395results 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 relates to the technical field of irrigation control, in particular to a water and fertilizer integrated irrigation control method based on a wheat growth cycle, which comprises the following steps: collecting soil humidity and wheat growth state data, and screening key soil and biological parameters influencing wheat growth by applying a random forest algorithm; and predicting the requirements of the wheat on water and nutrition in the differential growth stage according to the key parameters. According to the method, the water and nutrition requirements of wheat in different growth stages are accurately predicted by adopting a random forest algorithm, resource allocation is directly carried out according to the actual needs of plants, the problems of resource waste and insufficient supply of a traditional irrigation system are effectively avoided, and a dynamic adjustment strategy is based on current soil and plant states and also based on a dynamic adjustment strategy. And a water and fertilizer supply plan is optimized by using historical and predicted data, the capability of responding to environmental changes is enhanced, irrigation and fertilization parameters are finely adjusted by combining real-time feedback circulation with a PID controller, and the accuracy and effectiveness of each time of irrigation and fertilization are further ensured.

The invention relates to the technical field of production task decision making, in particular to a furniture production task automatic matching method and system based on intelligent manufacturing. And extracting personalized demand information corresponding to the production task, converting the personalized demand information into standard design parameters, and generating a customized task. The production task is disassembled into a plurality of sub-tasks, and each sub-task has independent attributes including process requirements, resource requirements and time requirements; and performing one-to-one mapping on the customized tasks and the subtasks to generate an execution plan. Performing resource matching by using a constraint algorithm based on the execution plan and the existing production resource information to obtain a production plan; and obtaining an optimal scheduling plan by using a constraint algorithm according to the production plan. And based on the real-time production data and the burst information, adjusting the optimal scheduling plan through the data analysis model to obtain a final scheduling plan. The method can cope with emergencies or real-time data changes, and manual decisions are reduced.

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 discloses a training method and device of a distributed computing system, a program product and a medium, which are used for solving the problems of performance difference and low communication efficiency of heterogeneous computing nodes. According to the scheme, the calculation capability of each calculation node is fully considered, so that dynamic allocation and iterative optimization of tasks are realized; specifically, the problems of idle resources and low efficiency are avoided by acquiring the computing power of each computing node and allocating the training task matched with the computing power to the computing node; the time length of single iteration is determined, and the number of iterations in the communication interval is adjusted according to the time length, so that each computing node can perform data exchange at a proper time, network bandwidth occupation and delay caused by frequent communication are reduced, the overall efficiency of the training process is optimized, mutual waiting between the computing nodes is avoided, and the network performance is improved. When the computing node runs the training process, the power consumption and heat generation are reduced, and the communication overhead is also reduced, so that the long-term stability and reliability of the computing node are improved.

The invention relates to the technical field of project management, and particularly discloses a construction project progress management platform, which comprises a data acquisition module, a data processing module, a dynamic adjustment module and a collaborative application module, the data acquisition module acquires real-time data of a construction site; the data processing module is used for processing the collected data; the dynamic adjustment module establishes a dynamic optimization model and a resource scheduling model; the collaborative application module supports a construction party, a supervisor and an owner to check the progress and feedback in real time; according to the invention, the actual data of the construction site can be monitored and acquired in real time, the construction plan and resource allocation are dynamically adjusted through the established model, and the construction efficiency and management level are improved.

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 provides an intelligent scheduling control method for an electric energy meter verification assembly line, and belongs to the technical field of electric energy meters. S1, features extracted from an original data set are expressed as a feature matrix, and a multi-level collaborative algorithm is designed; s2, the global scheduling layer is responsible for distribution of overall resources and macroscopic scheduling decision making, the input is a feature matrix, and the output is a task priority matrix; s3, the local scheduling layer is responsible for distribution of specific tasks and management of execution details, the input is a task priority matrix, and the output is a specific task distribution matrix; and S4, the equipment control layer is responsible for operation and state monitoring of verification equipment, inputting the specific task distribution matrix, outputting the specific task distribution matrix and the equipment control instruction set, and dynamically adjusting the specific task distribution matrix and the equipment control instruction set according to feedback. According to the invention, the Internet of Things and the big data technology are fully utilized, and the intelligent algorithm is combined, so that efficient, intelligent and automatic control of the electric energy meter verification assembly line is realized.

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 discloses a multi-center inherited metabolic disease information integration platform, and relates to the technical field of medical treatment, according to the multi-center inherited metabolic disease information integration platform, the normalization and availability of data are improved through a data standardization module; data from different centers are integrated into a unified database through the data integration module by utilizing a data fusion technology, redundant data are removed, the problem of heterogeneous data integration is effectively solved, and the integrity and consistency of the data are improved; the privacy protection module avoids data leakage and unauthorized access; through the resource calculation management module, the resource utilization efficiency is effectively improved, and the calculation cost is reduced; and secondly, the storage resources are dynamically adjusted and allocated according to the evaluation result of the storage index Cczs, so that the optimal management of the storage resources is realized, the problems of resource waste and resource insufficiency are avoided, and the efficient operation of the system is ensured.

