372 results about "Information environment" patented technology

The invention discloses a power industry robot collaborative inspection and fault self-diagnosis system and method, and belongs to the technical field of power inspection, and the system comprises a management module which receives an inspection task instruction and obtains inspection task information according to the inspection task instruction; the environment identification module is used for acquiring inspection environment data and identifying obstacles; the path planning module is used for generating an inspection path set by adopting a multi-target particle swarm optimization algorithm according to the obstacle and inspection task information; the scheduling module is used for acquiring the state data of each robot and distributing the routing inspection paths in the routing inspection path set to each robot; the fault feature extraction module is used for acquiring multi-sensor data acquired by the robot and generating a fault feature vector; and the fault diagnosis module performs fault analysis on the fault feature vector to obtain a fault risk analysis report. The obstacle is recognized by acquiring the environment data, the inspection path set is generated by combining the inspection task information and adopting the multi-target particle swarm optimization algorithm, and the method can adapt to the complex inspection environment.

The invention provides an electric two-wheeled vehicle collision detection and early warning method and related equipment, and the method comprises the steps: obtaining environment information in front of and behind a vehicle, the environment information comprising point cloud data collected by a radar sensor and image data collected by a camera; performing time synchronization and space coordinate conversion processing on the point cloud data and the image data, and constructing a sensing data frame under unified space-time reference; based on the sensing data frame, radar features and visual features of the target object are extracted respectively, feature fusion is carried out to form a joint feature vector, target matching and association are carried out based on the joint feature vector, and a fused obstacle sensing result is generated; and based on the obstacle sensing result, in combination with the current motion state of the vehicle, predicting and evaluating a potential collision risk, determining a collision risk level, and executing a corresponding early warning prompt according to the collision risk level. And the driving safety and the system response efficiency of the electric two-wheeled vehicle in a complex traffic environment are integrally improved.

The invention provides an unmanned aerial vehicle surveying and mapping method and system, electronic equipment and a storage medium, and relates to the technical field of unmanned aerial vehicle surveying and mapping. The method comprises the following steps: carrying out base station position planning based on geographic data and preset factors, and arranging a CORS platform base station; then, acquiring a remote sensing image to construct a three-dimensional model, and performing semantic segmentation to obtain key ground feature information; and generating a multi-modal environment semantic map for surveying and mapping route planning based on registration of the key surface feature information and the geographic data. The unmanned aerial vehicle obtains surveying and mapping data, CORS positioning information and surveying and mapping image data in real time according to the surveying and mapping route, and environment semantic positioning data is obtained based on the surveying and mapping image data and multi-mode environment semantic map registration. And finally, performing registration processing by comprehensively utilizing the CORS positioning information, the environmental semantic positioning data and the surveying and mapping data to obtain target surveying and mapping data. Through the scheme, the surveying and mapping accuracy of the unmanned aerial vehicle is improved.

The invention relates to the technical field of unmanned aerial vehicle flight attitude control, in particular to an unmanned aerial vehicle flight attitude adjustment method and system based on reinforcement learning. Comprising the following steps: constructing a dynamic environment grid map based on a digital map and a real-time semantic segmentation result, and generating an initial track by introducing a space-time constraint fast search random tree algorithm; collecting flight state information, environment perception information and image definition indexes of the unmanned aerial vehicle, inputting the flight state information, the environment perception information and the image definition indexes into a space-time attention encoder, and generating a semantic-fused state tensor; performing importance distribution on the state tensor through an information entropy weighting mechanism to obtain a weighted state vector; inputting into a Meta-SAC model with meta-learning ability, and outputting a target flight reference pose and an LQR controller dynamic gain coefficient; the control module drives the LQR controller to generate a flight control instruction based on the information; and a reward function is constructed based on the image quality and the energy consumption efficiency, and a reinforcement learning strategy is fed back in real time.

The invention discloses a park safety operation and maintenance management method based on a digital twinborn technology, and relates to the technical field of park safety operation and maintenance management, and the method comprises the following steps: S1, obtaining park information which comprises park structure information, environment information and real-time operation and maintenance data; and S2, constructing a digital twinborn model of the park based on the obtained park structure information. According to the park safety operation and maintenance management method based on the digital twin technology, the conditions after different emergency measures are implemented are simulated, and different evacuation routes and guide modes are simulated for multiple times to evaluate the advantages and disadvantages of the scheme, so that the optimal evacuation scheme is selected, the safety of personnel in an emergency is improved, and the safety of the personnel is improved. The response efficiency and effect when the security event occurs are improved, so that the security event can be timely and effectively controlled when the security event occurs, and greater loss is avoided.

