1440results about How to "Improve real-time performance" patented technology

The invention belongs to the technical field of tunnel and underground engineering intelligent construction and geotechnical engineering informatization, and discloses a high-energy geological environment surrounding rock classification and decision-making method based on digital twinning and multi-source feedback. The problems caused by difficulty in realizing surrounding rock state dynamic sensing, multi-source data fusion classification and construction decision closed-loop linkage in the prior art in a high-energy geological environment are solved. The method comprises the following steps: firstly, constructing a tunnel three-dimensional geology-structure digital twinborn body based on initial survey data; in the construction process, multi-source data such as geology, construction disturbance and surrounding rock response are collected in real time through the Internet of Things technology and mapped to the digital twinborn body, and virtual-real synchronous updating is achieved. And then, constructing a deep learning-parameter inversion hybrid model on the basis of the multi-source fusion data, outputting a dynamic surrounding rock classification index DRCI and key mechanical parameters, inputting the DRCI and the key mechanical parameters into a multi-objective optimization module, and giving a self-adaptive drilling and blasting scheme. And finally, reversely correcting the model through a construction feedback result to realize closed-loop self-learning.

The invention discloses a method and system for cooperatively positioning a vehicle through a door lock, and the method comprises the steps: obtaining and dynamically updating a neighbor door lock list through the detected door lock of the vehicle according to the broadcast and scanning data of a Bluetooth tag on a moving vehicle; according to the periodic detection report of the door locks on the vehicle and the global analysis of the management system, the cooperative timers of the related door locks are synchronized, and timing management for preventing repeated triggering is implemented; according to the response of the obtained neighbor door lock which initiates multicast query after the collaborative timer expires, the door lock which initiates the query verifies whether all responders are in the neighbor list, so as to judge whether the vehicle is stably parked; and according to the vehicle parking information confirmed by the door lock, the management system updates the global state and sends a parking position notification to the owner. According to the embodiment of the invention, accurate and low-misjudgment judgment of the parking state of the vehicle can be realized, and the reliability and real-time performance of vehicle positioning management are improved by means of global state synchronization.

The invention relates to the technical field of rural image inspection and recognition, and discloses a multi-source image collaborative inspection and recognition analysis system and method for a digital rural area, and the method comprises the steps: collecting multi-source image data in real time; obtaining a plurality of characteristic parameters corresponding to each image data item in the image data set, and carrying out space-time registration and multi-scale fusion processing on the plurality of characteristic parameters of each image data item; performing target detection and identification analysis on the plurality of feature parameters in the fusion feature parameter set, and constructing an abnormal point identification model; setting a plurality of abnormal point change thresholds according to the inspection coordinate data set for classification processing to obtain a plurality of abnormal point categories; and setting a corresponding co-processing scheme according to the plurality of abnormal point categories, and setting early warning information corresponding to the change trends of the plurality of abnormal point categories based on the co-processing scheme. According to the invention, the intelligent degree and response efficiency of rural inspection are improved, and the safety and sustainable development of digital rural construction are effectively guaranteed.

The invention relates to the technical field of control, in particular to a double-arm robot operation skill learning method and system based on big language model reasoning. The method comprises the following steps: firstly, carrying out context semantic modeling on an input natural language task instruction based on a large language model to generate a task semantic graph; in combination with a semantic entity in the task semantic graph and visual perception data collected by a robot, determining a three-dimensional space position of a target object through a multi-modal matching model, and constructing an environment semantic graph containing object nodes and spatial relation edges; generating an action sequence by using a language model according to the task semantic map and the environment semantic map, and generating a collaborative operation strategy based on the two-arm tail end state and an obstacle map; and finally, collecting feedback data of the sensor in real time when the action sequence is executed. According to the method provided by the invention, the understanding and execution capability of the two-arm robot on the unstructured natural language instruction is remarkably improved.

