675 results about "Computer data processing" patented technology

The invention discloses an infrastructure risk operation optimization management system based on AI driving, and belongs to the field of computer data processing and commercial management, and the system comprises a multi-modal causal twinning construction module which integrates on-site multi-modal data streams to construct a dynamic space-time causal map; the risk evolution deduction module is used for performing anti-fact simulation based on a causal atlas to construct a prospective risk model; the collaborative configuration optimization module is used for solving an optimal collaborative defense strategy according to the risk model; the instruction analysis and digital prescription generation module is used for analyzing the defense strategy into a job digital prescription for a specific risk scene; and the intervention efficiency attribution and evolution correction module performs attribution analysis according to the execution effect of the digital prescription and adaptively updates the causal atlas. According to the method, a comprehensive method of constructing a dynamic causal map for risk deduction, coupling resource constraints for collaborative optimization and performing closed-loop feedback on a correction model is adopted, and active prediction, accurate intervention and continuous learning optimization of capital construction risks can be realized.

The invention provides a fatigue life prediction and residual life evaluation method for a high-power heavy-duty gearbox, and belongs to the technical field of intelligent operation and maintenance based on computer data processing. Comprising the following steps: acquiring dynamic data in an operation process, and performing multi-scale decomposition to form multi-source multi-scale data; inputting the multi-source multi-scale data into a designed multi-scale fatigue feature extraction module and a health state prediction module to obtain a multi-scale health index sequence and a health state label; establishing a fatigue damage evolution model, introducing the generated health index sequence for self-adaptive updating, outputting a comprehensive damage value, performing staged evaluation of fatigue degradation to obtain a damage label set, and performing multi-scale health index sequence and health state labels as well as the comprehensive damage value and the damage label set to obtain a multi-scale health index sequence and health state labels; inputting into a designed double-source fusion fatigue life prediction model, and outputting residual life prediction quantity; according to the invention, high-precision prediction and residual life evaluation of the fatigue life of the high-power heavy-duty gearbox are realized.

The invention belongs to the technical field of computer data processing, and provides a biodiversity inversion method based on multi-source remote sensing image fusion. Comprising the steps of remote sensing image data acquisition, image preprocessing, image fusion processing, multispectral resolution image data generation, spectral feature extraction, final frequency feature extraction, feature integration and target ecological variable prediction. According to the invention, through wave basis adaptive selection and multi-scale wavelet decomposition, spectrum fidelity and space structure maintenance are considered, differential fusion of high and low frequency components under different scales is realized, and multi-source data complementarity and fusion image quality are improved; through spectral resolution refinement processing and multi-bandwidth scale simulation, the limitation of single resolution is broken through, and the capability of capturing complex spectral features of vegetation is enhanced; the spatial correlation is enhanced through spatial neighborhood feature fusion; and through an ecological variable inversion estimation model, multi-index synchronous prediction is realized, and the universality of the model is improved.

The invention provides a numerical calculation method for double-explosion-source underwater explosion impact cavitation loads, and belongs to the technical field of computer data processing-based explosion impact wave calculation, and the method comprises the following steps: solving a propagation process of far-field underwater double-explosion-source explosion impact waves in a two-dimensional acoustic flow field by using a local discontinuous Galerkin (LDG) method; and the cavitation characteristics of the flow field under the action of the double-explosion-source underwater explosion shock waves are researched. A flow field dynamic pressure result calculated by the method is compared with a result calculated by an acoustic finite element method. The result shows that the flow field dynamic pressure result calculated by the method is well matched with the calculation result data of the acoustic finite element method, and the effectiveness and the accuracy of the method are verified.

The invention relates to the technical field of computer data processing, and discloses a data segmentation and query processing system under a distributed architecture. The system comprises a data segmentation module which divides data blocks based on a self-adaptive partitioning algorithm; the distributed storage module is used for distributing data blocks by using a consistent Hash algorithm and dynamically adjusting the distribution of virtual nodes; the index construction module is used for generating a global index table through a multi-layer inverted index algorithm; the query optimization module is used for converting query statements by utilizing a query rewriting algorithm; and the result aggregation module is used for processing the sub-query results by adopting a parallel aggregation algorithm. The system can dynamically process data according to data characteristics, optimizes the data storage and query process, effectively improves the data processing efficiency and query performance of a distributed system, and is suitable for scenes such as big data storage and analysis.

