45results about How to "Satisfaction Guaranteed" patented technology

The invention relates to the technical field of power grid planning, in particular to a charging pile planning method based on dynamic analysis. The method comprises the following steps: obtaining electric vehicle use data in a target area, and carrying out charging demand prediction analysis according to the electric vehicle use data to obtain a charging demand prediction model; based on the charging demand prediction model, performing multi-scene simulation to obtain charging demand distribution map data, multiple scenes including different traffic flows, different driving modes and different charging demand scenes; obtaining geographic information data of the target area; and constructing a spatial analysis model according to the geographic information data, and evaluating the candidate position of the charging pile according to the charging demand distribution map data, thereby obtaining position suitability score data. According to the method, different scenes are simulated, so that the adaptability and robustness of planning are enhanced.

The invention discloses a cross-cloud resource scheduling system and method based on deep learning, and the system comprises a resource monitoring service module which is used for monitoring resource information applied to a plurality of cross-cloud platforms, and the output end of the resource monitoring service module is connected to a data preprocessing module; the data preprocessing module carries out preprocessing operation on monitored data and then sends the data to the CNN prediction model, the CNN prediction model processes time series data through multiple convolutional layers based on the preprocessed data and captures resource use trend and periodic changes, and the model outputs resource demand prediction in a period of time in the future. And the output end of the resource scheduling module is connected with the strategy optimization module, the strategy optimization module outputs an optimal resource scheduling strategy to the strategy implementation module through processing scheduling, and the strategy implementation module executes the resource scheduling strategy. According to the scheme, real-time dynamic resource scheduling can be realized to meet the requirement of cross-cloud platform resource scheduling.

The invention provides a bean product fermentation influence factor analysis method and system, and relates to the technical field of food safety detection.The method comprises the steps that multiple sets of key parameters are set within a preset range, physical and chemical index changes under the parameters are monitored in real time, and the physical and chemical index changes comprise the tannin variation, the acid production capacity, the fermentation rate and the active enzyme variation; the method comprises the following steps: establishing a key parameter, constructing a physical and chemical index prediction model, training by taking the key parameter as input, evaluating a fermented bean product, training a sensory evaluation index prediction model by taking the physical and chemical index as input, and finally, determining an optimal combination of the key parameter through a genetic algorithm by taking maximization of the sensory evaluation index as a target, thereby optimizing a fermentation process. By setting and adjusting key fermentation parameters in real time and combining physical and chemical index monitoring and sensory evaluation, the purposes of optimizing the fermentation process and improving the final quality of bean products are achieved.

The invention relates to the technical field of computers, in particular to a computer control method. The method comprises the following steps of performing user software interaction behavior log collection on a computer to obtain a user software interaction behavior log; determining to-be-updated software of the computer according to the user software interaction behavior log to obtain a to-be-updated software list; performing update time window analysis on the to-be-updated software list based on the user software interaction behavior log to obtain a software update time window list; carrying out target version software package downloading on the to-be-updated software list to obtain a target updating software package resource pool; performing current version software package metadata collection on the to-be-updated software list to obtain a current software package version data set. According to the method, the user experience is remarkably improved through an intelligent software updating strategy, updating is automatically executed in a time period when the user is inactive, and the user is prevented from being disturbed at a key or busy moment.

The present invention relates to the technical field of virtual scene construction, and discloses a model construction method and system based on virtual scenes, including: collecting construction requirements of virtual scenes; inferring a complete demand model based on the construction requirements; and constructing virtual scenes based on the inferred demand model. It can accurately capture user needs and improve the accuracy of demand capture. The initial population is composed of randomly selected element information, and a preliminary scene that meets the needs is quickly generated through an intelligent algorithm, and is continuously optimized during the iterative process, thereby realizing efficient virtual scene construction. When guiding the user's demand description, through diversified input methods and flexible interaction rules, the user can adjust the demand at any time. The scene can be optimized according to different demand dimensions, so that the virtual scene finally constructed is not only fully functional, but also reasonably laid out.