The invention provides a double-way buffering mechanism design method for Web services. Two buffering mechanisms based on dynamic pages and static pages perform double-way reading and writing through a database and an xml buffering file, synchronous operation is performed to trigger updated buffering mechanisms, an xml buffering module is used as a using library when a user accesses, a database in an application system is used as a standby library, and reading and writing operations of data are finished in the xml buffering file; and the design content comprises a management process, a reading process and a write-in process of a double-way buffering mechanism. The double-way buffering mechanism design method has the advantages that by means of the dynamic and static combined double-way buffering mechanism, timeliness and accuracy of a system in mass data processing can be improved to a large extent, and performances and service qualities of a Web server are improved.

The invention discloses a degradation scene-oriented multi-residual fusion laser radar positioning method, which comprises the following steps of: firstly, performing state prediction by adopting an iterative extended Kalman filtering framework and an IMU (Inertial Measurement Unit), and constructing three complementary observation models of a global map matching residual, a local point-to-plane geometry residual and a luminosity residual; secondly, designing a degradation sensing mechanism based on a covariance ellipsoid, representing absolute and relative degradation degrees through a condition number and an information entropy respectively, realizing quantitative evaluation of system observability, and dynamically adjusting fusion weights of observation residuals; meanwhile, a self-adaptive weight strategy based on luminosity Jacobi intensity is introduced; and finally, performing anomaly detection through deviation comparison between the IMU predicted pose and the IEKF estimated pose, inhibiting pose jump, and ensuring continuity of a positioning time sequence. The method effectively overcomes the challenges of geometric constraint deficiency, positioning drift accumulation and the like of the LiDAR positioning system in the geometric degradation environment, does not need to adjust parameters for a specific scene, and improves the precision, robustness and real-time performance of global positioning in the degradation environment.

The invention relates to the technical field of deep foundation pit monitoring, and discloses a deep foundation pit micro-deformation InSAR remote sensing method and system and a storage medium. The method comprises the following steps: synchronously acquiring data through a ground radar and an InSAR satellite, and obtaining a point target set and an original scattering characteristic sequence; obtaining a net deformation phase sequence without influence through registration residual correction, phase deduction and weighted fusion processing, and carrying out phase consistency quality control and unwrapping processing to obtain an accumulated deformation quantity sequence; constructing a deformation mechanism correlation characteristic matrix based on correlation analysis of the cumulative deformation quantity sequence and radar reflection intensity amplitude attenuation characteristics; and fusing the deformation mechanism correlation feature matrix and the real-time differential interference image group, extracting deformation rate and phase gradient change, and further generating a deep foundation pit local micro-deformation risk early warning index set. According to the invention, the tiny deformation of the surrounding area of the foundation pit can be effectively detected, potential risks are warned in advance, and the safety and monitoring efficiency in the construction process are improved.

The invention relates to the technical field of drilling parameter optimization control, and provides a petroleum drilling parameter optimization method and system and a storage medium. Comprising the steps that torque fluctuation amplitude and bit pressure response delay data are collected in real time, an original data sequence is constructed, and parameter trend data are obtained; analyzing the parameter trend data to identify a stratum change mode, extracting trend feature information, and when the trend feature information meets a preset condition, determining a corresponding abnormal mode identifier; generating a parameter adjustment direction of drilling control according to the abnormal mode identifier, and optimizing the parameter adjustment direction in combination with parameter weight analysis and a real-time feedback mechanism to obtain final optimization guidance data for guiding drilling operation; and mapping the final optimization guidance data to a control interface, and generating a dynamic adjustment instruction for drilling operation. The problems that in the existing petroleum drilling process, parameter adjustment lags behind, and stratum changes are difficult to adapt are solved, and intelligent optimization and dynamic control over drilling parameters are achieved.

The invention provides an interactive multi-dimensional data visualization display sand table and method for business data analysis, and the method comprises the steps: carrying out the semantic mapping of state parameters in an interaction control operation when the interaction control operation is received, obtaining a plurality of pieces of visualization intention metadata, and generating a structured query instruction; based on the structured query instruction, extracting a target data subset rendered visually by the heterogeneous commercial data source from a multi-dimensional data cube of the commercial data source, performing visual optimization on the visually rendered chart type, the visual coding channel and the initial visual parameter through the cardinal number of the dimension field in the target data subset and the data distribution type and the numerical range of the measurement field to obtain a visual configuration set of the target data subset; and using the visualization configuration set to render and generate a multi-dimensional data visualization view, and outputting and displaying the data visualization view in a graphical user interface. Based on the scheme, automatic closed loop generation from heterogeneous data to interactive views can be realized.

