137 results about "Growth data" patented technology

The invention relates to the technical field of concrete curing, in particular to an intelligent control method and system for a precast concrete component curing kiln, and the method comprises the steps: obtaining historical and real-time environment parameters of the curing kiln, generating monitoring data with an identifier in combination with a component type and a current process stage, and matching a corresponding preset temperature and humidity regulation and control model. And optimizing the model by using a historical temperature and humidity response sample and component strength increase data to generate a personalized target temperature and humidity regulation and control model. And analyzing real-time parameters based on the model, outputting a control instruction containing a target temperature rise rate, a humidity threshold value and heat preservation time, and converting the control instruction into a kiln body adjustment control signal according to a dynamically adjusted execution rule. And finally, sending to an execution terminal to realize cooperative regulation and control of heating, humidification, ventilation and heat preservation systems. According to the method, multi-source data and process characteristics are fused, the adaptive learning ability is achieved, the maintenance environment can be accurately regulated and controlled, and intelligent, refined and efficient operation of the maintenance process is achieved.

The invention relates to the technical field of automobile batteries, in particular to a battery cell matching system with improved cycle life, comprising: a battery cell parameter acquisition module for acquiring initial capacity, internal resistance value and voltage curve of a to-be-matched battery cell for a new energy automobile; the dynamic attenuation modeling module is used for establishing a dynamic attenuation model according to capacity attenuation and internal resistance growth data in the battery cell recycling process; the matching optimization module is used for combining the output of the dynamic attenuation model with the current matching strategy and adjusting the battery cell group in the matching scheme to obtain a battery pack; the high-temperature correction module is used for dynamically adjusting a matching correction value of a high-temperature cycle attenuation coefficient according to the attenuation performance of the battery cells in the battery pack under high-temperature cycle; and the control module is used for determining a screening threshold value of the static parameters of the battery cells according to the maximum difference quantity of the initial capacities of the plurality of battery cells in the same battery pack, or determining a dynamic characteristic weight coefficient according to the internal resistance difference of the plurality of battery cells in the battery pack. According to the invention, the stability of battery cell matching is improved.

The invention relates to the technical field of forestry intelligent monitoring, and particularly discloses an intelligent monitoring system for the growth condition of afforestation and greening nursery stocks based on deep learning, which is characterized in that multi-modal growth data of the nursery stocks are acquired through a multi-spectral imaging sensor, a three-dimensional laser scanning sensor and an environment monitoring sensor, and a standardized data set is formed through space-time alignment processing; extracting morphological structure and spectral response features through spatial domain and frequency domain parallel analysis, and generating multi-dimensional feature representation through cross-modal fusion; converting the parameters into growth state parameters by utilizing a feature recombination and space mapping technology; identifying an abnormal growth mode through time sequence dynamic analysis; and finally, adaptively adjusting the working parameters of the sensor according to the abnormal type to form closed-loop monitoring. The problem that an existing monitoring system cannot autonomously optimize a monitoring strategy according to the abnormal state is solved, and accurate monitoring and intelligent regulation and control of the nursery stock growth condition are achieved.

The invention discloses an AI-based rice planting environment data informatization management system, and relates to the technical field of agricultural informatization. The method comprises the steps that an environment data acquisition module obtains multi-dimensional space-time marked environment parameters; the disturbance modeling module is used for analyzing environment change, identifying a disturbance structure and calculating a sudden change value; the growth stage parameter module is used for constructing a stage characteristic parameter set; the growth state modeling module is used for generating growth state data in combination with disturbance and a growth stage; the strategy generation module formulates a resource regulation and control strategy based on the growth state and the feedback information; the resource coupling analysis module is used for identifying coupling and interference relations among resources; the strategy constraint module is used for adjusting a resource strategy to generate a control strategy; and the control execution feedback module is used for executing the regulation and control instruction and feeding back growth data. Through high-precision space-time environment data fusion, deep coupling analysis of environment disturbance and growth states and resource regulation and control closed-loop optimization management, intelligent and precise monitoring and regulation and control of a rice planting environment are realized.

