191 results about "Computational intelligence" patented technology

The invention provides a virtual workplace training task allocation method and system based on multi-agent collaboration, and relates to the technical field of intelligent training, and the method comprises the steps: decomposing a training task into subtasks through constructing a knowledge dependence graph; calculating intellectual body knowledge complementarity, cooperation proficiency and task bearing capacity scores, and fusing the scores into a collaborative score; establishing a knowledge topology network based on the difficulty coefficient and the collaborative score, and selecting an optimal agent combination; and recording a training data optimization distribution mechanism. According to the invention, the training efficiency is improved, the optimal configuration of agent resources is realized, and the pertinence and adaptability of training are enhanced.

The invention provides an enterprise product intelligent quotation collaborative decision-making platform. A platform architecture comprises a user interaction layer, a data processing layer and a decision-making layer, and platform functions are realized by relying on an intelligent agent. When a user releases a product quotation task, an authority management agent verifies the identity and access authority of the user, and a user interaction and demand analysis agent in a user interaction layer converts the natural language demand of the user into a structured instruction; the data processing layer receives an instruction, basic data and a special cost agent in the layer pull data from an external system through a cross-system calling agent, and a price calculation agent integrates the data through multi-objective optimization to generate candidate quoted prices; and finally, a man-machine cooperation and intelligent decision-making auxiliary agent in a decision-making layer displays all candidate schemes, and an expert performs fine adjustment to select an optimal quotation. According to the invention, rapid and accurate quotation is realized for enterprise products, the quotation model is continuously optimized through continuous feedback and self-learning, and finally a highly autonomous intelligent quotation system is formed.

The invention relates to the technical field of artificial intelligence, in particular to a self-organizing knowledge base construction method, system and device based on a multi-agent system and a storage medium, and the method comprises the steps: S1, monitoring a dialogue information flow, calculating the self-reply confidence coefficient of an agent, and if the self-reply confidence coefficient exceeds a preset dynamic threshold value, judging that a potential knowledge value exists and triggering an extraction request; s2, in response to the request, performing semantic coding on a dialogue fragment, extracting knowledge elements, and generating a structured knowledge unit by utilizing graph neural network modeling and relation calibration; s3, through a pre-training semantic coding model, mapping the multi-dimensional semantic coding model into a multi-dimensional semantic vector; s4, carrying out hierarchical clustering comparison and combination, and if a threshold value is exceeded, establishing a new classification and adjusting a knowledge base index; and S5, knowledge fingerprints are generated and compared, and fusion updating is carried out based on the reputation scoring model when semantics conflict or redundancy occurs. According to the method, unstructured dialogue high-precision knowledge extraction is realized, so that the knowledge base structure is dynamically self-organized according to semantics, multi-source knowledge is coordinated, and the knowledge management automation level and quality are improved.

The invention belongs to the field of multi-agent reinforcement learning, and discloses a relation-driven multi-agent reinforcement learning method and system based on a mixed game, and the method comprises the steps: enabling a strategy network to generate an agent action, and carrying out the interaction of an environment, so as to collect sample data; the centralized value evaluator calculates marginal influence values of the agents based on samples, and deduces a social influence weight set; assigning group external rewards as individual external social rewards based on the weights; processing the global state by using a random network distillation-driven method, updating a prediction network parameter to minimize a prediction error, and outputting an internal reward set; the external social rewards and the internal rewards are fused to form comprehensive rewards; and updating the strategy network and the value network by using the comprehensive reward, and circularly training until convergence. By adopting the method, accurate modeling of the individual interaction relationship is enhanced, invalid exploration is remarkably reduced, the learning efficiency and the strategy reliability are improved, and the overall performance of the system is enhanced through an explicit social mechanism.