The invention discloses a dynamic fusion prediction method and system for photovoltaic power generation power, and a storage medium. The method comprises the steps of collecting historical photovoltaic power generation power and corresponding multi-dimensional meteorological parameters; based on time sequence fragments in the historical data set, similarity measurement and clustering analysis are carried out on hour-level weather-power time sequence fragments in historical data by utilizing a dynamic time warping method, different weather-power mode clusters are divided, and a representative cluster center is determined for each cluster; training a long time sequence baseline prediction model and a plurality of short time sequence correction models in parallel; performing weighted fusion on the two prediction results by using the weight to obtain a final prediction value; and the prediction is updated by using the latest meteorological data by adopting an hour-by-hour rolling mechanism. According to the method, the problem that the long-period trend and the short-time fluctuation are difficult to consider at the same time is effectively solved, and the prediction precision, the stability and the adaptive capacity of the photovoltaic power generation power under the complex and changeable meteorological conditions are remarkably improved.

The invention provides an Internet of Things data acquisition and abnormity early warning method and system, and relates to the technical field of Internet of Things data processing, and the method comprises the steps: constructing a digital twin model for a physical entity at a cloud end, and issuing prediction data to an edge node; acquiring sensor data by an edge computing node at a basic frequency, and computing a residual error between real-time data and predicted data and an information entropy of a residual error sequence; dynamically adjusting the data acquisition frequency of the edge node based on a two-dimensional decision space formed by the residual error and the information entropy; and when the residual error and the information entropy both exceed the threshold values, potential abnormity is judged, an edge side abnormity analysis program is triggered, and abnormal event information is uploaded. According to the invention, through cloud edge cooperation and intelligent decision making, the acquisition frequency is reduced in a stationary period, resources are saved, the frequency is improved when an abnormal symptom appears, monitoring is enhanced, and optimal balance between resource consumption and monitoring precision is realized; double criteria are adopted to effectively filter noise interference, and the false alarm rate and the missing report rate are reduced.

The invention discloses a two-stage space-time diagram network and multi-source information fusion time sequence probability prediction method and device, and relates to the technical field of artificial intelligence and big data analysis. The method comprises the following steps: acquiring historical wind speed sequence data and corresponding target variable sequence data; preprocessing the data, and constructing a training sample according to a preset time sliding window; constructing a time sequence probability prediction framework of the two-stage space-time diagram network and multi-source information fusion; based on the fluctuation correlation of the historical sequence data, constructing an adjacent matrix between nodes; inputting the training sample and the adjacent matrix between the nodes into a wind speed prediction model, training the model through a designed threshold perception loss function of a wind speed-power nonlinear relationship, and outputting a multi-node wind speed prediction probability of a first stage; and inputting the multi-node wind speed prediction probability and the multi-source environmental factor data into the gradient boosting tree model for second-stage prediction, and outputting a multi-node wind power prediction result. According to the invention, prediction errors can be reduced.

The invention provides a self-adaptive collaborative security protection method based on attack path deduction, a medium and a system, and belongs to the technical field of network security. The method comprises the following steps: sampling possible paths from a current attack node to high-value assets based on an adaptive path sampling algorithm of Monte Carlo tree search to generate an attack path graph, calculating a protection effectiveness score and a service influence score according to key nodes and predicted attack actions, and generating an optimal protection instruction set based on a multi-target tradeoff function; an instruction is converted into a heterogeneous security device configuration command through a strategy execution cooperative controller, multi-device synchronous activation is realized by adopting a distributed transaction protocol based on a logic clock, and a protection effect is monitored and fed back to an artificial intelligence model for parameter optimization; the problem of protection strategy failure caused by asynchronous network security protection response and attack evolution is solved.

