10 results about "Potential assessment" patented technology

This invention belongs to the field of deep learning and renewable energy assessment technology, specifically a method and system for assessing urban photovoltaic potential based on multimodal deep learning. The method includes: acquiring and preprocessing multimodal data of the study area; constructing and training a multimodal deep learning model, which includes a roof recognition module based on a semantic segmentation network and a multimodal fusion module consisting of image branches, table branches, and a fusion output layer to output roof masks and potential correction parameters; based on the model output, sequentially calculating the roof's radiation potential, installation potential, technical power generation potential, carbon emission reduction potential, and economic potential, forming a multi-level potential assessment system. This invention, by fusing multi-source heterogeneous data, achieves high-precision, high-efficiency, and systematic assessment of rooftop photovoltaic potential at the urban scale, providing strong support for urban energy planning and decision-making.

This application relates to a method, apparatus, and electronic equipment for assessing the demand response potential of a zero-carbon hydrogen metallurgical system. The method includes: constructing an operating model of a hydrogen-based shaft furnace considering the metallization rate of direct reduced iron; establishing an objective function based on the production baseline load of the zero-carbon hydrogen metallurgical system; constructing a demand response potential assessment model for the zero-carbon hydrogen metallurgical system based on the objective function, constraints of the electrolyzer in the zero-carbon hydrogen metallurgical system, and constraints of the operating model of the hydrogen-based shaft furnace; determining the demand response potential assessment index for the zero-carbon hydrogen metallurgical system; and assessing the demand response potential of the zero-carbon hydrogen metallurgical system under real-time electricity pricing based on the assessment model and the assessment index. This solves the problems in related technologies, such as the lack of assessment of the flexibility of the zero-carbon hydrogen metallurgical system and the limited utilization of flexibility resources, leading to severe wind and solar power curtailment, grid backflow, and voltage exceedances, resulting in high production costs.

This invention provides a system and method for online monitoring and cathodic protection assessment of corrosion in well site oil pipelines, belonging to the technical field of online monitoring and cathodic protection for oil pipeline corrosion. The system includes an online corrosion monitoring system for well site oil pipelines, a cathodic protection potential assessment system, a corrosion and cathodic protection data acquisition unit, a local online corrosion and cathodic protection monitoring unit, a well site oil pipeline, and a remote monitoring center unit. The corrosion and cathodic protection data acquisition unit is electrically connected to the remote monitoring center unit via the local online corrosion and cathodic protection monitoring unit. The corrosion and cathodic protection data acquisition unit is also electrically connected to the online corrosion monitoring system and the cathodic protection potential assessment system located on the well site oil pipeline via corresponding network cables. This invention, through the combined use of multiple corrosion detection methods, ensures the reliability of the detection results and reduces the limitations of single-method detection results.

The application discloses a kind of air conditioner load demand response potential evaluation method and system, it is related to power system demand response technical field.The present application includes: constructing air conditioner user before response load data set and after response load data set;Adaptive load prediction model is constructed, and time series distribution adaptation mechanism is introduced, the distribution difference of different time period data on each layer output of encoder is calculated, and is added to model training target as regularization term;Before response and after response load data set is input into two adaptive load prediction models respectively for training, the feature data of the time to be evaluated are input into two models respectively, obtain before response load prediction value and after response load prediction value, calculate the difference value of both, it is evaluation result.The present application fuses Transformer network and distribution adaptation mechanism, can effectively capture air conditioner load long-term dependence relationship and adapt time series distribution drift, for realizing high-precision potential evaluation in the case of lack of historical response data.

This invention discloses a method, apparatus, terminal equipment, and storage medium for assessing the spatiotemporal load transfer potential of a data center, relating to the field of load assessment technology. The method includes: acquiring load transfer assessment parameters for the data center; determining a divisibility coefficient based on the maximum number of parallel subtasks and a preset parallelism benchmark value; determining a time flexibility coefficient based on the latest completion deadline and expected execution duration; determining a state dependency coefficient based on the total amount of migration state data and a preset state quantity benchmark value; determining a spatial transferability coefficient based on available link bandwidth, scheduling period length, data volume required for task migration, and data center transmission distance; and weighted summing of the above coefficients to obtain a load spatiotemporal transfer potential coefficient, thereby determining the load spatiotemporal transfer potential. By implementing this invention, the problem of prior art using only maximum tolerable latency to measure flexibility, leading to a deviation between the assessment results and actual schedulable capacity, can be solved, improving the matching degree of potential assessment results.

