395results about How to "Comprehensive assessment" patented technology

The invention provides an employee post matching and deploying method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: generating an optimized employee capability feature vector based on employee historical work data, extracting an employee capability development trend, and combining team cooperation data to obtain an optimized employee capability feature vector; a cross-modal contrast learning network is utilized to obtain an employee comprehensive ability vector, a development potential index is calculated, a post skill knowledge graph is constructed based on post instruction data and historical recruitment data, and a post skill feature matrix and a team structure feature vector are generated through a dynamic heterogeneous graph neural network and an adaptive graph attention network. Calculating an employee-post matching degree score matrix in combination with an employee comprehensive ability vector, inputting the matching degree score matrix and a development potential index into a distributed multi-agent reinforcement learning model, constructing a reward function, generating and evaluating a deployment scheme through hierarchical task decomposition and Monte Carlo tree search, and finally outputting a deployment execution scheme.

The invention provides a method and system for realizing electronic channel risk control processing based on a code insertion technology, and relates to the technical field of electronic channel risk control, and the method comprises the steps: encrypting user information and channel identification information through a dual Hash algorithm to generate a dynamic session identification code, and forming a target coding sequence through employing a self-adaptive segmentation algorithm and a business feature flag bit. And constructing a dynamic risk control rule model by using a graph neural network, extracting user multi-dimensional features, carrying out weighted fusion to generate a user risk score, comparing the user risk score with an adaptive risk threshold, analyzing abnormal behaviors based on a probability graph model, and generating an intelligent risk early warning report. And calling a reinforcement learning risk control strategy engine to determine an optimal disposal strategy, and issuing a multi-factor interaction verification instruction. And after verification, selectively activating a blocking mechanism according to a verification result, synchronizing interaction data and a result to a distributed risk control data center, and continuously optimizing the risk control model based on federal learning.

The invention relates to the technical field of disease follow-up visit, in particular to a health state assessment system applied to disease follow-up visit, which comprises a data acquisition module, a data preprocessing module, a health state assessment module, a personalized health management suggestion module, a dynamic health risk prediction module and a visualization module, the data acquisition module acquires multi-dimensional data of a patient and performs data fusion and integration, the health state evaluation module evaluates the health state of the patient based on multi-level feature extraction and a deep learning model, and the personalized health management suggestion module combines a health state evaluation result and historical data to obtain a personalized health management suggestion result. Customized health management suggestions are generated through a rule engine, and the dynamic health risk prediction module predicts future health risks of patients and provides real-time early warning by using a time sequence analysis model. The visualization module displays health assessment and management suggestion results through a chart and an interactive interface, and the accuracy and individuation degree of health management are improved.

The invention, which relates to the technical field of artificial intelligence and skill training examination, discloses a multi-crowd adaptive training evaluation system comprising the following components: a user data management module, an RAG module, an answer processing module, a score analysis module, an intensive training module and a question bank updating module. According to the method, comprehensive and accurate assessment of the user ability is realized by constructing a multi-crowd adaptive training assessment system, the system constructs the user ability portrait by collecting historical data and real-time performance of the user in multiple dimensions and utilizing advanced data processing and analysis technologies, and dynamically adjusts the training content and difficulty, so that the user ability assessment efficiency is improved. According to the method and the system, the ability level and the learning requirement of different users are adapted, the personalized training mode not only improves the participation degree and the learning efficiency of the users, but also helps the users to quickly and accurately identify the ability weakness of the users, and powerful support is provided for subsequent improvement.

The invention relates to a GNSS-based water conservancy facility deformation monitoring and early warning method, system and device and a medium, and the method comprises the steps: obtaining GNSS monitoring network data of a target part of a water conservancy facility, and carrying out the real-time dynamic differential processing, and obtaining corresponding three-dimensional coordinate data; acquiring environmental factor data, and performing multi-source feature fusion on the environmental factor data and the three-dimensional coordinate data to obtain a corresponding deformation feature vector; performing prediction analysis on the deformation feature vector according to a preset whale optimization algorithm to obtain a corresponding deformation trend prediction result; setting a multi-level early warning threshold according to the deformation feature vector, and performing early warning analysis on a deformation trend prediction result to obtain a corresponding initial early warning level; and obtaining standard deformation characteristic information of the deformation rate and the acceleration of the water conservancy facility, and performing comprehensive evaluation on the standard deformation characteristic information based on a deformation trend prediction result to obtain a corresponding early warning index. Potential risks can be found and processed in time, and the possibility of hydraulic engineering accidents is reduced.

