69results about How to "Increase profitability" patented technology

The invention provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

The invention discloses a mining area unmanned vehicle multi-mode competitive collaborative fusion sensing method, and relates to the technical field of fusion sensing, and the method comprises the following implementation steps: employing infrared thermal imaging and visible light image fusion to forward sense an obstacle, employing an ultrasonic radar and a laser infrared distance measurement sensor to sense a near-area obstacle, and employing a laser infrared distance measurement sensor to sense the near-area obstacle; whether an obstacle approaches or not is judged according to distance measurement information of sensors at four corners and four sides; a laser radar, a depth visible light imaging head and an inertial measurement unit are fused to perceive obstacles, and six modes are fused to construct a complete environment perception domain. According to the multi-mode competitive collaborative fusion perception method for the unmanned carrying vehicle in the mining area, posture changes are detected and tracked in real time, the angle of a vehicle steering engine is adjusted in advance, it is guaranteed that an image can accurately reflect an actual scene, the precision of state prediction of the carrying vehicle is further improved, and the safety and reliability of operation of the unmanned carrying vehicle in the mining area are improved.

The invention discloses a coal power multi-path transformation and new energy combination decision-making method under low-carbon transformation. The method comprises the following steps: firstly, constructing a system comprising first-level effect indexes such as environmental influence, economic benefit, energy safety and technical innovation and corresponding second-level indexes; then, alternative schemes of various coal power transformation technology and new energy project type combinations are generated; then quantitatively evaluating the effect index of each scheme, and calculating a comprehensive utility value by using multi-criterion decision analysis; identifying potential risk factors, and performing uncertainty analysis through Monte Carlo simulation; and finally, according to the low-carbon transformation target and policy evaluation scheme conformity, selecting a scheme which most conforms to the policy target, and continuously monitoring and adjusting. The coal-electricity multi-path transformation and new energy combination decision-making method under low-carbon transformation is beneficial to promoting energy low-carbon transformation, optimizing resource allocation, improving economic benefits, promoting technical innovation, enhancing risk management capability and ensuring that a project is consistent with policy orientation.

The invention discloses a method, a system and equipment for optimizing batching parameters in an industrial silicon smelting process. The method comprises the following steps: firstly, acquiring process parameters in an industrial silicon smelting process, standardizing the process parameters, then converting the process parameters into weight factors influencing a smelting result, and mapping a relation between the smelting result and the weight factors into a parameter equation based on a preset multiple linear regression model, obtaining a parameter set containing a weight factor and a linear regression coefficient; then, based on the parameter set, the initial raw material parameters and the reaction process parameters, an equivalent circuit model is constructed and simulated to generate smelting result data under different parameter combinations, and therefore parameter adjustment variables are obtained; according to the numerical range of the parameter adjustment variables and the initial raw material parameters, an optimal batching parameter combination is obtained; and finally, performing optimization treatment on batching parameters in the industrial silicon smelting process. According to the method, accurate control and rapid optimization adjustment of batching parameters can be stably achieved in the industrial silicon smelting process.

The invention relates to the technical field of inter-city cargo splicing and loading, in particular to an inter-city cargo splicing and loading transportation path optimization algorithm based on large model optimization, and solves the problems that in the prior art, an optimization algorithm based on a large model generally relates to a complex calculation process and needs a large amount of calculation resources and time, and especially in a real-time path planning scene, the calculation process is complicated. And the calculation delay can influence the transportation efficiency. The invention discloses an inter-city cargo splicing transportation path optimization algorithm based on large model optimization. The algorithm comprises data collection and preprocessing, model construction and training, algorithm optimization and verification, path optimization calculation, result evaluation and feedback, and algorithm deployment and application. According to the method, various possible transportation paths are analyzed and compared through an algorithm, the optimal path is selected, unnecessary detour and waiting time is reduced, so that the transportation efficiency is improved, and the algorithm can perform intelligent scheduling and reasonable arrangement of transportation vehicles and routes according to real-time traffic conditions, cargo demands, vehicle states and other information.

