98 results about "Screening algorithm" patented technology

The invention discloses an intelligent Chinese text error correction method integrating spelling and semantics, and relates to the technical field of data processing. Comprising the following steps: S1, according to an original Chinese text to be corrected and a correction intention instruction, obtaining an original candidate correction set of each agent through parallel operation of each agent; s2, processing the original candidate correction set according to the correction intention instruction, and obtaining the score of each original candidate correction item; and S3, comparing the original candidate correction items with the comprehensive evaluation scores through a diversity optimization screening algorithm, and determining a final candidate correction item set. According to the method, the candidate correction set is generated in parallel through multiple agents and is combined with the conflict resolution and multi-dimensional evaluation mechanism, so that the accuracy of error correction can be ensured, diversified expression selections can be provided, and the problems of single and rigid output of a traditional error correction system are avoided.

The application belongs to the technical field of multimedia information processing, and discloses a kind of monitoring video key frame extraction method, system, medium, equipment and terminal, collect original video stream data, original video stream data is decomposed into image frame set;Image frame set obtained by decomposition is sampled, and the image frame result set obtained by sampling is filtered;The image frame set after filtering is adaptively clustered, and the result after clustering is collected to form a video abstract.In order to better utilize the memory space of storage medium and let the user better quickly browse the general content of original video stream, the application provides a key frame screening algorithm for original video stream by sampling, filtering and clustering.The key frame extraction method of the application screens out similar frames, redundant frames and fuzzy frames in original video stream through key frame screening algorithm for original video data, forms a video abstract storage of a section of original video, thereby greatly reduces the occupied storage space.

The invention provides an efficient annotation-driven hierarchical fault positioning method, which comprises the following steps of: guiding a large language model to intelligently analyze and generate semantic annotations of a text based on a positioning algorithm of large model annotations, and performing fine-grained software fault positioning from a file level to a function level and then to a position level by using the annotation-based hierarchical positioning method. And an efficient hierarchical progressive sorting and screening algorithm is used for ensuring the software fault positioning efficiency. According to the method, the defect positioning efficiency can be ensured while the positioning accuracy is ensured. The positioning algorithm based on large model annotation strategically balances the information density, provides enough context clues, and improves the model understanding ability; according to the annotation-based hierarchical positioning method, positioning is divided into file / function / position hierarchies, and annotations are added by using a large model cue word project in sequence, so that the positioning accuracy is improved; and an efficient hierarchical progressive sorting and screening algorithm is provided, so that the optimal balance between the performance and the cost is realized.

The invention belongs to the technical field of artificial intelligence algorithm and bioinformatics crossing, and relates to a drug interaction prediction method based on multi-modal molecular characterization. According to the method, through the edge perception GCNII architecture and the Hop2Token multi-hop coding mechanism, effective modeling of atomic-level and bond-level local environments and a cross-substructure high-order dependency relationship in drug molecules is realized, and the accuracy and robustness of drug interaction prediction are improved. According to the method, Mol2Vec and MolT5 cross-modal molecular characterization is integrated, fusion of molecular overall semantics and substructure grammar semantic association is achieved, and the generalization ability of the model to complex molecules and unknown medicine combinations is remarkably improved. According to the method, the dynamic feature screening algorithm driven by the SHAP value of the artificial intelligence technology is adopted for biological verification, the feature redundancy problem is effectively solved, the molecular biological information analysis processing calculation efficiency and the model transparency are improved, and the traceability of the prediction process is guaranteed.

The invention discloses a method for detecting heart rate, respiration and oxyhemoglobin saturation based on micro-vibration images, and belongs to the technical field of physiological detection and image processing. A camera collects facial micro-vibration videos of a testee in real time, uses an ROI algorithm to lock a facial key area, extracts RGB micro-vibration change signals to replace red light and near-red light signals, and determines whether the micro-vibration videos are abnormal or not; performing wavelet transform five-layer decomposition, three rounds of peak valley screening and abnormal value elimination on the RGB microvibration time sequence signal, performing fine tuning training by using a yov11 model, and calculating to obtain heart rate, respiration and blood oxygen physiological indexes. According to the invention, non-contact detection is realized through video acquisition and signal processing of micro-vibration of a face area, and the comfort level and the use convenience of a testee are improved; interference caused by motion artifacts, baseline drift and expression changes is eliminated through multi-scale wavelet denoising; the three-wheel peak-valley screening algorithm deeply excavates the features of the face micro-vibration signals, and the recognition accuracy of the signal peak-valley pairs is improved; and an abnormity elimination and self-feedback parameter adjustment mechanism improves the calculation precision.

