11results about How to "Improve digitization" patented technology

The invention relates to the cross technical field of a computer information technology and a college management information system, and provides a college institution service intelligent response system fusing self-service questions and answers and task work orders. The system comprises a user interaction module, a self-service question and answer engine, a work order generation and distribution module, a background processing and feedback module and a service statistics and optimization module. According to the invention, an advanced natural language processing technology, knowledge graph construction and intelligent work order management are fused, so that a college institution service intelligent response system integrating self-service question answering and task work orders is constructed. The system not only realizes seamless access and efficient intelligent interaction of multiple channels and multiple terminals, but also ensures that various problems are timely and accurately processed through automatic classification, responsibility distribution and closed-loop management.

The application discloses a plasma-TIG integrated welding device capable of adjusting arc thermal power distribution, which comprises a plasma welding torch and a control circuit. The plasma welding torch comprises a plasma arc power supply, a high-frequency generator and a tungsten electrode; the plasma arc power supply is connected with the tungsten electrode through the high-frequency generator, and the tungsten electrode faces the workpiece directly; the control circuit comprises a dynamic push-pull device, and the dynamic push-pull device is connected with the tungsten electrode. The device improves the plasma welding circuit and the arc starting mode. When plasma welding is carried out, the dynamic push-pull device is used to make the tungsten electrode move downward before arc starting, the distance between the workpiece and the tungsten electrode is reduced, the arc is directly started with the workpiece, the dynamic push-pull device is used to make the tungsten electrode move upward after arc starting, and the tungsten electrode is recycled into the nozzle. Through the triple action of mechanical compression, thermal compression and magnetic compression, a stable constrained arc is formed, the plasma welding enters into a normal working state, and the conversion between the plasma welding and the TIG welding is realized.

The utility model relates to the technical field of automobile spraying equipment discloses a color changing valve structure and robot atomizer, include: color changing valve body, color changing valve body is rectangular, cleaning structure, cleaning structure includes first cleaning part and second cleaning part, first cleaning part includes the first limiting groove of setting up in the inside of color changing valve body, the inside of first limiting groove is provided with color changing valve inner chamber, the inside of color changing valve inner chamber is provided with the feed hole groove, the inside of feed hole groove is provided with first piston plate, one end fixed mounting of first piston plate has first fixed round plate, two groups of cleaning brush plates are fixedly installed on the outer wall of first fixed round plate, the outer wall of cleaning brush plate is close to the inner wall of color changing valve inner chamber, reach the pigment of color changing valve inner wall residue and clean up, reduce the condition that pigment mixes, improve the convenience of device use, improve the quality of paint spraying, improve the paint spraying effect of automobile appearance.

The invention provides a drilling cost parameter intelligent extraction method based on a natural language processing technology, and relates to the technical field of artificial intelligence and natural language processing, and the method comprises the steps: building a cost parameter standardized form, and carrying out the standardized definition of the form according to the requirements of a drilling project cost file; establishing a mapping relationship between the standardized parameters and scheme design contents, and forming a standardized form database model; an identification rule for data extraction is formulated, wherein the identification rule comprises inclusion relation matching and complete matching; keyword definitions are extracted, and professional keywords including well drilling, well logging, well logging, well cementation and the like are defined and recognized; performing traversal identification in the document; and carrying out framework extraction by adopting a keyword extraction model to realize extraction of cost parameters. According to the invention, through efficient and high-precision acquisition of estimation and budget calculation parameters, digital and intelligent development of cost business is promoted.

The application relates to the technical field of credit entry, in particular to a credit entry method and terminal equipment based on a smart contract, which comprises the following steps: S1, uploading credit proof materials, performing feature rough extraction on the credit proof materials to obtain rough recognition results; S2, matching the rough recognition results based on a preset certificate template library to extract key recognition results; S3, matching the key recognition results based on a smart contract deployed on a blockchain to obtain credit values; S4, performing hash calculation on the credit proof materials to obtain material hash values, and performing blockchain storage on the credit values and the corresponding material hash values to complete credit entry. Through the whole-process processing of credit material uploading, key information recognition and credit calculation to blockchain storage, the artificial input and time cost are greatly reduced, the efficiency, digitization and intelligentization of credit entry management are effectively improved, and the technical effect that the material storage is unalterable is achieved.

The invention discloses a multi-model intelligent agricultural combined operation optimization method and device and a medium, and relates to the field of intelligent agriculture. In the method, a first agricultural machine is controlled to execute first-stage operation on a target land parcel to obtain a first-stage result, and a second agricultural machine is controlled to execute second-stage operation based on the first-stage result to generate a process operation parameter set; a third agricultural machine is controlled to conduct harvesting operation on the target plot, and actual yield data are collected; dividing each geographical sub-region into different parameter job partitions; for each parameter job partition, a representative yield value is calculated; recognizing and marking disturbed geographical sub-regions; the yield value of the disturbed geographical sub-region is corrected into a representative yield value of the corresponding parameter operation partition, and a corrected yield data set is obtained; an optimized job policy is generated based on the set of process job parameters and the set of corrected yield data. By implementing the technical scheme provided by the invention, the accuracy of intelligent agriculture decision making is improved.