The invention discloses a multi-path suppression positioning method and system for a banded sparse single Beidou CORS network, and relates to the technical field of satellite navigation positioning. According to the multi-path suppression positioning method for the banded sparse single Beidou CORS network, initial pseudo-range time sequence data are acquired, the time sequence data and regional state data are received, the pseudo-range data are corrected and analyzed, then, filtering processing is performed by using a preset filtering rule, a virtual base station is established, data fusion is performed, and the multi-path suppression positioning method for the banded sparse single Beidou CORS network is obtained. According to the method, the pseudo-range time sequence data of each satellite is dynamically corrected, and particularly, through comprehensive analysis of the reflecting surface information, the environment data and the signal arrival time difference, the accurate position information of the receiver is obtained. Errors caused by reflecting surface and environment changes can be recognized and corrected in real time, and the stability and precision of a positioning system in a complex environment are remarkably improved.

Systems and methods are configured to generate a set of potential responses to a prompt using one or more data models with data from at least a plurality of data domains of an enterprise information environment that includes access controls. A deterministic response is selected from the set of potential responses based on scoring of the validation data and restricting based on access controls in view profile information associated with the prompt. These enterprise generative AI systems and methods support granular enterprise access controls, privacy, and security requirements. enterprise generative AI providing traceable references and links to source information underlying the generative AI insights. These systems and methods enable dramatically increased utility for enterprise users to information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems.

The invention discloses a material insurance supply method and system based on big data material performance monitoring, and belongs to the technical field of material supply, and the method comprises the following steps: constructing a supply topological graph according to the historical transportation data of suppliers; acquiring corresponding historical environment data based on the supply topological graph, and constructing an environment vulnerability weight of each topological side line in the supply topological graph based on an environment change frequency in the historical environment data; based on the supply topological graph, a supply association relationship of suppliers is obtained according to the supply type, and a supplier risk bearing coefficient is obtained according to historical supply data of the suppliers and the supply association relationship; and outputting a material insurance supply strategy based on the future environment information, the environment fragility weight and the supplier risk bearing coefficient. According to the method, the timeliness of adjustment of the material supply distribution strategy is ensured by considering the risk in the transportation process in advance, neglect of high-flexibility suppliers is avoided through the risk bearing capability of the suppliers, and the accuracy of material supply distribution is ensured.

The invention relates to the technical field of unmanned ship navigation control, and discloses an unmanned ship navigation state control method and system based on intelligent perception. Navigation environment information of multi-dimensional data is obtained, a multi-dimensional data fusion technology is adopted for construction of an environment state matrix, and discrete environment elements are converted into structured matrix expression. In the initial path generation stage, data acquisition value evaluation is fused, obstacle avoidance requirements are considered, and a high-value detection area is actively covered. In the path correction stage, a multi-objective cost function including energy consumption, threat degree and path length is constructed, and real-time path optimization in a dynamic environment is realized. In the propulsion control link, propulsion force and rudder angle parameters are calculated by integrating the attitude, the ship speed and the ocean current speed of the unmanned ship. According to the method, the contradiction among the real-time performance of dynamic obstacle avoidance, the data acquisition efficiency and the energy consumption optimization is solved through the cooperation of global-local double-layer path planning and the control of the propulsive force and the rudder angle of the unmanned ship.

The invention relates to the technical field of virtual simulation, and particularly discloses a sudden major respiratory infectious disease on-site epidemiological investigation virtual simulation experiment system which comprises a virtual scene management module, a data management module, a disease transmission simulation module, an epidemiological analysis module, a virtual on-site interaction module, a learning support module and an analysis and evaluation module. The virtual scene management module is used for creating and managing a simulation experiment environment and simulating an epidemic background and a development process of sudden major respiratory infectious diseases; the data management module is used for simulating the process of collecting case information, environment information and social contact data when students perform on-site epidemiological investigation; according to the system, big data analysis, the GIS technology and an AI intelligent analysis tool are integrated, a disease transmission path and a development trend are simulated, and virtual simulation not only helps students to master how to support a decision through scientific data, but also can improve the emergency response capability and the comprehensive analysis capability of the students in a complex epidemic situation.