The invention discloses a big data screening method and system, and relates to the technical field of data processing, and the method comprises the following steps: S1, user demand analysis and confirmation: obtaining and analyzing user data demands, determining key features and conditions of data screening, formulating and confirming a screening standard, obtaining user feedback, and carrying out adjustment; s2, demand priority determination: carrying out comprehensive analysis by utilizing the optimal parameters, determining the relative importance of each parameter by applying an analytic hierarchy process, allocating weights, calculating weighted scores, and determining demand priorities according to score sorting; according to the method, the efficiency and accuracy of big data screening are remarkably improved by comprehensively analyzing the user requirements, formulating the screening standard and adopting measures such as algorithm optimization, feature engineering and parameter adjustment, the requirement priority is determined through the analytic hierarchy process, the screening strategy is dynamically adjusted, and it is ensured that the screening result is highly matched with the user requirements.

The application discloses a special-shaped pipe automatic stacking equipment and method. The equipment comprises a PC system, a roller, a traction machine, a length sensor, a visual detection device, a mechanical arm, a grabbing device, a packing drum line and an automatic steel band packing machine. The method is as follows: the MES system transmits product order information to the PC system, the PC system selects a proper stacking program; the special-shaped pipe passes through the roller, the length of the special-shaped pipe is calculated and is converted into a theoretical weight and transmitted to the PC system; the visual detection device visually detects the special-shaped pipe, removes the special-shaped pipe with defects and sends the qualified special-shaped pipe into a turnover table mechanism; then the mechanical arm grabs the qualified special-shaped pipe through the grabbing device, stacks the special-shaped pipe and sends the special-shaped pipe into a packing area; and the automatic steel band packing machine packs the stacked special-shaped pipe through steel band arrow penetration. The application has the characteristics of high case packing efficiency, low labor cost, high automation degree, low error rate, reliable performance, strong maintainability and convenient operation.

The invention discloses a trusted data transaction platform and method based on a block chain and a big data technology, relates to the technical field of trusted data, and solves the problem that risk control and user experience cannot be effectively balanced in coping strategy selection based on risk prediction. According to the method, firstly, data such as collected user information and transaction amount are labeled and then trained based on a neural network model, and a risk prediction model capable of outputting a risk level after a transaction behavior and the user information are input is obtained. And then setting a corresponding risk coping strategy for the risk prediction model of each grade, establishing an objective function by taking the user satisfaction and the risk coping strategy grade as influence factors, and selecting the risk coping strategy by taking maximization of the objective function as a condition. Through the mode, when the current transaction risk is recognized, it is further ensured that after the transaction risk is recognized, the selected risk coping strategy can meet the satisfaction degree of the user and can also meet the requirement for risk coping.

The invention provides an evaluation analysis method and device for a logistics reserve alliance agent, a medium and equipment, and the method comprises the steps: obtaining the multi-dimensional data information of the alliance agent, and the multi-dimensional data information comprises operation data, market feedback information and financial data; constructing a prediction model, and training the prediction model by using the historical basic information of the franchise, the market environment factors and the characteristics of the franchise to obtain a trained prediction model; predicting a future operation risk score of the franchise by using the trained prediction model; and adjusting the priority of the reserve franchise according to the predicted future operation risk score of the franchise. According to the invention, decision-making efficiency is improved, and service interruption caused by abnormities of the franchise is reduced; the network stability is enhanced, and the service quality and customer satisfaction are ensured; and the alliance business structure is dynamically optimized, and the healthy development of the whole network is promoted.

The invention provides a recycling management and control method and system for cyclically leasing carriers and electronic equipment, and relates to the technical field of carrier leasing, and the method comprises the steps: inputting the obtained number of required carriers in a plurality of historical periods into a prediction model, and obtaining the number of required carriers in the next period; inputting the acquired overdue use times, the execution penalty average value and the order quantity of the user into a penalty perception model to obtain the penalty perception degree of the user; and inputting the predicted number of required carriers and the penalty perceptibility of the user into a target function, determining operation decision parameters by taking maximization of the target function as a target, and scheduling the carriers based on the operation decision parameters. By integrating the required number of the carriers in the next period, the sensitive degree of the user to punishment and the maximization of the objective function, the formulated operation decision parameters are more reasonable, the number of the carriers actively scheduled by the platform better meets the requirements of the next period, the scheduling of the carriers is optimized, and the recycling efficiency of the carriers is improved.

