159results about How to "Avoid resources" patented technology

The invention relates to the technical field of server scheduling, and discloses a large model reasoning scheduling method based on an off-network computing power server, which comprises the following steps of: collecting hardware information of the off-network computing power server, and constructing a server resource vector; decomposing a large model reasoning task into a plurality of sub-tasks, and constructing task modeling; according to the hardware information of the off-network server and the task dependency relationship, performing task division by using an optimization method; performing dynamic task scheduling on the sub-tasks based on the current load state of the server; according to the real-time calculation load of the server, dynamically migrating part of tasks to other servers to ensure the load balance among the servers; and executing the distributed task, monitoring the running state of the server and the task completion condition, and dynamically adjusting a task distribution strategy according to feedback data. Through modeling task requirements and server resources, in combination with real-time monitoring and optimal scheduling, efficient calculation and resource utilization maximization of large model reasoning in an off-network environment are realized.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a mooring unmanned aerial vehicle safety management system which comprises a multi-source data acquisition module, a data fusion and analysis module, an intelligent risk assessment module, a control instruction generation module and an execution mechanism module. The multi-source data acquisition module acquires flight and mooring cable data of the unmanned aerial vehicle by using various sensors, and transmits the flight and mooring cable data to the data fusion and analysis module through a time division multiple access protocol with low packet loss rate and low delay; the intelligent risk assessment module judges a risk level by combining a risk assessment model based on a support vector machine and a Bayesian network algorithm according to the information, and generates prompt information if the risk level exceeds a preset range; the control instruction generation module generates instructions according to a risk prompt and a preset strategy, ranks the instructions and sends the instructions to the execution mechanism module; a flight control system in the execution mechanism module cooperates with a mooring cable electric take-up and pay-off device and the like, real-time adjustment and control over the flight attitude of the unmanned aerial vehicle and the cable state are achieved, and it is ensured that tasks are safely and stably executed in the complex environment.

The invention belongs to the field of power distribution network low-carbon economic optimization scheduling, and provides a power distribution network source network load storage low-carbon optimization scheduling increment reinforcement learning method and system, and the method comprises the steps: carrying out the intelligent agent division of a power grid, obtaining a plurality of intelligent agent regions, and collecting the observation data of each intelligent agent region; on the basis of the observation data, constructing a low-carbon economic dispatching model of source-grid-load-storage cooperation by taking the operation cost and carbon emission of each agent regional power grid as objective functions; designing the source-network-load-storage coordinated low-carbon economic dispatching model into a reinforcement learning model in a multi-agent environment according to a state space, an action space, an environment and a reward function; and solving the reinforcement learning model in the multi-agent environment to obtain an optimal scheduling solution. According to the incremental optimization method, the environment can help the intelligent agent to detect environment changes in training, excessive exploration risks are avoided, the training time is shortened, and meanwhile the flexibility of the system is improved.

The invention discloses a multi-underwater-robot cooperative control system and method based on predictive control, and relates to the technical field of cooperative control. During operation of the system, underwater environment data are collected through sensor equipment, an underwater three-dimensional dynamic environment model is generated by using a data fusion technology, and the underwater three-dimensional dynamic environment model is established based on task requirements; tasks are decomposed and distributed to all the robots, through an optimization algorithm, optimal paths of the robots and the mechanical arms are planned, the motion trails of the underwater robot mechanical arms are dynamically optimized based on model predictive control MPC, a real-time control strategy is provided for task division among the multiple mechanical arms, and the real-time control strategy of the underwater robot mechanical arms is provided based on environment modeling and the real-time state. Future motion states of the robot and the mechanical arm of the robot are predicted, an optimization objective function including task completion time, energy consumption and mechanical arm operation precision is constructed, a rolling optimization method is utilized, a control instruction is updated in real time, and real-time data sharing among multiple underwater robots is supported based on an acoustic communication technology.

