1107results about How to "Improve sustainability" patented technology

The invention relates to the technical field of data processing, and discloses a power distribution method based on AI artificial intelligence and an intelligent power distribution cabinet. The method comprises the following steps: carrying out real-time data acquisition on key nodes of the power distribution system through an intelligent sensor network to obtain original operation data of the power distribution system, and carrying out cleaning and standardization processing on the original operation data to obtain a standardized data set; performing analysis modeling on the standardized data set through multi-level hybrid model training to obtain an AI analysis model of the power distribution system; processing and analyzing the power distribution data collected in real time to obtain a load prediction result and a dynamic balance strategy; performing abnormal mode identification and multi-path reasoning on the system operation parameters to obtain equipment health state evaluation and maintenance suggestions; and based on the equipment health state assessment and maintenance suggestions, performing multi-objective optimization calculation on renewable energy source supply and power consumption demands to obtain an intelligent energy scheduling scheme. According to the invention, the efficiency and accuracy of the power distribution method based on AI artificial intelligence are improved.

The invention relates to the technical field of crop production, in particular to a big data-based crop yield prediction and synergistic management system, which comprises a data acquisition module, a data processing module, a multi-scale crop growth prediction module and a plant stomata dynamic adjustment and transpiration monitoring module, the data acquisition module acquires multi-dimensional agricultural data; the data processing module receives and preprocesses the collected multi-dimensional agricultural data; the multi-scale crop growth prediction module predicts growth trends of crops in different time dimensions; and the plant stomata dynamic adjustment and transpiration monitoring module analyzes and predicts the transpiration rate of the crops, and adjusts the irrigation amount in real time in combination with the prediction result of the growth trend of the crops. According to the invention, the sustainability of water resource utilization is improved, the waste of agricultural resources is reduced, and finally the benefits of agricultural production and the sustainability of the environment are improved.

The invention provides a soil fertility quality analysis method and system based on corn planting cultivated land, and relates to the technical field of soil quality analysis and evaluation.The soil fertility quality analysis method comprises the steps that a visible-near infrared spectrum image of a soil sample is collected, reflectivity data of a key wave band is extracted from the spectrum image, and a soil health index and a nutrient balance index are calculated; calculating a soil fertility quality comprehensive score for comprehensively evaluating the soil fertility according to the soil health index, the nutrient balance index and the soil moisture content; comparing the comprehensive score of the soil fertility quality with a preset threshold value, and evaluating the soil fertility; a machine learning model is introduced, historical soil data is trained and predicted, a quality analysis model is constructed based on a random forest algorithm, the trained quality analysis model is put into use, and new data is continuously learned to dynamically adjust and update, so that the soil detection process is simplified, and the soil fertility change can be monitored in real time. And agricultural managers are helped to make scientific decisions.

The invention belongs to the technical field of engineering supervision, and discloses a concrete strength measurement system and method for civil construction engineering, and the method comprises the following steps: a test point planning module determines the number and position of detection points; the route planning module plans a running route of the detection mechanism; the detection mechanism sequentially moves to each detection point position according to the planned route; the positioning module analyzes the distance from the ultrasonic probe to the laser; the ultrasonic probe A is aligned with the ultrasonic probe B; the ultrasonic probe A and the ultrasonic probe B scan a concrete building; detecting the strength of the concrete building by using a rebound apparatus; the humidity acquisition module acquires humidity data of concrete; the temperature acquisition module acquires environment temperature data; and the ultrasonic analysis module comprehensively calculates the concrete strength by combining the rebound value, the concrete humidity, the environment temperature and the ultrasonic detection sound velocity. According to the invention, concrete strength on-site detection can be carried out on a high-rise building; by integrating various data, the strength of the concrete can be evaluated more accurately.