The invention belongs to the technical field of data processing, and particularly discloses an intelligent design method and device for long and short legs of an iron tower, electronic equipment, a medium and a program product. Generating a feasible solution space containing a tower leg length candidate set and a center pile adjustment height candidate set; the height of each candidate center pile is adjusted, a height difference matrix is constructed, the optimal tower leg length is determined through a conditional screening algorithm, and a leg matching scheme is formed; an error absolute value, an error variance and a center pile adjustment height are extracted as evaluation indexes, and after a comprehensive score is calculated through normalization and weighting, a scheme with the minimum score is selected. According to the method, automation and precision of iron tower long and short leg design are achieved, and design efficiency and scheme reliability are improved.

The invention relates to the technical field of inertial sensor state data analysis, and discloses a frequency domain processing-based inertial sensor data analysis method, which realizes frequency domain analysis on actual error composition of an inertial sensor, thereby improving the accuracy of effective system performance analysis of the inertial sensor. The method specifically comprises the following steps: S1, acquiring an original output signal by adopting an MEMS inertial measurement unit of a sensor; s2, decomposing a time domain signal in the original output signal into a solid mode equation and a residual term through a mode decomposition algorithm; extracting a solid-state model equation by using a screening algorithm through empirical mode decomposition, and updating a residual error; selecting a center frequency by using signal characteristics and priori knowledge through variational mode decomposition, extracting a solid-state mode equation at the center frequency, and modeling as a function about amplitude phase frequency; s3, performing Hilbert-Huang transform on the components of the solid mode equation to generate a Hilbert spectrum in time-frequency domain joint distribution; and S4, constructing a sensor performance evaluation model based on the Hilbert spectrum.

The invention discloses an interactive lattice radiotherapy planning method and system based on a functional image, and relates to the technical field of radiotherapy, and the method comprises the steps: obtaining a radiotherapy positioning CT image of a patient and a functional metabolism image after the space registration with the radiotherapy positioning CT image; based on the radiotherapy positioning CT image and a preset safety boundary parameter, determining a candidate area of lattice layout in the tumor target area; for each candidate position in the candidate region, adaptively calculating lattice parameters for the position based on the corresponding SUV value of the position in the functional metabolism image; determining a final lattice center set from the candidate positions through an iterative screening algorithm on the basis of the calculated lattice parameters of the candidate positions and preset geometric constraints and biological constraints, and generating a lattice target region set on the basis of the final lattice centers and the lattice diameters corresponding to the final lattice centers; and outputting the lattice target region set for dose calculation of a radiotherapy planning system. Individualized dosage improvement really based on tumor internal heterogeneity is achieved.

The invention relates to a batch material transfer method, system and equipment based on a workbin robot and a medium, and the method comprises the steps: obtaining the original code scanning data of a plurality of workbin robots for a target workbin, updating the original code scanning data based on a verification screening algorithm, and obtaining the target code scanning data; acquiring transfer sensing data of a target material box in a transfer time period based on the material box robot, determining a first transfer parameter of the target material box, and determining a second transfer parameter of the target material box according to the target code scanning data; and according to the first transfer parameter and the second transfer parameter, based on an accuracy prediction neural network, an accuracy parameter of the material transfer information in the target material box is determined, and the accuracy parameter is used for representing the accuracy degree of the material transfer process in the production plan.

The application provides a dynamic motion trajectory extraction method based on Kalman filtering and repeated data screening, which comprises the following steps: installing IMU on a plurality of key motion joints of a dynamic living body, generating an initial trajectory data set through data collected by the IMU; setting a state space model based on the measurement data in the initial trajectory data set and Euler angles, and performing noise compensation on the initial trajectory data set through a Kalman filter; optimizing the initial trajectory data set after Kalman filtering through a repeated data screening (RDS) algorithm to obtain an optimized trajectory data set; and calculating the joint axis direction through the optimized trajectory data set, and constructing the dynamic motion trajectory of the dynamic living body according to the calculated joint axis direction and the angular velocity data after Kalman filtering. The method combines the Kalman filtering algorithm and the repeated data screening algorithm to extract the trajectory, and significantly improves the robustness and accuracy of the dynamic motion trajectory extraction of the living body.

