343 results about "Strategy execution" patented technology

The invention provides a network security protection method and system applied to regional digital and intelligent asset businesses, and the method comprises the steps: collecting asset data flows of a plurality of asset business nodes in a target region, carrying out the threat feature extraction of the asset data flows based on a preset threat knowledge graph, and obtaining a threat feature extraction result; generating a dynamic threat feature vector corresponding to the asset service node; inputting the dynamic threat feature vector into a pre-trained dynamic protection model, and outputting a real-time protection strategy adaptive to the asset service node through a multi-layer decision network in the dynamic protection model; performing strategy execution on the network flow of the asset service node based on the real-time protection strategy, generating a strategy execution result and feeding back the strategy execution result to the dynamic protection model; and performing adaptive optimization on decision parameters of the dynamic protection model according to a strategy execution result, and generating an updated dynamic protection model for a protection decision of a next round of asset business nodes.

The invention discloses an AI data warehouse full-link blood relationship tracking method and device, and relates to the technical field of data processing. The method comprises the following steps: obtaining structured log data with business terms and entity tags; establishing a knowledge graph according to the structured log data and the business document; generating blood relationship metadata according to the knowledge graph and the data job execution log; constructing a blood relationship cognition map according to the blood relationship metadata, the data operation log and the business document; generating a governance strategy vector according to the blood relationship cognitive map, the system real-time index and the service SLO, and sending the governance strategy vector to an execution engine, so that the execution engine carries out processing according to the governance strategy vector; and obtaining a governance strategy execution result, user feedback information of the governance strategy execution result and a system log generated in a governance process. According to the method, complex semantics and causal relationships behind data operation can be captured.

The invention discloses a data management method based on a distributed storage system, relates to the technical field of data management, and is used for solving the problem of storage management behavior strategy mismatch. On the basis of dynamic perception of data access behaviors in a distributed storage system, a behavior feature vector fusing an access mutation rate, periodicity and an access span is constructed, behavior pattern recognition and strategy structure generation are completed, optimal deployment and hierarchical storage of copies are achieved through node resource state perception and construction of a cost function, and the strategy structure generation efficiency is improved. And after compression and consistency configuration are executed and the strategy falls to the ground, the compressibility and the offset trend of the behavior path are analyzed, scheduling management information is extracted, and a stable execution or strategy adjustment signal is generated, so that the problem of strategy execution mismatching caused by behavior perception deficiency in the distributed storage system is reduced, and the strategy execution efficiency is improved. The strategy closed-loop control and the resource scheduling optimization under behavior driving are realized, so that the data management efficiency and the strategy adaptation capability of the distributed storage system are improved.

The invention relates to the technical field of computer network security, and discloses a computer network security access control management method based on big data. The method comprises the following steps: constructing a network security situation knowledge graph, collecting a real-time access behavior sequence through a probe, and synchronizing the real-time access behavior sequence to the knowledge graph; simulating a network entity interaction state in the knowledge graph, and predicting a threat propagation path and a potential intrusion behavior; setting a dynamic access control strategy, constructing a multi-dimensional feature matrix in combination with a real-time access behavior sequence association influence degree and a strategy execution priority constraint condition, calculating a strategy conflict risk score by using a deep learning model, comparing with a preset threshold to judge whether a conflict exists or not, and if yes, reconstructing the strategy; and automatically executing access blocking, session termination and data encryption operations according to the reconstructed strategy, recording an execution log and security feedback data, and updating the knowledge graph in real time. According to the method, the dynamic property and the security of access control are improved, and security threats in a complex network environment can be effectively handled.