The invention discloses a diffusion model trajectory planning method based on historical guidance and collision suppression. The method comprises the following steps: collecting own vehicle information, environment information and routing lane information in normal vehicle driving data; constructing negative sample scene data containing vehicle collision; during model training, encoding static obstacle states, dynamic obstacle historical tracks and surrounding lane structure information in a current environment; training a diffusion model decoder based on the future trajectory of the own vehicle, inputting the environmental condition information, the routing lane information and the own vehicle planning result of the model at the previous moment as condition information into the decoder, and enhancing the time continuity of the planning result; and training a classifier based on the collision data, and applying negative guidance to a collision category by using a classifier guidance mechanism during reasoning and sampling of the diffusion model so as to suppress a trajectory generation result with a high collision risk. According to the invention, efficient, safe and stable planning track generation can be realized in a real road scene.

The invention provides a data center energy saving method and device based on artificial intelligence, and a server, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining equipment operation data, and machine room space layout information and environment parameters of a modular machine room; performing model updating processing on the initial self-adaptive real-time energy consumption prediction model through historical equipment operation data to obtain a target self-adaptive real-time energy consumption prediction model, and performing energy consumption prediction processing on the equipment operation data by using the target self-adaptive real-time energy consumption prediction model to determine an energy consumption prediction value; carrying out refrigerating capacity calculation processing on the machine room space layout information, the environmental parameters and the energy consumption predicted value through a basic heat load calculation model and an air enthalpy difference method cooling capacity calculation model, and determining a target refrigerating capacity; and according to the target refrigerating capacity, operation parameters of all refrigeration equipment of the data center are adjusted, so that all the refrigeration equipment operates with the lowest energy consumption meeting the refrigeration requirement of the data center. The energy utilization rate of the data center can be remarkably improved.

The invention discloses an automatic control system and method for an oil field power distribution network, and relates to the technical field of power system automation, and the method comprises the steps: a monitoring equipment configuration and deployment module is responsible for configuring monitoring equipment, and collecting the state information and environment data of each node of the power distribution network; the feature analysis processing module analyzes the collected data by using the intelligent operation module to generate control parameters and abnormal parameters; the remote connection module establishes remote connection with power distribution network equipment through communication equipment, and performs remote control or management according to control and abnormal parameters; the visual interface display module visually displays the state information, the environment data, the control parameters, the abnormal parameters and other data, and helps operators to monitor the operation of the power grid in real time. The technical problem that in an existing oil field power distribution network control system, the power grid operation state cannot be flexibly adjusted to adapt to oil field electric power characteristic changes is solved, and the technical effect of dynamically adjusting the power grid operation state based on oil field electric power characteristics is achieved.

The invention relates to the technical field of metal surface treatment monitoring, and discloses a black titanium surface coloring intelligent monitoring system, which comprises a spectrum acquisition module, an environment disturbance acquisition module, an environment disturbance comparison module and the like. The spectrum acquisition module dynamically adjusts a sampling frequency and a wavelength range in combination with environmental disturbance parameters; the environment disturbance acquisition module acquires information such as a temperature gradient change rate and an environment humidity fluctuation rate, and the environment disturbance comparison module generates a comprehensive index according to the information and judges an environment disturbance level. And the equipment state acquisition and comparison module monitors internal operation parameters of the system, generates an equipment health index and deals with abnormity. The spectrum and model synchronization module matches and evaluates the coloring quality by using wavelet transform and a pre-training model, and the coloring abnormity analysis module identifies defects. In addition, the system is also provided with a dynamic calibration module. The system can accurately monitor black titanium surface coloring, and the production efficiency and the product quality are improved.