The invention discloses an irrigation decision determination method considering a multi-source water conversion process, and relates to the technical field of agricultural water conservancy irrigation, and the method comprises the steps: obtaining a multi-spectral image and a thermal infrared image of crops in a target irrigation region, calculating a vegetation index and a leaf area index, and combining with the actually measured growth vigor data of the crops, a growth vigor model of crops is constructed by establishing a mapping relation, the rainfall, the irrigation volume, the canal system infiltration replenishment volume and the groundwater capillary rise volume of a target irrigation area are obtained, and a soil moisture income and expenditure model is constructed based on a water circulation process. According to the method, a plurality of paths such as rainfall, irrigation, canal system leakage and groundwater capillary rise are considered through the constructed soil water volume income and expenditure model, crop physiological moisture inflection points are accurately recognized by simulating the root zone volumetric moisture content and growth vigor response curve, threshold control irrigation based on crop requirements is achieved, and the crop yield is improved. And the multi-source input and conversion process of the farmland hydrological system is comprehensively reflected.

The invention discloses a hybrid model seedling growth prediction method based on ant colony algorithm optimization, and relates to the field of seedling cultivation, and the method comprises the following steps: collecting and preprocessing seedling growth data, and constructing a standardized data set; performing hybrid model training by using the standardized data set; the hybrid model training comprises the following steps: screening out key features by using an XGBoost sub-model, outputting a seedling growth prediction result through a GRU-KAN fusion sub-model, and optimizing hyper-parameters of the GRU-KAN fusion sub-model through an ant colony optimization algorithm; and inputting growth parameters of a to-be-predicted seedling into the optimized hybrid model, and outputting a corresponding seedling growth prediction result. According to the method, through a collaborative architecture of XGBoost and GRU-KAN and intelligent parameter adjustment of an ant colony optimization algorithm, adaptive feature screening, deep fusion and hyper-parameter efficient optimization of multi-source heterogeneous data are completed, and automatic and high-precision prediction of the seedling growth process is realized.

The invention discloses a live pig dynamic nutrition model construction method and system based on multiple breeding modes, and relates to the technical field of intelligent precise feeding with livestock breeding and information technology crossing, and the method comprises the following steps: S1, collecting macroscopic growth data and physiological indication data of live pigs through a sensor network deployed in a breeding environment; the macroscopic growth data comprises body weight, feed intake, water intake and environment temperature and humidity. According to the live pig dynamic nutrition model construction method and system based on the multiple breeding modes, by constructing a collaborative mechanism of data credibility grading, dual-core model cross validation and cyclic feedback optimization, the technical limitation that a traditional nutrition model depends on macroscopic data and neglects individual physiological state changes is effectively solved. According to the system, multi-source sensor data fusion analysis is utilized, and dynamic threshold judgment and a machine learning algorithm are combined, so that accurate perception and adaptation of real nutritional requirements of live pigs are realized, and the problem of disjunction between nutrition supply and physiological requirements is avoided from the source.

The invention discloses a hydraulic valve body unit machining process and device, and relates to the technical field of valve body machining, and the hydraulic valve body unit machining process comprises the steps that a hydraulic valve body unit is placed in an electrolyte tool container, a preset working voltage is set, and an electrolysis environment is formed; a plurality of observation points are arranged on the surface of the hydraulic valve body unit, and real-time thickness data of the oxidation film are obtained; calculating oxidation film growth data of the hydraulic valve body unit according to the oxidation film real-time thickness data of the plurality of observation points; comparing the oxide film growth data with a standard threshold range, and if the oxide film growth data accords with the standard threshold range, adjusting working parameters of an electrolyte tool container according to a comparison result of the oxide film growth data and the standard threshold range; and electrolytic machining is stopped, and the hydraulic valve body unit subjected to anodic oxidation is obtained. The oxidation film growth data are monitored in real time in the anodic oxidation process, and the process parameters are adjusted in real time, so that the oxidation film preparation quality of the valve body workpiece can be effectively improved.

The invention discloses a forage grass drought loss assessment method, and relates to the technical field of forage grass drought loss assessment, and the method comprises the following steps: S1, collecting forage grass basic data in a drought influence period, the data comprising meteorological data, growth data, quality detection data and field management data; s2, preprocessing the basic data to obtain standardized data; s3, constructing a two-dimensional loss evaluation model, wherein the model comprises a yield loss calculation sub-model and a quality loss calculation sub-model; s4, respectively inputting the standardized data into the two sub-models to obtain a forage grass yield loss rate and a quality loss coefficient; and S5, in combination with the yield loss rate and the quality loss coefficient, weight coupling is set according to the forage grass type to calculate the total loss rate, and evaluation is completed. According to the forage grass drought loss evaluation method, the quality loss is taken into evaluation, and the problem that the feeding cost is high due to neglect of the quality is avoided.