The utility model provides a commercial vehicle, and relates to the technical field of commercial vehicles, the commercial vehicle comprises an outside rear-view mirror arranged at the top end of a commercial vehicle door and further comprises a magnetic device and a mounting point arranged on the outside rear-view mirror, and when the commercial vehicle door is in a closed state, suction force is formed between the commercial vehicle door and a commercial vehicle body. Due to the arrangement of the magnetic device, the attraction force between the magnetic device and the vehicle body can resist the force generated by vibration of the vehicle under the working conditions of starting / flameout, bad roads, deceleration strips, other dynamic driving and the like as much as possible; the problems that in the prior art, when many types of commercial vehicles run at the speed of 60 km / h or above, vehicle doors flash outwards, meanwhile, outside rear-view mirrors shake at different degrees, noise in the vehicles is increased, and voice definition in the vehicles is reduced are solved as much as possible, the practicability of the commercial vehicles is improved, and the product quality and the satisfaction degree of customers are guaranteed to the maximum extent.

The present invention relates to the technical field of cold chain transportation, and specifically to a method for intelligent temperature control in the process of cold chain transportation, comprising the following steps: S1: real-time monitoring of the temperature distribution of each cargo unit and outputting temperature data; S2: calculating the thermal conductivity and thermal radiation characteristics of each cargo unit and predicting the trend of temperature change; S3: adjusting the working parameters of the refrigeration equipment according to the temperature change trend predicted in S2; S4: monitoring based on the temperature control result of S3, and automatically enhancing the refrigeration intensity of the cargo unit; S5: predicting the factors causing temperature abnormality and issuing an early warning to the transportation management personnel; S6: automatically generating a temperature control report after the transportation is completed. The present invention significantly improves the temperature management accuracy and response speed of cold chain transportation by real-time monitoring and automatic adjustment of temperature control parameters, while enhancing the energy efficiency of the system and the safety during transportation.

The present invention discloses a hospital calling system based on face recognition, and relates to the technical field of face recognition. The present invention comprises: a hospital reservation information acquisition module, a patient face punching module, a department calling adjustment unit and a local database. The present invention verifies the face information of each patient to ensure that the patient who sees a doctor offline and the patient who registers online are the same person, thereby reducing the secondary sale of reserved medical resources. The face recognition technology is used to monitor in real time the sorted signed-in patients of each department of the hospital. When calling the signed-in patients, if the signed-in patients do not see a doctor within the initial waiting time, the additional allowed waiting time of the signed-in patients is analyzed according to the time point and location of the patients in the hospital, thereby improving the utilization efficiency of medical resources. At the same time, a re-calling service is provided for these patients who miss the calling, and they are intelligently sorted, thereby ensuring the patient's satisfaction and improving the trust of the hospital.

The application discloses an Internet platform vehicle leasing operation management method, device and medium, and relates to the technical field of computers. The method comprises the following steps: obtaining the target and constraint condition of a multi-target optimization algorithm; based on operation data, vehicle state data and market environment data, a multi-target optimization algorithm is used to configure a target optimization operation management process, which comprises sequentially generating a candidate vehicle scheduling scheme, a candidate dynamic pricing strategy and a candidate user service strategy, and establishing a two-way feedback mechanism among the three to iteratively optimize; and performing vehicle leasing operation management according to the target optimization operation management process. Through the three-level progressive optimization architecture and the two-way feedback mechanism, the application realizes the collaborative optimization of vehicle scheduling, dynamic pricing and user service, solves the technical problems of single-point optimization, static decision and credit disconnection in the prior art, and improves the operation efficiency, income level and user satisfaction of the vehicle leasing business.

The invention relates to the technical field of transformer substations, in particular to an ultra-high voltage transformer substation GIS air chamber discharge fault positioning method. Comprising the following steps: S1, determining a fault range point according to a protection tripping range; s2, collecting current data of each switch in a trip fault range point, comparing the current data to obtain the maximum current data in the range, and narrowing the range of a fault air chamber; s3, measuring the information of the fault gas chamber, and if the information of the fault gas chamber is different from the information of the normal gas chamber, preliminarily judging the fault gas chamber; and S4, based on the fault gas chamber preliminarily judged in the step S3, carrying out test verification according to the SF6 decomposer, and confirming the fault gas chamber. Compared with a traditional strategy, the method provided by the invention has the advantages that the fault point positioning time is greatly shortened, the working efficiency is improved, and the non-planned power failure time and the load loss are reduced.

