644 results about "Analytical algorithm" patented technology

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

The invention discloses a smart factory management system and method based on MES, and relates to the technical field of smart manufacturing, and the method comprises the steps: calculating a health degree score based on equipment historical maintenance records, and generating a time-space correlation multi-dimensional analysis data set containing an equipment topological structure and health degree parameters in combination with a digital twin network; performing spatial-temporal feature coupling and topological weight dynamic adjustment on the spatial-temporal correlation multi-dimensional analysis data set through a dynamic topological analysis algorithm, and generating a high-risk equipment list and an anomaly control instruction set; based on the high-risk equipment list, constructing a standardized multi-dimensional abnormal feature vector, and generating an aging weighted danger level signal through an entropy weight method; according to the method, space-time alignment of equipment operation state data and physical topology data is realized through a multi-rate Kalman filtering algorithm and an iterative nearest point algorithm, a high-fidelity digital twin network is constructed in combination with a dynamic graph convolutional network, and accurate anomaly detection and health degree evaluation are supported.

The invention discloses an intelligent operation and maintenance fault early warning method for a rail transit variable-frequency power supply, and relates to the technical field of power electronics and intelligent operation and maintenance, and the method comprises the following steps: setting a multi-point synchronous collection device to carry out the high-precision real-time collection of voltage and current signals of a variable-frequency power supply system, and carrying out the digital coding processing of data; and performing frequency domain analysis on the acquired voltage and current signals by using a preset spectrum analysis algorithm, and extracting harmonic amplitudes and phase characteristic parameters of different orders. According to the invention, through high-precision synchronous acquisition and spectrum analysis, rapid and accurate identification of harmonic abnormity is realized; a machine learning model and dynamic threshold adjustment are fused, and the adaptability and intelligent recognition capability of the system to fault modes under multiple working conditions are enhanced; and a trend prediction and graphical early warning platform is introduced, so that the fault risk is pre-judged in advance and visually responded, and the operation and maintenance efficiency and the operation safety of the rail transit power supply system are remarkably improved.

The invention relates to the technical field of network security, and discloses a big data-based network security risk assessment method, which comprises the following steps of: 1, acquiring multi-source heterogeneous network security data; step 2, performing real-time cleaning and normalization processing on the data to generate a standardized feature vector; step 3, constructing a dynamic risk assessment model, and calculating a network node threat value by fusing a hybrid model of a correlation analysis algorithm, a random forest classifier and a long and short-term memory network; step 4, based on dynamic weight distribution of the threat value, optimizing a risk assessment result, and outputting a risk level and a protection suggestion; according to the method, the hybrid model fusing the correlation analysis algorithm, the random forest classifier and the long-short-term memory network is constructed and used for calculating the threat value of the network node, the advantages of different algorithms can be fully utilized through the hybrid model, the accuracy and robustness of risk assessment are improved, and meanwhile, the application of the dynamic weight distribution algorithm can improve the risk assessment accuracy and robustness. And the risk assessment result is more in line with the actual network condition.

The invention relates to the technical field of computer network management, and discloses an intelligent alarm preprocessing method of an adaptive rule engine, which comprises the following steps: firstly, acquiring real-time operation data of network equipment and a preset alarm baseline, and analyzing a historical alarm sequence through an association rule mining model to obtain the preset baseline; then inputting the data into an alarm decision model based on an event atlas analysis algorithm, processing the data by the model by using a multi-dimensional time window algorithm, calculating adaptive weight correction processing parameters in combination with node resource load parameters, and outputting strategy parameters; and finally, adjusting rule engine judgment logic according to parameters to realize alarm intelligent filtering and aggregation. In addition, a link emergency optimization step is provided. The method improves the accuracy and adaptive capability of alarm processing, and is suitable for a complex network environment.

The invention relates to a multi-point temperature control method and system based on intelligent temperature equalization control, and the method comprises the steps: collecting the temperature data of each region in frequency converter equipment in real time, and enabling the regions to comprise but not limited to a power module, a power module and a heat dissipation region; based on the temperature data, a deviation value between the temperature of each region and a preset target temperature is calculated through a preset analysis algorithm, and a temperature adjustment requirement of each region is generated based on the deviation value; based on the temperature adjusting requirements of all the areas, adjusting parameters of the cooling equipment are calculated through an optimization algorithm, and a corresponding control instruction is generated in combination with the load state of the frequency converter equipment; and the control instruction is transmitted to the cooling equipment, and corresponding parameters of the cooling equipment are adjusted according to the control instruction. The method has the effect of improving the temperature control precision.

