2680 results about "Distribution system" patented technology

The invention discloses a digital twinborn-based irrigation area water distribution scheduling method and system, and relates to the technical field of water distribution scheduling, and the method comprises the steps: collecting data, and constructing a digital twinborn model; dynamically simulating water resources in an irrigation area by using a hydrological model simulation technology based on a digital twinborn model; performing irrigation demand prediction by using a machine learning algorithm according to a simulation result; optimizing a water distribution scheduling model according to a prediction result; performing real-time scheduling control according to the scheduling strategy; performing scheduling effect evaluation according to the scheduling control data; adjusting a scheduling strategy according to an evaluation result; and performing irrigation area water resource management and long-term optimization by using an irrigation area water distribution scheduling system based on digital twinning according to an adjustment result. According to the method, the limitation of static weight distribution of a traditional method is overcome, the accuracy and flexibility of water distribution scheduling are improved, the actual requirements of a complex irrigation area environment are met, the maintenance cost and the operation risk are reduced, and efficient and stable operation of an irrigation area water distribution system is guaranteed.

The invention relates to the technical field of intelligent scheduling, in particular to a logistics storage task distribution system based on AI. According to the method, through fine calculation of energy consumption data of the equipment in the storage area and dynamic adjustment of the path energy consumption gradient, the material carrying path is optimized, the use frequency of the high-energy-consumption path is reduced, and the energy efficiency ratio of equipment operation is increased. In combination with density analysis of task execution time, task execution conditions in different time periods are monitored, the concentration degree of tasks in peak periods is identified, potential congestion risks are predicted based on trend analysis of task density, a task execution window is reasonably adjusted, and operation delay caused by excessive concentration of tasks is reduced. And analyzing the execution sequence of the storage tasks, and adjusting the tasks with close dependency relationships so that the tasks can be executed at reasonable time points. In the dynamic task allocation process, simulation optimization is carried out by using a reinforcement learning method, so that task allocation is more intelligent, and the overall operation cooperation capability of the warehouse is improved.

The invention relates to the field of intelligent recommendation, and particularly discloses an intelligent logistics distribution system and method based on an Internet of Vehicles system, which obtains goods label information, position information of a plurality of preset time points in a preset time period and vehicle sensing data information, and deep learning and cloud computing technologies are adopted to carry out semantic analysis and time sequence association on the cargo label information, the position information and the vehicle sensing data information, so that a reasonable vehicle speed value is generated and recommended to a driver. Through the method, the relationship among various data can be better understood by adopting deep learning and cloud computing technologies, so that the recommended vehicle speed value is more accurately generated, intelligent scheduling of logistics distribution is realized, the cargo safety and the vehicle stability are ensured, the distribution route is optimized, and the distribution efficiency is improved.

The invention relates to the technical field of digital relays, and discloses a digital relay telephone traffic real-time acquisition and intelligent distribution system for emergency scheduling, which executes the following steps through five modules: receiving and normalizing UDP (User Datagram Protocol) data through a time domain buffer management module, completing space-time matching of telephone traffic and media streams through a cross-protocol association engine, and completing real-time transmission of the telephone traffic and the media streams. And unsuccessful data triggers a reverse tracing service for remedy, the intelligent scheduling controller performs grading processing according to the emergency degree and dynamically deploys resources, and finally the data is intelligently distributed to a message queue cluster through an elastic load balancing module. By constructing a multi-level heterogeneous data processing framework and dynamic redundancy transmission, intelligent space-time alignment and elastic resource scheduling mechanisms, accurate routing of first-aid instructions and zero-loss guarantee of key data are achieved, meanwhile, the self-adaptive fault-tolerant capability of the system to abnormal working conditions such as equipment clock skew and burst flow is enhanced, and the system reliability is improved. And high-availability technical support is provided for digital upgrading of emergency dispatching.

The invention relates to the technical field of digital marketing, and discloses an omni-channel marketing funnel intelligent attribution model and an automatic budget distribution system, and the model comprises a data collection module which is used for collecting data through a contact recording device and a user identification device which are disposed at a marketing terminal, and a behavior tracking device and a channel classification device which are disposed at a user side. Acquiring information such as multi-channel marketing contact interaction data, user behavior data and channel types; the path analysis module realizes data path analysis through time sequence synchronization of a time alignment unit, feature matching of a path association unit and dynamic adjustment of a computing power scheduling unit; the attribution calculation module generates the conversion attribution weight of each channel based on the matching data; and the budget mapping module establishes a dynamic mapping relationship between the weight and the budget and updates the dynamic mapping relationship. The system outputs the attribution result through the model application module, and the budget execution module distributes the budget according to the dynamic mapping, so that the channel contribution can be accurately evaluated, the scientific distribution of the marketing budget is realized, and the marketing efficiency and the rate of return on investment are improved.