The application discloses a kind of fusions non-intrusive identification and thermal inertia decoupling resident air conditioner load portrait construction method.First, the intelligent electric meter total incoming line electrical data of the user to be monitored is obtained and synchronous outdoor meteorological data are acquired, and time series alignment is carried out;Second, the total incoming line electrical data is non-intrusively unmixed using a sequence-to-point convolutional neural network model, and the air conditioner equipment operating state sequence and operating power curve are extracted;Then, a dimension reduction thermal dynamics feature model is constructed, and the thermal-electric elastic coefficient, user comfort threshold temperature and normalized thermal inertia factor are decoupled, and a multi-dimensional user feature vector is constructed in combination with daily average power consumption;Finally, clustering is performed using a Gaussian mixture model, and the corresponding load portrait semantic label is assigned to generate a resident air conditioner load portrait.The application can identify the physical characteristics of the user's air conditioner load without installing an indoor temperature sensor, and improve the accuracy of demand response potential assessment.

This invention provides a flight potential prediction method based on expert experience and dynamic profile generation technology, belonging to the field of flight potential prediction technology. The invention first constructs a multi-dimensional evaluation table including flight capability dimensions and non-capability factor dimensions, with two experts independently scoring to form an initial subjective assessment; it then establishes a mapping relationship between expert assessment scores and key objective flight parameters, parametrically calibrating the initial subjective assessment; it aggregates the calibrated expert assessments, objective parameter aggregation indicators, and psychological test data, forming a flight potential classification model based on potential profile analysis; finally, it generates and dynamically updates the flight potential profile, constructs a future flight performance prediction model, and dynamically corrects it. This method improves the stability, completeness, and predictive accuracy of flight potential assessment.

The present application relates to the technical field of human resource management, and discloses an enterprise human resource management system based on artificial intelligence; an image construction module is used to construct a periodically updated digital twin image; a scene simulation module is used to simulate an enterprise virtual scene; a feature simulation module is used to simulate internal potential features; an employee screening module is used to screen potential employees from employees; a potential prediction module is used to identify training indicators and develop training management information; the present application can dynamically generate a virtual adjustable test simulation environment, ensure that employees can conveniently perform simulation tests in a virtual environment, avoid the subjectivity and one-sidedness of employee potential assessment in traditional human resource management, reduce the workload of the human resource department in employee assessment, potential screening and training planning, improve the work efficiency of human resource management, and achieve the effects of intelligentization, precision and high efficiency of human resource management.

This application relates to the field of artificial intelligence technology, and in particular to a method, apparatus, electronic device, and storage medium for generating promotional information, to improve the quality of generated promotional information. The method includes: responding to an input command triggered on a promotional information generation interface, presenting the input business promotion requirements, and initiating multiple intelligent agents to collaboratively execute a promotional information generation task; during task execution, the multiple intelligent agents collaboratively generate promotional information based on the business promotion requirements and at least one reference promotional material; each reference promotional material is selected from a preset material library based on the dissemination potential assessment value of each promotional material in the preset material library; presenting and continuously updating the generation progress of the promotional information on the promotional information generation interface, the generation progress reflecting the task completion status of the multiple intelligent agents; and presenting the target promotional information and interactive prompts for the target audience to evaluate the usability of the target promotional information upon task completion.

This application relates to a method and system for dynamic treatment of kitchen waste integrating deep learning. The method includes: collecting multimodal sensing data during the kitchen waste disposal process; performing spatiotemporal alignment and noise suppression on the multimodal sensing data to generate a structured feature vector set; inputting the structured feature vector set into a pre-set deep learning model to perform waste component distribution inference and degradation potential assessment, outputting a component proportion matrix and a bioactivity index; dynamically generating a phased treatment instruction sequence based on the component proportion matrix and bioactivity index, combined with the current operating conditions of the treatment equipment; and sending the phased treatment instruction sequence to the kitchen waste treatment terminal to complete the dynamic treatment decision identification for the disposed waste. This solution can achieve accurate assessment and dynamic treatment of kitchen waste, improving treatment efficiency and resource utilization efficiency.