1210results about How to "Improve objectivity" patented technology

The invention discloses an internet-based intelligent bid invitation management platform, which comprises a data integration and analysis module configured to establish an intelligent comprehensive economic analysis model by integrating internal data and / or external data of the intelligent bid invitation platform; the standardized bid invitation file module is configured to construct an industry lexical analysis and language model by utilizing natural language processing and a knowledge graph; the intelligent question-and-answer type bidding file filling module is configured to complete compiling of a bidding file by guiding a bidder to adopt an intelligent question-and-answer mode; the intelligent bid evaluation tool set module is configured to assist the bid evaluation process by establishing an intelligent bid evaluation tool; the risk early warning module is configured to construct a knowledge graph of a supplier, a subject matter and a project; and the large model application module is configured to construct an AI bid evaluation model by using a large model technology. Through intelligent bid invitation management, bid evaluation work efficiency is improved, and fairness and accuracy of bid evaluation results are ensured.

The invention provides a data asset multi-dimensional assessment method and system based on a block chain and machine learning, an assessment index system covering dimensions such as data quality, value, risk and security is constructed, a block chain technology is adopted to carry out assessment process and result evidence storage, a machine learning model is introduced to dynamically optimize index weights, and the assessment efficiency is improved. And a data asset marketization pricing mechanism is explored and formed. According to the method, the problems that an existing data asset evaluation method is single in dimension, high in subjectivity and lack of credibility are solved, scientific evaluation, marketization pricing and credible publication of the data asset value are achieved, important reference is provided for data element circulation, and the method has great significance in promoting prosperous development of the data element market.

The invention relates to the technical field of tea quality detection, in particular to a tea quality detection and analysis method and system. The method comprises the following steps: performing hyperspectral image acquisition on a tea sample, and performing RGB image acquisition to obtain an original hyperspectral image and an RGB image; performing spatial information registration on the original hyperspectral image according to the RGB image to obtain a registered hyperspectral image; performing spectrum-space data fusion according to the registered hyperspectral image and the RGB image to obtain spectrum-space fusion data; performing endoplasm and appearance feature analysis on the spectrum-space fusion data to obtain a multi-modal feature vector; and performing feature vector decoupling on the multi-modal feature vector to obtain an endoplasmic decoupling feature and an appearance decoupling feature. According to the method, the internal quality evaluation and the appearance evaluation of the tea leaves are organically combined, a unified evaluation system is constructed, and the comprehensive, objective and refined evaluation of the tea leaf quality is realized.

The invention relates to the technical field of safety engineering and artificial intelligence, in particular to a limited space operation safety risk assessment method and system, and aims to effectively improve the core defects of the prior art through a machine learning technology and a data analysis method. Due to the real-time monitoring and evaluation capability, the system can respond to environmental changes in time; the model can identify and adapt to new risk factors due to high adaptability; the accuracy and objectivity of evaluation are improved through a data driving method; an iterative optimization mechanism ensures that the model can be evolved continuously, the effectiveness of risk management is enhanced, a more efficient, more accurate and more reliable safety risk assessment method and system are provided for limited space operation, a solid technical support is provided for guaranteeing operation safety, and the method and the system have good application prospects. And an important step of intelligent and digitized development of the security risk assessment field is marked.

The invention is suitable for the technical field of tumor treatment and protection, and particularly relates to an intelligent classification prediction method, system and equipment for tumor treatment and protection, and the method comprises the steps: obtaining tumor-related molecular marker detection values and clinical diagnosis and treatment features of all target patients, and comprehensively integrating the multi-dimensional medical information of the patients; according to the relevance of molecular markers and clinical features, a scientific and quantitative multi-dimensional prognosis layering index set is constructed, and an accurate layering evaluation framework is formed; inputting the medical data into a pre-trained multilayer neural network model, and generating a high-resolution tumor characterization map through a cross-modal feature fusion technology; based on the dynamic matching of the atlas and the hierarchical index set, the hierarchical contribution degree parameter and the prognosis risk score are calculated in combination with the random forest model, the precise quantitative evaluation of the condition of the patient is completed, a high-credibility quantitative basis is provided for clinical decision making, the over-treatment risk is effectively reduced, and the prognosis management effect of the patient is improved.