The invention relates to the technical field of edge device security policy deployment, in particular to an edge device-oriented lightweight AI security policy adaptive deployment method. The method comprises the following steps: collecting operation state information of edge equipment, and constructing a current multi-dimensional environment vector and a sliding window feature vector; constructing a strategy candidate library, constructing a strategy adaptability scoring function based on the current multi-dimensional environment vector and a real-time perceived network threat event, and scoring and sorting all candidate strategy items to obtain a strategy execution candidate set; and performing scheduling optimization on the strategy execution candidate set by adopting a multi-objective optimization algorithm, and selecting a strategy combination with the highest deployment score as a deployment result. According to the method, a multi-dimensional strategy adaptability scoring function is constructed, candidate strategy items are screened according to current network threat events and system resource conditions, a strategy screening mechanism from fixed template type configuration to resource awareness and attack scene linkage is converted, and the pertinence and accuracy of strategy deployment are improved.

The invention provides a transformer area source load storage intelligent adjustment method based on multi-scene cooperation, and belongs to the technical field of intelligent power grid adjustment, and the method comprises the steps: a data collection module which collects the related data of a power supply, a load and a power grid, and generates corresponding feature parameters; the data analysis module is used for determining comprehensive adjustment parameters based on the characteristic parameters, obtaining a plurality of scenes and generating corresponding scene labels; the data prediction module is used for generating a prediction load curve and further determining a charging quantity parameter in a load trough period; the strategy execution module is used for executing a control strategy corresponding to the scene label based on the predicted load curve, the charging quantity parameter in the load trough period and the scene label; and the strategy updating module is used for comparing the real-time load curve and the predicted load curve after the strategy is executed, determining an adjustment deviation ratio, correcting the predicted load curve based on the adjustment deviation ratio, and then executing the control strategy again. And the energy utilization rate and the self-adaptive capability of the system are obviously improved.

The invention discloses a virtual power plant green energy consumption cooperation method and system fusing digital twinning and reinforcement learning, and the method comprises the steps: building a green energy power plant digital twinning model through a simulation tool, collecting data in real time, and carrying out the normalization and abnormal value cleaning; inputting the data into the digital twinborn model, mapping the operation state of a physical system, and rehearsing the influence of different energy scheduling strategies on the green energy consumption rate and the power grid frequency in a virtual environment; optimizing and updating the energy scheduling strategy based on a near-end strategy optimization PPO algorithm; the optimized and updated energy scheduling strategy is fed back to a physical system to be executed, the strategy execution effect is monitored in real time, the digital twin model parameters are updated, and closed-loop control is formed; the method can solve the problem that the intermittency of renewable energy sources is not matched with the dynamic demand of the load.

The invention provides a multi-modal LLM-based building risk prediction management and control method and system, and relates to the technical field of building safety management, and the method comprises the steps: analyzing sensor data, a text report, an image video and a voice instruction of a building construction site through a multi-modal feature extraction module, and generating a structured feature vector set; performing cross-modal semantic fusion and risk coupling analysis by using a multi-modal LLM inference engine to generate a potential risk identification set and a risk level assessment result; dynamically matching a management and control rule of the building safety specification library based on the risk identifier, and outputting a strategy set consisting of an equipment regulation and control instruction, a personnel early warning notification and a regional management and control suggestion; driving a field execution device to implement a control action, and collecting a multi-modal feedback data stream; and calculating a strategy execution efficiency index through a closed-loop optimization module, dynamically updating LLM model parameters and rule weights, and forming a self-adaptive optimization link. The system correspondingly comprises a multi-modal feature extraction and fusion module, an LLM inference engine module, a dynamic strategy generation module, an execution feedback module and a closed-loop optimization module. According to the method, the problems of key feature omission and risk response lag in traditional single-mode analysis are solved, and the risk prediction accuracy and the management and control real-time performance are remarkably improved.

