16761 results about "PathPing" patented technology

A network mapping behavior anomaly detection method and system based on machine learning is provided. The method includes: collecting dual-source traffic data, generating a structured log data set through dual-source log fusion engine; performing subgraph matching calculation to obtain a mapping behavior deviation degree; generating communication data containing a watermark identifier in a session corresponding communication path; verifying whether attack events carry the watermark identifier; generating a network mapping behavior anomaly detection report. According to the disclosure, an adaptive attack behavior model is constructed through a multi-modal feature vector based on structured logs and a graph protocol mapping rule base, so that the cognitive robustness to protocol camouflage and path drift is fundamentally enhanced, a real-time verification chain of detection results is built, and traditional passive detection is transformed into self-proof active defense through cross verification of watermark carrying state and behavior trajectory.

The invention relates to the technical field of communication scheduling, discloses a communication scheduling network management intelligent optimization system and method based on AI dynamic decision, and solves the problems of insufficient scheduling dynamics, closed loop deficiency and poor edge adaptation in the prior art. Comprising a multi-dimensional data fusion acquisition module, a dynamic AI decision engine module, a cross-domain collaborative scheduling module and an intelligent closed-loop feedback optimization module. The dynamic AI decision engine module evaluates business value and resource pressure based on an edge-center collaborative architecture, predicts transmission quality and quantifies strategy income, the cross-domain collaborative scheduling module realizes intra-domain resource slicing and inter-domain strategy negotiation and path optimization, the intelligent closed-loop feedback optimization module constructs a data closed loop to iteratively optimize model parameters, and the dynamic AI decision engine module performs multi-domain collaborative scheduling on the basis of the edge-center collaborative architecture. Intelligent scheduling and autonomous optimization of network resources are realized, and the real-time performance, the reliability and the resource utilization rate of a communication network are improved.

The invention discloses a multi-modal fusion AGV dynamic path planning and cluster scheduling system, and relates to the technical field of multi-modal perception and data fusion, and the system comprises a multi-modal perception module which generates a dynamic obstacle confidence map through multi-source data fusion in combination with a hardware-level time synchronization and Transform feature fusion network; the dynamic path planning module adopts an improved rolling window algorithm, integrates an LSTM space-time conflict prediction model and an adaptive weight cost function, and realizes dynamic obstacle trajectory prediction and non-oscillation global path generation; the cluster scheduling control module is used for optimizing multi-AGV task allocation and conflict resolution in combination with a dynamic priority preemption mechanism and digital twinborn simulation rehearsal based on a distributed contract network protocol of edge computing; and the data conflict resolution module is used for triggering a multi-modal re-calibration process through confidence weighting and sliding window time sequence verification. According to the system, in logistics storage and intelligent manufacturing scenes, the dynamic obstacle avoidance success rate and the robustness and operation efficiency of an AGV cluster are improved.

The invention relates to the technical field of energy scheduling, in particular to a multi-modal data fusion method and system based on energy scheduling and a storage medium. The method comprises the following steps: obtaining multi-modal data, and carrying out abnormal fluctuation feature extraction to obtain a space-time fusion abnormal feature labeling set; deducing a multi-objective optimization path according to the space-time fusion abnormal feature labeling set to obtain a dynamic scheduling decision map; performing edge node game equilibrium calculation according to the dynamic scheduling decision map to obtain a trusted scheduling verification chain; performing digital twinborn constraint optimization on the trusted scheduling verification chain to obtain a closed-loop scheduling digital twinborn body; compiling a dynamic scheduling instruction set based on the closed-loop scheduling digital twin to obtain an anti-disturbance energy scheduling strategy library; and obtaining real-time energy supply and demand data, and performing scheduling deviation tracing on the real-time energy supply and demand data according to the anti-disturbance energy scheduling strategy library to obtain an energy distribution decision. According to the invention, the efficiency and reliability of energy scheduling can be improved.

The invention discloses a construction engineering risk assessment method and system for multi-source anomaly monitoring, and particularly relates to the field of construction engineering risk assessment, comprising the following steps: analyzing equipment-to-component node mapping and return delay characteristics based on multi-source monitoring data received in an idle task period; establishing a cross-device time alignment model and a pseudo-synchronization section time migration trajectory, identifying a potential abnormal path set through a time delay drift trend, and providing a time feature basis for subsequent structure anomaly reasoning and risk assessment; deducing a node triggering sequence and a time causal chain based on the final pseudo-synchronization segment index matrix and channel path mapping; multi-source monitoring data is received in an idle task period, and a cross-device time alignment model is established, so that the problem of time sequence dislocation caused by mechanisms such as cache delay and packet loss re-sending is effectively identified, and historical abnormal data is prevented from being misjudged as a future risk from the source.