The invention discloses a wireless communication dynamic anti-interference optimization method and platform based on edge computing, and relates to the technical field of wireless communication anti-interference, and the method comprises the steps: collecting wireless channel state information and external interference source features in real time at an edge node, and constructing a real-time interference feature vector according to a collected data set; generating an interference prediction matrix; constructing a dynamic interference topological graph among network nodes, taking the dynamic interference topological graph as environment input, and utilizing reinforcement learning to generate an adaptive anti-interference strategy; and executing self-adaptive anti-interference strategy compensation, issuing a compensation result to the edge node, and executing anti-interference control. The technical problems that in the prior art, wireless communication anti-interference real-time response lags behind, global adaptability is insufficient, and local immediate response and overall reliable anti-interference requirements are difficult to consider are solved, and edge real-time anti-interference and cloud global collaborative optimization are achieved. And the technical effects of real-time performance, reliability and adaptability of wireless communication anti-interference are improved.

The invention discloses a marine main engine cabin entering simulation method and system based on multi-view measurement. The method comprises the following steps: acquiring cabin space depth image information through multiple view angles, constructing cabin environment three-dimensional point cloud data, and unifying multi-view angle point clouds to the same global coordinate system based on camera calibration; performing accurate fusion on the multi-view point clouds by adopting a point cloud registration algorithm which introduces a dynamic weight and a local rejection mechanism, and extracting a boundary of an effective space of a cabin by utilizing an alpha-shape algorithm; motion trail modeling is carried out on the main engine cabin entering process, meanwhile, rapid real-time calculation of the minimum gap between a main engine envelope body and a cabin boundary is achieved by constructing a gridding distance field of a cabin space, and therefore whether the risk of collision or insufficient space exists in a given cabin entering path of a main engine or not is judged. According to the invention, high-precision, high-efficiency and high-real-time host entry space verification can be realized in a complex and narrow in-cabin environment, and reliable technical support can be provided for optimization and decision-making of a marine host entry scheme.

The invention discloses an urban multi-unmanned aerial vehicle cooperative route planning method based on deep reinforcement learning, and the method comprises the steps: building a flight environment model based on urban three-dimensional point cloud data, and achieving the efficient obstacle query through an R-Tree index; for multiple unmanned aerial vehicles, acquiring distance information of surrounding obstacles by adopting a ray-based environment sensing mode, and constructing a state space containing a current unmanned aerial vehicle state, a target relative position, relative positions of other unmanned aerial vehicles and the distance information of the surrounding obstacles; outputting continuous flight control actions through a deep reinforcement learning strategy of parameter sharing; a multi-target reward function is designed, and the multiple unmanned aerial vehicles are guided to achieve safe and efficient collaborative route planning; in the execution stage, each unmanned aerial vehicle makes an independent decision only by depending on the shared strategy network and local observation information of the unmanned aerial vehicle, and distributed cooperative flight is realized. According to the method, the complexity of a traditional method for environment perception and decision calculation is effectively reduced, and the method is suitable for a multi-unmanned aerial vehicle planning task in a complex urban airspace.

The invention provides a target tracking compensation control method and system for a surgical robot in a dynamic breathing environment, and the method comprises the steps: collecting an ultrasonic image of a patient in real time, and carrying out the self-adaptive admittance control algorithm based on force feedback; force control output is adjusted in a self-adaptive mode according to the contact force between the ultrasonic probe and the skin surface and the change of the displacement and speed state of the ultrasonic probe, rigidity parameters of an admittance control model are adjusted in real time, constant contact force between the ultrasonic probe and the skin surface is kept, and it is ensured that the ultrasonic probe and the body surface keep stable contact force and meanwhile adapt to the dynamic change of the body surface; a focus is continuously identified and positioned by utilizing a real-time ultrasonic image through template matching and a dynamic template updating algorithm, real-time position information of an internal focus target spot is obtained and serves as a target pose, the motion compensation amount of a puncture mechanical arm is calculated, the track of a puncture needle point and the motion of the target spot are kept synchronous, and the target pose is accurately positioned. And target tracking and compensation control in a dynamic breathing environment are realized. Target tracking compensation control of the surgical robot can be carried out.