The invention discloses an intelligent war game deduction method based on reinforcement learning, and the method comprises the steps: constructing a dynamic battlefield model: constructing an adjustable battlefield simulation platform, and defining the landform, resources and army distribution elements in a battlefield; a reinforcement learning strategy is generated and optimized, an intelligent strategy generation algorithm based on deep reinforcement learning is designed, and an efficient combat strategy is generated in multiple rounds of training by constructing a state space and an action space and combining a situation reward function; a double-agent chess playing training mechanism is introduced, black parties and white parties are modeled into reinforcement learning agents, red parties and blue parties are modeled into reinforcement learning agents, and a real battlefield game is simulated through multiple rounds of alternate training; result visualization deduction: a dynamic decision visualization function is provided, and battlefield situation, troop dynamics and a strategy execution process can be displayed in real time; according to the method, the problems of rule solidification, limited strategy generation capability, insufficient antagonism and the like in the prior art are solved.

The invention provides a risk control strategy dynamic optimization method and system based on a large model, and the method comprises the steps: determining a current risk control strategy system and corresponding strategy execution data, calling the large model to carry out the conjoint analysis of the current risk control strategy system and the corresponding strategy execution data, and generating a strategy structure map and a deviation diagnosis report; based on the strategy structure map and the deviation diagnosis report, generating a strategy adjustment scheme containing a rule logic reconstruction suggestion and a rule priority optimization sequence, and according to the strategy adjustment scheme, updating the current risk control strategy system to obtain an optimized risk control strategy system; and finally, effect verification is carried out on the optimized risk control strategy system through a dynamic verification mechanism, and a verification result is used as newly added data of strategy execution data, so that dynamic optimization of the risk control strategy is realized, and the accuracy and adaptability of the risk control strategy are improved.

The invention discloses a dynamic network access control method and system based on a zero-trust architecture, and the method comprises the steps: integrating equipment health degree evaluation through the triple dynamic binding of biological feature dynamic binding, equipment fingerprint salt value hash verification and environmental state perception, and constructing a real-time trust basis; dynamic risk quantification is realized based on a multi-source heterogeneous data fusion machine learning model, real-time upgrading and degrading self-adaptive adjustment of authority is realized through an AI driving strategy generation module according to a real-time risk score, a zero-trust sandbox limitation sensitive operation is triggered for high-risk access, and a minimum authority channel is started for low-risk access; performing fine-grained access control and intercepting an unauthorized request in real time by adopting an agent-free API gateway technology, and monitoring an operation behavior in combination with a block chain non-tampering storage access log and an anomaly detection algorithm; finally, a continuous self-adaptive evolutionary cycle is formed through a risk assessment-policy execution-abnormal feedback closed loop mechanism, and the static lag problem of traditional network access control is systematically solved.

The invention relates to the technical field of operation and maintenance safety compliance, and discloses an operation and maintenance service automation safety compliance detection system and method. The system comprises an operation and maintenance event acquisition unit, a strategy execution unit, a compliance analysis unit, a strategy optimization unit and a violation tracing unit. The operation and maintenance event acquisition unit captures operation and maintenance operation events and execution environment parameters in real time, packages the operation and maintenance operation events and the execution environment parameters into event data packets and transmits the event data packets to the strategy execution unit; the strategy execution unit extracts a reference strategy rule from the security strategy library, performs matching verification on the event and generates a result; the compliance analysis unit analyzes compliance fluctuation characteristics through a multi-dimensional compliance deviation model in combination with historical records; the strategy optimization unit dynamically adjusts the event capture frequency and corrects a strategy matching rule according to the fluctuation characteristics; the violation tracing unit counts the risk cumulant and generates a violation tracing instruction when the risk cumulant exceeds a preset capacity. According to the system, automation and dynamic adjustment of operation and maintenance safety compliance detection are realized, and a risk accumulation process can be effectively tracked.

