944 results about "Electronic data processing" patented technology

The invention provides a high-temporal-spatial-resolution surface temperature reconstruction system for urban thermal environment refined monitoring, and relates to the field of electric digital data processing. Comprising a multi-source heterogeneous data intelligent acquisition and preprocessing module, an urban thermal environment multi-dimensional feature knowledge modeling module, a physical constraint deep learning surface temperature reconstruction module and an intelligent monitoring early warning and decision support module. The multi-source heterogeneous data intelligent acquisition and preprocessing module is responsible for collecting and preprocessing various remote sensing and ground observation data, and the urban thermal environment multi-dimensional feature knowledge modeling module constructs a knowledge system of an urban underlying surface, a three-dimensional form and a thermal process. The physical constraint deep learning surface temperature reconstruction module is used for realizing accurate reconstruction of high temporal-spatial resolution surface temperature, and the intelligent monitoring early warning and decision support module converts a reconstruction result into visual display, risk early warning and regulation and control decision suggestions; according to the system, high-precision, physically consistent and interpretable urban surface temperature reconstruction can be realized.

The invention relates to the technical field of electrical digital data processing, and discloses a statistical entropy and multi-factor fused text similarity data processing method, which comprises the following steps that: a processor extracts substring sets which do not contain maximum common values of a first data sequence and a second data sequence, and calculates the quadratic sum of the lengths of substrings to generate local statistical entropy; traversing the maximum common substring set to obtain storage address indexes of the maximum common substring set in the first data sequence memory space and the second data sequence memory space, and constructing a topological mapping vector of a mapping structure displacement relationship; calculating the total number of inverted pairs of the topology mapping vector by using a merge sorting algorithm, and generating a normalized topology dissipation index; and by taking the local statistical entropy as an information carrier and taking the topological dissipation index as a structural damping factor, executing nonlinear damping modulation operation to obtain a final similarity score, and solving the technical problem that the block-level displacement cannot be identified by linear scanning logic by quantizing topological entropy increase of data distributed in a storage space.

The invention relates to the technical field of electrical digital data processing, and discloses a source code security vulnerability semantic detection method based on a large language model, which comprises the following steps: analyzing a source code to be detected to extract execution path constraint features, and constructing an orthogonal feature base vector sequence through orthogonalization feature extraction; inputting the source code to be tested into the large language model to obtain an initial semantic tensor; orthogonal projection is carried out on the initial semantic tensor to a code security constraint subspace constructed by an orthogonal feature basis vector sequence, weighted aggregation is carried out in combination with attention weight distribution information entropy, and a refined semantic vector is generated; according to the hidden logic offset vulnerability recognition method, business semantic noise is eliminated by utilizing a logic subspace projection mechanism, the association between a detection conclusion and code execution logic is established, and the precision of recognizing hidden logic offset vulnerabilities is improved.

The invention relates to the technical field of electrical digital data processing, and discloses a system conflict point intelligent review system based on a knowledge graph and a large language model, which comprises the following steps: constructing a dual-mode storage space containing an unstructured index and a structured logic graph, analyzing target text extraction features and triggering graph-based generation logic; converting the topological structure of the associated sub-atlas into a natural language instruction sequence to construct a forced logic constraint template, filling the template with a text, and inputting a pre-training language model to generate a verification result; according to the method, the discrete atlas topology is mapped into the linear logic constraint, random divergence of the generative model is restrained on the calculation principle, and precise decoupling and dynamic evolution of unstructured semantics and structured logic are achieved.

The invention provides a blood pressure dynamic monitoring system integrating overall risk and local anomaly detection, and relates to the field of electric digital data processing. Comprising a multi-source blood pressure data fusion and acquisition module, a dual-path feature learning and abnormity pre-detection module, a local and overall interactive abnormity accurate identification module and a dynamic risk assessment and intelligent early warning decision module, and the multi-source blood pressure data fusion and acquisition module is used for acquiring blood pressure and context data; the dual-path feature learning and anomaly pre-detection module is used for extracting time sequence features and performing preliminary anomaly screening, and the local overall interactive anomaly accurate identification module is used for detecting and analyzing local anomaly. The dynamic risk assessment and intelligent early warning decision module is used for comprehensively assessing the risk, forming a feedback optimization mechanism and outputting a personalized early warning decision; the system can accurately identify abnormal blood pressure, realizes accurate assessment and prediction of risks, and provides effective support for clinical decision and personalized health management.