The invention discloses a method for constructing a sugarcane biomass 3D prediction model based on a dynamic video, and the method comprises the following steps: (1) actual measurement and investigation of sugarcane growth data: investigating the plant height, stem height, stem thickness, leaf area and stem leaf biomass of sugarcane every month until the sugarcane is mature; (2) carrying out 3D Gaussian spattering phenotype data extraction, data acquisition and data preprocessing; (3) 3D model construction and data verification, including three-dimensional reconstruction, point cloud data processing, phenotype analysis, growth trend tracking and biomass 3D prediction model establishment; and establishment of a biomass 3D prediction model: specifically, based on the key indexes extracted from the point cloud data and the actually measured biomass true value, the biomass prediction model established by adopting a random forest regression algorithm is the biomass 3D prediction model. The problems that in the prior art, data collection is prone to being interfered by the environment, the model generalization ability is insufficient, and the mapping relation between the form and the biomass is indefinite are solved.

The invention provides a forest sample plot monitoring system, the monitoring system comprises a three-dimensional live-action modeling subsystem, an Internet of Things monitoring perception subsystem and a data management analysis subsystem, the three-dimensional live-action modeling subsystem is used for collecting sky point cloud data and ground point cloud data, and constructing a forest sample plot three-dimensional model; the Internet of Things monitoring and sensing subsystem is used for collecting tree growth associated information collected by a sensor and gathering and transmitting the tree growth associated information to the data management and analysis subsystem; and the data management and analysis subsystem is used for combining the forest sample plot three-dimensional model with the tree growth associated information, and analyzing to obtain forest accumulation variation and tree growth data. Through high-precision, high-efficiency and automatic reconstruction of the three-dimensional real scene of the forest sample plot, automatic, real-time and continuous monitoring of key growth parameters such as diameters at breast height of all sample trees in the sample plot is realized, and the intelligent level and operation efficiency of forest monitoring are improved.

The application discloses an online semantic momentum stabilization system and method using a Welford algorithm and an optimistic concurrent control. In view of the semantic centroid calculation deviation and timing conflict problems caused by data noise, distribution drift and distributed out-of-order writing in the process of continuous incremental input of a large-scale vector database, the application combines a Welford online iterative algorithm to realize high-precision incremental updating of the semantic centroid mean and variance under constant space complexity. Meanwhile, a distributed optimistic concurrent control mechanism is used to implement version revision number checking on semantic segments and block obsolete data coverage caused by cross-node out-of-order arrival. The application guarantees the numerical stability and timing consistency of semantic feature expression in a dynamic growing data environment.

The invention relates to the field of seedling cultivation, in particular to a forestry seedling cultivation system and method.The forestry seedling cultivation method comprises the steps that seedling growth data of a plurality of nursery areas are collected to extract growth state characteristics of seedlings in the nursery areas; in combination with the growth state characteristics and the gap area uniformity of the adjacent nursery stocks, the growth quality characterization value of the nursery stocks in the nursery region is evaluated, and the nursery region is marked; in response to a marking result, analyzing growth interference characterization parameters aiming at the nursery stock according to soil state characteristics in the nursery region, and judging whether the nursery region accords with environment-related interference conditions or not in combination with the range of the diameter-height ratio of the corresponding nursery stock; and evaluating and analyzing the cultivation and growth conditions corresponding to the nursery stocks in the nursery area. According to the method, the growth quality of the seedlings is comprehensively evaluated, the overall seedling rate and the cultivation quality of the forestry seedlings are improved, the industrial development requirements are met, the seedling cultivation management accuracy is improved, and the management difficulty is reduced.

The invention belongs to the technical field of forestry engineering, and provides a castanopsis hystrix improved variety and eucalyptus mixed forest litter resource regulation and control method, which comprises the following steps: setting a quadrat region based on a castanopsis hystrix and eucalyptus mixed forest, and dividing an ecological conflict region; calculating a litter load threshold value of the ecological conflict area and dividing a high-load area; regulating and controlling litters in the high-load area, and performing demand analysis based on the regulated and controlled high-load area to obtain a decomposition demand coefficient; judging whether the high-load area is a strong decomposition area or not, performing secondary regulation and control on the strong decomposition area, obtaining a periodic decomposition rate and judging whether the periodic decomposition rate accords with expectation or not; if so, acquiring related growth data of the castanopsis hystrix in the regulation and control region twice, and performing growth analysis on the castanopsis hystrix to obtain an average growth index; evaluating the growth state of the castanopsis hystrix based on the average growth index; the method is beneficial for improving the pertinence and effectiveness of ecological management of the mixed forest.

