106 results about "State distribution" patented technology

The invention relates to the technical field of power distribution cabinet detection, and discloses an intelligent power distribution cabinet state dynamic monitoring method and system, and the method comprises the following steps: collecting multi-source data generated in the operation process of a power distribution cabinet; preprocessing the multi-source data to generate a standardized data set; a random field model is constructed and used for describing the spatial relevance between units in the power distribution cabinet and the relation between the state of each unit and multi-source data, and model parameters are estimated through the maximum logarithmic posterior probability. An intelligent power distribution simulation model and a multi-source data acquisition technology are adopted, multi-dimensional parameters are acquired in real time through a high-precision sensor, monitoring efficiency and decision accuracy are remarkably improved, data analysis integrity and model sensitivity are ensured through dynamic time modeling and capturing of space and time relevance of the power distribution cabinet, and the power distribution cabinet can be monitored more accurately. And the state distribution visualization and maintenance priority strategy generation module optimizes the maintenance plan, reduces the operation and maintenance cost, and guarantees the stability of the power distribution system.

The invention relates to the technical field of wind power generation system monitoring, and discloses a real-time monitoring method for the running state of a wind power generation host and a tower drum, which comprises the following steps of: installing sensors at key parts such as bearings and blade connecting parts of the host and the tower drum to collect running data samples; a risk assessment model and a state distribution model are respectively constructed by adopting deep learning and a clustering analysis algorithm, and a comprehensive monitoring model is formed through fusion. And solving a key performance index threshold boundary by using an optimization algorithm based on gradient descent, screening an early warning scheme set according to a dynamic priority decision-making method, implementing a maintenance strategy, and feeding back and adjusting according to new data. According to the method, data can be accurately collected, the operation state can be comprehensively evaluated, faults can be timely warned, stable operation of equipment is guaranteed through closed-loop maintenance, system reliability is enhanced through redundancy check of a fusion processing layer, and reliability, stability and economical efficiency of a wind power generation system are effectively improved.

The invention discloses an improved discrete velocity method for rotation unbalance calculation of diatomic molecules, and relates to the field of numerical simulation of rarefied gas dynamics. The method comprises the steps that a flow field is initialized according to actual working conditions, a Boltzmann equation based on a Rykov model is adopted as a control equation, a collision-free Boltzmann equation is solved to obtain a unit interface distribution function, a macroscopic equation flux corresponding to a local solution of the collision-free Boltzmann equation and a macroscopic equation flux corresponding to a Navier-Stokes equation considering the molecular rotation effect are calculated, and a unit interface distribution function is obtained; performing weighted calculation on the two types of fluxes according to the rarefaction degree of the gas to obtain a macroscopic flux, solving a macroscopic adjoint equation to obtain an estimated macroscopic quantity and an estimated equilibrium state distribution function, solving a microcosmic control equation to update the distribution function, and integrating the distribution function to update the macroscopic quantity and judge the convergence. On the basis of keeping the conciseness of the traditional semi-implicit discrete velocity method, the method can achieve the precise prediction of the full-basin unbalanced flow field of the diatomic molecular gas.

The invention provides a multi-agent collaborative exploration method for constructing an internal individual reward based on information gain. The method comprises the following steps: acquiring state information and action information of agents, creating a multi-agent reinforcement learning environment, and initializing parameters of a dynamic model; constructing a CVAE as a dynamic model, compressing a state space to a potential space, and predicting state distribution; optimizing model parameters by using reconstruction loss and KL divergence loss to obtain a trained CVAE model; estimating a priori entropy and a posteriori entropy of agent state transition through the trained model, and calculating an information gain; designing an exploration frequency factor, and calculating an internal individual reward in combination with information gain; and according to the internal individual rewards and the external rewards, generating a utilization strategy and an exploration strategy of the intelligent agent for collaborative exploration, and guiding the intelligent agent to select an action to be executed to complete a task target. The method provided by the invention is helpful for the intelligent agent to explore an unknown area, and the exploration capability and the task completion rate of the intelligent agent are remarkably improved.

