language model, and high-precision SQL query is automatically generated.

A computer system for compacting video data. The system acquires a video stream, reduces redundancy through pre-processing, and analyzes the stream to identify patterns and irregularities. It detects spatial or temporal anomalies in the video and produces three outputs: a conditioned video stream based on statistical analysis, an error stream reflecting adjustments made during conditioning, and an anomaly meta-stream containing metadata about detected anomalies. The system communicates with one or more remote systems to synchronize and negotiate a compatible compression codebook, optionally exchanging compact updates that represent differences between local and remote codebooks. The conditioned video stream is then compressed using the agreed codebook. The system outputs a compacted representation of the video that includes the compressed stream, the error stream, and the anomaly metadata, supporting efficient storage or transmission while maintaining the ability to detect, trace, and reconstruct key information within the video.

The invention discloses a dynamic S-box and cross-channel diffusion image encryption method based on six-dimensional hyper-chaos driving, and belongs to the field of information security, and the method comprises the implementation steps: 1) constructing a novel six-dimensional hyper-chaos system; 2) constructing a cross-channel diffusion scheme based on the six-dimensional hyperchaotic system; 3) constructing a dynamic S box library based on a six-dimensional hyperchaotic system; according to the invention, by introducing an inter-channel coupling mechanism, the potential safety hazard of traditional sub-channel encryption is effectively relieved. A dynamic S box generated based on chaotic system state self-adaption enhances the nonlinear confusion characteristic, and Arnodcat mapping transformation realizes global pixel scrambling. Theoretical and experimental results show that the method overcomes the limitation of a low-dimensional system, and the aspects of statistical analysis, key sensitivity, attack resistance and the like are remarkably improved.

The invention discloses a chemical plant safety early warning and multi-dimensional correlation analysis method and device and a storage medium. By collecting equipment operation parameters, personnel operation and environment data, a reference model library is constructed to realize dynamic threshold early warning; after the early warning is triggered, calculating a first correlation index, a second correlation index and a third correlation index respectively by adopting time sequence similarity analysis, historical statistical analysis and process knowledge graph reasoning; and positioning the core inducement through multi-index weighted fusion. The problems that a traditional method is poor in early warning adaptability, weak in system collaboration and insufficient in tracing depth are solved, and accurate early warning, deep tracing and closed-loop management are achieved.

The invention provides a test paper correction and personalized learning method based on OCR and a large language model. The method comprises the steps of S1, test paper scanning and preprocessing; s2, performing OCR (optical character recognition) and data structuring; s3, intelligently correcting the large model; s4, performing multi-dimensional statistical analysis; and S5, generating and pushing variable questions. According to the method, technologies such as OCR (Optical Character Recognition), a large language model and a knowledge graph are fused, so that the whole process intelligence from test paper correction to personalized learning guidance is realized, the efficiency and accuracy of education evaluation are improved, and technical support is provided for personalized teaching.

The invention discloses a rice yield estimation method based on unmanned aerial vehicle remote sensing and rice spike feature extraction, which comprises the following steps: firstly, assuming a ground section RTK base station and combining an unmanned aerial vehicle remote sensing platform to determine a coverage range and a spatial position of a rice region, and then collecting a rice image and canopy height and performing deblurring processing; then using a rice ear detection model to obtain a rice ear segmentation mask, determining a pixel scaling factor to construct a rice ear feature index, and carrying out statistical analysis and identification on key features to form a feature data set; performing yield estimation by using a rice yield estimation model, and optimizing the model in combination with the actual yield; and finally, transmitting the data to a ground end to generate a thermodynamic diagram and a statistical diagram, and carrying out regional comparison and time sequence trend analysis. According to the method, the influence of the flight height on yield estimation is eliminated based on the rice ear segmentation result in combination with high-precision depth information, and the accuracy and efficiency of rice yield estimation are effectively improved.

The invention relates to the technical field of image recognition, in particular to a plate defect recognition and detection method based on an image recognition model. The method comprises the following steps: aiming at the technical problems of insufficient texture direction identification precision and difficulty in defect type distinguishing in a traditional plate surface detection method, the invention provides an intelligent detection solution based on polarization imaging. A multi-angle polarization measurement system is constructed through a fixed polarizer and a rotatable analyzer, and omnibearing polarization characteristic collection of the surface of a steel plate is achieved. A polarization intensity change model is established based on the Malus law, the polarization degree and polarization angle parameters of each pixel point are extracted, and a polarization parameter spatial distribution diagram reflecting the surface microstructure is constructed; through statistical analysis of polarization angle distribution characteristics, a rolling direction dominant angle is identified by adopting a peak detection algorithm, and accurate classification and severity evaluation of defects are realized in combination with anisotropy characteristic analysis.