The invention provides a Web-based single-cell RNA sequencing data intelligent analysis system and method. The method comprises the following steps: receiving a cell group through a Web interface; performing differential gene analysis on the single-cell RNA sequencing data contained in the cell group to obtain an original differential gene list, and filtering the original differential gene list by adopting a multi-threshold screening algorithm to obtain a target differential gene list; performing species automatic identification processing on the target differential gene list, calling a target local gene set database based on a result of the species automatic identification processing, and performing parallel enrichment analysis independent of network connection on the target differential gene list according to the target local gene set database to obtain a gene enrichment analysis result; and generating an interactive chart by adopting an intelligent label anti-overlapping algorithm so as to visualize the interactive chart. According to the method, the problems of high operation threshold, low batch analysis efficiency, unstable result and poor interactivity in the prior art are solved.

The invention discloses a human body infrared spectrum intelligent screening feedback device and immune enhancement and anti-inflammatory application thereof, and relates to the technical field of human body feedback devices. The human body infrared spectrum intelligent screening and feedback device comprises an infrared detection unit, at least one biomarker sensor, a central processing unit and an infrared feedback unit. According to the human body infrared spectrum intelligent screening and feedback device, the infrared detection unit and the biomarker sensor are integrated, multi-source information is subjected to fusion analysis through the central processing unit, comprehensive judgment of the human body immune or inflammation state is achieved, the device dynamically calls a matched infrared spectrum screening algorithm according to the judgment result, and the human body infrared spectrum intelligent screening and feedback device is suitable for being applied to human body health. The infrared feedback unit is driven to carry out targeted irradiation, real-time sensing analysis and dynamic screening are achieved, the information is accurately fed back to uncomfortable body parts, and the problems that in the prior art, diagnosis and treatment are disjointed, and intervention parameters are solidified are effectively solved.

The present invention discloses a method for using a medical smart bed cabinet, comprising: obtaining relevant information data of people using the bed cabinet in a fixed area, and classifying and storing the relevant data in a database; collecting feature data required by a sorting and classification model, and sorting and classifying the feature data using the sorting and classification model; performing feature matching based on the sorted and classified data with the classified data stored in the database. If the matching degree is higher than a preset value, the match is successful, and accompanying personnel are assigned. When an emergency event occurs in the fixed area, a screening algorithm is used to quickly screen out vacant bed cabinets and corresponding accompanying personnel, thereby realizing the intelligent application of medical bed cabinets. The present invention combines smart bed cabinets with accompanying services to achieve the rational allocation and full utilization of hospital bed resources, improve the quality of accompanying services, and reduce patient expenses.

The invention provides a flow behavior recognition method and device, equipment and a storage medium, and relates to the field of big data, and the method comprises the steps: mapping user data used for representing time-space behavior characteristics of a user into a bitmap sequence according to a geographic space dimension and time resolution precision; generating migration behavior data based on the user data, wherein the migration behavior data is used for representing migration behaviors of the user from a starting point to an end point in a time period; based on task information, the bitmap sequence is processed through a bitmap sequence addition algorithm and a residence duration interval screening algorithm, a processed bitmap sequence is obtained, and the task information comprises a source area, a target area, a time period and residence duration; and based on the processed bitmap sequence and the migration behavior data, identifying a user conforming to the task information from a plurality of users corresponding to the user data. And the flexibility of user area flow behavior identification is improved.

The invention discloses a military crane chassis fault reproduction and practical training system based on working condition simulation, and relates to the technical field of military equipment practical training, and the system comprises an entity chassis building unit which selects and uses entity parts of subsystems of a military automobile crane to be arranged and installed according to a real automobile, retains a key relation, presets an interface, defines a safety threshold value and the like; the dual-mode fault generation unit constructs an initial library, and an intelligent agent is embedded to generate a fault; the multi-dimensional data acquisition unit responds to the trigger signal to acquire multiple types of data; the fault diagnosis practical operation unit is used for trainees to operate; the training evaluation and fault iteration unit generates a report and feeds back optimization suggestions; according to the invention, a dual-mode fault generation unit is constructed, an initial fault library and an intelligent agent exploration capability are integrated, a screening algorithm is matched, and multiple fault scenes are provided; a complete practical training system is built, multi-dimensional data acquisition is achieved, practical operation and evaluation iteration are standardized, the practical training quality is improved through closed-loop design, and the military crane chassis fault practical training requirements are met.

