18 results about "User expectations" patented technology

This application provides a model training method, training system, and related equipment. The method includes the following steps: acquiring positive sample data, wherein the positive sample data includes input samples and positive labels, the input samples include questions, and the positive labels include the answers corresponding to the questions; inputting the input samples into a first large model to be fine-tuned, obtaining the first negative label corresponding to the input sample; determining the first score of the first negative label based on the relevance of the first negative label to the question and the quality of the response to the first negative label based on a scoring model; inputting the input samples into the first large model; and fine-tuning the first large model based on a loss function to obtain a second large model. The loss function is used to guide the first large model to improve the prediction probability of positive labels and the prediction probability of negative labels with scores greater than a threshold, so that the model can not only learn standard answers that meet user expectations, but also generate more diverse answers, thereby improving the fine-tuning effect of the large model.

This application relates to the field of vehicle-related technology and discloses a parking method, apparatus, device, and vehicle based on voice interaction. The method includes: responding to a user's voice parking command and extracting parking constraints; the parking constraints include at least one of parking area, parking location attribute features, and parking method, each parking constraint including positive and / or negative requirements; while the vehicle is driving in the parking lot, sensing environmental information around the vehicle and obtaining multi-dimensional environmental features of available parking spaces; the multi-dimensional environmental features include at least the area and location attribute features; filtering available parking spaces based on the multi-dimensional environmental features according to the priority of the parking constraints, and controlling the vehicle to park in the available parking space when a parking space that meets the parking constraints is selected. Applying the solution of this application can solve the problems of existing automatic parking systems failing to understand users' personalized parking needs and resulting in parking results that do not meet user expectations.

The present disclosure provides a method for optimizing large language model conversation, including: in response to entering a current conversation turn, acquiring configuration information corresponding to the current conversation turn based on a type of the current conversation turn; acquiring user input information corresponding to the current conversation turn; determining whether the current conversation turn needs to be switched based on a conversation turn switching determination strategy corresponding to the conversation turn switching strategy configuration information; when the current conversation turn needs to be switched, determining a next conversation turn corresponding to the current conversation turn, and entering the next conversation turn; when the current conversation turn does not need to be switched, generating a response task, and performing the response task calling a large language model to generate a response message; and outputting the response message in the current conversation turn, to meet user expectations.

This application provides a voice control method, apparatus, device, system, storage medium, and program product. On one hand, voice control based on user expectations offers a novel voice control mode. Users can express their personalized needs by conveying their expected physical or emotional feelings, thereby enabling automatic device control. This achieves intelligent and flexible voice control, embodies a technological feel, and satisfies users' personalized needs. On the other hand, after generating control commands based on voice data including information related to the user's expected feelings, the accuracy of the control commands is determined. If the commands are accurate, home appliances are controlled according to the commands. This significantly reduces the possibility of misoperation, ensures the alignment of executed control commands with the user's actual intentions, and further guarantees a superior user experience.

Embodiments of the present disclosure provide a method, apparatus and electronic device for training a recommendation model. The method can include, in response to receiving a data deletion request of a user, determining data requested to be deleted in original data. The method can also include obtaining remaining data for the user based on the data requested to be deleted. Furthermore, the method can further include training the recommendation model using the remaining data. Through the technical solutions of the embodiments of the present disclosure, the long model training time and large amount of computing overhead caused by retraining the recommendation model can be avoided, and the model updating time is shortened. In addition, since the training of the recommendation model forgets the data expected to be deleted by the user, the performance of the recommendation model is improved, the recommendation result is more likely to hit the user's preference, and thus the user experience is improved.

This application relates to a method and apparatus for controlling vehicle lighting, a vehicle, and a program product. The method includes: acquiring target lighting interaction habit data associated with the vehicle's current driving route from a pre-built memory module; acquiring initial lighting control parameters for the current driving route based on the target lighting interaction habit data; optimizing the initial lighting control parameters based on real-time driving environment information of the current driving route to obtain target lighting control parameters; and sending the target lighting control parameters to a lighting execution system; the lighting execution system is used to control the vehicle's lighting using the target lighting control parameters. This method enables more efficient lighting control that better meets user expectations for lighting performance, thereby improving the user's driving experience.

