17 results about "Rurality" patented technology

The application discloses a population migration dynamic attribution assessment method and system for air pollution health risk, and belongs to the technical field of environmental health risk assessment and spatio-temporal big data analysis. The method firstly fuses multi-source spatio-temporal data and carries out standardization preprocessing; secondly, based on night light and built-up area statistical data, a provincial adaptive threshold algorithm is used to dynamically identify three types of spatial units, i.e. urban core area, expansion area and rural area; then, a "no migration" counterfactual population distribution scenario is constructed based on the population pattern in the base year; finally, the health risk in the real and counterfactual scenarios is calculated in parallel, and the net health risk caused by population migration is quantified by difference. The application effectively solves the exposure misclassification and attribution capability loss problems caused by the use of static population and fixed boundaries in the traditional assessment method, improves the spatio-temporal accuracy of the assessment and the calculability of the attribution result, and provides a standardized analysis tool for environmental health risk management.

This invention relates to the field of rural economic data analysis technology, specifically an artificial intelligence-based method for rural economic data analysis. The method includes: delineating an economic data perception domain in a geographic information system based on the administrative division vector data of the target administrative village; collecting multi-source heterogeneous economic data through a protocol parser of an agricultural IoT terminal and converting it into standardized economic behavior data; extracting entities using natural language processing technology to generate a structured rural economic behavior fact table; inputting the fact table into a pre-trained economic activity association model and constructing a rural industry relationship graph using a graph embedding algorithm; and calculating the centrality index of economic entities based on the graph topology to identify core operating entities. This method enables accurate processing and association analysis of rural economic data, clearly presenting industry relationships and providing effective technical support for rural economic analysis.

This invention relates to the field of water demand prediction technology, specifically to an intelligent water demand prediction system and method for rural areas. By quantifying the crop growth process into continuous growth state parameters and constructing a dynamic crop coefficient model based on a sine function, it solves the problems of discontinuous water demand calculation and physiological distortion caused by traditional step-type models during growth stage transitions. It corrects root depth and combines measured soil moisture content with preset field capacity to dynamically constrain effective precipitation, overcoming the shortcomings of existing technologies that ignore basic soil moisture conditions and overestimate the contribution of natural precipitation. By constructing a priority score based on water depletion time and growth sensitivity, it achieves rational scheduling in scenarios with limited water resources across multiple plots, ensuring the continuity of crop physiological water demand and improving the precision of irrigation decisions and water resource utilization efficiency.

The application discloses a linear drainage ditch for rural areas and a permeation testing method thereof, relates to the technical field of sewage collection, and solves the problems of high use cost and easy damage of the existing concrete drainage ditch for rural areas, reduces the material consumption, and reduces the construction difficulty, and the specific scheme is as follows: the drainage ditch main body is provided with a grid on the groove, the drainage ditch main body is made of a mixed material, the main agent of the mixed material is epoxy resin, the curing agent is modified amine T31 synthesized by phenol, formaldehyde and fatty amine, the diluent is acetone, the toughening agent is dibutyl phthalate, the filler is fly ash, and the aggregate is fine aggregate produced by using construction waste.

The application discloses a rural house and land integrated investigation method based on multi-source data fusion and intelligent cooperation, and has the characteristics that house and land data are acquired and preprocessed and coded to obtain first data; the first data includes real estate unit number; external operation right ownership investigation data is acquired to serve as second data; the second data includes land ownership information; supplementary cadastral data is acquired to serve as third data; the third data includes a repaired house graph; the first data, the second data and the third data are subjected to investigation office processing to realize rural house and land integrated investigation. The method integrates multi-source data to form high-precision and high-present-situation cadastral results, the results provide the most direct and most accurate data support for land space planning compilation, old village reconstruction, land expropriation compensation, illegal land use treatment and the like, and improve the problems of operation difficulty and low efficiency of traditional measurement in dense house areas.