The application discloses a data screening, large language model output acceleration method, device, medium and product. The data screening method comprises the following steps: selecting a thread bundle and determining a local comparison value of the thread bundle; triggering each thread bundle to execute an operation of comparing the numerical value between each current probability value and the baseline value of itself, and updating the key value of itself using each current probability value greater than the baseline value of itself; comparing the screening threshold of a target screening algorithm with the key value of each thread bundle; if the screening threshold and the key value of each thread bundle do not match, updating the target set and the current probability value set according to the identified first thread bundle and second thread bundle; and if the screening threshold and the key value of the third thread bundle match, updating the target set according to the third thread bundle as a screening result. The technical scheme of the embodiment of the application effectively reduces the calculation complexity of the data screening algorithm in a soft and hard cooperative manner, and maximally utilizes the parallel computing hardware in the artificial intelligence acceleration computing chip.

The invention relates to a retrieval enhancement generation method, system and device based on information optimization and a storage medium. Aiming at the technical problems of high calculation cost and damaged generation quality caused by context information redundancy and noise accumulation due to simple Top-K retrieval in the existing retrieval enhancement generation technology, the invention creatively introduces the concept of ''in engineering thermodynamics, establishes isomorphic mapping of information retrieval and thermodynamics, and improves the information retrieval efficiency. A high-value information unit is preferably selected from a retrieval result by calculating an'information 'value fusing semantic correlation (information temperature) and redundancy (information invalid performance) and adopting a self-adaptive dynamic screening algorithm based on marginal maximization and dead-state temperature threshold truncation. According to the scheme, the length of the input context and the calculation cost can be remarkably reduced while the generation quality is ensured, the robustness of the system in a noise environment is improved, and high-efficiency and high-quality retrieval enhancement generation is realized.

Embodiments of the present application provide a method and device for obtaining well logging curves and an electronic device. The method for obtaining well logging curves comprises: obtaining seismic response data of each well logging in a target work area; processing the seismic response data of each well logging using a trained well logging facies recognition model to obtain predicted well logging facies of each well logging in the target work area; obtaining a plurality of target well loggings according to the predicted well logging facies of each well logging in the target work area, wherein the target well logging is a well logging within a dominant facies range; and performing the following processing on each target well logging: obtaining a plurality of original well logging curves of the target well logging using different curve logging methods; determining a sensitive curve from the plurality of original well logging curves using a preset sensitive curve screening algorithm; and obtaining a final well logging curve of the target well logging according to the sensitive curve using a preset curve optimization algorithm.

The invention relates to a rapid detection method for food-borne pathogenic bacteria based on the action of meat-derived symbiotic microorganisms, belongs to the technical field of rapid detection and spectral analysis of fresh meat quality, and aims to solve the problems of tedious detection operation and high dependency on biochemical reagents caused by interference of a complex substrate and symbiotic microorganisms of fresh meat in the prior art. The method comprises the following steps: firstly, carrying out elution separation-homogeneous centrifugation-coating culture pretreatment on a fresh meat sample to eliminate the interference of a fresh meat matrix, and then preferably selecting a high-discrimination characteristic wave band from high-dimensional spectral data by applying a combined characteristic screening algorithm; and finally, detecting the food-borne pathogenic bacteria of the fresh meat by utilizing a qualitative and quantitative model established on the basis of a culture sample under the interference of the meat-borne symbiotic microorganisms in the earlier stage. The detection accuracy and sensitivity of the food-borne pathogenic bacteria in the fresh meat can be improved, meanwhile, the method has the advantages of being rapid, easy and convenient to operate and economical in cost, and technical support can be provided for detection of the food-borne pathogenic bacteria in the fresh meat.

The invention discloses a two-dimensional image feature extraction method under adaptive polar coordinate mapping. The method comprises the following steps: step 1, intercepting a short-time sequence by using a short-time sliding window function; step 2, generating a time domain signal short-time matrix energy-time distribution map; step 3, positioning a key signal sequence representing fault characteristics through a peak screening algorithm; step 4, adaptively adjusting scale parameters of polar coordinate mapping, and constructing a multi-scale polar coordinate space; 5, generating a two-dimensional image under adaptive polar coordinate mapping; and step 6, eliminating the influence of rotation speed fluctuation on image features through normalization processing. And step 7, extracting spatial features by using a convolutional layer, and realizing adaptive diagnosis under a variable-speed working condition. According to the method, the fault features of the rotating equipment under the variable-speed working condition can be effectively extracted, and the features have robustness in a time-varying speed strong noise environment.