This invention relates to the field of smart grid and electric vehicle dispatching technology, specifically providing a method and apparatus for assessing the adjustable potential of electric vehicle battery swapping stations. The method includes: importing the battery swapping demand of the electric vehicle battery swapping station into a pre-constructed battery charging coordination control model and solving it to obtain the battery charging strategy in the electric vehicle battery swapping station; determining the charging power of the electric vehicle battery swapping station based on the battery charging strategy; and determining the adjustable potential of the electric vehicle battery swapping station based on the charging power; wherein the electric vehicle battery swapping station meets the battery swapping demand constraints. The technical solution provided by this invention effectively solves the problems of unquantified user satisfaction, inaccurate charging power modeling, and lack of peak-shaving-oriented assessment methods. It can significantly improve the accuracy of the adjustable potential assessment of battery swapping stations, effectively improve peak-shaving effects while ensuring user satisfaction, and provide reliable technical support for battery swapping stations to participate in grid interaction.

The invention relates to the technical field of electric hydrogen system coordination, and provides an electric hydrogen system coordination optimization operation system. The energy input module receives input hydrogen energy and converts the hydrogen energy into electric energy; the energy storage and output module can store energy from the energy input module, and supports electric energy scheduling, electric energy hydrogen production storage and conversion of hydrogen into electric energy for output; the monitoring management module monitors the energy input module and the energy storage and output module in real time and collects power utilization feedback and energy supply feedback; the optimal scheduling module generates a scheduling strategy according to the electricity demand and the energy supply condition; the scheduling strategy is transmitted to the energy storage and output module, and the energy storage and output module adjusts electric energy storage and output according to the scheduling strategy; cooperative cooperation among various energy sources is achieved through the energy input module and the energy storage and output module, the scheduling strategy is generated by the optimization scheduling module, the scheduling unit accurately controls the energy storage and output module to carry out power supply distribution according to the scheduling strategy, and continuity and stability of power supply are ensured.

The embodiment of the invention provides a payment request response method and device, electronic equipment, a storage medium and a program product. The method can be used in financial cloud field. The method comprises the following steps: in response to a payment request initiated by a target object at a first client, identifying a payment contract pre-stored by the target object at the first client; sending a plurality of contract payment systems corresponding to the payment contract to the first client, and receiving a target payment system fed back by the first client and selected by a target object based on the contract payment system; forwarding the payment request to the target payment system, and receiving a processing result fed back by the target payment system based on the payment request; and sending the processing result to the first client to respond to the payment request.

The present invention discloses a laboratory information management system based on digitization. The present invention receives a patient's test data packet and determines whether the patient is a follow-up patient or a newly added patient. When the patient is determined to be a follow-up patient, the present invention extracts the patient's relevant historical test data and the test data packet from a database for comprehensive analysis. By constructing a change trend graph and calculating a slope, an evaluation of the change trend of abnormal indicators is provided. At the same time, the concepts of hidden danger points and hidden danger levels are introduced to make the hidden danger evaluation clearer and more specific, thereby generating a follow-up report for the follow-up patient and a new diagnosis report for the newly added patient, thereby solving the problem in the prior art of failing to effectively distinguish and adopt different treatment strategies when dealing with follow-up patients and newly added patients.

The invention belongs to the technical field of intelligent questions and answers, and discloses an intelligent questions and answers system based on a large language model (Large Language Mode l), the intelligent questions and answers system is composed of a retrieval recall module, a correlation sorting module and a large language model induction and summary module, the retrieval recall module comprises a similarity model of question and answer query and document paragraph level; the correlation sorting module comprises a question-answer query and a document paragraph level discrimination model; and the big language model induction and summarization module is realized on the basis of a home big language model AutohomeChat. According to the method, related document paragraphs of the query can be effectively recalled by constructing a similarity model of the question and answer query and the document paragraph level, then the related paragraphs can be effectively and finely ranked by training a discrimination model of the question and answer query and the document paragraph level, and finally the related paragraphs are sent into a home big language model AutohomeChat to be aggregated and concluded. Correct answers can be effectively summarized, and satisfactory experience is provided for users.