The embodiment of the invention discloses a remote state monitoring method and system based on an intelligent water meter, and the method comprises the steps: obtaining pipeline BIM model data of an intelligent water meter pipeline network of a target building, and synchronously collecting a real-time monitoring data set of a plurality of sensing nodes in the intelligent water meter pipeline network; performing abnormal state recognition processing on the real-time monitoring data set, and generating an abnormal state distribution map matched with the spatial position of the pipeline BIM model data; based on the abnormal state distribution map and a preset pipeline topology constraint condition, performing dynamic visual reconstruction processing on the pipeline BIM model data, and generating a visual output interface with an abnormal state mark; and performing association mapping on the visual output interface and the real-time monitoring data set to generate a state monitoring feedback strategy, and triggering maintenance response operation of the intelligent water meter pipeline network through a remote service port.

The invention discloses an energy storage control method and device of a target system, a storage medium and an electronic device, and relates to the field of computers.The energy storage control method of the target system comprises the steps that real-time weather data of a target area and real-time operation data of the target system in the target area are obtained, the real-time operation data comprises real-time load data and real-time photovoltaic output data; predicting first net load state distribution data of the target system in a first time period according to the real-time weather data, the real-time operation data and a probability dynamical model of the target system; and performing strategy planning on the first net load state distribution data through an optimal strategy model, and performing energy storage control on the target system according to an obtained real-time optimal strategy.

The invention discloses an unmanned ship cooperative target surrounding method, and belongs to the technical field of unmanned ship cooperative control. According to the method, a YOLOv8 target detection method is adopted to train a data set of a collaborative surrounding target, and a real-time target detection model is obtained; a particle filter is utilized to realize collaborative target path determination and conversion, target state distribution is approximated through weighted particles, particle weights are updated in real time according to recognized target states, weighted averaging is carried out, a target trajectory is finally formed in combination with time recursion, and continuity and robustness of the trajectory are ensured. According to the method, the target needing to be surrounded can be identified, the trajectory of the target is obtained through a particle filter algorithm, and the trajectory is converted into a determined path.

The invention relates to the technical field of underground communication, in particular to an underground deterministic network optimization scheduling method. According to the method, underground roadway humidity, electromagnetic interference and channel blocking data are obtained in real time, a blocking state distribution diagram is generated, a reconfiguration demand threshold value is determined, and then a data packet dynamic reconfiguration sequence is formulated; dynamically adjusting time slot division granularity and antenna gain parameters by comprehensively evaluating multi-path reflection interference, dust influence and an equipment moving track, and generating an optimized time slot allocation scheme; and finally, adaptively adjusting time slot configuration according to a comparison result of the signal attenuation dynamic distribution and a preset threshold value. According to the invention, the dynamic influence of the underground complex environment on the communication quality is effectively overcome, the reliability, real-time performance and network resource utilization efficiency of data transmission are remarkably improved, and reliable communication guarantee is provided for mine safety production.

The invention discloses an unmanned aerial vehicle path planning method, which comprises the following steps: carrying out environment data acquisition and state modeling, and generating an initial task data set containing starting point distribution, end point distribution, a reference random process and original and real-time environment data; modeling and inferring by using a Gaussian process potential force model, and outputting a disturbance force prediction result containing a mean value and uncertainty; on the basis of the starting point, the terminal point, the reference random process and the disturbance force, constructing SRoudinger bridge distribution evolution and multi-objective optimization to obtain an optimal state distribution track; according to the uncertainty and the battery capacity, self-adaptively adjusting the regularization coefficient of the SRosine-Bridge entropy, and updating and optimizing the weight; and mapping the optimal state distribution track into a control input sequence, and triggering re-optimization when the environment suddenly changes. According to the method, Gaussian process potential force modeling and SRoudinger bridge optimization are fused to improve path precision, energy efficiency and environmental adaptability.

The invention discloses a method for searching individuals in a distributed manner in a big data set, and aims to solve the technical problems of conflict avoidance and state uniqueness distribution in a large-scale node structure data set. The method comprises the following steps of undirected graph establishment, structure mapping, four-class state initialization, adjacency detection and syn-position adjustment until the states of all adjacent nodes are different. Compared with the prior art, the method is based on graph structure mapping, odd-even state classification and syn-position adjustment, is used for carrying out distributed search and accurate positioning on individuals in a big data set, constructs a globally unique and locally adaptive coding method system through four-color state division and local adjustment, and has the advantages of unique state traceability, accurate recheck, high efficiency and the like. The method is flexible in state compression and expansion, can be expanded into various states, is high in distributed deployment capability, is high in universality, and is suitable for multiple fields of plane maps, image modeling, graph neural networks and file systems.