The invention relates to the technical field of intelligent power distribution system state monitoring, in particular to a cable connection state online monitoring method for a low-voltage cable branch box, which comprises the following steps of: applying a disturbance excitation signal to a connection node of the cable branch box, and acquiring transient voltage, current and phase response parameters of the node in different excitation periods; establishing a mapping relation between the node electrical characteristics and the connection state; performing dynamic reference calibration on the acquired signals based on a multi-node space-time coupling matrix to form an adaptive judgment reference of electrical coupling between nodes; a connection degradation sensitive index is extracted through feature sequence differential calculation, a connection state quantitative evaluation result is generated, and real-time updating and anomaly recognition are carried out according to the state change trend; according to the method, the problems of misjudgment and delay caused by a monitoring mode depending on indirect parameters such as temperature and current are solved, high environmental adaptability and degradation identification precision are achieved, and the operation safety and monitoring reliability of the low-voltage power distribution system are greatly improved.

Systems, devices, and techniques are disclosed for network security policy generation and distribution. A security policy written using a Domain Specific Language (DSL) for network security may be received. The security policy may be associated with a service owner and a control plane. A representation of the security policy may be generated from the security policy. A configuration bundle of the service owner may be updated with the representation of the security policy. The security policy may be determined to be approved. A rule set may be generated from the representation of the security policy. A differential between the rule set and a current rule set may be determined. A security component associated with the control plane based on the differential may be configured.

The invention discloses a dynamic task distribution system based on multi-source data fusion, belongs to the technical field of intelligent task scheduling, and aims to solve the problems of response delay and execution failure caused by undifferentiated rule verification, fuzzy resource matching logic and inaccurate real-time state evaluation in a traditional task distribution system. According to the system, a task request is obtained through a task receiving module, multiple indexes are converted through a data standardization processing module, a rule matching engine carries out multi-rule verification and outputs a matching degree score, a real-time capability evaluation module calculates a current capability value of an executor, and a dynamic decision module dynamically adjusts double weights according to a task emergency degree and generates a comprehensive score. And time, resource and skill conflicts are eliminated through the conflict detection unit, and finally the optimal executor is selected by the task allocation unit to issue the task. The system is suitable for various fields requiring efficient and accurate task allocation, such as grid command center order distribution, emergency response, logistics distribution, maintenance service and the like.

The invention discloses a ship plane segmented welding multi-robot task distribution system based on digital twinning, and relates to the technical field of ship intelligent manufacturing, and the system comprises a four-dimensional digital twinning body module which is used for collecting workpiece assembly errors, welding seam positions and robot state data in real time; and constructing a twin model comprising a physical engine layer, a process knowledge base and a dynamic topological graph. According to the ship plane segmented welding multi-robot task allocation system based on digital twinning, workpiece assembly errors, robot motion states and welding process rules are fused in real time through the digital twinning body, a weld joint task topological graph is dynamically constructed, and multi-robot task accurate allocation is achieved in combination with an anti-conflict auction algorithm. By means of a space-time gridding cooperation mechanism, motion conflicts between a large gantry mechanism and a small gantry mechanism are effectively solved, and zero interference of a welding path is guaranteed; the local path optimization module adaptively adjusts the posture and the dry extension length of a welding gun for a narrow space, and eliminates the physical collision risk.

The present invention discloses a charging stack power distribution system and method, and relates to the technical field of charging stacks. The system includes a main control module, at least one charging component, at least one contactor component and multiple charging terminals, the charging component includes multiple charging modules, the contactor component includes multiple contactors, each charging terminal is directly connected to at least one charging module, and is connected to another charging module through at least one contactor, the main control module is used to determine the combined charging module from other charging modules according to the terminal identifier of the target charging terminal; determine the target contactor identifier according to the module identifier and charging status of the combined charging module; control the contactor corresponding to the target contactor identifier to close. The present invention can dynamically adjust the power distribution between charging modules according to the charging demand, effectively integrate the output power of multiple charging modules to meet the charging demand of a single charging terminal, while maintaining the flexibility and efficiency of the system.