The invention relates to the technical field of commodity trade management, and discloses a commodity trade business management system based on a block chain. A block chain data acquisition module of the system obtains commodity basic information, trade enterprise qualification information and logistics node information in real time through a preset data interface, and synchronizes the information to a block chain distributed account book; the trade risk quantitative analysis module generates a trade link real-time risk coefficient set based on account book real-time data in combination with a trade compliance rule base, historical order fulfillment data and current logistics state data; a risk control strategy collaborative analysis module generates a plurality of candidate risk control strategy schemes when the risk coefficient exceeds a preset threshold value, calculates a collaborative execution cost factor when the enterprise qualification state is changed in combination with logistics node information, and obtains an interference influence value set of each scheme on the trade process; and a risk control strategy execution feedback module updates a block chain smart contract execution instruction according to the optimal risk control strategy scheme, and facilitates efficient management and control of commodity trade business.

The invention provides an intelligent e-commerce behavior event decision-making method and system fusing multi-source perception, and the method comprises the steps: obtaining a user behavior data set, a commodity attribute data set and an environment context data set through a preset data collection interface, and carrying out the intention recognition processing of multi-source data; generating an intention feature set containing preliminary intention features and refined intention features, then generating an interactive guidance strategy tree containing node branch weights and strategy execution priorities based on the intention feature set, and performing dynamic path adjustment processing on the interactive guidance strategy tree according to a real-time feedback data set to obtain an optimized strategy tree set; and finally, pushing the optimization strategy tree set to a target interaction interface to activate an interaction guide operation, thereby providing personalized and intelligent interaction guide for the user by fusing multi-source sensing data, deeply understanding the intention of the user and dynamically adjusting a decision strategy, and improving the operation efficiency of an e-commerce platform and the satisfaction of the user.

The invention relates to the technical field of network slice resource allocation, in particular to a 6G intelligent load balancing and fault self-healing method based on AI and network slices, which comprises the step of building an AI decision-making layer model architecture, a self-adaptive optimization mechanism, a resource dynamic scheduling mechanism and a strategy execution guarantee architecture. According to the method, the network load trend is predicted in real time through the AI technology, the resource allocation between the slices is dynamically adjusted, and the problem of low resource utilization rate caused by static allocation and periodic adjustment is solved. Meanwhile, an automatic fault detection and recovery mechanism is designed, when a node fault is detected, standby resource takeover can be quickly triggered, session continuity can be kept, the service interruption time is remarkably shortened, and the requirements of a 6G network for high reliability and low time delay are met.

The invention discloses an integrated information automatic supervision system and method for an edge device, and relates to the technical field of edge computing, an edge node management module of the system accesses the edge device, generates basic attributes and carries out containerized application deployment; the real-time monitoring and diagnosis module is used for collecting equipment operation data, identifying abnormity, positioning a fault type and triggering an alarm; the automatic strategy generation module is responsible for generating a customized repair strategy; the MESH communication and repair execution module constructs an edge device ad hoc network, realizes networking and offline strategy transmission and executes a repair instruction; the solution knowledge base module stores equipment fault types, historical repair cases and strategy templates, and updates strategy matching logic; the application deployment management module packages application programs through container mirror images, and distributes the application programs to edge nodes in batches according to scene templates; and the centralized management and control module visually displays the equipment state, the fault alarm and the strategy execution record, and provides manual intervention and global strategy configuration.

The invention relates to the technical field of chip scheduling and optimization, in particular to a neural network-based main control chip task scheduling and dynamic performance optimization method, which comprises the steps of data acquisition and feature engineering, neural network model design, simulation environment training, model compression and deployment preparation, real-time state monitoring, dynamic decision reasoning, scheduling strategy execution and performance optimization. The data acquisition and feature engineering comprises the following steps: S1, hardware index acquisition; analyzing task attributes (calculation-intensive / IO-intensive), a dependency relationship (DAG), deadline (Deadline) and a resource demand (CPU / GPU occupancy rate); collecting data during chip operation through a performance counter (IPC, cache hit rate and branch prediction error rate), a temperature sensor and a power consumption monitoring unit (PMU); the neural network scheduler can achieve the energy efficiency ratio which is 20%-40% higher than that of a traditional method (such as a CFS scheduler), meanwhile, the neural network scheduler adapts to sudden load changes, and the practicability and the application range of a main control chip are wider.