The invention provides an automatic driving decision-making and planning algorithm applied to a complex intersection. The automatic driving decision-making and planning algorithm comprises a prediction module and a planning module, the prediction module is provided with a condition prediction model and is used for predicting and outputting predicted trajectories of surrounding intelligent agents under different candidate trajectories by taking the candidate trajectories of the vehicle as conditions; the planning module comprises a trajectory tree construction module which is used for generating candidate trajectories of a vehicle and forming a trajectory tree; the scene tree construction module is used for generating predicted trajectories of other traffic participants around the vehicle to form a scene tree; the cost evaluation module is combined with a neural network-based feature extractor and a weight decoder to calculate the cost of each planning trajectory branch in the joint trajectory of the intelligent agent; the dynamic planning module is used for exporting candidate planning trajectories from the candidate trajectories output by the trajectory tree construction module and outputting the candidate planning trajectories to a vehicle control system; and the node pruning module is used for pruning low-fraction nodes in the trajectory tree and reducing the number of branches in the scene tree.

The invention discloses a property operation report generation method and device based on multiple agents and a related medium, and the method comprises the steps: calling an intention recognition agent to carry out intention analysis, and then calling a task planning agent to carry out path construction and scheme solving; determining an execution sequence of tasks in combination with the task planning data to obtain task processing data; calling a data collection agent to carry out data collection on the task processing data to obtain primary processing data; and calling a code calculation agent to carry out code generation and model operation on the primary processing data, calling an outline making agent to carry out outline arrangement and report generation processing, calling a report writing agent to complete report packaging, and outputting a property operation report. According to the invention, the structured analysis result obtained through calculation is subjected to outline arrangement and report generation processing, and the property operation report is packaged and output, so that manual report arrangement is not needed, the process is simplified, and the report output efficiency is improved.

The invention relates to the technical field of edge computing, and discloses an edge computing intelligent decision-making system based on edge cloud collaboration, which comprises a terminal sensing layer, an edge computing layer, a network transmission layer and a cloud center layer. The terminal sensing layer is configured to collect original data; the edge computing layer comprises edge computing equipment, a resource scheduling module and a distributed synchronous triggering module which are respectively used for data preprocessing and model reasoning, dynamic resource allocation based on multi-agent deep reinforcement learning and multi-equipment synchronous acquisition realized through a precision time protocol; the network transmission layer dynamically adjusts a transmission strategy based on a reinforcement learning algorithm during data transmission; and the cloud center layer carries out training optimization on the model through a federal learning mechanism and issues the model to an edge side. According to the method, millisecond-level real-time intelligent decision making of an industrial site and sustainable evolution of the AI model are realized through a side cloud collaborative architecture, and the efficiency, the reliability and the self-adaptive capability of the system are effectively improved.

The invention provides a trailing suction dredger loading operation parameter optimization real-time recommendation system and method, and relates to the technical field of trailing suction dredger dredging engineering.The system comprises a data collection layer, a real-time calculation layer, an intelligent recommendation layer and a feedback control layer, the data collection layer is used for collecting trailing suction dredger loading operation parameters in real time, and the real-time calculation layer is used for calculating the real-time calculation layer; the real-time calculation layer can calculate energy consumption and yield in real time based on edge calculation equipment, the intelligent recommendation layer can generate a Pareto optimal solution set through a multi-objective optimization algorithm, and the dynamic weight adjustment module adjusts energy consumption and yield weight coefficients in real time according to the construction stage. The feedback control layer issues the collected loading operation parameters of the trailing suction dredger to an execution mechanism in real time through a PLC, and monitors the operation state of equipment to update a historical database, so that collaborative optimization of energy consumption and yield can be achieved, and the optimization precision is continuously improved through historical data.