The application discloses a kind of federal feature forgetfulness method and system based on blockchain, it is related to computer technology field.The method comprises: when client receives the multi-feature forgetfulness request initiated by user to global model, client adds Gaussian noise to the feature to be forgotten in multi-feature forgetfulness request;Global model is trained based on the local dataset of all clients;Local dataset includes multiple features;The feature to be forgotten is the feature that user expects to remove from global model;Client calculates the sensitivity of the feature to be forgotten after adding Gaussian noise, and determines the mask weight of the feature to be forgotten based on sensitivity;Sensitivity is used to quantify the degree of influence of the feature to be forgotten on global model;Blockchain updates global model based on the mask weight of the feature to be forgotten, and obtains global model after feature forgetfulness.The method reduces the complexity of model calculation in the process of feature forgetfulness.

This specification discloses an adaptive operation control method and device for an air purifier. The method constructs multi-source time-series data by acquiring indoor environmental parameters, user operation behavior data of the target air purifier, and time context data. After preprocessing, a sequence of samples is constructed using a sliding time window, with the user's actual operations at future moments serving as a monitoring signal. The sequence of samples is input into a trained Long Short-Term Memory (LSTM) network model, which, through its gating mechanism and state calculation, outputs a predicted probability distribution of the user's expected operating mode. Based on the predicted mode and its confidence level, and combined with real-time indoor environmental parameter thresholds, a final control strategy is generated through a strategy decision engine to control the air purifier's operating mode and fan speed. User manual intervention is monitored; when the system's automatic control actions deviate from the user's expectations, the LSTM model parameters are updated based on this deviation.

This application provides a graphical user interface (GUI) operation method, apparatus, device, and medium. The method includes: performing intent recognition and parsing on natural language commands to obtain a structured intent sequence; performing parsing and semantic mapping operations on the candidate GUI operation sequence to obtain a structured GUI operation sequence; constructing a matching score matrix between the structured GUI operation sequence and the structured intent sequence based on a local sequence alignment algorithm; determining the element with the largest value in the matching score matrix as the matching degree; and executing the candidate GUI operation sequence if the matching degree is greater than a threshold. This solves the semantic difference between the GUI operation sequence and the natural language commands, and robustly handles the mismatch in granularity and length between the two sequences, thereby improving the accuracy of the calculated matching degree and thus increasing the consistency between the GUI operation result and the user's expectations, enhancing the user experience.

The invention describes a computer-implemented method for planning and realizing a work process in engineering and production, comprising conducting a dialogue with a user, including displaying (130) known work steps of the work process and, for each of the work steps, displaying possible implementations of the work step; recording (140) a first selection by the user, wherein the first selection for one or more of the work steps includes one of the displayed implementations, the selected implementations being implementations currently used by the user; recording (150) a second selection by the user, wherein the second selection for each of the work steps includes one of the displayed implementations, the selected implementations being implementations desired by the user;Based on the first and second selections, determine (160) the additional work steps, implementations and technical resources required for the intended implementations that must be provided by the user; and display (170) the required work steps, implementations and resources.

This invention provides an image generation method, apparatus, storage medium, and computer program product. The method includes: acquiring an image to be processed, a reference style image corresponding to the image to be processed, and a prompt word, wherein the image to be processed contains a target object, and the prompt word is used to describe the visual feature information expected to be presented. An image generation model is determined by fine-tuning a target low-rank adaptive model corresponding to a first style feature, and the reference style image has a second style feature. The image to be processed, the reference style image, and the prompt word are input into the image generation model to generate a target image containing the target object. Because the image generation model is configured with a target low-rank adaptive model corresponding to the first style feature, and can obtain the second style feature from the reference style image, the generated target image can present the first style feature, the second style feature, and the visual features corresponding to the visual feature information, consistent with the user's expectations.