The invention discloses a multi-mode fusion driven multi-agent Transform collaborative power dispatching method and system, and the method comprises the steps: fusing three types of heterogeneous data, namely power grid topology, time sequence operation and equipment state, through a multi-mode projection network, and forming unified state representation; a Transform encoder is used for modeling a dependency and cooperation relationship between intelligent agents, and a mask decoder is used for generating a cooperation scheduling strategy in an autoregression mode; a strategy is evaluated and optimized by adopting a near-end strategy optimization algorithm and a joint reward function, and finally an instruction is converted into a control signal and a closed-loop learning mechanism is formed. The system comprises a multi-modal state perception and fusion module, a multi-agent collaborative representation module, a collaborative decision generation module, a joint strategy optimization and learning module and a scheduling strategy execution and feedback interface module. According to the method, the multi-source information utilization efficiency and the agent cooperation capability are effectively improved, and safe, stable and economical operation of the power grid under high-proportion new energy access is guaranteed.

The invention relates to the technical field of artificial intelligence model optimization, and discloses a multi-granularity dynamic pruning method and system for a generative AI model. The system comprises a model state acquisition module which acquires output of a middle layer in real time through a probe, and constructs a feature information set of neuron activation distribution, weight matrix norm and connection topology; the sparseness evaluation module outputs a sparseness risk value based on the feature information set, the initial sparseness parameter and the real-time computing resource state; a pruning planning collaborative analysis screening module generates a multi-granularity pruning scheme when the risk value exceeds a threshold value, calculates a collaborative interference value of precision recovery operation and screens an optimal scheme; and the pruning strategy execution feedback module updates model parameters, generates logs and feeds the logs back to the management terminal. The system realizes adaptive structure optimization and resource scheduling of the generative model in the reasoning process.

The invention relates to the technical field of computers, and discloses a multi-agent reinforcement learning fault diagnosis method based on edge-center hybrid optimization, and the method comprises the following steps: S1, collecting and preprocessing equipment data; s2, layered fault diagnosis: an intelligent diagnosis agent adopts a layered reinforcement learning structure and is composed of a high-layer strategy network and a low-layer strategy network; s3, edge-center hybrid optimization: designing a center strategy optimization agent, training a high-layer strategy network and a low-layer strategy network in stages, and performing compression and distillation on the trained strategy networks by adopting a teacher-student strategy structure; executing strategy fusion and global updating based on the received strategy execution information; and S4, strategy migration. According to the method, continuous learning and strategy updating are carried out in a scene in which fault samples are extremely scarce, diagnosis knowledge sharing, strategy synchronization and cross-device migration are realized by using a multi-agent cooperation mechanism, efficient deployment and operation at an edge device end are supported, and field state change is adapted in real time.

The invention provides a real-time command and control strategy optimization method and device based on multi-agent reinforcement learning, equipment and a storage medium, and the method comprises the steps: deploying a pre-trained cooperative intention network on edge equipment of each combat unit, and coding local observation data and a historical sequence thereof into a low-dimensional local cooperative intention vector; replacing original high-dimensional data as inter-unit communication content; whether broadcasting is carried out or not is dynamically determined according to observation uncertainty and a channel state by combining a self-adaptive intention broadcasting mechanism, and communication resources are distributed according to needs; after each unit receives an adjacent collaborative intention vector, a lightweight space-time attention module in a pre-trained local strategy execution network carries out space weighting on adjacent intentions and fuses time features of own historical intentions, and a collaborative and consistent real-time command and control instruction is generated under the condition that global information convergence is not needed.