The present invention relates to the field of virtual pre-assembly matching of bridge engineering components based on 3D point clouds, and particularly relates to a method for virtual pre-assembly matching of prefabricated beams based on design-measured point cloud models. An oriented bounding box is computed respectively for 3D point clouds of two prefabricated beams with assembly relationship therebetween, and two point cloud slices and design point cloud are formed; the two point cloud slices are respectively registered with the generated design point cloud by the iterative closest point algorithm; boundary features and corner features of a pre-assembly interface of two components to be assembled are fitted and extracted; and coarse matching and fine matching of the assembly interface are achieved by the Procrustes analysis algorithm and the iterative closest point algorithm in sequence, a matching degree error of the interface is computed, and an assembly result is evaluated. According to the present invention, design point cloud is introduced to achieve the screening of point clouds near features to be extracted, and posture adjustment on components to be assembled is performed by combining a plurality of algorithms, which not only improves the degree of automation and reduces the manual intervention, but also improves the precision of virtual pre-assembly matching and saves the computation time.

The invention provides an AI-based nuclear power station high-risk operation compliance real-time monitoring method and system, and the method comprises the steps: processing human body joint point coordinate data of an operator through employing an AI dynamic contour modeling algorithm, and generating a limb movement track sequence; scanning the preset operation area to generate a relative motion vector between the special operation tool for the nuclear power station and the operator; detecting an action deviation mode of the operator based on the limb movement track sequence, and generating a detection result; fusing the relative motion vector and multi-channel video stream frame synchronization data obtained by applying an AI video analysis algorithm to generate a space intrusion relation between the nuclear power station special operation tool and the nuclear power station high-risk equipment; and when the action deviation mode exists in the detection result and the space intrusion relation exceeds a preset safety threshold, generating an operation compliance signal. According to the invention, real-time dynamic evaluation of human motion compliance and tool equipment safety distance in the high-risk operation process of the nuclear power station is realized, and the safety monitoring precision and real-time early warning capability of high-risk operation are improved.

Artificially intelligent systems and methods for financial coaching provide personalized, fiduciary-compliant financial guidance through advanced machine learning architectures with measurable performance criteria. The systems implement privacy-preserving processing pipelines that detect personally identifiable information using multi-layered pattern recognition including regular expressions for formatted data sequences, named entity recognition with confidence thresholds above 0.85, and contextual analysis algorithms. A multi-step artificial intelligence processing workflow includes automated language detection, emotional tone classification with confidence scoring, financial profile transformation using predefined templates, context-aware question rephrasing, and semantic similarity matching employing vector embeddings with financial domain vocabulary weighting applying multiplier values between 1.3-2.0. Specialized training methodologies expand datasets through mathematical transformation functions utilizing statistical standard deviations with incremental variations between 0.5-2.0. Mood-based escalation logic automatically transfers users to human advisors when emotional indicators exceed confidence thresholds above 0.8. The systems maintain response times below 5 seconds while providing regulatory compliance through curated content sources and predefined fiduciary instruction parameters.

The invention relates to a context-driven fault solution suggestion generation method, which specifically comprises the following steps of: firstly, collecting various log data generated in system operation, and preprocessing the data as a basis for subsequent analysis; then, a BERTomaly model based on a multi-head cross attention mechanism is constructed, feature extraction, feature fusion and template matching can be performed on the log data, anomaly detection of system faults can be realized, and then, based on the system operation log data, the system fault detection efficiency is improved. The method comprises the following steps of: firstly, constructing a fault solution library which is of a context-driven type and has a dynamic updating capability by using a large language model in combination with the output of a BERTomally model and the output of an FP-Growth association analysis algorithm, and carrying out real-time detection on each fault, so that the fault solution library has a dynamic updating capability. And a targeted solution is output.