This invention discloses an artificial intelligence-based method, system, equipment, and storage medium for rural pollutant assessment, relating to the field of ecological assessment technology. The method includes: dividing the assessment area into multiple units and encoding them, and using a sliding window assessment on a monthly cycle. It involves collecting multi-source data, including remote sensing, meteorological, pollution monitoring, and rural statistics data, processing them uniformly, and constructing pollution pressure characteristics (including monthly and cumulative effects) and ecosystem stability characteristic sequences. An artificial intelligence discrimination model is used to train and infer from the two types of features, outputting pollution risk classifications and a list of high-risk units. A database is established to store the results, supporting incremental model updates, and finally, the regional risk distribution results are output periodically. This invention enables traceable and reusable long-term management ledgers, improving the consistency, automation level, and engineering application value of assessments.

The invention relates to the technical field of big data and artificial intelligence, and discloses a rural traffic accident high-risk crowd identification method based on signaling data fusion and a space-time diagram convolutional network, and the method comprises the steps: taking mobile signaling data as a core, fusing multi-source data, carrying out privacy protection preprocessing, extracting the space-time features of rural users through dynamic diagram modeling, and carrying out the reconstruction of the space-time features of the rural users; space-time association is captured by means of a space-time diagram convolutional network fusing an attention mechanism, a rural scene exclusive rule is accurately captured through dynamic graph modeling and regional risk factor injection, and cross-domain privacy security training is realized in combination with federated learning and homomorphic encryption. And finally, through integration of a space-time diagram convolutional network and a federated safety tree model, risk grade division is assisted by a gray system, and risk probability distribution of rural traffic accident high-risk crowds is accurately output, so that a clear object is provided for targeted propaganda and education, police waste caused by indifference propaganda and education is reduced, the rural traffic accident prevention and control efficiency is effectively improved, and the accident rate is reduced.

The application discloses a kind of cultivated land abandoned risk prediction methods of fusing counterfactual reasoning ideas, comprising the following steps: step 1, based on global urban and rural residential grid data set, generate rural residential mask data set;Step 2, the aging trend of rural residential area on the grid scale of northeast area is extrapolated by fusing statistical yearbook data, and the corresponding average walking speed is calculated;Step 3, 15-minute accessibility calculation is carried out based on forbidden area, aging population speed and slope data;Step 4, based on 15-minute accessibility range and cultivated land data, predict potential abandoned risk area;Step 5, based on the future cultivated land abandoned after the type determination of abandoned ring turns out.The present application is based on counterfactual reasoning perspective, objectively and quantitatively identifies cultivated land plots with abandoned risk, to provide data support for abandoned prevention means.

This invention provides a route optimization method for truck-drone collaborative delivery in rural areas. It relates to the field of route planning technology. The method includes: constructing a delivery network comprising delivery centers, ground delivery points, and flight delivery points; dividing customer nodes into multiple delivery clusters; selecting a cluster center for each cluster as the truck's stopping point and the drone's take-off and landing starting point; establishing an integer programming model with the objective of minimizing delivery costs and waiting time costs; solving the integer programming model using a two-layer interactive optimization algorithm; dynamically determining the optimal connection point between the truck's next stopping point and the drone's current path using the golden section method, minimizing the time difference between the truck and the drone reaching that point; and outputting the optimal collaborative delivery route for the truck and drone, including the truck's driving path, the drone's flight path, and the location information of each dynamically connected point.

The system according to this embodiment aims to reduce mismatches in relocation by simulating life in rural areas in advance. [Solution] The system according to the embodiment comprises a generation unit, a search unit, and a simulation unit. The generation unit analyzes geographic data and lifestyle information to generate a 3D virtual space. The search unit explores the 3D virtual space generated by the generation unit. The simulation unit performs a simulation based on the user's occupation and family structure.

