271 results about "Evaluation algorithm" patented technology

The invention relates to the technical field of Internet of Things gateways, and discloses an Internet of Things gateway device and system based on edge computing, and the device comprises a hardware heterogeneous computing module and a multi-interface protocol adaptation module. The edge calculation local processing module is used for performing preprocessing, feature extraction and local decision on the acquired terminal data; the network redundancy and self-healing module is used for realizing accurate network disconnection detection, seamless switching of a main network and a standby network and equipment fault self-recovery through a multi-dimensional network health degree evaluation algorithm; the local autonomous storage module is used for realizing hierarchical storage of high-frequency data and network disconnection emergency data and seamless synchronization after network recovery through network disconnection data hierarchical caching and an incremental synchronization algorithm; and the cloud collaborative management module is used for realizing remote configuration, OTA upgrading and state monitoring. The method is high in integration, low in delay and fast in response, adapts to real-time scenes, saves bandwidth cost, improves cloud efficiency, is high in reliability, and realizes network disconnection and continuous operation.

The invention relates to a rail transit engineering digital twin management and control method based on unmanned aerial vehicle honeycomb inspection, and solves the problem that a twin model is disjointed from the real-time state of an engineering field, and the method comprises the steps: firstly obtaining multi-source data containing BIM, GIS and Internet of Things data and an unmanned aerial vehicle initial image; carrying out BIM lightweight processing, carrying out registration with a GIS (Geographic Information System) and establishing an engineering entity-environment-event dynamic association network to form a digital twin reference model; based on the real-time state data of the model and external factors, a multi-dimensional evaluation algorithm is used to generate an inspection task priority; an adaptation rule is established in combination with an unmanned aerial vehicle honeycomb performance threshold, and unmanned aerial vehicle obstacle avoidance inspection is scheduled; the inspection data is preprocessed and then fused to the twin model, and the twin model is driven to dynamically iterate so as to realize real-time synchronization with the site. The method has the following effects: real-time and precise rail transit engineering management and control are realized through digital twin virtual-real mapping and unmanned aerial vehicle honeycomb intelligent inspection closed loop, and the core management and control efficiency is improved.

The invention belongs to the field of underground transportation, and particularly relates to a cascade failure-based subway freight network operation robustness evaluation method. Making a subway freight network cascade failure redistribution strategy; constructing a subway freight network cascade failure load redistribution model; constructing a subway freight network robustness evaluation index; and designing an evaluation algorithm for the robustness of the metro freight network based on the established cascade failure redistribution strategy of the metro freight network in combination with the constructed cascade failure load redistribution model and the proposed robustness evaluation index system, thereby realizing quantitative evaluation of the operation robustness of the network in the cascade failure situation. According to the invention, a scientific basis can be provided for planning and operation management of a subway freight system, and the reliability and safety of an urban comprehensive transportation system are effectively improved.

The invention discloses a highway intelligent risk monitoring method and system based on multi-modal information, and the method comprises the steps: collecting multi-modal traffic data, constructing a road network holographic portrait through a road network holographic portrait risk prediction model, positioning a potential risk point through a dual time-space mask anomaly detection model, and carrying out the detection of the potential risk point. Lane-level risk assessment is completed by means of a lane-level risk dynamic assessment algorithm, and risk information is output after multi-dimensional fusion analysis is carried out through a traffic risk intelligent research and judgment platform. All the models and algorithms are operated cooperatively, data integration, feature mining, anomaly recognition, risk assessment and fusion research and judgment are achieved step by step, a whole-process monitoring system from data collection to result output is constructed, refined and dynamic risk monitoring from the global road network to the local lane is achieved, the risk recognition accuracy and monitoring comprehensiveness are effectively improved, and the risk monitoring efficiency is improved. And technical support is provided for safe operation of the expressway.

