6results about How to "Satisfaction Guaranteed" patented technology

The application discloses a special-shaped pipe automatic stacking equipment and method. The equipment comprises a PC system, a roller, a traction machine, a length sensor, a visual detection device, a mechanical arm, a grabbing device, a packing drum line and an automatic steel band packing machine. The method is as follows: the MES system transmits product order information to the PC system, the PC system selects a proper stacking program; the special-shaped pipe passes through the roller, the length of the special-shaped pipe is calculated and is converted into a theoretical weight and transmitted to the PC system; the visual detection device visually detects the special-shaped pipe, removes the special-shaped pipe with defects and sends the qualified special-shaped pipe into a turnover table mechanism; then the mechanical arm grabs the qualified special-shaped pipe through the grabbing device, stacks the special-shaped pipe and sends the special-shaped pipe into a packing area; and the automatic steel band packing machine packs the stacked special-shaped pipe through steel band arrow penetration. The application has the characteristics of high case packing efficiency, low labor cost, high automation degree, low error rate, reliable performance, strong maintainability and convenient operation.

The application discloses an Internet platform vehicle leasing operation management method, device and medium, and relates to the technical field of computers. The method comprises the following steps: obtaining the target and constraint condition of a multi-target optimization algorithm; based on operation data, vehicle state data and market environment data, a multi-target optimization algorithm is used to configure a target optimization operation management process, which comprises sequentially generating a candidate vehicle scheduling scheme, a candidate dynamic pricing strategy and a candidate user service strategy, and establishing a two-way feedback mechanism among the three to iteratively optimize; and performing vehicle leasing operation management according to the target optimization operation management process. Through the three-level progressive optimization architecture and the two-way feedback mechanism, the application realizes the collaborative optimization of vehicle scheduling, dynamic pricing and user service, solves the technical problems of single-point optimization, static decision and credit disconnection in the prior art, and improves the operation efficiency, income level and user satisfaction of the vehicle leasing business.

This invention relates to the field of smart grid and electric vehicle dispatching technology, specifically providing a method and apparatus for assessing the adjustable potential of electric vehicle battery swapping stations. The method includes: importing the battery swapping demand of the electric vehicle battery swapping station into a pre-constructed battery charging coordination control model and solving it to obtain the battery charging strategy in the electric vehicle battery swapping station; determining the charging power of the electric vehicle battery swapping station based on the battery charging strategy; and determining the adjustable potential of the electric vehicle battery swapping station based on the charging power; wherein the electric vehicle battery swapping station meets the battery swapping demand constraints. The technical solution provided by this invention effectively solves the problems of unquantified user satisfaction, inaccurate charging power modeling, and lack of peak-shaving-oriented assessment methods. It can significantly improve the accuracy of the adjustable potential assessment of battery swapping stations, effectively improve peak-shaving effects while ensuring user satisfaction, and provide reliable technical support for battery swapping stations to participate in grid interaction.

This invention relates to the field of smart grid and electric vehicle scheduling technology, specifically providing a multi-objective collaborative scheduling method and apparatus for photovoltaic-storage-charging-battery swapping stations. The method includes: using the state space set of the photovoltaic-storage-charging-battery swapping station as input to a pre-trained agent to obtain the battery charging strategy output by the pre-trained agent; and scheduling the charging of the batteries in the photovoltaic-storage-charging-battery swapping station using the battery charging strategy. The state space set includes at least one of the following: time period, SOC state of each battery, battery swapping quantity requirement, photovoltaic output, and electricity price. The pre-trained agent is trained using a near-end strategy optimization algorithm. The technical solution provided by this invention achieves multi-objective collaborative scheduling of photovoltaic-storage-charging-battery swapping stations, improves the global optimality and practicality of the scheduling strategy, and balances the interests of multiple stakeholders.

According to the knowledge retrieval method and device based on multi-path recall, the target query statement of the user is expanded, rewritten and split through the first large language model so as to form the multiple candidate query statements under the same semantics, problem rewriting is achieved, retrieval elements can be refined, and the accuracy of recall results can be improved. Multi-path content retrieval is adopted, vector retrieval and ES retrieval are adopted at the same time, database data of ES retrieval are updated in real time, the problem of knowledge limitation can be eliminated, delay is reduced, and information integrity is guaranteed. A Rerank model or a double-tower model is introduced to score and sort the first type of query results and the second type of query results which are recalled preliminarily, the query results with the higher score are selected to be integrated and supplemented by a second large language model, natural language reply aiming at the target query statement is generated, the accuracy and integrity of the final knowledge retrieval result are improved, and the query efficiency is improved. And the double-tower model can be iteratively updated according to user feedback, so that the user satisfaction is ensured.