The invention provides a service area customer group portraying method and system based on deep clustering. The method comprises the steps that S1, customer group information of a service area is acquired and preprocessed; s2, performing feature processing on the customer group information to obtain feature variables; s3, further extracting feature variables by using the trained model to obtain deep features; s4, performing clustering analysis on the customer group information of the service area, and outputting a clustering result; s5, calculating a contour coefficient to evaluate the clustering quality; optimizing and adjusting the model according to an evaluation result; and S6, according to a clustering result, dividing the customer group information of the service area into multiple types to construct customer group portraits. According to the method, the deep clustering algorithm is introduced into the customer group portrait of the high-speed service area, the limitation of a traditional method is effectively solved, different types of customer groups are identified, and the accurate customer group portrait is constructed.

The invention relates to the technical field of vending machine inventory management, in particular to a vending machine automatic inventory management method based on image recognition, and the method comprises the steps: collecting internal and external images of a vending machine in real time, extracting the internal images of the vending machine, recognizing and analyzing the types of commodities, and generating a commodity display list; extracting a selling number evaluation index and a selling speed evaluation index of various types of commodities corresponding to each monitoring time point, calculating a first selling evaluation value, extracting an external image of the vending machine, identifying and analyzing consumer behaviors, calculating a ratio of a new consumption value, an old consumption value and a target consumption number, determining a second selling evaluation value, and calculating a selling speed evaluation value; the first sales evaluation value and the second sales evaluation value are combined and calculated through normalization processing to obtain the total sales evaluation value of various commodities, the change trend of the total sales evaluation value is analyzed, the sales trend value of various commodities is determined, and inventory management parameters are adjusted accordingly, so that the real-time monitoring of the commodity inventory state is realized, and the commodity inventory management efficiency is improved. The operation efficiency and the profitability of the vending machine are improved.

The invention provides a multi-farm picking and distribution integrated scheduling method based on an improved artificial bee colony algorithm, and relates to the technical field of agricultural product supply chain management and optimization, and the method comprises the steps: setting a constraint condition for a multi-farm picking and distribution integrated scheduling problem; defining an agricultural product freshness loss function; defining two objective functions, including an operation cost minimization function and a customer satisfaction maximization function; based on the constraint condition and the objective function, establishing a dual-objective model of multi-farm picking and distribution integrated scheduling; and solving the dual-objective optimization model to obtain an optimal solution set, and performing agricultural product scheduling according to picking and distribution information in the optimal solution set. According to the invention, picking and distribution links are effectively integrated, the operation cost is reduced, the freshness of agricultural products is guaranteed, the service satisfaction is improved, and an innovative and efficient solution is provided for an agricultural product supply chain in a community-supported agricultural mode.

A novel flue gas recycling system for four cooling sections of a circular cooler comprises the first cooling section, a first cooling fan, a second cooling section, a second cooling fan, a third cooling section, a third cooling fan, the fourth cooling section and a cooling pipeline of the circular cooler. The first cooling section, the second cooling section, the third cooling section and the fourth cooling section are distributed in the circumferential direction and connected into a continuous cooling loop through an overall rotary frame. The first cooling section, the second cooling section and the third cooling section are respectively connected with a first cooling fan, a second cooling fan and a third cooling fan; the fourth cooling section is connected with a cooling pipeline; and the second cooling fan extracts flue gas of the fourth cooling section of the circular cooler through the cooling pipeline and blows the flue gas into the second cooling section of the circular cooler to cool pellets. According to the novel flue gas recycling system for the four cooling sections of the circular cooler, a fourth cooling fan is omitted in the hot air process of the circular cooler, waste gas of the four cooling sections is extracted through a second cooling fan to cool pellets, the flue gas recycling efficiency is improved, and the cost is saved.

