241results about How to "Achieve co-optimization" patented technology

The invention relates to the technical field of image classification, and discloses a breast image classification evaluation method and system based on deep learning, and the method comprises the steps: carrying out the preprocessing of sample breast image data, and obtaining comprehensive feature data; performing feature enhancement based on the comprehensive feature data to obtain enhanced feature data; performing multi-modal bidirectional feature fusion on the enhanced feature data to obtain multi-modal fusion feature data; based on the multi-modal fusion feature data, constructing and training a semi-supervised local feature reservation dense connection context-aware graph convolutional network to obtain a graph volume integral class model; according to the multi-modal fusion feature data, constructing and training a multi-branch residual attention reconstruction network to obtain a multi-scale feature fusion model; through the image volume integral class model and the multi-scale feature fusion model, classification evaluation and prediction result integration are carried out on the real-time mammary gland image data to obtain a target mammary gland image classification result, and the method improves the accuracy and reliability of mammary gland image classification.

The invention discloses a tunnel segmented exhaust method and system based on train working conditions, a medium and a product, and relates to the technical field of ventilation. The method comprises the steps that a target tunnel is divided into a plurality of control sections according to fan installation configuration of the target tunnel; acquiring train passing data and train configuration information of the control section, and calculating a flue gas growth value of the control section in a preset time period; collecting flue gas concentration data of the control section, and calculating a ventilation demand index of the control section in combination with the flue gas growth value; collecting wind direction parameters of the control sections, determining a ventilation control relationship between the adjacent control sections based on the ventilation demand indexes and the wind direction parameters, and generating a ventilation cooperation scheme; according to the ventilation cooperation scheme, operation parameters of the tunnel fans in all the control sections are calculated, and control instructions corresponding to the operation parameters are sent to the tunnel fans. By implementing the application, the fan operation can be adjusted according to the actual train operation condition, and the ventilation effect is optimized.

The invention provides a task allocation and optimal scheduling method and system for a multi-robot collaborative industrial assembly line, and relates to the technical field of robots, comprising the steps of constructing a task allocation strategy network through deep reinforcement learning, encoding workpiece types and attribute information into a state vector input network, a multi-objective optimization function including total assembly time minimization, load balance degree maximization and energy consumption minimization is designed, a near-end strategy optimization algorithm is adopted to train network parameters, a robot task mapping matrix is output as a distribution scheme, and the network parameters are dynamically optimized and updated in an online learning mode. The task allocation efficiency of the multi-robot collaborative assembly production line can be remarkably improved, the dynamic optimization scheduling of the production line is realized, the system energy consumption is reduced, and the assembly efficiency is improved.

The invention relates to the technical field of panoramic video processing, in particular to a multi-modal fusion panoramic video intelligent content analysis method, which comprises the following steps of: acquiring a panoramic video frame and a synchronous audio thereof; performing spherical projection transformation on the panoramic image data, and converting the equidistant projection image into a spherical image; generating a plurality of plane images based on the spherical image; performing candidate target position detection based on the plurality of plane images; based on a candidate target position detection result, an image target detection model is utilized to realize instance segmentation of a target, and target features are extracted to generate a target embedded description vector sequence; based on the synchronous audio, an audio recognition processing module is used for carrying out time sequence information extraction, and an acoustic attention sequence is formed; a spectrum sensor is adopted to realize association fusion of the target embedded description vector sequence and the acoustic attention sequence, and an audio and video collaborative attention sequence is generated; and based on the audio and video collaborative attention sequence, establishing an audio and visual event identification model, and obtaining event content described by the panoramic content.

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 belongs to the technical field of human body posture estimation, and discloses an ultra-low light human body posture estimation method based on image frequency division feature enhancement, and the method comprises the following steps: collecting ultra-low light human body images to construct a training data set; constructing an ultra-low light human body posture estimation model based on image frequency division feature enhancement; in the training stage, training data is enhanced by using a frequency sensing image enhancement module, and then the training data is input to a network trunk for training to obtain a trained model; and a test stage: inputting a test data set into the trained network model to obtain a human body posture estimation result. The network model provided by the invention can guide the network to generate richer and clearer image features by using the frequency perception image enhancement module, and collaborative optimization of image enhancement and human body posture estimation is realized, so that the accuracy of extremely low light human body posture estimation is improved.