The invention relates to the technical field of electrocardiogram monitoring, in particular to an electrocardiogram monitoring method and system based on heart rehabilitation data. The method comprises the following steps that electrocardiosignals in different motion states are collected through a multi-lead electrocardiograph and motion monitoring equipment, and fatigue electrocardio mapping is carried out to obtain fatigue signals. Then, heart rate changes of the fatigue electrocardiogram mapping signals are analyzed, the disordered scatter point trend of the heart rate is quantified, dynamic inflection point distribution is fitted, and trend dynamic data are obtained; multi-scale rule intersection analysis is carried out based on the data, a fatigue heart rate change rule is extracted, and rehabilitation deviation quantitative evaluation is further carried out. Finally, the rehabilitation deviation evaluation result is sent to the terminal for real-time electrocardiogram monitoring, and heart rehabilitation management is supported. According to the method, the electrocardiogram monitoring technology is optimized, so that the electrocardiogram monitoring technology is more perfect.

The invention provides an intelligent health risk assessment method and system, and relates to the technical field of health risk assessment, health big data is constructed, feature selection is performed through the health big data to construct a gradient boosting decision tree model, risk prediction of individual diseases is performed, and time sequence analysis of health indexes is performed according to a first health risk score. Adjusting the first health risk score, carrying out health risk interpretation based on the second health risk score, carrying out health risk hierarchical evaluation according to the personalized health optimization suggestion and the second health risk score, making a hierarchical intervention strategy, carrying out health detection on the target individual, and triggering health early warning according to an abnormal detection result. The technical problems that in the prior art, health risk assessment data sources are single, so that health assessment is not comprehensive and inaccurate, and targeted intervention strategies are lacked are solved. The technical effects of comprehensively and accurately evaluating the individual health risk and providing a targeted intervention strategy are achieved.

The invention discloses a big language model evaluation system and method, and belongs to the technical field of big language model evaluation. The system comprises a data preparation module, a basic performance evaluation module, an efficiency evaluation module, a robustness and multi-mode evaluation module, a distributed task scheduling module and a feedback optimization module. Through the system, the technical effect of multi-dimensionally, efficiently and automatically evaluating the large language model is achieved.

The invention provides a distributed photovoltaic access distribution network voltage fluctuation evaluation method, medium and system, and belongs to the technical field of distributed photovoltaic, and the method comprises the steps: firstly collecting the parameters of a photovoltaic system and a distribution network, and building a power grid response equation set considering the parameters; more parameter samples are obtained through data augmentation and expansion, and indexes such as voltage, power and harmonic waves of each node are calculated according to the response equation set. Constructing a voltage fluctuation evaluation index matrix including voltage deviation, fluctuation, harmonic waves, short-circuit capacity change and the like; and decomposing the matrix by adopting singular value decomposition to obtain a voltage stability and fluctuation evaluation matrix, and finally calculating a voltage stability index and a fluctuation index. If the voltage fluctuation evaluation index exceeds a preset threshold value, an alarm is triggered, and decision support is provided for distribution network planning and operation. According to the invention, the technical problems of short evaluation time scale and difficulty in capturing the dynamic characteristics of voltage fluctuation in the evaluation of the distributed photovoltaic access distribution network in the prior art are solved.

The invention discloses an electric energy safety management system and method based on a cloud platform, and relates to the technical field of electric energy safety management, and the scheme comprises the steps: collecting the operation data of a micro-grid unit in a power grid, and transmitting the operation data to the cloud platform; judging whether the operation data at each time point is abnormal or not; constructing a topological structure of the power network according to the physical connection between the micro-grid units; dividing all nodes into different areas, calculating a comprehensive evaluation value of each node, and selecting the node with the highest comprehensive evaluation value as a local center node of each area; and when a plurality of micro-grid units in any area have an electric energy safety fault, calculating the priority of each fault node according to the local aggregation coefficient of the fault node and the connectivity between the fault node and the local center node, and performing maintenance according to the priority of the fault node. And comprehensive monitoring and management of the electric energy quality are realized.