The invention discloses an agent sorting method for a trust domain multi-agent reinforcement learning algorithm, and relates to the technical field of reinforcement learning. The invention provides an agent sorting method for a trust domain multi-agent reinforcement learning algorithm in order to solve the defects that in the prior art, a training sequence cannot be reasonably arranged according to agent observation similarity and information richness, and consequently training stability and collaboration are insufficient. Comprising the following steps: collecting observation information of each agent; calculating the observation similarity between the intelligent agents; sorting is carried out according to the similarity, and an intelligent agent training sequence is generated; the sorting comprises the following steps: preferentially selecting agents with low observation similarity, and performing clustering optimization in combination with a centralization mechanism so as to determine a final sorting sequence. The method is suitable for a complex task decision-making system which needs to improve the multi-agent reinforcement learning training stability and cooperation performance.

The invention discloses a secondary water supply tail end pressure balance edge computing AI intelligent gateway, and relates to the technical field of secondary water supply. The secondary water supply tail end pressure balance edge calculation AI intelligent gateway comprises a data acquisition and transmission module, an edge calculation and intelligent analysis module and a control instruction generation and execution module, and the secondary water supply tail end pressure balance edge calculation AI intelligent gateway disclosed by the invention brings significant improvement in multiple aspects for a secondary water supply system; in the aspect of pressure balance performance, the terminal pressure can be accurately monitored and dynamically regulated in real time by means of advanced edge computing power and an intelligent algorithm, pressure change can be effectively predicted and measures can be taken in advance by continuously analyzing a large amount of pressure data and a water consumption trend, and the fluctuation range of the terminal pressure is strictly controlled within an extremely small range. And the pressure can be stabilized within + / -0.01 MPa of a set pressure value, so that users on all floors can enjoy stable and sufficient water pressure at any time, and inconvenience in water use caused by pressure fluctuation is eliminated.

The “Industrial Digestive System” encapsulates a novel paradigm in waste management and resource recovery, bridging the gap between sophisticated computational intelligence and industrial automation. The hardware component replicates the natural digestive system's efficiency in processing a diverse range of inputs, including municipal and industrial waste, transforming them into valuable outputs like biofuels, chemicals, and advanced composite / conglomerate materials. This process is achieved through a series of mechanized operations. Complementing the hardware, the software facet of the invention is rooted in advanced machine learning and optimization algorithms that optimize multivariable functions for the most efficient conversion of input to resources, orchestrating the transformation from varied waste streams, elements, and molecules into solid, liquid, and gaseous outputs. The result is an automated, intelligent manufacturing machine that enhances product quality and operational efficacy. “The Industrial Digestive System” can be seen as a new branch of cybernetics, a cyber-physical system inspired by nature.

The invention relates to a semantic collaborative SLAM map fusion and splicing method based on a multi-modal large model. The method comprises the following steps: deploying a plurality of intelligent agents, collecting original data and preprocessing the original data to obtain multi-modal perception data; inputting the data into a local SLAM (Simultaneous Localization and Mapping) module, combining with a semantic consistency loss fine-tuning multi-modal large model, and constructing a local geometric and semantic map of each agent; fusing the features and mapping to obtain a local and global semantic feature map, and calculating relative poses between the intelligent agents to register the map; and finally, optimizing, reasoning and integrating the initial global map to form a final global semantic map with complete semantics and standard geometric accuracy. According to the method, the problems of insufficient precision, poor robustness, lack of semantic information, difficulty in adapting to dynamic scenes and the like in the existing map splicing process can be effectively solved, and the efficiency, the accuracy and the intelligent level of global map construction are effectively improved.

The invention relates to the technical field of artificial intelligence, in particular to a storage supervision-oriented AIoT edge computing intelligent analysis system and method, and the system comprises an edge computing node, a multi-modal data collection module, an intelligent analysis engine, a dynamic resource optimization module and a secure communication module. The system has the beneficial effects that real-time data acquisition and localized intelligent analysis of storage environments (temperature and humidity, illumination, gas concentration and the like) and cargo states (stacking integrity, displacement, damage and the like) are realized by integrating edge computing nodes and a multi-mode sensor; and the deep learning algorithm is used to carry out autonomous identification and early warning on abnormal events (such as cargo dumping, environment exceeding and illegal invasion).

