66 results about "Product optimization" patented technology

The invention belongs to the technical field of atomized liquid component analysis, and discloses an intelligent analysis system and method based on atomized liquid components, and the method comprises the following steps: carrying out multi-parameter sampling on atomized liquid to obtain pyrolysis data; performing temperature gradient analysis to generate a pyrolysis spectrogram; identifying trace markers to generate a feature library; analyzing the component evolution track to generate a dynamic evolution spectrum; carrying out partition demarcation processing to generate an atomization characteristic spectrum; performing thermodynamic analysis to generate a reaction map; analyzing component diffusion to generate quantized data; constructing a sensory response prediction engine; performing intelligent analysis to generate response feature data; evaluating and generating a quality report; and performing optimization processing to generate formula suggestions and executing accurate blending. According to the method, accurate analysis and evaluation of atomized liquid components are achieved through multi-dimensional analysis, a scientific basis is provided for product optimization, and safety and taste experience are effectively balanced.

Software product optimization is provided. Expressed sentiment in a set of opted in user-to-user business-related conversations among a group of users corresponding to an entity is associated with a software product in response to determining that the software product is identified by mentioned keywords in the set of opted in user-to-user business-related conversations based on a keyword corpus. It is determined whether the expressed sentiment associated with the software product is negative sentiment. A related software product listed in a software product catalog of the entity is identified as an optimization to the software product in response to determining that the expressed sentiment associated with the software product is negative sentiment. A recommendation to implement the related software product as the optimization to the software product is generated. The recommendation to implement the related software product as the optimization to the software product is sent to the group of users.

The invention belongs to the field of product recommendation, and discloses a campus product optimization method and platform based on user data analysis, and the method comprises the steps: dividing students into a plurality of small groups, and generating a normalized consumption behavior matrix of the small groups based on the consumption behavior data of members in the small groups; acquiring a collective consumption preference model corresponding to the small group based on the normalized consumption behavior matrix; obtaining a dynamic demand model of the small group based on the collective consumption preference model; obtaining individual demand models of members in the small group based on the dynamic demand model of the small group; generating a recommendation model based on the dynamic demand model and the individual demand model of the small group, and adopting the recommendation model to score and sort commodities in the candidate commodity set to obtain a recommendation list; and continuously optimizing the dynamic demand model and the individual demand model of the small group based on the feedback data of the members to the commodities. According to the invention, double responses of the recommendation result to the student social group and the real-time demand are realized.

The invention relates to the technical field of product optimization, and discloses a product optimization method and device based on user feedback, equipment, a medium and a product, and aims to perform association analysis on multi-modal evaluation data of a user, realize semantic association of texts, voices and pictures and obtain user feedback data with relatively high reliability. And extracting a plurality of to-be-processed feedback suggestions from the user feedback data of the plurality of users, realizing multi-dimensional classification of the to-be-processed feedback suggestions by utilizing a knowledge graph architecture, and realizing classification refinement of the to-be-processed feedback suggestions. The scores of the to-be-processed feedback opinions in the multiple basic dimensions are evaluated by using the multi-dimensional classification result, and the quantum state priorities of the to-be-processed feedback opinions are obtained according to the quantum amplitude of each basic dimension, so that priority judgment is more flexible. And finally, sorting each to-be-processed feedback suggestion based on the quantum state priority, and generating a target product optimization scheme according to a sorting result, so that product optimization is performed, and the use experience of a user on the product is improved.

The invention relates to the technical field of circulating fluidized beds, and discloses a circuit board resin continuous gasification control system based on a circulating fluidized bed, comprising: a component identification module for calculating a component concentration vector through an identification model; the layered temperature control module is used for calculating a target temperature vector, collecting the actual temperature and calculating the average temperature of a bed layer through the actual temperature; the intelligent feeding module is used for determining the feeding rate according to the component concentration vector and the target product composition; the product optimization module is used for generating an operation parameter adjustment instruction according to the gas concentration deviation and the average bed temperature; the coordination control module is used for executing a target temperature vector, a feeding rate and an operation parameter adjusting instruction; according to the method, different temperature intervals are established, so that the problem of incomplete gasification caused by temperature mismatching of different components is solved, high-value utilization of the waste circuit board resin is realized, wastes are converted into synthesis gas with high additional value, and green recycling of solid waste resources is realized.