This invention discloses a framework and method for quantitative assessment of combustion pollutants based on heat load, belonging to the fields of building energy conservation technology and environmental science. Its purpose is to address the problems of unclear correlation mechanisms between pollutant emissions and building heat load, and the lack of quantitative basis for emission reduction measures in existing technologies. The method includes: obtaining the envelope structural parameters of rural buildings with different energy efficiency in a target area, and constructing heat load models for each rural building based on the envelope structural parameters; inputting geographical location data and meteorological data of the target area into each heat load model to simulate the heating heat load of each rural building under different geographical locations; calculating the heating fuel consumption corresponding to different fuels based on the heating heat load, combined with preset fuel calorific value and combustion equipment thermal efficiency; inputting the heating fuel consumption into a pre-constructed pollutant emission estimation model to obtain multi-dimensional pollutant emissions generated by the combustion of different fuels in various types of rural buildings; and determining the key factors affecting pollutant emissions through multiple regression analysis based on the multi-dimensional pollutant emissions, and generating a collaborative emission reduction path based on the key factors. This application can provide theoretical support for the scientific formulation of collaborative emission reduction paths in rural areas, and achieve precise quantification and collaborative control of rural heating pollutants.

The application discloses a homestead house surveying and mapping method based on handheld SLAM and semantic segmentation, which acquires multi-source sensing data collected by a handheld SLAM device; performs coupling processing on the multi-source sensing data based on a SLAM algorithm to generate a global point cloud map with absolute coordinates; uses a pre-trained special semantic segmentation model for homestead houses to perform component-level classification on the global point cloud map, and extracts building contour features; constructs a three-dimensional building model based on the building contour features, and generates homestead surveying and mapping results in combination with field ownership investigation information. The application solves the positioning drift problem of the handheld device through multi-source sensing coupling and global coordinate constraint, solves the automatic contour extraction problem under the shielding of rural house sundries through the special semantic segmentation model, and realizes high-precision and automatic surveying and mapping of rural homestead houses in a complex shielding environment.

This invention relates to the field of remote sensing image processing and discloses a method for detecting complex rural roads based on a novel MACER-UNet deep learning network model. The method includes: Step S1, selecting corresponding rural road distribution areas based on geographical partitions; Step S2, downloading high-resolution remote sensing images of typical rural road distribution areas determined in each geographical partition in Step S1 and performing preprocessing; Step S3, designing and constructing the structure of the MACER-UNet deep learning network model; Step S4, training, validating, and testing the MACER-UNet deep learning network model in Step S3 based on rural road image data of different materials in the geographical partitions selected in Step S2; and Step S5, using the trained MACER-UNet deep learning network model to process the high-resolution remote sensing image under test and extracting all rural roads within the image. This invention improves the topological connectivity of the road extraction results and significantly reduces the phenomenon of broken lines in the extraction results of long and narrow rural roads.

This invention belongs to the field of power system operation optimization technology, specifically disclosing a rural microgrid cluster collaborative scheduling method and related device driven by a hybrid firefly algorithm. This method addresses the problems of dispersed and highly volatile renewable energy in rural areas by constructing a cluster structure including wind, solar, and traditional biomass energy microgrids. An optimization model is established with the goal of minimizing the total daily operating cost, comprehensively considering operating costs and energy curtailment penalties. By introducing an adaptive adjustment factor and a dynamic decay step size mechanism, the firefly optimization algorithm is improved, enhancing its global optimization capability and convergence speed, achieving efficient collaborative scheduling under multiple constraints such as microgrid output, energy storage, and interactive power. Simulation results show that this method can significantly reduce cluster operating costs, improve energy utilization, and provide effective technical support for the economical, low-carbon, and stable operation of rural microgrids.

The application discloses a rural three-capital intelligent early warning method and system based on multi-modal data fusion, and belongs to the technical field of rural collective economy management. Through multi-modal data fusion and AI dual-engine identification, the complex risk identification work of traditional manual checking is converted into automatic processing, the fund violation rate is reduced, the discovery rate of cadres and relatives' illegal possession of assets and resources is improved, and the passive checking is changed into active early warning. Through automatic data acquisition, the data entry workload of grassroots personnel is reduced. Cold and hot separation storage reduces the annual data storage cost. Blockchain collaborative disposal eliminates artificial data transmission between departments and reduces the disposal cycle. Through knowledge graph and visualization technology, the three-capital management short board is directly presented, and the supervision department is assisted in accurate policy making. Data transmission adopts national encryption algorithm SM2, the storage layer implements AES-256 encryption on sensitive fields, and the permission control granularity is refined to the field level.