A simulation system of a railway system for a Medium-Voltage Direct Current (MVDC) distribution network includes: a computing device which stores a simulation program including a converter station model configured to simulate a converter station included in the MVDC distribution network and a train operation model configured to simulate an operation state of a train that is supplied with power from the converter station and runs; a converter controller which controls a DC / DC converter included in the converter station model; and a Hardware In the Loop Simulation (HILS) device which performs a simulation based on the converter station model, the train operation model, and the converter controller. Herein, the converter station model and the train operation model are executed in software by the HILS device, and the converter controller is connected to the HILS device and driven to control the converter station model.

The invention relates to the technical field of distributed energy storage system control optimization, and discloses a cooperative control and energy optimization distribution system of a distributed energy storage system, and the system comprises an energy storage node data sensing module which obtains a basic sensing unit and a state data collection interval through hierarchical division of an energy storage node operation environment; the multi-source information fusion processing module collects multi-type energy storage operation information according to a preset period, and generates time sequence fusion information; a cooperative control strategy modeling module constructs a state abstract model according to the state abstract model and extracts multi-dimensional control features; the energy distribution state evaluation module identifies a local distribution state and determines an overall distribution state; the dynamic optimization strategy generation module dynamically adjusts energy distribution parameters, and optimizes an energy storage operation environment in real time to obtain a real-time distribution state. According to the system, accurate cooperative control and efficient energy distribution of the distributed energy storage system are realized, and the operation performance of the system is improved.

Data integration and distribution systems. A system includes a graphical user interface (GUI). Weather and market data are collected. A weather symbology including symbol elements linked to segments of the collected weather data and rules for generating weather symbology instructions are stored. The GUI is generated for display on a user device. A weather symbology instruction is determined based on at least one requested symbol element indicated in a weather data request and the rules. A weather forecast dataset is created from among the collected weather data based on the weather symbology instruction. A presentation package including the weather forecast dataset and the collected market data is generated such that the weather forecast dataset is integrated with the collected market data. The presentation package is presented on the GUI and updated concurrent with changes to at least one of the weather data, the market data and user input.

The invention provides a remote intelligent control distribution box, which belongs to the technical field of distribution boxes and comprises a control chip, a wireless communication device, a current monitoring device, a voltage monitoring device, a temperature monitoring device, a leakage protection device, a circuit breaker state monitoring device, a grounding state monitoring device, a humidity monitoring device and a power management device. A control module is arranged in the control chip and is used for acquiring output data of the current monitoring device, constructing a linear temperature rise prediction model, identifying potential fault types by using a decision tree algorithm, operating a lightweight power distribution network sensing model to process dimension-reduced time sequence data and realizing low-calculation-amount feature extraction by adopting a sparse attention mechanism; and when the system health state risk score exceeds a threshold value, a protection strategy is adjusted, and fault precursor characteristics are recorded. The technical problem that early warning of the abnormal state of the power distribution system is difficult to realize in a low-computing-resource environment is solved.

The invention provides an optimal design method of an ultralow-temperature multi-load heat insulation support, a support assembly and an experimental research method, and relates to the field of large superfluid helium low-temperature distribution systems. Comprising the following steps: selecting thickness parameters of a casing pipe in each stage of temperature zone of a supporting assembly and a supporting panel as optimization design parameters to carry out parametric modeling, and introducing a stress maximum value and a heat leakage value as target values; and based on the thickness parameters of the cold shield sleeve and the supporting panel of each stage of temperature zone, taking a genetic algorithm as an optimization function, performing iterative calculation in combination with simulation software, obtaining the thickness parameters of the cold shield sleeve and the supporting panel under the optimal boundary, determining the heat leakage value and the maximum stress value of the supporting assembly according to the optimal boundary, and then processing the supporting assembly. By combining various simulation technical means and experimental research, the problems of poor performance of the support assembly, lack of process verification and the like caused by the adoption of a classic model are solved, and the rigidity and robustness of the large-scale superfluid helium transmission and distribution system are effectively optimized and improved.

The invention provides an urban low-altitude unmanned aerial vehicle instant distribution scheduling and path planning method considering demand prediction. The method comprises the following steps: establishing a three-dimensional environment map of an unmanned aerial vehicle distribution system by adopting a grid-topology hybrid modeling strategy based on three-dimensional urban environment map data; according to the three-dimensional environment map of the unmanned aerial vehicle distribution system, the starting point and the target point of the unmanned aerial vehicle, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost are obtained by using an unmanned aerial vehicle path planning algorithm of space-time improvement A *; and according to the order data, the unmanned aerial vehicle performance parameters, the optimal flight path of the unmanned aerial vehicle, the time required by the optimal path and the transportation cost, using the CNN-LSTM-Attention order prediction model and the task allocation model to output a task allocation scheme including a virtual order and an actual order. Accurate spatial data support is provided for path planning of the unmanned aerial vehicle, the reasonability of low-altitude logistics distribution path planning is improved, and finer data support is provided for urban airspace management.