The invention relates to the field of data processing, in particular to a remote detection method and system for an electric energy meter, and the method comprises the steps: obtaining the operation data of the electric energy meter, carrying out the window division of the operation data according to a time sequence through a variational mode decomposition algorithm, obtaining a comprehensive frequency distribution curve of the operation data in a window, and generating a time-frequency diagram; the method comprises the steps of performing peak point detection based on a time-frequency diagram, determining a main frequency peak point and an interference peak point, calculating a harmonic interference degree value of the frequency of electric energy meter data based on distribution characteristics between the main frequency peak point and the interference peak point, and using change consistency and frequency amplitude of the interference degree value to calculate an abnormal degree value of an intelligent voltmeter. And the abnormal condition of the electric energy meter is judged. According to the method, the change consistency of data of different dimensions is calculated, so that single-dimension noise interference is suppressed, and the reliability of abnormal criteria is improved.

The invention discloses a battlefield multi-agent dynamic cooperation method based on an attention mechanism. The method comprises the following steps: S10, acquiring local observation information; s20, establishing a strategy network and a value network, embedding a multi-head attention mechanism in each of the strategy network and the value network, allowing each agent to dynamically query the internal representation of a teammate, and converting a search problem of an exponential level joint action space into dynamic attention on key information; s30, the strategy network generates action probability distribution of the intelligent agent through the attention layer, and the intelligent agent is forced to depend on a cooperation signal; the value network outputs the value estimation of each agent and provides a reference function; the collaborative loss function calculates similarity penalty between agents based on the embedded representation output by the attention layer; and S40, the training process is updated by adopting a strategy gradient method, strategy network parameters are updated to maximize accumulated rewards, value network parameters are updated by minimizing total loss, and a highly cooperative joint strategy is output. According to the invention, the agent is ensured to always integrate team information during decision making.

The invention provides a dynamic frame rate control multi-target tracking method based on reinforcement learning, and aims to improve the adaptive capability and tracking performance of a video processing system in a computing resource limited environment. According to the method, a video is divided into fixed time windows, multi-dimensional features including target motion features, frame rates, system state information and the like in the windows are extracted, and state vectors are constructed and input into a reinforcement learning model to predict frame rate adjustment actions of the next window. The system controls frame rate switching according to the action confidence, and calculates an award by comparing the current tracking performance with the reference performance for feedback policy performance. The method combines proportional frame rate control, frame rate smooth transition and a cooling period mechanism, realizes dynamic balance between tracking performance and computing resources, and is suitable for scenes of edge computing, intelligent monitoring and the like.

The invention relates to the field of multi-agent reinforcement learning, and discloses a reinforcement learning method based on causal reasoning and a hierarchical attention mechanism, which makes up the defects of inaccurate role allocation and low cooperation efficiency in a traditional multi-agent system and improves the overall cooperation performance. According to the method, a causal perception multi-agent cooperation model is constructed, firstly, a dynamic causal graph is constructed by using a structural causal model, and a causal influence vector between agents is calculated by using an optimized variational distribution estimator; then generating a causal-guided hierarchical attention weight based on a causal influence vector; then cooperative information transmission is realized through a cross-agent attention sharing mechanism, and enhanced feature representation is generated by using a cross-layer fusion mechanism; a causal perception role selector is further designed based on the global causal contribution degree and the local causal contribution degree, and dynamic allocation of agent roles is achieved; and finally, performing model training optimization through a causal influence internal reward mechanism.

