1677 results about "On demand" patented technology

A generative AI-supported system (100) for real-time demand forecasting and inventory optimization in supply chain management, which includes the following: a data collection and integration module configured to aggregate and preprocess structured and unstructured data from multiple sources, including historical sales records, IoT sensors, external market trends, and environmental factors; a generative AI-based forecasting module configured to process input data, recognize complex patterns, and generate highly accurate real-time demand forecasts using deep learning techniques; an inventory optimization module configured to dynamically calculate and adjust inventory levels based on demand forecasts, replenishment plans, lead times, and safety stock requirements; a decision support and analysis module configured to generate actionable insights, provide simulations of multiple scenarios, and recommend cost-effective strategies for supply chain operations; a real-time warning and notification module configured to monitor supply chain activities and inform stakeholders of potential disruptions, anomalies, or demand spikes; a user interface and integration module configured to provide outputs via a user-friendly dashboard and seamlessly integrate with existing enterprise resource planning (ERP), warehouse management systems (WMS), and logistics platforms.

The invention provides a demand side response-based distributed power supply optimal scheduling method and system, and relates to the technical field of power grids, and the method comprises the steps: building a distributed power supply prediction model based on a deep neural network, and predicting the photovoltaic power generation power, the energy storage charge state and the user load. Then, a robust optimization model is constructed, prediction errors and energy storage nonlinear characteristics are considered, an optimal time-phased load scheduling scheme and an energy storage charging and discharging strategy are generated, the scheme comprises an interruptible load execution time sequence, and the strategy comprises an energy storage charging and discharging power curve; and finally, establishing a hierarchical optimization execution system based on model predictive control, monitoring the state of the system in real time, and correcting a control instruction according to the deviation to realize optimal scheduling of the distributed power supply. The method can improve the consumption capability of the distributed power supply, reduce the power consumption cost, and enhance the stability of the power grid.

The invention relates to the technical field of front-end resources, in particular to an intelligent caching method and device for loading front-end resources on demand, equipment and a medium, and the method comprises the following steps: obtaining user behavior data, predicting an access page, identifying required resources, and constructing a resource dependency graph; calculating resource use frequency and importance, and determining a target resource; selecting a resource format and a compression mode according to the user equipment type; according to the resource dependency relationship graph, loading and caching target resources required by a next page possibly accessed by the user in advance; meanwhile, the caching strategy is dynamically adjusted based on the resource use frequency and the importance index; the method comprises the following steps: monitoring a network condition, dynamically adjusting a resource loading strategy, pre-loading resources before a user requests by using a resource dependency graph and a server pushing technology, and determining a target loading mode when the user accesses. Therefore, the problems that in the prior art, resource loading is low in efficiency, a caching strategy is rigid, environment adaptability is poor, user behavior analysis is insufficient, and performance optimization automation is lack are solved.

The invention discloses a big data stream-oriented memory cache storage system, relates to the technical field of memory cache storage, and is used for solving the problem of poor memory cache storage management. According to the method, the access frequency, the timeliness and the service importance of the data are evaluated, the priority ranking model is constructed, the cache strategy is optimized based on the ranking result, the data storage and scheduling efficiency is improved, the write-in and read operation of the memory cache is adjusted in real time in combination with the flow prediction model, memory overload is effectively avoided, and the data storage and scheduling efficiency is improved. According to the method, non-emergency data writing is delayed by adopting a delayed writing and lazy loading strategy, data is loaded as required, meanwhile, a cache elimination mechanism is designed according to data priority, access frequency, survival time and size, data migration is carried out when a cache space is insufficient, reasonable resource allocation is ensured, and through a multi-level and dynamic cache management scheme, the cache management efficiency is improved. Therefore, the system performance, the resource utilization rate and the response speed are remarkably improved, the data processing efficiency is optimized, and cache conflicts are reduced.