The invention discloses a multi-modal end-to-end reinforcement learning four-footed robot dog reconnaissance and control method for an alley battle, and the method comprises the following steps: (1) generating a global path through an A * algorithm, and carrying out the real-time planning through combining with a dynamic window algorithm, thereby obtaining a local path; (2) inputting multi-modal perception data (local path, point cloud information, environment depth map, motion state information and environment privilege information), training a teacher strategy by combining a parallel near-end strategy optimization algorithm of a variational auto-encoder, and generating a corresponding action instruction; (3) inputting multi-modal data with noise, minimizing the loss of the teacher strategy and updating the student strategy in combination with a belief encoder; and (4) deploying the student strategy to a robot dog, inputting path information, street battle environment information and motion state information in real time, and generating an action instruction. According to the invention, multi-modal data and end-to-end reinforcement learning are fused to realize efficient sensing of the robot dog to the street battle environment, and the method is suitable for robot dog reconnaissance and control tasks in the street battle scene.

The invention provides an error information instant detection method which can provide more accurate, timely and more credible detection results in a dynamic and complex error information environment. The method comprises the following steps: (1) a retrieval enhancement generation step: (1) retrieving context information related to declarations needing to be classified on a network; (2) the context information is further extracted, and several contents with the highest ranking are extracted; (3) inputting several extracted contents with the highest ranking into a large language model, and summarizing context information by the large language model; (4) enabling the large language model to output a prediction result of statement classification and related evidence, naming the content output by the large language model as priori knowledge, and then combining the statement with the priori knowledge to complete the expansion of the statement; (2) an information classification step; and inputting the expanded declaration into a large language model for classification to obtain a final detection result.

The invention discloses an error calibration method and system for intelligent electric energy metering equipment, and belongs to the technical field of error calibration, and the system comprises an electric energy data acquisition module which is used for collecting electric energy parameter information metered by the electric energy metering equipment in real time; the environment data acquisition module is used for acquiring related environment parameter information in real time; the analysis module carries out analysis processing according to the obtained parameter information so as to judge whether the metering of the electric energy metering equipment has errors or not; and the calibration module is used for further analyzing and generating a gain adjustment value to adjust the electric energy gain of the electric energy metering equipment. According to the invention, the operation state of the electric energy metering equipment can be monitored in real time, once errors occur, calibration can be carried out in time, error accumulation is avoided, the metering accuracy is greatly improved, manual intervention is not needed during calibration, the related electric energy gain can be automatically adjusted according to the gain adjustment value condition, environmental factors are considered, and the calibration accuracy is improved. And the measurement precision is ensured.

The invention discloses a smart park security early warning system and method based on artificial intelligence, and relates to the technical field of early warning systems, the system comprises a security center platform, and the security center platform is in communication connection with a data acquisition module, an intelligent analysis module, an early warning decision module, a data transmission module, a data processing module and an execution feedback module. According to the invention, a multi-type video acquisition assembly, a distributed environment perception assembly and a multi-mode identity verification assembly are integrated through a data acquisition module, and park dynamic image information, environment parameter information and personnel biological characteristics and identification information are respectively acquired, so that a park security basic data set covering three dimensions of personnel, objects and environment is formed; the comprehensiveness and the dimensional integrity of park security data acquisition are improved, and the problems that blind areas exist in security monitoring and compound potential safety hazards cannot be accurately recognized due to incomplete data coverage of an existing system can be solved.

The invention provides a shopping guide method and a shopping guide device for a physical store, and a computer readable storage medium. The shopping guide method comprises the steps that obtained visual information, voice information, individual attribute information and environment information of a customer and attribute information of multiple commodities in a physical store are input into a trained shopping guide model, and the shopping guide model is a deep learning model based on a multi-modal large language model; controlling the shopping guide model to determine a target recommendation demand subject and a target demand of the target recommendation demand subject, and determining a target recommendation commodity corresponding to the target demand; guiding the client to walk from the current position to the position of the target recommended commodity; wherein the individual attribute information at least comprises part or all of information of age, gender, height, figure and dressing style, and the environment information at least comprises part or all of information of city, weather condition and season where the physical store is located. According to the embodiment of the invention, the intelligence and experience of shopping can be improved.

The invention provides a cooperative control method and device for low-voltage power consumption of a whole vehicle and electronic equipment, and relates to the technical field of automotive electronics, and the method comprises the steps: obtaining multi-modal original data (including vehicle state information, environment information and user behavior information), and carrying out the preprocessing; performing feature extraction and fusion processing on the preprocessed multi-modal original data to generate a high-level semantic event used for representing the vehicle leaving intention of the user or the parking scene; matching a global power consumption management strategy of a high-level semantic event through a hierarchical finite-state machine model; and according to the global power consumption management strategy, executing hierarchical power supply control on the low-voltage electricity load of the whole vehicle, and in the hierarchical power supply control process, dynamically adjusting control parameters in the global power consumption management strategy based on a comparison result of the actual power consumption monitoring data and a preset baseline model. Depth power shortage of the storage battery caused by redundant power consumption is effectively avoided, the risks that the vehicle cannot be started and equipment is abnormal are reduced, and the reliability of the whole vehicle and the user experience are improved.