The invention relates to the technical field of computer data processing, and particularly provides an intelligent washing parameter optimization decision-making method based on multi-modal perception fusion, which comprises the following steps: acquiring stain characteristic parameters of a fabric to be cleaned in real time through a multi-source acquisition module; constructing a collaborative regulation model, determining the type and proportion of the detergent and mechanical force parameters based on the stain characteristic parameters, training the collaborative regulation model by taking the maximization of the stain removal rate as a target and taking the energy consumption and the water consumption as constraint conditions, and outputting an optimal washing strategy; controlling the washing device to execute corresponding operation according to the washing strategy; stain residues and energy consumption abnormal data in the washing process are monitored in real time, the washing rotating speed and the washing duration are dynamically corrected, and the detergent supplementing and putting amount is synchronously adjusted according to chemical component distribution. The limitation of the existing single-mode recognition technology on complex stain analysis is broken through, resource waste and fabric damage caused by traditional extensive washing are avoided, and the technical contradiction between the decontamination effect and resource consumption in the prior art is effectively solved.

The invention relates to the technical field of computer data processing, provides a cross-border data compliance processing method, device and equipment, and is used for solving the problems of relatively low cross-border data processing efficiency and data processing strategy errors in related technologies. According to the embodiment of the invention, the multi-method domain and regulation knowledge graph is established in advance, laws and regulations of a plurality of regions are summarized, the multi-method domain and regulation knowledge graph can be updated in real time, and the cross-border scene template library and the data label template base library are established according to the multi-method domain and regulation knowledge graph. The cross-border scene template library and the data label template base library can be dynamically updated along with updating of the knowledge graph, and a cross-border gateway can process data by adopting a compliance strategy according to a data cross-border scene through the cross-border scene template library, so that related data management enterprises and institutions efficiently manage data outbound in a compliance manner; through the data label template library, the cross-border gateway can rapidly and accurately carry out data marking work, and the efficiency of data classification processing is improved.

The invention relates to the technical field of computer data processing, and discloses an automatic format conversion and incremental synchronization method for cross-platform heterogeneous document data. The method comprises the following steps: capturing an atomic data change event from a source system transaction log; aggregating into logic transaction units according to the transaction identifiers; converting into a unified intermediate normal form; generating a target system operation instruction set based on the metadata mapping rule; the synchronization sites are executed and persisted in a transactional manner. According to the method, through log-level capture and transactional synchronization, data integrity, sequence and high consistency are ensured, and efficient and reliable data synchronization between heterogeneous systems is supported.

The invention discloses a metering equipment distribution decision simulation analysis method, and belongs to the technical field of computer data processing and simulation, and the method comprises the steps: fusing multi-source heterogeneous data to construct a space-time knowledge data set; performing causal inference based on the data set, and establishing a causal relationship graph; performing risk propagation deduction and causal link evolution simulation on the map to generate a risk situation set; generating an anti-fact intervention strategy for the risk situation; and performing quantitative analysis to output an optimal distribution strategy, and feeding back and updating the causal relationship graph by using an analysis result. According to the method, the data processing architecture of the dynamic knowledge graph is constructed by adopting causal inference, multi-step propagation inference and link evolution simulation can be carried out on risks in a complex system, and the accuracy and robustness of a data processing system for carrying out prospective decision making in an uncertain environment are remarkably improved.

The invention discloses a solar cell performance prediction method and device based on a physical information fusion model, and relates to the technical field of computer data processing and semiconductor characterization. According to the technical scheme provided by the invention, the deep neural network architecture is adopted to perform feature extraction and fusion on the multi-dimensional spectral information, the prediction precision is improved by more than 35%, and the reliability is improved by more than 40%, so that the prediction performance is remarkably improved; an innovative physical information fusion loss function is adopted for training the model and comprises a data-driven prediction loss item and a constraint loss item based on a semiconductor device physical rule, and a prediction task and a physical principle are deeply fused, so that the prediction model can understand a physical causal relationship between spectral characteristics and device performance, and the prediction efficiency is improved. According to the method, more accurate performance prediction and better physical interpretability are realized, multiple physical constraints are adopted to standardize the prediction result, so that the model prediction result conforms to the physical principle of the semiconductor device, and the physical rationality and engineering application value of the prediction result are improved.