The invention belongs to the technical field of oil and gas pipeline safety monitoring and unmanned aerial vehicle image analysis, and discloses a two-stage oil and gas pipeline occupation pressure monitoring method and system based on unmanned aerial vehicle space perception and deep learning fusion, and the method comprises the steps: obtaining image data and space data of a to-be-monitored region, combining the image data and the space data with a DEM, and obtaining a DEM image; realizing projection of geographic coordinates of the pipeline to image pixel coordinates to obtain a plane image of the pipeline; constructing and deploying a lightweight target detection network DRF-YOLOv8n, performing reasoning on a plane image of the pipeline based on the lightweight target detection network DRF-YOLOv8n, outputting a candidate bounding box, and performing screening and key frame judgment; a Promp-guide SAM pixel level segmentation module is used to realize pixel level segmentation of the key frame, and a candidate pixel mask is generated; and judging whether occupation occurs or not through the intersection of the pipeline projection line and the candidate pixel mask, and outputting alarm information with geographic coordinates.

The embodiment of the invention provides a vehicle communication data processing method, a vehicle and a storage medium, and the method comprises the steps: obtaining target feature data and real-time state data of a target vehicle, the target feature data being used for representing multi-dimensional physical features associated with the target vehicle, and the real-time state data being used for representing real-time state data of the target vehicle; the real-time state data is used for representing an engine running state and an ignition state of the target vehicle; generating key seed data based on the target feature data and the real-time state data; a vehicle session key is generated according to the key seed data, and the vehicle session key is used for encrypting and decrypting the session data packet; and performing data communication of the target vehicle by using the vehicle session key. According to the method and the device, the technical problems of low security of a fixed key management scheme and great influence of an encryption authentication mechanism on real-time performance in related technologies are solved.

The invention provides a cross-border trade supply chain risk early warning system based on digital twinning, and relates to the technical field of cross-border trade. The cross-border trade supply chain risk early warning system based on digital twinning comprises an entity layer, a data acquisition layer, a digital twinning modeling layer, a data processing layer, a risk early warning layer, a safety protection layer and an application output layer, and the entity layer is a monitoring object basis of the system and covers all key physical objects in a cross-border trade supply chain; comprising suppliers, logistics carriers, storage facilities, customs nodes and purchasers in the cross-border trade supply chain. The system integrates a multi-source data acquisition module, covers full-dimensional data of policies, logistics, finance, markets, Internet of Things perception and the like, ensures the comprehensiveness of risk factor extraction, improves the coverage of risk identification, greatly improves the accuracy and real-time performance of early warning, and solves the problem that a traditional early warning mode is inaccurate and lagged in evaluation.

The invention discloses an underwater target detection method based on differential attention. The method comprises the following steps: acquiring an underwater image to be detected; based on the underwater image data set, constructing a lightweight underwater target detection network model based on a differential attention mechanism; constructing a joint loss function, and performing end-to-end training on the lightweight underwater target detection network model to obtain a trained lightweight underwater target detection network model; performing mathematical equivalence fusion on the lightweight underwater target detection network model to generate a lightweight reasoning model; and inputting the obtained underwater image to be detected into the lightweight reasoning model to realize target detection of the underwater image. According to the method, a differential attention feature interaction mechanism is introduced, common-mode noise is stripped through differential calculation, and the detection precision and real-time performance of an underwater fuzzy scene and a tiny target are remarkably improved in combination with a feature pyramid network integrating dynamic scale perception and semantic gating.