This invention relates to the field of smart grid and electric vehicle scheduling technology, specifically providing a multi-objective collaborative scheduling method and apparatus for photovoltaic-storage-charging-battery swapping stations. The method includes: using the state space set of the photovoltaic-storage-charging-battery swapping station as input to a pre-trained agent to obtain the battery charging strategy output by the pre-trained agent; and scheduling the charging of the batteries in the photovoltaic-storage-charging-battery swapping station using the battery charging strategy. The state space set includes at least one of the following: time period, SOC state of each battery, battery swapping quantity requirement, photovoltaic output, and electricity price. The pre-trained agent is trained using a near-end strategy optimization algorithm. The technical solution provided by this invention achieves multi-objective collaborative scheduling of photovoltaic-storage-charging-battery swapping stations, improves the global optimality and practicality of the scheduling strategy, and balances the interests of multiple stakeholders.

The invention relates to the technical field of fault detection, in particular to a distribution line fault automatic positioning and isolating method, which comprises the following steps of: installing an intelligent fault indicator at each distribution node of a distribution network; the data of all the intelligent fault indicators are transmitted to the central processor in real time by using a wireless communication technology; the central processor predicts and positions potential line fault points by using a machine learning algorithm according to the received data; after a fault point is detected, an optimal isolation scheme is calculated, and rapid isolation of a fault area is realized by controlling an intelligent circuit breaker or an isolation switch in a power distribution network; after the fault is isolated, the dynamic self-adaptive control module is automatically started, and the dynamic self-adaptive control module dynamically adjusts the load distribution and the current path of each node in the power distribution network based on the real-time monitoring data and the network state. According to the method, the fault response capability of the power distribution network is remarkably improved, the fault processing time is shortened, and the loss and inconvenience caused by the fault are reduced.

According to the knowledge retrieval method and device based on multi-path recall, the target query statement of the user is expanded, rewritten and split through the first large language model so as to form the multiple candidate query statements under the same semantics, problem rewriting is achieved, retrieval elements can be refined, and the accuracy of recall results can be improved. Multi-path content retrieval is adopted, vector retrieval and ES retrieval are adopted at the same time, database data of ES retrieval are updated in real time, the problem of knowledge limitation can be eliminated, delay is reduced, and information integrity is guaranteed. A Rerank model or a double-tower model is introduced to score and sort the first type of query results and the second type of query results which are recalled preliminarily, the query results with the higher score are selected to be integrated and supplemented by a second large language model, natural language reply aiming at the target query statement is generated, the accuracy and integrity of the final knowledge retrieval result are improved, and the query efficiency is improved. And the double-tower model can be iteratively updated according to user feedback, so that the user satisfaction is ensured.

The invention discloses a traceable post-operation whole-process management follow-up visit system and method and electronic equipment, and relates to the technical field of post-operation follow-up visit management, and the system comprises a follow-up visit strategy simulation module, a follow-up visit strategy selection module, a strategy tracing reset module, a follow-up visit strategy simulation module and a follow-up visit strategy selection module. Postoperative follow-up visit strategies are customized in a personalized mode according to the actual situation of a patient, follow-up visit of each postoperative follow-up visit strategy is recorded, traceability of whole-process postoperative management is guaranteed, a strategy traceability reset module is arranged, the actual follow-up visit process of a final follow-up visit strategy can be tracked, analyzed and managed, and the follow-up visit management efficiency is improved. And on the basis of ensuring the dynamic adaptability of the follow-up visit strategy, the postoperative medical management quality and the patient satisfaction are ensured.

The present invention discloses a distribution network equipment status evaluation method based on an improved rough set optimized fireworks algorithm. First, historical ledger data, status monitoring records and fault entry data are combined to obtain a balanced data set based on an undersampling method based on K-means clustering in order to solve the problem of data set imbalance in the distribution network equipment status evaluation. Then, the rough set theory is used to discretize and simplify the attribute values ​​of many continuous evaluation indicators in the distribution network equipment status evaluation, and a reduction method based on attribute importance is used to simplify the attributes of the evaluation indicator decision table. Then, the improved fireworks algorithm is introduced to find the global optimal solution in a large number of attribute simplification results, and finally a set of simplified evaluation indicator decision tables are obtained for the status evaluation of distribution network equipment. This method effectively improves the accuracy and reliability of the distribution network equipment status evaluation results, can effectively reduce and avoid the occurrence of distribution network equipment failure accidents, and ensure power supply reliability and customer satisfaction.