The invention provides a model fine tuning training method and device, an answer output method and device and electronic equipment, and aims to solve the problem of low safety of a large language model. The method comprises the following steps: acquiring a plurality of question texts to be processed; for each question text in the plurality of question texts, acquiring a principle keyword corresponding to the question text, and generating a prompt text corresponding to the question text according to the question text and the principle keyword corresponding to the question text; inputting a prompt text corresponding to the question text into a large language model to enable the large language model to output a plurality of answer texts; determining the answer text with the highest security from the plurality of answer texts; and taking the prompt text corresponding to each question text as training data, taking the answer text with the highest security as a training label, and performing fine tuning training on the large language model to obtain the large language model after fine tuning training.

The embodiment of the invention discloses a power transmission line path planning method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining geographic information data of a power transmission line path planning region, carrying out the preprocessing of the geographic information data through a geographic information system, and constructing a grid cost surface considering influence factors; constructing an optimization model based on the grid cost surface according to the requirement of power transmission line path planning; solving the optimization model by using a heuristic search algorithm to obtain a preliminary path; further optimization processing is carried out based on the preliminary path, unnecessary corners and redundant paths are eliminated, and a target planning path is obtained; according to the method, a mathematical optimization model and a corresponding solution algorithm can be introduced, the optimal line path can be automatically generated on the basis of comprehensively considering various factors, the dependence on artificial experience is reduced, and the objectivity and scientificity of planning are improved, so that the efficiency and accuracy of high-voltage transmission line planning are remarkably improved.

The invention discloses a craniocerebral injury degree diagnosis auxiliary system based on a medical image, and relates to the technical field of medical treatment, a primary judgment module can accurately judge whether a patient has the risk of interstitial encephaledema or not by analyzing the accumulation degree of interstitial fluid in white matter and quantitatively calculating a penetration anomaly coefficient Syxs, the ability of early risk screening is provided, and the risk of interstitial encephaledema can be accurately judged. Potential problems can be recognized in time before disease deterioration, a clinician is prompted to intervene rapidly, and therefore the prognosis effect of a patient is remarkably improved. When the risk of interstitial encephaledema exists, the type diagnosis module further distinguishes lesion types (such as brain contusion or other injuries) by extracting related distinguishing features in a multi-modal feature set, calculates a locking coefficient Sdxs, and provides a scientific quantitative basis for diagnosis of lesion types based on specific features of the lesions. According to the accurate type diagnosis method, diagnosis errors can be reduced, and reliable support is provided for clinical decision making.

The invention discloses an agent evaluation method and device, electronic equipment, a storage medium and a program, and the method comprises the steps: determining a test case set of a target agent based on an agent role; generating a test result of the target agent based on the test case set; and determining an evaluation result corresponding to each test result according to an intelligent agent evaluation model. According to the embodiment of the invention, automatic evaluation of the intelligent agent can be realized, quantitative evaluation is carried out on the intelligent agent evaluation process, and objectivity of intelligent agent evaluation can be improved.

The invention belongs to the technical field of data asset management, and discloses an enterprise data asset auditing method and system based on big data, and the method comprises the steps: collecting financial information data, business operation data, management decision data and external industry data of an enterprise; preprocessing the collected financial information data, business operation data, management decision data and external industry data to obtain a financial feature data set, a business operation feature data set, a management decision feature data set and an external industry feature data set; performing feature extraction and weighted fusion on the financial feature data set, the business operation feature data set, the management decision feature data set and the external industry feature data set to obtain a comprehensive feature data set; constructing an audit risk prediction model, inputting the comprehensive feature data set into the audit risk prediction model, and predicting to obtain an audit risk coefficient of the enterprise data assets; and a more comprehensive view angle is provided, and the data asset risk of the enterprise can be evaluated more accurately.