The invention discloses a porous rock multiphase fluid lattice Boltzmann simulation method based on GPU acceleration, and belongs to the technical field of computational fluid mechanics, a D3019 speed discrete model is utilized to identify porous rock to obtain a digital core model, a speed direction and a weight coefficient are defined, a distribution function is defined by means of an LBM method, and a multi-phase fluid lattice Boltzmann simulation model is obtained. Calculating a macroscopic physical quantity by utilizing a relation between a distribution function and a velocity moment, respectively describing a gas-liquid interface and a complex boundary of the porous rock by virtue of a scalar tracer function and a step function, and constructing a physical model; constructing an equilibrium state distribution function, performing LB equation calculation by adopting a forming schema equation under an external force, constructing a pseudo-potential multiphase model by virtue of a Shang-Chen potential energy formula, and processing the solid-liquid boundary of the porous rock by using a semi-rebound format; and carrying out a numerical solution pseudo code process based on the lattice Boltzmann method. According to the method, the characteristics of multithreading, memory sharing and the like of GPU hardware are fully utilized under the LBM framework, and the steps of the core algorithm are highly parallelized, so that high-performance calculation is realized.

The invention discloses a GNSS (Global Navigation Satellite System) and INS (Inertial Navigation Satellite System) tight coupling method and system. The method comprises the following steps: acquiring first state distribution of a first target system, wherein the first target system is a GNSS and INS tight coupling system; presetting a first algorithm, and performing state estimation operation based on the first state distribution in combination with the first algorithm; and correcting the first target system according to the state estimation operation result. By adopting the particle filtering technology, the nonlinear noise problem of the GNSS signal in a high electromagnetic interference environment can be effectively solved. Particle filtering serves as a recursive Bayesian filtering technology based on a Monte Carlo method, probability distribution is represented through a group of random samples (particles), and a posterior probability density function in a nonlinear and non-Gaussian noise environment can be well approached. Therefore, even under the condition that the GNSS signal quality is obviously reduced, the method provided by the invention can also provide a more accurate and robust positioning result.

The invention discloses a spaceflight equipment test system based on scripts and plug-ins, and belongs to the technical field of LVC test verification. The system comprises a master control module, a plurality of slave control modules and an operation support library. Each slave control module is a plug-in sub-module designed according to a unified coding format, the plug-in sub-module defines implementation logic of an object model, and after the plug-in sub-module is loaded, the object model in the operation support library is called to execute an internal function. And the main control module reads external configuration parameters, assigns values to internal variables of the plug-in sub-modules, loads all the plug-in sub-modules, and controls the corresponding plug-in sub-modules to execute in sequence according to a set execution sequence. The operation support library provides an object model library for each plug-in sub-module, and objects defined in an object model comprise messages, state distribution objects and remote methods. Through the application of the invention, communication between subsystem models is realized, and subsystems of aerospace equipment are associated with each other to form a digital equipment overall model.

The invention relates to the field of unmanned aerial vehicle navigation and control, and particularly discloses an unmanned aerial vehicle three-dimensional track prediction method based on a pre-distribution state estimation model and particle swarm optimization, which comprises the following steps: initializing a particle set, and calculating an initial state mean value and a covariance matrix by adopting unscented Kalman filter (UKF); predicting the state of the particles by using unscented Kalman filter (UKF); sampling the particles based on the state distribution predicted by the UKF; particle weights are calculated and normalized; carrying out resampling on the particles; and calculating a three-dimensional track estimation value of the unmanned aerial vehicle and predicting a future track. According to the method for predicting the three-dimensional flight path of the unmanned aerial vehicle, high-precision state estimation of the UKF is introduced on the basis of particle filtering, so that the algorithm can effectively solve the problems of particle degradation and sample depletion encountered by a traditional PF algorithm in the three-dimensional flight path prediction of the unmanned aerial vehicle, and the prediction precision and stability are improved; and the future trajectory of the unmanned aerial vehicle can be accurately predicted in a complex three-dimensional environment.