The invention provides an industrial furnace temperature adaptive optimization control method and system based on a multivariable chaotic time sequence, and belongs to the technical field of industrial process control, and the method comprises the steps: collecting operation parameters to form a multivariable time sequence, and carrying out the processing verification of chaotic characteristics, and obtaining an analyzable sequence; extracting features through phase-space reconstruction and quantifying variable coupling strength to obtain multivariable chaotic features; a prediction model and an early warning model are constructed based on the above, and a temperature change trend and early warning information are obtained; and finally, setting and optimizing control parameters, generating strategy execution and combining feedback to form closed-loop control. According to the method, the multivariable chaotic characteristics of the industrial furnace are analyzed, the prediction and early warning model is constructed, and self-adaptive optimization control is carried out, so that accurate regulation and control of the temperature are realized, the product quality stability is improved, and the energy consumption and the production cost are reduced.

The invention provides a user behavior prediction method and system applied to an online marketing service platform, and the method comprises the steps: firstly obtaining a multi-scene user interaction record set which comprises browsing, searching, collecting and ordering interaction records in the online marketing service platform of a user; constructing a scene behavior association mapping table for recording user interaction behavior triggering association relationships in different marketing scenes, and performing dynamic tendency evolution modeling based on the scene behavior association mapping table to obtain a user behavior tendency evolution model; according to the method, a user behavior tendency evolution model is established, marketing scene behavior prediction adaptation is performed according to the user behavior tendency evolution model to obtain a user behavior prediction scheme, and finally the user behavior prediction scheme is transmitted to a platform marketing decision module to generate marketing strategy execution guidance, so that user behaviors can be comprehensively and accurately predicted, and the platform marketing accuracy is improved.

The invention belongs to the technical field of building construction automation and intelligent engineering management, and discloses a BIM-based electromechanical pipeline intelligent avoidance self-adjustment installation system, which comprises the steps of generating a three-dimensional point cloud model based on point cloud data and UWB positioning data, further constructing a four-dimensional space-time model, synchronously generating composite conflict quantification data, and performing BIM-based electromechanical pipeline intelligent avoidance self-adjustment installation. Correlation analysis is carried out in combination with construction specifications, and a space-time coupling semantic model is generated; fusing the penetrating scanning data of the millimeter wave radar and the UWB positioning data, and constructing a two-dimensional conflict thermodynamic diagram of a space margin-time window; generating a risk level conflict list in combination with a historical case library; generating a dynamic avoidance strategy list, refining the dynamic avoidance strategy list into an avoidance path execution scheme, verifying a strategy execution effect, judging a conflict elimination state, and generating an avoidance strategy execution state report; and evaluating the effectiveness of the avoidance strategy, dynamically adjusting the priority of the strategy, synchronously updating the dynamic BIM semantic model and the two-dimensional conflict thermodynamic diagram, and forming a strategy iteration closed loop.

The invention relates to the technical field of AR glasses data flow scheduling, in particular to an AR glasses data flow scheduling method and system for a replaceable module, and the method comprises the steps: carrying out the dynamic topology perception of a module, and monitoring the connection state and function attributes of the module in real time to generate a topological graph; data flow path modeling: constructing a virtual data flow path diagram and marking constraint conditions; generating a multi-target scheduling strategy, and generating a dynamic strategy by taking time delay, power consumption and bandwidth utilization rate as targets in combination with task requirements; strategy distributed execution: decomposing the strategy into sub-instructions and distributing the sub-instructions to each module for execution; the system applies the method and comprises the hot-pluggable module group, the central scheduling controller and the local scheduler. According to the invention, the dynamic adaptation of the data stream in the hot plug scene of the module is realized, the user experience and the system resource efficiency are balanced, and the flexibility, the reliability and the industrialization adaptability of the AR glasses are improved.

