426results about How to "Optimize allocation" patented technology

The invention relates to the technical field of loaders, in particular to a loader multi-sensor data fusion fault diagnosis and early warning method, which comprises the following steps: S1, multi-source data acquisition and preprocessing: acquiring running state data of a loader in real time; s2, multi-modal data fusion: carrying out multi-modal data fusion on the preprocessed operation state data to form comprehensive data; s3, energy flow fault propagation prediction: analyzing energy flow distribution of each component in real time, and identifying a potential fault component and a propagation path thereof; and S4, fault assessment and graded early warning: comprehensively assessing the fault risk of the loader, and generating a graded early warning signal according to a set multi-stage early warning rule. According to the invention, the operation efficiency and safety of the loader are improved, the distribution of maintenance resources is optimized, the operation cost is reduced, the overall reliability of the loader is further improved, and the service life of the loader is further prolonged.

The invention discloses a control method and device of a building cold and heat source system self-adaptive to load requirements. The control method comprises the following steps: acquiring historical operation data, physical characteristics and equipment system data of a building, and establishing a building load prediction model; generating the hourly load curve, and predicting the cooling or heating demand of each time period in the future according to the curve; dynamically adjusting the refrigerating or heating load demand, and generating a corresponding cooling or heating curve; and constructing a machine learning algorithm based on an equipment mechanism model, optimizing a control strategy of the cold and heat source system based on the historical operation data of the equipment, and outputting control parameters of the equipment. According to the technical scheme, the load of the building cold and heat source system is intelligently predicted and adjusted, the energy efficiency of the system is improved, invalid energy consumption is reduced, energy utilization is optimized, control parameter optimization is achieved through a machine learning algorithm, efficient operation is ensured, indoor comfort is kept, and the purpose of saving energy is achieved.

The invention relates to the technical field of smart power grids, and discloses a distributed renewable energy source and energy storage cooperative scheduling system of Internet of Things data, comprising a data acquisition and monitoring module used for acquiring the running states of distributed energy sources and energy storage units through the Internet of Things technology; the energy supply and demand prediction module is used for predicting energy supply and load demand according to historical data, real-time data and meteorological information and generating basic data of a scheduling strategy; the collaborative scheduling strategy module formulates a collaborative scheduling scheme of the renewable energy and the energy storage unit based on energy supply and demand prediction, and adjusts the charging and discharging time of the energy storage unit; the double-layer optimization decision module is responsible for long-period energy storage planning and short-period real-time scheduling optimization; and the real-time monitoring and feedback module dynamically adjusts a scheduling strategy based on feedback data. According to the invention, through real-time monitoring, supply and demand prediction and cooperative scheduling of renewable energy sources and energy storage equipment, the system can optimize distribution and use of energy to the greatest extent, and energy waste is reduced.

The invention relates to the field of parking lot management, in particular to a parking management method and system based on data analysis. The method comprises the following steps: acquiring real-time state information of a parking lot and a historical parking monitoring log of the parking lot; analyzing the dynamic characteristics of the vehicle in the fixed parking space according to the real-time state information of the parking lot, and predicting the available time so as to generate the predicted available time of the fixed parking space; acquiring traffic flow data of a target parking lot area; performing multi-section traffic flow analysis on the traffic flow data of the target parking lot area, and performing traffic flow time sequence trend evolution to generate real-time road state characteristics of the target area; and performing time sequence parking demand analysis on the historical parking monitoring log of the parking lot, and performing dynamic parking demand prediction of the target parking lot according to the real-time road state characteristics of the target area so as to generate a dynamic parking demand prediction value. According to the invention, parking lot management with high efficiency and high utilization rate is realized.

The invention relates to the technical field of energy storage configuration, in particular to an energy storage configuration method and system for photovoltaic power generation. The method comprises the following steps: acquiring information data of a photovoltaic power generation energy station; performing distributed synchronous energy storage network construction on the photovoltaic power generation energy station information data to obtain a distributed synchronous energy storage network; performing real-time power generation data acquisition on the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; performing photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data; performing historical electricity load data acquisition on the distributed synchronous energy storage network to obtain historical electricity load data; and performing load curve curvature trend analysis on the historical electricity load data to generate load curve curvature trend data. According to the invention, through real-time photovoltaic power generation prediction, load demand dynamic analysis and adaptive energy storage strategy optimization, the accuracy and flexibility of photovoltaic energy storage configuration are improved.