The invention discloses a liquid chromatography-tandem mass spectrometry detection method and system, and the method comprises the steps: designing a multi-dimensional gradient elution program according to the physicochemical properties of a target compound, and obtaining a chromatographic separation spectrogram; performing efficient ionization on the target compound according to the chromatographic separation spectrogram to obtain an ionization signal; according to the ionization signal, fragmenting the target ions, and obtaining a fragment ion spectrogram through fragment ion screening; according to the fragment ion spectrogram, a multivariable data analysis algorithm based on machine learning is adopted, a standard curve of a target compound and an interferent database are combined, quantitative analysis is carried out, and a quantitative result is generated through a dynamic interference correction technology in combination with principal component analysis and a partial least square method. By utilizing the embodiment of the invention, the sensitivity, the accuracy and the real-time performance of detection can be improved through multi-dimensional gradient elution, dynamic ionization and fragmentation parameter optimization in combination with an analysis means of machine learning.

The invention discloses an adaptive distributed network threat detection and response system and method, and the system comprises a data collection module which is used for capturing network traffic in real time and converting the network traffic into structured data; the data analysis module is used for performing parallel analysis on the structured data by adopting a plurality of anomaly detection algorithms; the algorithm fusion module is used for counting the abnormal judgment times of each algorithm on the same sample through a voting mechanism, and outputting a final classification result according to a preset threshold value; the method comprises the following working steps: concurrently capturing network traffic through distributed nodes, analyzing the network traffic into structured data, operating an isolated forest algorithm, a DBSCAN algorithm and a K-means algorithm in parallel, and outputting an anomaly judgment result; and fusing multi-algorithm results and generating final classification through a voting mechanism. The invention provides an efficient, intelligent and distributed network security protection system, which can effectively detect and respond to various network threats, and is particularly suitable for large-scale distributed environments and dynamic network security defense requirements.

A water and soil loss pattern spot landing system based on land utilization information weak constraint comprises a land utilization information acquisition module, a factor evaluation and analysis module, a time sequence analysis and pattern spot landing module and a data integration and visualization module. The factor evaluation and analysis module is used for calculating a water and soil loss erosion modulus and evaluating and analyzing influence factors, the time sequence analysis and pattern spot landing module is used for analyzing water and soil loss changes and generating landing pattern spots, and the data integration and visualization module is used for data integration and pattern spot visualization display. According to the water and soil loss pattern spot landing system based on the land utilization information weak constraint, an erosion modulus calculation algorithm based on data fusion and pixel unmixing is proposed to calculate an erosion modulus, and a water and soil loss pattern spot change analysis algorithm based on time sequence classification is proposed to analyze pattern spot change.

The invention discloses an intelligent warehousing dynamic optimization system based on a digital twinborn technology. The system comprises the following steps: S1, creating a digital twinborn model: acquiring basic data of an entity warehouse and constructing an accurate digital model; s2, data collection and integration: deploying Internet of Things equipment and realizing real-time transmission and processing of data; s3, constructing an analysis algorithm: developing an advanced analysis algorithm and implementing a machine learning model; s4, dynamic optimization and adjustment: automatically adjusting a storage layout and optimizing a warehouse-in and warehouse-out process; and S5, prediction and early warning: analyzing and predicting potential problems and setting an alarm mechanism based on historical data. According to the invention, the real-time performance and accuracy of data are obviously improved, the environment monitoring capability is enhanced, the cargo safety is guaranteed, and the space utilization rate is greatly improved.

The invention discloses an air conditioner resource allocation method and system based on a cold accumulation prediction algorithm, and relates to the field of air conditioner cold accumulation, and the method comprises the steps: collecting historical load operation data of an air conditioner, analyzing the historical load operation data through employing a grey relational analysis algorithm, and constructing a cold load prediction model in combination with the cold accumulation prediction algorithm; training the cold load prediction model by using a back propagation neural network algorithm, inputting load operation data collected in real time into the trained cold load prediction model, and outputting a cold load prediction result; and in combination with the cooling load prediction result and the electricity price information, an air conditioner resource allocation strategy is formulated, and a model prediction control algorithm is used for optimizing the allocation strategy. Through integration of the load prediction technology and the real-time electricity price information, the cold load demand of the whole day can be accurately predicted, energy waste is reduced, the energy utilization efficiency is improved, the dynamic electricity price market can be responded, and economic maximization is achieved.