The invention relates to the technical field of electric digital data processing, and discloses a multi-dimensional data logic processing method based on an artificial intelligence algorithm, which comprises the following steps: receiving a target discrete data packet, extracting metadata, and calculating to generate a dimension entropy feature vector representing data logic complexity; inputting the vector into a preset topological mapping model, and outputting an initial adjacent matrix; calling a feedback suppression mask matrix generated based on a historical operator utility state, and executing bitwise logic AND operation with the initial adjacent matrix to generate a corrected effective topological matrix; the matrix is analyzed, a logic operator function pointer is dynamically indexed in an instruction cache, and a directed acyclic execution linked list is constructed; according to the method, redundant logic nodes in AI prediction are definitely eliminated through a bit operation mask mechanism based on historical feedback, and deterministic convergence of processing delay and optimal matching of computing power resources are achieved.

The invention relates to the technical field of electrical digital data processing, and discloses a train ice melting simulation optimization method of an electromagnetic thermal coupling model fused with a deep learning method, which comprises the following steps of: inputting a geometric representation tensor and a physical working condition parameter vector containing an electromagnetic excitation frequency and a reference environment temperature into a feature mapping neural network; outputting a dual-channel space source item tensor containing basic heat source power density and a heat source to temperature change sensitivity distribution matrix through nonlinear convolution operation; constructing a heat conduction discrete numerical value evolution operator configured with an active item linear correction interface; time stepping operation is executed according to the heat conduction time scale, a basic heat source is corrected in real time through the Hadamard product of a sensitivity distribution matrix and temperature deviation, and an operator is substituted for solution. On the premise that electromagnetic-thermal nonlinear coupling characteristics are reserved, decoupling of the time scale is achieved, and the calculation efficiency in high-frequency physical field simulation is effectively improved.

The invention relates to the technical field of electrical digital data processing, and discloses a lightweight-based multi-modal data enhancement method, which comprises the following steps: acquiring a multi-modal feature tensor; extracting a vector orthogonal projection scalar and determining the novelty; updating the global covariance matrix when the novelty is greater than a redundancy threshold, and maintaining the global covariance matrix in a register state when the novelty is not greater than the redundancy threshold; extracting a main diagonal variance component to determine a differential modulation coefficient; calculating a second-order moment manifold projection operator according to the global covariance matrix, and calibrating the operator by using a differential modulation coefficient; the calibrated operator is used for carrying out orthogonal projection on random noise to generate a structured disturbance vector, the structured disturbance vector is superposed to a multi-modal feature tensor to output enhanced features, a novelty judgment mechanism is used for restraining statistical deviation caused by steady-state redundant data, computing resource occupation is reduced, and it is ensured that semantic alignment between modals is maintained in enhanced feature distribution.

The invention relates to the technical field of electric digital data processing, in particular to a radiator working state monitoring method and system based on data analysis, and the method comprises the steps: collecting multi-dimensional parameter data of a radiator at different moments, and taking the multi-dimensional parameter data of the radiator at the same moment as a data point; clustering all the data points to obtain a plurality of clusters; the abnormal degree of any cluster is calculated, the abnormal degree is in inverse correlation with the number of the data points in each cluster and is in positive correlation with the standard deviation of the data points in each cluster, the data points in the cluster with the abnormal degree larger than a set threshold value are responded to be abnormal data points, and it is judged that the radiator is abnormal at the moment corresponding to the abnormal data points. The problem that the monitoring efficiency and accuracy are not high in the process of monitoring the abnormality of the radiator is solved.

The invention relates to the technical field of electrical digital data processing, and discloses an AI semantic enhanced unstructured manufacturing document data structuring method, which comprises the following steps: acquiring a character recognition stream of a target document to extract a semantic anchor point and topological coordinates thereof, and vectorizing a to-be-structured field to generate a to-be-processed vector; querying an engineering logic mapping table to determine association intensity, and calculating a logic gravitational field intensity value of the to-be-processed vector relative to the semantic anchor point according to the association intensity; associating the to-be-processed vector to a target semantic anchor point according to the field intensity value, starting a logic polarization program when the to-be-processed vector is identified to be interfered by the homogeneous semantic anchor point in the association stage, extracting bias characteristics to generate a polarization vector, and modulating the native gravitational field intensity; according to the method, the attribution ambiguity of similar semantic entities in a dense distribution scene is solved by introducing the logic gravitation constraint, the structured conflict caused by spatial distribution and engineering logic decoupling is eliminated, and the topology consistency in the data conversion process is ensured.