The invention relates to the technical field of program control systems, in particular to a pentahedron machining center precision calibration method and system based on multi-sensor fusion, and the method comprises the steps: a sensor system construction and calibration module is used for field calibration, drift correction and redundancy deployment to ensure data precision, and achieves the whole-course traceability of a calibration process through a block chain technology; the multi-source data preprocessing and fusion module is used for time-space synchronization of heterogeneous data and dynamic fusion of multi-source information; the intelligent modeling and state prediction module is used for performing real-time and multi-task prediction on key states such as tool wear and thermal deformation; based on the prediction result, the adaptive compensation and path optimization module is used for dynamically optimizing the tool path; meanwhile, through an online learning mechanism, the calibration model is continuously updated by utilizing a processing result; the distributed cooperative control module executes data processing and calibration algorithms locally and makes a cooperative decision with a numerical control system, and low-delay and intelligent response to machining abnormity is achieved.

The invention relates to a digital resource sharing method and system based on a pedigree mapping relation, and belongs to the technical field of enterprise digital resource management, and the resource sharing method comprises the steps: obtaining multi-source heterogeneous data from an internal distributed system of an enterprise through a preset interface protocol; performing standardization processing on the multi-source heterogeneous data to generate a structured resource pool; a graph dictionary module is constructed, the resources in the structured resource pool are subjected to association topology analysis, and a resource relation topological graph is output; establishing a pedigree relationship between resources and business scenes based on the three-dimensional mapping model, respectively generating and fusing a business process demand map, a production stage demand map and a product capability matching map, and constructing a visual digital resource capability platform; and outputting the target resource identifier and the associated path in response to a resource calling instruction input by a user. Integration and sharing of internal and external multi-source heterogeneous data of an enterprise can be realized, the problem of data islands is solved, and meanwhile, the intelligent degree of a resource management system is improved.

The invention discloses a cloud desktop security access control method based on a zero-trust architecture, belongs to the technical field of network security, and is used for solving the problems of difficulty in hidden attack detection, cross-cloud attack chain breakage and conflict between security control and service continuity in a multi-cloud environment. Firstly, user identity attributes, session metadata and service call logs are aggregated, an identity-resource-behavior triple dynamic graph is constructed, cross-session association features are extracted, and a multi-dimensional behavior baseline is generated. And quantifying the access deviation degree based on the behavior baseline, triggering sensitive operation traceability analysis, constructing a time sequence risk propagation model, identifying latent attack features, predicting a penetration path and outputting a risk propagation coefficient. And finally, dynamically generating a process-level micro-isolation strategy according to a risk result, gradually adjusting the authority through a nonlinear authority attenuation function, inserting a secondary authentication node when unexpected resource jump is detected, reconstructing a communication white list, and realizing collaborative optimization of security protection and service continuity.

The invention discloses an intelligent substation communication link fault self-healing regulation and control method and system, and relates to the technical field of fault self-healing regulation and control, and the method comprises the following steps: constructing a link disturbance intensity sequence reflecting link quality fluctuation; performing trend fitting and mutation identification on the link disturbance intensity sequence based on a sliding window mechanism, and generating a link state transaction index; when the link state transaction index exceeds a first threshold value, identifying an affected key easy-to-disturb service set according to the service path topological data of the abnormal link; constructing a context-aware path migration cost model based on the key easy-to-disturb service set, and generating an optimal switching path sequence; and implementing hierarchical link regulation and control according to the path switching optimal sequence. According to the method, trend fitting and mutation detection are carried out by adopting an overlapped sliding window mechanism and a mutation scoring function, so that intermittent or hidden link degradation abnormity which is difficult to capture can be effectively identified, and a link state transaction index has higher judgment precision.

