284 results about "Scene labeling" patented technology

The invention discloses a power market large language model evaluation method and device based on dynamic scene perception, and the method comprises the steps: building a large model evaluation system covering the three dimensions of understanding, generation and safety according to a power market application scene; the method comprises the following steps: constructing a test question bank, evaluating a power market large language model according to various evaluation methods, endowing three dimensions with different weights based on a large model evaluation system in the evaluation process, constructing a test question-scene label fine tuning data set, and performing scene classification fine tuning on the model by using a fine tuning method. Evaluating scene classification performance of the fine-tuned model, and distributing corresponding initial evaluation dimension weights for different scenes; according to the requirements and differences of different scenes, the initial evaluation dimension weight is adjusted, the scene is judged to be matched with the corresponding evaluation dimension weight according to the input content, weighted summation is carried out from the three dimensions of understanding, generation and safety, and an evaluation result is obtained. According to the method, accurate evaluation of the performance and reliability of the large language model in the power field can be realized.

The invention belongs to the technical field of machine room safety monitoring, and discloses an image review method fusing semantic understanding and visual recognition, which comprises the following steps: calculating visual / semantic feature dynamic credibility in real time through an exponential weighted moving average algorithm in combination with environment interference and equipment state parameters; resNet50 is adopted to extract visual features in global and local branches, and a BERT model is adopted to encode semantic features; correcting the feature correlation degree according to the scene, calculating a dynamic weight, carrying out heterogeneous calibration through an attention mechanism, and calling priority rules such as'physical security features are higher than behavior features' for arbitration during conflicts; the method is advantaged in that low-credibility feature interference fusion is avoided, abnormity identification accuracy in a complex scene of a machine room is improved, multi-modal data cooperation demands are adapted, scene labels are marked based on time, work orders and historical data dimensions, an exclusive sub-model is constructed for a high-density scene through DBSCAN density clustering, and low-frequency scene parameters are migrated to a similar model.

The invention belongs to the technical field of data centers, discloses an energy efficiency evaluation method of an artificial intelligence data center, and solves the problem that a static evaluation method is disjointed with the real-time operation state of the data center in the prior art by constructing a dynamic feature library and an AI adaptive weight model. The dynamic feature library collects and associates service scene labels and time-space coupling data in real time, and provides a scene basis for an AI adaptive weight model, so that weight adjustment is converted from passive response index mutation in the prior art to active adaptive service scene change. Nonlinear influences of dynamic factors such as server load fluctuation and environment adjustment on energy efficiency are effectively captured, so that an energy efficiency evaluation result better fits the actual operation state of the data center, energy efficiency factor relevance changes caused by service switching of the AI data center are adapted, scene-based self-adaptive adjustment of weights is achieved through a deep learning model, and energy efficiency evaluation accuracy is improved. The limitation that the fixed weight distribution ignores the difference of different service scenes in the prior art is solved.

The invention relates to a man-machine interaction method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring interaction content input by a user; scene recognition is carried out on the interaction content through a large language model, and a scene label of the interaction is determined; determining a target long-term memory partition corresponding to the scene tag from a plurality of long-term memory partitions preset for the user; inputting target long-term memory content in the target long-term memory subarea and short-term memory content stored in a short-term memory area preset for the user into an intelligent agent; the intelligent agent is used for generating reply content for the interaction content according to the target long-term memory content and the short-term memory content. By adopting the method, effective association and collaborative calling of different scene memories can be realized, and the dynamic requirement of a user on coherent interaction in multi-scene switching is met.

The invention relates to an identification method for matching scene behaviors by using multi-modal features, and belongs to the technical field of scene behavior matching. The method comprises the following steps: acquiring multi-modal scene behavior data, and carrying out noise self-adaptive purification processing on the multi-modal scene behavior data to obtain a preprocessed scene image, scene audio data and a scene label text; secondly, performing feature collaborative extraction on the preprocessed data to obtain visual features, audio features and text features, inputting the cooperatively extracted features into a scene behavior matching network, and performing scene feature fusion and behavior feature fusion respectively to obtain a scene feature vector and a behavior feature vector; and constructing a bipartite graph according to the scene feature vector and the behavior feature vector, calculating the semantic similarity between nodes of the bipartite graph, dynamically updating the edge weight of the bipartite graph according to the semantic similarity between the nodes, and normalizing the updated bipartite graph to obtain a scene behavior matching result. According to the method, the association degree of the scene and the behavior can be accurately quantified, and the accuracy of a matching result is greatly improved.