The invention provides a big data-based material demand analysis and prediction method and device and a storage medium, and the method comprises the steps: obtaining a multi-source historical material data set of a supply chain link where a target enterprise is located, carrying out the cross-link integration processing of the multi-source historical material data set, and obtaining a cross-link integrated data set, performing demand feature extraction processing on the cross-link integrated data set to obtain a material demand dynamic feature and a material trend evolution feature of each material circulation sequence, and based on a preset dynamic analysis model, performing joint trend matching processing on the material demand dynamic feature and the material trend evolution feature to obtain a material demand dynamic feature and a material trend evolution feature; and generating a predicted demand distribution result of the material circulation sequence, generating a material supply optimization strategy according to the predicted demand distribution result, and feeding back the material supply optimization strategy to the supply chain management system to trigger material allocation operation. According to the method, the hysteresis of manual decision making can be avoided, and meanwhile, the accuracy and the anti-interference capability of strategy execution are enhanced.

The invention discloses a data processing system and method based on a federated architecture interface and related equipment, and relates to the technical field of computer software, in particular to a federated architecture distributed system formed based on a central coordination node and a plurality of regional autonomous nodes. The central coordination node is used for performing global strategy making, metadata block chain evidence storage, conflict detection and federated monitoring and self-healing to realize global management and control, and the regional autonomous node adapts to a physical machine heterogeneous environment to perform local strategy execution, heterogeneous protocol conversion and resource autonomous regulation and control so as to realize flexible regulation of a regional strategy. According to the scheme, through a federated and intelligent treatment system, inherent contradictions between centralized management and control and department autonomy, standardization and rapid iteration and global safety and local flexibility are solved, technical support is provided for an enterprise to construct a standardized, high-reliability and easy-to-expand API ecological system, digital transformation is assisted, and the enterprise safety is improved. And global controllability and local autonomy of enterprise-level API ecology are realized.

The invention discloses a central control system of a digital multimedia exhibition hall, and relates to the technical field of central control systems, a time-space correlation analysis module constructs a fault propagation probability graph of exhibition hall equipment and audience behaviors, and a coupling degree analysis module verifies a cross-layer causal relationship by using a directional disturbance injection method based on the fault propagation probability graph; a coupling degree index is fitted by analyzing an incidence matrix of a resource scheduling operation and a fault propagation chain, a dynamic priority decision module fits a fault propagation cost gradient, the coupling degree index and the fault propagation cost gradient are input into a priority function, and a collaborative priority of a short-term suppression action and a long-term eradication action is calculated. And the strategy execution module calls the execution sequences of the two types of actions through the interface of the central control platform, and executes corresponding strategy actions. The central control system is not only high in dynamic adaptive capacity, but also capable of responding to audience demands and equipment state changes in real time, improving exhibition management efficiency and enhancing immersive experience of audiences.

The invention provides an information processing method and device in a game, electronic equipment and a storage medium. According to the method, a signal identifier set is displayed by responding to a first trigger operation, the signal identifier set comprises a plurality of orientation partitions, and each orientation partition is associated with at least one candidate game object; in response to the selection operation, determining at least one target azimuth partition; determining a target marking sequence of the candidate game objects associated with the target orientation partition, wherein the target marking sequence is used for indicating an attack sequence of the candidate game objects; and displaying a prompt signal corresponding to the target mark sequence. According to the scheme, through the combination of the spatial orientation partition and the marking sequence, an intelligent locking mechanism with the area as the unit is achieved, interruption on the heart stream of a player is reduced, the operation efficiency under the multi-target combat scene is remarkably improved, and the strategy execution accuracy is enhanced through visual prompt.

The invention relates to the field of retail management, and discloses a retail chain response type system. The system comprises a user behavior perception module, an inventory dynamic mapping module, a cross-store collaborative scheduling module, a price response optimization module and a strategy execution feedback module. A commodity popularity model is dynamically constructed by sensing customer behavior data and combining real-time inventory information, and cross-store inventory collaborative allocation and price strategy response are realized. The system also continuously optimizes the overall response effect through a closed-loop feedback mechanism, and improves the sales conversion rate and the inventory turnover efficiency. According to the method, a multi-dimensional popularity scoring model and a dynamic pricing formula are particularly introduced, and responsive and high-adaptability retail operation management is realized.