13 results about "Object level" patented technology

The invention provides an automatic object-level authorization vulnerability detection method based on a large language model, which comprises the following steps of: firstly, identifying a sensitive resource table through SQL (Structured Query Language) grammar analysis, and analyzing an operation path from a tracking request parameter to the sensitive resource table by utilizing a forward data stream; then, performing context sensing analysis on the database operation by adopting LLM to infer object-level sensitive operation; thirdly, recognizing program logic and a conditional protection mechanism in database query through static analysis, and recognizing custom verification logic such as method-level annotation and administrator permission in combination with an LLM and character string matching method; and finally, judging whether the sensitive operation lacks user-defined verification, ownership SQL constraint and access control conditions at the same time or not by integrating the mechanism, and generating a vulnerability report according to the judgment result. The invention further provides an automatic object-level authorization vulnerability detection system based on the large language model. Therefore, the detection precision can be effectively improved, and false alarm and missing alarm are remarkably reduced.

The application discloses a search platform object-level data permission management method for a non-uniform permission system and multi-source data, which sequentially comprises classification of multi-source data collectable by a search platform, establishment of search platform permission data standard specification, multi-source data permission requirement analysis, data source permission data processing, search platform target permission data processing, search platform target permission data collection, search platform knowledge production, search platform user permission configuration and personnel authentication in a search request; the method solves the problem that a permission model based on RBAC in the prior art cannot adapt to object-level data permission management requirements in a non-uniform permission system and a multi-data-source enterprise search platform, realizes compatibility of data permission management of different data sources, different permission systems and differentiated organization mechanisms, and the granularity of search platform data permission management reaches the object level.

The application discloses a kind of intelligent campus-oriented electromechanical installation management method and system, it is related to computer platform load balancing technical field, including constructing electromechanical installation object basis set and object level installation constraint set.Further generate electromechanical installation task, task branch regulation is executed in combination with construction state data, form three kinds of tasks of executable, to be made up and collaborative adjustment.For executable task, installation process data is collected, object level installation process record is constructed, object level installation verification and abnormal disposal path regulation are executed, and object level installation closed loop result is formed.Based on installation closed loop result, systematized debugging, acceptance collection, file construction and constraint update processing are implemented, and constraint optimization result is formed.The method described in the application realizes the fine flow of intelligent campus electromechanical installation task, abnormal closed loop disposal and result feedback optimization, significantly improves the accuracy, cooperativity and traceability of installation management.

A method for interactive controlling in a game includes: in response to an interactive behavior operation, controlling a controlled virtual object to execute an interactive behavior against a specified object; in response to an interaction condition being satisfied, updating an attribute parameter of the specified object, and increasing an experience parameter of the controlled virtual object according to a preset rule; in response to the experience parameter reaching a preset experience threshold, increasing an object level of the controlled virtual object in a game match, determining at least part of candidate items from a specified item set, and displaying the candidate items; in response to a selection operation on a first item from the candidate items, controlling the controlled virtual object to acquire the first item; and in response to a trigger operation on the first item, displaying a game behavior corresponding to the first item in the game scene.

The application discloses a kind of multi-feature supported spillway remote sensing identification method and system, and the object level target is obtained by image segmentation algorithm;Spectrum, shape, geometric stereo feature of object level target is extracted, and preliminary extraction of spillway object is realized by multi-feature joint decision;The spatial direction and distance relationship between objects are comprehensively considered, and the fine spot around the preliminary identification result is identified and integrated again, contour modification is carried out simultaneously, and finally the relatively neat spillway boundary vector is output.This method specifically designs the geometric stereo feature parameters of spillway to highlight the unique features of spillway in three-dimensional space, effectively overcoming the difficulty of distinguishing spillway from general buildings.At the same time, the method also provides an automatic remote sensing spillway identification process to improve the accuracy of spillway identification, which has important practical application value in the fields of spillway remote sensing monitoring and water conservancy construction.

Embodiments of the present disclosure relate to application programming interface (API) billing based on an API object level. A method performed by an API open function (AEF) in a universal application programming interface (API) framework (CAPIF) is provided. The method includes receiving, from an API caller, a request to invoke a serving API for an API object lifecycle or access operation of an API object. The method includes determining that an API object lifecycle or access operation is associated with at least one API object-related trigger condition of a satisfied charging event. The method includes triggering a CAPIF core function to report charging information for a charging event to a charging function. And the method includes sending a response to the request to the API caller.