The invention relates to the technical field of overhead line system deicing, and discloses an intelligent deicing method and system for an overhead line system. The method comprises the following steps: firstly, acquiring ice layer monitoring data such as ice layer thickness distribution information, environment temperature information and wind speed information of a contact network area in real time; inputting the data into a simulation modeling tool, and constructing a three-dimensional ice layer distribution model of the overhead line system; judging whether deicing is needed or not according to matching of ice layer thickness distribution information in the model and a preset ice layer thickness threshold value mapping table; if yes, deicing equipment position parameters and deicing agent type parameters are extracted from a preset parameter database, and the deicing agent applying process is simulated in the three-dimensional model; and finally, outputting an ice layer fracture path, an ice melting range and deicing efficiency data, analyzing rationality, matching degree and effectiveness, and generating a parameter optimization instruction to adjust subsequent deicing parameters.

An AR system enables creation and viewing of AR content for sharing of information relating to a physical environment. The AR environment may include one or more virtual objects, which may be pinned to specific locations in the real-world physical environment. The virtual objects may provide information (or access to such information) relating to the object or region of interest. To create pinned virtual objects, the AR system maps a localized physical space and enables tracking of an AR device with respect to the mapped physical space. The AR device may then be physically positioned at the desired location in the physical space and a pinning action may be initiated to generate a pinned virtual object associated with the selected position.

The invention relates to the technical field of intelligent traffic, and discloses an intelligent servo barrier gate management system. According to the system, vehicle identification information, detection information, environmental parameters and a barrier gate equipment state are acquired in real time through the data acquisition unit, comprehensive monitoring of traffic flow is ensured, the processing and analysis unit processes the acquired data by using a deep learning model, a control instruction is quickly generated, and the vehicle passing efficiency is evaluated, so that the traffic flow is ensured. The servo control unit is combined with an adaptive control algorithm to dynamically adjust the switching speed, the torque and the acceleration of the barrier gate, the vehicle passing experience is optimized, the queuing time is reduced, the bidirectional communication capability of the communication unit realizes remote monitoring and adjustment of the state of the barrier gate, and the traffic management decision making efficiency is improved. A manager can timely respond to sudden traffic conditions, the response speed and accuracy of the system are improved, the intelligent system effectively relieves the bottleneck of traditional barrier gate management, and the intelligent level of urban traffic is improved.

The invention relates to the field of inventory management and cloud computing application, in particular to a commodity inventory management method and system based on cloud computing, and the method comprises the steps: collecting commodity dynamic information, environment state data and commodity state data, and generating enhanced data; constructing a dynamic scene graph and simulating resource flow through a distributed simulation system; generating scene prediction data in combination with a quantum heuristic optimization algorithm; constructing a hierarchical inventory structure based on the prediction data, realizing resource dynamic allocation between inventory layers through a multi-agent collaborative optimization algorithm, and generating inventory optimization data; and recording a resource allocation task execution process, and generating transparent inventory state data by using a block chain technology. According to the invention, the inventory structure can be dynamically adjusted in real time, the inventory management precision is improved, the inventory overstock and shortage risks are effectively reduced, and accurate support is provided for supply chain management.

The embodiment of the invention provides an intelligent penetration testing method and device based on deep reinforcement learning, and the method comprises the steps: constructing a network information environment, setting basic parameters, initializing the network information environment, and constructing an act-critic structure with an independent strategy network and an independent value network; modeling the Markov decision process of the penetration test into a quaternion group lt; s, A, R, Pgt; a Markov model is constructed; constructing a priority playback experience pool; performing penetration testing based on a Markov model, calculating and outputting an attack action to a network information environment through a strategy network based on a current penetration state to obtain a next penetration state, rewarding the attack action through a value network, and storing data in a priority playback experience pool; and calculating and determining a mode of preferentially selecting sampling according to the priority of experience, repeating the penetration test operation of the Markov model until the function is converged, and outputting an optimal penetration strategy.