The invention relates to a real-time process monitoring method based on data analysis, and the method comprises the following steps: S1, building a three-dimensional component priority evaluation model based on a component operation scene type, a real-time resource occupancy rate and a data dependence degree, and dynamically matching an acquisition strategy; s2, a cleaning rule is adapted according to a data source, a feature extraction dimension is adjusted in combination with process dynamic features, and an improved time sequence decomposition algorithm is used for separating data trends, fluctuations and abnormal residual errors; s3, constructing an exclusive baseline sub-model by using an online learning algorithm according to a scene label, and establishing a scene switching mechanism; s4, in combination with component interaction anomaly features, an anomaly level is judged through a mixed detection model; s5, on the basis of exception processing and user feedback, constructing an error correction model optimization parameter; and S6, generating a report containing an abnormal propagation path, and triggering hierarchical collaborative response of the associated component. The invention aims to solve the problems of single acquisition dimension, no scene adaptability in preprocessing, incomplete abnormal detection and the like in the existing monitoring technology.

The invention relates to the related technical field of intelligent scene labeling, in particular to an intelligent driving scene collaborative labeling method and system based on multi-modal fusion, and the method comprises the steps: constructing a dynamic feature fusion model, carrying out the cross-source correlation analysis of a multi-modal scene data set, setting organization structure elements, and setting a multi-dimensional scene labeling model. And configuring a potential danger interaction path and a scene risk index, and generating an annotation instruction set to carry out collaborative annotation operation. The technical problems that under interference of severe weather such as sensor noise, rain and fog and strong light and abnormal traffic behavior scenes, the labeling accuracy is insufficient, and complex scenes and multi-modal data requirements are difficult to adapt are solved, labeling noise data are introduced into an adversarial sample configuration assembly, a multi-dimensional scene labeling model is constructed in combination with organizational structure elements, and the multi-dimensional scene labeling accuracy is improved. The technical effects of improving the training data reliability, adapting to complex environment labeling requirements, configuring potential risk interaction paths and scene risk indexes, carrying out risk-oriented priority labeling and improving the risk scene labeling precision are achieved.

The invention relates to the crossing field of artificial intelligence technology and e-government, and discloses an e-government enterprise service intelligent response method based on AI cue word engineering, comprising: constructing and maintaining a government knowledge graph and a scenarized cue word template library; receiving and analyzing a service request input by an enterprise, and extracting an enterprise feature tag and a business intention; selecting a target cue word template based on the matching degree of the enterprise feature tag and the scene tag of the template in the scene cue word template library, and generating an adapted cue word template; retrieving associated knowledge data from the government affair knowledge graph according to the service request, and filling the adapted cue word template with the retrieved associated knowledge data to generate a structured cue word; inputting the structured cue word into an AI model to drive the AI model to generate a government affair service response; and collecting multi-dimensional evaluation feedback of government affair service response, and carrying out iterative optimization. The demand understanding precision is improved, and the risk of core information misjudgment is reduced.

The invention discloses a double-arm clothes folding robot control method and device, equipment and a storage medium. The method comprises the following steps: determining three-dimensional point cloud data of a target workbench according to a collected image through a visual perception assembly; determining a scene label graph and a desktop edge line set through a scene division assembly according to the three-dimensional point cloud data, determining a clothing mask of the to-be-processed clothing according to the scene label graph through a clothing classification assembly, and determining a clothing description vector according to the clothing mask; determining a low-risk action set and a protection area mask from the candidate clothes folding action set through a safety protection component according to the scene label graph, the desktop edge line set and the clothes mask; determining a target clothes folding action sequence through an action planning component according to the clothes description vector, the low-risk action set and the protection area mask; and folding the to-be-processed clothes through the robot control assembly according to the target clothes folding action sequence. According to the scheme, the clothes folding efficiency and reliability of the robot are improved.

The invention belongs to the technical field of Internet data security, and discloses a multi-dimensional Internet data security fusion processing system, which is characterized in that an acquisition module depends on a three-dimensional decision model, combines reinforcement learning dynamic allocation tasks, only acquires threat associated key data in a low-computing-power and high-threat scene, and starts full-amount lightweight acquisition in a high-computing-power and low-threat scene; the data preprocessing module adds scene labels for data, filters logic and unifies formats according to scene design, and eliminates data isomerism. The identification module builds a basic threat library and a scene adaptation layer, adapts a new scene through transfer learning, calculates a risk value in combination with a four-dimensional quantitative model such as propagation probability, generates an attack link map, and effectively eliminates a monitoring blind area caused by edge computing power limitation; the decision-making module constructs a five-dimensional trust evaluation model of cooperation duration, compliance records and the like, calculates weights by using an analytic hierarchy process, establishes a compliance and trust linkage engine, synchronizes industry specifications in real time and adapts to scene adjustment rules.