The invention relates to the technical field of electrical digital data processing, and discloses a data analysis system for dynamic monitoring and intelligent prediction of physical parameters and an implementation method, and the method comprises the steps: setting a sensor node comprising a measurement oscillation circuit and a reference oscillation circuit, so as to obtain a differential frequency signal which is mapped by a physical agent and is subjected to temperature compensation; and the central processing unit calculates the drift rate of the differential frequency signal to predict chronic degradation, and triggers the sensor node to switch to a multi-principle electrical detection mode when the rate exceeds a threshold value so as to diagnose the cause of an acute event. The inherent contradiction between the high sensitivity and the wide temperature range operation universality of the existing monitoring technology is avoided, meanwhile, the system is endowed with the capability of performing cause discrimination on the acute sudden risk, and the monitoring precision and the decision effectiveness are remarkably improved.

The invention relates to the technical field of electric digital data processing, and discloses an intelligent data processing method for clinical research. The method comprises the following steps: constructing a basic clinical knowledge graph fused with an international medical ontology; the method comprises the following steps: performing deep learning driven entity recognition and knowledge graph linking on original data from heterogeneous sources such as an electronic medical record and an inspection system; executing cross-source entity alignment and knowledge fusion based on the graph attention network; performing automatic data quality verification and restoration according to clinical logic rules embedded in the atlas; and finally, extracting and generating a standardized analysis ready data set from the enhanced atlas according to research requirements. According to the technical scheme, high-quality, automatic and semantic integration and flexible delivery of clinical research data are realized, and the data processing efficiency and research reliability are improved.

The invention relates to the technical field of electric digital data processing, in particular to a shock absorber data processing method and system based on digital twinning, and the method comprises the steps: obtaining a multi-channel data signal of a shock absorber, and constructing a panoramic vibration data tensor; extracting a discrete digital sequence on a panoramic vibration data tensor time dimension, executing a variational mode decomposition algorithm, and separating and extracting a transient impact feature vector flow; constructing a double-time-axis sliding mapping window, and mapping the transient impact feature vector flow to a future node of a prediction response time axis based on a front and rear wheel preview mechanism of a vehicle wheelbase; operating the time sequence recurrent neural network model, and predicting and generating a target state data matrix of future nodes; and in the numerical constraint space, a prediction type damping adjustment instruction data packet is solved and generated through a rolling optimization algorithm. Through prospective state prediction and optimization solution, the problem of data timeliness caused by calculation delay in digital twinning is solved, and real-time, predictive and safe adjustment of the shock absorber is realized.

The invention relates to the technical field of electric digital data processing, and discloses a power cable laying planning simulation method and system based on digital twinning, and the method comprises the steps: constructing a digital twinning space containing a power equipment three-dimensional model and a cable laying path model based on a real power system, and determining the geographic positioning information of the digital twinning space; generating an optimal laying path according to the cable starting point and terminal point positioning information; responding to an automatic planning instruction, and dynamically simulating a laying process in a twin space in combination with a safety verification rule; and meanwhile, a user is supported to manually edit a path and carry out safety distance verification in real time. According to the method, a compliant and efficient cable laying scheme can be automatically generated, the material consumption is accurately counted, and the design precision and the construction efficiency of electric power engineering are remarkably improved.

The invention relates to the technical field of electric digital data processing, and discloses a multi-level circulation box quantity distribution scheduling method and system, and the method comprises the steps: extracting a request numerical value in a resource request instruction, comparing the request numerical value with a reference limit, and when the request numerical value exceeds a preset triggering proportion, executing the step 1; splitting the resource request instruction into an activated detection sub-instruction and a frozen main body sub-instruction; issuing a detection sub-instruction and receiving a processing state updating signal fed back by the target node; calculating a state circulation rate of the detection sub-instruction, and when the state circulation rate is higher than a preset circulation rate threshold value, unfreezing the main body sub-instruction and triggering an issuing operation; according to the invention, through an active interaction mechanism of data instruction fragmentation and sequential logic gating, prediction of an uncertain demand is converted into determinacy verification of instant physical feedback; the problems of system resource locking and scheduling deadlock caused by input data distortion in a distributed network are solved.