The invention provides an end-side multi-modal large model accelerated reasoning method and system, and the method comprises the steps: carrying out the two-stage screening and rearrangement of visual tokens based on the CLS attention and text-to-visual attention in a visual encoder and pre-filling stage, and constructing a sparse attention and sparse key value cache; in a decoding stage, an important neuron set is judged according to activation gating or historical statistics, only a corresponding feedforward network weight is pulled and calculated, missed weights are loaded on demand through asynchronous I / O, and hot neurons are maintained in a high-speed memory to utilize model sparsity, so that video memory / memory occupancy and calculation overhead are remarkably reduced on an end side; throughput and time delay performance are improved. According to the method, the internal memory and computing resources required by reasoning of the multi-modal large language model are reduced from two dimensions by utilizing the endogenous sparsity of the end-side large language model in input and the model, so that a higher reasoning speed is achieved by utilizing fewer resources on the premise of keeping the size of the model unchanged, and the performance of the whole system is improved.

The invention discloses a data processing system, method and device, a medium and a program product in the technical field of computers. In the application, the plurality of acceleration cards are directly connected with the plurality of memory devices through the management device, and the actual operation information of each acceleration card is collected through the management device, so that the memory space is allocated to each acceleration card as required according to the actual operation information of the acceleration cards. In other words, each memory device is not perceived by the host and is directly used as the extended memory of each acceleration card, so that the host does not need to distribute the memory for each acceleration card, thereby avoiding the condition of scrambling resources such as bandwidth easily occurring when multiple acceleration cards apply for the memory at the same time, and being not easy to cause data transmission delay. The purpose of expanding the memory for the accelerator card on the premise of not influencing the system operation performance is achieved.

An AI-based automated pricing and inventory management system (100) for retail marketplaces, including: a data collection module configured to collect real-time data from sales transactions, competitor pricing, market trends, inventory levels, and customer behavior; an AI engine configured to process the collected data using machine learning algorithms for demand forecasting, price optimization, and customer segmentation; a price optimization module that is communicatively connected to the AI ​​machine and configured to dynamically adjust product prices based on demand patterns, competitive strategies and predefined business rules in order to maximize profitability and maintain competitiveness in the market; an inventory management module configured to predict optimal stock levels, generate replenishment alerts, and recommend inventory reduction strategies, thereby minimizing overstocking and stockouts; a dashboard interface configured to display actionable insights, pricing strategies, inventory levels, and key performance indicators in real time; an integration layer configured to interface with existing retail systems (100), including point-of-sale (POS), enterprise resource planning (ERP) and e-commerce platforms, enabling seamless operation and data exchange; the system dynamically coordinates (100) decisions on pricing and inventory management based on real-time data analysis, thereby improving operational efficiency, profitability and customer satisfaction.

The invention relates to the technical field of intelligent war game deduction, in particular to an intelligent war game deduction simulation system based on a digital twin fusion large model. Comprising a digital twin modeling unit which adopts a dynamic precision adaptive modeling mechanism; a large model decision unit; a deduction execution unit; and a dynamic interaction unit. A dynamic precision self-adaptive modeling mechanism is adopted by the digital twin modeling unit, the model precision can be dynamically switched on demand, the detail simulation demand and the computing resource consumption are balanced, and meanwhile, the sub-second-level dynamic synchronization of a virtual model and a war game deduction scene is realized through a time sequence calibration algorithm; and space confrontation, time sequence decision and rule triggering features are fused through the multi-modal feature analysis module, and the Wargame rule verification module is combined to embed entity performance boundaries and scene rule constraints, so that compliance confrontation decision parameters conforming to Wargame deduction logic can be generated, and the problem of insufficient model precision adaptation and decision constraints is solved.