The invention provides an inspection method and system for electric power marketing, and belongs to the technical field of electric power marketing, and the method comprises the steps: employing an unmanned plane to inspect a high-voltage line, and employing a robot to move along the high-voltage line; a laser radar is used for carrying out three-dimensional modeling on a high-voltage line, online equipment is used for collecting temperature data, vibration data and sag data of the high-voltage line in real time, and the collected data are transmitted to a monitoring center through the Internet of Things; images shot by the unmanned aerial vehicle and images shot by the robot are analyzed, and the defects of the high-voltage line are automatically recognized; the modeling information, the data information, the image information and historical inspection data of the high-voltage line are combined for analysis, and the operation state of the high-voltage line and the fault position of the high-voltage line are predicted. According to the inspection method for electric power marketing provided by the invention, the accuracy of high-voltage line inspection operation is improved, and meanwhile, the functionality of high-voltage line inspection is also perfected.

The invention provides a real-time disaster situation multi-dimensional verification alarm method and system based on a large model, and relates to the technical field of disaster situation response, and the method comprises the steps: carrying out the backtracking and calling of multi-source disaster situation associated data after a disaster situation early warning signal is received, and obtaining a heterogeneous disaster situation data set; starting a dynamic task chain scheduling mechanism, executing multi-priority thread trend analysis, and outputting a multi-dimensional index analysis result; executing multi-dimensional index cross verification, and outputting a disaster verification conclusion; disaster decision making is carried out, and a disaster decision instruction is generated; carrying out township-level disaster situation grid positioning, and outputting a disaster boundary coordinate set; after the thermal rescue target spot is framed, geological disaster avoidance analysis is carried out, and a safety buffer area is positioned; and executing an alarm verification response. The technical problems that in the prior art, a disaster monitoring and early warning system mainly depends on a step-by-step reporting mechanism, so that disaster information transmission is lagged, accuracy is lost, and timeliness and accuracy of post-disaster rescue work are affected are solved.

The invention discloses an intelligent glasses double-target speech enhancement method and system based on a multi-microphone array, terminal equipment and a medium, and relates to the technical field of speech enhancement. The method is applied to the intelligent glasses, and comprises the following steps: collecting noise-containing multichannel original time domain voice signals through a multi-microphone array of the intelligent glasses, and performing time-frequency domain conversion to obtain a frequency spectrum set; frequency domain complex spectrum amplitude-phase characteristics of each channel are extracted, a local time sequence context is introduced, and multi-channel space acoustic characteristics of amplitude-phase decoupling are constructed; and regulating and controlling the double-tone-area complex spectrum through pre-trained deep neural network modeling to obtain a target complex spectrum, and performing inverse time-frequency domain conversion and reconstruction to obtain an enhanced independent wearer voice signal and a preset direction target voice signal. According to the method, double-target voice parallel enhancement and non-target interference suppression are realized, the power consumption is low, the real-time performance is high, the method adapts to the harsh performance constraint of the end side of the intelligent glasses, and the robustness of voice enhancement in a complex noise scene is excellent.

This invention provides a radiometric calibration and temperature inversion method for a cooled infrared thermal imager, comprising: constructing a temperature calibration device, acquiring multiple calibration temperature points and their corresponding blackbody radiance to obtain corresponding datasets, acquiring two sets of response image grayscale value datasets to obtain a radiometric calibration model; plotting response curves to simultaneously solve for unknown parameters in the radiometric calibration model to obtain a near-range radiometric calibration model; using the modified radiometric calibration model for back-calculation to obtain the actual radiance of the target; pre-setting the constructed lookup table in the embedded processing unit of the infrared thermal imager to perform online temperature inversion output to obtain the absolute temperature of the measured target, and then outputting the absolute temperature of the measured target to a host computer display module. This invention can significantly reduce the calibration workload, enabling the infrared thermal imager to flexibly adjust the integration time and maintain high-precision temperature measurement in complex scenarios, significantly improving its engineering practicality and environmental adaptability.

The application relates to the technical field of data processing, and discloses a data security weight-based dynamic adjustment data trusted space permission control method. The method comprises the following steps: constructing a correlation permission matrix; assigning a basic security weight value to a data object, assigning a credit basic value to a data account, coupling the basic security weight value and the credit basic value to obtain a data security weight value; monitoring the attribute change of the data object and the behavior characteristic change of the data account in real time, updating the data security weight value to obtain an updated permission matrix; receiving an operation request, calling a north-south control permission, and checking the access permission of a target data object; when the north-south control permission is checked, the real-time security weight value is obtained, the east-west control permission in the updated permission matrix is matched, and the target operation permission is confirmed; after the target operation permission is confirmed, an operation record is generated, and the behavior characteristic is updated; and the application can improve the efficiency of dynamically adjusting the data trusted space permission control.