The invention discloses a medical ultrasonic image intelligent processing method and system, and relates to the technical field of medical image.According to the main scheme, normal feature coordinates of different organs and tissues are calculated by obtaining normal ultrasonic image data, to-be-processed image data and historical medical record information of a patient; extracting coordinates of a to-be-detected image and comparing the coordinates with normal coordinates for preliminary judgment; performing secondary judgment by combining ovality and circularity; setting a lesion gray threshold to judge whether a lesion exists or not; calculating the size of a lesion area when the lesion exists; according to the method and the system, the comprehensive blood flow evaluation index is calculated in combination with color Doppler blood flow imaging data, the disease evaluation value is calculated according to known medical record data and the like, the disease level is judged, the diagnosis report is automatically generated, the accuracy and the efficiency of medical ultrasonic diagnosis are improved, and powerful support is provided for clinical diagnosis.

The invention discloses a tongue picture characteristic change rule analysis method based on the rehabilitation process of a chronic disease patient, and relates to the technical field of health big data analysis, and the method comprises the steps of tongue picture data standardized collection, tongue picture data recording system establishment, tongue picture characteristic data analysis, result visualization and report generation. According to the invention, through a standardized tongue picture data acquisition mechanism, the quality and consistency of the tongue picture data are ensured, and through controlling illumination conditions, background colors, diet restrictions and posture requirements, the influence of external factors on the tongue picture data is reduced, and the accuracy and reliability of the data are improved. By comprehensively evaluating definition, noisy points, contrast ratio and color accuracy verification parameters, it is ensured that collected tongue picture data reaches a preset quality standard, and a high-quality data basis is provided for subsequent analysis; meanwhile, a comprehensive tongue picture data recording system is established, and tongue picture data are effectively stored and managed.

The invention provides a tumor immunotherapy curative effect prediction system based on polarization characteristics of neutrophil, and relates to the technical field of biology, the system comprises a neutrophil detection module used for determining neutrophil information; the patient information module is used for acquiring tumor information and immune information of a patient; the immunotherapy scheme information module is used for acquiring immunotherapy scheme information of the patient; the historical data acquisition module is used for acquiring historical patient information; the immune state coefficient module is used for determining the immune state coefficient of the patient; the prediction model module is used for obtaining the predicted tumor size through the trained tumor size prediction model; the immune state prediction module is used for determining a predicted immune state coefficient; and the curative effect prediction module is used for determining a predicted tumor immunotherapy curative effect score. According to the invention, the predicted tumor immunotherapy curative effect can be evaluated according to two aspects of predicting the tumor size and predicting the immune state coefficient.

The invention provides an industrial big data micro-service API structure optimization method, medium and system, and belongs to the technical field of industrial big data, the system comprises an analysis data acquisition API, a data preprocessing API, a data analysis API and a data display API, and is divided into a data access layer micro-service module, a data processing layer micro-service module and a data service layer micro-service module; analyzing an API (Application Program Interface) responsibility range of the data access layer micro-service module by adopting a first machine learning model, and extracting an API calling link index; performing in-participation and out-participation standardization processing on the micro-service module of the data processing layer; an API interface version management module is established, and API interface change records are collected; analyzing the API interface version evaluation model by adopting a second machine learning model; and an API optimization evaluation function is established, API structure recombination operation is executed, and the problem that existing API responsibility division is not reasonable enough is solved.

The invention relates to a pilot SA ability evaluation system based on eye movement tracking and space directional flight simulation, and the system comprises a space directional flight simulator which is used for simulating a real flight environment; the eye tracker is used for recording an eyeball movement track of a pilot in real time in a simulated real flight environment to obtain eye movement track time sequence data and shooting a flight training video; the microprocessor evaluation module comprises a time sequence data processing module and a Ti-SA model; the time sequence data processing module obtains corresponding AOI fixation time sequence data based on the eye movement trace time sequence data, the flight training video and a preset AOI subdivision area; the Ti-SA model is used for predicting a current SA capability value of a pilot based on the eye movement trace time sequence data and the AOI fixation time sequence data and judging whether the predicted SA capability value is lower than a preset SA threshold value or not, and if the predicted SA capability value is lower than the preset SA threshold value, an early warning instruction is sent to the early warning and voice broadcasting module; and the early warning and voice broadcasting module is used for early warning when the SA capability value is lower than a preset SA threshold value.