The invention discloses a logistics transportation scheme evaluation method and system, and relates to the technical field of logistics management, and the method comprises the steps: obtaining a training sample; extracting a multi-dimensional feature vector; inputting a deep learning model to obtain prediction cost; determining a difference value between the predicted cost and the real cost, and adjusting the deep learning model to obtain a trained deep learning model; obtaining a to-be-evaluated logistics transportation scheme; extracting key information; inputting the key information into the trained deep learning model to obtain the estimated cost of each logistics transportation scheme to be evaluated; and selecting an optimal scheme. According to the invention, the cost and benefit of the transportation scheme can be evaluated according to an objective and unified standard, the optimization of the transportation route and resource allocation is facilitated, the overall efficiency of logistics transportation is improved, and intelligent monitoring and management of the logistics transportation process are realized by introducing advanced technologies such as deep learning and NLP.

According to the regional load aggregator-oriented transformer area user flexible load scheduling method provided by the invention, the temperature control load, the electric vehicle, the energy storage equipment and the like are optimally scheduled, so that the balance of a power grid and the maximization of economic benefits are realized. The method comprises the following steps: predicting the load demand of a transformer area user through modeling analysis, and formulating an optimal scheduling strategy in combination with external signals such as real-time electricity price and environment temperature; a home energy efficiency management system (HEMS), a vehicle-to-power grid (V2G) control platform and an intelligent energy management system (EMS) are utilized to dynamically dispatch and control various flexible loads. The implementation result shows that the method effectively reduces the electric charge of the user, improves the revenue of the aggregator, improves the stability of the power grid, and has remarkable practicability and popularization value.

The invention discloses an electricity consumption information monitoring method and system based on hardware industry electric power big data, and belongs to the technical field of electric power information monitoring, and the method comprises the steps: S1, calculating segmented energy consumption data and classified energy consumption data based on electricity consumption data; s2, correcting the classified energy consumption data based on the working duration and rated power of the production equipment, and detecting whether the classified energy consumption data is abnormal or not; s3, constructing a fitting function by using the classified energy consumption data, the raw material feeding data and the finished product data, and judging whether the fluctuation information is abnormal or not; s4, judging the relationship between the power consumption data of the hardware feeding equipment and the threshold value to determine whether to adjust the feeding speed; s5, calculating the number of theoretical products based on the power consumption of the heating equipment, and adjusting the feeding speed if the actual number is smaller than a theoretical value; and S6, judging whether the heating equipment is abnormal or not according to the load current of the die forging equipment, and sending warning information. According to the scheme, multi-level power fault analysis is carried out on the hardware industry, so that the coverage range of power fault detection can be remarkably expanded.

The invention relates to a multi-demand response resource day-ahead market optimal bidding method and device, and the method comprises the steps: defining the structure and operation mechanism of a day-ahead electricity market, considering various demand response resources, and building demand response bidding models corresponding to the various demand response resources; on the basis of the demand response bidding model and contracts of various demand response resources of the signed customer, constructing a load aggregator decision optimization model by taking the maximum total income of a demand response load aggregator as a target; and solving the load aggregator decision optimization model to obtain an optimal bidding strategy. According to the method, the energy use efficiency can be improved, and meanwhile, the demand response utilization efficiency of large-scale data processing facilities with special demand characteristics is improved.

The invention provides a thermal power plant day-ahead transaction quotation scheme optimization method and device, equipment and a medium, and belongs to the field of data index evaluation management. The method comprises the steps that a thermal power plant revenue function with revenue maximization as a target is constructed, factors such as medium and long term electric quantity and contract electricity price are considered, the optimal day-ahead bid winning electric quantity and real-time on-grid electric quantity are determined through optimization, and theoretical optimal revenue is achieved. After actual operation, the coverage rate of the actual income and the theoretical optimal income is calculated, and if the income coverage rate of the thermal power plant in the target time period is smaller than the preset income coverage rate, the thermal power plant can adjust the quotation scheme in the day-ahead transaction by improving electricity price prediction and the like so as to optimize the income. By optimizing the quotation scheme, the resource allocation efficiency, the market competitiveness, the risk resistance and the economic benefits of the thermal power plant are remarkably improved.