The invention discloses a mobile robot adaptive path planning method based on enhanced Q learning and multi-strategy cooperation, and relates to the field of robot autonomous navigation and computing intelligence. The method comprises the following steps: firstly, establishing a two-dimensional grid map model containing obstacle information through an environment sensing module; the initial position of a gold scrubber population is generated by utilizing the low-difference characteristic of a golden section sequence to enhance the global ergodicity of a solution space, three stages of migration, gold scrubber and cooperation of a gold scrubber optimization algorithm are modeled into a discrete action space, an optimal search action is dynamically selected through a nonlinear attenuation greedy strategy, and the optimal search action is realized. According to the method, adaptive switching of exploration and development behaviors is realized, a search gradient is constructed by using a differential vector of survival of the survival of the survival of the population, and a lens imaging reverse learning strategy is introduced to dynamically disturb an elite individual so as to jump out of a local extremum trap. And finally, outputting an optimal collision-free path through a multi-objective evaluation function including path length, safety and smoothness.

The invention relates to the technical field of public safety personnel evacuation, discloses a public emergency emotional infection simulation method, and aims to solve the problems that an existing emotional infection simulation model does not fully consider a group emotional propagation theoretical mechanism and the influence of personality characteristics of an intelligent agent and is insufficient in simulation precision and authenticity. The method is based on an improved SIRS (Susceptable-Infected-Recovery-Susceptable) model and an OCEAN large five-personality model, and is realized through the following six core steps: 1) constructing a three-dimensional simulation model of a public place; 2) initializing personality characteristics, emotional states and behavior states of the agents; 3) calculating an emotion propagation process (including a propagation range, an influence value, a distance weight and emotion conflict processing) between the intelligent agents; the method comprises the steps of (1) acquiring the emotion of the intelligent agent, (2) calculating the emotion attenuation of the intelligent agent, (3) determining the evacuation speed and completing analogue simulation, (4) realizing the behavior logic of the intelligent agent in different emotion states, (5) calculating the emotion attenuation of the intelligent agent, and (6) determining the evacuation speed and completing analogue simulation. The method can accurately restore the emotion infection and evacuation behaviors of the crowd in an emergency scene, improves the simulation authenticity and lightweight level, and provides decision support for emergency evacuation scheme making and emergency management.

The invention belongs to the cross technical field of computational intelligence and machine learning, and discloses a particle swarm optimization method for a high-dimensional expensive problem fusing a DBN and an agent model, and the method comprises the steps: extracting the features of a high-dimensional optimization problem through an IDBN dynamic dimension reduction module, and carrying out the dynamic dimension reduction of the features; filling a multi-stage collaborative screening mechanism of the samples, and selecting a plurality of representative samples; according to the local search strategy of the elite solution, local development is carried out on the elite solution so as to improve the convergence speed and the precision of the algorithm. According to the particle swarm optimization method for the high-dimensional expensive problem fusing the DBN and the proxy model, through the IDBN dimension reduction module, the search efficiency of an algorithm in a high-dimensional space is remarkably improved, and the construction cost of the proxy model is reduced; through a multi-stage collaborative screening mechanism of filling samples, the precision of the agent model in a desired area and a potential area is enhanced; a local search strategy of an elite solution is introduced, so that the local development capability of the algorithm is improved, and the precision of an optimal solution is improved.

The invention relates to a fan vibration quantity testing device and a testing method thereof.The fan vibration quantity testing device comprises a rack, double testing stations are arranged on the rack, each testing station is provided with a two-dimensional cooperative clamping mechanism and an acceleration detector, and each two-dimensional cooperative clamping mechanism comprises a transverse driving air cylinder and a longitudinal driving air cylinder; a transverse clamping plate is arranged at the end of a piston rod of the transverse driving air cylinder, a longitudinal clamping plate is arranged at the end of a piston rod of the longitudinal driving air cylinder, the transverse clamping plate and the longitudinal clamping plate are vertically matched to form an L-shaped clamping face, an industrial personal computer is integrated in the rack, and the industrial personal computer is electrically connected with the transverse driving air cylinder, the longitudinal driving air cylinder and the acceleration detector. And through hardware initialization and self-calibration, double-station self-adaptive clamping and positioning, multi-dimensional vibration data acquisition, signal preprocessing and feature extraction, vibration quantity fusion calculation, intelligent judgment and error compensation, detection of the vibration quantity of the fan is realized. The method has the effect of improving the efficiency of fan testing.