The invention provides a dynamic life reactor maintenance platform and method, and relates to the technical field of power management, and the platform comprises a data collection module which is used for obtaining the operation state data of a target reactor, and the operation state data comprises an operation condition data column and an operation environment data column; the state analysis module is used for extracting scene operation state distribution of the target reactor and defining a scene life consumption coefficient; the dynamic life prediction module is used for initializing a dynamic life model to perform dynamic life prediction; the maintenance strategy generation module is used for updating the maintenance strategy of the target reactor according to the dynamic life prediction result; and the maintenance report generation module is used for applying the updated maintenance strategy of the target reactor and generating a maintenance report of the target reactor. According to the method, the technical problem that the formulation of a maintenance strategy is lack of pertinence and flexibility due to neglect of the actual operation environment and load condition change of the electric reactor because most of traditional electric reactors are based on regular maintenance is solved.

The invention discloses a simulation method for partial discharge and near-field electromagnetic radiation of power equipment, which comprises the following steps of: simulating a gas discharge process of partial discharge of the power equipment, and acquiring particle state distribution at each moment in gas discharge time based on a model of each particle in the gas discharge; obtaining electric field distribution under each time step length based on the particle state distribution; based on a plurality of excitation sources arranged on a gas discharge path and the electric field distribution, obtaining electric field time sequence change at each excitation source; the near-field electromagnetic radiation caused by gas discharge is simulated, the transient electric field of each excitation source at each simulation moment is used as the input of near-field electromagnetic radiation simulation, and the electromagnetic field change in a near-field electromagnetic radiation simulation area is acquired.

The invention relates to a deep learning compensation method and system for mechanical assembly errors, and relates to the technical field of mechanical assembly optimization, and the method comprises the steps: synchronously collecting dispensing images and dispensing point cloud data according to a preset monitoring time interval, and recognizing and outputting a dispensing state distribution sequence; key state recognition and feature extraction are carried out on the dispensing state distribution sequence, window fluctuation analysis of assembly errors is carried out, and a compensation time window sequence is output; a predicted dispensing state distribution sequence is obtained through prediction, optimal optical performance is taken as a target, lamination control parameter optimization is carried out, and an optimal lamination parameter sequence is output; and according to the compensation time window sequence and the optimal pressing parameter sequence, the fitting assembly process in the preset time zone is optimized and controlled. According to the invention, the problems of insufficient fitting precision, low production yield and uncontrollable camera optical performance of the 3C camera module caused by the fact that the traditional mechanical assembly only depends on a global fixed pressing strategy are solved.

The invention relates to the technical field of operation state evaluation, in particular to a switching power supply health degree evaluation method based on data analysis, which comprises the following steps of: establishing operation load correlation characteristics through mean value and range calculation, extracting continuous increasing paragraphs in a ratio sequence by using a difference mode, and calculating the health degree of a switching power supply; when the length of the interval exceeds a threshold value, a degradation starting point is recorded, a data segment is input into a long short-term memory network for time sequence modeling, offset is accumulated point by point in the modeling process and is compared with the threshold value in sequence, an abnormal path range is output, a degradation trend judgment result is generated, and a heat dissipation unit temperature rise and magnetic element jitter sequence is divided into four types of states; adjacent state changes are counted one by one, a proportion matrix is generated, convergence distribution is obtained through a Markov model through power accumulation, serious degradation and failure state distribution values are compared with a threshold value point by point, a critical interval is marked when the threshold value is exceeded, and life prediction distribution is formed; and life prediction can be combined with multi-dimensional state changes instead of depending on a single index.

The invention discloses an acceleration sensor output value correction method, and relates to the technical field of data processing, and the method comprises the steps: S1, analyzing the data drift trend of historical operation big data, and carrying out the clustering division according to the drift fault type of a known acceleration sensor; s2, on the basis of fault development type state distribution of a plurality of known acceleration sensors, obtaining a fault development type pointed by real-time operation data of a target acceleration sensor, and marking support factors in the fault development type pointed by the real-time operation data of the target acceleration sensor, determining that the real-time operation data of the target acceleration sensor points to the fault development type feature data set; and S3, predicting the hardware drift state of each stage of the fault development type of a plurality of known acceleration sensors, generating the hardware drift state of the stage to which the real-time operation data of the target acceleration sensor points, marking a hardware drift interval, and generating an output value correction value of the acceleration sensor.

The invention provides an agent inference method and a related device. The method comprises the steps of obtaining information of a robot and an environment, and constructing a generation model of an intelligent agent according to the information of the robot and the environment; the intelligent agent interacts with the environment through the generative model, the generative model deduces the real state and outputs the prior state distribution, and the generative model is adjusted according to the prior state distribution to minimize the surprise degree; wherein the free energy is obtained according to the expected value of the logarithm of the KL divergence of the variation distribution and the prior state distribution and the quotient of the variation probability and the conditional probability, and the free energy is minimized to minimize the surprise degree; the intelligent agent has more efficient perception ability in an incomplete information environment, the model training efficiency is improved, environment interaction develops towards an interested state, and falling into a local optimal solution is avoided.