The invention discloses a multi-agent real-world clinical curative effect evaluation and accurate decision-making system, and relates to the technical field of medical data processing. According to the invention, a research demand analysis agent generates a research scheme after understanding the input content of a user and transmits the research scheme to a data management agent, a statistical analysis modeling agent, a result report generation agent, a data security agent and the data management agent privacy and encrypt data uploaded by the user; meanwhile, additional feature construction is carried out according to the research requirement of a user to form brand new analysis data used by a subsequent statistical analysis modeling agent, and after the statistical analysis modeling agent obtains the data, an analysis result is obtained by calling external software and is transmitted to a result report generation agent; and generating a clinical evaluation report together with the research scheme transmitted by the research demand analysis agent. According to the invention, full-process automatic, specialized and safe research support is realized.

The invention provides an emergency rescue path planning method and system for adaptive risk avoidance. The method comprises the following steps: establishing an emergency rescue road network structure; the method comprises the following steps: before an actual natural disaster comes, carrying out risk entropy driven adaptive Monte Carlo simulation analysis, carrying out path information statistical analysis on a Monte Carlo simulation result, and establishing a pre-planning path library; when an actual natural disaster comes, prior risk distribution of each planned path in the pre-planned path library is established; performing dynamic correction on the prior risk distribution of each planned path in the emergency rescue process by adopting sequential Bayesian reasoning; and carrying out emergency rescue path dynamic planning based on the path posterior risk mean value of each planning path after sequential Bayesian reasoning, selecting an optimal rescue path, and executing an emergency rescue task until the emergency rescue activity is finished. According to the invention, through a path dynamic adjustment strategy, a decentralized transportation strategy, a path pre-planning strategy and a network-free communication strategy, rescue resources are ensured to efficiently and safely arrive at a target location in natural disaster scenes such as flood and typhoon.

The invention discloses an automatic cast film defect detection method and system based on an improved YOLOv8 model and a multi-threshold mask segmentation algorithm, and the method comprises the steps: obtaining a defect picture for defect detection through the collection and preprocessing of surface image data of a cast film; a defect detection model based on an improved YOLOv8 model is constructed, the detection precision and stability of the model on a tiny target are improved through an NWD loss function, defect pictures are input into the detection model, and center point coordinates of each prediction defect target frame are output after model detection, so that accurate position information of the defect is provided; according to the predicted position of the defect target frame, extracting the target frame and segmenting the area by adopting a multi-threshold mask segmentation algorithm to obtain a fine defect area and features; and carrying out statistical analysis on the extracted defect features, and generating a batch detection report. According to the invention, automatic detection and classification of the surface defects of the casting film can be realized, and high-precision real-time detection capability is realized.

The invention discloses a rotor acoustic anomaly detection method, system and device based on an auto-encoder and wavelet packet energy entropy and a medium, and belongs to the technical field of hydroelectric generating sets, and the method comprises the steps: collecting an original acoustic signal, carrying out the noise reduction of the original acoustic signal, and carrying out the multi-channel data fusion to obtain an integrated acoustic signal; inputting the integrated acoustic signal into a wavelet packet for transformation processing to obtain wavelet packet coefficients under different scales; calculating energy values of different frequency band signals based on wavelet packet coefficients to form an energy entropy feature vector; constructing an implicit feature model of the hydroelectric generating set rotor in a normal state according to the energy entropy feature vector; and carrying out feature reconstruction on the energy entropy feature vector, calculating a reconstruction error, carrying out dynamic statistical analysis, and carrying out real-time monitoring and abnormity judgment on the operation state of the hydroelectric generating set rotor. According to the method, the detection precision and the response speed are improved in actual hydroelectric generating set rotor acoustic anomaly detection, and automatic identification and dynamic threshold adaptive adjustment of irregular perturbation are realized.

The invention discloses an application of an OsPES gene and an encoding protein thereof in regulating and controlling the tiller number of rice, and belongs to the technical field of biology. According to the application disclosed by the invention, two OsPES BRCT structural domain homozygous knockout strains Ospesc-cas9-1 and Ospesc-cas9-5, two OsPES overexpression strains OE-OsPES-3 and OE-OsPES-7 and two RNAi (Ribonucleic Acid Interference) interference strains RNAi-OsPES-1 and RNAi-OsPES-6 are respectively obtained by taking japonica rice'cloud guide 'as a background material and utilizing related technologies. Phenotypic observation and statistical analysis find that compared with a wild type, the tiller number of an overexpressed OsPES strain is increased, and the tiller number of an OsPES interference strain and the tiller number of a BRCT structural domain knockout strain are reduced. It is found for the first time that OsPES can positively regulate the tiller number of rice and has application significance in regulation of the tiller number of rice and new strain breeding.