The invention discloses a patient pain prediction method and device based on wearable equipment. The method comprises the steps that historical physiological data and activity data of a patient are collected through the wearable equipment; acquiring a pain assessment report of the patient through an electronic medical record system, wherein the pain assessment report comprises a pain intensity score, the frequency and duration of pain attack and a treatment and intervention scheme; establishing a pain multi-modal evaluation model according to the physiological data and the pain evaluation report; wherein the pain multi-modal evaluation model comprises a learning identification module, a 1-all strategy module, a time sequence prediction module and a multi-modal fusion evaluation module; inputting the current physiological data into the pain multi-mode evaluation model, and predicting the pain intensity and the pain mode of the patient. Through the model comprising rule learning, the 1-all strategy, time sequence prediction and multi-mode fusion evaluation, the prediction precision of the pain intensity and the pain mode of the patient is improved, and the diagnosis and treatment effect of the patient, especially the chronic pain patient, can be remarkably improved.

The invention relates to the technical field of database capacity evaluation, in particular to a method for dynamically evaluating database capacity. The technical problems that in the prior art, a method for evaluating the capacity of a database may only pay attention to the current or short-term data volume, the possibility of future data increase is ignored, evaluation on the performance of the database may not be comprehensive or deep enough, and consequently capacity configuration cannot meet the performance requirement are solved; according to the technical scheme, the method for dynamically evaluating the database capacity comprises the steps that a prediction model is constructed, future storage requirements and load changes are predicted based on historical data, a machine learning algorithm is used for analyzing a use mode, resources are dynamically adjusted, and the resources are automatically increased or decreased according to the requirements; according to the method, by estimating the data growth rate and reserving the extra space, it is ensured that capacity configuration can meet future service requirements, and database performance is comprehensively evaluated and optimized by means of load testing, scene simulation and user behavior analysis.

The invention relates to the technical field of power transmission lines, in particular to a power transmission line toughness evaluation method and system considering important loads under an ice disaster. The method comprises the following steps: firstly, constructing an icing thickness real-time growth model considering multiple parameters such as water-ice density, an ice precipitation rate, a wind speed and air water content, carrying out regionalization analysis on a power transmission line according to weather parameters such as the wind speed and the precipitation rate, respectively calculating a unit length fault rate of each section, and comprehensively obtaining an overall fault probability of the whole line; then establishing a toughness evaluation system comprising a disturbance performance index, a recovery speed index and an important load maximum loss index, and dividing the affected process into five stages of system normal operation, performance reduction, performance maintenance, preliminary recovery and complete recovery; and finally, carrying out normalization processing on the evaluation indexes, determining the weight by adopting an optimization algorithm, and calculating the toughness value of the system. The fault prediction accuracy and the system recovery efficiency are remarkably improved through regional analysis and multi-dimensional evaluation, and the power supply reliability of important loads is effectively guaranteed.

The invention discloses an automatic environment air quality monitoring method and system, a product and a medium, and relates to the field of environment monitoring, and the method comprises the following steps: obtaining concentration data of malodorous chemical substances based on monitoring sensors in a plurality of monitoring stations; selecting the target monitoring point with the highest concentration data as a reference point, and marking a plurality of potential pollution sources based on the current wind direction and current wind speed data of the target monitoring point at the current monitoring time point; obtaining the reference distance between each potential pollution source and the reference point, the consistency degree with the current wind direction and emission historical data; calculating a pollution score of each potential pollution source based on the reference distance, the consistency degree and the emission historical data; determining the potential pollution source with the highest pollution score as a pollution starting point; and sending the position coordinate of the pollution starting point to a target client. By implementing the method, the accuracy of pollution source speculation can be improved.

The invention discloses a multi-agent path planning method, device and equipment, and the method comprises the steps: dividing a map according to first grids of a preset size, carrying out the global planning of all agents on the map, and generating a global path, the global path being a road section passing through a plurality of first grids from a starting point to a target point; on the basis of each path section of the global path, local planning is conducted on all agents, a local path is generated, and each path section is a road section formed by the center points of every two adjacent first grids; real-time data are obtained by monitoring changes of dynamic obstacles and dynamic targets in a map in real time, and the local path and / or the local path are / is updated and planned according to the real-time data to obtain an updated path. Potential collision risks can be quickly found, obstacle avoidance measures can be taken in time, and the safety of a multi-agent system and the smooth completion rate of tasks are improved.

The invention belongs to the technical field of unmanned aerial vehicles, and particularly provides a transformer substation unmanned aerial vehicle autonomous inspection route generation method based on an optimization A star algorithm. According to the method, a heuristic function is improved, the Euclidean distance between a target point and a current node is comprehensively considered, obstacle avoidance processing is carried out, and a safer and more efficient inspection route is generated. And by calculating the total cost of each node, the node with the minimum total cost is selected for expansion until a target node is found. According to the improved A star algorithm, the unmanned aerial vehicle can be effectively prevented from flying into an obstacle dense area, the obstacle keeps a safe distance, and the flying safety is remarkably improved. The distance and the obstacle density are comprehensively considered, the generated inspection route is smoother and more efficient, and unnecessary flight distance and energy consumption are reduced.