A reinforcement learning-based system for adaptively controlling computing performance is provided. The system includes an environment module and an agent module. The environment module is configured to collect environment information from the application environment. Based on the collected environment information, the environment module calculates a reward value using a reward function and then outputs the state data and the reward value to the agent module. The agent module receives the output from the environment module, including the reward value and the state data. Based on the received reward value and state data, the agent module determines a performance adjustment action to take, which is then fed back to the application environment. Upon receiving the performance adjustment action, the application environment executes the performance adjustment operation in response, causing the environment module to collect the updated environment information as a result of the performance adjustment operation.

The invention discloses a heterogeneous terminal behavior baseline auditing system and method based on artificial intelligence, and the method comprises the steps: carrying out the time sequence modeling and clustering analysis of large-scale and diversified traffic data collected by bypass monitoring or network equipment through introducing a streaming machine learning model supporting an online learning capability, and carrying out the clustering analysis of the data, normal behavior state mode clusters of various terminals are effectively extracted and dynamically maintained; meanwhile, in the application stage, a deviation measurement network is combined, real-time calculation of a deviation score between newly generated update behavior data and a historical normal mode is achieved, and a concept offset signal and an adjustment mechanism are automatically triggered accordingly, so that the system can continuously adapt to environmental changes. Therefore, the defects of the traditional scheme in generalization ability, self-adaptive ability and real-time performance are overcome, and the new requirement for efficient and safe management of the heterogeneous terminal in the current complex and changeable informatization environment can be better met.

The present disclosure relates to the technical field of audio control. Provided are a computer-implemented method for audio control, and an electronic device and a storage medium. The computer-implemented method for audio control comprises: on the basis of a specified measurement signal, performing acoustic effect testing on a target space, in order to obtain environmental response information, wherein the environmental response information comprises impulse response information and post-separation impulse response information; analyzing the environmental response information, in order to obtain acoustic characteristic information of the target space; on the basis of the acoustic characteristic information, performing compensation processing on audio to be processed, in order to obtain regulated audio; regulating gains of multiple channels corresponding to the regulated audio, in order to obtain re-regulated audio; and performing multi-channel collaborative optimization processing on the re-regulated audio, in order to obtain audio having a target effect. The method achieves precise compensation for spatial acoustic defects, resolves inter-channel crosstalk, improves the acoustic image localization and spatial perception, significantly improves the perceptual audio quality, and optimizes an output.

The invention discloses an intelligent camera system for a bicycle, and belongs to the technical field of image data processing, and the system comprises a riding vehicle data obtaining module which is used for obtaining the related data information of a preset bicycle in the riding process in real time, and the related data information comprises the riding speed and riding positioning information of the preset bicycle; the environment image acquisition unit is used for acquiring image information of objects around the preset bicycle according to the riding positioning information; the environment image acquisition unit comprises a main calculation module, an auxiliary calculation module and a judgment module, and the main calculation module is used for calculating the distance between the preset bicycle and the surrounding object based on the riding positioning information. Through intelligent environment perception, dynamic risk assessment and precise early warning functions, early warning can be performed in advance before a potential collision risk occurs, the occurrence probability of a riding accident is effectively reduced, the safety of bicycle riding and user experience are improved, meanwhile, data support is provided for riding safety, and early warning logic is optimized.

The invention provides a task deployment method, a model training method, equipment and a storage medium, and relates to the technical field of industrial internet, and the method can fully utilize communication resources and computing resources to deploy industrial tasks on the basis of meeting the service quality. The method comprises the following steps: acquiring task information of a to-be-executed task of terminal equipment and environment information of an edge-end collaborative scene; the to-be-executed task is any one of all to-be-executed tasks in one calculation period in the edge-end collaborative scene; determining a deployment scheme of the to-be-executed task based on the task information, the environment information and a trained task deployment model; the deployment scheme of the to-be-executed task is used for indicating to deploy the to-be-executed task on the local computing device and / or the edge computing device, so that resources are utilized to the maximum extent under the condition that the processing delay of the to-be-executed task meets a delay threshold value required by the to-be-executed task; and deploying the to-be-executed task based on the deployment scheme.