The invention relates to an electric vehicle self-adaptive energy recovery method and system fusing driver behaviors, and the method comprises the following steps: analyzing the current driving data and historical driving data of a vehicle, judging a driving style, and predicting the occurrence probability of the driving style; according to the current driving data of the vehicle, the mass of the whole vehicle is estimated, and two values of the gradient are calculated through two different algorithms respectively; setting a weight according to a preset vehicle speed critical value, and weighting the two values to obtain a fusion gradient; according to the current driving data of the vehicle, the whole vehicle mass, the fusion gradient and the driving style occurrence probability, corresponding correction factors are determined respectively, the initial braking torque corresponding to the current motor rotating speed is corrected to obtain a reference braking torque, and the reference braking torque is compared with a preset motor torque range; and the optimal braking torque is obtained, braking is executed, and energy recovery is achieved. According to the method, distribution of regenerative braking energy is effectively optimized, and the energy recovery rate is increased.

The invention relates to the related technical field of integrated service management, in particular to a digital intelligent technology and software and hardware integrated service management method and platform and a storage medium, and the method comprises the steps: connecting an industrial internet, receiving integrated service requirements, deploying software and hardware based on specifications, fitting a service scene, setting a topological architecture conforming to the specifications, and collecting data to establish a performance model. According to the software and hardware matching topology architecture, a communication architecture is adopted, a monitoring mechanism is set, and abnormal response management is performed on each component and each service in the software and hardware matching topology architecture, so that the technical problems that a service scene is fixed and a static deployment mode is difficult to quickly respond to dynamically changing service requirements in a software and hardware integration process are solved; the technical effects that the industrial internet is connected, the integrated service demand information is received in real time, changes of service demands are flexibly coped with in the software and hardware configuration process, dynamic configuration and adjustment are carried out according to different service scenes and demands, service changes are rapidly responded, and the flexibility and adaptability of resource configuration are improved are achieved.

The invention relates to a data verification method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining to-be-verified data, and determining a verification priority corresponding to the to-be-verified data based on a data type of the to-be-verified data; dividing the to-be-verified data into a plurality of to-be-verified data sets according to the verification priority; determining a target verification mechanism corresponding to each to-be-verified data set based on the verification priority corresponding to each to-be-verified data set, and determining a target verification strategy corresponding to each to-be-verified data set based on a corresponding relationship between the verification mechanism and a preset verification strategy; and according to the target verification mechanism and the target verification strategy corresponding to each to-be-verified data set, performing data consistency verification on each to-be-verified data to obtain a data verification result. By adopting the method, the efficiency of data verification can be improved.

The invention relates to the technical field of tea garden disease and insect pest monitoring, in particular to a tea garden disease and insect pest monitoring method and system based on photovoltaic light supplement. The method comprises the following steps that photovoltaic light supplementing equipment is arranged, and tea garden environment data are obtained; carrying out photovoltaic light supplement operation according to the tea garden environment data, and carrying out tea tree image acquisition through camera equipment to obtain tea tree image data; tea tree trunk feature extraction and tea tree tea feature extraction are carried out according to the tea tree image data, and tea tree trunk feature data and tea tree tea feature data are obtained; performing pest assessment according to the tea tree trunk feature data and the tea tree tea feature data to obtain pest assessment data; and performing pest distribution generation according to the pest evaluation data, the tea tree trunk feature data and the tea tree tea feature data to obtain pest distribution data. According to the invention, an intelligent and automatic tea garden disease and insect pest monitoring and prevention and control system is formed by combining photovoltaic light supplement and disease and insect pest simulation.

The invention provides an energy management system based on a cloud and edge integrated optical storage system. The energy management system comprises a cloud module and an edge module, the EMS controller is used for collecting information of the optical storage equipment module and the load module and uniformly sending the collected data to the cloud module; the cloud module predicts future photovoltaic power generation power / electric quantity through a photovoltaic power generation prediction model by receiving data acquired by the EMS controller; predicting a future load through the load prediction model; analyzing peak and valley time periods of the electricity price through the electricity price management model, and sending prediction data to the edge module; and the edge module carries out energy management through the control logic of energy management according to the battery SOC, the chargeable amount, the photovoltaic predicted power and the generating capacity, and superposition peak-valley arbitrage and safe anti-countercurrent logic. Through the arrangement, the whole energy management system has high intelligence and automation levels, energy distribution and use are optimized, and the energy utilization efficiency is improved.