The invention particularly relates to an intelligent monitoring method and system for the service life of an ion exchange resin mixed bed, and belongs to the technical field of ion exchange resin water treatment. The method comprises the following steps: collecting real-time flow and real-time conductivity of mixed bed inlet water; executing a mixed bed service life intelligent evaluation algorithm, and dynamically estimating a mixed bed exchange capacity consumption rate and a mixed bed residual operation duration; displaying the exchange capacity consumption rate of the mixed bed and the residual running time of the mixed bed in real time in the form of numbers, progress bars or trend curves; and when the exchange capacity consumption rate of the mixed bed and the residual operation duration of the mixed bed are lower than a preset threshold value, triggering an early warning prompt. The system implements the steps of the method. The invention provides the universal, convenient and economic intelligent monitoring method and system for the service life of the ion exchange resin mixed bed, and the real-time dynamic monitoring of the residual exchange capacity of the ion exchange resin mixed bed and the accurate prediction of the residual operation service life are realized.

The invention relates to the field of energy storage and reactive compensation capacity optimization configuration, in particular to a new energy pooling station energy storage and reactive compensation configuration method based on big data analysis. The method comprises the following steps: firstly, acquiring data, respectively obtaining a sensitivity coefficient of active power and a sensitivity coefficient of reactive power through a disturbance response sampling linearization identification method, and introducing a dynamic active-reactive decoupling voltage improvement evaluation algorithm based on sensitivity weighting to obtain a voltage improvement amplitude of a node; then, based on the voltage improvement amplitude of the node, constructing a target function; and carrying out iterative solution on the constructed objective function, and outputting the optimal active power regulation quantity of each energy storage unit and the optimal reactive power regulation quantity of each reactive compensation device. The technical problems that the sensitivity coefficient is difficult to update in real time, the calculation scale is huge, the response speed of real-time optimization scheduling, unnecessary active loss and battery life loss are seriously influenced, and the physical balance of the voltage improvement amplitude between the nodes is ignored are solved.

The invention discloses a multi-modal data acquisition and processing system for dynamic pressure assessment, and the system comprises a multi-modal data acquisition module which synchronously acquires dynamic pressure signals of different modals through a plurality of high-frequency response pressure sensors; the data preprocessing module is used for carrying out real-time signal conditioning processing on the dynamic pressure signals in different modes to obtain preprocessed multi-mode dynamic pressure signals; the multi-modal data fusion module is used for carrying out space-time alignment and feature extraction on the preprocessed multi-modal dynamic pressure signals by adopting a multi-factor coupling model, and generating fusion pressure data through a fusion algorithm; and the dynamic pressure evaluation module is used for executing time-frequency domain analysis and mode recognition on the fusion pressure data through an analysis model, extracting key characteristic parameters, calculating a dynamic pressure index through a pressure evaluation algorithm, and generating an evaluation result containing the dynamic pressure index. According to the invention, high-precision and real-time dynamic pressure assessment can be realized, and the comprehensiveness and reliability of data acquisition and analysis are improved.

The application is suitable for the technical field of data storage, and provides a data disaster recovery method and device, comprising the following steps: determining a fault type in a distributed cluster system and a range of data to be recovered; acquiring state parameters of multiple normal nodes in the distributed cluster system, and service sensitivity features and data block access features of the data to be recovered; performing data preprocessing operations; respectively adopting a service sensitivity evaluation model and a node state evaluation model to determine a data recovery priority list and a node priority list; determining a fault processing strategy according to the data recovery priority list and the node priority list; and executing the fault processing strategy. Through the above method, the real-time evaluation algorithm of the heterogeneous big data node is increased on the basis of dynamically updating the data recovery priority based on the service sensitivity evaluation model, and compared with the traditional technology, the fault perception of the business system can be reduced while the data disaster recovery efficiency is improved.

The invention belongs to the technical field of photoelectric detection imaging, and particularly relates to a dim target enhancement device and method based on range gating polarization imaging, the device comprises a transmitting system and a receiving system, the transmitting system comprises a pulse laser, a signal modulation circuit and a transmitting optical system; the pulse laser is used for emitting laser beams; the signal modulation circuit is used for generating a high-frequency modulation electric signal and modulating a laser beam emitted by the pulse laser; the emission optical system is used for focusing and adjusting the light beam direction and the emergent light polarization state; the receiving system comprises a receiving optical system, a focal plane polarization detector, a synchronous control board, a signal processing board, an image processing board and display control equipment; according to the device, the pulse illumination module, the time synchronization control module and the polarization imaging module are integrated, and an image definition evaluation algorithm is combined, so that synchronous acquisition and fusion of target polarization information and distance information are realized.