The invention relates to a method convenient for data consanguinity collection and analysis, which comprises the following steps of: obtaining original consanguinity data comprising a task execution log, application metadata and a cross-system dependency relationship, and carrying out standardization processing on the original consanguinity data to generate structured consanguinity information comprising an asset unique identifier, an upstream and downstream association relationship and a data operation type; structured consanguinity information is synchronously written into a graph database and a distributed data warehouse, the graph database stores real-time association topology, the distributed data warehouse stores full-amount historical versions, and a transaction consistency algorithm is adopted to ensure the atomicity of double-write operation, so that the data storage efficiency is improved. Single-asset-level consanguinity tracking is performed based on real-time topology of a graph database, global consanguinity analysis is performed based on batch computing power of a distributed data warehouse, a direct dependence path, a deep association network and a closed-loop link detection result are generated, and a closed-loop management mechanism from data acquisition, analysis to optimization is formed. And the problem that an analysis result is disjointed from an acquisition end in a traditional scheme is solved.

The invention relates to the technical field of network security, in particular to a computer network information security monitoring method, system and device and a medium, and the method comprises the steps: obtaining encrypted traffic data and application log data in a network environment, and constructing a space-time associated original data set based on the encrypted traffic data and the application log data; performing protocol analysis on the original data set with the time-space association to generate a protocol fingerprint feature vector; inputting the protocol fingerprint feature vector and the application log data into a preset heterogeneous multi-modal analysis model, and obtaining a multi-dimensional security situation assessment result containing a threat level and an attack path; and based on the multi-dimensional security situation assessment result, generating a dynamic defense strategy instruction set through a reinforcement learning algorithm, and issuing a strategy instruction to a network execution node in real time. The method and the device have the effects of realizing real-time accurate detection of encryption threats and constructing a dynamic defense system with balanced security and efficiency.

The invention discloses an Internet of Things data transmission optimization system based on intelligent routing, and relates to the technical field of data transmission optimization. The heterogeneous network access module supports adaptive switching of multiple protocols such as 5G, NB-IoT and LoRa, and monitors link quality; intelligent routing decision selects an optimal path by means of deep reinforcement learning, and end-to-end delay is reduced; data distribution scheduling split data streams are transmitted in a multi-path parallel mode, and forward error correction is combined to guarantee reliability. The network state prediction uses an LSTM to predict bandwidth, and a route is adjusted in advance; the secure transmission integrates iris authentication and dynamic encryption. According to the invention, efficient, reliable and secure transmission is cooperatively realized, energy collection and dormancy strategies are combined, the equipment endurance is greatly prolonged, the network energy consumption is reduced, and the method is suitable for industrial control, telemedicine and other scenes with high requirements on real-time performance and reliability.

An illustrative method includes scanning a compute environment associated with an entity and identifying one or more attack paths from a network to one or more datasets associated with the entity. The one or more attack paths each include a series of risk artifacts within the compute environment that can be exploited by an attacker to access the one or more datasets. The method further includes generating one or more attack path risk scores associated with the one or more attack paths and indicative of one or more levels of risk that the one or more attack paths could be exploited to access the one or more datasets. A risk mitigation operation associated with the one or more attack paths is performed based on the one or more attack path risk scores.

The invention relates to the technical field of computer networks, in particular to a monitoring method and system based on industrial computer network fault data, and the method comprises the steps: collecting the heterogeneous fault data of each layer of equipment in an industrial control network in real time through distributed probe nodes; performing multi-modal normalization processing on the original fault data; constructing a fault knowledge graph, and dynamically associating an equipment topological relation, a historical fault mode and a current production task context; fault root cause analysis is carried out by adopting a hybrid inference engine, and a potential fault propagation path is predicted in combination with a rule-based expert system and an LSTM-GNN joint model; generating a grading alarm strategy, triggering a self-adaptive fault-tolerant mechanism, and dynamically adjusting network bandwidth allocation or starting redundant equipment switching according to the fault grade; according to the invention, by constructing the industrial knowledge graph and the adaptive fault-tolerant mechanism, efficient, accurate and interpretable fault diagnosis and prediction are realized, and the reliability and operation and maintenance efficiency of an industrial network are improved.

The invention discloses a production process state monitoring scheduling optimization method based on real-time data acquisition, relates to the technical field of manufacturing process scheduling, and is used for solving the problem of insufficient real-time performance and stability of process scheduling. According to the method, a process monitoring mechanism based on real-time acquisition and closed-loop scheduling is constructed, a time sequence structured data frame is formed under a unified time reference, operation stability and quality offset characteristics are extracted, a data credible label and a current process state factor vector are generated, an optimized scheduling model is input, and task conflicts and resource bottlenecks are identified according to the data credible label and the current process state factor vector. According to the method, path compression and sequence adjustment are implemented in combination with scheduling priority mapping and a resource path diagram, scheduling deviation vectors are constructed through task response time delay and process blocking in operation, rules and parameters are triggered to be updated online and locally rearranged, and solidification is performed after verification in a prediction window, so that equipment idling and switching fragmentization in the production process are reduced, and the production efficiency is improved. And the real-time performance of the production process, the resource utilization rate and the system stability are improved.