The invention provides a federated learning-based cloud edge size model collaboration method and system, and electronic equipment, and the method makes full use of cloud edge resources, releases the size model capability: a large-scale model is trained at a cloud end, a small model suitable for a local scene is trained by an edge server and terminal equipment, and through a federated learning mechanism, a large-scale model is trained at the cloud end; and model collaborative optimization among different levels of nodes is realized. Data are available and invisible, data privacy and safety are guaranteed, private data of clients on the edge side do not need to be out of the domain, the cloud side shares the model capacity, the data are available and invisible, and data privacy and safety are guaranteed. The invention provides an efficient, flexible and extensible model training and optimizing method, computing power resources of cloud edges can be fully utilized, user privacy is protected, and a global large model is continuously optimized.

The invention relates to the technical field of sound console, and discloses a balance adjustment method, system and device for a sound console volume controller, and a medium. The method comprises the following steps: processing an audio signal of an input end of the sound console to obtain an initial system parameter matrix; performing probability density function calculation on the initial system parameter matrix to obtain an audio state data matrix and a noise feature data matrix; performing first-order volume control calculation based on the audio state data matrix to obtain a volume control data matrix; performing second-order frequency equalization calculation according to the volume control data matrix to obtain a frequency equalization control data matrix; performing iterative optimization calculation on the control parameters to obtain an optimal control parameter matrix; and performing dual-channel parallel calculation on the transfer function of the volume control channel and the transfer function of the frequency equalization channel to obtain an audio signal of the output end of the sound console, thereby realizing independent adjustment of volume control and frequency equalization, and ensuring high quality of audio output.

The invention provides an edge computing resource allocation method and system based on multi-objective optimization, and relates to the field of data processing, and the method comprises the steps: determining the resource demand feature of each unmanned retail store and the resource demand association feature between any two unmanned retail stores according to the historical computing resource demand information of a plurality of unmanned retail stores, determining an edge calculation layout scheme, and setting a plurality of edge calculation devices; predicting future computing resource demands of the plurality of unmanned retail stores according to the resource demand feature of each unmanned retail store, the resource demand association feature between any two unmanned retail stores and the demand prediction auxiliary information; acquiring resource allocation auxiliary information; according to the method, the edge computing resource allocation scheme is determined according to the future computing resource demands and the resource allocation auxiliary information of the unmanned retail stores, the edge computing resources are allocated to the unmanned retail stores, and the method has the advantage of improving the flexibility of edge computing resource allocation in an unmanned retail scene.

The invention discloses an energy storage system control method based on multi-target cooperative operation. The method comprises the following steps: acquiring state data of an energy storage system in a current control period and state data of the energy storage system connected with a power grid; based on the state data of the energy storage system and the state data of the power grid, calculating an operation state comprehensive evaluation value of the energy storage system in the current control period; and determining a priority regulation and control target based on the comprehensive evaluation value of the operation state in combination with the state data of the energy storage system and the state data of the power grid, and controlling the energy storage system to regulate and control the priority regulation and control target in the next control period. A plurality of regulation targets of the energy storage system are comprehensively considered, and the comprehensive evaluation value of the operation state is calculated according to the real-time state data of the system to determine the priority regulation target, so that the energy storage system is promoted to regulate the corresponding targets in a targeted manner in the next period, the relationship among different targets is effectively coordinated, the one-sidedness caused by single target regulation is avoided, and the regulation efficiency is improved. And overall collaborative optimization is realized.

The invention discloses an optimization system for intelligent operation and maintenance of a new energy generator set. The optimization system comprises a data acquisition module for acquiring operation state data and external environment data of the generator set through a sensor network; the data processing and storage module is used for carrying out preliminary data processing by utilizing edge computing equipment; the algorithm model module comprises a maintenance optimization model and a dynamic energy efficiency optimization model based on reinforcement learning; the invention discloses an optimization method for intelligent operation and maintenance of a new energy generator set. The optimization method comprises the following steps: S1, data acquisition and preprocessing; s2, constructing a maintenance optimization model based on reinforcement learning; s3, constructing a dynamic energy efficiency optimization model; s4, fault prediction and maintenance strategy optimization; and S5, system integration and feedback execution. According to the method, the operation parameters of the generator set are adjusted in real time, it is guaranteed that the generator set is always in the optimal operation state under different external environment conditions, and the dual purposes of maximum utilization of resources and minimum energy consumption are achieved.