The invention relates to the technical field of food monitoring, in particular to a nutritious food freshness monitoring and early warning method and system, overcomes the limitation of a traditional method, and improves the level of nutritious food freshness monitoring. The method comprises the following steps: collecting freshness influence factors in the process from production to distribution of the nutritious food; carrying out attribute marking on the collected freshness influence factors to generate a freshness characterization data set; classifying the freshness characterization data set by using a pre-constructed freshness characterization classification model to generate a plurality of freshness characterization type sets; performing freshness evaluation on each freshness characterization type set, and sorting the multiple freshness characterization type sets based on an evaluation result to generate a freshness risk sequence; each freshness characterization type set in the freshness risk sequence corresponds to a freshness risk value; on the basis of a preset freshness requirement threshold value, all freshness characterization type sets in the freshness risk sequence are compared.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a retrieval enhancement generation method based on multi-source knowledge fusion, which comprises the following steps: selecting multi-field knowledge sources such as web pages and Mock APIs through a proxy routing system, extracting related information by utilizing sparse retrieval, dense retrieval and text rearrangement technologies, and performing entity recognition and naming in combination with a large language model. And updating the data by adopting a noise fragment fusion technology. Meanwhile, the invention further provides a system and a performance verification method, and accuracy and consistency of generated results are evaluated through regular matching and a large model. According to the method, multi-source data integration, accurate retrieval and generation evaluation are combined, the accuracy, logicality and continuity of the generated content are improved, and the method is suitable for generation tasks in multiple fields. According to the method, dynamic knowledge selection, multi-step retrieval and reasoning and noise fusion technologies are combined, the overall performance of the retrieval enhancement generation model is effectively improved, and accurate evaluation is carried out.

The invention discloses an engineering investigation risk assessment method based on AI, and relates to the technical field of engineering investigation risk assessment, and the method comprises the following steps: data collection: collecting data related to engineering investigation, including geological data and environmental data; the geological data collection method comprises the following steps: firstly, determining a prospecting purpose and required information, determining the boundary of a prospecting area so as to determine a prospecting target and range, and collecting existing geological reports, maps and research paper data in the prospecting area; according to the method, the engineering investigation risk is evaluated by adopting an artificial intelligence technology, and a large amount of geological and environmental data are deeply analyzed by utilizing a machine learning algorithm, so that the defect that a traditional method excessively depends on subjective experience judgment is overcome, and the accuracy and objectivity of risk evaluation are improved; through the automatic feature selection and data preprocessing process, it is ensured that the evaluation result is more accurate, and interference of human factors is reduced.

The invention discloses a drawing projection talent selection test system and method based on combination of hierarchical quantum evolution and particle swarm optimization. The system comprises a sensing data acquisition module, an intelligent situation identification preprocessing module, a multi-dimensional feature collaborative extraction module, a distributed intelligent quantitative analysis module, a self-adaptive intelligent prediction module and a multi-source data dynamic evaluation module. All the modules work cooperatively, and perceptual acquisition, intelligent preprocessing and depth feature extraction of multi-dimensional drawing projection data are achieved, and efficient quantitative analysis, intelligent prediction and dynamic evaluation are achieved. Through the combination of the hierarchical quantum evolutionary algorithm and the improved particle swarm algorithm, the drawing projection data can be subjected to multi-level and refined analysis, an accurate and automatic talent screening tool is provided, the accuracy and the intelligent level of evaluation are remarkably improved, and the method has a wide application prospect.

The invention provides a peaceful treatment and care effect evaluation method, which belongs to the technical field of peaceful treatment and care effect evaluation, and comprises the following steps: establishing a physiological index anomaly detection model and removing abnormal data by collecting physiological index data and subjective score data of a patient in real time and applying technologies such as multivariate mixed decomposition and time sequence feature analysis. And establishing a function relationship between the physiological index stable component and the subjective score data, calculating a physiological fluctuation influence coefficient matrix, and performing weighted fusion calculation by using the corrected physiological stability coefficient matrix and subjective score coefficient matrix according to a personalized reference range in a patient baseline database to obtain a comprehensive score of the calming treatment and protection effect. And finally, according to a preset scoring interval, generating an evaluation result of the calming treatment and protection effect. According to the method, the physiological index data and the subjective score data are fully utilized, and the problem that the current calming treatment effect depends on subjective evaluation of medical staff and is difficult to reflect accurately is solved.