The invention relates to the technical field of information retrieval, and discloses a distributed storage and indexing method, which comprises the following steps of: dynamically fragmenting an original file through an improved consistent Hash algorithm to generate a plurality of data blocks with timestamps; constructing a three-dimensional Bloom filter index matrix containing timestamps, data types and content features for the data blocks with the timestamps, associating a B + tree local index with an inverted global index through a hierarchical index structure, and establishing an index update priority queue by adopting a two-channel synchronization mechanism, performing real-time increment synchronization on the hotspot index through a heartbeat mechanism, performing batch synchronization on the cold data layer index according to a cold data synchronization period, predicting a data distribution probability through a distributed query statistics probability table during query, and initiating multi-path query in parallel based on probability weight, and the query path is dynamically optimized according to the node group storage medium type and the inter-node network transmission delay, so that rapid distribution adjustment and access of distributed storage are realized.

The invention discloses an electric power data anomaly detection method and system combined with edge computing, and particularly relates to the field of electric power data anomaly detection.The electric power data anomaly detection method comprises the steps that a periodic abnormal behavior chain structure is established by extracting a continuous judgment result, actual physical feedback and a historical standard response track of edge nodes to electric power data; and a weighted residual trend path is generated, so that identification of a node judgment offset state and quantification of an abnormal trend are realized. By constructing a power data abnormal behavior chain and a multi-cycle residual error trend graph, an edge node misjudgment offset state is identified, and path suppression and channel structure correction are executed, so that the node safety judgment accuracy and the abnormal response stability in an edge computing environment are improved.

The invention discloses a mobile storage and charging robot remote scheduling and path planning system based on the Internet of Things, and relates to the technical field of robot control. Comprising a path dependency modeling module, a path conflict analysis module, a resource dependency graph construction module, a decoupling rearrangement scheduling module, a time sequence offset evaluation module and a path weight regulation and control module, and obtaining a path node sequence and an access time period of a current to-be-executed task of each mobile storage and charging robot, and generating a task path pre-occupation graph. By constructing the path dependence model, the conflict prediction mechanism and the task decoupling rearrangement strategy, accurate identification and effective intervention of path conflicts and resource deadlocks in the multi-robot scheduling process are realized, the stability of the scheduling system and the task execution continuity are improved, efficient completion of energy supply is ensured, and the scheduling efficiency is improved. And the operation efficiency and safety of the system are obviously optimized.

The invention discloses a cloud-edge collaborative intelligent storage node dynamic deployment method and system. The method comprises the following steps: monitoring performance indexes such as edge node data traffic and storage resource state in real time; a deep learning algorithm combining time sequence analysis and an LSTM neural network is adopted to analyze traffic information, and a data access demand is predicted; edge nodes and cloud storage resource configuration are dynamically adjusted based on a prediction result, and an intelligent scheduling algorithm, a data cold and hot separation strategy and a self-adaptive fragmentation technology are introduced to allocate resources; the storage node layout is optimized in real time, and an optimal data distribution path is selected through a reinforcement learning strategy; data security and access control are realized by adopting end-to-end encryption, multi-level access control and block chain technologies. The system comprises a monitoring acquisition module, a prediction analysis module, a resource scheduling module, a path optimization module and a security control module. According to the method, the utilization efficiency of storage resources is improved, data delay is reduced, system stability and data security are enhanced, and the method is suitable for a cloud edge collaborative storage scene.

The invention relates to the technical field of communication data analysis, and discloses a communication network service health degree analysis method, system and device and a medium, and the method comprises the steps: obtaining real-time link state data, carrying out the quantitative analysis, and judging whether the link jitter exceeds a preset threshold value or not; when abnormality is detected, abnormal points can be positioned and the influence range of the abnormal points can be analyzed, so that the influence degree of nodes on the abnormal diffusion path can be identified; calculating a service quality index change value, and determining a starting point and a diffusion direction of chain degradation; by analyzing historical traffic data of the key nodes, a service traffic change trend and traffic dense area distribution characteristics are obtained. According to the method, the bandwidth allocation strategy is dynamically optimized, the bandwidth resources at the junction are reconfigured, and the regional service health degree is evaluated. And if the degradation is not effectively controlled, monitoring and detection mechanism parameters can be adaptively adjusted, and continuous analysis and control of link abnormity are realized, so that the network performance and the service quality are effectively improved.