The application relates to a duct production information input method, computer equipment and a storage medium. The method comprises the following steps: in response to a trigger operation of a user, determining a target object level selected by the user; acquiring all duct fittings under the target object level; taking a first duct fitting in all the duct fittings as a target duct fitting, and extracting target information of the target duct fitting; obtaining target production information of the target duct fitting based on the target information and a preset tray configuration table; writing the target production information into an attribute of the target duct fitting, taking a next duct fitting of the target duct fitting as a new target duct fitting, and repeating the execution until the target duct fitting is the last duct fitting in all the duct fittings. The application can realize batch automatic input of duct production information, and manual discrimination and input of the user are not required, so that the efficiency and accuracy of duct production information input are improved.

A process for sensing a scene. The process includes receiving sensor data from a plurality of sensor modalities, where each sensor modality observes at least a portion of the scene containing at least one of the objects of interest and generates sensor data conveying information on the scene and of the object of interest. The process further includes processing the sensor data from each sensor modality to detect objects of interest and produce a plurality of primary detection results, each detection result being associated with a respective sensor modality. The process also includes fusing sensor data from a first sensor modality with sensor data from a second sensor modality to generate a fused 3D map of the scene, processing the fused 3D map to detect objects of interest and produce secondary detection results and performing object level fusion on the primary and the secondary detection results.

The invention discloses a video question-answering method and system based on a multi-modal multi-layer attention model, and the method comprises the steps: carrying out the segment division and key frame extraction of a video, obtaining object-level, frame-level and segment-level visual features, and carrying out the semantic coding of a question text and a candidate answer; modeling a cross-time object relationship by using a global object attention mechanism, fusing visual and text features layer by layer at an object level, a frame level and a fragment level in combination with layered cross-modal attention, and generating problem-aware video multi-modal feature representation; and finally completing answer matching and prediction based on comparative learning. According to the method, a lightweight layered fusion structure is adopted while the object-level semantic relationship is explicitly modeled, so that the video semantic understanding and question and answer accuracy are effectively improved, the model calculation complexity is remarkably reduced, and the method has good engineering practicability and deployability.

The invention discloses a multi-target tracking method and system based on self-adaption during testing, and belongs to the field of multi-target tracking. The method comprises the following steps: constructing different types of enhanced views for an input video frame image; a teacher-twin student model double-branch module is established, a spatial entropy method and object-level KL divergence calculation are adopted to obtain a spatial entropy and object-level uncertainty corresponding to a student model, dynamic threshold determination of different layers of the student model is realized based on the object-level uncertainty, and then learning rate dynamic allocation is realized. The enhanced detection consistency loss between the enhanced views is obtained through calculation according to the spatial entropy, the enhanced detection consistency loss and the local patch comparison loss between the enhanced views are added, and a total loss function is obtained; and optimizing the student model according to the total loss function of all the images in the video frame sequence so as to reversely update the teacher model. According to the method, tracking precision, time consistency and calculation efficiency are effectively balanced, and compared with an existing method, all aspects are remarkably improved.

The application discloses a tampered image detection method based on object semantic consistency, and relates to the field of image passive forensics. Most of the existing splicing tampering detection methods based on deep learning are realized based on anomaly detection, and there are limitations in the receptive field due to convolution operation. In view of the problem, the application adopts an encoder constructed based on an attention mechanism to respectively extract and represent the semantic information of the object level and the tampering trace information of the pixel level in the image, generates object semantic representation and tampering trace representation, and fuses and decodes two different encodings through a decoder, so that splicing tampering detection and positioning of the image are realized. The application can combine object semantic consistency and suspicious tampering traces, effectively improve the detection accuracy of the tampered image, and has practical value.

Methods, systems, and devices for data management are described. A data management system (DMS) may receive a federated login request from a user associated with one or more tenants of the DMS. The DMS may direct the federated login request to a centralized management service. The DMS may receive a security assertion markup language (SAML) assertion that indicates an identity of the user, a set of object-level permissions assigned to the user, and an identifier of a first tenant associated with the user. The DMS may identify one or more computing objects in a cluster of storage nodes that correspond to the first tenant based on the identifier from the SAML assertion. The DMS may determine that the user is authorized to perform a set of actions on the one or more computing objects based on the set of object-level permissions indicated by the SAML assertion.

An object evaluation method and an object evaluation device. The object evaluation method includes: obtaining an image of a first object to be evaluated; determining first level information by means of a first object level classifier according to the image of the first object to be evaluated; determining adjustment weights of various object levels according to distribution proportions of the object images of the various object levels in a plurality of object images having the same object type as that of the image of first object to be evaluated and having known object levels, wherein an object level having a relatively high distribution proportion has a relatively low adjustment weight; and respectively generating second rating confidences of the various object levels according to first rating confidences and the adjustment weights of the various object levels to form second level information.