9results about How to "Timely" patented technology

This utility model discloses a safety protection device for ultra-high formwork support, including a safety buckle, a rotation auxiliary component, and a safety belt. The safety buckle and rotation auxiliary component are both mounted on a first guide rail and a second guide rail. An adjustment box is provided on the safety buckle. One end of the safety belt is connected to a retractor, and the other end passes sequentially through the safety buckle, the rotation auxiliary component, and the adjustment box, connecting to a safety ring. Multiple first elastic protrusions are evenly distributed on the upper and lower surfaces of the safety belt. Two limiting plates are provided inside the adjustment box. Multiple second elastic protrusions are evenly distributed on one side of each limiting plate, with the first and second elastic protrusions interlocking. A support mechanism is provided on the other side of the limiting plate. This utility model, by incorporating a safety buckle and an adjustment box, allows workers to easily and slowly adjust the length of the safety belt extending beyond the adjustment box at any time, expanding the workspace, preventing accidental activation of the centrifugal mechanism, and providing high flexibility and safety.

This invention relates to the field of data retrieval technology, specifically disclosing a database-based method for rapid retrieval of skills learning data, comprising the following steps: S1: Preset a retrieval period, statistically analyze the types, occurrences, and frequencies of retrieval fields within the period, and stop if the threshold is not met; S2: Construct binary retrieval field combinations, calculate the frequency and correlation coefficient of each combination field; S3: Single-field retrieval matches initial courses, calculates popularity based on the number of enrollees and collections, and sorts them; S4: Divide primary and secondary fields to correct popularity, sort and filter popular courses, and establish a connection between combinations and courses; S5: Prioritize displaying related popular courses, continuously update the period, and repeat the process after decomposing multi-field combinations. This method can improve retrieval accuracy and efficiency, ensure course timeliness and recommendation quality, adapt to the needs of skills training platforms, optimize resource allocation, and enhance the platform's core competitiveness.

The utility model discloses an absorption tower slurry PH and density measuring device which comprises an absorption tower, a first drainage pipe and a second drainage pipe which are communicated with the absorption tower are installed at the bottom of the absorption tower, and the first drainage pipe is connected with a first sampling pipe and connected to an inlet in the bottom of a first measuring tank through the first sampling pipe. An outlet in the top of the first measuring tank is connected with a first liquid outlet pipe and connected to a discharge pipe through the first liquid outlet pipe, a first PH meter is inserted into the top of the first measuring tank, the second drainage pipe is connected with a second sampling pipe and connected to an inlet in the bottom of the second measuring tank through the second sampling pipe, and an outlet in the top of the second measuring tank is connected with a second liquid outlet pipe. The second measuring tank is connected to the discharge pipe through a second liquid outlet pipe, and a second PH meter is inserted into the top of the second measuring tank. The slurry in the first measuring tank and the second measuring tank adopts a bottom-in and top-out mode, so that the drained slurry has stability, timeliness and representativeness, and the measurement accuracy of the first PH meter and the second PH meter is guaranteed.

This invention provides a method and system for constructing an industry graph based on scenario-based marketing, belonging to the field of industry graph construction technology. The method includes: processing media text to generate a text dataset using a BERT pre-trained model; processing scenario data to generate a behavioral dataset using a spatiotemporal calibration algorithm; aggregating these to generate an unstructured marketing dataset; combining the structured marketing dataset to generate a multimodal marketing scenario dataset; using a stream-batch integrated framework and entity disambiguation technology to process the multimodal dataset and construct a four-dimensional data lake; constructing a three-dimensional feature library and training a marketing scenario weight model; mapping the data lake to a graph database to form an industry chain topology; adjusting the strength of node relationships based on the weight model; learning the implicit features of nodes using the GraphSAGE algorithm; and generating a scenario-sensitive industry graph topology, thereby reflecting the relationships between industry entities in different marketing scenarios in real time and improving the accuracy of the industry graph in marketing decisions.