The embodiment of the invention discloses a power grid load prediction method and device based on fractal analysis, and the method comprises the steps: obtaining historical load time series data of a power grid, and carrying out the preprocessing, and generating a standardized load data set; fractal features of the load data set are extracted by using a fractal analysis algorithm; judging dynamic behavior characteristics of the load time sequence based on fractal characteristics, adaptively selecting short-term prediction or long-term prediction model parameters, and dynamically adjusting the time scale of a prediction model to adapt to load fluctuation characteristics; analyzing an abnormal mode in the historical load time sequence based on fractal features, and generating an abnormal detection result; constructing a load prediction model based on the fractal features, the anomaly detection result, the model parameters and the external features; and predicting the power grid load in the future time period by using the load prediction model to obtain a prediction result. According to the power grid load prediction method provided by the invention, the prediction accuracy and stability are improved.

The invention belongs to the technical field of energy consumption optimization, and particularly relates to a digital twin building energy consumption dynamic optimization system, which comprises a modeling module for acquiring physical, structural and equipment layout parameters of a building, and constructing a target building three-dimensional region segmentation model containing geometric transmission constraints by combining three-dimensional modeling and a grid segmentation algorithm; the control mapping module is used for collecting data such as regional people flow density and equipment energy consumption, and constructing multi-level association control mapping including people flow-rate first association mapping, rate-energy consumption second association mapping and comfort level score-based second association feedback mapping through an association analysis algorithm; the analogue simulation module constructs a generator and a discriminator through association mapping, performs synchronous confrontation simulation in combination with a three-dimensional region segmentation model, and solves an optimal energy consumption rate; and the feedback adjustment module compares actual operation with feedback data, corrects mapping parameters and optimizes algorithm weights, realizes building energy consumption dynamic optimization, and ensures that the overall energy consumption and comfort level score meet preset thresholds.

The invention discloses a forest carbon sink dynamic monitoring system based on multi-source data fusion, and relates to the technical field of carbon sink monitoring, and the system comprises a multi-source data collection and fusion module which collects forest-related multi-source data, and generates a standardized fusion data set; the carbon sink reserve estimation module is used for constructing a carbon sink reserve estimation model according to the fused data set, and calculating forest carbon sink reserves based on a model output result; the carbon sink reserve prediction module is used for predicting the forest carbon sink reserve at the next moment by using a time sequence analysis algorithm; the state change monitoring module is used for analyzing a carbon sink dynamic change trend based on forest carbon sink reserve prediction data and historical reserve data; and the early warning and decision support module is used for triggering an early warning signal of a corresponding level based on the abnormal change information. Through fusion of federated learning, ARIMA time sequence analysis, a sliding window and a linear regression technology, efficient and continuous tracking of a carbon sink reserve change trend is realized, and change abnormity can be identified in time.

The invention relates to the technical field of multi-modal behavior anomaly detection scheme design, in particular to a multi-modal behavior anomaly detection method and system under the condition that encrypted traffic is not decrypted. The method comprises the following steps: capturing a network encrypted traffic data packet in real time; on the premise that decryption is not carried out, multi-mode non-decryption features such as flow layer statistics, time sequence interaction, encryption handshake and context association are extracted in parallel; establishing a dynamic normal behavior baseline model of each feature based on historical data; the real-time features are compared with the baseline model, comprehensive judgment is carried out through a multi-mode correlation analysis algorithm, and an anomaly detection result is output; and when the abnormal condition is judged, the network control equipment is automatically linked for blocking. The method thoroughly gets rid of dependence on traffic decryption, realizes high-precision and self-adaptive detection and rapid automatic response to abnormal behaviors in encrypted traffic through multi-dimensional feature fusion and dynamic baseline technologies, and effectively solves the problem of failure of a traditional detection technology in an encrypted environment.

The invention relates to a historical building disease diagnosis method and device assisted by artificial intelligence, equipment and a medium. The method comprises the steps of obtaining a multi-angle image sequence of a historical building and performing preprocessing to obtain a standardized building structure image, performing edge feature extraction on the standardized image to identify dislocation feature points to generate an edge information graph, and segmenting a dislocation region through a region growing algorithm based on the edge information graph to generate an initial dislocation region distribution graph. Performing deformation degree and displacement calculation on each region in the distribution diagram to determine a dislocation severity level, comparing dislocation region changes in each period through a time sequence analysis algorithm by combining an image sequence to generate a dynamic change mode, and optimizing boundary division according to the severity level and the dynamic change mode to generate a diagnosis report; automatic identification and evolution tracking of historical building dislocation diseases are realized, the problems of high subjectivity, fuzzy boundary judgment and lack of disease evolution prediction of traditional manual investigation are solved, and the scientificity and long-term effectiveness of repair decision are improved.