The invention particularly relates to a website user behavior record analysis method based on rrweb. According to the website user behavior record analysis method based on the rrweb, an rrweb initialization method is rewritten, the rrweb is repackaged into a plug-in unit based on a B / S architecture system, a cache mechanism and a database sub-table storage strategy are added in an rrweb service, a Power UI interface development tool suite is used for carrying out visual data analysis, and according to the data type and the analysis purpose, the user behavior record of the website is analyzed. Chart types are selected in a customized mode, and analysis results are sorted into reports to be exported in a unified mode. According to the website user behavior recording and analyzing method based on the rrweb, the limitation of the prior art in the aspects of user behavior data collection, analysis and playback is overcome, the behavior of the user in the webpage or the application is efficiently and accurately recorded and played back, and the user behavior recording and analyzing efficiency is improved. Powerful data support is provided for website operation, product optimization, user research, operation behavior safety verification and the like.

The invention provides a precipitation product optimization method and device, electronic equipment, a storage medium and a product, and relates to the technical field of computers, and the method comprises the steps: obtaining current precipitation product observation data and position information of a target precipitation field; inputting the current precipitation product observation data and the position information of the target precipitation field into the precipitation product optimization model to obtain a precipitation product optimization result output by the precipitation product optimization model; wherein the precipitation product optimization model is a model for performing optimization analysis on precipitation space and precipitation time based on current precipitation product observation data and position information of the target precipitation field to obtain a precipitation product optimization result of the target precipitation field. According to the technical scheme, the precipitation space and precipitation time optimization analysis is performed on the current precipitation product observation data and position information of the target precipitation field based on the precipitation product optimization model to obtain the precipitation product optimization result of the target precipitation field, the richness and accuracy of precipitation products are improved, and the data missing condition is reduced.

The invention relates to an apparatus for controlling the production process of a product from several pre-products, particularly in a recycling process. A supply unit (131) provides expected production data indicating the pre-products and the planned production parameters for manufacturing the product. A receiving unit (132) receives, for each planned pre-product, pre-production data from several independent production units. An optimization unit (133) determines an optimal product output with respect to a predetermined target related to one or more pre-production parameters, based on the production and pre-production data. A control unit (134) provides control signals to regulate product output based on the determined optimized output.

The application discloses a production quality real-time monitoring and optimization management method based on an AI technology, and particularly relates to the field of artificial intelligence, and comprises S1: interval division, S2: production management feature extraction, S3: production planning, S4: production management rationality analysis, S5: correlation analysis, S6: production management prediction, and S7: production management optimization. The application carries out production management in the product production process, carries out production planning based on the production management features, further extracts production quality features, thereby carries out production management rationality analysis, and obtains a production management relationship formula by planning a production frequency, planning production equipment and planning a production speed, thereby providing data support for subsequent actual production frequency, actual production equipment and actual production speed adjustment, simultaneously generates a production quality optimization scheme, reduces product optimization management and adjustment time length in the production process, and evaluates the optimization effect according to the quality improvement amplitude, thereby ensuring that quality improvement is combined with production efficiency.

The application provides an e-commerce demand analysis method and system based on a large language model, which optimizes product characteristics and market strategies by analyzing user-generated content (UGC). The method includes: first, collecting UGC data such as product reviews and user Q&A from e-commerce platforms. Then, using a large language model to deeply analyze these data, extract product attributes and sentiment information, and convert them into structured data. Next, generate a dual importance matrix at the market level to evaluate the importance of product attributes in purchase decision and post-purchase satisfaction. Further, for the products of the merchant, calculate the actual performance value of the attributes, generate a dual importance-performance matrix at the product level, and identify optimization needs. Finally, the analysis results are visualized and a report is generated to provide market insights and decision support for merchants. This method helps merchants accurately position product optimization direction and improve market competitiveness.