The invention provides a distributed photovoltaic output power estimation method, and the method comprises the steps: installing a load sensor and a photovoltaic output sensor at an access point, and collecting original data including load data and photovoltaic output data in real time; processing the collected original data; carrying out time synchronization on the load data and the photovoltaic output data; key features are extracted from the cleaned original data, and photovoltaic output is predicted through a time sequence analysis model; inputting load data and photovoltaic output data which are acquired in real time into the trained time sequence analysis model; the time sequence analysis model performs real-time operation according to the load data and the photovoltaic output data, and estimates the photovoltaic output power at the current moment; and based on the estimated photovoltaic output power and the real-time load data, the intelligent coordination controller performs dynamic adjustment, so that the photovoltaic output is matched with the load power. According to the invention, efficient estimation and optimal control of photovoltaic output are realized.

The invention relates to the field of cerebral apoplexy rehabilitation management, in particular to a cerebral apoplexy patient rehabilitation training management system and method based on an internet platform. The stroke patient rehabilitation training management system based on the internet platform comprises a training plan generation module, a training time dynamic adjustment module and a training intensity dynamic adjustment module. According to the method, personalized exercise priority indexes and cognitive priority indexes are calculated according to the specific exercise function recovery degree and cognitive function recovery degree of each cerebral apoplexy patient, the fields in which the patient needs more attention and training are determined, a targeted exercise function training plan and a targeted cognitive function training plan are generated according to the fields, and the training efficiency of the cerebral apoplexy patient is improved. Therefore, the allocation of rehabilitation resources is optimized; the personalized rehabilitation training plan can better meet individual differences of patients, the rehabilitation efficiency is improved, and the recovery progress is accelerated.

The invention relates to the technical field of data storage, and discloses a lossless compression algorithm-based data storage method, which comprises the following steps of: receiving to-be-stored data through a data interface, automatically identifying a data format by using a data analysis tool, and performing feature analysis to obtain the size, the type and the complexity of the data; through deep analysis of data features, the method can flexibly select a proper Huffman coding processing mode, and optimizes a compression process according to different data types and complexity. In addition, a context modeling optimization mechanism is introduced, the coding mode is dynamically adjusted, and the compression ratio and the storage efficiency are further improved; efficient storage and reliability verification of data are ensured through a block storage strategy in combination with metadata management; the problem that an existing compression algorithm is low in efficiency is solved, the integrity and reliability of data storage are improved, and the method is suitable for storage requirements of large-scale data and diversified data.

The invention discloses a task scheduling method and device, a medium and equipment. And obtaining task description information of the calculation task. And inputting the task description information into a pre-trained first prediction model to obtain first execution time of the calculation task output by the first prediction model. And determining a resource scheduling strategy of the calculation task according to the task description information and the first execution time. And executing the calculation task according to the resource scheduling strategy and the task description information. Determining resource utilization information in the execution process of the calculation task, and determining second execution time of the calculation task according to the resource utilization information. According to the second execution time, a resource scheduling strategy of the calculation task is adjusted, and the calculation task continues to be executed according to the adjusted resource scheduling strategy. By dynamically adjusting the resource scheduling strategy of the calculation task, the utilization efficiency of calculation resources and the execution efficiency of the calculation task are improved.

The invention discloses a management system for combined use of multiple battery packs of an electric vehicle, and relates to the technical field of monitoring analysis, and the system comprises an information collection module which is used for collecting basic information corresponding to each battery pack and load demand information corresponding to the electric vehicle; the power distribution module is used for analyzing the basic information corresponding to each battery pack, determining the operation mode corresponding to each battery pack, and further determining the power distribution information corresponding to each battery pack based on the operation mode and the load demand information corresponding to the electric vehicle; the analysis module is used for analyzing and processing the basic information corresponding to each battery pack, performing safety analysis on each battery pack according to an analysis and processing result, and formulating an early warning management strategy based on a safety analysis result; and the early warning module is used for responding to the early warning management strategy and managing each battery pack based on the early warning management strategy. The effect of improving the overall performance of the electric vehicle is achieved.