The invention discloses an online solution annealing process for austenitic stainless steel. The online solution annealing process comprises the following steps that S1, smelted molten steel is subjected to continuous casting to form a plate blank; s2, a heating furnace is adopted for heating the casting blank, the temperature of a preheating zone is controlled to be 990-1130 DEG C, the temperature of a heating zone is controlled to be 1175-1280 DEG C, and the temperature of a soaking zone is controlled to be 1280-1285 DEG C; s3, seven passes of rough rolling and calendering are carried out; s4, 5-7 passes of finish rolling and calendering are carried out, wherein the finish rolling temperature of a finish rolling machine of the steckel mill is controlled to be 980-1000 DEG C; the rolling speed of final pass finish rolling is 210-320 mpm, and the recrystallization time of the casting blank is controlled to be 7-10 S; s5, the casting blank is rapidly and evenly cooled to 500 DEG C at the speed of 23-25 DEG C / S in a laminar water cooling mode and then coiled; and S6, pickling to obtain the austenitic stainless steel coiled material. The method has the advantage that online solution annealing of the austenitic stainless steel can be achieved.

The invention relates to a market-oriented new energy power supply and distribution system resource modeling method and system, belongs to the field of new energy power, and provides a market-oriented new energy power supply and distribution system resource modeling method for solving the problem that an existing mode is difficult to meet the requirements of efficient operation and transaction optimization of a new energy power supply and distribution system. Comprising the steps of classifying resources according to resource types contained in the new energy power supply and distribution system based on characteristic parameters of the resources; establishing a mathematical model to which each resource type belongs; respectively establishing an association model to which each resource type belongs, wherein the association model is used for associating offers between the resources and the new energy power supply and distribution system; and based on the mathematical model and the correlation model, constructing a new energy power supply and distribution system scheduling model, realizing cooperative operation between new energy power generation resources and energy storage resources, further realizing optimized storage and release of energy, and realizing accurate modeling and efficient optimization management of various resources in the new energy power supply and distribution system.

The present subject matter discloses a system and a method for integration application environment migration utilizing hub and spoke architecture. In one implementation, the method for application environment migration comprising assessing at least one source application code of corresponding integration application environment by a processor (122) of an integration application server. The processor forecasts an assessment statistic (302) that provides at least one functional readiness (304) and a timeline (306) to complete the migration of the each source application code. The processor (122) further scans the each source application code of corresponding source application for generating the integration applications compatible with a target application environment and creates a relationship map to match components of the integrating applications with components of the target application environment. The processor (122) generates a re-factored code for the each source application code by breaking the each source application code into macro-services (426a, ..., 426n) and repackaging the macro-services (426a, ..., 426n) in accordance with the target application code. Thereby, updating components of the integration application environment as per the forecasted assessment statistic (302) and the re-factored code and thus migrating the integration application environment to the target application environment while re-platforming the updated components and the re-factored code of the integration application environment to the target application environment.

This invention belongs to the field of virtual power plant technology and relates to a dynamic resource aggregation method for virtual power plants oriented towards the electricity spot market. The invention extracts distributed resource feature vectors and maps them to response type labels; determines the price fluctuation cycle based on the spot electricity price sequence, calculates the temporal matching degree between each resource and the market rhythm based on the feature vectors, and integrates clustering weights and response type labels to construct a comprehensive distance; clusters resources into dynamic aggregation units with the objective of minimizing the variance of the comprehensive distance; generates resource aggregation strategies based on the aggregation units and issues control commands; iteratively updates the clustering weights and aggregation parameters based on the clearing deviation rate fed back by market clearing feedback. This invention solves the technical problems of existing static partitioning ignoring market dynamics, clustering models easily merging heterogeneous resources, and lacking closed-loop optimization, achieving improved market responsiveness of virtual power plants, consistency of aggregation unit control, and adaptive optimization effects for continuous transactions.

The present invention relates to the field of data analysis, and in particular to a resource management method, device, equipment and storage medium based on a neural network model, the method comprising: constructing a neural network model, obtaining resource training data, inputting the resource training data into a module corresponding to the neural network model, simulating the demand data of the next year corresponding to the resource training data, and obtaining resource simulation training data; inputting the resource training data and resource simulation training data into a corresponding module, obtaining resource error data, and performing several training and error verification on the corresponding module according to the resource error data to obtain a target module of the neural network model. Compared with the prior art, it is possible to configure resources from multiple aspects, realize the reasonable configuration of resources, improve the efficiency of resource configuration, prevent resource waste, and enhance the benefit effect obtained by using resource configuration.