The application provides a network element state management method and device, electronic equipment and a storage medium, wherein the method comprises: decoupling a service area in a to-be-processed satellite network from a preset satellite, and defining the service area as a geographical location area, wherein the service area in the to-be-processed satellite network is coupled with the preset satellite; determining a mobile network location state of the to-be-processed satellite network based on the geographical location area; hosting other states of the to-be-processed satellite network to a local user equipment to obtain a distributed local state library; running a state management process of key signaling of the to-be-processed satellite network based on the distributed local state library; and obtaining a stateless space-ground integrated mobile network based on the mobile network location state, the distributed local state library and the state management process, wherein the stateless space-ground integrated mobile network is a network composed of satellites and ground networks without state migration. The application can avoid signaling storm.

The invention provides an electric power field operation safety management and control method and device based on Internet of Things data deep fusion, and the method comprises the steps: obtaining deep fusion data through an Internet of Things equipment fusion model, building a multi-dimensional time series data set based on time and space, dividing a first feature of the data set through information gain evaluation, and obtaining a second feature of the data set; normalizing the first feature to obtain a second feature, constructing a hydropower station full-dimensional data deep fusion model to obtain a data development trend of deep fusion data, judging whether the data development trend accords with standard normal distribution or not, if yes, indicating that the deep fusion data is in a stable state, and if not, indicating that the deep fusion data is in a non-stable state. And if the state distribution does not conform to the standard state distribution, indicating that the deep fusion data is abnormal, evaluating the abnormal deep fusion data, and identifying potential safety hazards and risk points. According to the method, the deep fusion data is obtained through the Internet of Things equipment fusion model, the abnormal deep fusion data is obtained, potential safety hazards and risk points are identified after evaluation, and the whole operation process is visible, known and controllable.

A method and system for reserving resources includes determining a state of the distributed computing system based on resource needs of applications executing on the distributed computing system and system resource constraints (302). A behavior is determined using the state of the distributed computing system as an input to a trained reinforcement learning model (304). A resource request (306) is issued for the application to reserve resources based on the behavior.

The present invention provides a method and system for collaborative optimization scheduling of a virtual power plant based on deep reinforcement learning, which relates to the technical field of virtual power plant optimization scheduling, including: extracting multi-scale time series features through a hybrid perception network, obtaining equipment association features using a dynamic heterogeneous graph neural network, constructing a continuous state evolution model to predict system state trajectory, calculating state distribution in combination with a probabilistic process network, extracting decision-making key features based on a multi-agent meta-learning model in combination with a variational information bottleneck network, generating a candidate action space using a soft actor critic algorithm, optimizing strategy parameters through an experience replay mechanism, obtaining an enhanced decision-making plan, ensuring model security through a knowledge distillation framework of adversarial training and a homomorphic encryption algorithm, and outputting optimal control instructions in combination with a hybrid expert system, thereby achieving stable and efficient scheduling of virtual power plants and improving the reliability and economic benefits of the system.

Proposed is a new posterior distribution estimation technique. This estimating device comprises at least one memory and at least one processor, wherein, when estimating a posterior distribution of a state at a second time point based on observed values up to the second time point, in a case of a non-linear state space model in which the state distribution at the second time point, evolved over time on the basis of the state at a first time point, is described by a linear Gaussian distribution, and the distribution of observed values at the second time point, based on the state at the second time point, is described by a non-linear Gaussian distribution, the at least one processor uses a model that estimates a linear Gaussian distribution and that has parameters that are optimized to maximize a lower bound on a log-likelihood, to calculate a posterior distribution of the state at the second time point based on the observed values up to the second time point.