The invention relates to the technical field of urban green development evaluation, and particularly discloses an urban green development level comprehensive evaluation method based on a digital technology, which comprises the following steps: constructing an urban green development level comprehensive evaluation index system and a digital technology development level comprehensive evaluation index system; urban index data are selected and preprocessed; a comprehensive evaluation model is constructed based on an entropy weight-TOPSIS method, the entropy weight method is used for determining the weight of each index, and the TOPSIS method is used for determining an evaluation index; analyzing a green development level index and a digital technology development level index of each city by using a comprehensive evaluation model; a spatial statistical analysis method, a random forest model and a VAR model are adopted to analyze a space-time effect and an interaction mechanism of digital technology development and urban green development. According to the method adopted by the invention, the limitation of traditional static evaluation is broken through, multi-dimensional coupling analysis of the digital technology and urban green development is realized, and scientific support can be provided for formulating a regional differentiation green development strategy.

The invention discloses a fish underwater behavior identification method, medium and device, and the method comprises the steps: collecting the original video information and environment parameter information of fish, and carrying out the underwater image restoration and foreground segmentation preprocessing of the original video information; performing motion feature extraction on the standardized video information, and constructing a kinematics feature vector by combining fish body key point coordinate information obtained by three-dimensional attitude estimation; performing multi-modal feature fusion on the kinematics feature vectors, and screening out discriminative feature subsets; and performing pattern recognition on the discriminative feature subset by adopting a space-time classifier constructed by a graph convolutional network, performing individual matching and result correction, and finally outputting a comprehensive monitoring report including individual behavior recognition, group statistical analysis and an environment correlation model. According to the method, the influence of an underwater complex environment on behavior recognition is overcome, accurate recognition and intelligent analysis of fish behaviors in a complex culture environment are achieved, and the method has the advantages of being high in recognition precision, high in environmental adaptability and comprehensive in analysis dimension.

A system and method for real-time time series forecasting using a compound large codeword model with integrated supervisory neurons. The system processes diverse inputs through adaptive codebook generation and codeword allocation. A projection network fuses different data types for a latent transformer-based machine learning core. A hierarchical supervisory network, comprising low-level, mid-level, and high-level nodes, monitors local neural network regions, performing real-time statistical analysis and implementing structural modifications. The system efficiently handles multi-modal data, capturing complex relationships between input types. An adaptive codebook generation method, coupled with the supervisory architecture, ensures responsiveness to evolving data patterns and task requirements. This approach provides accurate and timely forecasts by leveraging diverse data types in a sophisticated, integrated manner, while continuously adapting its structure during operation to maintain optimal performance.

The invention provides a sealed self-adaptive soft package lithium battery packaging image detection method and system, relates to the technical field of image detection, and obtains feature information by analyzing and processing image information of a to-be-detected soft package lithium battery packaging area. Based on the feature information, whether material batch switching occurs or not is judged, when material batch switching is detected, an image sample of a current product is collected, local visual features are extracted for statistical analysis, and reference information is constructed. The method comprises the following steps: acquiring current image information of a to-be-detected product, extracting local visual features of the to-be-detected product, comparing the local visual features with reference information, identifying an abnormal region, quantifying the geometric shape, size and strength of the abnormal region, performing similarity matching and multiple verification in combination with a preset defect mode, and judging whether the abnormal region is a real defect or not. The problem that in the prior art, when facing different batches of raw materials, an automatic image detection system cannot adapt to material characteristic changes, so that qualified products are frequently misjudged as defects is effectively solved.

The embodiment of the invention provides a face image anti-counterfeiting detection method and device based on frequency spectrum reconstruction, and the method and device achieve the effective utilization of frequency domain information through the innovative design of a frequency spectrum reconstruction system and the shielding reconstruction and feature extraction. And an anti-counterfeiting classification mechanism is constructed, and a reliable detection model is established in combination with reconstruction features and threshold discrimination. And score calibration is introduced, and the detection accuracy is ensured through parameter fitting and statistical analysis. According to the method, the defects of the traditional technology in the aspects of spectrum processing, anti-counterfeiting classification, score calibration and the like are effectively overcome, and a technical guarantee is provided for face anti-counterfeiting.