The invention provides an automatic driving system reliability self-diagnosis method and platform based on multi-modal sensor fusion. The method comprises the following steps: acquiring image modal data, performing feature extraction by using a visual Transform model, generating an environment scene label, and representing the type of a current environment; carrying out statistics on key modal characteristics in actual automatic driving operation data to obtain a modal score vector corresponding to each environment scene label to reflect the prior credibility of each sensor modal in the environment, and calculating a modal trust weight through softmax to obtain a confidence weight vector; performing weighted fusion on the key modal features based on the confidence weight vector to obtain fusion features; generating a modal reconstruction feature according to the fusion feature, and generating a modal consistency error vector based on the modal reconstruction feature; and in combination with the confidence weight vector and the modal consistency error vector, determining an early warning signal of the current system for judging whether the system has a fusion degradation or modal failure risk.

The invention relates to a peripheral feedback effect control method and system, a storage medium and electronic equipment, and relates to the technical field of peripheral control, and the method comprises the following steps: determining whether a current target user has a target scene label in a target game according to actual game state data; if the target scene label exists, determining a first light parameter and a first vibration parameter of the actual peripheral according to the target scene label; optimizing the first light parameter and the first vibration parameter according to the actual electric quantity to obtain a final light parameter and a final vibration parameter, and controlling the feedback effect of the actual peripheral according to the final light parameter and the final vibration parameter; and if the target scene label does not exist, determining a second feedback parameter of the actual peripheral according to the user game state data, and controlling a feedback effect of the actual peripheral according to the second feedback parameter. The method and the device have the effect of improving the peripheral feedback effect and the user adaptability.

The invention relates to the technical field of video content analysis, and discloses a video scene content label determination method based on a knowledge graph. The method comprises the following steps: inputting a multi-modal feature sequence formed by visual, audio and text features extracted from a video stream into a knowledge graph inference engine comprising an entity relationship network and a semantic association rule base; performing node mapping through an engine to generate an initial scene entity set; performing hierarchical reasoning on the set based on a semantic association rule base to obtain a scene semantic topological structure; screening core entities according to entity weight distribution in the topological structure, and generating a candidate tag set; performing time sequence consistency verification on the candidate tag set, and correcting tag time sequence offset in combination with timestamp information; performing cross-modal disambiguation on the corrected label set by using an entity relationship network to eliminate semantic conflicts; and according to a disambiguation result, constructing a scene label knowledge sub-graph containing entity attributes and relation path constraints.

The invention provides a dynamic protection control method for a compressor and an electronic starter controller, and the method comprises the steps: carrying out the scene labeling of the operation data of the compressor, dividing various load scenes, and setting a basic critical value offset coefficient; parameters of the compressor are collected in real time, the real-time load intensity is calculated through a weighting algorithm, and a buffer area is determined; the method comprises the following steps: acquiring data of a compressor, converting the data into a time sequence feature sequence, performing normalization processing, then inputting the time sequence feature sequence into a pre-trained neural network model of a double-branch network structure, and outputting a trend level and a PID parameter interval; according to the trend level and the PID parameter interval, determining a basic PID parameter, and introducing a risk deviation coefficient to determine a final PID parameter; and smooth transition amplitude limiting is set for the final output quantity of the PID parameters, the compressor is operated under the smooth transition amplitude limiting, and the problems of time delay and curve fluctuation of traditional critical value control are solved.

The invention provides an intelligent quality inspection method and device based on RAG and a dual-enhancement mechanism, and the method comprises the steps: determining a retrieval text based on a transcriptional text of voice data to be subjected to quality inspection and an initial quality inspection result; based on the semantics of the retrieval text and the scene label of the voice data, retrieving from a preset knowledge base to obtain matched entries and the relevancy of the matched entries; whether an IDK enhancement mechanism is triggered or not is judged based on the matching items, if not, a direct preference optimization DPO model is called to conduct preference matching degree scoring on the initial quality inspection result, and the DPO matching degree of the initial quality inspection result is obtained; and determining the accuracy and confidence of the initial quality inspection result based on the correlation degree of the matched items, the confidence of the large model, the DPO matching degree and the accuracy of the historical similar cases, and determining a final quality inspection result of the voice data based on the accuracy and confidence. According to the method and the device provided by the invention, the result accuracy, the illusion resistance and the service adaptation efficiency of intelligent quality inspection are remarkably improved.