The invention relates to the technical field of ecological data analysis, in particular to a pumped storage power station slope ecological restoration effect evaluation method, which comprises the following steps: monitoring a pumped storage power station slope ecological restoration area; calculating a dry season restoration state characterization value; calculating a frozen season restoration state characterization value; calculating the representation value of the restoration state in the rainy season; outputting a corresponding repair effect evaluation prompt according to each repair level; and correcting the preset evaluation duration. Through real-time monitoring and dynamic weight adjustment, the accuracy and adaptability of an evaluation result are ensured, environmental changes can be responded in time, comprehensive ecological restoration state analysis is provided through comprehensive consideration of various environmental parameters, different restoration levels are clearly distinguished through the evaluation result, and the method has the advantages of being high in practicability and the like. Clear guidance is provided for taking targeted restoration measures, and the problem that the restoration effect assessment is inaccurate due to the fact that the adaptability to extreme climate conditions is insufficient and ecological restoration measures cannot be adjusted in time is effectively solved.

The invention discloses an early-stage prostate tumor recognition method based on a hyperspectral image, and belongs to the technical field of image recognition, and the method comprises the steps: collecting early-stage prostate tumor samples with different lesion degrees; performing hyperspectral scanning on the prostate tumor early tissue sample to generate corresponding hyperspectral image data and performing preprocessing; extracting features of the preprocessed hyperspectral image data; organizing feature data and label data, wherein the label data classifies and marks a sample based on a known pathological diagnosis result; constructing an early prostate tumor recognition model based on the feature data and the label data, and performing optimization training; and performing early prostate tumor recognition on a new sample to be detected by using the trained recognition model. The method has the beneficial effects that the actual canceration condition can be easily mastered, and compared with an existing tissue slice inspection mode, the method is easier to operate, the detection result can be obtained more quickly, the result accuracy is higher, and the objectivity is higher.

The invention relates to the technical field of enterprise supervision, in particular to a method for constructing an enterprise classification and grading supervision index system. According to the technical scheme, the method comprises the following steps of 1, determining a supervision target and a supervision range, and clearly supervising enterprise types and enterprise projects; 2, collecting various data related to the enterprise, including but not limited to financial data, operation data, market data and compliance data; and step 3, screening out key indexes having significant influence on enterprise classification and grading. According to the method, comprehensive and accurate evaluation is realized through multi-dimensional index screening and scientific classification, comprehensive weight determination and accurate quantitative evaluation; dynamic changes are adapted by means of regular updating, dynamic feedback early warning and flexible adjustment factors; and refined supervision and resource optimization are assisted by scientific threshold setting, enterprise development is promoted, and a benign interaction situation between supervision and enterprises is formed.

The invention relates to the technical field of quality evaluation, and discloses a lightweight beef quality evaluation method based on unsupervised feature learning, and the method comprises the steps: obtaining original hyperspectral data of beef as real data, and generating high-quality sample data based on a generative adversarial network model; preprocessing the high-quality sample data, mapping, reconstructing and fusing the preprocessed sample data, extracting spectral and spatial features of different levels, and fusing or splicing the spectral and spatial features with feature values of other auxiliary data; the fusion sample features are mapped and fused, sample data of specific tiny disturbance similar to real data are generated, the sample data and the real data are alternately input into a Transform model, and parameters of the Transform model are updated; the beef quality is evaluated on the basis of the Transform model with the updated parameters; according to the method, the accuracy, objectivity, rapidness, non-destructive property and comprehensiveness of beef quality evaluation are improved.

A large model agent generation content quality evaluation method based on double baselines comprises the steps that data generated by human beings is acquired, preprocessing and semantic segmentation are carried out, and data samples after semantic segmentation are formed; performing theme extraction on the data samples after data segmentation to form a theme list; the content of the data forming the theme list is generated through an intelligent agent workflow and the content of the data forming the theme list is directly generated through a large model, and an evaluated sample and a contrast sample are obtained; comparing and scoring the data sample after semantic segmentation, the evaluated sample and the contrast sample; selecting a sorting algorithm to carry out score conversion on the graded and compared samples; and performing statistical verification and result analysis. According to the method, a comprehensive, reliable and efficient solution is provided for quality evaluation of the generation content of the large model agent. The method not only can accurately evaluate the content quality, but also provides powerful support for continuous optimization of an intelligent agent system.