This invention relates to a decoupled evolutionary modeling and service recommendation method for user interest state distribution. The method first collects user interaction data within a service system to construct a sequence of user-item-time triples. A graph convolution algorithm is used to perform initial static embedding learning of users and items, extracting collaborative signals from the user-item interaction graph. A decoupled state space model is constructed, representing user interest states as distributed variables composed of an orthogonal rotation matrix and a diagonal scaling matrix, respectively capturing the evolution of interest direction and the changing diversity of interest. A user interest precision matrix is ​​generated based on the rotation-scaling transformation, and the anisotropic Mahalanobis distance is used to measure the degree of match between users and items. A recommendation list is constructed based on the distance scores, with the top K best items selected as the recommended results. This method overcomes the static embedding limitations of user interest modeling, achieving invariant preservation and potential preference extrapolation in a dynamically evolving structure, thereby improving the expressiveness and generalization performance of the recommendation system.

The invention relates to the technical field of industrial Internet of Things, and discloses an industrial Internet of Things automatic storage intelligent scheduling system and method, and the method comprises the steps: determining a parameter range of storage equipment in a target scheduling scene, setting different scheduling parameters, and enabling the equipment to schedule a storage task according to the parameters; then acquiring position information of articles in the storage tasks under different parameters, generating a storage space state distribution diagram, acquiring equipment operation data, determining a response delay rate, and fitting to obtain a scheduling efficiency coefficient; and finally, determining the task matching degree based on the efficiency coefficient and the state distribution diagram, and further obtaining the optimal scheduling time. Through data-driven intelligent scheduling optimization, efficient utilization of the storage space and improvement of the equipment operation efficiency are realized, the optimal scheduling time can be accurately determined, the scheduling efficiency and the intelligent level of automatic storage in the industrial Internet of Things environment are improved, and the industrial application value is wide.

The invention provides a flow state distribution uniformity monitoring system in a wet anaerobic digestion reactor, and belongs to the technical field of online monitoring of anaerobic digestion reactors, the system comprises a reactor main body, a monitoring device arranged on the outer side of an observation port on the side wall of the reactor main body, an online monitoring sensor arranged in the reactor, and an AI computer connected with the reactor main body and the online monitoring sensor. According to the method, internal fluid images or videos are collected through monitoring equipment, and physicochemical parameters are collected through a sensor; processing the image data by using an AI computer to generate a flow field vector diagram; fusing the flow field and physical and chemical parameter data, and calculating a flow state uniformity quantitative index based on the evaluation model; and generating a regulation and control instruction for the stirring system according to the evaluation result, and displaying and transmitting the regulation and control instruction. According to the invention, real-time, online and quantitative monitoring and accurate regulation and control of the internal flow state of the reactor are realized, the problems of lagging, non-intuition, experience-dependent regulation and control and the like of the traditional method are effectively solved, and the operation efficiency, stability and economical efficiency of the anaerobic digestion process are remarkably improved.

The invention discloses a scheduling method for real-time frequency regulation and control and distributed unit collaborative optimization of a power grid, and the method comprises the steps: calculating a power distribution proportion through employing a dynamic programming algorithm according to the charge state distribution of unit clusters, and determining a priority response sequence of each cluster; transmitting the preliminary allocation instruction to a corresponding cluster through a communication network, and correcting a transmission delay influence by adopting a feedback mechanism to obtain an adjusted power output scheme; according to the adjusted power output scheme, if it is judged that a local fault signal exists, switching to power distribution of a standby cluster is carried out, and a final batch coordination instruction is determined; extracting an execution parameter from the final batch coordination instruction, and testing a response effect by adopting a simulation verification module to obtain an optimized scheduling strategy; and generating a real-time monitoring report through the balanced resource use data, and determining whether the system stability index meets the requirement or not.

The invention relates to the technical field of remote scheduling, in particular to a remote scheduling method and system for roadbed and pavement construction equipment, and the method comprises the following steps: carrying out the division based on task units, recognizing an equipment track inflection point and an operation state conversion sudden change interval, analyzing the equipment operation direction consistency and operation state distribution, and evaluating the linkage task scheduling efficiency. And analyzing a stress distribution trend of the construction area, calibrating an abnormal level, identifying a corresponding number, and outputting an intelligent collaborative scheduling instruction set of the construction equipment. According to the method, by accurately extracting and analyzing the running track and the operation state of the equipment, the abnormal interval and the deviation area can be effectively recognized, resource allocation can be balanced, operation consistency can be improved, resource waste and low efficiency caused by improper task allocation can be avoided, risk level calibration of task units can be achieved, and the method is suitable for popularization and application. Potential problems are found in advance, effective countermeasures are taken, scheduling decision precision and flexibility are improved, and efficient and smooth execution of construction tasks is ensured.