The invention provides a method for detecting the quality of a foundation steel bar welded joint, and belongs to the technical field of steel bar welded joint quality detection.The method comprises the steps that a stress distribution matrix is obtained through ultrasonic detection equipment, and a fatigue distribution matrix is established in combination with a fatigue analysis algorithm; a stress fatigue correlation analysis method and a bipartite graph maximum matching algorithm are used to construct a stress fatigue correlation matrix, a damage evaluation deep learning model based on a Transform architecture is used to generate a damage distribution matrix, and a time sequence prediction algorithm is used to consider a fatigue center change effect to establish a damage expansion trend matrix. A damage prediction matrix is deduced through a damage prediction deep learning model of a bidirectional long-short-term memory network, and finally, a quality detection result matrix is comprehensively evaluated and output by utilizing a game model, a multi-objective optimization algorithm and a gating weighting function, so that the technical problem that the quality detection accuracy of the steel bar welded joint is not enough is solved.

The invention relates to the field of financial information management, and discloses a fund exchange information management system based on big data analysis, and the system comprises the steps: obtaining multi-source fund exchange data based on a data collection interface, carrying out the data cleaning and standardization processing, and constructing a fund information basic database; extracting fund exchange feature parameters from the fund information basic database through a preset feature extraction rule, and generating a fund feature data set; and based on the fund feature data set, mining a fund exchange association relationship by adopting an association analysis algorithm, and establishing a fund exchange relationship graph. According to the method, the association relationship among the transaction subjects is fully mined by constructing the fund exchange relationship graph, and the risk assessment is performed in combination with the fund exchange characteristic parameters and the relationship graph characteristics, so that the problem of inaccurate risk assessment caused by neglecting the association relationship of the transaction subjects in a traditional mode is solved, and the accuracy of risk level judgment is improved.

The invention discloses a PDF contract file identification method and system, a medium and a program product, and relates to the technical field of information identification, and the method comprises the steps: carrying out the image analysis of an obtained PDF contract file through employing a deep learning model, generating an editable text, and synchronously identifying the page layout of the document, outputting structured data including page numbers, text paragraphs and coordinate information; performing multi-dimensional feature matching on the text paragraphs according to an adaptive semantic analysis algorithm, a preset contract template library and a dynamic keyword library, and positioning contract core element information; performing entity relationship verification on the content of the contract core element information, correcting an extraction error through dependency syntactic analysis and semantic role labeling, and establishing a contract element data set containing a confidence coefficient weight; and performing serialized packaging on the verified contract element data set to generate a contract element list conforming to electronic signature authentication. According to the invention, the processing efficiency and recognition precision of the PDF contract file are improved.

The invention provides a high-precision numerical control machine tool operation state abnormity monitoring method and system, and relates to the technical field of numerical control machine tool operation state abnormity monitoring. The method comprises the steps that the displacement deviation of a feeding system, the oil supply pressure of a lubricating system, real-time temperature data of a driving motor stator and original signals during machining are collected; the three are preprocessed to obtain a final time sequence data set, and an original signal is processed to obtain a normalized smooth physical signal; then obtaining associated features by using a multi-parameter association analysis algorithm, and comparing the associated features with a preset dynamic reference feature library to judge the running state; if the operation state of the high-precision numerical control machine tool is abnormal, the fault tree diagnosis model is used for positioning an abnormal root part and process parameters, then a targeted solution is generated, full-process monitoring of the operation state of the high-precision numerical control machine tool can be completed, and data collection, preprocessing, correlation analysis, abnormality judgment, and root positioning and targeted solution generation during abnormality are achieved.