The invention discloses an on-line discharge capacity checking system of a station DC storage battery pack, and relates to the technical field of power systems. According to the method, voltage, temperature and internal resistance parameters of the storage battery in the continuous discharging process are continuously collected and are respectively analyzed to obtain a voltage evaluation index, a temperature evaluation index and an internal resistance evaluation index of the storage battery in the continuous discharging process, and the voltage evaluation index, the temperature evaluation index and the internal resistance evaluation index are further extracted; the method comprises the following steps: setting respective weight coefficients, multiplying and summing to obtain a health assessment index, presetting a threshold index of the health assessment index, comparing the health assessment index of each storage battery with the threshold index, judging that the storage battery is unqualified if the health assessment index is higher than the threshold index, and judging that the storage battery is qualified if the health assessment index is higher than the threshold index, thereby comprehensively assessing the use state of the storage battery. The problems that in the prior art, the health state of the storage battery is difficult to comprehensively and accurately assess, and multi-parameter comprehensive analysis and quantitative assessment means are lacked are solved.

The invention discloses an oil and gas reservoir shale content logging curve intelligent prediction method, and belongs to the technical field of rock physics and logging curve processing and interpretation, and the method comprises the steps: in step 101, determining Gardner formula parameters through combining a determination coefficient and a grid optimization method, and determining a Gardner formula parameter; and then generating a virtual density data set capable of covering the actually measured density data set by using a Gardner formula. According to the method, the optimal parameters of the Gardner rock physical formula and the Larionov rock physical formula are determined by using the decision coefficient and the grid optimization method, so that the abnormal value of the actually measured logging data can be effectively corrected, and a fusion data set for training various machine learning models can be generated; a cross validation method, a correlation coefficient, a determination coefficient and a multi-dimensional SHAP method are comprehensively utilized to more comprehensively evaluate and explain a prediction result, compared with a traditional rock physics method, the shale content prediction precision is improved by 12%, a logging curve data set can be effectively corrected and expanded, and the prediction accuracy of the shale content is improved by 12%. And the accuracy and the reliability of predicting the shale content of the oil and gas reservoir can be obviously improved.

The invention relates to a microbial index-based estuary and coastal environment ecological evaluation method, which comprises the following steps of: selecting representative sampling points, collecting sediment samples, analyzing and evaluating pressure gradients generated by human activities to estuary and coastal environments through physicochemical indexes, and constructing pressure indexes according to a reference standard, the comprehensive environment pressure index of each sampling point is constructed and calculated to reflect the overall condition of the target area environment; evaluating the health condition of large invertebrate communities in estuary and coastal environments and the response of the communities to human pressure, and calculating a marine organism index AMBI, so as to display the ecological health condition of a region; a high-throughput sequencing technology is used for identifying microorganism composition, pollution indicator bacteria are defined, a microorganism index M-AMBI value is calculated according to the pollution indicator bacteria, and the ecological health condition of each sampling point is graded; and finally, verifying the reliability of an ecological evaluation result through statistical analysis. According to the method, multi-dimensional data are integrated, the ecology of the estuary and coastal environment is comprehensively evaluated, a scientific basis is provided for revealing an environment pressure source and an ecological effect thereof, formulation of a precise environment management strategy is assisted, pollution risks are effectively prevented and controlled, and sustainable development of an estuary and coastal ecological system is promoted.

The invention relates to a GIS particle defect evaluation method and equipment fusing vibration and partial discharge, and the method comprises the steps: obtaining an ultrasonic signal and an ultrahigh frequency signal caused by the partial discharge of a GIS, generating a phase distribution diagram of the partial discharge, comparing the phase distribution diagram with a reference map, and judging whether a particle defect exists or not; for particle defects, the particle size is calculated based on the average discharge capacity and the maximum discharge capacity of the ultrahigh-frequency signals; and acquiring a vibration signal, combining the calculated particle size, outputting the predicted particle horizontal diffusion area and the maximum jumping height, and calculating a danger degree index of the particle defect so as to evaluate the danger degree of the GIS particle defect. Compared with the prior art, the method has the advantages that the sensitivity, the accuracy and the real-time performance of detection are improved, and more comprehensive evaluation is provided for the health state of the GIS equipment.