The invention provides a small sample image classification method fusing global and adaptive local information, and relates to the technical field of image processing, and the method comprises the steps: S1, obtaining small sample images to establish a data set, and dividing the data set into a support set and a query set; s2, inputting the data set into a global sensing network DCCP-Net for pre-training; s3, inputting the support set and the query set into a task aware network QSA-TAN for meta-training; s4, inputting a pre-training result and a meta-training result into a global-local knowledge collaborative network GLKC for joint training to output probability distribution differences of the small sample images, and classifying the small sample images according to the probability distribution differences to obtain a classification result; according to the method, through collaborative optimization of knowledge in the global data space and the local task space, image characteristics and category information to which the images belong can be better highlighted, generalization of a small sample image classification model is remarkably improved, and therefore the intra-class change problem of the images is effectively solved.

The invention belongs to the technical field of the Internet of Things and energy management, and particularly relates to an energy consumption data collector based on the Internet of Things and a collection method, the collector comprises a data processing module, a network state evaluation module, a data value evaluation module and a communication and control module, real-time protocol analysis and data conversion are carried out on original data, and the energy consumption data is obtained. Generating a standard energy consumption data packet with a unified timestamp and a device identifier, storing the standard energy consumption data packet according to a receiving sequence, and eliminating a time reference deviation caused by an instrument clock difference; communication signal strength and round-trip time with a remote Internet of Things platform are collected in real time, through multiple times of detection and normalization processing, a maximum value is selected as a network state factor, a dynamic change reference and a self-adaptive fluctuation interval are calculated based on a historical power data sequence, an instantaneous deviation rate and a continuous deviation duration are determined, and the network state factor is determined. A data value factor is generated through processing, and the network state factor and the data value factor are matched with a preset period adjustment mode to realize collaborative optimization.

The invention discloses an integrated energy system planning and scheduling method and device considering demand side response. The method comprises the following steps: establishing load models of price type demand response, excitation type demand response and alternative type demand response; establishing an initial planning scheduling model by taking the lowest annual operation cost and the lowest demand side response cost of the full-life-cycle integrated energy system as a first optimization target; solving the initial planning scheduling model in combination with operation constraint conditions and user satisfaction constraints to obtain load transfer quantity and reduction quantity after price-type demand response and excitation-type demand response, and a real-time pricing mechanism; carrying out alternative demand response based on a real-time pricing mechanism to obtain an optimized price curve and load substitution quantities of various energy sources; and solving the planning scheduling optimization model taking the lowest annual operation cost of the full-life-cycle integrated energy system as a second optimization target to obtain an optimal capacity configuration result and an operation scheduling plan of each energy device in the integrated energy system.

The invention belongs to the technical field of berth distribution, and discloses a dry bulk cargo wharf berth distribution and ship loader combined dispatching system based on self-adaptive MOEAD. The invention provides a dry bulk cargo wharf berth allocation and ship loader combined dispatching system based on self-adaptive MOEAD. The system comprises an input module, an algorithm module, a berth allocation module, a loading and unloading dispatching module, a notification module, a control module, a loading and unloading operation module and a port leaving module. The input module is connected with the scheduling module and is used for inputting a ship plan table; the scheduling module is connected with the berth distribution module and the loading and unloading scheduling module and is used for scheduling through a scheduling algorithm; the berth distribution module is connected with the notification module and is used for carrying out berth distribution on the ship; and the loading and unloading scheduling module is connected with the control module and used for scheduling loading and unloading equipment. The operation efficiency of port loading and unloading is improved. The energy consumption of port loading and unloading equipment is reduced; the algorithm is high in convergence speed and has good convergence and distribution.

The invention discloses a wireless communication dynamic anti-interference optimization method and platform based on edge computing, and relates to the technical field of wireless communication anti-interference, and the method comprises the steps: collecting wireless channel state information and external interference source features in real time at an edge node, and constructing a real-time interference feature vector according to a collected data set; generating an interference prediction matrix; constructing a dynamic interference topological graph among network nodes, taking the dynamic interference topological graph as environment input, and utilizing reinforcement learning to generate an adaptive anti-interference strategy; and executing self-adaptive anti-interference strategy compensation, issuing a compensation result to the edge node, and executing anti-interference control. The technical problems that in the prior art, wireless communication anti-interference real-time response lags behind, global adaptability is insufficient, and local immediate response and overall reliable anti-interference requirements are difficult to consider are solved, and edge real-time anti-interference and cloud global collaborative optimization are achieved. And the technical effects of real-time performance, reliability and adaptability of wireless communication anti-interference are improved.