The invention discloses a multi-agent cooperation method, system and device based on shared memory data, and a medium, mainly relates to the technical field of multi-agents, and aims to solve the problem that a traditional reasoning system cannot recognize semantic similarity between requests and cannot sense a context relationship of agent tasks, so that the efficiency is improved. The intelligent agent system is often deployed at multiple nodes, and the cache result cannot be shared across the nodes. Comprising the steps of obtaining an output result and an intermediate result generated by each path node; caching the semantic signature and the output result to a preset result cache layer; caching an intermediate result generated by each path node as context information to a preset context cache layer; and according to a preset time window, detecting inference frequencies of different semantic signatures generated by all the agents, and according to the inference frequencies, adjusting storage time of output results corresponding to the semantic signatures in a preset result cache layer.

The invention provides an intelligent salary calculation method and device, electronic equipment and a storage medium, and relates to the field of enterprise human resource management.In the method, a user can flexibly configure a salary formula and automatically carry out salary calculation, intelligence is good, salary strategy simulation (namely salary back-test calculation) is supported, an execution track during formula execution can be displayed, and the method is suitable for popularization and application. And the method has result interpretability.

The invention discloses a power distribution network situation deduction method and system considering multi-type source-load interaction, and relates to the technical field of power distribution network situation prediction. The method comprises the following steps: acquiring operation states of various types of source loads and power distribution equipment in a power distribution network; capturing a collaborative prediction relationship between the source load and the power distribution equipment by using a multivariable time sequence coupling model; constructing an electric power AI large model, and pre-training the electric power AI large model by using an autoregression method; performing parameter fine tuning on the pre-trained electric power AI large model by adopting a plurality of fine tuning technologies; and deducing the situation of the power distribution network according to the operation states of the source load and the power distribution equipment and the collaborative prediction relationship between the source load and the power distribution equipment by using the fine-tuned power AI large model. According to the method, on the premise that large-scale flexible resources are contained and the flexible resources continuously respond to the interaction strategy of the power distribution network, calculation intelligent deduction of multi-type source-load interaction of the short-term operation situation of the power distribution network can be achieved.

The invention discloses an energy efficiency evaluation method and system for a source network load storage multi-element load, relates to the technical field of energy efficiency metering, and aims to solve the energy efficiency metering problem of the source network load storage multi-element load, and the method comprises the following steps: S1, calculating an optimal configuration scheme of an intelligent sensing device, and forming an industrial park energy efficiency metering general scheme; s2, performing adaptive synchronous sampling on each energy consumption measurement node to obtain equipment energy consumption data; s3, training a load energy consumption prediction model and a photovoltaic output prediction model based on the equipment energy consumption data to form an overall energy efficiency prediction model; and S4, according to the energy efficiency metering general scheme and the overall energy efficiency prediction model, forming an industrial park energy efficiency metering and intelligent analysis system, and realizing energy consumption prediction of different links of source network load storage of the industrial park and overall energy efficiency metering and evaluation of the industrial park.

The invention discloses an intelligent low-carbon service model and method based on block chain fusion, particularly relates to the technical field of low-carbon management, and is used for solving the problem that the credible recognition and settlement of carbon behaviors are not clear. According to the method, identity verification, time marking and caliber standardization are carried out on energy consumption and travel multi-source data through a trusted data gateway, a carbon behavior voucher containing a signature and an abstract is generated, minimum disclosure under an index upper chain and an original text chain is adopted, it is ensured that the source and the caliber are verifiable and cannot be tampered, and then merging is carried out through a unified event model; and performing conflict resolution and consistency verification to form a one-event one-key event, attaching a consistency label and a credibility index, performing calculation by a rule base and a scene parameter set, performing intelligent contract accounting, and providing rollback correction by using a unique identifier of the event, enabling the credibility index to participate in integral and right confirmation, performing low-credibility entering and re-verification, and suppressing abnormities. And the fusion accuracy and the auditing efficiency are improved.