The invention relates to a computer-implemented method for planning and implementing a work process in the area of engineering and production, having the steps of carrying out a dialogue with a user; displaying (130) known work steps of the work process and, for each work step, displaying possible implementations of the work step; receiving (140) a first selection of the user, the first selection comprising one of the displayed implementations for one or more of the work steps, and the selected implementations comprising implementations currently being used by the user; receiving (150) a second selection of the user, the second selection comprising one of the displayed implementations for each work step, and the selected implementations comprising implementations desired by the user; on the basis of the first and the second selection, determining (160) the work steps, implementations, and technical means which are additionally required for the desired implementations and are to be provided to the user; and displaying (170) the required work steps, implementations, and means.

This invention discloses a BIM-based method, apparatus, equipment, and medium for manufacturing door and window components. The method includes: acquiring a target door and window component that a user wishes to manufacture; generating at least one prompt message based on the target door and window component according to a preset program, and pushing the at least one prompt message to the user one by one, wherein each prompt message is used to instruct the user to input a corresponding design operation according to the door and window manufacturing process; receiving at least one design operation performed by the user according to the at least one prompt message; generating at least one door and window model in response to the at least one design operation; and generating and displaying the target door and window component based on the door and window model generated by each design operation when the user completes all the design operations indicated by the prompt messages according to the door and window manufacturing process. This reduces the user's learning curve and provides a better experience and higher efficiency for designers and project developers.

This invention relates to the field of autonomous driving, and more particularly to an autonomous driving method and related equipment. The method includes: executing an autonomous driving function according to a set autonomous driving gear; wherein the autonomous driving function has multiple autonomous driving gears, including at least one of a side distance gear and a following distance gear; upon detecting a correction command, acquiring correction information for the autonomous driving gear; determining whether the correction information poses a safety hazard; and if no safety hazard exists, updating the gear parameters of the autonomous driving gear based on the correction information. In this embodiment of the invention, by setting multiple autonomous driving gears, users can switch between them according to their actual needs. Furthermore, users can fine-tune the gear parameters of each autonomous driving gear, thereby making the following distance or side distance more consistent with user expectations and improving the user experience.

The present disclosure provides a method for optimizing large language model conversation, including: in response to entering a current conversation turn, acquiring configuration information corresponding to the current conversation turn based on a type ofthe current conversation turn; acquiring user input information corresponding to the current conversation turn; determining whether the current conversation turn needs to be switched based on a conversation turn switching determination strategy corresponding to the conversation turn switching strategy configuration information; when the current conversation turn needs to be switched, determining a next conversation turn corresponding to the current conversation turn, and entering the next conversation turn; when the current conversation turn does not need to be switched, generating a response task, and performing the response task calling a large language model to generate a response message; and outputting the response message in the current conversation turn, to meet user expectations.

A language model is used to generate autosuggestions to complete or revise a user's partial search query. An initial partial query is applied to the language model to generate query candidates for completing the search query. The language model may generate the query candidates as additional or alternate tokens for the partial search query. When the user revises the partial query, the previously-generated candidates can be re-used to reduce subsequent processing time for generating additional candidates. The previously-generated candidates are compared with the revised partial query to select which of the candidates to be re-used and expanded for generating additional tokens. Additional tokens can be generated in parallel for the previously-generated candidates or with model values from the previous generation, enabling the tokens to be generated effectively with reduced latency consistent with user expectations for search-related autosuggestions.

This application relates to the field of artificial intelligence technology, and provides a device pre-installation processing method, apparatus, device, storage medium, and program product. The method includes: identifying a device based on an image to be processed or based on the image to be processed and device gateway information to obtain device identification information; the image to be processed includes at least a device and the area where the device is located; the device gateway information is information about the device collected through a gateway device; determining the actual size information of the area based on the device identification information; and pre-installing the device to be installed in the area based on the actual size information and the size information on the drawing to obtain a device pre-installation result. This application, by pre-installing the device based on the actual size information and the size information on the drawing of the device installation area, can simulate the effect of actual device installation, ensuring that the installation result meets user expectations and that the device can be successfully installed during the actual installation process.