The application discloses an image classification model regression error reduction method based on uncertainty perception, and aims at the problem that a new version of an image classification model is difficult to be compatible with an old version of the image classification model after the image data is updated or the model architecture is updated. A model integration strategy is used to estimate and obtain an integrated prediction result of the model according to the uncertainty of the new and old image classification models in the prediction result. The integrated strategy is discussed for two cases of data-free and a small amount of label-free. For the data-free case, the uncertainty of the image classification model is estimated by using an image disturbance or a model disturbance. For the case of a small amount of label-free, the uncertainty of the old version of the image classification model is calibrated by using a temperature scaling method, so as to be aligned to the new version of the image classification model. The application reduces the regression error and obtains an integrated image classification model with forward compatibility, and has a better performance effect.

The invention belongs to the technical field of data analysis. The method comprises the following steps of: converting discrete textualized technical entities and relationships in a maintenance technical document into a group of mathematical constraint sets with internal logic by constructing a semantic geometric conformation library, so that a computable geometric representation is constructed for each possible technical semantic state of equipment; the establishment of the evolution path set further explicitly models an empirical rule contained in historical maintenance data into evolution logic and probability between different technical semantic states, and reveals a typical mode and an internal mechanism of equipment state recession; by calculating the docking affinity of the real-time data probe and the semantic geometric conformation, the current complex state, formed by multi-dimensional sensor data, of the equipment can be accurately mapped to a specific semantic state; this state is then projected to a set of evolutionary paths. The maintenance instruction generated by the invention not only has timeliness, but also comprises traceability explanation of a fault evolution path.

The present application relates to the technical field of natural language processing of clinical trial medical texts, and specifically discloses a method for enhancing the reliability of large model output based on a heterogeneous double model, which comprises: performing deep learning semantic analysis on user input, fusing symptom and time feature encoding, combining fruit fly algorithm collaborative optimization, accurately positioning implicit semantic drift, and identifying implicit symptoms and time features; according to the identified symptoms and time features, dynamically activating and matching the corresponding localized knowledge sub-graph in the knowledge graph; the present application introduces time perception coding and symptom entity vector matrix in the semantic feature space through deep learning semantic analysis and fruit fly algorithm collaborative optimization, can accurately identify the implicit semantic drift in the user input due to the difference in symptoms or time, the two-stage search mechanism of the fruit fly algorithm gradually converges the semantic analysis range to the global optimal position, locates the semantic center of the user's real intention, and provides a semantic benchmark for knowledge retrieval and reasoning.

The utility model belongs to the technical field of kitchen appliances, and discloses a small-size food processor which comprises a cup body, a shell matched with the cup body in a sealing mode, a motor arranged in the shell and located below the cup body and a handle located on the side portion of the cup body, a smashing device is arranged in the cup body, and a rotating shaft of the motor penetrates through the bottom wall of the cup body to be in transmission connection with the smashing device. The shell is provided with an air inlet and an air outlet which are located below the cup body, a first fan for pumping outside air into the shell is arranged at the air inlet, a second fan for pumping the air in the shell to the outside is arranged at the air outlet, and the first fan and the second fan are arranged towards the cup body. The first fan and the second fan jointly provide power for circulation of airflow, the heat dissipation efficiency of the cup body is improved, cooling of the cup body and slurry in the cup is accelerated, the waiting time needed by drinking of a user is shortened, and the use experience of the user is improved.

The invention provides a business acceptance order processing method and device, and the method comprises the steps: obtaining at least one commodity recovery order and at least one commodity maintenance order, determining at least one usable recovery assembly from the to-be-recovered commodities of the at least one commodity recovery order according to the first commodity information and the second commodity information, the recovery assembly is used for maintaining the to-be-maintained commodities in the commodity maintenance order; for each recovery assembly, determining the total processing duration required for disassembling the recovery assembly from the to-be-recovered commodity and delivering the recovery assembly to the position of the to-be-maintained commodity; and selecting a target recovery assembly with the shortest total processing duration from all the recovery assemblies, and distributing the target recovery assembly to the position of the to-be-maintained commodity. Therefore, by intelligently matching the available components and the maintenance requirements in the recovery order and selecting the option with the shortest processing duration, rapid maintenance can be realized, the resource utilization efficiency and the service response efficiency can be improved, and the user experience can be improved.