The invention discloses an environmental parameter prediction method based on GRU-LSTM-Attention, and the method comprises the following steps: 1, collecting experimental bacterium growth data, dividing a time period, and sorting the experimental bacterium growth data into a data set; 2, preprocessing the data set, and constructing a training data set and a test data set; step 3, a GRU-LSTM-A model is constructed; 4, a GRU-LSTM-A model is trained; 5, carrying out the performance evaluation of the trained GRU-LSTM-A model, and obtaining a prediction model; and step 6, predicting the test data set by using the prediction model to obtain environmental parameters. According to the environmental parameter prediction method based on GRU-LSTM-Attention, the problems that in an existing environmental parameter prediction technology, due to the fact that an RNN model or an LSTM model is singly used, the operation efficiency is low, and the prediction result is not accurate enough are solved.

The application relates to the technical field of traditional Chinese medicine planting, and discloses a traditional Chinese medicine planting soil quality monitoring method and system based on big data analysis. The method comprises the following steps: collecting related data of traditional Chinese medicine planting, the related data comprising historical soil data, planting history data and traditional Chinese medicine growth data, pre-processing the related data, taking the pre-processed related data as learning data to train a prediction model; obtaining stage related data of a first growth stage of traditional Chinese medicine, and demarcating a key data range based on first data in the stage related data; generating multiple data input combinations based on the key data range, and generating a soil optimization scheme based on the multiple data input combinations and the prediction model; and judging whether the soil optimization schemes of all growth stages of traditional Chinese medicine have been generated, and if not, generating the soil optimization schemes of all growth stages of traditional Chinese medicine by using the above method. The growth soil optimization scheme improves the quality and yield of traditional Chinese medicine.

The application relates to a crop planting information intelligent management method and system based on a pesticide knowledge graph, which comprises the following steps: acquiring a pesticide knowledge graph of pesticide and crop entity relationships, wherein nodes in the pesticide knowledge graph are expressed by a ternary group constituted by pesticides, crops and crop diseases; acquiring growth data of crops in real time, and writing the growth data into a block chain; matching the growth data with the pesticide knowledge graph through hyperbolic space, and outputting pesticide use suggestions to planting personnel; generating a planting log based on the planting operation records of the planting personnel through prompt learning; and writing the planting log into the block chain. The application recommends pesticide use suggestions through a knowledge graph technology and a hyperbolic space technology, and automatically generates a raw material planting log through a machine learning method based on prompt learning, so that the credibility, integrity and safety of crop planting information are realized.

The application provides a crop yield prediction method and device based on multi-modal information fusion and a computer storage medium, multi-modal crop data including daily growth data and historical annual yield data of crops are acquired; a crop yield prediction model is established and the crop yield prediction model is used for crop yield prediction, the model comprises a representation module, a first modeling module, a first fusion module, a second fusion module, a second modeling module and a crop yield prediction module, on one hand, the application introduces crop daily growth picture time series data to perform fine-grained modeling on the growth trend of crops; on the other hand, the daily growth data and the historical annual yield of crops are modeled through a hierarchical time series modeling method, and the potential correlation between the historical annual yields of different crops in a region is considered, so that the crop yield prediction effect is further improved. The method has the characteristics of easy implementation and good effect.

The invention relates to the technical field of intelligent management of water and fertilizer integrated equipment, and discloses an intelligent management method and system for water and fertilizer integrated equipment based on artificial intelligence, and the method comprises the steps: obtaining the growth data and soil environment information of crops, and calculating the absorption efficiency of the crops for main nutrients; when the absorption efficiency does not reach the preset normal efficiency threshold value, or the absorption efficiency reaches the preset normal efficiency threshold value and the growth data is deviated from the preset health reference value, trace element lack screening judgment is carried out; and adjusting a nutrient supply scheme or a soil environment management scheme according to a trace element deficiency screening judgment result. According to the invention, the crop growth data is acquired by using an image recognition technology, non-contact and high-efficiency crop growth monitoring can be realized, an accurate and real-time basis is provided for subsequent absorption efficiency calculation and abnormality judgment, and the automation and accuracy of data acquisition are improved.