This invention relates to the field of computer technology, specifically to a method and apparatus for integrating third-party algorithm components based on dual-core collaboration. This application directly integrates third-party algorithm components into the BCM dual-core MCU, eliminating reliance on ZCU and CAN bus communication. This fundamentally eliminates the latency and security overhead caused by cross-ECU transmission and E2E protection, improving signal real-time performance and communication security. By clearly defining the functions and allocating resources of the BCM dual-core, a dedicated storage area is planned for the third-party algorithm components, and a standardized integration environment is built, achieving efficient and stable component integration. Simultaneously, peripheral access strategies are optimized, unnecessary protections and redundant polling are disabled, releasing idle computing power and hardware resources of the BCM and improving utilization. Without increasing the ZCU hardware cost, the chassis braking algorithm is deployed and executed locally, reducing performance losses caused by cross-domain communication and effectively improving the overall system operating efficiency and resource utilization efficiency.

The application provides a logistics transportation management method and system based on artificial intelligence, relates to the field of logistics transportation, and solves the technical problems that the prior art is difficult to balance real-time performance, distribution feasibility and resource utilization efficiency in the sudden interruption scene of a logistics vehicle, resulting in slow scheduling response, frequent secondary conflicts or failure of high-value order fulfillment. The method comprises the following steps: obtaining a set of orders to be delivered by a target vehicle at present and real-time position information; based on a preset hard constraint condition, screening candidate vehicles and constructing a task migration feasibility graph; inputting the task migration feasibility graph into a pre-trained lightweight graph neural network model to output the compatibility scores of each candidate vehicle for each order to be delivered in the order set; based on the compatibility scores, evaluating the urgency of the order to be delivered; and based on the urgency of the order to be delivered, updating the delivery task scheme of each candidate vehicle. The application is used in the process of logistics transportation.

The invention discloses a line loss prediction method and system based on integrated DBN-BP, and belongs to the technical field of power system data analysis. The method comprises the steps that firstly, a plurality of parallel DBN sub-networks are constructed, all the sub-networks adopt different activation functions, unsupervised pre-training is carried out with N antenna loss historical data and corresponding weather data as input, and high-dimensional robust features are automatically extracted; and then, taking the output of each sub-network as a feature, inputting the feature into a BP integrated network for supervised training, and finally fusing to obtain a high-precision line loss prediction value. Through the architecture of "unsupervised feature extraction + supervised integrated decision", the problems of strong dependency on annotated data and weak feature extraction ability in the prior art are effectively overcome; meanwhile, forward prediction can be simplified into efficient matrix operation through the full-connection structure of the model, the requirement for real-time dispatching of the power grid is met, excellent generalization ability is achieved, and reliable data support is provided for economical and safe operation of the power grid.

The application relates to the technical field of batteries, and provides a battery negative electrode potential evaluation system, a battery negative electrode potential evaluation method and an electric vehicle. The system comprises a data acquisition device configured to acquire battery surface stress data; and a controller configured to: determine a current high stress concentration area and an area of the current high stress concentration area based on current battery surface stress data; determine a current change rate of the area of the high stress concentration area with respect to a battery SOC based on the area of the current high stress concentration area, a current battery SOC, a historical area of the high stress concentration area and a historical battery SOC; and in the case that the current change rate is greater than a preset change rate threshold, substitute the current battery SOC into a mapping relationship model of the SOC and the negative electrode potential to obtain a current battery negative electrode potential. The application does not need to depend on a complex electrochemical model or additional hardware, has low calculation cost, good real-time performance, can be deployed on a large scale at a vehicle end, and is beneficial to early warning of lithium precipitation risk before lithium precipitation occurs.