The invention relates to the technical field of AI computing power evaluation scoring, and provides a console AI computing power evaluation scoring system which comprises a computing power data set management unit, a console task scheduling unit, a multi-hardware computing power execution unit, a computing power comprehensive calculation unit, a safety monitoring and adapting unit and a visual output unit. The data collection unit is responsible for collecting, cleaning and marking multi-type data sets related to console computing power evaluation and establishing association mapping of the data sets and console hardware parameters, and the console task scheduling unit dynamically creates evaluation tasks based on console hardware loads. Through automatic data set management and task scheduling, the evaluation period is greatly shortened, and the pain points that manual evaluation is low in efficiency and prone to making mistakes are thoroughly solved; by means of a hardware adaptation related module, the comparability of computing power scores among hardware of different brands and architectures is remarkably enhanced, and evaluation algorithm adaptation of newly-accessed small-scale industrial hardware can be rapidly completed.

The invention relates to the technical field of information processing and commanding and dispatching, and discloses an information commanding and disposing method and system based on multi-source information fusion. According to the method, information flow features are analyzed through an event emergency degree evaluation algorithm, and an emergency degree score is calculated in combination with an event type rule base and an arrival rate deviation degree; obtaining the queue length and the average waiting time of the processing queue, and calculating a queue congestion factor; a dynamic priority value is calculated according to the product relation of the emergency degree and the queue congestion factor, and when the system load is increased, the priority difference between the high and low emergency degree information flows is increased; reordering the processing queues based on the dynamic priority values; when the queue congestion factor exceeds a threshold, the sampling frequency of the low priority information stream is reduced and the processing node extension is triggered. According to the method, the technical problem that key information is delayed to be processed in a multi-source information flow concurrence scene is solved, and the real-time performance of information processing in an emergency scene is improved.

The invention belongs to the field of battery thermal runaway early warning, and particularly relates to an early warning method and device for thermal runaway of a lithium ion battery pack, and the method comprises the steps: firstly collecting the voltage, temperature and current data of a battery cell, and judging the grade of a current working condition according to a preset rule; self-adaptively matching a corresponding alternating current excitation strategy according to the working condition grade and applying excitation; synchronously acquiring the compensated excitation current and the voltage response of each battery cell through a sampling chip configured with a compensation strategy; based on the response information, a multi-dimensional impedance anomaly judgment strategy is combined, and a judgment parameter set containing impedance deviation, thermal runaway risk, voltage and temperature deviation flag bits is calculated and generated; and finally, determining the thermal runaway risk level of the battery pack through an evaluation algorithm according to the parameter set, and packaging and reporting the thermal runaway risk level through CAN communication. According to the invention, on-line and accurate sensing and early warning of the internal state of the battery under various working conditions such as standing and dynamic operation are realized.

The invention provides a vocational education teaching quality evaluation system, and belongs to the field of teaching quality evaluation, and the system comprises a data collection layer which is configured to collect multi-source evaluation data for teaching quality evaluation; evaluation subjects of the multi-source evaluation data comprise ideological and political course teachers, specialized course teachers, tutors, enterprise tutors and students; the micro-service layer is configured to evaluate the teaching quality based on a time sequence individual entropy weight evaluation algorithm according to the multi-source evaluation data; the user interface layer is configured for each evaluation main body to log in the evaluation system through a webpage or a mobile terminal and input evaluation data for each service unit of the micro-service layer to call; and the block chain layer is configured to package and store the multi-source evaluation data in a block chain according to a time sequence. The vocational education teaching quality evaluation method can solve the technical problems that only the teaching effect of the students during the school is considered, and the effect feedback in the aspects of practical application ability and quality after the students participate in work is not introduced in vocational education teaching quality evaluation.