The present application relates to the technical field of field trait detection, and particularly relates to a field trait detection method based on crop breeding, comprising the following steps: determining an environment penalty value according to detected field crop color data and soil data, determining a yield reward value according to detected field crop growth data and nutrient data stored in a crop management platform, and determining a fertilizer utilization value according to detected field soil element data; performing weighted summation to generate a fertilization adjustment reward function, wherein a dynamic coefficient is determined according to crop breeding stage big data fitting of the crop management platform; updating a fertilization amount reinforcement learning model based on the fertilization adjustment reward function at regular time intervals, and determining a breeding multi-element recommended fertilization amount based on the fertilization amount reinforcement learning model by combining a Critic network with an Actor network. The present application realizes collaborative closed-loop detection of stress resistance, yield increase and fertilizer saving and field detection data mining by combining a reinforcement learning model with breeding.

The invention provides an optimal design method for a vegetation recovery nutritional agent formula of a power grid project slash in a high-altitude region, and the method comprises the steps: collecting growth data and landform data of pioneer plants in a target region, and constructing a vegetation growth demand model of the target region based on the growth data and the landform data; acquiring a nutrient element detection result of the multi-region soil sample of the target region, and inputting the nutrient element detection result into the vegetation growth demand model for plant growth nutrition supply simulation; and according to the plant growth nutrition supply simulation result, respectively determining an optimal nutritional agent component proportion corresponding to each region in the power grid project slash, and generating a corresponding nutritional agent formula and sending the nutritional agent formula to a display end. According to the method, accurate optimization of a nutritional agent formula in the vegetation recovery process of the high-altitude region is realized, so that the survival rate and the growth speed of vegetation recovery of the high-altitude region are effectively improved, and ecological restoration of a power grid project slash of the high-altitude region is promoted.

This invention relates to the field of food security monitoring technology, specifically a method for monitoring and warning of food security yield based on big data analysis. The method includes: collecting multi-source monitoring data such as remote sensing vegetation index time-series data, meteorological observations, soil moisture, crop growth surveys, and historical yield statistics. An improved time-series decomposition algorithm combining crop phenological patterns and interannual cycle characteristics is employed to decompose multiple components of the remote sensing time-series data. Spatiotemporal fusion and feature derivation are performed on farmland environment and growth data to construct a multi-dimensional feature vector set. A yield prediction integration model is used to calculate the predicted grain yield and confidence interval, and combined with yield security thresholds, graded risk warning information is output. Multiple data types are integrated to generate a structured warning report, which is then pushed to a decision support platform, achieving accurate grain yield calculation and standardized risk warning.

The invention discloses a biological data analysis processing method and system, and the method comprises the steps: building a combined model based on a resistance phenotype prediction model and a yield prediction model, and obtaining the current planting data of the combined planting of different genotypes of oat, including growth data and genotype data; inputting the genotype data into a resistance phenotype prediction model to obtain resistance phenotype prediction output; and inputting the resistance phenotype prediction output and the growth data into a yield prediction model to obtain yield prediction output. The invention provides a comprehensive solution combining a resistance phenotype prediction model based on an attention mechanism and a yield prediction model based on a recurrent neural network, and the accuracy and practicability of oat disease resistance and yield prediction are remarkably improved.

The application provides a farm load prediction method, device and equipment based on multi-source data, and relates to the field of power load prediction.The method comprises the following steps: obtaining historical load data, breeding animal growth data, environmental data and equipment data of a farm; decomposing the historical load data according to a variational mode decomposition algorithm to obtain a plurality of load components; wherein the variational mode decomposition algorithm optimizes parameters through a sparrow search algorithm, and the sparrow search algorithm establishes a fitness function about the load components according to the breeding animal growth data; after the plurality of load components, the breeding animal growth data, the environmental data and the equipment data are spliced, the spliced data is input into a preset Transformer model to obtain a power load prediction curve of the farm.The application can improve the accuracy and availability of the power load prediction result of the farm.