The invention provides a self-adaptive communication equipment fault prediction and maintenance system based on artificial intelligence. According to the system, data analysis and machine learning technologies are utilized, multi-mode sensor data are analyzed in real time, and a fault prediction model is dynamically adjusted to adapt to equipment state changes. Performance evaluation, intelligent maintenance decision, predictive maintenance and fault diagnosis functions and self-healing capability are integrated, and timeliness and accuracy of maintenance are ensured. Through cloud edge cooperative computing, the system quickly responds to data processing, and the security is enhanced. Continuous updating of the knowledge base is maintained, and the system is optimized to adapt to the new technology. The system improves the reliability and maintenance efficiency of equipment, provides a personalized intelligent maintenance scheme for a user, and has wide application prospects and practical values.

The invention discloses a Beidou-based slope monitoring and early warning system evaluation method, particularly relates to the technical field of system monitoring, and is used for solving the problem that a fault of an existing slope monitoring and early warning system is not found in time. Analyzing a time synchronization function of the Beidou system to realize data synchronization between the target sensors and analyzing automatic calibration conditions of the target sensors; on the basis that the data synchronism is normal and the sensor is reliable to use, the consistency of data fusion is further analyzed through an entropy theory, vibration influence evaluation and adaptability evaluation caused by a vehicle are analyzed, key monitoring is conducted for a high-risk time period, and through comprehensive analysis, the accuracy of data fusion is improved. The system can generate detailed early warning information for a specific monitoring scene, the safety of slope monitoring and the accuracy of early warning are enhanced, the data processing efficiency is improved through the staged processing flow, and disasters and accidents caused by inaccurate data or improper analysis are effectively prevented.

The invention provides a collaborative computing method, system and device based on SIMD and SIMT, and belongs to the technical field of computer processing. The method is applied to a Warp composed of a plurality of SPs, and the Warp comprises an SIMT front end and an SIMD rear end. Each SP corresponds to a vector register combined by a plurality of short registers; generating instruction fetching indication information under the condition that a thread bundle scheduler at the front end of the SIMT determines that a first thread bundle corresponding to the calculation task is in an activated state; obtaining a first cache instruction corresponding to the first thread bundle through an instruction fetching unit, and sending the first cache instruction to a decoding unit at the front end of the SIMT for analysis; obtaining one or more corresponding required operands through an operand collection unit at the rear end of the SIMD; and distributing the required operands to the corresponding calculation management units through the SIMD rear end to execute the corresponding calculation tasks, and writing the calculation results corresponding to the calculation tasks back to the storage unit through the write-back unit.

The invention discloses a topological optimization method for an optical storage integrated AC / DC system based on an adaptive algorithm, and the method comprises the following steps: obtaining an initial topological structure and operation parameters of the optical storage integrated AC / DC system, including photovoltaic power generation, configuration of an energy storage system, operation state parameters of a microgrid, and external environment parameters; optimizing and adjusting the topological structure, and dynamically generating a plurality of candidate topological structures according to system operation data and environment change which are acquired in real time; carrying out analogue simulation on the candidate topological structure, and evaluating the stability, power distribution and energy utilization efficiency of the system under different operation conditions; performing reliability analysis on the optimized topological structure, and monitoring and adjusting the topological structure of the system in real time; and performing simulation verification on the adjusted topological structure to obtain an optimal topological structure. According to the invention, the system stability, reliability and fault processing capability can be improved.

A fire risk dynamic assessment and early warning system based on K-class mean value clustering relates to the technical field of big data and artificial intelligence, and comprises a data acquisition module, a data processing module, a fire risk classification module, a fire risk assessment module, an early warning decision module and a feedback mechanism module. Through combination of clustering analysis and multi-factor evaluation, the system can quickly generate a risk evaluation result and timely give an early warning according to a preset threshold value. The efficient response mechanism can quickly inform related departments when the fire risk is aggravated, so that the safety of personnel and property is guaranteed.