The invention provides an intelligent charging pile site selection evaluation method and system. The method comprises the following steps: acquiring communication data of a mobile terminal in a target area in real time, and constructing a demand time period division model to distinguish daytime quick charging and night conventional charging demands; dynamically adjusting the position of an edge computing device based on demand distribution to ensure that the service range is matched with a people flow density peak region in real time; controlling the multi-port wireless charging system to switch an adaptive charging power mode according to the identified charging demand type; and generating a candidate charging pile layout position set in combination with equipment layout and power mode data, and screening out an optimal deployment scheme through safety distance verification. According to the technical scheme provided by the invention, efficient matching between the charging pile layout and the dynamic people flow demand is realized, and the daytime charging efficiency and the night equipment utilization rate are improved.

The invention provides a multi-warehouse demand management method, device and equipment and a storage medium, and the method comprises the steps: carrying out the exchange rate sensitivity correlation analysis processing of demand fluctuation data in a cross-border e-commerce multi-warehouse network, and obtaining the exchange rate elasticity coefficient early warning information of each warehouse node; performing distributed coordination decision processing on the logistics constraint condition of each warehouse node according to the early warning information to obtain a decision scheme of resource allocation between warehouses; performing block chain trusted measurement processing on inventory distribution information in the multi-warehouse logistics alliance chain according to the decision scheme to obtain a multi-warehouse resource reconfiguration execution instruction based on the smart contract; and performing adaptive exchange rate risk avoidance processing on the inventory configuration strategy of each warehouse node according to the execution instruction to obtain a multi-warehouse collaborative inventory optimization management result. According to the method, the problem of influence of exchange rate fluctuation on multi-warehouse demand management is effectively solved through exchange rate sensitivity analysis and distributed coordination decision.

The invention provides a script preservation method which can be applied to the technical field of artificial intelligence. The script preservation method comprises the following steps: carrying out semantic understanding on demand related files by utilizing a large model, extracting core elements, and generating a structured demand change list; analyzing to obtain an abstract syntax tree of an existing script code, and constructing a mapping relationship between the software automation test element and a script code block; dynamically comparing the structured demand change list with an existing script code structure tree based on a mapping relation between software automation test elements and script code blocks, identifying a script range influenced by demand change, and outputting a change influence matrix; and based on the change influence matrix, generating an automatic script code snippet needing to be updated, and realizing preservation of the automatic script. The invention further provides a script preservation device and equipment, a storage medium and a program product.

The invention relates to the technical field of traffic engineering, and discloses an urban air traffic vertical take-off and landing field site selection optimization method based on demand maximization, and the method comprises the following steps: S1, obtaining and preprocessing online car-hailing original data; s2, dividing grids for the research area range, and taking the grid center as an alternative address of a vertical take-off and landing field; s3, screening out online car-hailing travels which are impossible to turn to urban air traffic; s4, constructing a vertical take-off and landing field site selection model based on potential demand coverage maximization; s5, outputting the selected vertical take-off and landing field address and the corresponding urban air traffic travel demand; and S6, outputting the current optimal site selection and demand as a final solution of the model. According to the method, under the condition of the specific number of vertical takeoff and landing fields, the vertical takeoff and landing field addresses which enable potential requirements of urban air traffic to achieve maximum coverage are selected, an effective solution is provided for development of the urban air traffic, and construction of a more efficient, convenient and environment-friendly urban traffic system is facilitated.

Disclosed in the present invention is a general-purpose intelligent agent. The general-purpose intelligent agent comprises: an input module, which is configured to acquire a preprocessed input signal; a consciousness module, which is configured to perform perception and cognitive operations in a transparent and interpretable abstract thinking form; a self-awareness module, which is configured for perception and cognition of an intelligent agent body; a subconsciousness module, which is configured to use a data-driven model to implement perception and cognitive functions at a subconscious level; an information exchange module, which is configured to perform mutual exchange between perception, cognition and other information generated by the consciousness module, the self-awareness module and the subconsciousness module, thereby enabling integration and utilization; and an output module, which is configured to output a processing result on demand. In a machine intelligent agent of the present invention, a machine uses a general-purpose language as an underlying language for thinking and interaction, a thinking process and result are completely transparent, and thus autonomous decision-making, autonomous learning and continuous evolution are realized under fully controllable conditions. Further disclosed is a method for constructing a general-purpose intelligent agent and a general-purpose language. Compared with the prior art in which a natural language is used, the machine intelligent agent based on the general-purpose language has wide versatility in application.