The invention relates to a verification cascade failure early warning method and device for a complex simulation system. The method comprises the following steps: constructing a multi-layer verification network according to various verification tasks of the complex simulation system; and dividing each verification task into a plurality of continuous time slices according to the multi-layer verification network, wherein each time slice corresponds to a complete operation and index acquisition process of the complex simulation system. And calculating the state transition probability between the nodes of the multi-layer check network, so that the Bayesian network is adopted to obtain the failure probability of the check node state in the multi-layer check network. And performing failure evaluation on the joint state between the check nodes on the key propagation path to obtain a joint failure probability. And constructing a three-level early warning mechanism according to the failure probability and the joint failure probability, and performing early warning state sample iterative learning through an optimization evaluation algorithm to generate a cascade failure early warning scheme. By adopting the method, the verification efficiency and the self-adaptability of the complex simulation system can be improved.

The invention provides a network autonomous operation and maintenance method and device fusing topology discovery and situation awareness, and relates to the technical field of network operation and maintenance, and the method comprises the steps: achieving the automatic recognition of network equipment, the automatic construction of a connection relation, and the real-time updating of a topological graph through a distributed deployment IP probe and a protocol adaptive collection technology; then collecting multi-source heterogeneous data, and performing quantitative evaluation and security threat identification of the network situation based on grey correlation analysis and a threat evaluation algorithm; and finally, carrying out automatic repair and performance optimization on a network fault based on dynamic load balancing and an autonomous decision-making mechanism. According to the invention, a topology automatic discovery module, a situation awareness analysis module, an autonomous decision and control module and an operation and maintenance management module are integrated; according to the invention, through integration of multiple modules for cooperative work, automatic discovery of network topology, accurate perception of situation, self-repairing of faults and efficient management of operation and maintenance are realized, manual intervention is greatly reduced, and the intelligent and automatic level of network operation and maintenance is improved.

The invention discloses a spaceflight structure strength analysis method based on a multi-physics field, which comprises the following steps: establishing a parameterized finite element model integrated with a friction heat generation effect by acquiring in-orbit temperature time sequence data and material creep characteristic parameters, and simulating inelastic strain accumulation of a structure under a thermal cycle load; a nonlinear damage model considering a creep-fatigue interaction effect is adopted to calculate an accumulated damage degree, an effective stiffness matrix is constructed to deduce a structure inherent frequency drift distance and a shape retention capability parameter, and finally, through a safety margin evaluation algorithm, the effective strength of a material and the maximum equivalent stress of the structure are dynamically compared, so that the performance of the material is evaluated. And accurate prediction of quantitative residual strength and safe service life is realized. According to the method, the technical problems that the local thermal coupling effect is not depicted enough and the creep fatigue nonlinear interaction effect is ignored are solved, and the precision and reliability of on-orbit performance degradation prediction of the spaceflight flexible structure are remarkably improved.

The invention discloses a hybrid enhanced power distribution network business expansion power flow and reliability cooperative verification method and system, and the method comprises the steps: inputting the topological data and historical power flow data of a power distribution network into a power flow calculation engine for large model reasoning acceleration, and obtaining the data of the power distribution network; inputting the data of the power distribution network and the wind, light and storage output models into a reliability evaluation algorithm containing new energy randomness to obtain a reliability index; performing digital twinning and federal learning data correction processing on the reliability index and the equipment real-time measurement data to obtain a corrected reliability index and an equipment parameter update value; performing N-1 fault simulation and self-healing strategy generation processing on the corrected reliability index, the equipment parameter update value and the equipment importance degree to obtain a fault simulation index and a self-healing strategy; and performing feedback optimization closed-loop processing on the fault simulation index and the self-healing strategy to obtain a structured improvement suggestion. According to the invention, an innovative solution is provided for business expansion verification of the high-proportion new energy grid-connected power distribution network.