The invention discloses a coal mine ventilation optimization and air quality regulation and control method based on adaptive learning, and relates to the technical field of coal mine ventilation optimization, and the method comprises the following steps: arranging a plurality of sensors in a mine, carrying out the real-time collection and monitoring of key environment parameters, and uploading the collected data to a central control system; and comparing the collected real-time data with historical data, and dividing the collected real-time data into conventional working condition data and abnormal working condition data according to environment states. Through real-time monitoring of various sensors and a deep learning algorithm, accurate ventilation control is achieved, a problem area is rapidly positioned in an emergency, airflow is dynamically adjusted, and safety of miners is guaranteed. According to the system, an LSTM model is adopted to predict an event trend in advance, preventive regulation and control measures are taken in time, real-time adjustment is supported, and the strain capacity is improved. Through a multi-stage ventilation mode, the system optimizes energy consumption and equipment operation efficiency, reduces unnecessary energy waste and maintenance cost, and realizes balance between safety and economy.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention relates to the technical field of multimedia interaction, in particular to an intelligent interaction control method and system for digital exhibition hall multimedia equipment, and the method comprises the following steps: S1, carrying out the arrangement of a sensor, and carrying out the intelligent sensing and data collection; s2, generating an intelligent interaction strategy; s3, equipment cooperative control and execution; and S4, performing real-time feedback and system optimization. According to the method, user behaviors and environment data are collected in real time through the multi-modal sensor, user requirements are accurately speculated in combination with a deep learning algorithm, the optimal interaction strategy and task priority are generated, individuation and interaction efficiency of user experience are remarkably improved, multi-device cooperation is optimized through distributed task scheduling, and user experience is improved. According to the method, display content and environment parameters are dynamically adjusted, self-optimization of the system is realized by utilizing user feedback, real-time performance, smoothness and reliability of interactive experience of the exhibition hall are ensured, rapid fault response and accurate maintenance demand prediction are realized through anomaly detection and equipment log analysis, and the stability and sustainability of operation of the exhibition hall system are greatly improved.

The invention discloses an environment adaptability illumination control system applied to a travel landscape, and relates to the technical field of intelligent illumination control. The system comprises a lighting point data acquisition module, a region division module, a use condition monitoring module, a tourist distribution monitoring module, a data analysis and prediction module, an intelligent control module, a user feedback collection module and an adaptive adjustment module. The lighting point data acquisition module is used for acquiring position and state information of all lighting points in a scenic area; identifying all lighting equipment in the scenic spot; by integrating advanced data acquisition, region division, use condition monitoring, tourist distribution monitoring and data analysis and prediction modules, the intelligent level of the lighting system is remarkably improved, the system can automatically perform real-time adjustment according to environmental changes and tourist behavior modes, optimal configuration and accurate control of lighting are realized, and the intelligent level of the lighting system is improved. Therefore, the lighting effect is highly matched with the actual demand of the tourism landscape.

The invention provides a smart park optimization method and device based on digital twinning and BIM, and the method comprises the steps: collecting the actual information of a park, and carrying out the preprocessing; based on the configured basic BIM model, integrating the basic BIM model by using actual information; constructing a three-dimensional digital twinborn model, and fusing the integrated BIM model with the three-dimensional digital twinborn model; and analyzing and processing the park operation data by using the current fused three-digit digital twinborn model so as to realize park optimization. The digital mirror image of the smart park is constructed by integrating digital twinning, BIM, Internet of Things, big data, cloud computing, GIS and other frontier technologies, and comprehensive perception, accurate analysis, intelligent decision and efficient management of the park are realized. According to the scheme, the intelligent level, the operation efficiency and the sustainable development capability of the park are improved, and convenient, efficient and intelligent service experience is provided for park managers, settled enterprises and individual users.