The invention relates to the technical field of health monitoring, in particular to a health state alarm response system based on multi-dimensional monitoring, which comprises a dynamic threshold module, a physiological monitoring module, an anomaly positioning module, an inducement output module, an anomaly intervention module and an early warning response module, according to the method, multi-dimensional original data of a user is collected, meanwhile, a timestamp alignment and scene label binding technology is adopted, an exclusive physiological index normal fluctuation interval library is constructed for different large classes of scenes, then the problem of misjudgment of a static threshold value is fundamentally solved, the cause of'abnormity 'is deeply and accurately positioned, and the accuracy of the abnormal condition is improved. The abnormity-scene-behavior-cause full-link association not only enables the user to clearly know the cause of the abnormity, but also provides data support for the medical personnel to formulate a targeted intervention scheme, and reasonably formulates the targeted intervention scheme based on the intervention list, thereby avoiding the ineffectiveness of general suggestions, enabling resources to be inclined to high-risk users, and improving the user experience. The complication occurrence rate is effectively reduced.

The invention provides an intention recognition method, device and equipment and a computer program product, and relates to the technical field of computers and Internet. The method comprises the following steps: in multiple rounds of sessions, obtaining session content expressed by a target object in a current round; obtaining a dynamic memory matrix, a plurality of preset intention categories and a dialogue scene label, the dynamic memory matrix comprising at least one group of historical intention conversion information, wherein one group of historical intention conversion information comprises an intention conversion pair, a semantic similarity between two intentions in the intention conversion pair and an intention transition probability corresponding to the intention conversion pair; and performing intention recognition on the session content according to the session content, the dynamic memory matrix, the plurality of preset intention categories and the session scene label to determine a corresponding intention category in the session content of the target object. The intention recognition method and device can improve intention recognition accuracy.

The invention relates to the technical field of medical data analysis, in particular to a clinical team cooperation ability assessment method and system based on multi-modal data dynamic fusion, and the method comprises the steps: collecting physiological signal data based on wearable equipment, collecting audio / video data through an environment sensor, and collecting physical operation data through simulated human equipment; adding a timestamp and a scene label; carrying out sampling frequency synchronization and time axis alignment on the multi-modal data by taking the first-aid event triggering time as a reference; calling an attention mechanism based on the first-aid scene type, and automatically adjusting the weight of each modal data; and integrating the technical operation score and the non-technical cooperation feature to generate an individual and team visual evaluation report. According to the invention, the problem of blank collection of team non-technical skill data in traditional training is solved, and the effects of quantitative evaluation and real-time feedback of collaboration capability are realized.

The invention discloses an audio and video processing system and method supporting an AI intelligent repair technology, and relates to the technical field of audio and video data processing, and the method comprises the following steps: carrying out the scene recognition based on a playing source type, a user interaction form, an audio and video code rate, an audio sampling rate and network transmission time delay, and generating live broadcast, film watching and singing scene labels; determining a time delay upper limit, a resolution target, a detail reduction level, an audio fidelity index and a beat alignment threshold according to the scene label, and forming a performance constraint vector; according to the invention, a closed-loop mechanism of scene identification, performance constraint setting, repair link arrangement, cloud and terminal task allocation, real-time adaptive adjustment and parameter iterative optimization is constructed for different requirements of live broadcast, film watching, singing and other scenes, so that low delay, high image quality, high tone quality and computing power adaptation are realized; and the stability and the continuous optimization capability of the system are improved through versioning and staged release.

A digital human generation method, a model training method and apparatus, a device, and a medium are provided. An implementation solution is: obtaining material content; determining a plurality of scenarios from the material content based on a pre-trained scenario division model, where each of the plurality of scenarios corresponds to a content fragment of the material content that has complete semantic information; and for each of the plurality of scenarios, determining, based on a corresponding content fragment, target content corresponding to the scenario; determining scenario label information of the scenario based on the corresponding target content; and configuring a digital human specific to the scenario based on the scenario label information.