The invention belongs to the field of mechanical engineering, particularly relates to a pinwheel tooth form error detection system based on laser point cloud adaptive calibration, and aims to solve the problem of inaccurate tooth form error detection caused by mechanical deformation, point cloud distortion, coordinate registration misalignment and environmental disturbance in a traditional measurement mode. The system integrates a laser scanning array, a six-degree-of-freedom fine tuning platform, an environment sensing unit and a point cloud processing engine, and real-time updating of calibration parameters and offset correction of a coordinate system are realized through dual mechanisms of static internal reference self-calibration and dynamic external reference compensation; and a closed-loop detection process is constructed by combining multi-view point cloud adaptive weighted fusion and an improved Hausdorff distance evaluation algorithm. The gear surface point cloud integrity and the measurement stability can be effectively improved, multi-model rapid remodeling is supported, full-automatic and high-repeatability online detection is achieved in a non-constant-temperature workshop environment, and the quality control efficiency and the process consistency of the pin wheel transmission part are remarkably improved.

This invention provides a method for determining the carbon dioxide huff and puff mechanism in fractured reservoirs, belonging to the technical field of determining the carbon dioxide huff and puff mechanism in fractured reservoirs. This invention establishes a unified digital core by selecting outcrop cores to create cast thin sections and employing multi-scale fusion reconstruction technology. Topological connectivity is evaluated based on a seepage topological invariant fracture network connectivity evaluation algorithm. During well shut-in, a diffusion-convection hybrid encoding / decoding model is used to predict migration in real time, and the model learning rate is adjusted by a dynamic learning rate adjustment function. After well shut-in, a feedforward-feedback composite control strategy is used to gradually reduce back pressure to complete the production stage. The experiment ends after cleaning the chip with anhydrous ethanol. Finally, the degree of crude oil recovery is statistically analyzed using image processing software. This invention solves the technical problem of not being able to predict the migration process in fractures and matrix of fractured reservoirs in real time during well shut-in.

The invention discloses a pneumatic rapping ash removal efficiency optimization method under a high-ash working condition, and relates to the technical field of flue gas electrostatic precipitation, and the method comprises the steps: carrying out the time window time frequency feature extraction of a high-ash working condition rapping data set, obtaining a rapping multi-mode feature set, quantifying the rapping performance state through a rapping state evaluation algorithm, and generating a rapping comprehensive state set; constructing a double-constraint rapping optimization model based on the rapping comprehensive state set, and generating a regional rapping optimization strategy by performing optimal configuration on rapping strength parameters; an area rapping optimization strategy is executed, the marginal dust removal effect of pneumatic rapping is recognized through sound and vibration characteristics in combination with a high-ash working condition rapping data set, and a rapping execution result is obtained; and performing self-adaptive adjustment on the double-constraint rapping optimization model according to a rapping execution result, and performing ash removal performance trend analysis in combination with the rapping comprehensive state set to obtain an ash removal efficiency optimization report. By constructing the vibrating comprehensive state set, the ash removal efficiency is improved, and the unit ash removal energy consumption is reduced.

The invention relates to the technical field of computer vision, in particular to an end-to-end single target tracking method combining underwater frequency domain scene prompt and light parameter fine tuning. The method comprises the following steps: acquiring a tracking template graph and a search area image through a to-be-tracked video; generating a scene feature map by using a frequency domain enhancement algorithm; recoding the underwater target features by using a feature embedding extraction algorithm; taking the token mark sequence and the scene feature token mark sequence as the input of an underwater scene prompter, respectively calculating the attention scores of the two token mark branches by using a target perception attention algorithm and an auxiliary modal importance evaluation algorithm, and generating new scene features; introducing a cross-layer weight fusion mechanism, performing further feature extraction on the underwater scene in combination with shallow and deep features, and outputting target frame information through a prediction head network; in a network model training process, the performance of executing a specific underwater task is improved by adjusting a partial multi-head attention mechanism algorithm of a trunk vision Transform layer encoder.