The invention discloses a block chain multi-node secure communication method for an industrial internet, and the method comprises the steps: constructing a multi-chain cooperation mechanism, and obtaining an inter-chain interaction path table; determining a task partition optimization scheme and inter-chain interaction path update data based on the inter-chain interaction path table; matching an adaptive node resource for each task according to a task partition optimization scheme; determining an encryption mechanism between a cross-chain protocol and a block chain according to the security requirement of the task; constructing a multi-level block chain node hierarchical architecture and an intra-chain and inter-chain communication architecture, and formulating a parallel communication scheduling scheme; formulating a dynamic load balancing strategy based on the node resource use condition and the task execution state; and obtaining the task execution progress and the resource use condition of each block chain node, and adjusting the task scheduling and cross-chain communication path. Through the multi-dimensional collaborative design, the execution efficiency, the resource utilization rate and the communication security of the industrial internet are remarkably improved, and the stability and the reliability in a complex multi-chain collaborative scene are ensured.

The invention provides a wireless communication and computing resource collaborative optimization method and system. The method provided by the invention comprises the following steps: establishing an initialized global model matched with a business demand for each service provider, and matching service resources of each service provider according to the business demand; establishing a service agent corresponding to each service provider; each service agent generates an optimal strategy based on the corresponding service resources and the state information of other service providers; sending the initialized global model to a distribution client according to an optimal strategy; each distribution client trains the received to-be-trained model based on the distributed service resources, and sends the trained model to the corresponding service provider; and aggregating the received trained models by each service agent to obtain a global model of the corresponding service. The method and the system provided by the invention are used for realizing collaborative optimization of wireless communication and computing resources and improving the overall performance and the resource utilization efficiency of federated learning.

The invention relates to the technical field of computing power optimization, and provides a computer and cloud computing power optimization method based on multi-task collaboration. The method comprises the following steps: receiving Q to-be-executed tasks of a local cloud collaborative network; performing priority constraint on the Q to-be-executed tasks according to the multivariate priority evaluation factors, and establishing a task chain; collecting callable computing resource information of a local cloud collaborative network, and establishing a resource space; based on the resource space, performing task chain resource allocation by utilizing a computing resource scheduling optimization algorithm to obtain a scheduling strategy chain; performing cloud computing power balance optimization according to the scheduling strategy chain to obtain an optimized chain; and executing the task chain according to the optimization chain based on the cloud collaborative network. The technical problems of unbalanced scheduling and low resource utilization rate between the cloud and the local computing resources are solved, and the technical effects of improving the cooperative work efficiency and the resource utilization rate of the cloud and the local computing resources through priority constraint and computing resource scheduling optimization are achieved.

The invention discloses insect prevention, irrigation and fertilization integrated equipment based on vegetable greenhouse planting. The insect prevention, irrigation and fertilization integrated equipment comprises a walking frame, a lifting mechanism, an inclination angle adjusting mechanism and an operation mechanism. The lifting mechanism is installed on the walking frame, and the dip angle adjusting mechanism is connected with the lifting mechanism and ascends and descends along with the lifting mechanism. In the operation mechanism, an assembling support is matched with the inclination angle adjusting mechanism, and a main folding and unfolding support is installed on the assembling support in a sliding mode. The alignment adjusting assembly is in power connection with the main folding and unfolding support. And the folding and unfolding adjusting assembly is in power connection with the auxiliary folding and unfolding supports. The driving assembly is in power connection with the alignment adjusting assembly and the folding and unfolding adjusting assembly; the spraying assembly comprises a nozzle, a liquid supply connecting pipe and a scissors fork type mechanism, the scissors fork type mechanism is mounted on the main and auxiliary retractable brackets, the liquid supply connecting pipe is uniformly assembled on the scissors fork type mechanism, and the nozzle is connected to the liquid supply connecting pipe. The production efficiency of greenhouse vegetables can be effectively improved, the cost is reduced, the resource waste is reduced, and the requirements of efficient, energy-saving and sustainable agricultural development are met.