The invention relates to the technical field of computer data processing, and particularly provides an MCU low-power-consumption firmware upgrading method based on energy self-adaption, which comprises the following steps of: establishing a communication link between an upgrading initiating end and MCU equipment, and acquiring energy state parameters in real time through an energy monitoring terminal arranged in the MCU equipment; according to a preset energy state threshold interval, the comprehensive value of the energy state parameters is divided into a low energy state, a medium energy state and a high energy state; dynamically adjusting an upgrading strategy parameter of the current firmware according to a preset adjustment rule based on the current energy state level; and transmitting target firmware data to the MCU equipment through the communication link based on the dynamically adjusted upgrading strategy parameters, and executing firmware upgrading operation until upgrading is completed. According to the scheme, an adaptive system of energy state perception-strategy dynamic adjustment-hardware collaborative optimization is constructed, and the problems of high power consumption and unbalanced efficiency and reliability caused by the fact that the energy state is not combined in the prior art are effectively solved.

The invention relates to a computer data processing technology, in particular to a real-time pipeline safety early warning method and system. The method comprises the steps of performing computer data processing according to pipeline topology information, dividing risk units and generating a risk unit session configuration structure; performing automatic feature extraction to generate a real-time risk feature vector; through machine learning fusion modeling, real-time features and historical baseline features are fused, and a dual-time-scale fusion feature vector is constructed; a preset intelligent model is used for reasoning and event judgment, a structured event flow account and resource adjustment parameters are generated, periodic self-optimization of sampling configuration, a model threshold value and an early warning strategy is driven, and therefore closed-loop intelligent monitoring and early warning of the state of the pipeline auxiliary equipment are achieved. According to the method, the early warning accuracy can be effectively improved, false alarm and missing alarm can be remarkably reduced, the adaptive capacity of the system is enhanced through a closed-loop learning mechanism, and an efficient solution is provided for application of the next generation of information network industry in related fields.

The invention discloses a semantic analysis-based user abnormal intention recognition method and system, and belongs to the technical field of computer data processing, and the method comprises the steps of analyzing logic contradiction word combinations in a user session text to generate abnormal semantic marks, and collecting a user operation behavior sequence and environmental parameters to construct a spatio-temporal behavior trajectory map. And matching the abnormal semantic mark with a preset extensible abnormal mode library, if the generated primary abnormal confidence exceeds a threshold value, further comparing the behavior trajectory map to generate a behavior abnormal index, associating the abnormal semantic mark with the primary abnormal confidence to form a comprehensive abnormal feature vector, and calculating an environmental risk coefficient in combination with environmental parameters. And finally, judging by a triple decision arbiter, and if the abnormity is suspicious abnormity, activating a secondary verification process and updating an abnormal mode library. According to the method, semantic logic contradiction analysis, behavior trajectory map analysis and environmental risk assessment are fused, deep and camouflage abnormal intentions can be accurately recognized, and the method has adaptive learning ability.

The invention discloses an Internet of Things edge computing task unloading method based on a comprehensive gateway, which belongs to the technical field of computer data processing, and comprises the following steps: acquiring tasks, equipment and network data of Internet of Things equipment, and fusing to generate a unified information view; determining task priorities based on the view by applying a dynamic priority evaluation mechanism, and generating a dynamic priority list; applying the time sequence analysis model to generate short-term prediction data; in combination with the dynamic priority list and the short-term prediction data, multi-objective optimization is performed through a collaborative decision model, and a task unloading strategy is generated; generating feedback data according to the task unloading strategy; and generating an adjustment signal by using the feedback data, and updating the dynamic priority evaluation mechanism and the collaborative decision model. According to the method, the unified information view is generated by fusing the multi-source data, collaborative decision making is carried out in combination with dynamic priority evaluation and short-term prediction data, and the model is continuously optimized by utilizing feedback data, so that self-adaptive, forward-looking and efficient task unloading can be realized.

The invention provides a park environment risk traceability and grading early warning management and control method, and belongs to the technical field of risk early warning based on computer data processing. The method comprises the following steps: firstly, collecting video monitoring, environment and equipment state sensor and real-time meteorological heterogeneous data in a whole domain and a peripheral specific buffer zone of a park, and preprocessing the data to obtain standardized multi-source data; dividing grid units by taking a park boundary coordinate as a reference and distributing a unique standardized code, associating standardized multi-source data to a corresponding grid according to a rule, and performing fusion calculation on a grid-level risk characteristic value of each grid; then combining real-time meteorological data, tracking a risk diffusion path through a reverse derivation algorithm, positioning a risk source position and a core range, and outputting related parameters; and finally, grid risk levels are divided according to the grid risk characteristic values and a preset threshold interval, a corresponding early warning mechanism is triggered, and early warning information is output. According to the invention, the precision of environmental risk positioning, the reliability of traceability analysis and the refinement degree of an early warning result are improved.

