7results about How to "Uniform configuration" patented technology

The application discloses a kind of urban sanitation vehicle collaborative perception management systems based on Internet of Things, and the application relates to the technical field of sanitation cleaning, lack of system analysis and mining of historical cleaning and transporting data, and the problem that the characteristics of garbage generation interval of each single route cannot be accurately mastered is solved, the application is based on the dynamic programming logic of the total amount of operation by combining analysis processing center, the route combination satisfying the requirement of transport capacity is filtered, and the optimal cleaning and transporting route is determined with the shortest travel distance as the core sorting principle, which effectively reduces the invalid driving mileage of sanitation vehicle, such as empty driving and detour, improves the cleaning and transporting efficiency of single sanitation vehicle per unit time;At the same time, through the scientific combination of multiple groups of to-be-planned routes, it is ensured that the transport capacity of sanitation vehicle is fully utilized, the resource waste phenomenon of part of vehicles overloading and part of vehicles idling is avoided, and the balanced allocation of regional sanitation vehicle transport capacity is realized.

The invention provides an active equalization method and system for a large-scale electrochemical energy storage system, and belongs to the technical field of electric energy storage. According to the method, an equalization switch method is used, and an equalization switch is used for controlling the electric quantity to be transferred from an electric core with high electric quantity to an electric core with low electric quantity in the same cluster of electric cores. According to the invention, the unbalanced state in the battery cell cluster can be quickly responded, the transfer of the electric quantity is realized through the quick change-over switch, and compared with the equalization of the traditional battery cell management system, one-stage conversion is omitted, the efficiency is higher, and the flexibility and expandability are good.

The application discloses a preparation method and application of an antistatic composite fiber, and belongs to the technical field of fiber preparation, and is used for solving the technical problem that the antistatic performance and tensile resistance of the fiber in the prior art need to be further improved; graphene oxide, dopamine hydrochloride and choline lactate are used to prepare a static conduction functional powder, a phosphorus-silicon reaction oligomer, imidazole and a static conduction polyester particle are used to prepare a phosphorus-silicon functional base material, and the static conduction functional powder and the phosphorus-silicon structural unit are further introduced into a polyester system in a stable and cooperative manner to improve the interface organization state and component matching relationship, so that the structural stability, processing adaptability and antistatic function maintaining capacity of the material are improved.

The invention relates to the technical field of unmanned aerial vehicle spraying, in particular to an unmanned aerial vehicle spraying device for pesticide spraying, which comprises an unmanned aerial vehicle body, a mounting cavity arranged on the unmanned aerial vehicle body and a pesticide box fixedly mounted at the mounting cavity, the pesticide box comprises an upper pesticide barrel and a lower pesticide barrel, the upper pesticide barrel is arranged to penetrate through the mounting cavity, and the lower pesticide barrel is arranged to penetrate through the mounting cavity; the lower pesticide barrel is mounted at the bottom of the upper pesticide barrel, and a through hole is formed between the top of the lower pesticide barrel and the bottom of the upper pesticide barrel; a liquid guide pipe penetrates through the through hole, a liquid guide mechanism is arranged in the liquid guide pipe, and the liquid guide pipe guides liquid medicine in the medicine discharging barrel to the medicine feeding barrel through the liquid guide mechanism; according to the unmanned aerial vehicle spraying device for pesticide spraying, when pesticide is prepared, a secondary dilution method can be directly adopted between the upper pesticide barrel and the lower pesticide barrel, so that pesticide liquid is prepared more evenly, precipitation is not likely to be generated, and by means of the secondary dilution method, sufficient dissolution and even distribution of the pesticide can be facilitated; therefore, the possibility of precipitation and blockage is effectively reduced.

The invention discloses a power supply configuration method for guaranteeing capacity adequacy based on selective options, and relates to the technical field of power system optimal configuration. The method comprises the steps of constructing a hybrid capacity adequacy mechanism, generating a market framework allowing a power generator and a user to autonomously select and participate according to the hybrid capacity adequacy mechanism, and generating operation profit and option cashing data according to a contract distribution scheme, and based on the operation profit and the option cashing data, constructing a power generator residual model considering risk preference, wherein the power generator residual model generates a residual value evaluation result. According to the method, the hybrid capacity adequacy mechanism including a scarcity pricing mechanism and a reliability option mechanism is constructed, indication information is selected according to the generated market framework and mechanism, a power generator and a user are allowed to autonomously select to participate in the corresponding mechanism, the operation efficiency of the energy market is optimized, the risk of a power system is reduced, and the capacity adequacy is ensured.

This invention belongs to the field of wireless communication technology and discloses a wireless communication resource scheduling method and system based on LSTM and QoS demand awareness in multi-slice networks. This invention introduces Long Short-Term Memory (LSTM) networks and time-series prediction technology, and integrates an improved SAC reinforcement learning algorithm to construct a QoS-aware multi-slice resource scheduling framework. This framework accurately predicts users' future movement trajectories and slice service demands through LSTM, and dynamically allocates RBGs (Resource Blocks) based on the exploration-utilization balancing capability of the improved SAC algorithm, achieving coordinated optimization of multi-slice QoS demands and global resource utilization efficiency. Extensive simulation results show that the scheduling scheme combining LSTM and the improved SAC algorithm proposed in this invention outperforms traditional DDPG and PPO algorithms, demonstrating superior performance in latency control, throughput improvement, and packet loss rate optimization.

The present application belongs to the field of computer technology, and specifically relates to a data processing method, a data processing device, a computer-readable medium, an electronic device, and a computer program product. The method includes: splitting a data query request into a first sub-request corresponding to a first storage system and a second sub-request corresponding to a second storage system; the first storage system and the second storage system have different system configurations, and the system configuration includes the distribution ratio of computing tasks between the computing layer and the storage layer; executing the first sub-request on the first storage system to obtain a first query result, and executing the second sub-request on the second storage system to obtain a second query result; aggregating the first query result and the second query result to obtain the query result of the data query request. The present application can improve the resource allocation balance of the database system.