The invention discloses a multi-variable measurement sensor state lightweight evaluation method based on knowledge distillation, and belongs to the technical field of electric digital data processing and multi-sensor data fusion. The method comprises the following steps: firstly, carrying out time synchronization and physical consistency constraint modeling on original data of multiple sensors, and extracting feature representation; a high-precision teacher model is trained at the cloud end, and the high-dimensional mapping relation of the sensor state is learned; intermediate features, soft output and uncertainty information of a teacher model are extracted to serve as distillation knowledge, lightweight student model training is guided, and effective migration of discrimination knowledge is achieved in combination with soft label constraint, feature alignment and an uncertainty guiding mechanism; and finally, compressing, quantifying and optimizing the student model, and deploying the student model to a vehicle-mounted end to realize real-time evaluation and dynamic updating of the states of multiple sensors. According to the method, the model complexity is greatly reduced while the evaluation precision is ensured through a knowledge distillation framework, and efficient and reliable state perception and fault-tolerant control support is provided for an intelligent driving system.

The invention provides a ticket business full-life-cycle anti-counterfeiting and verification system based on block chain trusted triple mapping, and relates to the field of electric digital data processing. Comprising a trusted triple construction and cryptography binding module, a block chain hash anchoring and consensus evidence storage module, a trusted triple dynamic evolution and state transition module and a multi-level anti-counterfeiting verification and anomaly detection module, the block chain Hash anchoring and consensus evidence storage module is used for storing a trusted triple in a block chain in a tamper-proof manner, and the trusted triple dynamic evolution and state transition module is used for realizing state tracking and evolution management of ticket business in a full life cycle. The multi-level anti-counterfeiting verification and anomaly detection module is used for providing static identity verification, dynamic anti-counterfeiting vouchers and abnormal behavior detection capability; according to the system, the security problem in a ticketing system is solved by enhancing the security of the entity identifier and protecting state transition privacy.

The invention discloses an intelligent key response method, a user terminal, a storage medium and a program product, and relates to the field of electric digital data processing.The method comprises the steps that based on first time sequence touch data obtained from a sample user and collected when a mouse device does not generate a displacement signal, a two-hand mistaken touch recognition model is obtained through training; based on second time sequence touch data acquired from the sample user and acquired during the period that the mouse device generates the displacement signal, training to obtain a single-hand mistaken touch recognition model; during the operation period of the user terminal, monitoring a target displacement signal of the mouse equipment; according to the target displacement signal, determining one of a two-hand mistaken touch recognition model and a single-hand mistaken touch recognition model as a current effective recognition model; inputting the real-time touch data into the current effective recognition model for calculation to obtain a key position effective score; and when the key position effective score is lower than a preset effective threshold value, preventing a corresponding error key pressing signal. By implementing the method and the device, the misjudgment detection accuracy on the high-sensitivity keyboard can be improved.

The invention relates to the technical field of electric digital data processing and energy storage system safety control, in particular to an optical storage and charging integrated station energy storage system. Comprising a data acquisition module which is used for acquiring a state feature vector of an energy storage system, planned scheduling power of an upper-layer energy management system, an external power grid interference index, and a real-time safety state and an economic value index of a battery management system in real time; the risk assessment module is used for generating a predictive risk score; the decision threshold module is used for calculating a dynamic intervention threshold; the arbitration decision module is used for generating a negative sign; the instruction correction module is used for determining a derating factor in response to the override mark and generating a correction scheduling instruction; or when the negative sign is not generated, outputting the planned scheduling power as a corrected scheduling instruction. According to the invention, early warning and active prevention of the potential thermal runaway risk are realized, and the intrinsic safety level and the operation reliability of the system are obviously improved.

The invention discloses an auditing report automatic generation method based on natural language processing, and relates to the technical field of electronic data processing, and the method comprises the steps: collecting multi-source heterogeneous data, building a corresponding relation among auditing items, auditing evidences, regulation basis and auditing states according to an auditing record, time information and a document identifier, and generating auditing associated data; constructing an audit evidence constraint graph according to the audit associated data, calculating the evidence support degree, conclusion conflict degree and risk propagation intensity of each audit item, and generating a core audit item set; extracting evidence nodes, law and regulation basis nodes and risk analysis results corresponding to the audit items from the core audit item set, and performing combination and arrangement to generate audit report paragraph data; and performing consistency matching on the audit report paragraph data and the corresponding audit evidence and regulation basis to generate an audit report first draft. According to the method, the evidence correspondence, the regulation suitability and the conclusion consistency of the paragraph content in the audit report generation process are improved.