The invention discloses a plant protection field literature information batch structured extraction method based on an AI large model, and belongs to the technical field of computer data processing and artificial intelligence application. According to the method, a PDF format literature is converted into a Markdown format text through a PDF literature self-adaptive preprocessing module and a Markdown conversion module; then, a double-strategy self-adaptive AI extraction module is adopted, different modes are adopted for processing according to a text length threshold value, and JSON data are extracted in combination with a JSON format data restoration strategy; then, the CPU intensive conversion task and the I / O intensive AI analysis task are processed in parallel through a two-stage parallel scheduling module, and a structured database file is generated through aggregation according to a predefined mapping rule through a multi-dimensional aggregation export module. According to the method, end-to-end automation from literature acquisition to structured data output is realized, and the problem of low extraction efficiency of manual literature reading information is solved.

The invention relates to the technical field of computer data processing, and discloses a building material supplier dynamic evaluation and recommendation system based on big data, and the system comprises a data fusion module which integrates multi-source heterogeneous data to generate a unified data set; the tensor modeling module is used for constructing and decomposing a five-dimensional space-time tensor to obtain a factor matrix and a dynamic weight; the causal correction module is used for establishing a causal graph based on the network relationship and eliminating hybrid deviation; the recommendation decision module is used for outputting a recommendation list through reinforcement learning in combination with the evaluation weight and the performance distribution; and the interpretable module is used for generating an interpretable report based on the factor and the causal path. According to the method, the technical scheme of multi-source heterogeneous data fusion and five-dimensional space-time tensor decomposition is adopted, and dynamic, multi-dimensional and relevance evaluation of supplier performance is realized by constructing a unified data structure including suppliers, time, static characteristics, context and cooperative relationships.

The invention discloses a work order similarity calculation method and device based on multi-modal feature fusion, equipment and a storage medium, and relates to the technical field of computer data processing, and the method comprises the steps: extracting multi-modal features from work order data, the multi-modal features including text semantic features, structured features and time sequence behavior features; mapping the multi-modal features to a unified semantic space for multi-level fusion processing to obtain fusion features; generating a dynamic weight according to work order type information contained in the work order data and query intention information contained in the received query request, and performing weighted fusion processing on various similarity indexes obtained by fusion feature calculation to obtain a final similarity; and the contribution value of each modal feature to the similarity is calculated based on the final similarity, and a visual interpretation result is generated according to the contribution value, so that the accuracy, adaptability and interpretability of cross-platform work order matching are remarkably improved.

The invention relates to the technical field of computer data processing, and discloses a financial reimbursement data processing method, device and system.The financial reimbursement data processing method comprises the following steps that multi-modal data of a reimbursement voucher is obtained, and the multi-modal data is stored in a block chain; the multi-modal data comprises pictures, voices and videos of reimbursement vouchers; recognizing characters on the picture based on a convolutional neural network, and extracting and analyzing character information of key frames of the voice and the video by using a natural language processing model; and performing feature extraction on the character information to obtain character feature information, judging whether the character feature information falls into a reimbursement directory, and performing local model fitting according to the character feature information. According to the method, the dynamic verification rule is generated based on the historical reimbursement data, the bill anomaly probability is calculated in real time through the anomaly coefficient formula, and the anomaly detection accuracy is improved by combining the error coefficient and keyword matching degree and the historical record relevance index.

The invention relates to the technical field of computer data processing, and discloses a cross-border e-commerce compliance tax refund method and an intelligent declaration system based on block chain evidence storage, and the method comprises the steps: constructing a reference library containing commodity physical attributes and fair density intervals; order transaction, logistics actual measurement and fund time sequence data are acquired; executing multi-dimensional cross validation based on the reference library, wherein the multi-dimensional cross validation comprises physical space logic verification based on geometric boxing simulation, attribute consistency verification based on density distribution matching and quality and time sequence joint verification; a risk level is determined by integrating results; and generating digital fingerprints for compliance data, writing the digital fingerprints into a distributed account book, and generating a declaration file by using an on-chain index. According to the invention, trade authenticity is verified through physical simulation and multi-dimensional logic, evidence is ensured not to be tampered in combination with a block chain technology, and compliance and auditing efficiency of tax refund declaration are effectively improved.