The invention discloses a self-adaptive resource scheduling method for improving cluster reconstruction efficiency, and relates to the field of resource scheduling distribution, and the method comprises the steps: monitoring the resource use data of each node in a cluster in real time through a monitoring agent, and obtaining the health state of the node through a heartbeat mechanism; collecting input task requests, predicting the resource demand quantity of each task, and performing preliminary queue processing on the tasks to obtain a task queue group; setting a resource competition constraint according to the task queue group and the resource use data and the health state of each node, obtaining dynamic resource scheduling corresponding to each task by using an optimization algorithm, and allocating the tasks to each node; a resource scheduling dynamic graph is constructed, a visual interface is set, and the resource scheduling dynamic graph is arranged on the visual interface and used for displaying resource scheduling, providing a user-friendly interface and realizing high-efficiency scheduling of adaptive resources in a cluster reconstruction process.

The invention relates to the field of industrial automation and intelligent logistics, and discloses a vision-based operation management system, which comprises a visual perception module, a traffic modeling module, a path optimization module, a scheduling optimization module and a feedback adjustment module, the visual perception module obtains image or video data in the operation area and extracts the dynamic state of the target equipment; the traffic modeling module constructs a dynamic weighted graph model based on the equipment state data; the path optimization module generates an optimal path of the equipment by solving a dynamic Hamiltonian path problem; the scheduling optimization module optimizes the passing sequence and speed of the equipment based on a game theory model; and the feedback adjustment module monitors the running state of the equipment in real time, detects abnormal conditions and dynamically adjusts modeling and optimization results. The method can effectively solve the problems of path conflict and traffic jam in multi-device collaborative operation, improves the operation efficiency and the resource utilization rate, has high real-time performance and adaptability, and is suitable for various scenes such as logistics storage, industrial production and intelligent transportation.

The embodiment of the invention provides a computing resource configuration method and device of FPGA equipment and the FPGA equipment. The method comprises the steps that a heterogeneous query execution plan is generated according to a query request of a user, and the heterogeneous query plan comprises heterogeneous operators and non-heterogeneous operators; and determining a first number of resource blocks required to be configured for executing the heterogeneous operator based on the calculation throughput for processing a non-heterogeneous operator adjacent to the heterogeneous operator and the calculation throughput which can be provided by a single resource block in a programmable array logic FPGA (Field Programmable Gate Array) device. Through the embodiment of the invention, the problems that the throughput of the FPGA equipment is not matched with the calculation throughput of the CPU and the resources of the FPGA equipment cannot be reasonably utilized in the current dynamic configuration scheme in the related technology are solved, and the effect of ensuring that the resources of the FPGA equipment are reasonably utilized in the query acceleration process of the database is achieved.

The invention relates to the technical field of computer data management, and discloses a data management method and system capable of reducing computing power occupancy, and the method comprises the steps: S1, based on data distribution analysis, employing a sparse representation algorithm and a principal component analysis method, carrying out the operation elimination of the redundancy of high-dimensional data features through setting an L1 regularization constraint condition, and obtaining an L1 regularization constraint condition; decomposing the data correlation matrix in combination with a matrix operation mode, extracting feature vectors with top-ranked contribution values, and converting the feature vectors into a sparse low-dimensional matrix; and generating a sparse low-dimensional data matrix. Through combination of sparse representation and principal component analysis, L1 regularization is creatively applied to perform feature screening on high-dimensional data, redundant data items are removed, and principal component features with high correlation are extracted, so that the data dimension is effectively reduced, consumption of computing resources is reduced, and meanwhile, main information features of the data are reserved; the efficiency and accuracy of subsequent processing are improved, and distributed scheduling of tasks and efficient utilization of equipment computing power are realized.