The invention discloses a power station photovoltaic field area automatic inspection unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle inspection, and the unmanned aerial vehicle comprises the following components: a meteorological self-adaptive intelligent inspection flight system, a power supply module, a rotor wing device and a communication module; the power supply module is used for providing power support for each component of the unmanned aerial vehicle; the communication module is used for realizing data interaction between the unmanned aerial vehicle and the ground control station and between the unmanned aerial vehicle and the photovoltaic field area monitoring equipment; by carrying the meteorological self-adaptive intelligent inspection flight integrated system, meteorological conditions of a photovoltaic field area can be perceived and analyzed in real time, and an inspection path and a flight attitude are dynamically optimized according to the meteorological conditions, so that the unmanned aerial vehicle can preferentially inspect an area where a problem is most likely to occur, and the inspection efficiency is remarkably improved; meanwhile, the unmanned aerial vehicle can keep stable flight under complex meteorological conditions such as strong wind, flight accidents are avoided, and the accuracy of inspection data is ensured.

The invention discloses a data security transmission method for an edge gateway of the Internet of Things, particularly relates to the technical field of data transmission, and is used for solving the problem that an encryption mode of existing low-power-consumption equipment is not flexible in data encryption transmission. Sensing data are collected and preprocessed through low-power-consumption equipment; analyzing a multi-dimensional load state of a communication channel through network state tensor decomposition, and evaluating a future load change risk; time distribution of encryption and decryption links is analyzed through a distributed time optimization game, and link delay balance is evaluated; and dynamically adjusting whether to use the lightweight encryption algorithm based on the analysis result. When a lightweight encryption algorithm is selected, the equipment encrypts data to generate preliminarily encrypted data and transmits the preliminarily encrypted data to the edge gateway; and the edge gateway performs high-strength encryption upgrading on the preliminary encryption data to generate deep encryption data and transmits the deep encryption data to the cloud server. According to the invention, the energy consumption of the low-power-consumption equipment is reduced while the data security is ensured, and the reliability of data transmission is improved.

The invention provides a photovoltaic power station design system and method based on ecological collaborative optimization, and relates to the technical field of data processing. The system comprises an initial configuration module which is used for generating an initial layout structure of a photovoltaic module, performing energy-saving and consumption-reducing calculation and economic analysis according to generating capacity data, and obtaining initial energy-saving and consumption-reducing data and initial economic analysis data; the secondary optimization module is used for optimizing the parameters of the photovoltaic module based on the economical efficiency analysis data and determining energy-saving and consumption-reducing data and economical efficiency analysis data through the generating capacity optimization data; the ecological evaluation module is used for determining ecological benefit data of the photovoltaic power station according to the ecological influence parameters; and the collaborative optimization module is used for setting a power generation income weight and an ecological benefit weight so as to optimize the photovoltaic module layout structure and generate a photovoltaic power station design scheme based on ecological collaborative optimization. The method is used for realizing collaborative optimization of power generation benefits and ecological benefits of the photovoltaic power station, so that the power station gives consideration to ecological protection while improving the power generation efficiency.

The invention relates to the field of construction planning visualization, in particular to a construction planning visualization method and system for a refuse transfer station. The method comprises the following steps: acquiring a satellite positioning map and a refuse transfer station design drawing of a to-be-developed area; image detail reconstruction is carried out on the satellite positioning map, road space distribution fitting is carried out, and a regional road distribution network is constructed; performing surrounding building positioning calculation on the detail optimization reconstruction satellite image based on a regional road distribution network, performing an optimal site selection decision, and extracting an optimal address region image; carrying out transfer station key feature point identification on the refuse transfer station design drawing, then carrying out three-dimensional point cloud modeling, and constructing a transfer station three-dimensional structure model; and according to the optimal address area image, carrying out corresponding foundation accurate matching on the transfer station three-dimensional structure model, carrying out spatial positioning mapping, and constructing a transfer station address twinborn model. According to the method, the operation efficiency of the transfer station is improved, and the rationality of building planning is enhanced.