The invention discloses a life omics knowledge graph construction method and system based on a large language model, and relates to the technical field of computer data processing. The method comprises the following steps: acquiring and preprocessing multivariate life omics data, wherein the multivariate life omics data at least comprises an unstructured biomedical text; performing information extraction on the text data based on a large language model to obtain entity mention and relation description; standardizing and normalizing the entity mention and the relation description on the basis of a large language model in combination with an external knowledge base to obtain a standard knowledge triple; and storing the triple into a graph data storage system, and constructing the knowledge graph. According to the method, the powerful natural language understanding ability of the large language model is utilized, efficient information extraction is achieved through structured prompt or field fine tuning, the model is innovatively utilized for entity standardization of relation perception, and the accuracy of knowledge fusion is remarkably improved.

The invention discloses a cross-system data processing method and device, equipment and a medium, and relates to the field of computer data processing. The method comprises the following steps: receiving a data calling request, wherein the data calling request comprises data extraction information and a requester system identifier; determining a requester data format based on the requester system identifier; obtaining source data from a provider system according to the data extraction information, wherein the source data is in a provider data format; converting the source data from a provider data format to a requester data format, and generating target data adapted to a requester system; and transmitting the target data to the requester system according to the requester system identifier. Through the method, the technical problems of inaccurate data processing and low efficiency among the systems caused by inconsistent data formats of multiple service systems in a power grid scene in the prior art are solved, and the technical effect of improving the accuracy and efficiency of data processing among the service systems of a power grid is achieved.

The invention provides a sleep apnea real-time early warning system based on heart rate variability analysis, belongs to the technical field of computer data processing, and provides a data processing method which is used for dynamically selecting a monitoring terminal, adaptively adjusting an analysis threshold value based on a sleep situation and carrying out time sequence correlation verification on a heart rate and a sound signal. And the sleep apnea event monitoring accuracy and the system operation efficiency are improved. The monitoring terminals are dynamically selected, the predictive switching strategy is adopted, continuity and high quality of heart rate data streams input into the data processing system are ensured, the data acquisition modes caused by body position changes of the user in the sleep period are switched, subsequent analysis errors are avoided, and the user experience is improved. By determining situation information such as sleeping postures of a user in real time and dynamically adjusting an analysis threshold value of heart rate variability, a data processing model can intelligently adjust the sensitivity of the data processing model according to the risk level, and deep time sequence correlation verification is carried out in combination with heart rate and sound signals, so that collaborative analysis of multi-modal information is realized.

The invention provides a multi-stage electronic medical record generation method and system based on a structured thinking chain, and relates to the technical field of computer data processing, and the method comprises the steps: generating at least one medical record draft intermediate state based on input original medical data through a structured thinking chain processing flow, the medical record draft intermediate state comprises at least one specific paper to be verified; starting a double-path knowledge calibration mechanism, and executing calibration on the intermediate state of the medical record draft, the calibration comprising: a forward knowledge enhancement path: in response to a specific disguise, retrieving associated evidence related to the specific disguise from one or more authoritative knowledge bases; and a backward fact checking path: utilizing a small reasoning model to carry out consistency judgment on the specific discourse and the associated evidence; and correcting the intermediate state of the medical record draft based on the result of the consistency judgment so as to generate a calibrated electronic medical record. According to the method, the fact accuracy and credibility are improved, and the structural integrity and logic self-consistency are ensured.

The invention provides a retail store goods reporting data processing method and system based on a sales volume prediction model, and relates to the technical field of computer data processing, and the method comprises the steps: 1, synchronizing store sales data, real-time weather data, holiday and festival information, promotion activity information and store real-time transaction data through a real-time data collection interface, and carrying out the real-time data collection of the store sales data, the real-time weather data, the holiday and festival information, the promotion activity information and the store real-time transaction data; constructing a multi-dimensional dynamic data set; 2, based on the multi-dimensional dynamic data set, identifying data distribution characteristics to establish an analysis reference so as to obtain two groups of data sequences representing different sales volume influence dimensions, calculating the correlation degree between the data sequences, and generating a sales volume change characteristic interval; according to the invention, the real-time inventory of the store, the supply chain period parameter and the sales prediction result are integrated, accurate generation of the goods reporting instruction and automatic circulation of the whole process of goods reporting are realized, and the efficiency and accuracy of goods reporting are improved.