The invention relates to the field of transportation, and provides an icebreaking test method and system for an electric pedal, and the method comprises the steps: firstly obtaining test environment information of the electric pedal to analyze a test application scene, configuring test operation parameters according to the test environment information, and constructing an icebreaking test simulation environment; analyzing running state data of the electric pedal in a simulation environment, calculating a power loss rate, adjusting a power supply strategy according to the power loss rate, then carrying out multiple times of icebreaking tests, obtaining an icebreaking record set, calculating an icebreaking efficiency index, constructing an evaluation framework, predicting icebreaking effects under different ice layer hardness by using the framework, and analyzing stress change and key influence factors; and finally, connecting the road surface detection module according to the key factors to obtain a test pedal, collecting real-time icebreaking data, detecting optimization points, and generating an icebreaking optimization report. The use quality of the electric pedal in the cold ice and snow environment can be improved.

A resource management system based on cloud computing relates to the technical field of resource management and comprises a cloud server, and the cloud server is in communication connection with a data acquisition module, a data storage module, a flow prediction module, a resource extension module, a resource activation module, a real-time flow monitoring module and a data aggregation module. The data acquisition module is used for acquiring data information; the data storage module is used for storing data information; the traffic prediction module is used for acquiring predicted data traffic of each edge equipment terminal; the resource extension module is used for presetting to-be-activated extension elastic computing nodes; the resource activation module calculates a pre-estimated activation time period of the node according to the determined expansion elasticity; the real-time flow monitoring module is used for judging whether a to-be-aggregated terminal is generated or not and determining to-be-aggregated flow of the to-be-aggregated terminal; the data aggregation module is used for generating an aggregated data packet and a verification list; and the cloud server is used for carrying out integrity verification on the aggregated data packet to ensure that the cloud server provides stable, safe and efficient cloud services.

The invention provides an intelligent operation system for a pre-post training examination of a power construction unit, and relates to the technical field of intelligent operation, and the system comprises an examination task analysis module which is used for analyzing examination task information to obtain a plurality of construction operation tasks; the operation task mapping module is used for mapping a plurality of construction operation tasks to a plurality of operation functional blocks; the time window configuration module is used for configuring a scheduling time window for the operation functional block according to the operation duration identifier; the scheduling and sequencing module is used for scheduling and sequencing the operation functional blocks according to the examination sequence identifiers to obtain a functional block scheduling sequence; and the examination behavior monitoring module is used for performing examination behavior monitoring on the examination user and outputting fusion cheating alarm information. The technical problems that traditional examination monitoring mostly depends on manual inspection or fixed camera monitoring, the behaviors of examinees cannot be comprehensively covered, the abnormal behavior detection accuracy is low, and the accuracy and fairness of examination monitoring are insufficient are solved.

The invention relates to a database operation instruction execution method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: receiving a database operation instruction; the database operation instruction is used for executing data operation on a distributed database; the distributed database stores data in a distributed table through a plurality of fragments; splitting the database operation instruction to obtain a plurality of subtasks; and according to the dependency relationship between the sub-tasks and the task attributes corresponding to the sub-tasks, executing the sub-tasks on the fragments corresponding to the sub-tasks. By adopting the method, the operation and maintenance cost can be reduced.

The invention discloses a container system process life cycle management method and system, and relates to the technical field of computers, and the method comprises the following steps: constructing a container cluster system covering a single machine and a cluster mode; a configuration management mapping scheme from container process management to host process management is designed, and process configuration in a container is converted into process configuration on a host by using a namespace and a resource management subsystem of a Linux kernel. According to the container system process life cycle management method and system, the resource utilization rate and the system performance are improved through fine resource allocation and NUMA optimization, under the NUMA architecture, by identifying and configuring NUMA nodes, it is ensured that the process preferentially uses a memory on a specific NUMA node, memory access delay is reduced, the data processing efficiency is improved, and the service life of the container system process is prolonged. CPU and memory resource allocation is dynamically adjusted according to the priority and the dependency relationship of the processes, and it is ensured that the key processes obtain enough resources to maintain the optimal performance.