The invention relates to the technical field of mine potential safety hazard identification, in particular to a mine potential safety hazard identification method and system based on an unmanned aerial vehicle. The method comprises the following steps: acquiring a global image of a mining area by using an unmanned aerial vehicle camera, and constructing a three-dimensional model; thirdly, performing rock-soil landslide probability estimation through weathering erosion mechanism reduction in combination with the three-dimensional model and the global image, and calculating landslide coercing force potential energy increment; then, according to the landslide probability and the coercing force incremental data, carrying out risk grade endowing on the local area of the mining area; and finally, constructing a mine potential safety hazard identification optimization model based on the risk level, and sending the model to a terminal for potential hazard identification. According to the invention, the mine potential safety hazard identification technology is optimized, so that the mine potential safety hazard identification technology is more perfect.

The invention discloses a coal mine fireproof early warning method based on intelligent monitoring, and relates to the technical field of coal mine fireproof early warning, and the method comprises the following steps: obtaining coal mine environment information data parameters in real time through a coal mine fireproof early warning system, including humidity dynamic parameter information and air pressure parameter information; the environment is intelligently evaluated through the generated environment safety change index, when the environment is judged to be a high-risk environment, the system dynamically adjusts the sampling frequency based on the preset sampling frequency, fine changes such as methane diffusion or air static pressure fluctuation in the early stage of a fire disaster are captured in time, rapid early warning is ensured, and the safety of the fire disaster is improved. The accuracy and timeliness of signal capture are improved, and early intervention time is won for managers. And in a low-risk environment, the system collects data at a fixed sampling frequency, so that unnecessary calculation tasks and energy consumption are reduced, and long-term monitoring and trend analysis are supported. Through the dual regulation and control mechanism, the system not only ensures the data integrity, but also optimizes the resource utilization and operation efficiency.

The invention discloses a near-zero energy consumption building energy supply method and system based on multi-objective optimization, and relates to the technical field of building energy efficiency and renewable energy application, and the method comprises the steps: collecting energy consumption data in a building, and constructing a multi-objective evaluation system according to the energy consumption data; performing weight optimization on the multi-target evaluation system to obtain an optimal weight coefficient; combining the optimal weight coefficient with a solar photovoltaic system and a ground source heat pump system, and establishing a renewable energy supply strategy model; and outputting an energy supply scheduling instruction of the building in each time period based on the renewable energy energy supply strategy model, and adjusting operation parameters of a solar photovoltaic system and a ground source heat pump system. According to the invention, by adjusting the operation mode of the equipment in real time, the balance between the energy efficiency and the comfort level is ensured, the common problem of energy efficiency waste or insufficient comfort level in the traditional method is avoided, and by means of intelligent scheduling and optimization, the energy consumption of the building can be effectively reduced, and the sustainability of energy utilization of the building is improved.

The invention belongs to the technical field of intelligent agricultural planting, and particularly relates to an agricultural planting decision-making method and system based on a large language model.The technological process of the method includes the steps that firstly, historical field data and field data are preprocessed, and then processing such as feature extraction, clustering and sensor data fusion is conducted; and finally, predicting according to the agricultural data set and sending to the user. The concrete structure of the system comprises a data processing module, an intelligent agent module and an agricultural interaction model module. The system can be widely applied to precision agriculture, and helps farmers to implement precise management strategies such as precise fertilization, irrigation and pest control according to specific environment and crop requirements, so that the yield and quality of crops are improved. According to intelligent greenhouse management, environment parameters such as temperature, humidity and illumination are monitored in real time through an environment sensor, and after data are processed, an intelligent agent can provide an optimized management strategy to adjust conditions in the greenhouse, so that an optimal environment is created for growth of plants.

According to the intelligent energy-saving analysis method based on the building information model (BIM), building thermal performance parameters and an intelligent simulation algorithm are integrated, building heat conduction, a heat bridge effect and solar heat gain are accurately simulated, and a scientific basis is provided for design. Meanwhile, an energy-saving optimization strategy is automatically generated, such as wall thickness adjustment and window design, so that energy consumption is reduced. Besides, a multidisciplinary cooperative working platform is established, effective implementation of energy-saving measures is ensured, and energy use and maintenance tracking of the whole life cycle of the building are realized and energy-saving performance is continuously optimized in combination with an energy management system. The innovation of the method lies in the automatic and intelligent analysis process, and a comprehensive solution is provided for the field of building energy conservation.