The invention discloses a multi-modal fusion vehicle dynamic sensing method, device and equipment and a storage medium, and the method comprises the steps: calculating the depth confidence of an intelligent driving vehicle, and dynamically adjusting the weight of each sensor according to the depth confidence; performing multi-sensor fusion according to the sensor weights to obtain fusion features; the fusion features are input into a self-supervised BEV generation module, a BEV feature map is generated, vehicle dynamic perception is performed according to the BEV feature map, and a failure protection mechanism is triggered according to depth confidence, so that multi-sensor fusion environment adaptive optimization can be realized, and perception robustness and feature consistency can be significantly improved in complex scenes such as severe weather or high-density traffic; the generated BEV feature map can effectively unify the view angle of multi-source sensor data and provide high-precision and low-delay environment perception input for intelligent driving, so that the adaptability and decision reliability of a system in a dynamic environment are greatly enhanced while the driving safety is guaranteed.

The invention provides a smart financial product recommendation method and device, equipment and a storage medium, and the method comprises the steps: firstly generating a key pair and a digital ID according to a user certificate, and constructing a safety identity identification system; and obtaining user financial data through the digital ID, obtaining a credit value by combining a credit calculation intelligent contract, and screening selectable financial products from a product library. And then the data is stored in a distributed manner according to business departments to form a multi-node computing architecture. And each node adopts a K-means + + algorithm to perform initial clustering and calculate model parameters, and optimal model parameters are determined through target function evaluation and neighbor node parameter migration. And calculating the posterior probability of the user data point based on the optimal parameter, determining the category of the user data point, and outputting a clustering result. And finally, generating a grouping label according to a clustering result, and screening and recommending financial products from the selectable products. According to the method, through multi-technology fusion, accurate grouping and recommendation are realized while the data security is guaranteed, and the recommendation efficiency and reliability are improved.

The invention belongs to the field of automatic medicine feeding machine control and computational intelligence and optimization algorithms, and discloses an automatic medicine feeding machine medicine feeding method based on a particle swarm optimization algorithm, which comprises the following steps: initializing particles of the particle swarm optimization algorithm according to the residual capacity of a medicine bin and a medicine storage rule, and according to the use frequency of the medicine bin and the frequently-used degree of medicines, calculating the medicine feeding efficiency of the medicine bin; adjusting the positions of the particles obtained by partial initialization; an objective function is constructed based on the number of remaining effective days of the medicine, the medicine bin space utilization rate and the medicine feeding efficiency, the fitness value of each particle is calculated according to the objective function, and the individual optimal position and the global optimal position are determined; updating the speed and the position of the particle; outputting a global optimal position as an optimal medicine applying scheme after an iteration ending condition is met; and the automatic medicine feeding machine completes the medicine feeding operation of the medicine to be fed according to the optimal medicine feeding scheme. The medicine storage position is optimized, the medicine feeding efficiency is improved, and medicine changes can be dynamically adapted.

The invention provides a data processing method, a data processing device, a computer storage medium and electronic equipment, and relates to the technical field of communication. The method comprises the following steps: in response to receiving a service request sent by user equipment for a target service, performing demand analysis on the service request based on a session management function (SMF) network element to obtain a service type demand of the target service; the SMF network element determines a to-be-coordinated network element based on the service type demand; a to-be-coordinated network element receives a service request and a service quality demand parameter corresponding to a target service, and obtains a node state parameter of an initial computing node matched with the to-be-coordinated network element, thereby determining a target computing node from the initial computing node based on the service request, the node state parameter and the service quality demand parameter, and sending the target computing node to the to-be-coordinated network element. And providing services for the target business based on the target computing node. According to the invention, multi-element cooperation of communication, calculation, intelligence and the like can be realized, and then integrated fusion services of communication services, calculation services, intelligent services and the like are provided for users.