The invention discloses a computer network information security monitoring method, which relates to the technical field of network information security monitoring, and comprises the following steps of: monitoring and collecting network flow data in real time by using a monitoring and collecting module, extracting and recording historical network flow data, judging the abnormal degree of the current flow by using a comprehensive evaluation and adjustment module, and judging the abnormal degree of the current flow. The method comprises the following steps: evaluating the overall risk level of network security, adjusting a network security policy, generating a policy adjustment report, executing the adjusted network security policy by using a security execution module based on the policy adjustment report, and continuously monitoring network traffic by using a monitoring acquisition module. The beneficial effects and optimization innovation of comprehensive assessment, dynamic risk assessment, flexible strategy adjustment and sensitivity improvement are realized, and the optimization and innovation not only solve the problems and defects in the prior art, but also improve the accuracy and reliability of the monitoring system. And a more comprehensive and effective guarantee is provided for computer network information security.

The invention discloses an existing building outer wall internal and external heat insulation energy consumption optimization method based on a building model, and belongs to the technical field of building design. In order to solve the problems of insufficient data comprehensiveness and incomplete thermal insulation scheme evaluation, the thermal insulation performance of a building can be evaluated more accurately by checking an outer wall structure on site, recording material types, damage degrees and the current situation of a thermal insulation layer and combining scientific methods such as thermal resistance testing and heat transfer coefficient measurement; digitization and visualization of building information are achieved through the BIM technology, historical energy consumption data are collected from multiple data sources and systematic arrangement and classification are carried out, the readability of the data is improved, rapid import and analysis can be conveniently carried out in energy consumption simulation software, and therefore the energy consumption condition of a building is reflected more accurately; according to the method, comprehensive coverage of multiple possibilities is achieved, the influence of different heat preservation schemes on energy consumption can be evaluated more comprehensively, differentiated design is conducted on the thickness of the heat preservation layer based on the energy consumption simulation result, and the heat preservation performance of a building is effectively improved.

The invention discloses an agricultural precision planting pre-training and fine-tuning training method based on an AI large model. Based on a deep learning technology, key information is obtained from large-scale agricultural data, an optimal planting mode is predicted, and the crop yield and quality are improved. Through analysis of historical data and real-time monitoring, peasants are predicted and reminded about possible plant diseases and insect pests, so that measures are taken in advance. For different regions and different environmental conditions, the model can adapt to specific planting requirements through fine adjustment, and the universality and accuracy of the model are improved. The prediction of soil, weather and plant growth modes provides intelligent decision support for agricultural production, helps farmers to optimize a planting scheme, improve the yield and reduce resource waste, thereby improving the scientization and production benefits of agriculture. The model can help farmers to scientifically plant, improve crop yield and quality, save resources, reduce environmental burden and promote agricultural sustainable development.

The invention relates to the technical field of pilot state monitoring, in particular to a pilot state monitoring and flight data real-time synchronous fusion system, which comprises an information acquisition module used for acquiring physiological state information of a pilot and flight data information of an airplane in real time; the intelligent control module is used for controlling the operation of the camera, the electroencephalogram acquisition device and the electrocardiograph; the information processing module is used for carrying out preprocessing operation on the collected pilot physiological state information and the collected aircraft flight data; and the data fusion module is used for fusing the physiological state information of the pilot and the flight data parameters of the aircraft. The early warning assistance module is used for immediately sending out an early warning signal when an abnormality is found; and positioning information and rescue guidance are provided for a pilot under the emergency condition that the aircraft encounters danger. According to the current attention state and the task requirement of the pilot, the system can dynamically adjust the collection frequency and the collection position of the electroencephalogram collection device so as to improve the efficiency and the pertinence of data collection.

The invention discloses a driving behavior risk detection method, system and device and a storage medium, and relates to the field of driving safety. The method comprises the following steps: collecting vehicle internal data, external environment data, driver biological characteristic data, vehicle-mounted video data and vehicle-mounted audio data; extracting a first feature from the vehicle internal data, extracting a second feature from the external environment data, extracting a third feature from the driver biological feature data, extracting a fourth feature from the vehicle-mounted video data, and extracting a fifth feature from the vehicle-mounted audio data; fusing the first feature, the second feature, the third feature, the fourth feature and the fifth feature to obtain a feature vector, and inputting the feature vector into a preset machine learning model to obtain a risk detection score; and when the risk detection score exceeds a threshold value, displaying risk information on a vehicle-mounted display screen and giving a voice prompt. By implementing the technical scheme provided by the invention, the driving risk can be comprehensively evaluated by integrating multi-source data, the early warning is timely performed, and the driving safety is improved.