The present invention discloses an intelligent fault diagnosis method and system for explosion-proof motors in the natural gas industry, the method comprising the following steps: S10: acquiring multi-source heterogeneous data of explosion-proof motors; S20: performing enhancement processing on the multi-source heterogeneous data of explosion-proof motors to obtain an enhanced multimodal data set of explosion-proof motors; S30: performing feature extraction on the enhanced multimodal data set of explosion-proof motors using a deep learning model to obtain a high-dimensional feature vector; S40: based on the high-dimensional feature vector, a multi-task learning network is used to perform fault classification and fault severity evaluation to obtain a fault type probability distribution and a fault severity index; S50: generating an intelligent diagnosis report of the explosion-proof motor including maintenance suggestions according to the fault type probability distribution and the fault severity index. The method can make full use of multi-source heterogeneous data and realize an explosion-proof motor fault diagnosis method of intelligent diagnosis, so as to improve the diagnostic efficiency and accuracy and realize preventive maintenance.

The invention discloses an AI marking closed-loop method based on active learning and difficult case mining, and the method comprises the steps: carrying out the parallel calculation of uncertainty, representativeness and diversity three-dimensional value indexes after training an initial model, and carrying out the dynamic weighting screening of a high-value sample through a closed-loop feedback controller; difficult cases are recognized, DBSCAN clustering is adopted, similar sample expansion is retrieved, and the samples are stored in a dynamic pool; annotations are distributed, intelligent judgment is triggered for dispute samples after consistency verification, and labels are determined by fusing node reputation, model confidence and feature similarity; a mixed loss function is adopted for incremental training, and if the performance does not reach the standard, sampling, difficult examples and training parameters are adjusted in a linkage mode and then retry is conducted; the effectiveness of the difficult cases is evaluated after each round of iteration, the invalid difficult cases are eliminated, redundant clusters are combined, and the pool capacity is adaptively maintained; and the performance of the monitoring model is iteratively optimized until the standard is reached. According to the method, the labeling cost is reduced, and the boundary sample and long tail category recognition capability is improved.

The application provides an amorphous alloy magnetocaloric performance prediction method and system based on interpretable machine learning, relates to the technical field of material science and engineering, and comprises the following steps: step 1, constructing an amorphous alloy magnetocaloric performance data set, wherein the data set comprises alloy composition data, physical and chemical descriptors, external conditions and structural characteristics of a plurality of amorphous alloy samples and corresponding experimental values of maximum magnetic entropy change and Curie temperature; step 2, screening and optimizing the features in the data set, eliminating the collinearity between the features and performing recursive feature elimination to determine the final feature subsets for predicting the maximum magnetic entropy change and the Curie temperature respectively; and step 3, establishing machine learning models for predicting the maximum magnetic entropy change and the Curie temperature respectively through the final feature subsets, and training and optimizing the models to obtain the trained prediction models. The application realizes a closed loop of high-precision performance prediction, physical mechanism analysis and directional composition design.

The application relates to the technical field of hydrological prediction and deep learning, and provides a conceptual hydrological neural network hydrological prediction method and system considering space-time dynamic parameters, the method comprising the following steps: acquiring digital elevation model data of a target region, extracting a river network structure, and dividing the target region into a plurality of sub-basin units; constructing a sub-basin feature set and a river channel feature set based on underlying surface information data of the target region; constructing an original sample set based on historical hydrological and meteorological data of the target region; fusing the sub-basin feature set and the original sample set to obtain a standardized time series sample set; training a conceptual hydrological neural network model based on the standardized time series sample set, the original sample set, the sub-basin feature set and the river channel feature set to obtain a trained conceptual hydrological neural network model; and inputting real-time hydrological and meteorological data into the trained conceptual hydrological neural network model to obtain hydrological prediction information of the target region. Thus, the hydrological prediction accuracy is effectively improved.