The invention discloses an enterprise data evaluation and analysis system and method based on big data, and belongs to the technical field of data processing, and the method specifically comprises the steps: building a scene ontology grammar rule base, disassembling ERP, CRM and supply chain management fields into grammar atoms, marking entity relationship semantics according to a business scene, and summarizing and sorting results into a reusable grammar atom template; deploying a grammar perceptron at a data acquisition end, capturing field names, data formats and association logics, and generating grammar fingerprints; according to the grammar fingerprint and the grammar atomic template, generating field naming mapping, format mapping and association mapping, and forming an alignment mapping table as input of structure normalization; according to the scene label and the alignment mapping table, a structure normalization assembly line is constructed, field renaming, format conversion and association relationship assertion verification are executed, and a unified structure interlayer table is generated; the method comprises the following steps: sensing table structure and field change of a source system, updating grammar fingerprints, and aligning a mapping table and a structure normalization pipeline.

The invention belongs to the field of man-machine interaction, and particularly discloses an intelligent AI dialogue interaction method and system based on an RICH normal form, and the method comprises the steps: obtaining current dialogue context data and a user input signal, processing the data through an intention recognition model, and obtaining a user intention vector and a scene label; according to the user intention vector and the scene label, acquiring hierarchical state information in a preset multilayer interaction framework, and if the matching degree of the vector and the current hierarchical state is lower than a preset threshold value, activating an information sharing mechanism to obtain an updated shared knowledge graph; through the shared knowledge graph, a dynamic scene adaptation module is fused, a potential switching path and intention extension of a conversation are determined, and an optimized interaction path sequence is obtained; the objective of the invention is to solve the problems of inaccurate intention recognition, stiff interaction path switching and incoherent response logic of a traditional dialogue system in a complex scene in the prior art.

The invention relates to the technical field of automobile infotainment, and discloses an automobile infotainment theme production binding method, system and device and a storage medium, and the method comprises the steps: obtaining an original data flow of a vehicle multi-mode sensor; analyzing the original data stream to generate a standardized feature vector; outputting a scene label corresponding to the current vehicle by analyzing the feature vector; dividing the to-be-started application process into different grades according to a preset scene grading mapping table; allocating corresponding hardware resources to different levels of application processes, and dynamically adjusting an allocation scheme of the hardware resources of the vehicle-mounted system; according to the adjusted resource allocation scheme, the application processes of the corresponding levels are started in sequence; and when it is detected that the feature vector has a change exceeding a preset threshold, re-outputting the scene label, and synchronously updating the application process grading and resource allocation scheme according to the re-output scene label. Therefore, quick starting of the application process, reasonable distribution of system resources and optimization of user experience are realized.

The invention discloses a semantic information generation method and device, a storage medium and an electronic device, and relates to the technical field of artificial intelligence and Internet of Things crossing, and the semantic information generation method comprises the steps: encoding a plurality of pieces of first metadata in a first scene and a first natural language instruction corresponding to the first scene sent by a target object, wherein the first metadata at least comprises one of a device list in the first scene, a capability attribute of each device in the device list and a scene label of the first scene; according to the encoded target vector, determining an attention weight corresponding to an attention mechanism in the edge device, and according to the attention weight, performing fusion processing on the plurality of first metadata and the first natural language instruction to generate target semantic information corresponding to the first natural language instruction, the target vector comprises a first metadata coding vector corresponding to the multiple pieces of first metadata and a first instruction coding vector corresponding to the first natural language instruction.

The invention provides a full-scene adaptive intelligent recommendation method and system based on a graph neural network, and the method comprises the steps: constructing a parallel candidate recommendation model for generating a diversified candidate pool; a scene label of a user is determined through a CoT inference rule, the scene label is used as a query, a most matched strategy document is retrieved from a strategy knowledge base by utilizing an RAG module, and a currently recommended dynamic execution strategy is formulated; normalizing the original score of the parallel candidate recommendation model based on a dynamic execution strategy, calculating a preliminary fusion score according to a dynamic weight retrieved from a strategy knowledge base, and generating a preliminary sorting list; and performing rearrangement based on big language model reasoning on the preliminary sorting list in combination with scene and strategy guidance, and introducing evidence retrieved from a fact knowledge base based on an RAG module into a rearrangement result to obtain a final recommendation list with language interpretation. According to the method, the recommendation list which is highly personalized, self-adaptive in scene and clear in natural language interpretation can be obtained.