The invention refers to method and devices for on demand paging for executing a sidelink relay path switch, the method including: Receiving a message from a remote user equipment, UE, including a measurement report and a list of candidate relay UEs; Selecting a target relay UE for the sidelink relay path switch from the list of candidate relay UEs; and performing one of: Transmitting a paging request to an entity of a core network, CN, including instructions to page or instruct the target relay UE to transit to an RRC_CONNECTED state for sidelink relay path switch purposes; and Transmitting a paging request to the target relay UE, including a request or instructions to transit to an RRC_CONNECTED state for sidelink relay path switch purposes.

The utility model discloses an efficient logistics sorting system which comprises sorting devices arranged in multiple layers and further comprises a lifting machine capable of conveying goods to the sorting devices. The sorting device comprises a delivery unit, the delivery unit runs along a delivery path, and a goods placing unit is arranged on the side edge of the delivery path; the lifter is provided with a plurality of material supply units which are aligned up and down and staggered in the vertical direction, so that the lifter can supply materials to the sorting devices at the same time; the vertical position of the input device is fixed, and the input device can convey goods to the goods supply unit. The plurality of supply units can supply goods to the same sorting device for temporary storage and supply goods to a plurality of sorting devices at the same time according to needs, so that the moving stroke of the lifting frame is reduced, and the sorting efficiency is improved.

The MOD system according to an embodiment of the present invention includes a dispatch calculation unit for dispatching a mobility on demand (MOD) bus based on a boarding request from a user terminal including a departure point and a destination point; a boarding and alighting point calculating unit for specifying a boarding / alighting point at which the user boards or alights the MOD vehicle; a travel management unit for planning a route interworked with transfer means including public transportation means, metropolitan transportation means, or other MOD means other than the MOD vehicle when the departure point and the destination point belong to different areas; and a punctuality guarantee unit for performing an operation to guarantee punctuality of the arrival time when the boarding request includes the arrival time.

The invention discloses a cross-film video stream non-inductive fusion playing method based on an HLS protocol, and belongs to the technical field of civil aircraft scene monitoring, and the method comprises the steps: 1, high-frame-rate screen recording preprocessing, 2, meta-information and video stream storage, 3, dynamic playlist construction, and 4, client non-inductive playing and rapid positioning. Through dynamic calculation of a playlist and a video frame index positioning technology, real-time loading and playing of any number of video clips with any time span, loading of target TS slices as required and asynchronous preloading of adjacent frames are supported, the smooth experience of zero lagging of cross-clip playing and comparable to real-time monitoring is realized, and a micro-batch preprocessing and on-demand loading mechanism is adopted, so that the real-time monitoring effect is improved. According to the method, the resource consumption of the server is reduced by more than 90%, the delay and resource bottleneck of a large-video multi-fragment scene are solved, the second-level event positioning capability of cross-day recording and broadcasting is provided for a civil aviation scene monitoring system, and the abnormal event analysis efficiency is greatly improved.

The disclosed embodiments relate to systems / methods for low latency regulation of distributed, e.g., geographically and / or logically, transaction processing, such as trading of financial instruments, in accordance with centralized demand-based dynamically reallocated post-trade-based limits, e.g., credit / risk limits. In particular, the disclosed embodiments relate to monitoring credit demand across execution venues, as opposed to actual utilization, and, based thereon, modifying execution specific limits to reallocate of portions of a credit / risk allotment, such as a credit / risk limit, to multiple execution venues for use in approving transactions, which may implement order driven and / or quote driven markets, so as to dynamically rebalance the allocations to the execution venues based on demand / need, rather than utilization, thereby avoiding having to mitigate latencies in determining and responding to utilization. In addition, the disclosed embodiments may maximize the total available amount for reallocation by recognizing inter-execution venue limit offsetting positions, i.e., trades, during the rebalancing process.