The invention provides a breathable film air permeability testing device which comprises a base, connecting seats are fixedly connected to the two ends of the base, stand columns are fixedly connected to the four ends of the upper portion of the base, a top plate is fixedly connected to the tops of the stand columns, first lead screws are rotationally connected to the two ends of the lower portion of the top plate, and movable plates are in threaded connection to the first lead screws; the winding rollers are arranged at the two ends of the lower cylinder, so that the breathable film can be wound and tightened, and the situations that the air film possibly forms wrinkles or local deformation in a loose state, so that the air passing path is not uniform, the test result is possibly high, and the real air permeability cannot be reflected are prevented; meanwhile, ventilation and waterproof performance tests are integrated, the test efficiency can be improved, equipment replacement or sample re-clamping is not needed, two key performance tests are completed at a time, time and labor cost are saved, and the device is suitable for research and development iteration or production line quality inspection, product optimization acceleration and qualification judgment.

The invention discloses an information recommendation method and device, equipment, a medium and a program product. The method comprises the steps of obtaining a multi-source data set; performing multi-dimensional association on the equipment performance data, the cloud platform resource data and the security equipment data according to a target identifier to obtain a global data link diagram corresponding to the target identifier; performing risk detection based on the security equipment data to obtain a risk detection result; performing information analysis based on the equipment performance data, the cloud platform resource data and the security equipment data to obtain an information analysis result; safety potential and performance potential analysis is carried out according to the global data link diagram, the risk detection result and the information analysis result, an information list is obtained, and the information list comprises a target risk customer and a corresponding product deployment list and / or product optimization list. According to the embodiment of the invention, data can be comprehensively collected, information recommendation is performed by integrating multiple dimensions, and information can be flexibly and accurately recommended.

The invention relates to the technical field of text classification, in particular to a user evaluation classification method, system and device and a storage medium. The user evaluation classification method comprises the following steps: exporting evaluation information of a user as an Excel table, labeling category labels, carrying out word segmentation processing and data statistics, and indexing, cutting or filling segmented words of evaluation contents into one-dimensional vectors; a Mish activation function, a global attention module (GAM) and a weighted cross entropy loss function are introduced into an encoder structure of the Transform model; and classifying user evaluation information collected in real time based on the trained model, and outputting a classification result. The user evaluation classification method, system and device and the storage medium are high in feasibility and robustness, can accurately classify the user evaluation information, improve the value of the evaluation information, and promote a product optimization mode to tend to informatization and intellectualization.

The invention provides a data distribution method and system, a program product, equipment and a storage medium, which are applied to online air ticket booking platform product testing based on flight information units, and the method comprises the steps: obtaining a plurality of flight information units, and determining the flight information units based on air routes and air route take-off days; based on a preset grouping rule, dividing the plurality of flight information units into at least two groups so as to carry out a comparison test; and performing different product optimization operations on the flight information units of different groups according to a preset product test strategy. According to the method, the problems of overlong experimental period and low experimental efficiency in the prior art are effectively solved, the experimental efficiency is improved, the evaluation period is shortened, strategy evaluation of various marketing scenes is supported on the premise of ensuring the consistency of user experience, A / B testing can be effectively carried out by grouping ODT, and the experimental efficiency is improved. Therefore, the product effect can be evaluated more quickly.

The application discloses an agent development and product optimization method and equipment. The method comprises the following steps: calling a product design agent encapsulating an industry knowledge graph; generating an initial design scheme through an inter-agent communication protocol; searching for optimization in a design parameter space by using a proxy model; constructing a physical simulation model; inputting physical sensor data as a boundary condition into the physical simulation model to simulate and predict data; calculating the error of the sensor and the simulation prediction data by using a neural network algorithm based on physical information; if the error exceeds a preset setting value, updating the material physical parameters by using a loss function containing data items and physical constraint items and transmitting the updated material physical parameters to the physical simulation model for re-simulation; if the scheme does not exceed the preset setting value, judging whether the performance index meets the design requirement, and if yes, outputting a final product design scheme. The application improves the prediction accuracy of the simulation model.