The invention relates to the technical field of intelligent switch cabinet monitoring, and particularly discloses a multi-mode fusion monitoring system for an intelligent switch cabinet. The system is provided with a use environment data analysis module, a monitoring error item matching module, a first monitoring signal analysis module, a second monitoring signal analysis module, a fusion monitoring factor acquisition module and a fusion monitoring result output module. The problems that a traditional single-mode monitoring method is difficult to comprehensively and accurately evaluate the overall state of the intelligent switch cabinet, information islands exist in the single-mode monitoring method, and relevance among various monitoring information cannot be fully utilized are solved. By monitoring electrical parameters, vibration characteristics and the like and combining and fusing monitoring error terms, the errors can be quantized and compensated, the system can evaluate the state of the switch cabinet more comprehensively, the limitation of single-factor evaluation is avoided, and association among different modes can be mined.

The invention provides a software code optimization method and system based on big data, equipment and a medium, and relates to the technical field of computers. The method comprises the steps that a target code library and corresponding running log data are acquired, and a first data set is obtained; cleaning, converting and aggregating the first data set from the programming language dimension, the framework dimension and the project specification dimension to obtain a second data set; training a machine learning model by using the second data set, so that the machine learning model can identify code quality, performance bottlenecks and potential defects; analyzing a code module written by a user by using the trained machine learning model to obtain a code analysis result; and according to a code analysis result, generating a corresponding code segment from the target code library, and displaying the code segment by using a visual interface. According to the technical scheme, accurate and comprehensive code optimization suggestions can be given.

The invention discloses a method for evaluating the quality of a metal part, which comprises the following steps of: determining the laser type according to the characteristics of the metal part, obtaining various laser parameters for evaluating the quality of the metal part through an experimental method, and performing laser irradiation by adopting the accurately controlled laser parameters. In combination with real-time monitoring and regulation of instruments such as a power meter, a spectrograph, a roughmeter and an optical microscope, a laser parameter-grain boundary definition data set is formed, and finally a quality evaluation model is established to realize accurate evaluation of the quality of the metal parts. According to the method, the quality of the metal parts can be evaluated quickly and economically.

The invention relates to the technical field of neodymium iron boron detection, in particular to a hardness detection device and method for sintered neodymium iron boron, and the device comprises a bottom plate and the like; the bottom plate is a bearing carrier of the detection device, a plurality of air cylinders are fixedly mounted on the bottom plate, and the mounting frame is a square frame and is fixedly mounted on telescopic rods of the air cylinders; the guide seat is fixedly arranged on the top surface of the bottom plate, a sliding groove is formed in the middle of the guide seat, a threaded seat is fixedly arranged at one end of the guide seat, and a fixing block is fixedly arranged at the other end of the guide seat; the supporting plate is installed in the sliding groove of the guide base in a sliding mode. Through the adjustable clamping piece and the replaceable extrusion contact, the device can adapt to sintered neodymium iron boron blanks with different shapes and hardness, the application range of the device is expanded, then the hardness detection mechanisms at multiple positions are used for synchronously carrying out multi-point-position hardness detection on the neodymium iron boron blanks, and the comprehensiveness and high efficiency of neodymium iron boron blank detection are achieved.

The invention provides a premature delivery prediction method and device, electronic equipment and a readable storage medium, and relates to the technical field of data analysis. The method comprises the following steps: based on a preset model, scoring acquired comprehensive information of a pregnant woman in an early pregnancy stage; wherein the comprehensive information comprises demographic information, clinical information and biochemical index information; and predicting the premature delivery risk of the pregnant woman according to the scoring result. According to the method, the comprehensive information of the pregnant women at the early pregnancy stage is scored through the preset model, the premature delivery risk of the pregnant women is predicted according to the scoring result, quantitative evaluation of premature delivery risk prediction of the pregnant women at the early pregnancy stage is achieved, and objectivity and certain accuracy are achieved; and the difficulty and the cost of obtaining the comprehensive information of the pregnant women in the early pregnancy are relatively low, the difficulty and the cost of predicting the premature delivery risk of the pregnant women in the early pregnancy are reduced, and medical resources are saved.