The invention provides an enterprise management method and system and computer equipment, and the method comprises the steps: determining initial enterprise data of a plurality of enterprises and a corresponding enterprise management strategy, carrying out the preprocessing of the initial enterprise data, obtaining target enterprise data, generating an enterprise global database, and carrying out the calculation of the enterprise global database according to the enterprise management strategy. Determining at least one target evaluation algorithm and a corresponding service providing strategy, and based on each target evaluation algorithm, according to the target enterprise data of the plurality of enterprises in the enterprise global database, performing evaluation processing on the development conditions of the plurality of enterprises to obtain an enterprise development condition evaluation result corresponding to each target evaluation algorithm, and providing a service matched with the development condition of the target enterprise for the target enterprise based on the enterprise development condition evaluation result and the service providing strategy corresponding to each target evaluation algorithm. Therefore, operation complexity of enterprise management is reduced, timeliness and accuracy of service providing and enterprise management are improved, enterprise satisfaction is improved, and corresponding management of multiple enterprises is facilitated.

The invention relates to the technical field of data transmission, in particular to a processing and risk assessment method and device for mass files and asset data and a medium, and the method comprises the steps: dividing to-be-processed mass files and asset data into a plurality of data clusters; based on a duration evaluation algorithm and a risk evaluation model, calculating the expected total processing duration and the risk level of each data cluster, and processing each data cluster according to the expected total processing duration and the risk level; performing real-time feature extraction and vectorization representation on the asset data, and matching asset features with a risk information feature library to identify and evaluate the risk of the asset features; when it is monitored that the data cluster transmission state or asset data analysis is abnormal, an auxiliary processing thread is started to continue executing the next task in the data cluster, the average processing speed of the corresponding data cluster is updated, and the total processing duration of the remaining tasks is evaluated again. Therefore, the data processing efficiency and the data security of the power system are improved.

According to the multi-sensor fusion evaluation algorithm and device based on the continuous time sequence, modeling is carried out based on continuous time, physical reality is better met, motion modeling with better precision is achieved, intra-frame motion can be accurately described, smoother and more accurate track estimation can be provided, and the method and the device are suitable for being applied to multi-sensor fusion evaluation. Especially, the advantages are obvious in high-speed and high-frequency vibration scenes; meanwhile, according to the technical scheme disclosed by the invention, when LiDAR / Camera matching fails, the constraints of the IMU and the GNSS are seamlessly and continuously applied to the whole track through a continuous time model instead of only acting on a discrete frame, so that error accumulation is greatly inhibited, system collapse is avoided, the robustness of a degraded scene is enhanced, application in specific fields such as surveying and mapping is greatly facilitated, and the method is suitable for popularization and application. And the method has the capability of flexibly adding constraints.

The invention discloses a cultural tourism economic operation data monitoring method, which comprises the following steps of: constructing a multi-dimensional value radar index system: establishing a multi-dimensional evaluation framework, and quantifying bridge and tunnel values; deploying a multi-source data acquisition network: constructing an integrated data acquisition system, and performing real-time mapping between a physical space and a digital space; and adaptive fusion analysis: constructing a quantum state superposition classifier, representing bridge and tunnel characteristics by using quantum bits, constructing a real-time digital twin mirror image engine, carrying out dynamic evaluation algorithm loading through linkage of a data acquisition network, a mirror image body and an algorithm pool, and introducing an ecological correction coefficient and a hidden value correction coefficient to carry out negative adjustment on an economic contribution value. Dynamically distributing an index weight by combining an entropy weight method and a Delphi method with a particle swarm optimization algorithm, and outputting an economic equivalent value in combination with the index weight; building a four-dimensional visual decision platform; and displaying the interactive radar map based on WebGL three-dimensional rendering and data binding technology.