The invention discloses a martensite reinforced self-lubricating copper-based alloy coating and a laser cladding preparation method thereof, the martensite reinforced self-lubricating copper-based alloy coating comprises the following components in percentage by mass: 66 to 84 weight percent of Cu, 10 to 20 weight percent of Al, 3 to 10 weight percent of Ni, 3 to 10 weight percent of Mn, 0 to 2 weight percent of Sn and 0 to 2 weight percent of Pb. In the preparation process, the laser input energy density is controlled within the range of 8.96-13.27 J / mm, and the double strengthening effects of grain refinement and martensite content increase can be synchronously achieved. The main phase structure of the obtained coating is twin-crystal martensite, meanwhile, the coating contains a small amount of alpha-Cu lubricating phase, gamma phase and Fe-rich hard phase, and the dry friction coefficient of the coating is 1t; 0.35, and the solidification hardness gt; and the voltage is 350HV. Compared with the existing self-lubricating tin-based babbitt metal and copper-tin alloy with similar friction coefficients, the coating disclosed by the invention realizes synergistic improvement of high hardness and low friction performance, and is particularly suitable for dynamic friction and wear working conditions with higher impact resistance requirements, such as robot motion joints, precision mechanical transmission parts and the like.

The application discloses a kind of integrated energy system low carbon operation method, belong to power grid technical field.The method includes based on baseline method allocation system free carbon emission quota, calculate actual net carbon emissions;Set integrated energy system objective function;Adopt two-stage robust optimization method, handle renewable energy and load uncertainty, first based on predicted worst-case scenario information obtains pre-dispatch optimization result, subsequently based on pre-dispatch information seeks worst-case scenario, ensure stable operation of system by iterative solution;Build and include integrated energy system, integrated energy system and photovoltaic production and sales between and photovoltaic production and sales between multilayer transaction framework, adopt Nash negotiation model and multistage robust method, realize the energy distribution between multiple stakeholders;By solving integrated optimization model, obtain the optimal strategy of integrated energy system low carbon operation.Thereby realized the coordination optimization of carbon emission right and renewable energy consumption, further improve the low carbon operation effect of system.

The invention provides an image generation model construction method which is used for generating images with high complexity and diversity, and the method comprises the steps: obtaining a preset training set, and enabling a plurality of images and a target image to have partial common features; an initial image generation model is constructed, the initial image generation model comprises a pre-training image generation model, a first feature learning module and a second feature learning module, and the pre-training image generation model comprises a coding module and a decoding module; a model training step: adopting a preset training set to carry out multiple rounds of iterative training on the initial image generation model to obtain a target image generation model, and keeping parameters of a pre-trained image generation model unchanged in the iterative training to obtain a target image generation model; and collaborative optimization of the first feature learning module and the second feature learning module is realized by adopting an asynchronous optimization mode so as to optimize the image generation model. Compared with the existing method, the method provided by the invention has the advantage that the generated image has higher authenticity and diversity.

The application discloses a kind of federal positioning method based on individualized patch and dynamic fusion, belong to indoor positioning and federal learning technical field.When applying federal learning to indoor Wi-Fi fingerprint positioning of Internet of Things, global model is often difficult to capture local data feature preference, local training cost is high and poor adaptability alone, the present application is by setting individualized patch and dynamic parameter fusion mechanism, reduce the influence of above-mentioned problem on positioning performance.The algorithm configures private position decoder as individualized patch for each local client, extracts local data features in training and uploads to server, server calculates the similarity of data distribution between clients to generate combination weight, for local clients with similar data distribution, individualized patch can selectively linearly combine with the decoder of the local model with higher relevance, avoid local model overfitting, improve the positioning performance of global model in local scene.

This invention discloses a graphic calibration system and method for CNC mold machining. The system includes: a data acquisition module configured to receive CNC machining tasks and acquire graphic data and measurement data of the mold to be machined based on the machining tasks; a data calibration module configured to perform geometric feature calibration on the graphic data based on preset calibration data, generate calibrated calibrated graphic data, and spatially register the measurement data and calibrated graphic data to obtain deviation data; a data processing module configured to map the deviation data to a machining parameter library in the machining task, calculate the correction values ​​of each machining parameter to obtain calibration parameters; and a data feedback module configured to feed back the calibrated graphic data and / or calibration parameters to the system, and perform the remaining graphic data input and machining parameter input for the machining task. This invention significantly improves mold machining accuracy and production efficiency, providing a high-confidence graphic input benchmark for CNC mold machining.