The invention discloses a cross-system heterogeneous metadata intelligent acquisition method and system based on LLM, and the method comprises the following steps: S1, collecting and preprocessing heterogeneous system data, and constructing a heterogeneous graph; s2, analyzing nodes by the large language model, and splicing structural features to form initial representation of the nodes; s3, constructing a heterogeneous graph Transform, executing multi-layer propagation and low-rank decomposition, and outputting final node representation; s4, generating a field alignment path, an interface mapping path and a dependency path, and planning an acquisition path and an acquisition sequence; and S5, executing an acquisition task, converting a structured format, writing into a metadata center, and performing closed-loop optimization. According to the method, intelligent acquisition, semantic analysis and automatic integration of cross-system heterogeneous metadata are realized, and the efficiency and intelligent level of metadata management in a multi-source data environment are greatly improved.

The invention provides a multi-source heterogeneous distributed computing power fusion scheduling method and system, and the method comprises the steps: carrying out the multi-dimensional collection and preprocessing of heterogeneous scheduling source data, generating a scheduling joint data set, carrying out the key demand prediction processing of a task set based on a preset quantum channel topology, generating a key pre-distribution matrix, and carrying out the key pre-distribution matrix, and generating a real-time task queue according to a task set of the scheduling joint data set, and performing multi-target matching degree calculation processing on the key pre-distribution matrix and the real-time task queue based on a dynamic priority quantification model to generate a distribution decision matrix. Then adaptive key supplement and migration path optimization processing are performed on the allocation decision matrix to obtain a migration task set, and finally hidden node resource prediction and preset quantum channel topology updating are performed on the migration task set, so that the rationality of key allocation is improved, the problem of compatible conflicts of resource scheduling is solved, and the resource scheduling efficiency is improved. Therefore, the task migration success rate is improved.

The invention discloses a heterogeneous AI computing power resource scheduling method and system, and the method comprises the steps: constructing a heterogeneous AI computing power resource pool, wherein the heterogeneous AI computing power resource pool integrates the computing resources of a plurality of heterogeneous AI acceleration chips; obtaining a scheduling demand of the AI task, wherein the scheduling demand comprises a task type, a resource request quantity, a priority identifier and a task group association relationship; generating a multi-dimensional scheduling strategy according to task requirements, wherein the scheduling strategy comprises a priority scheduling rule, an affinity scheduling rule and a resource preemption rule; based on a multi-dimensional scheduling strategy, the AI tasks are dynamically allocated to target computing power nodes of the heterogeneous AI computing power resource pool, and the task execution state and the resource utilization rate are monitored in real time; and dynamically adjusting computing resource allocation according to the resource utilization rate. Through the heterogeneous AI computing power resource pool, the resource utilization rate is remarkably improved, dynamic resource allocation is realized through a multi-dimensional scheduling strategy, and meanwhile, a communication path is optimized through an affinity scheduling strategy, so that the problem of task starvation caused by resource fragmentation is avoided.

The invention discloses a network security threat research and judgment method, system and device and a storage medium, and the method comprises the following steps: S1, obtaining network traffic, terminal logs, application program interface calling records and threat intelligence data in real time, carrying out the standardized cleaning and format conversion of the data, and building a unified data lake; s2, matching, identifying and determining threats through a preset known threat feature library, constructing a normal behavior baseline by using an unsupervised learning algorithm, and marking suspicious events deviating from the baseline; s3, for the suspicious event marked in the step S2, mining a potential attack path and an attack intention by combining knowledge graph technology associated asset information, a historical attack chain and a homologous IP address; and S4, based on the attack success probability, the influence asset importance and the diffusion speed, calculating a threat level by adopting a fuzzy comprehensive evaluation model, and generating a research and judgment report containing disposal suggestions.