The invention relates to the technical field of smart home, and discloses a smart home real-time energy consumption monitoring and dynamic distribution system, which comprises a data acquisition module, a spatial-temporal feature fusion module, an energy distribution module and a dynamic control module. The data acquisition module acquires household energy supply and demand real-time data, the spatial-temporal feature fusion module processes the data to generate an energy state feature matrix, the energy distribution module constructs a model to optimize an energy distribution sequence according to the matrix, and the dynamic control module constructs a hierarchical control framework to complete energy dynamic distribution. According to the system, energy can be accurately and efficiently distributed, and the power utilization cost is reduced according to a real-time electricity price and equipment energy consumption optimization strategy. The service life of the energy storage battery is effectively prolonged by considering the charge-discharge cycle times of the energy storage battery. Meanwhile, the decision-making layer, the scheduling layer and the execution layer cooperate to improve the intelligent level of equipment management. The execution layer introduces a fault-tolerant mechanism to enhance the reliability and stability of the system, ensure stable supply of household energy and improve the user experience.

The invention discloses a flexible power distribution network interaction energy control method supported by a reconfigurable intelligent soft switch, and the method comprises the following steps: 1, constructing a reconfigurable intelligent soft switch MOP constraint, and building a power flow model of a flexible power distribution system based on the reconfigurable intelligent soft switch support; 2, constructing a power distribution network operator model, and constructing a reconfigurable intelligent soft switch MOP constraint and a power grid operator constraint; step 3, constructing a production and disappearing person model, obtaining a target function of a production and disappearing person, and constructing a production and disappearing person constraint; and 4, carrying out model linearization and relaxation processing. According to the invention, the power flow of the flexible power distribution network based on the reconfigurable intelligent soft switch is accurately regulated and controlled, and the safety guarantee capability is enhanced; a source-load multi-resource refined time sequence space-time coordinated regulation and control model is provided, so that the voltage safety under large-scale distributed photovoltaic access can be guaranteed, and the economy of system operation can be improved, namely the safe and economic operation performance of the system is improved.

The invention discloses an AI data scheduling distribution system and method based on cloud computing, and relates to the technical field of intelligent cloud computing, and the system comprises an AI data collection module, a feature extraction module, a decision parameter generation module, a scheduling distribution module and a visual feedback module. The AI data acquisition module acquires equipment video data and temperature data, the feature extraction module extracts video temperature features, the decision parameter generation module outputs equipment vibration intensity and temperature rise rate, and the scheduling distribution module calculates equipment shutdown probability and node load score and triggers a shutdown instruction and a task migration decision. And the visual feedback module maps the equipment health state and node load distribution, and optimizes the feature weight. Through multi-modal data fusion and a closed-loop feedback mechanism, accurate early warning of equipment abnormity and dynamic optimal allocation of resources are realized, visual decision assistance and adaptive weight adjustment are combined, equipment safety and cluster load balancing efficiency in a cloud computing environment are improved, and manual intervention requirements and operation and maintenance cost are reduced.

Disclosed are twin-based systems and methods for predicting solar power generation and optimizing power generation and distribution processes. Employed are a digital twin model of a solar power plant, which includes detailed representations of various components, such as solar panels, inverters, and transformers, as well as real-time weather data and historical data. This advantageously allows for accurate simulations of plant performance under various weather conditions and operational scenarios. Our systems and methods Incorporate novel machine learning algorithms that are trained on historical and real-time data from the digital twin model, weather data, solar power generation data, and other relevant factors. These algorithms utilize an advanced ensemble learning approach, which combines multiple predictive models, such as deep learning, support vector machines, and decision trees, to achieve higher accuracy and robustness in predicting solar power generation

A system includes an application generating website building system (AGWBS) for converting a website to an application (app) which operates on a mobile device. The AGWBS includes at least one database storing at least one layout option for the app and BI (business information) for the website, where the BI includes at least use of settings and features by a designer of the website and of apps of the AGWBS from previous editing sessions and an app-editor to determine an editing mode for a designer of the app according to at least the use of settings and features and to provide at least one layout option from the at least one database for the app according to the editing mode and to receive modification to a designer selected layout.