This invention relates to the field of hydropower technology and discloses a method for early warning of vibration swing of bulb turbine units under different operating conditions based on big data. Through steps S1-S5, the method specifically performs intelligent early warning of vibration swing of the bulb turbine unit under different operating conditions. It combines data from multiple Chinese turbine models and the SVR method to fit the health status of the bulb turbine unit, i.e., historical data from the initial operation or after major overhaul. Furthermore, by using different operating condition standards, it achieves personalized judgment based on a single unit. The method compares the real-time vibration swing value of the unit with standard values, alarm values, and shutdown value ranges, providing real-time warnings of exceeding limits and shutdowns for interval operation. It also makes timely intelligent diagnoses, effectively improving the effectiveness of the vibration swing early warning system, reducing false alarms and misreporting, effectively helping to reduce the probability of unit failure, improving the stable operation frequency of the unit, and creating significant economic benefits.

The present application relates to the technical field of state detection, in particular to a hanging basket contraction state detection system based on cloud, which comprises a data reporting module, a time sequence verification module, a contraction detection module, a trend inference module and an event index module. Based on the key nodes of the hanging basket, the displacement sensor monitoring data is analyzed, and the time characteristics of the collected data are identified item by item. In the present application, the multi-source monitoring data of each node of the hanging basket is automatically time-labeled on site, and then is collected to the cloud platform through wireless mode. The data is efficiently screened and standardized time-sequenced verified in the cloud, the contraction state change is automatically identified through dynamic trend analysis in the continuous period, the structural linkage anomaly and the attribution characteristics are classified, the event correlation index and the spatial position mapping are supported, the multi-level data archiving strengthens the information comparison and tracking between cross nodes, and the digitalization and intelligent management level of the whole process of the bridge hanging basket contraction are improved.

The invention discloses a heavy metal ecological risk evaluation and processing technology recommendation method and device based on machine learning, and a medium. The ecological risk evaluation method comprises the following steps: constructing a sample data set; an occurrence form prediction model based on machine learning is constructed, feature vectors formed by heavy metal types and physicochemical properties serve as input, and the proportions of the four occurrence forms serve as output; training an occurrence form prediction model by using the sample data set; obtaining heavy metal types and physicochemical properties of a to-be-evaluated sample to construct feature vectors, inputting the feature vectors into the trained occurrence form prediction model, and predicting four occurrence form proportions of the to-be-evaluated sample; and calculating an evaluation index RAC of a risk evaluation coding method based on the proportions of the four occurrence forms of the to-be-evaluated sample, and determining an ecological risk level according to the evaluation index RAC. The efficiency and the intelligent level of heavy metal pollution ecological risk assessment and treatment decision making are improved.

The utility model discloses oil storage tank measuring equipment which comprises a main body support, a wheel disc and a guide assembly, a cable ruler belt is wound on the wheel disc, scale marks are arranged on the cable ruler belt, one end of the cable ruler belt is fixed on the wheel disc, and the other end of the cable ruler belt bypasses the guide assembly and is connected with a medium detection sensing device. The medium detection sensing device is used for sensing the liquid level position, the oil-water interface position and the tank bottom platform position in the oil storage tank; a cable electrically connected with the medium detection sensing device is arranged in the cable ruler belt, the end, not connected with the medium detection sensing device, of the cable penetrates through the wheel disc and is electrically connected with an alarm, and the alarm sends out a corresponding alarm signal along with a current medium detected by the medium detection sensing device. The energy supply of measuring the liquid level position, the oil-water interface position and the tank bottom platform position is integrated, the number of carried single-function measuring instruments is reduced, unified operation and management of various measuring data are conveniently achieved, and the digitization and intelligence level of a using unit is improved.

The application discloses a kind of high-precision prediction method of friction coefficient based on ceramic coating, it is related to ceramic coating friction coefficient prediction technical field, comprising the following steps: step one: obtaining the experimental data and literature auxiliary data of APS spraying ceramic coating;Step two: complete field alignment, training / test set division, missing value processing and training set enhancement;Step three: construct five-level physical feature chain, convert original variable into descriptor with tribological physical meaning;Step four: descriptor input machine learning model for training and prediction;Step five: robustness verification and explainability analysis are carried out, the method constructs five-level physical feature chain, and further maps original material parameter and working condition parameter into physical descriptor related to contact stiffness, pore weakening, bearing capacity and lubrication response, so that model input no longer stays in surface experimental variable, but can more fully characterize tribological mechanism.