The invention relates to the technical field of nondestructive testing, in particular to a gear defect intelligent detection method and system based on a magnetic memory technology. If the service gear is an abnormal service gear, a plurality of abrupt change magnetic flux signals are calculated by moving a sliding window so as to draw a magnetic flux line trend distribution diagram, the magnetic flux line trend distribution diagram is identified and analyzed, and an actual magnetic domain structure of each quasi magnetic flux leakage characteristic point in the magnetization process is obtained; and analyzing the actual magnetic domain structure to obtain a plurality of magnetic moment vector variations of each quasi-magnetic leakage flux feature point, analyzing defect features of the service gear based on the change amplitude of the magnetization intensity of the strong magnetic field for the magnetic moment vector of the normal service gear and the plurality of magnetic moment vector variations, and finally outputting an accurate defect type. According to the invention, the stress defect of the gear can be detected, positioned and identified based on the magnetic memory technology, so that a reliable fault detection basis is provided for the maintenance of the gear, and the accuracy and sustainability of the use and maintenance of the gear are improved.

The invention provides a microbial agent for promoting growth of medicago sativa and application of the microbial agent, and relates to the technical field of agriculture, the microbial agent is prepared from sinorhizobium meliloti MCSWL1, sinorhizobium meliloti MCSD1 and nitrogen-fixing rhizobium sp. MCDC3 according to the mass ratio of 1: 1: 2, and the mass ratio of the sinorhizobium meliloti MCSWL1, the sinorhizobium meliloti MCSD1 to the nitrogen-fixing rhizobium sp. MCDC3 to the nitrogen-fixing rhizobium sp. MCDC3 to the nitrogen-fixing rhizobium sp. MCDC3 is 1: 1: 2. The microbial agent for promoting the growth of the medicago sativa, provided by the invention, can form a close symbiotic relationship with the medicago sativa (particularly grassland No.3), and provides sufficient nitrogen nutrition for the growth of the medicago sativa, so that the soil fertility is effectively improved, and the healthy growth of plants is promoted.

The invention discloses an intelligent model data processing method applied to metal smelting, and relates to the technical field of metal smelting, and the method comprises the following specific steps: S100, collecting metal smelting data in real time, S200, establishing different sub-models, S300, outputting a prediction result based on each sub-model, and carrying out uncertainty evaluation. The invention designs a multi-target model output fusion mechanism capable of simultaneously considering the product quality, the energy consumption and the production efficiency in the metal smelting process, and through a weighted average method, the optimal decision output is obtained through the intelligent fusion of the output results, and the comprehensive optimal decision output is obtained, and S500, the actual data in the production process is continuously monitored, and the operation parameters of equipment are adjusted. The invention designs a multi-target model output fusion mechanism capable of simultaneously considering the product quality, the energy consumption and the production efficiency in the metal smelting process. And the model prediction results corresponding to different targets are intelligently fused to obtain a comprehensive optimal decision output, so that the metal smelting process can realize balance and optimization among a plurality of key indexes, and the demand of an enterprise for maximizing comprehensive benefits is met.

The invention relates to the technical field of soil health analysis, in particular to a tobacco planting soil health analysis and fertilization prediction system. Comprising a server, a health analysis module and a prediction management module, soil state parameters are obtained through soil microorganism activity action state analysis, soil nutrient state analysis and soil moisture state analysis of tobacco planting soil, then the soil change degree is analyzed, a prediction updating instruction is generated, and comprehensive detection, analysis and evaluation of the soil are achieved. By comprehensively analyzing the state parameters of the soil, the demand change of the soil, including the opportunity and quantification of fertilization, pesticide application and irrigation, can be accurately predicted, so that the growth efficiency and yield of crops can be improved; meanwhile, through the generated prediction notice, a farmer or a manager can be guided to take corresponding measures in time, it is guaranteed that soil and crops grow in the optimal state, and malnutrition, pest and disease damage and other problems are avoided.