The invention relates to a method and a system for an adaptive elastic expansion strategy of an Android distributed edge container. Monitoring the resource use condition of each Android container in real time; wherein the resource use condition of the Android container is determined; according to the resource use condition of each Android container obtained by the resource monitoring module, a preset calculation resource is dynamically allocated to each preset Android container by applying a preset algorithm; and according to different service flow characteristics and specifications of resources required by services, adjusting preset rearranged available container resources, or calling elastic resources to carry out resource expansion. The resources are flexibly allocated according to actual resource requirements and business load conditions of the Android containers, the problem of unbalanced resource allocation and utilization is solved, container expansion can be quickly and flexibly performed when the business volume is increased, manual intervention is not needed, the response time is shortened, the quick increase requirement of the business for the container resources is met in time, and the business efficiency is improved. And the service continuity is ensured.

The invention relates to the technical field of path planning, in particular to a bucket wheel machine unmanned collaborative operation reclaiming path planning method, which comprises the following steps of: constructing a three-dimensional stacking model of a stock ground, and determining an operation overlapping area of adjacent bucket wheel machines according to operation positions and operation ranges of the adjacent bucket wheel machines; dividing the stockpile in the operation overlapping area into a plurality of stockpile blocks, and generating a priority distribution table of the stockpile blocks in the overlapping area through priority calculation; based on the priority distribution table, the stacking blocks in the operation overlapping area are distributed to the two adjacent bucket wheel machines; planning a preliminary path for each bucket wheel machine by using the distribution result, wherein the path planning needs to be followed; and updating the stacking form and adjusting the path in real time. According to the invention, the conflict of processing the same stockpile block by two bucket wheel machines is effectively avoided, dynamic balance of task load is realized, and high efficiency and continuity of collaborative operation are ensured.

The invention discloses a control device and method for an anti-unmanned aerial vehicle jammer, and the method comprises the steps: enabling a radar system to scan a set air area, and obtaining the time period, distance and movement track of an unmanned aerial vehicle invading the air area; the spectrum analysis module is used for carrying out full-band scanning on the air radio signal and recording a spectrogram in the whole frequency band; the interference identification module identifies and separates the local highest signal intensity value in the same time period from the spectrogram as the target intensity, and the peak frequency corresponding to the local highest signal intensity value as the target frequency; the radio frequency front end adjusts the radio frequency of the directional transmitting antenna in real time according to the target frequency, and adjusts the radio frequency power of the directional transmitting antenna in real time according to the target intensity and the distance of the unmanned aerial vehicle; the omni-directional rotating device carries a directional transmitting antenna according to the motion trail to transmit interference electromagnetic signals to a dynamic directional area where the unmanned aerial vehicle is located. According to the invention, accurate interference on the unmanned aerial vehicle is realized, and the problem of space electromagnetic pollution is avoided while the airspace safety is guaranteed.

The invention provides a distributed computing system based on pre-scheduling resource collaborative adaptation, and relates to the technical field of resource collaborative allocation systems. The distributed computing system based on pre-scheduling resource collaborative adaptation aims at solving the problems that in the prior art, resource scheduling is not flexible, and application configuration and resource scheduling are lack of coordination. By introducing a dynamic scheduling mechanism of the resources, organic combination of resource pre-scheduling and flexible allocation is realized. Meanwhile, through cooperation of application configuration and resource scheduling, optimal distribution and execution of calculation tasks are achieved, the system is composed of a server side and a client side, the client side has pre-scheduling resources and is used for feeding back the resource state in real time and receiving and executing the calculation tasks, and the server side is responsible for monitoring and scheduling of global resources and task distribution. And through a designed heuristic collaborative adaptation algorithm, the server can dynamically schedule resources in the pre-scheduling resources, and high efficiency and stability of task execution are ensured.