The invention discloses a breeding management system based on an amoeba management mode, which significantly improves the refinement and intelligence level of breeding management by fusing three modules of dictionary management, amoeba pricing management and hog house cost profit analysis, the dictionary management module accurately records pig transfer reasons, provides powerful data support for breeding decision making, and improves the breeding management efficiency. The ameeba pricing management module sets reasonable prices according to pig transfer conditions to ensure accurate evaluation of transfer values, and the hog house cost and profit analysis module comprehensively calculates hog house revenue, cost and profit based on pricing management to provide a scientific basis for breeding decision-making. Data transformation of breeding management is achieved, each hog house is promoted to become an independent accounting unit, resource allocation is optimized, the profit level is improved, meanwhile, the enthusiasm of breeding personnel is stimulated, enterprise competitiveness is enhanced, and new vitality is injected to sustainable development of the breeding industry.

The invention discloses a tariff calculation method based on access times and traffic. The tariff calculation method comprises the following steps: S1, monitoring and counting user access times and traffic consumption conditions; s2, establishing a user calling habit portrait; s3, designing a tariff scheme and performing tariff calculation; and S4, sending the tariff scheme to the user, and adjusting the tariff scheme according to user requirements. According to the invention, the actual use condition of the user is reflected more fairly and reasonably, and network resources are distributed reasonably; users are encouraged to save resource use, and the overall resource utilization efficiency is improved; more flexible tariff selection is provided for the user, and different requirements are met; and the service provider can improve the service quality and the profitability.

The invention, which relates to the technical field of orchard picking management, discloses an orchard picking adaptive management system comprising an environment monitoring module, a data analysis and prediction module, an automatic control module, an intelligent picking module, a pest and disease monitoring module and a pest and disease control module. The environment monitoring module is used for collecting orchard environment data and transmitting the orchard environment data to the data analysis and prediction module, and the data analysis and prediction module is used for preparing and preprocessing the data, determining a model order and establishing, checking and optimizing an ARIMA model for yield prediction. According to the invention, through the integrated ARIMA model, the orchard picking adaptive management system can carry out accurate yield prediction based on the historical time sequence data of the orchard; the problems of inaccurate orchard yield prediction, complex data processing and model establishment, difficult model optimization and selection, lack of basis for picking plan making and low orchard management efficiency can be solved.

The application discloses a method for flocculating and clarifying cheese whey and separating milk minerals, and belongs to the technical field of dairy processing. The method comprises two parts of whey flocculation and clarification and milk mineral separation. The whey is made clear and transparent by flocculation, and then the milk minerals are separated from the flocculation by multiple centrifugal separation, so that the method is simple and easy to implement. The method greatly improves the clarity and transparency of whey, provides great convenience for enterprises to process and utilize whey, can obtain milk minerals and some protein products, effectively utilizes waste, and improves the profit of enterprises.

The invention discloses a method for modifying smelting industrial pure iron top slag, and belongs to the technical field of ferrous metallurgy steelmaking processes. A top slag modification process is carried out mainly by adopting a three-step method, that is, the top slag modification process is carried out after tapping is finished, an LF treatment process and an RH treatment process are finished, and reasonable steel slag chemical components of the IF steel are obtained according to calculation and simulation; the influence of the adding amount of the modifier on the oxygen activity in the molten steel and the oxidability of the steel slag after converter tapping is studied; the slag forming effect of the steel slag after tapping is controlled; the steel slag oxidability change rule after RH oxygen blowing is analyzed; controlling the steel slag oxidability at the smelting end point; the optimal adding amount of the modifier and the deoxidizing agent is obtained; the influence of the oxidability of the steel slag on nozzle nodulation and the number of continuous casting furnaces is explained; and the influence of steel slag oxidability on aluminum loss is researched.