The application relates to a recognition method for matching scene behaviors by using multi-modal features, and belongs to the technical field of scene behavior matching. The method acquires multi-modal scene behavior data, and carries out noise self-adaptive purification processing on the multi-modal scene behavior data, so as to obtain preprocessed scene image data, scene audio data and scene label text. Then, feature collaborative extraction is carried out on the preprocessed data, so as to obtain visual features, audio features and text features. The collaboratively extracted features are input into a scene behavior matching network, scene feature fusion and behavior feature fusion are respectively carried out, scene feature vectors and behavior feature vectors are obtained, a bipartite graph is constructed according to the scene feature vectors and the behavior feature vectors, the semantic similarity between nodes of the bipartite graph is calculated, the edge weight of the bipartite graph is dynamically updated according to the semantic similarity between the nodes, the updated bipartite graph is normalized, and a scene behavior matching result is obtained. The application can accurately quantify the correlation degree of scenes and behaviors, and greatly improves the accuracy of the matching result.

The invention discloses a digital annotation adaptive optimization method and system based on deep learning, and relates to the field related to deep learning, and the method comprises the steps: uploading an annotation target to an annotation platform, carrying out the decoupling reconstruction of the annotation target based on the annotation confidence according to a meta-cognitive threshold, and determining a first annotation target and a second annotation target; according to the first labeling component, performing automatic labeling processing on the first labeling target and the second labeling target to generate a first labeling result and a second labeling result; according to an annotation judgment component, anti-fact judgment based on physical attributes and annotation ambiguity is conducted on the second annotation result, a second annotation component is triggered to execute directional projection annotation, a second optimized annotation result is generated, and the first optimized annotation result and the second optimized annotation result are coupled to serve as a target annotation result; and labeling a platform display port for displaying. The technical problem that existing digital labeling is poor in complex scene labeling effect is solved, and the technical effect of improving the adaptability to complex scenes and the overall output quality is achieved.

The invention belongs to the technical field of clean energy photovoltaic power generation, and particularly relates to an intelligent operation and maintenance method for a photovoltaic power station based on artificial intelligence, which takes basic data, historical operation and maintenance cases and a scene label library of the photovoltaic power station as working objects, and imports the basic data and the historical cases into a vector database by using an RAG technology and a secondarily developed DeepSeekR1 model; a scene tag is marked for a power station through a scene adaptation unit, a corresponding centralized sub-library and a corresponding distributed sub-library are constructed, a self-iteration unit is started, and an operation and maintenance personnel case auditing process is set. A dynamic knowledge base is constructed through a unit RAG unit technology, a secondarily developed unit DeepSeekR1 unit model and a self-iteration unit, the knowledge updating period is greatly shortened, centralized and distributed sub-libraries are divided based on a scenarized adaptation unit, the operation and maintenance requirements of different power station types and environment characteristics are accurately matched, the knowledge retrieval efficiency is improved, and the knowledge retrieval efficiency is improved. And the problems of lag and retrieval redundancy of the traditional static knowledge base are avoided.

The invention provides a bicycle accounting evaluation system and method and electronic equipment, and relates to the technical field of bicycle data management, and the system comprises a data collection module which is used for obtaining the operation data flow of a bicycle in a preset time period; the scene classification module is used for carrying out dynamic clustering on the operation data flow of each bicycle through a scene segmentation model, generating a plurality of scene labels and obtaining a scene label set of the bicycles according to the scene labels; the loss mapping module is used for performing data mapping according to the scene label set and a preset loss cause library to obtain a loss matrix; the weight adjustment module is used for performing dynamic weight adjustment on the initial depreciation rate corresponding to the bicycle according to the loss matrix to obtain a dynamic depreciation weight vector; and the accounting generation module is used for carrying out apportionment calculation on a plurality of preset indexes of the bicycles according to the dynamic depreciation weight vectors, and generating an accounting result of each bicycle in the target area. According to the invention, the accuracy of bicycle accounting is improved.

The present disclosure provides an object portrait information generation method, device, equipment and storage medium, which can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation, auxiliary driving, financial risk control, network security and the like. The method comprises the following steps: analyzing historical operation information to obtain a business operation information sequence and a corresponding time interval sequence; calling a trained operation sequence encoding network to perform encoding processing on the business operation information sequence and the corresponding time interval sequence to obtain sequence operation features; respectively performing identification processing on the sequence operation features, and based on obtaining a plurality of corresponding operation identification sub-results, generating object portrait information of an object. Thus, in the case that the object data is not comprehensive, the object portrait information can be accurately generated, and the dependence on object data and scene labels is reduced.