The invention belongs to the technical field of pricing decision making, and particularly relates to a manufacturer pricing decision making method and system under multiple influence factors, and the method comprehensively considers the social learning behaviors of consumers, the return freight insurance policy of an e-commerce platform and the cooperation mode between a manufacturer and the e-commerce platform. By collecting consumer social learning behavior data, e-commerce platform return freight insurance policy data and manufacturer product sales data, a pricing decision model including a manufacturer, an e-commerce platform and consumers is constructed. The model can derive an optimal pricing strategy combination and a sales strategy combination of a manufacturer, thereby helping the manufacturer to accurately position a target customer group in a proxy sales mode, and maximizing the potential profit of the manufacturer. According to the invention, a scientific pricing decision basis is provided for manufacturers, and the market competitiveness can be improved.

The invention provides a data processing method which can be applied to the financial field and the technical field of big data. The data processing method comprises the steps that in response to a first operation executed on a display interface by a user, the display interface is controlled to be switched to a first interface, and first inventory data is obtained and represents inventory data of inventory articles; starting a control on the first interface in response to the condition that the first inventory data meets a first preset condition; in response to the fact that the user clicks the control and starts to execute a second operation, determining first target information corresponding to the user based on the first inventory data and the extraction probability of the inventory articles; in response to the fact that the first target information meets a second preset condition and the user completes the second operation, the first interface is switched to the second interface, second prompt information is generated on the second interface, and the second prompt information is used for prompting that target inventory data corresponding to the user exists. The invention further provides a data processing method and device, electronic equipment, a storage medium and a program product.

The invention relates to a high-speed logic analysis method for an image processing chip, which relates to the technical field of image processing, and comprises the following steps: acquiring color tendency test data of a plurality of test users, and determining color tendency characteristics of the plurality of test users; obtaining color tendency test data of the current user, and determining an initial color tendency feature of the current user; obtaining a to-be-adjusted image; extracting image features of the to-be-adjusted image; determining an optimal color adjustment scheme of the to-be-adjusted image according to the image feature of the to-be-adjusted image, the initial color tendency feature of the current user and the color tendency features of the plurality of test users; according to the optimal color adjustment scheme, color adjustment is carried out on the to-be-adjusted image, the adjusted image is generated, and the method has the advantage that personalized image color adjustment is achieved according to the color tendency of the user.

The present invention relates to the field of data processing technology, and discloses an order violation detection method, device, equipment and storage medium. The method sets corresponding monitoring rules according to different merchant types, sets monitoring tasks based on the monitoring rules to regularly monitor the update of logistics data of orders on each electrical commodity platform, obtains logistics data recorded in the logistics update node in each order through the monitoring rules, and checks whether the logistics records of each update record in the detected logistics data meet the conditions specified in the monitoring rules. If not, the order is determined to be in violation, and the violation events in the order are determined. Based on the violation events, the corresponding penalty method is selected to achieve constraints on each merchant, improve the delivery efficiency of the order, and ensure customer satisfaction. In addition, the present invention also relates to blockchain technology, and the monitoring rules and orders can be stored in the blockchain.

The invention discloses a music teaching recommendation method and system based on a knowledge graph, and relates to the technical field of teaching recommendations, the method recommends a target node to a student according to each node music teaching resource in the knowledge graph and the learning record of the learned student in each node, and then according to the historical learning record of the student, the target node is recommended to the student. According to the method and the device, a music score recommendation mode of a student is confirmed, a target teaching music score is recommended to the student, and a learning period of the target teaching music score is confirmed according to a historical learning condition of the target teaching music score and learning conditions of the student in different periods, so that the adaptability of the music score and the learning period to the student is improved, and the music score recommendation accuracy and reliability are improved; personalized and targeted recommendation of the music score of the student is realized, the learning interest of the student is effectively stimulated, the learning requirements of different students are met, and the learning effect of the student is guaranteed, so that the music teaching recommendation effect is improved, and the satisfaction degree of the student is improved.