The invention discloses an ANTLR4-based multi-manufacturer network equipment configuration analysis method and system, and the method comprises the steps: carrying out the preprocessing of a configuration file; based on a machine learning algorithm of multi-dimensional feature analysis, automatic identification of configuration file manufacturer types is realized; based on the identification result of the manufacturer type, loading an ANTLR4 grammar parser corresponding to the manufacturer type; a lexical analyzer in ANTLR4 realizes lexical analysis on the configuration file through lexical rule definition, a Token classification system and an optimized analysis algorithm; a syntactic analyzer in ANTLR4 constructs an abstract syntax tree according to a lexical analysis result and a syntactic rule defined based on a context-independent grammar; traversing the abstract syntax tree, and extracting key configuration information; and converting the specific configuration format of each manufacturer into a unified standard model. According to the method and the system, network equipment configuration files of different manufacturers can be automatically identified and analyzed, and key configuration information is extracted and converted into a unified model.

The invention relates to a human body health state assessment system and assessment method based on multi-point acquisition, and the system comprises an event sensing module which is used for collecting an image data signal, a physiological signal and an environment data signal of a human body, and outputting an asynchronous pulse event flow when the signal change is detected; the pulse coding module is used for generating a space-time pulse sequence through a preset adaptive threshold coding technology; the neural network processing module is used for performing event driving processing by utilizing leakage integral distribution neurons in a preset pulse neural network according to the space-time pulse sequence to obtain a current health characteristic pulse mode; and the health state decoding module is used for obtaining a health state score and a risk classification result of the current human body through a preset pulse distribution rate analysis algorithm and a preset time sequence decoding technology according to the current health characteristic pulse mode. Therefore, the problems of high power consumption, high delay, low data transmission efficiency, low early pathological feature recognition accuracy and the like of a human health state evaluation system are solved.

The invention provides a high-precision road network topology model construction method based on multi-source data fusion, and belongs to the technical field of traffic transportation information processing, and the method comprises the steps: obtaining traffic industry management data, open source map data and map provider navigation data, carrying out the data fusion, and obtaining fusion data, constructing a topological network to perform validity and connectivity verification, and performing processing by using a fracture stitching algorithm to generate a road network topological structure; inheriting and standardizing attribute fields according to a road section space overlapping relation to obtain a structured attribute set, and optimizing by utilizing a space analysis algorithm and structural logic judgment to obtain an optimized road network topological structure; and combining the structured attribute set with the optimized road network topology structure to obtain a high-precision road network topology model. According to the method, the problem that a set of universal, efficient and iteratively-updated data fusion and topology modeling framework is lacked in existing high-precision road network topology model construction is solved.

The invention discloses an electronic file content anti-counterfeiting and tampering detection method adopting a locked signature, and belongs to the field of information security. According to the method, file content is automatically segmented according to semantics through a context coding self-adaptive segmentation algorithm, and each segment of content fingerprint is generated by applying a content embedding disturbance weighted digest algorithm. And constructing each segmented abstract into a directed relation chain by adopting an embedded relation chain transformation signature mechanism, and carrying out digital signature overall locking. During detection, the segmented abstract and the relation chain structure are compared again, and precise positioning and tracing of tiny tampering, segmented change and structure adjustment of the text content are achieved. And through a signature duration chain entropy analysis algorithm, performing comparison and evolution path analysis on previous version states, and outputting a detailed traceability report. According to the method, authenticity verification and tampering detection of the electronic file content are greatly improved, and the method is suitable for the fields of data anti-counterfeiting, compliance supervision, electronic evidence management and the like.

The invention provides an intelligent wire harness adaptive control method and system, and the method comprises the steps: S2, extracting a corresponding current range and a communication protocol from an output mode library according to an equipment type judgment result, and determining an initial output parameter; s4, executing the adjusted output parameters through a low-power-consumption communication module, obtaining equipment operation state data, and judging whether the real-time operation requirement is met or not; s6, after the pre-adjustment parameters are executed, equipment response data and energy consumption data are obtained, and whether system overload or power consumption exceeding exists or not is judged; s8, updating the equipment feature database and the output mode library according to the adjusted operation configuration, and obtaining updated collaborative performance indexes and energy consumption indexes; and S9, if the collaborative performance index and the energy consumption index do not reach the performance threshold, repeatedly executing the combined optimization process of the dynamic adjustment algorithm and the feature analysis algorithm. According to the invention, high-efficiency cooperation and energy consumption optimization of the wire harness and the intelligent equipment are realized.