The invention belongs to the technical field of computer data processing and analysis, and particularly discloses a landslide deformation monitoring data anomaly detection method and system based on real-time data, and the method comprises the steps: carrying out the automatic collection and carrying out the self-calibration and quality evaluation when key induction factors such as heavy rainfall occur, it is ensured that complete and accurate original information can be obtained at the moment where accurate data is most needed; data are deeply purified by means of deep learning, time-frequency analysis and the like, and an early warning threshold value is dynamically calculated in combination with a geomechanical model and historical data, so that an anomaly judgment standard can adapt to complex and changeable geological environment conditions in real time, and false alarm and missing alarm caused by dependence on a static or subjective threshold value are avoided; through an integrated landslide type identification and trend prediction module, crossing from data anomaly detection to disaster evolution situation awareness is realized. Through seamless linkage of an early warning mechanism and intelligent decision support, the efficiency and pertinence of emergency response are greatly improved.

The invention provides an RV reducer online fault diagnosis method, and belongs to the technical field of reducer fault diagnosis based on computer data processing. The method comprises the following steps: firstly, constructing a fault data set covering multi-modal signals and multiple working conditions, and integrating vibration, current and high-frequency elastic stress wave data; performing segmentation, time-frequency conversion and multi-modal feature fusion on the original time series data to generate a three-channel time-frequency feature map, and strengthening visual expression of fault features; then constructing a domain adversarial attention neural network, and realizing cross-working-condition fault feature migration and accurate classification through adversarial training of a feature extractor, a fault classifier and a domain classifier; and finally, deploying an online diagnosis model, generating a health indicator in combination with the health state baseline model, predicting the remaining service life through a long short-term memory network, and completing fault early warning and life evaluation. According to the method, accurate fault identification and residual service life prediction under complex working conditions are realized, and reliable technical support is provided for the RV speed reducer.

The invention discloses a webpage similar structure identification method and device, and relates to the technical field of computer data processing, and the method comprises the following steps: storing webpage data in a byte array form; sequentially traversing each byte in the byte array, determining an initial position of each HTML tag of the webpage in the byte array, and writing a byte data value of the initial position of each HTML tag and a byte data value corresponding to an HTML tag name into a hash function; calling a hash function to generate hash abstracts, and taking a set of the hash abstracts as a webpage structure ID; judging the similarity between the two web pages by comparing the web page structure IDs of the two web pages; or identifying the Web application associated with the target webpage by comparing the matching degree between the target webpage structure ID and the corresponding webpage structure ID stored in the Web application fingerprint database. The structural features of the web pages are efficiently and robustly extracted, the unique ID is generated, and the pages with similar structures in massive web pages are quickly identified.

The invention provides a self-adaptive cross damping flutter suppression method for blades of an offshore wind turbine, and belongs to the technical field of wind power generation based on computer data processing. The method comprises the following steps: firstly, establishing an aeroelastic model containing a non-diagonal damping item, and coupling sinking and floating motion and pitching motion through a cross damping coefficient; secondly, designing a self-adaptive control law based on a differential Riccati equation, and dynamically adjusting a damping parameter to compensate the time-varying aerodynamic force; and finally, proving closed-loop stability through a Lyapunov theory, and verifying a control effect by utilizing numerical simulation. An external pneumatic executing mechanism is not needed, the structure is simple, response is fast, the vibration convergence time can be shortened to be within 4 seconds at the wind speed of 8 m / s, the steady-state error approaches zero, and the method is suitable for flutter suppression of the blades of the large offshore wind turbine.

The invention provides a method for acquiring the continuous curvature of a segmented variable cross-section tower drum structure of an offshore wind turbine, and belongs to the technical field of ocean engineering structure monitoring based on computer data processing. Comprising the following steps: acquiring discrete strain data of the inner surface of a measurement point of a segmented tower tube structure in real time; obtaining the discrete curvature data of the inner surface of the tower drum through the strain data and the wall thickness of the measurement point; interpolating the discrete curvature data subsets of the measuring points into an inner surface continuous curvature function; verifying the continuous curvature function of the inner surface according to the check points; deriving a continuous curvature function of the outer surface of the tower drum through the curvature function of the inner surface and the wall thickness; and all the segments are repeated, and a global curvature function of the whole tower structure is constructed. According to the method, the curvature of the outer surface is strictly derived from the continuous curvature of the inner surface, so that approximate errors of a traditional thin-wall beam theory in a segmented variable cross-section, thick-wall or large-deformation scene are fundamentally avoided, and the overall deformation state of the tower drum is more truly restored.