The application discloses a sound insulation foam production optimization method and system based on machine learning, and relates to the technical field of building protection.The method comprises the following steps: based on an acoustic camera, sound insulation detection is performed to determine a sound wave spectrum, and a sound insulation coefficient is determined in combination with a building sound insulation standard; an optimization decision module is built, and the optimization decision module comprises an optimization decision module and an acoustic evaluation module; based on the sound wave spectrum and the sound insulation coefficient, product optimization decision and sound insulation evaluation are performed in combination with the optimization decision module to determine a product optimization strategy, wherein the optimization dimensions include sound insulation optimization and lightweight optimization, and the sound insulation optimization comprises the sound insulation coefficient; the product optimization strategy is subjected to process conversion based on an initial process flow to determine an optimized production process; and based on the optimized production process, optimized production management of the sound insulation foam is performed.The application solves the technical problem that the sound insulation foam production process is less intelligent in the prior art, and the sound insulation effect of the sound insulation foam product is poor in different frequency bands and complex environments.

The invention provides a method and device for monitoring the abnormal state of a sweeper, and relates to the technical field of smart home. The method comprises the steps of collecting working parameters in real time; preprocessing the working parameters to obtain preprocessed data; constructing an anomaly detection model, optimizing hyper-parameters by adopting a snake-egret algorithm, inputting pre-processed data, and outputting a working state score; setting an abnormal state scoring threshold value and dynamically adjusting the abnormal state scoring threshold value; judging whether the sweeper is abnormal or not; and analyzing the abnormity, sending an alarm and taking control measures. According to the invention, by constructing the anomaly detection model and combining with the optimization algorithm, the precise judgment of the complex working state is ensured; threshold value adjustment enables anomaly detection to be more flexible and accurate, different working scenes and changes can be adapted, problems can be rapidly positioned and solved, valuable reference is provided for product optimization and improvement, meanwhile, losses and risks caused by anomaly are reduced to the maximum extent, safe and reliable operation of the sweeper is guaranteed, and the method is suitable for large-scale popularization and application. And the service life is prolonged.

The embodiment of the invention discloses an optimization effect analysis method based on distributed link tracking and electronic equipment. The method comprises the steps of obtaining an interface execution SQL snapshot formed in the execution process of an interface and a database, and obtaining test data of multiple versions in the interface execution SQL snapshot; inputting the test data of the current version of the target interface into an interface risk prediction regression model generated by pre-training to obtain an interface risk prediction value of the current version of the target interface; comparing the test data of the multiple versions of the target interface, and determining the category coverage degree between the optimization category covered by the execution of the current version of the target interface and all the optimization categories when the distributed link is tracked; and determining an optimization effect of the target interface during distributed link tracking according to the interface risk prediction value and the type coverage degree of the target interface. According to the method, the interface optimization effect in distributed link tracking can be automatically evaluated, and the test efficiency during product optimization is improved.

This invention discloses a method for calculating the critical preload margin of bolts under external loads. The method includes establishing a finite element model of an assembly connected by bolts and setting contact relationships and constraints; applying an external load to the finite element model and solving it to obtain the bolt working load; extracting the force values ​​in the x, y, and z directions at the contact position between the bolt head and the clamping element from the bolt working load; and calculating the shear load and axial load of the bolt working load using these force values; and calculating the critical preload margin required to ensure the clamping element does not separate using the shear load and axial load. This invention, through the contact settings used in finite element analysis, can obtain the actual working load borne by the bolt according to different external loads, and then evaluate the minimum bolt preload force required to prevent the clamping element from separating, i.e., the critical preload margin. This critical preload margin can then be used to guide bolt selection, safety assessment, and product optimization design.

The invention discloses an automobile sealing rubber strip compression state image analysis system and method, and belongs to the technical field of detection equipment, automatic adjustment of a pressing angle is realized through a linkage gear set, a fan-shaped rack and a chute mechanism, and the cooperation of a fan-shaped limiting batten and an arc-shaped batten ensures that the motion trail is accurate and controllable. The angle and the position of the transparent pressing strip plate can be flexibly adjusted through mutual cooperation of the inward folding assembly, the half folding assembly and the downward pressing assembly, so that longitudinal, transverse and oblique multi-angle compression testing of the sealing rubber strip is achieved, deformation characteristics in different stress directions are comprehensively analyzed, and the testing accuracy is improved. According to the invention, a conventional plane compression test can be carried out, compression behaviors of complicated parts such as sharp corners and edges can be simulated, the compression resilience performance and the sealing effect of the sealing rubber strip can be comprehensively evaluated in combination with an image analysis technology, and data support is provided for product optimization.