The invention relates to the technical field of software testing, and discloses a unit test case generation large model fine tuning method based on feedback reinforcement learning, which comprises the following steps: constructing a unit test fine tuning data set based on demand driving; an Actor model is constructed, and optimization is carried out through a demand-driven unit test fine tuning data set; a Critic model is constructed, and optimization is carried out through a demand-driven unit test fine tuning data set; and generating a unit test case based on the optimized Actor model, mapping the unit test case into a single instant reward based on the optimized Critic model, and obtaining a large model generated based on the unit test case through reinforcement learning. The unit test case generation method capable of automatically generating high-quality unit test cases with aligned requirements and consistent styles is constructed, the test efficiency and quality are remarkably improved, and the bottlenecks of traditional test generation in manual dependence, coverage depth and requirement consistency are broken through.

A wireless device may request system information, such as a system information block, from a base station on demand. The base station may indicate to the wireless device a change in system information via a message, for example, during a time period that the base station is not sending system information to the wireless device. The wireless device may expect to receive updated system information, for example, based on receiving the message, without having to send a request to the base station for system information.

Cybersecurity is a critical requirement for current and future vehicles, to protect against catastrophic cyber attacks. Vehicles today (including land vehicles, waterborne vehicles, and aircraft including such embodiments as airplanes, helicopters, and air taxis) are constructed with myriad separate sensors and actuators, which generally have only limited cyber protections—a worrying vulnerability. Therefore, procedures are disclosed for a vehicle-wide 5G / 6G network in which each ECU (electronic control unit) is a separate user device. Each ECU is also the manager of a set of sensors and actuators, forming a local sub-network with tightly regulated wireless protocols. Each ECU and each end sensor / actuator may include an AI model to detect and defeat cyber attacks. Each sensor / actuator boots from ROM and executes from ROM, performs its specific tasks on demand, and then communicates the results solely with its ECU in a nested-star topology, thereby providing vehicle-wide cybersecurity on par with 3GPP standards.

The invention relates to the technical field of network operation and maintenance, and relates to an intelligent management and control method and device for an operation and maintenance work order, a computer program product and electronic equipment. The method comprises the steps of performing intention recognition on demand description information in an operation and maintenance work order based on a work order management and control agent, and determining a work order type and a work order task of the operation and maintenance work order according to an intention recognition result; according to the work order type and the work order task, generating an operation and maintenance guidance scheme of the operation and maintenance work order based on the operation and maintenance corpus knowledge base through the work order management and control agent; determining a target operation and maintenance worker based on the work order type, a work order adaptation model and a work order intelligent scheduling strategy through a work order management and control agent, wherein the work order adaptation model is a model representing the adaptation degree of the operation and maintenance worker and the operation and maintenance work order; and automatically distributing the operation and maintenance work order and the operation and maintenance guidance scheme to the target operation and maintenance personnel, and performing automatic order staring management and control on the operation and maintenance work order through the work order management and control agent so as to complete the disposal of the operation and maintenance work order. The management and control processing efficiency and quality of the operation and maintenance work order can be improved.

This invention introduces a sophisticated system for deploying projects on cloud platforms, combining Unikernels, Cyber Security Mesh Architecture (CSMA), MQTT protocol with SHA256 encryption, an Unikernel Orchestration Rules Engine (UORE), generative AI, and an innovative caching mechanism. Unikernels offer a secure, isolated environment for applications, reducing overhead and boosting performance. CSMA provides extensive security through analytics, identity management, and policy enforcement. The MQTT protocol, secured with SHA256, ensures the integrity and confidentiality of communications. UORE automates deployment, integrating a TLS terminator and data management for streamlined operation. Generative AI proactively resolves deployment challenges, particularly for complex applications, while the caching mechanism enhances performance and efficiency by minimizing latency. This integrated approach automates and secures the deployment process, enabling scalable, efficient, and real-time project creation and deployment in the cloud, thereby addressing the key challenges of cloud application hosting.