The invention discloses an offshore wind power peak regulation system meeting peak load based on methanol synthesis, and relates to the technical field of energy, the offshore wind power peak regulation system comprises an offshore synthesis platform, the offshore synthesis platform comprises an automatic power generation frequency modulation device, a PEMFC hydrogen production system, a DMFC power supply system, a methanol synthesis system and a computer control system, the computer control system is used for being connected with and controlling the automatic power generation frequency modulation device, the PEMFC hydrogen production system, the DMFC power supply system and the methanol synthesis system, the automatic power generation frequency modulation device is used for scheduling the output direction of current, and the methanol synthesis system and the PEMFC hydrogen production system are used for controlling conversion between electric energy and methanol. And the DMFC power supply system is used for transmitting electric energy generated by methanol combustion to a power grid through the automatic power generation frequency modulation device. The impact on a power grid caused by unstable power generation of the offshore wind plant can be avoided, meanwhile, the base load with the low economic value is converted into methanol with the high economic value, and the income of the offshore wind plant is increased.

The utility model belongs to a daily chemical grade humic acid product production device. Comprising a raw material pretreatment tank which is connected with a low-speed centrifugal machine through an alkali extraction reaction kettle, a centrifugate outlet of the low-speed centrifugal machine is connected with a high-speed centrifugal machine, a centrifugate outlet of the high-speed centrifugal machine is connected with an acid precipitation tank, and a precipitate material outlet of the acid precipitation tank is connected with a precipitate washing tank. A precipitate outlet in the precipitate washing tank is connected with an ultrafiltration device through a PH adjusting tank, and an outlet of the ultrafiltration device is connected with a daily chemical grade sodium humate production unit and / or a daily chemical grade humic acid production unit; a centrifugal residue outlet of the low-speed centrifugal machine and a centrifugal residue outlet of the high-speed centrifugal machine are respectively connected with an inlet of a waste residue collecting tank; an outlet of the waste residue collecting tank is connected with an agricultural fertilizer raw material storage tank; the method has the characteristics of avoiding the waste of humic acid and improving the profitability of enterprises.

The invention discloses a biological preparation capable of disinfecting and retaining freshness of mulberry leaves, which belongs to the technical field of biological preparations and comprises the following components: food-grade chlorine dioxide, sericin, creeping oxalis extract and fructo-oligosaccharide. The invention further discloses a preparation method of the biological agent with the disinfection and fresh-keeping functions on the mulberry leaves. The food-grade chlorine dioxide has the effects of disinfecting and inhibiting various bacteria, the sericin protein has oxidation resistance, a hydration film can be formed on the surfaces of mulberry leaves after spraying, the moisturizing effect is achieved, the effect of inhibiting diseases and insect pests is achieved, the fresh-keeping effect is achieved, the creeping oxalis extract has the fresh-keeping effect, and the fresh-keeping effect is good. The fructo-oligosaccharide has the effect of improving the nutrition of the mulberry leaves, and the four components are combined, so that not only can the mulberry leaves be sterilized, but also the mulberry leaves can be preserved, the taste and nutrition of the mulberry leaves are improved, the food intake of silkworms is increased, and the disease resistance of the silkworms is improved while the growth speed of the silkworms is increased.

The invention provides an operation work order generation method for an unmanned retail terminal, and the method comprises the steps: employing an operation work order generation server, pre-storing a historical operation work order in the operation work order generation server, carrying out the work order analysis of the consumption data corresponding to each region and the environment data corresponding to each region through the operation work order generation server, and carrying out the calculation of the work order. Analyzing to obtain a matching value corresponding to the region; according to the matching value corresponding to the area, the unmanned retail terminal of the area analyzes a service demand index corresponding to the current matching value of the area; analyzing a current goods demand index of the area according to the service demand index corresponding to the area; and obtaining the historical data processing efficiency of each work order in a preset area of the unmanned retail terminal, and calculating the service demand time corresponding to the service area of each work order according to the historical data processing efficiency of each work order and the service demand quantity corresponding to the area corresponding to the service demand index corresponding to the service area of each work order.