The invention relates to the technical field of supply chain tracing, and particularly discloses a fresh flower supply chain tracing system and method based on a block chain, and the system comprises a growth supervision module which is used for carrying out the supervision and analysis of the real-time states of all fresh flowers in the same batch in the growth process, by shooting the multi-angle appearances of different flowers in the same batch, the growth data of the different flowers at multiple angles can be obtained, and by combining the growth data at multiple angles, the growth data can be corrected, so that the data is closer to a real value, and the situation that the growth data is more accurate due to the fact that the postures of the flowers in the growth process are changed is avoided. According to the method, the situation that the real-time growth states of all the fresh flowers in the same batch are abnormal or not in the growth process can be analyzed by establishing a fresh flower growth state influence coefficient calculation model on the basis of the data when the real-time growth states of all the fresh flowers in the same batch are abnormal, so that the accuracy of a fresh flower quality analysis result is improved.

The invention discloses a method and a device for autonomic optimization of a database all-in-one machine. The method comprises the following steps: firstly, acquiring query behavior, system load and storage space growth data; then preprocessing the data and carrying out anomaly identification analysis to determine a key bottleneck causing performance reduction; automatically generating an optimization scheme including index optimization, SQL optimization, execution plan adjustment and database parameter adjustment based on the bottleneck; the scheme is implemented after being audited, and performance changes after implementation are verified and evaluated; and finally, updating the adjustment and optimization knowledge base, historical samples and problem feature information according to a verification result to form an autonomous optimization closed loop. According to the invention, complete circulation from data acquisition to autonomous optimization is realized, manual dependence is reduced, and the automation and intelligence level of database all-in-one machine management is improved.

The invention relates to the technical field of intelligent monitoring of plant maturity, and discloses an intelligent monitoring method and system for the maturity of cauliflower flower buds, and the method comprises the steps: analyzing the flower bud distribution density according to a pre-obtained cauliflower image, carrying out the multi-angle image collection of an area with the flower bud distribution density greater than a preset density threshold value, and obtaining a first image set; extracting morphological parameters of the flower buds, identifying flower bud contours, and calculating flower bud volumes; extracting morphological characteristics of an area in the flower bud contour, and calculating a probability value of the flower bud belonging to a preset maturity level through characteristic fusion analysis in combination with the flower bud volume to obtain the maturity of the flower bud; in combination with the maturity of the flower buds and historical growth data, the growth process of the flower buds is simulated, the maturity time of the flower buds is predicted, and the maturity of the flower buds of the cauliflower is intelligently monitored; according to the method, the maturity of the flower buds of the cauliflower is intelligently monitored, so that the prediction accuracy of the flower bud maturity time can be improved, and accurate harvesting, yield increase and efficiency increase of the cauliflower are realized.

The invention discloses a carbon sequestration capability assessment method based on ecological restoration, and belongs to the technical field of carbon sequestration assessment, and the method comprises the following steps: S1, obtaining growth data of each plant in an ecological restoration region in a restoration period; s2, determining functional leaves of each plant, and obtaining a carbon sequestration transition coefficient of the plant during the restoration period according to the growth data of the functional leaves during the restoration period; s3, inputting the carbon sequestration transition coefficient of each plant in the restoration period into the carbon sequestration capability stage model to obtain the carbon sequestration capability weight of each plant at each moment in the restoration period; and S4, determining a restoration result of the ecological restoration area according to the carbon sequestration capability weight of each plant at each moment in the restoration period and the base carbon library of the ecological restoration area. According to the method, systematic evaluation of the carbon sink capacity of the whole restoration area is realized, and the accuracy and reliability of ecological restoration carbon sink evaluation are greatly improved.

The invention relates to the technical field of forestry management, in particular to a forest thinning necessity assessment method based on environmental data analysis, and solves the technical problems of incomplete and inaccurate assessment in a forest thinning necessity assessment process due to limited field survey coverage and insufficient remote sensing image definition in the prior art. The method comprises the following steps: acquiring multi-source remote sensing image data of a forest region, and dividing the forest region into a plurality of sub-regions according to the multi-source remote sensing image data; determining an ecological optimization index of each sub-region according to the multi-source remote sensing image data of the plurality of sub-regions; screening out a target sub-region of which the ecological optimization index is greater than a preset threshold value from the plurality of sub-regions, and obtaining vegetation growth data of the target sub-region; and according to the ecological optimization index and the vegetation growth data of the target sub-region and the multi-source remote sensing image data of the target sub-region and the peripheral sub-regions, determining the demolishing necessity of the target sub-region.