The invention discloses a hydroelectric generating set health state assessment method based on multi-dimensional data, and the method comprises the steps: S1, obtaining the operation data of a hydroelectric generating set, and carrying out the data preprocessing; s2, sensing data extraction is carried out on the preprocessed hydroelectric generating set operation data; s3, performing anomaly detection on the operation sensing data of the hydroelectric generating set; s4, carrying out sensing abnormal frequency statistics on the operation sensing abnormal data of the hydroelectric generating set; s5, thermal efficiency and operation water conservancy efficiency calculation is carried out on the operation sensing data of the hydroelectric generating set, and operation performance index analysis is carried out; s6, performing health state calculation according to the analyzed hydroelectric generating set operation performance index data; and S7, constructing a health assessment model to assess the health state of the hydroelectric generating set, uploading assessment data to the cloud platform, and executing a preset health early warning task. According to the method, the health state is calculated by combining the operation performance index and the sensing abnormal frequency data, so that more comprehensive health assessment is realized.

The invention relates to the technical field of multi-modal sentiment analysis, in particular to a multi-modal sentiment analysis method and system based on pre-training and online learning. The method comprises the following steps: acquiring sentiment analysis multi-modal data, constructing a sentiment analysis model, namely constructing a multi-head attention mechanism to respectively encode text, image and audio modals, and splicing and fusing each encoded modal feature; mining an interaction relationship among all modes in the fusion features by using a feedforward neural network and a multi-head attention mechanism; evaluating the contribution degree of each mode in the joint features to the sentiment analysis task by constructing a full-connection neural network; according to the sentiment analysis method based on the multi-head attention mechanism, the multi-head attention mechanism is constructed to carry out coding processing on each mode, the coded mode features are spliced and fused, and therefore the sentiment analysis accuracy is remarkably improved. The multi-head attention mechanism based sentiment analysis method based on the multi-head attention mechanism has the advantages that the multi-head attention mechanism is constructed to carry out coding processing on each mode, and the coded mode features are spliced and fused.

The invention provides an innovative integrated system which aims at monitoring the workload of medical staff and providing psychological support for patients at the same time. The system collects physiological and behavioral data of medical staff and physiological and psychological state information of a patient in real time through a multi-modal data acquisition technology. And by utilizing an advanced machine learning algorithm, the system can intelligently evaluate the workload of the medical personnel and predict the overwork risk. Meanwhile, according to individual requirements of the patient, the system generates customized psychological support schemes including emotion recognition, psychological education, social support and professional consultation. In addition, the system has the real-time feedback and intervention functions, and it is ensured that medical workers and patients get attention and help in time. The intelligent user interface design improves the user experience, and strict data security and privacy protection measures guarantee the security of user information. According to the design of the system, the efficiency and quality of medical care work are improved, the overwork risk of medical staff is reduced, comprehensive psychological care is provided for patients, the psychological welfare of the patients is enhanced, and overall rehabilitation of the patients is promoted.

The invention discloses a natural disaster risk relieving method and device for a power system, and the method comprises the steps: constructing an hour-level composite cascade event set generation module which is used for generating hour-level data sets in various natural disaster scenes, capturing the cascade effects of different disasters, and generating a data set; data support is provided for power system risk influence quantification and risk mitigation module strategy generation; a distributed resource evaluation module is constructed and used for evaluating the availability and power generation potential of distributed resources in the third-level region, and the power generation potential of the distributed resources in various natural disaster periods is analyzed in combination with weather and disaster factors; a natural disaster influence module is constructed, the influence of various natural disasters on the power system is simulated based on the hour-level composite cascade event set, the damage degree of the disasters on loads, power generation facilities, power transmission and distribution lines and the like is analyzed, and the risk influence on the power system is quantified; and constructing a risk relief module, for an area with risk influence, formulating a disaster emergency scheme based on a three-level cooperation mechanism, and based on the power generation potential of the distributed resource assessment module, by scheduling various distributed resources, repairing the power grid, adjusting the load and reducing the disaster influence, ensuring rapid recovery and stable operation of the power grid.

The invention discloses a large model training and application method and system based on a city brain, and relates to the technical field of artificial intelligence and smart city management. Comprising the steps of 1, collecting and preprocessing data, 2, training a large-scale language model, 3, utilizing the trained large-scale language model to understand and predict city dynamics, and utilizing the large-scale language model to carry out semantic understanding and relation reasoning on city data, and 4, displaying a model analysis and prediction result by using a visual interface, and performing interactive question and answer by using the visual interface. And 5, continuously learning and updating the large-scale language model according to the feedback: continuously learning and improving the large-scale language model according to the newly acquired city data and the user feedback, regularly evaluating the performance of the model, and adjusting the structure and parameters of the model according to the evaluation result.