The invention belongs to the technical field of network security, provides a network attack detection method based on dynamic graph coding, and solves the problems of poor dynamic adaptability of an attack path and missing of timing constraint in the prior art. The method comprises the following steps: constructing a dynamic threat map, extracting a triple of heterogeneous threat intelligence by using a RoBERTa model, and adding a timestamp and a confidence attribute; a dynamic graph encoder for time sequence perception is designed, semantic and evolution laws are fused through periodic time coding and a multi-head time sequence attention mechanism, and feature weights are adjusted in combination with a gating residual layer; an event-driven incremental updating strategy is adopted, and node similarity is calculated to achieve local subgraph updating; a time sequence rule base is established, three-dimensional parameter verification attack chain time sequence logic is defined, and abnormity is judged through conflict scores; and finally, integrating a graph updating module, a dynamic coding module and a constraint analysis module to realize multi-source threat feature matching and attack detection. According to the method, the adaptability of attack path evolution is improved through dynamic graph modeling and real-time increment updating.

The invention provides an APT (Advanced Persistent Threat) attack detection method based on a large language model, which comprises the following steps of: S1, extracting original system call event data from a kernel audit log of an operating system, and preprocessing the data; s2, constructing a multi-model collaborative detection architecture based on a large language model, and realizing fine-grained classification of network entities according to the preprocessed data through prompt construction, model fine tuning and a confidence scoring mechanism; s3, constructing an adaptive graph search algorithm based on multi-modal feature correlation modeling, driving attack path topology reconstruction, and realizing maximum reduction of a malicious sub-graph topology structure; s4, carrying out combination with MITRE ATTamp; the CK tactical knowledge base constructs a cyclic enhancement analysis framework, a cyclic enhancement technology is adopted to drive a large language model to execute hierarchical association reasoning, a mapping relation from malicious subgraphs to attack tactics and tactical chains is derived step by step, and finally an attack report summary and a targeted defense strategy are generated. According to the invention, APT attack detection with high accuracy and high interpretability is realized.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

The invention discloses a sensing data fusion-based radiator abnormal state detection method, relates to the technical field of radiator state detection, and is used for solving the problem of poor radiator state detection efficiency. According to the method, multiple types of sensors are arranged in combination with a radiator structure, acquisition time alignment is completed, segmentation and de-noising processing is performed on multi-modal data based on a sliding time window, and a frequency domain texture feature is constructed by extracting a frequency spectrum information entropy and a high-frequency energy ratio; constructing the feature values into graph nodes, establishing a complete connection graph, compressing the complete connection graph into a skeleton graph through conditional independence test, generating a directed acyclic graph in combination with an intersection structure and topological sorting, and calculating a causal weight to realize structure updating; and extracting a current window node state, comparing the current window node state with a prediction state, identifying an abnormal node, executing path backtracking, calculating path cost, tracing to a root cause node, extracting path evaluation information, and generating a response signal, thereby improving the abnormality diagnosis precision and scheduling linkage responsivity of the radiator.

The invention discloses a file uploading method and device, a storage medium, electronic equipment and a program product, and relates to the technical field of computer networks and distributed storage, and the method comprises the steps: collecting metadata of a to-be-uploaded file and a network state of an uploading path between a client and a server in real time; calculating the size of a target fragment of the to-be-uploaded file for data uploading between the client and the server through the metadata and the network state; segmenting the to-be-uploaded file based on the target fragment size to obtain a plurality of data blocks; according to the method, the plurality of data blocks are used for uploading the file from the client to the server, and the uploading parameters of the different data blocks are recorded, so that the uploading result of the file to be uploaded transmitted to the server is determined through the uploading parameters, the technical problem that the uploading efficiency and robustness of the object storage system are poor is solved, and the uploading efficiency of the object storage system is improved. The technical effect of improving the uploading efficiency and robustness of the object storage system is achieved.

The invention discloses an end-side cloud collaborative intelligent fire-fighting fire monitoring system based on image fusion, and relates to the technical field of intelligent fire-fighting, the system is composed of a plurality of functional modules, and the system comprises a multi-modal image fusion module which generates a dynamic scanning priority map based on prior data, distinguishes a natural heat source from an abnormal fire by using a dual-light fusion algorithm, and sends an image fusion result to a cloud server; a scanning area is divided according to the thermal risk grade, and the thermal imaging resolution is dynamically adjusted; the distributed edge computing module is used for carrying out space-time synchronization on cross-modal data through a multi-modal feature alignment network, and carrying out dynamic allocation on a CUDA core and CPU resources through adaptive computing scheduling; an improved artificial bee colony algorithm is adopted, the bandwidth of the multi-sensor data flow is dynamically allocated through a time-sharing multiplexing protocol, and three-dimensional path planning is carried out; and the end-side cloud collaborative decision module constructs a federated learning driven model sharing network, and each edge node trains a lightweight YOLOv5s pruning model based on local data.