This invention discloses a membership big data analysis system and method based on data mining, belonging to the field of membership data analysis technology. The system includes: a data acquisition module, a scene recognition module, a dynamic weight allocation module, a time-series correlation mining module, a feature extraction module, and a feature output module. This invention constructs a membership full-scene data association and fusion framework, dynamically allocates multi-source data weights based on scene correlation, and combines behavioral time-series causal correlation mining to correct analysis biases. It accurately extracts the true needs of members and achieves standardized output, solving the problems of homogenization in multi-source data fusion and the disconnect between time-series features and behavioral intentions in existing technologies. This improves the accuracy and timeliness of membership feature extraction, providing direct data support for personalized service delivery, marketing strategy formulation, and product optimization and upgrading. It helps enterprises optimize resource allocation, improve member service experience and stickiness, and further enhance their market competitiveness and operating efficiency.

This invention relates to the technical field of inverter testing systems, and particularly to a full-scenario energy feedback inverter testing system, comprising a scenario simulation unit, an energy feedback inverter testing platform unit, a data acquisition unit, a control unit, a data analysis unit, and a comparison unit. The scenario simulation unit includes a load simulation module, an environment simulation module, and a power grid simulation module. The load simulation module uses mechanical equipment to simulate various loads of different sizes on a motor under different operating conditions, enabling dynamic load testing of the inverter. The environment simulation module, through the setting of an environmental simulation chamber, precisely controls environmental parameters such as temperature, air pressure, and vibration intensity within the chamber to test the inverter's starting performance and the reliability of its components. It comprehensively simulates various complex operating conditions, accurately tests the inverter's performance under different conditions, provides rich data for subsequent analysis, and provides a strong basis for product optimization and selection, thereby comprehensively improving the level of inverter R&D, production, and application.

The invention relates to a roller press pole piece thickness control method and a roller press control system. The control method comprises the steps that the running speed of a roller press is detected in real time; according to the detected speed change, the rolling pressure is adjusted according to a preset gradient so as to change the thickness of the pole piece; and the rolling pressure is adjusted according to the pole piece thickness detected in real time. By detecting and adjusting the rolling pressure in real time, the consistency of the thickness of the pole piece is improved, and the battery performance is ensured, so that the yield of products is increased, the production efficiency is improved, the rejection rate is reduced, and the safety risk is avoided.

The invention belongs to the technical field of knowledge maps, and discloses a knowledge map updating method introducing a time sequence analysis and incremental learning algorithm. Comprising the following steps: S1, collecting new cases of an enterprise, and performing knowledge extraction on the new cases to form a new case sub-graph; s2, performing time sequence analysis on the new case sub-graph to obtain a development trend of a new case; according to the method, the new case sub-graph can be conveniently fused with the sub-graphs of the same category of products with the same development trend in the original knowledge graph, and an enterprise can conveniently formulate a subsequent sales strategy and a product optimization strategy of the products corresponding to the new case sub-graph; by recording the resolution path and setting the jump burying point, the fusion processing time of the new case sub-graph and the original knowledge graph can be greatly shortened, and the updating efficiency and stability of the original knowledge graph are improved.

This application provides an automated testing method, system, and medium for stepper servo drives. The method includes: automatically generating a unique number and binding it to a record; sending data packets; detecting external network communication; collecting first fault information; acquiring bus data; detecting board status; collecting second fault information; connecting the drive module port and performing automatic polling and internal communication detection; collecting third and fourth fault information; reading preset synchronization register data; performing synchronization status detection and collecting fifth fault information; reading back motor data to perform motor performance detection and collecting sixth fault information; storing test data; obtaining a test report for quality analysis and product optimization. Through fully automated dynamic analysis technology, IO self-closed-loop testing, and centralized data management, data transmission and collaborative work are achieved, improving the testing efficiency and accuracy of servo drives and realizing automated testing.