The invention discloses a network virtualization resource management method and system, and relates to the technical field of intelligent resource management, and the method comprises the steps: initializing an AoL virtual queue, dynamically updating the queue through a Lyapunov drift method, dividing scene feature groups in combination with an ODSS algorithm, fusing prediction results of multiple models, correcting deviation through a Lyapunov-ODSS objective function, and refining the prediction results according to priorities. And an OPT-JR multi-objective function is defined, and an optimal resource allocation vector is solved by using an NSGA-II algorithm. According to the method, the Lyapunov drift theory is introduced to carry out resource regulation and control, the ODSS feature screening algorithm is combined to carry out feature selection, the robustness and real-time performance of resource management are improved, multiple models are fused to carry out collaborative prediction, the precision and stability of resource demand prediction are improved, and the utilization efficiency and intelligent scheduling of resources in a virtualized network are realized.

The application relates to the technical field of medical information, and specifically discloses a multi-modal Alzheimer's disease early screening algorithm based on a large language model. The algorithm first collects speech data and demographic information of participants. The speech data is processed through noise suppression, voice enhancement and speaker separation, and is transcribed into descriptive text. Then, the eGeMAPS feature set is extracted from the speech data and time-series spliced, and combined with the demographic information to form a multi-modal feature matrix. Key features are obtained using an attention pooling module, and feature classification is performed using a support vector machine to distinguish potential Alzheimer's disease patients from healthy individuals. The classification results, descriptive text and demographic information are input into the large language model, and zero-shot thinking chains are used to analyze four language barriers: reduced information content, lexical semantic defects, poor referential coherence and language loss of psychological state, and a comprehensive screening report is finally generated. The algorithm improves the accuracy of early diagnosis by combining audio and semantic features.

The invention discloses a public building peak regulation feature screening method, system and device based on multi-algorithm fusion sorting and a medium, and belongs to the technical field of building energy data analysis and intelligent load management, and the method comprises the steps: obtaining the power utilization load data of a target public building, and constructing an original multi-dimensional load feature set based on the power utilization load data; independently evaluating the relevance between each load characteristic and the building peak regulation capability by adopting a plurality of characteristic screening algorithms to obtain a plurality of groups of characteristic importance sorting results; based on the actual operation state information of the building, dynamically adjusting the weight and parameter configuration of each screening algorithm in the fusion process, and constructing a comprehensive feature importance sorting system; and according to the comprehensive feature importance sorting system, comprehensively determining a final sorting result of each feature on the building peak regulation capability, and according to the final sorting result, forming a dynamically updated building peak regulation feature set. According to the method, the problems of high subjectivity and poor adaptability of a single algorithm feature screening result are solved.

The invention relates to a fixed asset investment screening method and system, and the method comprises the steps: displaying a user interaction interface in response to a preset operation, and obtaining and displaying a plurality of fixed asset data from a database in response to an input operation on the user interaction interface; a setting interface is displayed in response to the trigger operation, a corresponding investment priority and a capital demand of each fixed asset every year are set for each fixed asset corresponding to each fixed asset data in response to an input operation on the setting interface, and the capital demand comprises a capital source and a requested capital amount; obtaining fund information for fixed asset investment from a database, wherein the fund information comprises a plurality of fund sources, a fund available amount of each fund source and a fund use constraint condition; and based on an investment screening algorithm, fund distribution screening is performed on each fixed asset by using the fund information, the investment priority corresponding to each fixed asset and the annual fund demand of each fixed asset to obtain an invested fixed asset list.

The invention provides a log anomaly detection method and system based on deep learning, and relates to the technical field of log analysis and operation and maintenance monitoring, and the method comprises the steps of log access and preprocessing, connectable testing, data screening and analysis debugging; carrying out log mode recognition and anomaly detection, adopting a dynamic algorithm aggregation mode, eliminating noise and grading anomalies; aggregating alarms and faults, generating alarms, and analyzing cross-service line faults; intelligent analysis: constructing a knowledge base, generating an intelligent report and optimizing the intelligent report; and fault overview and disposal, operation and maintenance data display, and full-life-cycle operation support. According to the invention, by fusing the dynamic similarity aggregation and the mode stability screening algorithm, the log mode recognition accuracy is improved, the false report and the missing report are reduced, and the anomaly detection efficiency is improved; and meanwhile, an exclusive knowledge base and a business adaptation retrieval algorithm are constructed, intelligent fault analysis is realized in combination with a large language model, the troubleshooting and repairing period is shortened, a closed-loop optimization mechanism is formed, and the operation and maintenance efficiency is improved.