The invention discloses an art education intelligent course arrangement method and system based on demand analysis, and relates to the technical field of data processing, and the method comprises the steps: connecting an art training management platform, and receiving M classes, course type labels, student labels and teacher labels, M being an integer greater than 1; performing multi-course conflict analysis based on the class student labels to generate a first rigid demand constraint; based on the class teacher label and the course type label, distributed demand analysis of the same type of course teachers is carried out, and a second rigid demand constraint is generated; in combination with the first and second rigid demand constraints and class hour load balance soft constraints of students and teachers, course arrangement is optimized in a preset course arrangement period, and an optimal course arrangement scheme is generated, so that the technical effects of improving course arrangement efficiency and optimizing course arrangement flexibility and individuation level are achieved.

The invention provides a bill component design period and operation period micro-front-end extension method and device. The method comprises the following steps: developing a micro-front-end extension module according to a preset extension development specification; the micro-front-end extension modules are separated and packaged into different resource files according to different use scenes; registering the micro-front-end extension module as an operation period component or a design period component, and recording an access path and metadata of a corresponding resource file; when a receipt application is started, analyzing the configuration information of the receipt to obtain a core function and an extension point definition, and querying and identifying an extension list needing to be loaded by using registered extension description information and an access path of a resource file; and according to the extension list and the corresponding resource file path, dynamically loading the registered resource file of the running period component or the design period component as required. Through on-demand loading and dynamic module loading, a user only needs to load necessary extensions when opening a receipt, and the initial loading time is remarkably shortened.

The present invention discloses an intelligent information recommendation system for network sales, which belongs to the technical field of sales information recommendation, and comprises a user module, a retrieval module and a recommendation module; the user module is used for users to input demand information, and send the identified demand information to the retrieval module; the retrieval module is used to retrieve sales information according to the demand information, set target channels, and the target channels refer to various selected e-commerce platforms; the obtained sales information is input into each target channel for retrieval, a large amount of sales information is obtained, the obtained sales information is evaluated, the corresponding recommendation value is obtained, each sales information is arranged in order from high to low according to the recommendation value, and a first sequence is obtained; and corresponding quality characteristic data is supplemented; the recommendation module is used to recommend the sales information to the user, obtain the corresponding first sequence, and recommend the sales information to the user according to the first sequence.

Aspects of the disclosure include using piezoelectric devices to break laminated glass assemblies on demand (e.g., a windshield of a vehicle). An exemplary vehicle includes a laminated glass panel having an outer glass layer, an inner glass layer, and a bonding layer between the outer glass layer and the inner glass layer. The vehicle further includes a piezoelectric device including an actuator and a displaceable element coupled to the actuator. The actuator is made of a piezoelectric material that, responsive to receiving an activation voltage, induces a mechanical displacement in the displaceable element against at least one of the outer glass layer and the inner glass layer. The vehicle includes a controller electrically coupled to the actuator. The controller is configured to deliver the activation voltage to the actuator.

Some embodiments provide order packaging control systems, comprising: a product packaging (PP) control circuit communicatively couple with an automated custom package fabrication (CPF) system configured to fabricate on demand custom packages, an automated product retrieval (PR) system configured to autonomously retrieve one or more products, pick stations, and a set of sensor systems. The PP control circuit can be configured to receive orders; prioritize the orders and define an order fulfillment sequence, track states of the PR system and the CPF system; determine product retrieval timing, and correspondingly determine fabrication timing of when the CPF system is to fabricate corresponding packaging; and control the PR system and the CPF system in controlling synchronous operation of the PR system and the CPF system to synchronize an availability of products of an order at a pick station and an availability of a custom package at the pick station.