The invention discloses a wastewater treatment device for the semiconductor industry, and belongs to the technical field of wastewater treatment.The wastewater treatment device for the semiconductor industry comprises a main body, a supporting frame is fixedly connected to the outer wall of the main body, a wastewater inlet pipe is fixedly connected to one side of the top of the main body, and a driving mechanism is installed on the inner wall of a storage cavity; a spraying mechanism is mounted at the top of the main body, a stirring mechanism is mounted on the outer wall of the driving mechanism, and a wastewater outlet pipe is fixedly connected to the outer wall of the main body. By designing the driving mechanism, the spraying mechanism and the stirring mechanism, wastewater and an adsorbent can be quickly mixed, the wastewater can be treated, meanwhile, the angle of stirring blades can be selected, the treatment rate can be increased in the wastewater treatment process of different methods, in addition, the spraying mechanism can move up and down along with stirring, and the treatment efficiency is improved. Therefore, the treatment efficiency is improved, and the method has wide application prospects in the field of wastewater treatment.

The invention discloses a satellite on-orbit uploading data transmission method and device based on a CFDP protocol. According to the method, the fixed-length advantage of a fixed-length virtual channel data unit VCDU defined in the CCSDS is adopted, system design is carried out in cooperation with frame counting of the VCDU and a CRC verification result, and operation steps needing complex calculation, such as error detection and retransmission communication, in an error detection data frame retransmission mechanism in the CFDP are simplified. Under the clamping of the FPGA parallel computing advantages, the satellite on-orbit uploading transmission system supporting efficient transmission of multi-user multi-channel parallel on-orbit uploading data based on the CFDP protocol is achieved with the minimum resources. The on-orbit uploading data transmission device can be realized from a single satellite to multiple satellites in an engineering manner, a data management function can be changed along with the change of a link position in an inter-satellite and in-satellite multi-user multi-channel topology network, and the functions of data sending, data forwarding, data receiving and the like are respectively realized in a system.

The invention discloses a large model reasoning hardware accelerator based on data flow execution, and belongs to the technical field of large model reasoning hardware acceleration. Each node comprises a linear calculation kernel used for sequentially completing query / key / value projection and attention output projection in multi-head attention calculation, dimension raising projection and dimension reduction projection calculation in a feedforward neural network layer, and inverse quantization operation after each projection is output; the multi-head attention calculation kernel is used for completing loading and quantization of query / key / value vectors, multi-head division and cache management, attention score calculation, Softmax normalization, context vector generation and KV cache updating; the RLA kernel is used for completing residual addition, layer normalization, quantization / inverse quantization and nonlinear activation calculation of each layer; the three cores in the same node are located in the same SLR area, and data transmission is carried out through a hardware queue and an HLS blocking mechanism. According to the invention, high hardware utilization rate and low-delay reasoning can be realized.

The invention discloses a power line carrier communication enhancement method and system, a carrier machine and a wave trapper. The method comprises the following steps: determining a target power line, acquiring information of the target power line and environmental data of the target power line in real time, correspondingly determining a distance grade and a power line topology complexity grade according to the length of the power line and a power line topology structure, and determining an environmental grade according to the environmental data; monitoring channel and signal state data and power line impedance data in real time; correspondingly and dynamically matching a first power line carrier communication enhancement strategy and a second power line carrier communication enhancement strategy according to the real-time output of the distance grade, the environment grade and the power line topology complexity grade of the target power line; and in combination with channel and signal state data and power line impedance data obtained by real-time monitoring, respectively applying matched enhancement strategies to complete power line carrier communication enhancement processing. According to the invention, an actual power line application scene can be considered, and effective long-distance power line carrier communication enhancement is realized.