The application discloses a Brillouin sensing device based on optical parallel differential pulse pairs and a working method, and belongs to the technical field of optical fiber sensing. In view of the problem that the prior art is difficult to consider high resolution, high precision and fast measurement, the device used by the application comprises: an optical source module, which is used to generate an optical carrier; a first optical path module, which is connected with the optical source module and is used to receive the optical carrier and generate optical parallel differential pulse pairs containing anti-Stokes and Stokes components and having different pulse lengths as pump light through double-frequency double-sideband pulse modulation; a second optical path module, which is connected with the optical source module and is used to generate double-sideband probe light; and a sensing optical fiber, which is connected with the first and second optical path modules respectively and is used to receive the pump light and the probe light and directly complete signal differentiation in the optical domain through stimulated Brillouin scattering. Thus, high resolution, high precision and fast measurement are synergistically optimized, the gain / loss mechanism can be flexibly switched, and the scene adaptability is enhanced.

The invention relates to the technical field of lithium ion batteries, and discloses a disulfide bond dynamic network binder for a silicon-based negative electrode plate, and a preparation method and application thereof, and the preparation method comprises the following steps: carrying out ring opening polymerization on lipoic acid and derivatives thereof, and reacting with a functional monomer containing carbon-carbon double bonds capable of free radical polymerization to obtain a disulfide bond dynamic network binder; the binder containing the disulfide bond dynamic network structure is obtained; the functional monomer is a cross-linking / film-forming monomer or a mixture of the cross-linking / film-forming monomer and a monomer carrying a functional side group; according to the binder disclosed by the invention, reversible network reconstruction is realized through disulfide bond exchange, high binding power and pulverization resistance are considered, continuity of a conductive / ion channel can be maintained, surface resistance of a pole piece is reduced, and cycling stability and rate capability are improved. No additional solvent is added in the whole process, the process is simple, cost is low, and the method is suitable for continuous large-scale production.

The invention provides an operation path planning method and system integrating environmental perception and occupational health, and the method specifically comprises the steps: obtaining a three-dimensional map of a farm, and building a harmful gas prediction model related to an exhaust state; taking a personnel starting and ending point as a root node, performing bias sampling on a low-concentration region according to the model, and respectively constructing a forward and backward random search tree, a path cost comprehensive geometric distance and a predicted average concentration; performing reconnection optimization on the double trees, and updating father nodes of lower-cost adjacent nodes; connecting the optimized dual trees to generate an initial path; accumulated harmful gas exposure of the path is calculated, the gas exposure is the integral of the product of the retention time and the concentration, and target operation parameters of all exhaust equipment along the line are determined accordingly; and updating the prediction model by using the new parameters, repeatedly executing path search and exhaust parameter optimization until the total cost change of adjacent iteration paths is smaller than a convergence threshold value, and outputting an operation path and exhaust cooperation scheme.

The invention relates to the technical field of server hardware resource optimization, in particular to a server hardware resource intelligent optimization method and system based on an adaptive neural network, and the method comprises the steps: obtaining hardware resource information and system performance indexes of a server; constructing topological resource mapping based on the hardware resource information and the system performance indexes; executing a group theory resource scheduling algorithm according to the topological resource mapping; performing matrix resource state prediction based on a result of the group theory resource scheduling algorithm; executing number theory resource allocation optimization according to a result of matrix resource state prediction; updating the weight of the adaptive neural network based on a number theory resource allocation optimization result; according to the method, an optimized server hardware resource configuration scheme is output, and by introducing topological resource mapping, the method can comprehensively and accurately represent a complex relationship among multi-dimensional resources, so that a solid foundation is provided for subsequent optimization decision making; the application of the group theory resource scheduling algorithm enables the resource scheduling operation to be more flexible and systematized.

The embodiment of the invention provides a method and equipment for determining a power system configuration scheme and a medium. The method comprises the following steps: determining the initial inertia of a system; if the initial inertia of the system is smaller than the inertia demand value, the reactive response capability of each new energy node is calculated, and a first node is determined; if the first node is different from the previous configuration scheme, taking a new energy node of which the short-circuit ratio is smaller than a short-circuit ratio threshold value as a second node after simulating to carry out network construction type transformation on the first node; determining a current configuration scheme and a corresponding configuration cost; after the second node is simulated to be subjected to synchronous phase modifier configuration, the initial inertia of the system is determined again, and the step of judging whether the initial inertia of the system is larger than or equal to the inertia demand value or not is executed again; and if the first node is the same as the previous configuration scheme, selecting the scheme with the lowest configuration cost. According to the scheme, various configuration schemes of the new energy power system can be determined, the scheme with the lowest cost is selected as the target configuration scheme, and the economical efficiency of system transformation is guaranteed.