The application provides a graph question answering method and system based on context semantic retrieval and a computer readable medium, comprising: performing error correction processing on a question input by a user to generate a question sentence conforming to a format requirement; constructing a pre-training model based on context semantic retrieval to perform semantic retrieval on the question sentence to obtain candidate S, P and O; performing fine screening on the candidate S, P and O in combination with a multi-layer sliding window fine screening algorithm to obtain target S, P and O; judging a question type of the target S, P and O, generating a graph query statement corresponding to the question type through a graph query statement generation module; executing the graph query statement on a graph database and analyzing a query result to generate a question answer conforming to a format requirement to feed back to the user. The method solves the demand for a large amount of labeled data and model training to adapt to a scene in which an existing knowledge graph system needs to quickly experience a question answering effect and lacks training data.

The application discloses a model security migration method based on iterative screening and random mask learning, and the steps include: 1) fixing the weight value of an existing unknown channel teacher model and initializing the weight of a student model; 2) screening the channels output by each level of the teacher model using an iterative reinforcement screening algorithm, strengthening the high semantic information channels to improve the transmission information accuracy, and weakening and deleting the channels that cause significant changes in the output after being disturbed to avoid triggering the backdoor attack mode; 3) inputting a target training data set, shielding the student model output using different random masks in each round of training to regularize the learning of the teacher network channel output, avoiding the continuous learning of untrustworthy channels and learning the global strong semantic channel until the student model converges. The method can be applied to the migration of a pre-training model trained by an unsafe source to a student model, can solve the potential harm of the backdoor migrated to the student model and improve the accuracy of the student model.

The invention discloses a drug virtual screening task scheduling method and device based on an intelligent agent and a medium. The method comprises the steps that drug virtual screening task information is received; a directed acyclic graph of at least one task execution path is determined according to the medicine virtual screening task information through the trained intelligent model, the directed acyclic graph of the task execution path is composed of a plurality of cascaded atomic task nodes, and each atomic task node is responsible for a corresponding medicine virtual screening sub-task; according to the directed acyclic graph of the task execution path, determining a calling sequence of services matched with the atomic task node; and calling the services according to the calling sequence of the services so as to execute the medicine virtual screening sub-tasks in charge of the atomic task nodes through the services, and the services matched with the atomic task nodes are obtained by packaging a medicine virtual screening algorithm or tool through a model context protocol (MCP). Automation and intelligentization of the whole process of virtual drug screening are achieved, the use threshold is greatly lowered, and the drug research and development efficiency is improved.

The invention provides a sewage treatment and filtration system for urban renewal environment improvement, and relates to the technical field of water treatment. The sewage treatment and filtration system for urban renewal environment improvement comprises the following modules: a pretreatment module, a cyclone separation module, a dynamic membrane treatment module, an intelligent control module, a back-flushing regeneration module, a rotary optimization module and a tail water reuse module, the preprocessing module is based on a grating interception method, adopts a gradient screening algorithm, and realizes graded interception of floating objects through a three-stage stepped screen to generate primary purification liquid; and the cyclone separation module adopts a tangential flow centrifugal separation algorithm based on the primary purification liquid. Gradient screening is implemented through a three-stage stepped screen, and the solid-liquid separation efficiency is enhanced in combination with a centrifugal force field generated by tangential flow, so that the removal rate of suspended solids breaks through the bottleneck of a traditional process; a dynamic membrane assembly is integrated with a gradient aperture composite filter material, and the membrane surface pollution rate is reduced under the synergistic effect of cross-flow scouring and periodic rotation.

The invention discloses a benchmarking sample vehicle reverse development cooling system performance detection test method, and belongs to the technical field of automobile cooling system performance. The method comprises the following steps: driving a benchmarking sample vehicle into a drum test bed and fixing the benchmarking sample vehicle, performing upshift and downshift operation under a full-accelerator extreme working condition, acquiring speed point data, optimizing working condition points by utilizing a working condition screening algorithm based on normal distribution, acquiring thermal parameters of a transmission under a selected working condition, and calculating thermal power by utilizing a non-equilibrium heat transfer correction model. And finally, reverse development comparison is completed. The core innovation of the method is to solve the problems of lack of scientific basis of working condition point selection and insufficient precision of unbalanced calorific value measurement and calculation in the prior art, and by constructing a complete reverse development test system, the thermal load parameter extraction precision is significantly improved, the heat dissipation system matching period is shortened, and the heat dissipation efficiency is improved. And a key technical support is provided for rapid adaptive development of the automatic transmission.