The invention discloses an electric power big data automatic reasoning platform and an electric power distribution system, and relates to the technical field of electric power systems, and the platform comprises a knowledge acquisition unit which collects and preprocesses electric power internal and external data, and converts the data into structured data through a knowledge processing unit; organizing the triple data by the dynamic knowledge graph construction unit to form an initial graph; the updating unit monitors a new dynamic state in real time and corrects or adds atlas content; the reasoning unit is based on the dynamic graph, power fault accurate reasoning and future demand trend prediction are achieved through a fault diagnosis and demand prediction algorithm, efficient integration and dynamic evolution of power knowledge are achieved through construction and real-time updating of the dynamic knowledge graph, timeliness and accuracy of the graph are ensured, and reliable data are provided for fault diagnosis; through a closed-loop architecture of data acquisition-inference analysis-scheme making-execution control, an intelligent sensor and an optimization algorithm are combined to generate a distribution scheme adaptive to a scene, and the power distribution adaptability and economy are improved.

A gas-liquid contactor includes at least one packing section, a liquid distribution system, one or more liquid collection devices, and a fan. The at least one packing section includes a plurality of packing supports. Each packing support is removably attached to at least one other packing support and includes a structure defining a bottom surface and a packing perimeter and a plurality of structured packings positioned on the bottom surface within the packing perimeter. The structure includes at least one first attachment and at least one second attachment. The liquid distribution system is configured to flow a carbon dioxide (CO2) capture solution through at least one packing section. The one or more liquid collection devices include a bottom basin positioned beneath the at least one packing section. The fan is operable to form a CO2 lean stream and flow the CO2 lean stream from the at least one packing section.

The invention discloses an oil-water well acidification continuous construction electric control system based on multi-parameter monitoring, and relates to the technical field related to acidification control in oil-gas field development, and the system comprises a multi-source data fusion monitoring module which integrates wellhead pressure, temperature, flow, pH value, H2S concentration and acid liquid conductivity, and an underground microwave imaging sensor carries out multi-physical quantity collection; a multi-objective optimization controller is designed, and the acid etching depth, the crack flow conductivity and the construction cost are balanced; the self-adaptive construction control module is based on a variable-frequency and variable-speed acid pump system controlled by a fuzzy sliding mode and is used for realizing stepless regulation of displacement; an intelligent shunting system is arranged, and acid liquor distribution is dynamically adjusted according to the suction volume of each layer section. According to the variable-frequency acid pump system based on fuzzy sliding mode control, stepless adjustment of the displacement is achieved; the intelligent flow distribution system dynamically prepares liquid according to the suction volume of each layer section; and through a three-stage safety interlocking mechanism, power can be cut off and emergency acid discharge can be started within 0.5 second when the temperature rise is greater than or the pressure suddenly changes, so that the well control risk is reduced.

The invention discloses an intelligent charging priority distribution system and method based on multi-device identification, and particularly relates to the technical field of charging priority distribution. Equipment identity and charging demand information are obtained through an intelligent identification technology, a priority score is calculated in combination with a preset weight parameter, the real-time performance of priority distribution is ensured by adopting a dynamic threshold adjustment strategy, power demand fluctuation in a short time in the future is predicted based on a load prediction algorithm of machine learning, power distribution is optimized in advance, and the power distribution efficiency is improved. The method comprises the steps of reducing instantaneous load impact, monitoring equipment power in real time in the charging process, comparing the equipment power with the maximum distributable power, and dynamically adjusting the charging power or switching the charging sequence through a self-adaptive power adjustment mechanism if abnormality is detected, so as to guarantee the stability of a power grid and the safety of the equipment, and the method can effectively improve the utilization efficiency of charging resources and reduce the energy consumption. Overload of a power grid is prevented, multi-device charging scheduling is optimized, and safety, reliability and intelligence of the charging process are ensured.

The invention discloses a task load balanced distribution system and method based on a node resource state, and relates to the technical field of distributed computing, and the method comprises the steps: collecting and associating multi-dimensional node resource data and dependency relationship data between tasks; establishing a virtual synaptic connection for each pair of tasks with data interaction, and constructing a task-dependent neural network model; the node resource state is monitored in real time, and the synaptic connection strength is adjusted; dynamically generating an optimal task allocation strategy; when the node resource saturation is continuously too high or the task execution efficiency is obviously reduced, starting a task migration process; and in the migration process, a synaptic cache region is created in the intermediate node, interaction data is cached, a dependency task node mapping relation is updated, data is synchronized, and the collaboration efficiency is verified. According to the method, CPU and GPU resource states and task dependency are considered at the same time, accurate matching and cooperative scheduling are achieved, overload or idling of a single resource is avoided, and the resource utilization rate is increased.