The invention relates to the technical field of electric digital data processing, and discloses a data processing system for message management, which comprises a look-ahead buffer unit for maintaining a sliding window with variable window size parameters and pre-reading a message sequence; the mapping logic circuit is used for converting the resource identifier into a resource request mask; the state register is used for storing a global occupancy bitmap for representing resource occupancy; the scheduling controller is used for distributing messages according to bit operation results, counting bitmap setting quantity based on the population counting instruction to generate a resource saturation index, and then adjusting window size parameters in a negative feedback mode. The resource contention entropy value is directly sensed by utilizing a hardware instruction, nanosecond-level self-adaptive breathing control of a scheduling window is realized, and the service life of the scheduling window is prolonged. The problem of physical computing power collapse caused by bitmap scanning jitter in a high-concurrency scene is solved.

The invention discloses a WAL log playback method based on a PostgreSQL database, and relates to the technical field of electric digital data processing. The method comprises the following steps: analyzing a read WAL log to obtain basic meta-information corresponding to the WAL log; according to the basic meta-information, identifying a playback dependency relationship between the WAL logs from a plurality of preset dependency dimensions so as to construct a playback dependency graph corresponding to the WAL logs; dividing the WAL log into corresponding playback execution units and issuing the playback execution units to a playback execution thread so as to execute a WAL log playback operation; predicting a playback pressure level corresponding to the main control process, and adjusting a parallel scheduling strategy of a parallel playback unit according to the playback pressure level; and when the playback of all the WAL logs is completed, controlling the PostgreSQL database instance to exit from the recovery state.

The invention discloses a method for managing service nodes capable of being dynamically expanded based on a Camuda process engine, which belongs to the technical field of electric digital data processing and comprises the following steps of: uniformly configuring service process nodes into a pointing proxy delegation class; querying an external mapping table based on the execution context to obtain a business logic unit identifier; obtaining metadata from a logic registration center according to the identifier, and dynamically loading and instantiating a business logic implementation class through a sandbox class loader; driving service logic execution through a security context object of a white list only exposure method; and recording a full-link audit log. According to the method, structural decoupling of process definition and service logic is realized, so that the process definition does not need to be modified during service change, and dynamic replacement and gray release are supported; through sandbox isolation and security agent, isolated operation of dynamic codes and security and stability of an engine are ensured; and in combination with an audit tracking and fusing mechanism, the observability, the reliability and the operation and maintenance efficiency of the system are improved.

The invention discloses a multi-dimensional metering system and metering method based on an FPGA, and belongs to the technical field of electronic data processing, and the system comprises a data collection module which is used for obtaining and preprocessing multi-source sensing data; the simulator construction module is used for constructing a multi-physics field interference simulator and loading a geometric model and a physical response coefficient of a reference shadow workpiece, the reference shadow workpiece is digital representation of an actual measured workpiece, and the geometric model and the physical response coefficient of the reference shadow workpiece are mutually decoupled data sets; the vector field generation module is used for generating a pre-distortion vector field based on the output of the interference simulator and the reference shadow workpiece; and the interference identification module is used for extracting primary features from the multi-source sensing data. Through constructing a multi-physical field interference simulator and generating a pre-distortion vector field, physical state drift of a cross-sensing channel is accurately modeled, a multi-sensor data fusion error is reduced, and data consistency and reliability in a high-speed dynamic measurement scene are improved.

The invention provides an operator-level data consanguinity automatic generation method based on a large model, and relates to the field of electric digital data processing, and the method comprises the steps of S1, heterogeneous script preprocessing and semantic normalization, S2, symbol enhanced LLM consanguinity reasoning, S3, causal logic verification and consanguinity correction, S4, dynamic adaptation, and S5, consanguinity intelligent analysis and visualization. The method is suitable for multi-source, multi-format and multi-language data processing scenes, the automation degree, the refinement capability and the intelligent level of data blood relationship construction can be improved, and more accurate and efficient technical support is provided for data governance, data security, system migration / reconstruction, data quality management and control, cost optimization and the like.