The invention discloses an artificial intelligence resource scheduling system and method, and relates to the technical field of scheduling systems, and the technical scheme is that the artificial intelligence resource scheduling system comprises a resource scheduling platform module, the resource scheduling platform module comprises a basic-level facility module, a resource management module, an intelligent scheduling module, a scheduling monitoring module, a safe scheduling management and control module and a connection module, the resource scheduling platform module is in data connection with the basic-level facility module, the resource management module, the intelligent scheduling module, the scheduling monitoring module, the safety scheduling management and control module and the connection module, and the connection end of the resource management module is provided with the resource library management module. The resource library management module comprises a resource registration module, a resource library construction module, a resource state monitoring module and a resource dynamic adjustment module, and the system has the beneficial effects that the compatibility and flexibility of the system are improved, the operation and maintenance efficiency is improved, the automation degree is high, intelligent decision making, resource allocation optimization and task scheduling are carried out, the operation cost is low, and the operation efficiency is improved. And resource waste is reduced.

The invention provides a wireless network load prediction method, device and equipment, a medium and a program product, and belongs to the technical field of artificial intelligence, and the method comprises the steps: determining a covariant input vector based on wireless network load data and covariant load data; inputting the covariant input vector into a load prediction model to obtain predicted load data at a first preset moment output by the load prediction model; the load prediction model is obtained by training on the basis of historical covariant input vectors and load data labels of the historical covariant input vectors. According to the wireless network load prediction method and device, the equipment, the medium and the program product provided by the invention, the index data related to wireless network load data covariant is considered during wireless network load prediction, so that predicted load data with higher precision is obtained; and the network performance and the resource allocation are better optimized by depending on the predicted load data with higher precision.

The invention relates to the technical field of logistics, in particular to a digital twin network traffic prediction method, device and equipment and a storage medium, and the method comprises the steps: obtaining historical traffic data of a digital twin network of a logistics circulation center, and obtaining traffic training data from the historical traffic data; constructing an initialization model based on the TimeGPT time sequence model, and performing parameter configuration on the initialization model to obtain a basic prediction model; performing training processing on the basic prediction model based on the traffic training data to obtain a traffic prediction model; acquiring updated traffic data from the digital twin network of the logistics circulation center according to a preset period, and performing updating training on the traffic prediction model according to the updated traffic data to obtain an updated traffic prediction model; acquiring real-time traffic data of the digital twin network of the logistics circulation center, and performing traffic prediction based on the updated traffic prediction model and the real-time traffic data to obtain a predicted traffic result; according to the method, the problem of overfitting of the model can be effectively prevented.

The invention discloses a bay water quality monitoring station layout optimization method, and the method comprises the steps: carrying out the grid division of a research region, and obtaining data points; determining an initial range of algorithm search according to the characteristic values of the data points; the optimal step length of data point updating is determined by calculating gradient and curvature information of a target function, the position of the data point is updated based on the comparison result of the actual decline amount and the predicted decline amount of the target function, loop iteration is carried out until the preset convergence condition is reached, and the finally obtained position of the data point is the bay water quality monitoring station site. On the basis of the trust region mapping method, the site selection and layout problem of the bay water quality monitoring stations is rapidly solved through the particle swarm optimization algorithm, the appropriate number of stations can be given according to requirements when the number of the monitoring stations is not restrained, calculation reasonability is high, and the method has high application value.

The invention discloses an electric carrying equipment drive-by-wire chassis multi-domain collaborative anti-rollover control method, which comprises the following steps of: 1, constructing a rollover risk assessment and control strategy selection method, and calculating key parameters such as yaw velocity and lateral acceleration of a vehicle in real time; step 2, based on the rollover risk index model and the rollover critical threshold in the step 1, establishing a multi-domain collaborative rollover prevention control model, and performing collaborative scheduling on independent steering, hub motor differential steering and an ESP brake control system; step 3, based on the multi-domain collaborative anti-rollover control model in the step 2; and 4, based on the priority dynamic allocation and control strategy execution result in the step 3, the central control unit coordinates the complex interaction relationship among the subsystems by using a fuzzy logic control algorithm. The invention provides a reliable control strategy priority and use frequency method, and provides a more reliable anti-rollover solution for the electric carrying equipment under a complex dynamic working condition.