The invention discloses a power supply and distribution control system based on a virtual power plant, and relates to the technical field of power distribution systems. The system comprises five core modules: a digital twin simulation control closed loop module constructs a complete closed loop of data acquisition, simulation calculation, control execution and model iteration, and realizes real-time synchronization of a physical system and a digital mirror image and accurate issuing of a control instruction; the multi-agent source-load-storage optimization module improves the source-load-storage collaborative scheduling efficiency through distributed agent collection and reinforcement learning; the multi-scene energy storage adaptive control module generates a scene-based energy storage strategy based on meteorological, load and electricity price multi-dimensional perception and model prediction control; and the multi-type energy storage ratio linkage module realizes multi-type energy storage cooperative operation based on an energy storage characteristic database and scenarized weight optimization. All the modules work cooperatively, the system response speed, the control precision and the power supply stability are effectively improved, and the system is suitable for industrial parks and other complex power supply and distribution scenes.

The embodiment of the invention discloses a logistics distribution method and device based on an unmanned aerial vehicle cluster, equipment and a medium. The method comprises the steps of obtaining to-be-processed order information and state information of the unmanned aerial vehicle cluster; determining a to-be-delivered task based on the order information, and determining a target unmanned aerial vehicle from an unmanned aerial vehicle cluster based on the delivery information and the state information; determining a flight strategy of the target unmanned aerial vehicle based on a preset relay link, and controlling the unmanned aerial vehicle to execute a distribution task according to the flight strategy; and monitoring real-time state information of the target unmanned aerial vehicle, and determining a real-time control strategy of the target unmanned aerial vehicle based on the real-time state information so as to adjust a flight strategy of the target unmanned aerial vehicle based on the real-time control strategy. According to the embodiment of the invention, an efficient, flexible and safe unmanned aerial vehicle distribution system is constructed, and a solid technical support is provided for large-scale commercial application.

The invention relates to the technical field of automatic warehouse material, and discloses an intelligent goods allocation distribution system for automatic warehouse logistics, and the system comprises a goods allocation data collection module; a dynamic state detector; a multi-source data fusion unit; a three-dimensional storage modeling module; intelligently distributing a strategy engine; and a PLC control interface. According to the intelligent goods allocation allocation and scheduling system for automatic warehouse logistics, a goods allocation allocation scheme is generated through a multi-target Pareto optimization algorithm, the balance between carrying distance minimization min sigma alpha * Di and space utilization rate maximization Ut is achieved, and compared with a proportional static allocation rule, the warehouse space utilization rate is increased to 82%; a dynamic grid map technology is integrated, and a track cross risk area (red early warning grid) of the stacker and the AGV is marked in real time, so that the equipment collision probability is reduced by 90%; and in combination with a goods relevance knowledge graph, special goods such as medicines are clustered and stored according to temperature zones, and the energy consumption of cross-temperature-zone operation of equipment is reduced by 35%.

The invention relates to an energy efficiency optimization method for a dynamic load of a central air conditioner, which comprises the following steps of: establishing a response relationship among a cold and heat source, a transmission and distribution system and end equipment, identifying a nonlinear decoupling characteristic of a cold and heat response behavior by monitoring operation deflection of each level of equipment under a low-load working condition, and defining and updating a cooling capacity response deviation threshold in real time; on the basis of the cooling capacity response deviation threshold, delay compression control is conducted on response actions of the refrigerant side and the air medium side, and by adjusting the air side response time sequence in advance, dynamic thermodynamic equilibrium is formed between refrigerant transfer delay and air medium response in advance; by establishing the response relationship among the cold and heat source, the transmission and distribution system and the end equipment and introducing a cold capacity response deviation threshold dynamic updating mechanism, nonlinear coupling mismatch in a cold and heat transmission chain can be accurately identified, so that the system is more flexible to operate under low-load and variable-load conditions, frequent start and stop or redundant operation of the cold and heat source is avoided, and the system reliability is improved. And therefore, the overall COP value of the system is remarkably increased, and unit cooling capacity consumption is reduced.