The invention discloses a data processing method for predicting early-stage eccentric wear of a tire through tire grounding pressure distribution. The method comprises the following steps: step 1, performing static load simulation calculation on tire modeling, extracting stress distribution data, determining the number of ribs according to the number of pattern grooves, and extracting all stress distribution value data of each rib after tread deformation; 2, processing the extracted data, and calculating an average stress distribution value of each rib; step 3, evaluating the impression quality, the average pressure and the pressure distribution non-uniformity parameters by adopting a data processing method for predicting eccentric wear through pressure distribution; according to the method, impression and pressure distribution analysis is carried out on the tire, unit stress data are collected, parameters such as impression shapes, average pressure distribution and unevenness are evaluated by adopting a uniform distribution stress data processing method, the smaller the average pressure and pressure distribution unevenness value are, the better is, product optimization is realized by analyzing and processing the extracted data, and the product quality is improved. And the design period and the design cost are greatly reduced.

The utility model belongs to the technical field of electric core rubbing device, concretely relates to a cylindrical battery rubbing station center needle subassembly and rubbing device, center needle subassembly includes center needle base and rubbing center needle, the center position on center needle base is provided with bearing, the lower extreme of rubbing center needle is connected with center needle base rotatory through bearing, rubbing device includes the cylindrical battery rubbing station center needle subassembly, still includes rubbing base, center needle subassembly sets up in the center position on rubbing base, is provided with a plurality of rubbing wheel fixed base on rubbing base, is rotatory and is provided with rubbing wheel on every rubbing wheel fixed base, the utility model can make the rubbing center needle relative electric core keep static in the operation process, effectively prevent the pole lug contact center needle when driving pole lug rotation, avoid center hole collapse and pole piece drop powder, can effectively promote product optimization rate and performance.

The invention relates to the technical field of green product life cycle optimization, and discloses a green product optimization method and system based on artificial intelligence, and the method comprises a database of historical optimization intervention events; distinguishing sample groups with / without intervention records, analyzing the correlation between the monitoring frequency and the intervention effect for the recorded sample groups, and screening out a target sample group; extracting multi-source sensing data of the target sample group, determining key change nodes and constructing a monitoring frequency adjustment response model; when it is monitored that similar products meet the model in real time, a frequency adjustment instruction is automatically transmitted. The system comprises a sample grouping module, a database construction module, a target sample analysis module and the like, and full-process automation is achieved. According to the method, by dynamically adjusting the monitoring frequency, the accuracy and efficiency of optimization intervention are improved, resource waste is reduced, and the method is suitable for full-life-cycle optimization of various green products.

The invention provides a causal reasoning analysis method and system for public praise of automobile users and a program product, and relates to the technical field of product public praise analysis, and the method comprises the steps: firstly extracting event mentions conforming to automobile field semantics from unstructured public praise texts, and aggregating the event mentions into a high-level event concept based on semantic similarity; a large language model is introduced to generate an explanatory knowledge sequence related to event concepts, and the understanding ability of the model for domain logic is enhanced; a heterogeneous graph containing event mention nodes and event concept nodes is constructed, semantic information spreading is performed in combination with an interpretive knowledge sequence, and a cross-sentence and cross-fragment global dependency relationship is fully mined; and finally predicting causal relationship probability distribution between event concepts by using a graph probability model. Therefore, the depth and the intelligent level of automobile user public praise analysis are improved, and interpretable data support is provided for product optimization and user experience improvement.

Software product optimization is provided. Expressed sentiment in a set of opted in user-to-user business-related conversations among a group of users corresponding to an entity is associated with a software product in response to determining that the software product is identified by mentioned keywords in the set of opted in user-to-user business-related conversations based on a keyword corpus. It is determined whether the expressed sentiment associated with the software product is negative sentiment. A related software product listed in a software product catalog of the entity is identified as an optimization to the software product in response to determining that the expressed sentiment associated with the software product is negative sentiment. A recommendation to implement the related software product as the optimization to the software product is generated. The recommendation to implement the related software product as the optimization to the software product is sent to the group of users.