The invention relates to an intelligent earth-rock filling and digging balance optimization method and system for a water conservancy project. According to the method, firstly, a laser scanner, unmanned aerial vehicle aerial survey equipment and a GPS positioning device are used for carrying out data collection on a construction site, and detailed topographic data and earth-rock distribution information are obtained; subsequently, construction requirements related to the hydraulic engineering are determined, including construction progress, construction sequence, resource availability, environmental limitation, and cost control. And based on the collected data and set construction requirements, a genetic algorithm is used for optimization, and an optimal earth-rock filling and digging balance scheme is generated. The optimization scheme covers a construction route, an equipment scheduling plan and a resource allocation scheme. And finally, hydraulic engineering construction is guided according to the optimization scheme, and efficient balanced filling and excavation of the earthwork are achieved. The construction efficiency is effectively improved, the cost is reduced, reasonable utilization of resources is ensured, and the method is suitable for various complex hydraulic engineering projects.

The invention provides a separate hospital guidance method and system for a large medical model, electronic equipment and a storage medium. The method comprises the following steps: receiving symptom description information of a natural language of a patient; performing natural language processing on the symptom description information by utilizing a large medical model, and determining symptom entity information and basic information of the patient; inputting the symptom entity information into a pre-constructed medical knowledge graph for matching, and determining candidate diseases and departments thereof corresponding to the patient; generating personalized triage suggestions by using the medical big model according to the candidate diseases and the departments thereof corresponding to the patient and in combination with the basic information of the patient; that is to say, according to the medical big model and the medical knowledge graph, symptom entity identification is performed on the symptom description of the patient, and then the corresponding candidate disease and the department thereof are determined, so that a manual separate hospital guide mode is avoided, the separate hospital guide efficiency and accuracy are improved, the waste of medical resources is reduced, and the fairness and sustainability of medical services are realized.

The invention discloses a computer artificial intelligence system based on a large model, which comprises a computer artificial intelligence system body, and the computer artificial intelligence system body comprises an LLM Agent architecture platform, an optimization design platform, an intelligent storage platform and an Agent platform. According to the computer artificial intelligence system based on the large model, the optimization design platform is arranged, the reasoning speed and the cost efficiency are optimized, the performance of a multi-agent system is improved, and the system has the advantages of being high in practicability and high in practicability. Stronger adaptability and sustainability are shown in the aspects of user interaction and long-term development of the system, and higher efficiency and accuracy are shown when a specific task is processed; by setting the Agent platform, the customizable and dialogue Agents are provided, the limitation of the Agents is solved by integrating the Agents with people and tools and performing dialogue among the Agents through automatic chat, and the cost of information analysis of the Agents is reduced.

The invention discloses a method for degrading vomitoxin in a corn deep processing process, and relates to the technical field of corn deep processing, and the method specifically comprises the following steps: obtaining temperature dynamic distribution information of corn slurry in each region, judging whether the temperature of the corn slurry in each region is too fast or too slow or not, and if yes, judging whether the temperature of the corn slurry is too fast or too slow or not; dividing each area into a temperature acceleration area, a temperature equalization area and a temperature delay area according to the judgment result; based on the division result of each area, an adjustment mechanism of heating power and time is constructed, and different temperature adjustments are respectively carried out on the temperature acceleration area, the temperature equalization area and the temperature retardation area; in the process that the adjustment mechanism adjusts the temperature of each area, whether the temperature adjustment effect of the adjustment mechanism in each area reaches the expectation or not is evaluated, and the adjustment mechanism is optimized according to the evaluation result. The problem that the temperature is not uniform in the corn slurry heating process is solved, and the effect of uniformly degrading vomitoxin is achieved.