The invention relates to the technical field of solid waste recycling, in particular to a bean dreg recycling method which comprises the following steps: adding a first compound enzyme into bean dregs and first water to perform enzymolysis, performing ultrasonic treatment and first heating treatment on the prepared first mixture, and performing first centrifugal treatment to prepare first liquid and first solid; adding a second compound enzyme into the first solid and second water for enzymolysis to obtain a second mixture, and carrying out second heating treatment and second centrifugal treatment on the second mixture to obtain a second liquid and a second solid; an oligosaccharide concentrated solution prepared from the first liquid can be used as an additive of a soymilk beverage, and a soybean hydrolysis concentrated solution prepared from the second liquid; the soybean hydrolysis concentrated solution can be used as a freshener; the insoluble fiber polysaccharide prepared from the second solid can be used as a dietary fiber supplement or used for preparing feed. According to the method, recycling and comprehensive utilization of the bean dregs are achieved, secondary pollution is avoided, the application range of recycled products is wide, and the economic value is high.

The invention belongs to the technical field of office automation, and discloses an intelligent office process automation service system and method based on reinforcement learning, and the system comprises a data obtaining module which is used for obtaining office process structure data; the data processing module is used for preprocessing the office process structure data to obtain an office interaction feature data set; the task time evaluation module is used for training according to the office interaction feature data set to obtain a task allocation matching degree prediction model, and predicting an office task allocation matching degree based on the task allocation matching degree prediction model; the work intelligent distribution module is used for performing task distribution on the employees by utilizing an intelligent distribution algorithm according to the predicted office task distribution matching degree; the feedback early warning adjustment module is used for establishing a feedback channel, acquiring the work feedback rate of the employees in real time, and judging whether adjustment early warning information needs to be generated or not according to the work feedback rate of the employees; the ability and task requirements of the employees are matched more reasonably, and the working efficiency is improved.

The invention discloses a medical instrument demand management system and method based on big data, and the system comprises a demand trend module, a demand estimation module, a market analysis module, and a supply module, and relates to the technical field of medical instrument management. The technical key points are as follows: the use proportion of each age group is analyzed according to the deviation condition of the demands of different age groups for various medical instruments, so that the market demand of each medical instrument can be accurately speculated according to the age change condition of population; secondly, the diffusion trend and prevention and control requirements of various infectious diseases in a market area are known by analyzing treatment data, so that adjustment and planning are conveniently made before virus outbreak, the hysteresis quality of medical instrument supply is changed, and the requirements during market change are met; meanwhile, the upper limit of expansion of each supplier in the current market area can be known in combination with competitive product information and the market proportion, so that the production scale can be reasonably adjusted, and excessive competition and resource idleness are avoided.

The invention belongs to the technical field of battery health state evaluation, and particularly relates to a retired battery health state evaluation method and system based on an auto-encoder, and the method comprises the steps: forming a to-be-processed data set through collecting the charging and discharging data and charging curve characteristics of a retired single battery, and obtaining a sequence sample comprising temperature, voltage and capacity changes; and performing feature learning and dimension reduction processing on the sequence sample by using an auto-encoder to obtain a low-dimensional feature vector, and inputting the low-dimensional feature vector into a multi-layer perceptron model for iterative training to obtain an evaluation model. The system comprises a data acquisition module, a sequence acquisition module, a feature extraction module, an evaluation model training module and a health state evaluation module. According to the method, the maximum available capacity of the battery can be estimated only through the charging test data, the test time and energy consumption are reduced, the evaluation precision and the calculation efficiency are improved, a scientific basis is provided for secondary utilization of the retired battery, and resource waste and safety risks are avoided.

The invention provides a multi-robot task allocation optimization algorithm in an old-age service scene, and the algorithm comprises the steps: building an old-age demand model through a Petri network, and extracting a conflict transition set; forming a genome by the conflict transition set and robot numbers, and randomly generating an initial population; taking the satisfaction degree of the old as a fitness function, and screening chromosomes to obtain a first new population; performing genome interchange variation to obtain a second new population; screening and modifying infeasible solutions by using a Petri net state equation to obtain a third population; and repeating the process to obtain an optimal scheme. By establishing the model based on the elderly needs, the operation process of the elderly service institution can be simulated more accurately, and a reliable reference framework is provided for robot task scheduling. By extracting the conflict transition set, a potential resource conflict problem can be identified and solved in advance, so that the task execution efficiency is improved.