The invention relates to the technical field of medical information, in particular to an accurate operation name matching method and system based on a semantic matching model, and the method comprises the steps: S1, constructing a training data set, and carrying out the fine tuning of a pre-training language model through employing a comparative learning strategy, and generating the semantic matching model; s2, performing semantic coding on the standard operation name to generate a standard library semantic vector set; s3, in response to the matching request, reading a to-be-matched original operation name, splitting the to-be-matched original operation name into a basic operation name element set by adopting multi-separator regular matching, and cleaning the basic operation name element set; s4, performing semantic coding on the cleaned elements to generate a query vector set; s5, calculating the cosine similarity between the query semantic vector and the standard library semantic vector, and selecting the first K candidates to form a candidate matching set; and S6, performing optimization sorting on the candidate matching set to generate a final matching report. The method solves the problems that a traditional method is insufficient in semantic understanding, inaccurate in splitting and low in efficiency, and is suitable for medical data standardized management, coding association and statistical analysis scenes.

The invention discloses a postoperative sub-delirium syndrome risk prediction system for a cardiac surgery patient, and relates to the technical field of medical treatment, and the system comprises a clinical data collection module which is used for collecting multi-dimensional clinical data of the cardiac surgery patient in a perioperative period under extracorporeal circulation, the data comprises patient baseline information, preoperative neurocognitive function evaluation indexes, intraoperative physiological parameters and postoperative early-stage vital signs; and the risk identification module is used for utilizing a single-factor statistical analysis mechanism. According to the postoperative sub-delirium syndrome risk prediction system for the cardiac surgery patient, accurate prediction of the sub-delirium syndrome risk is achieved by collecting multi-dimensional clinical data of the patient in the perioperative period, such as baseline information, preoperative neurocognitive function, intraoperative physiological parameters and postoperative early-stage vital signs; the independent risk factors are scientifically screened and quantified by using single factor analysis and a multivariate Logistic regression model, and the problem of incomplete data dimensions in traditional evaluation is effectively solved.

The invention provides a comprehensive evaluation method for dissemination characteristics of minerals in coal of different particle sizes, and relates to the field of coal quality analysis. The method comprises the following steps: collecting coal analysis samples of different size fractions, and preparing coal polished sections of all the size fractions; the method comprises the following steps: collecting microscopic images, and constructing a representative microscopic image sample database of each size fraction; carrying out size fraction judgment, generating a component segmentation image on a single particle and a component segmentation image on a microscopic image, and extracting dissemination characteristic parameters of mineral substances in coal of each size fraction; key parameters are screened out, and comprehensive evaluation is carried out on the dissemination characteristics of the minerals in the coal of different size fractions. According to the method, an image processing technology and multivariate statistical analysis are combined, and qualitative and quantitative analysis of the dissemination characteristics of the minerals in the coal is achieved. Compared with an existing microscopic image analysis method, the method has the advantages that the human subjective influence and the labor intensity are remarkably reduced, and the analysis result has higher objectivity and reproducibility.

The invention discloses a method and equipment for compressing multi-dimensional data based on column storage and self-adaption, and the method comprises the steps: recombining original multi-dimensional data into a column data set structure, and organizing data block storage for the column data set structure according to a hierarchical structure; each data block stores observation grid data of a single variable of multi-dimensional data at a certain moment so as to support column type storage and self-adaptive compression; and carrying out real-time statistical analysis on characteristics or parameters of variables in the data blocks, and dynamically selecting and configuring a filter and parameters thereof to compress the multi-dimensional data. The data of the same variable are stored together through column storage, so that the data access efficiency is greatly improved, especially during variable-level analysis. Meanwhile, the self-adaptive compression technology can dynamically select a compression mode according to the distribution characteristics of the data, so that the compression rate of the data is effectively improved, and the occupied storage space is reduced. The processing speed of the meteorological grid point data can be obviously improved, and the storage cost is reduced.

The invention relates to an industrial park-based comprehensive energy flow direction on-line monitoring system and method, and the system comprises an energy modeling module which is used for representing instrument information, carrying out the modeling of collection labels and collection frequencies of different types of instruments, constructing nodes of different energy layered structures, carrying out the modeling of a directed flow direction relation between the nodes, and carrying out the modeling of the directed flow direction relation between the nodes; binding the logic relationship between the node and the instrument; the data acquisition module is used for performing data acquisition through an instrument, performing edge processing and encrypted transmission through communication equipment, and storing the data in a database; the energy monitoring module is used for monitoring and counting the state of the instrument and monitoring the energy flow direction of the enterprise in real time according to the directed flow direction of the node; and the statistical analysis module is used for providing energy balance analysis for different links of the enterprise energy in the conversion and use process according to the nodes, evaluating the energy consumption loss degrees of the different links, visualizing the nodes with abnormal loss and giving an alarm. Through the real-time and accurate calculation of the energy loss among the nodes at all levels, the backtracking of the nodes with abnormal loss and the timely pushing of the alarm information, the enterprise can timely discover the problems occurring in the energy supply, and can quickly find out the corresponding solution, reduce the energy consumption loss and guarantee the robustness of the energy flow network.