The invention relates to the field of arid region oasis ecological protection, and discloses an arid region oasis protective barrier construction pattern and plant species configuration method, according to the characteristics of the arid region oasis structure, a protective barrier is laid, and according to the habits, uses and morphological characteristics of plants and the functional difference of different plants, the arid region oasis protective barrier construction pattern is constructed. Proper arbor, shrub and herbaceous plant configuration is carried out, and the method comprises the steps that a photovoltaic, mechanical sand stabilization and shrub and grass sand blocking belt is constructed in the edge zone of the desert, a windproof backbone forest is arranged at the junction of the desert and the oasis, forest network construction is carried out in a farmland area in the oasis, and greening and beautifying are carried out on the periphery and the interior of a residential area. By constructing a multi-layer and multifunctional oasis protection barrier system, desertification control and oasis protection system construction are achieved, regional economic development and biodiversity recovery are promoted, and the win-win situation of ecological benefits and economic benefits is achieved.

The invention discloses a soft foundation treatment method under the condition of reinforced flow-state muddy soft soil. According to the intelligent soft foundation treatment method, an intelligent monitoring and control system is introduced, the whole soft foundation treatment method achieves accurate monitoring and automatic adjustment of the construction process, and remarkable beneficial effects are achieved. Firstly, key parameters such as pore water pressure, soil deformation and underground water level are monitored in real time, so that engineers can quickly respond to changes of soil properties and timely adjust preloading load and vacuum degree, construction efficiency is optimized, and unnecessary construction period delay is reduced. And secondly, due to automatic data acquisition and processing, the accuracy and speed of data processing are greatly improved, personal errors are reduced, and the construction quality is ensured. In addition, the intelligent system can give out early warning in time when potential risks are found, construction safety is guaranteed, and the possibility of accidents is reduced.

The invention discloses an intelligent self-adaptive cleaning system and method for a photovoltaic module, and mainly solves the problems that the cleaning effect of a photovoltaic module cleaning system on stains (such as bird droppings and oil stains) with strong adhesive force and fine dust particles is limited, and it is difficult to ensure that the surface of the module keeps good cleanliness all the time. The cleaning system comprises an environment sensing module used for detecting the cleaning state of the photovoltaic module, an intelligent cleaning module connected with the environment sensing module and used for executing the cleaning function, and self-adaptive control connected with the environment sensing module and used for controlling the intelligent cleaning module. And the energy recovery module is connected with the intelligent cleaning module and is used for realizing energy recovery and feeding back power supply to the environment sensing module. By means of the design, the multi-sensor fusion environment sensing technology and the intelligent cleaning technology are combined, the stain type and the pollution degree can be more accurately recognized, a targeted cleaning mode is adopted, and compared with a traditional cleaning technology, the cleaning effect is remarkably improved.

The invention relates to the technical field of remote water meter abnormity early warning, and discloses a remote water meter abnormity early warning method and system based on historical water volume, and the method comprises the steps: dividing a user water meter into three classes A, B and C according to the installation time of the user water meter, and employing a differentiated abnormity detection strategy. For the A-type users, establishing a water consumption prediction model based on historical consumption data of the A-type users; for the B-type users, finding similar reference water meters from the A-type users by using a K-means clustering algorithm; and for the C-type users, monitoring whether the accumulated water consumption for continuous N days is completely equal or not after the C-type users are installed. After real-time usage data is obtained, usage deviations of various users are calculated respectively, if the deviations exceed a set threshold value, abnormal early warning is triggered, and the early warning level is dynamically adjusted according to the number of abnormal duration days. According to the method, technologies such as machine learning and clustering analysis are comprehensively utilized, a set of comprehensive, flexible and efficient water consumption abnormity early warning system is constructed, and the refinement and intelligence level of urban water supply management can be remarkably improved.