The invention relates to the technical field of data processing, in particular to a sheep house-oriented environmental data monitoring method, device and system, and the method comprises the steps: obtaining a feed consumption sequence, an environmental temperature sequence, an infrared image data set and an ammonia concentration sequence of a plurality of historical excrement cleaning periods of a sheep house; the feed consumption degree of the sheep house at the latest moment in the latest excrement cleaning period is obtained; obtaining the influence degree of the temperature on the ammonia gas concentration value at the latest moment; the environment temperature fluctuation degree of the sheep house at the latest moment is obtained; according to the influence degree, the environment temperature fluctuation degree, the current ammonia concentration sequence and the stable class cluster, the real ammonia concentration value of the sheep house at the latest moment is obtained. Under the condition that the ammonia concentration of the sheep house is influenced by the environment temperature, the excrement accumulation amount of the sheep house can be accurately judged, excrement in the sheep house can be cleaned in time, and a proper breeding environment is created for the sheep house.

The invention relates to the technical field of water traffic safety supervision, and particularly discloses a water traffic safety risk grading management and control system and method, and the system comprises a data collection and processing module, a risk judgment module, and a risk grading management and control module. The data acquisition and processing module is used for acquiring and preprocessing risk data; the risk judgment module is used for carrying out risk level judgment on the preprocessed risk data to obtain a risk level judgment result; the risk grading management and control module is used for constructing a risk grading management and control measure library, and performing automatic matching of management and control measures in the risk grading management and control measure library based on a risk grade judgment result to obtain risk management and control measures; and transmitting the risk level judgment result and the risk management and control measures to different institution users. The system provides relatively objective and accurate risk level judgment rules, supports risk information level management and control, and supports collaborative linkage of the risk information among different users.

The invention relates to the technical field of anorectal operation suturing, in particular to an anorectal operation suturing effect evaluation method and system based on image recognition, which can improve the objectivity and accuracy of evaluation and realize real-time feedback and immediate adjustment. The method comprises the following steps: acquiring treatment scheme data of an anorectal operation, and extracting a suture feature influence parameter set from the treatment scheme data; the suturing feature influence parameter set comprises a suturing tissue structure, a suturing wound shape, a suturing wound width and a suturing wound length; inputting the stitching feature influence parameter set into a pre-trained standard stitching feature contrast model to generate a standard stitching feature set; the standard suture feature set comprises a standard suture needle density, a standard suture edge distance and a standard suture needle distance; acquiring a surgical suturing image in real time, and segmenting and extracting suturing part sub-images from the surgical suturing image; and inputting the suturing part sub-image into a pre-constructed suturing feature image recognition model.

The invention relates to the technical field of construction site risk identification, and particularly discloses an infrastructure line construction site environment risk identification system. The system is provided with a three-dimensional distance measurement risk grade determination module, a natural environment risk grade determination module, a construction state risk grade determination module, an environment risk initial evaluation grade determination module and a construction site environment risk judgment module. The problems that monitoring of the safety area and the safety distance of high-risk operation is low in precision, low in efficiency, greatly affected by the environment and difficult to meet the requirement of a modern infrastructure line construction site for safety management exist in the prior art are solved, the construction site can be dynamically monitored under different environments and construction conditions, and through a comprehensive and efficient risk identification and early warning system, the safety management efficiency is improved. The accident rate of a construction site can be reduced, and construction quality and project progress are guaranteed.

The invention discloses a method for rapidly detecting the content of a residual flocculating agent in washing machine-made sand, which comprises the following steps of: screening out particles with the particle size of more than 4.75 mm from machine-made sand to be detected, respectively testing the critical water contents of degraded and untreated machine-made sand samples, and determining the mass concentration range of the flocculating agent on the basis of the difference value of the critical water contents of the two kinds of machine-made sand. Compared with tests through neat paste, mortar and concrete systems, deviation possibly caused by introduction of other materials is avoided, and the method is more convenient, faster and more accurate. A'critical water absorption 'test method is provided according to the stability of the stacking state of sand, a transparent circular truncated cone test mold with scales is designed, the filling height and the tamping method of each layer of sample are accurately stipulated, and the test normalization and the test result repeatability of different time, different places and different operators are improved.