The invention discloses a point cloud-based reverse modeling curved surface generation method and system, and relates to the field of electric digital data processing, and the method comprises the steps: obtaining three-dimensional point cloud data of a to-be-processed engineering design product, and projecting the three-dimensional point cloud data to a preset cutting plane to obtain a two-dimensional projection point set; and performing dimension reduction processing on each projection point, calculating a local geometrical relationship between the projection point and a neighborhood point, and mapping the local geometrical relationship to a one-dimensional parameter space to obtain a one-dimensional parameter value. Clustering analysis is conducted on the two-dimensional projection point set based on the one-dimensional parameter values, the two-dimensional projection point set is divided into a plurality of point cloud clusters, and each point cloud cluster corresponds to one geometric feature curve. And sorting the projection points in each point cloud cluster according to a spatial position relationship to obtain an ordered point sequence. And identifying the geometric feature type of each ordered point sequence, performing curve fitting, and extracting parameter information to obtain the feature parameters of the engineering design product. And generating an accurate curved surface model according to the characteristic parameters. By implementing the method, the accuracy of point cloud clustering can be improved.

The invention provides an urban built-up area air quality space-time evolution and exposure risk prediction method, and relates to the technical field of electrical digital data processing.The space-time evolution method comprises the steps that a to-be-monitored target area is discretized into a cubic sampling domain, an execution flight path of an unmanned aerial vehicle is planned, and a sparse data set containing five-dimensional features is constructed; executing iterative up-sampling operation, and converting the sparse data set into a dense feature point set covering a sampling domain; calculating a pollutant transmission flux vector of each node in the dense feature point set, constructing a weighted reverse tracking vector pointing to a suspected source, and constructing a three-dimensional traceability probability cone in combination with turbulence intensity; extracting a centroid offset coefficient and an axial dispersion, and identifying an emission working condition label of the pollution source through an emission working condition ternary discrimination model; executing multi-source probability field superposition fusion, and determining an estimation coordinate of a pollution source; and executing forward space-time diffusion simulation based on the estimated coordinates, and outputting a three-dimensional risk early warning graph.

The invention discloses a method and system for packaging a webpage embedded sign language translation JS library, and belongs to the field of electrical digital data processing.The method comprises the steps that by constructing an ECS core architecture with perception feedback, a bidirectional data flow between modules is established to achieve dynamic collaboration of rendering and grammar; predictive streaming resource management is adopted, and resources are intelligently preloaded based on gesture sequence contexts; multi-instance physical isolation is realized by utilizing a sandboxed container and a ShadowDOM (Document Object Model); implementing multi-level adaptive rendering, and dynamically switching a rendering path and a frame rate according to equipment performance; and finally, shielding the bottom layer difference through cross-end unified rendering of the abstraction layer. According to the method, the problems of animation and semantic dislocation, resource loading conflict and poor equipment compatibility in a multi-user concurrent scene are effectively solved, and high-fidelity, low-delay and cross-end consistent experience of sign language translation animation is realized.

The invention relates to the technical field of digital data processing, in particular to an automatic session quality detection method applied to a customer service scene. The detection method comprises the following steps: model training: selecting an LLaMA pre-training large language model as a core engine, and guiding training by means of cue words with session key element extraction and semantic consistency comparison functions; judging the format of the target session data to be subjected to quality detection, and converting the voice format into a text format; performing key element extraction and comparative analysis on the session data in the text format by means of the trained model; consistency comparison is carried out on the robot reply and a corresponding standard reply in a knowledge base; verifying whether business data contained in the robot reply is consistent with real data of a back-end business system; and generating a detailed report of the quality inspection result. Defects are accurately recognized, understood and expressed by means of the large model reasoning ability, and robot reply and a semantic verification closed loop of a knowledge source are achieved.

The invention discloses a linear scanning tire image target detection method and system, and relates to the field of electrical digital data processing, and the method comprises the steps: a YOLOV8 network employs an FAE feature extraction module to replace original convolution, extracts channel features through depth separable convolution, and obtains a transverse scanning feature map through point-by-point convolution fusion. Inputting the feature map into a CCA mixed attention module, executing global maximum pooling to obtain a position code, and weighting with channel attention to obtain an adjustment weight feature map; and the shallow and deep features of the backbone network are spliced through a tiny target detection layer to form a fusion feature map, a detection head is input to execute rotation frame prediction, and a target frame containing a rotation angle is output. Angle classification loss and frame regression loss are calculated, a total loss value is obtained through weighted summation, and network parameters are updated through back propagation to complete training. And inputting the to-be-detected tire long image into the trained network to output a character region detection result. By implementing the method, the detection precision of the character target in the linear scanning tire image can be improved.