The invention discloses a safe real-time monitoring system and method for lifting and lowering a drilling barrel of a heavy slurry cap in pressure control drilling, and the method comprises the steps: collecting the real-time data of the shaft in a drilling process, and calculating the pressure change trend of the shaft, the density fluctuation range of drilling fluid and the thickness attenuation condition of the heavy slurry cap in a future preset time period through a multivariable coordination nonlinear prediction algorithm; in combination with a calculation result, the drilling fluid density is dynamically adjusted by utilizing a drilling fluid density self-adaptive adjustment algorithm, and the sealing performance, the pressure transmission efficiency and the anti-pollution capacity of the heavy slurry cap are evaluated by virtue of a heavy slurry cap action efficiency evaluation algorithm; and the adjusting parameters and the evaluation result are input into a heavy slurry cap pressure control intelligent analysis engine to calculate pressure control parameters and generate a control instruction, the state of the shaft is monitored in real time according to the control instruction in combination with the safe real-time monitoring parameters of the drilling barrel, and data closed-loop updating is formed. According to the invention, multi-parameter collaborative management and control and precise evaluation of the efficiency of the heavy slurry cap are realized, and the safety monitoring precision of the shaft is improved.

The application discloses a kind of paving thickness and flatness real-time evaluation and subthreshold correction method on machine, it is related to data processing technical field.The method comprises the following steps: step one: the sensor height data of the local working area behind the screed is obtained to generate observation data containing virtual paving thickness deviation information and multi-scale flatness index information;Step two: based on the observation data, a pre-set sparse Gaussian process wavelet Bayesian conditional risk assessment algorithm is called, and combined with short-term prediction and conditional risk assessment, a subthreshold correction instruction representing the target correction level is generated;Step three: based on the subthreshold correction instruction, the correction action corresponding to the subthreshold correction instruction is executed by the actuator.The application can maintain stable, continuous and forward-looking control effect under complex disturbance conditions.

A user can define an evaluation algorithm at a borehole system using sensor parameters collected at one or more locations throughout the borehole and equipment used at the borehole. The evaluation algorithm can implement statistical analysis on the parameters collected. The evaluation algorithm can combine two or more parameters to generate complex parameter relationships. The evaluation algorithm can implement one or more alarms using the analysis of the parameters. The alarms can specify levels or thresholds for different types or severities of alarms. Alerts can be defined using the alarms where the alerts are communicated to users or borehole systems for action. The evaluation algorithms can be packaged and forwarded for a technical review and a business management review, where evaluation algorithms that are approved can be made available to other users or at other borehole systems. Evaluation algorithms can be made available to select borehole systems or borehole operations.

The present application relates to the technical field of data processing, and provides a task execution optimization method and system based on an environmental protection law enforcement inspection intelligent terminal. The method comprises the following steps: obtaining core performance indexes of the environmental protection law enforcement inspection intelligent terminal, and calculating a comprehensive performance score by using a nonlinear dynamic evaluation algorithm; obtaining ambient light intensity and the temperature of the environmental protection law enforcement inspection intelligent terminal, and calculating a saturation degree dynamic adjustment amount of the screen; calculating a screen contrast dynamic gamma correction amount according to the comprehensive performance score and the ambient light intensity, so as to adjust the brightness and contrast of the screen of the environmental protection law enforcement inspection intelligent terminal; obtaining a task queue to be executed, and evaluating the estimated resource consumption, subsequent dependency degree and historical risk coefficient of each task to be executed, so as to calculate the comprehensive priority weight of each task; and preferentially executing tasks with high comprehensive priority weight.

The present application relates to the field of intelligent agricultural control, and more particularly to an intelligent agricultural control system and method based on the Internet of Things. The method comprises: collecting environmental parameters, calculating predicted environmental parameters by a dynamic adaptive trend prediction algorithm; based on the predicted environmental parameters, calculating membership values using a multi-level fuzzy membership environmental adaptability evaluation algorithm; based on the current environmental parameters, the predicted environmental parameters and the membership values, generating control signals using a dynamic control algorithm, and intelligently controlling the equipment. The present application solves the technical problems that the traditional system cannot effectively predict environmental data, but simply responds to the current environmental data; the traditional control method can only determine environmental suitability based on a single numerical value, and cannot achieve fine evaluation of the degree of parameter deviation from the ideal value; and the traditional agricultural control system is difficult to effectively suppress emergencies, which easily causes the system to overrespond or even become unstable.