The invention relates to a distribution box with an autonomous heat dissipation mechanism, and belongs to the technical field of distribution box equipment. The distribution box with the autonomous heat dissipation mechanism comprises a box body, an electric push rod and a top cover, the electric push rod is arranged between the box body and the top cover, a controller is arranged at the bottom end of the top cover, a raindrop sensor is arranged in the middle of the top end of the top cover, and a temperature sensor is arranged in the box body; the heat dissipation device can be switched and adjusted in multiple states, a ventilation heat dissipation state can be kept in a conventional state, and in a rapid heat dissipation state, an air inlet of the device can be arranged, the air inlet amount can be increased, and a heat dissipation channel is formed in the box body under the influence of an L-shaped plate and a blocking plate; and meanwhile, the device can be matched with a temperature sensor, a controller and an electric push rod according to needs, self-adaptive switching adjustment can be conducted according to the actual operation state of the device, autonomous heat dissipation of the device is achieved, the device is not affected by external factors, and stable and efficient operation of the device can be achieved.

The invention relates to the technical field of gas component detection, in particular to a public place gas detection method based on gas chromatography, which comprises the following steps: a mobile robot covers all areas of a public place according to a predetermined path, collects an air sample, and pretreats the collected gas sample through a filter or an adsorbent; a gas sample enters a detector after being separated by a gas chromatographic column, and harmful gas components are identified; according to the detected harmful gas concentration, combining historical data and a threshold value, calculating the danger level of each region, and carrying out danger level division on the region of the public place; according to the danger level, the system automatically selects and executes corresponding countermeasures; and the system records a historical data set of all countermeasures, and optimizes the countermeasures through a machine learning algorithm. According to the invention, full coverage, multi-area dynamic monitoring, accurate risk prediction and grading processing can be realized, and the response speed and the use effect of the system are improved.

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.

The invention discloses a database AI question answering system and method based on a large language model and retrieval enhancement, and relates to the technical field of database retrieval. The system comprises a front-end interaction module, a distributed cache management module, a task scheduling center, a large language model processing sub-module, a retrieval enhancement sub-module, an answer optimization sub-module and a database management sub-module. All the sub-modules are mutually independent and work cooperatively, through an efficient task scheduling mechanism, the system can monitor the load and resource states of all the sub-modules in real time, accurate allocation of tasks is conducted according to an intelligent algorithm, in a large-scale concurrent request scene, single-point faults and resource waste are effectively avoided, stable operation of the system is ensured, and meanwhile, the system can be widely applied to large-scale concurrent requests. The distributed cache management module further optimizes the storage and access of the data, reduces repeated data query and transmission, and improves the overall performance.

The invention provides a virtual reality urban environment design system based on an AR technology, and relates to the technical field of urban planning design, and the system comprises a data collection module, a modeling module, a rendering module, an interaction module, a display module, a physical simulation module and a real-time updating module. The system provides a visual and immersive design environment for city planners. Through high-precision data acquisition and vivid modeling rendering, a plan can clearly observe details and overall layout of a city. And the real-time updating module ensures that the planner obtains the latest city change information and adjusts the planning scheme in time. The optimization module ensures smooth operation of the system and improves the working efficiency. The physical simulation module can predict the effects of different planning schemes under the influence of various natural and human factors, so that the schemes are optimized, the scientificity and feasibility of urban planning are improved, and planning errors and resource waste are reduced.

The invention relates to the technical field of computer networks, in particular to an implementation method and system for an RDMA network card request queue, and the method comprises the steps: checking whether a residual space exists in an RDMA Cache module or not when a write request is received; if yes, writing the writing information of the writing request into an RDMA Cache module, updating a free bit, and recording a queue pair number and an unfinished pointer; if not, writing the writing information of the writing request into a host memory module; when a read request is received, checking whether the queue pair number and the unfinished pointer carried by the read request are matched with historical records in the RDMA Cache module or not; if yes, reading from the RDMA Cache module; and if not, reading from the host memory module. According to the invention, multiple QPs in the RDMA network card share the internal resources of the Cache, the resource consumption is reduced, and the utilization rate of the cache is 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 relates to the technical field of power distribution network regulation and control, in particular to a power distribution network intelligent regulation and control system for high and low voltage power transmission, comprising a global optimization layer module which is responsible for global regulation and control of the whole power distribution network and comprises a global data acquisition unit, a global data processing unit and a global control unit; the regional optimization layer module is used for performing intra-regional optimization regulation and control on the plurality of divided regions according to geographic distribution and load characteristics of the power distribution network, and the regional optimization layer module comprises a plurality of regional data acquisition units, a regional data processing unit and a regional control unit; and the adaptive learning module adaptively optimizes global and regional regulation and control strategies through a machine learning algorithm according to historical data and real-time data in operation of the power distribution network, and the adaptive learning module comprises a data training unit and a model updating unit. According to the invention, the multi-level regulation and control mode can effectively reduce the power loss in the region, and improves the overall operation efficiency of the power grid.

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 embodiment of the invention provides a resource management method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining resource data for a target server type, performing resource use estimation based on historical resource data, computing resource supply data, current resource data and a current inventory resource total amount, and obtaining a target reference resource amount corresponding to the target server type at a current time, based on the target reference resource quantity and the current inventory resource total quantity, generating resource scheduling information, so that the inventory calculation resource quantity corresponding to the target server type is smaller than the target reference resource quantity after resource scheduling is carried out on resources under the target server type according to the resource scheduling information, the difference value between the target reference resource quantity corresponding to the target server type at the current time and the current actual use resource total quantity is within a preset threshold value range. By adopting the method, inventory resources can be automatically adjusted, and the resource utilization rate is improved.