29 results about "Early warning score" patented technology

The invention relates to the technical field of medical information, in particular to an osteoporosis risk intelligent early warning method and system based on a big database. The method comprises the following steps: extracting user bone health data in a multi-source health database; evaluating a bone mass state based on the user bone health data; screening osteoporosis risk users in the user bone health data according to the bone mass state; inquiring a medical image of the osteoporosis risk user; extracting a bone mineral density value by using the medical image, and determining a bone mineral density change trend according to the bone mineral density value; detecting the degeneration degree of the bone microstructure by using a medical image; constructing an osteoporosis risk early warning model according to the bone mineral density change trend and the bone microstructure degeneration degree; and inputting the user bone health data into the osteoporosis risk early warning model to generate a risk early warning score. According to the invention, accurate identification and dynamic monitoring of the osteoporosis risk user are realized based on the medical information technology, and the accuracy and response rate of osteoporosis risk early warning are improved.

The invention discloses an asset health assessment method and system based on UWB-RFID tags, and belongs to the technical field of asset assessment, each asset corresponds to one UWB-RFID tag, and the assessment method comprises the steps: classifying assets in a region, carrying out the tag quality scoring and tag computing power calculation of the UWB-RFID tags of the assets in real time, and obtaining the risk early warning scores of the assets; performing initial health scoring on each asset by using the trained XGBoost model, and punishing the initial health scores based on asset abnormal movement identified in real time to obtain final health scores of the assets; and obtaining the health score of each type of assets and the asset health score of the whole area based on the final health score of each asset. According to the invention, the health conditions of all assets can be obtained in real time, and the refinement level of management and control and decision making is improved under the condition of ensuring that the health assessment result is objective, accurate and comprehensive.

The present application relates to the technical field of construction site safety early warning, in particular to a multi-modal construction site safety early warning method and system with dynamic weight distribution, comprising the following steps: deploying a multi-modal data acquisition module, collecting and transmitting construction site data to a central processing unit in real time; constructing a correlation model according to a historical accident database; pre-processing real-time data and detecting abnormalities, and dynamically adjusting the multi-modal weight according to the results; inputting the adjusted weight into the model, calculating the comprehensive early warning score and grading the early warning; correcting the model according to the early warning and accident matching degree. The present application collects three types of data, namely sensors, vision and text, by deploying a multi-modal data acquisition module, and combines a dynamic weight distribution mechanism, which can adjust the weight of each mode in real time according to the abnormal detection results, significantly improves the accuracy and comprehensiveness of safety hazard identification, and effectively avoids the one-sidedness and missed reporting problems of single-mode monitoring.

The invention relates to an unmanned vehicle control software evaluation method based on physiological signals, belongs to the technical field of software man-machine work efficiency evaluation, and solves the problem of low reliability of an unmanned vehicle control software evaluation mode in the prior art. The unmanned vehicle control software evaluation method comprises the following steps: collecting first physiological signals generated when a plurality of operators operate unmanned vehicle control software according to a standardized task process; constructing an early warning scoring baseline and a man-machine work efficiency scoring baseline of the unmanned vehicle control software according to the first physiological signals generated by the plurality of operators; acquiring a second physiological signal generated when a plurality of operators operate the unmanned vehicle control software according to the normal task process; second physiological signals generated by the multiple operators are counted, and whether the unmanned vehicle control software is qualified or not is determined based on the early warning scoring base line and the man-machine work efficiency scoring base line. The reliability of quantitative evaluation of the unmanned vehicle control software is higher.

The application discloses a short-term and impending roof fall early warning method for underground mines based on multi-source monitoring and partition early warning values, and comprises the following steps: S1, multi-source monitoring is conducted on roof fall to obtain multi-source monitoring data; S2, based on existing several stope falling rock mass influencing factors, specific influencing factors are screened, and partition early warning is conducted based on the specific influencing factors, and an early warning value early warning scoring system of rock mass internal fracture and surface deformation is established; and S3, the early warning value early warning scoring system of rock mass internal fracture and surface deformation is utilized to conduct actual partition early warning. The method is based on multi-source monitoring means, and meanwhile, the specific engineering geological condition partition of the monitoring object is considered, different early warning values are set for different partitions, and the short-term and impending early warning accuracy is remarkably improved.

The invention discloses an abnormity early warning platform and method based on energy data flow type calculation, and the method comprises the steps: obtaining an energy data flow, and carrying out the preprocessing of the energy data flow, and obtaining a standardized energy data flow; when the monitoring data greater than the corresponding early warning threshold exists in the standardized energy data flow and the corresponding duration is greater than the preset duration threshold, directly performing early warning reminding; otherwise, determining the first data stream through the standardized data stream; performing sub-window selection on the first data stream, and determining a sub-window of each piece of monitoring data; analyzing according to the sub-windows of the monitoring data, and determining one or more overlapping intervals; and for each fault type, respectively determining a total early warning score and a corresponding early warning level, and carrying out corresponding early warning reminding. Through real-time acquisition, intelligent preprocessing and multi-energy data comprehensive analysis of energy data, timeliness and accuracy of energy system abnormity monitoring are improved, and energy waste and operation and maintenance risks are reduced.

The application discloses a battery state evaluation method and device, electronic equipment and storage medium, comprising: when receiving a battery product evaluation request, obtaining fault handling data, early warning data, SOH analysis data and working condition analysis data of the battery product to be evaluated, and determining a fault score, an early warning score, an SOH score and a working condition score of the battery product; using the fault score, the early warning score, the SOH score and the working condition score to determine a comprehensive score of the battery product; and determining the battery state of the battery product according to the comprehensive score. The fault score, the early warning score, the SOH score and the working condition score reflect the battery state from different angles, and the four types of scores are used for comprehensive scoring, the battery state of the battery product is evaluated according to the final comprehensive score, the problem product can be fully identified, the battery state can be represented from all aspects and multiple levels, and it is beneficial to investigate the risk product and reduce the after-sales cost.

The invention relates to the technical field of coal mine safety, and discloses a gas disaster cloud early warning method and system based on multi-dimensional data driving, and the method comprises the steps: determining discretized data based on a plurality of pieces of historical coal mine data, determining an influence coefficient based on the discretized data and a pre-constructed correlation analysis algorithm, determining a gas state monitoring mechanism, and determining a gas state monitoring mechanism; a coal mining working area is obtained, gas pressure is confirmed based on the coal mining working area, a pressure sensor and a hole sealing air bag, a firmness coefficient is confirmed based on the coal mining working area and a crushing testing machine, and a gas emission risk index is confirmed based on the coal mining working area, a gas flow meter and a gas concentration sensor. And calculating a disaster early warning score according to the pressure influence index, the hardness influence index, the emission speed influence coefficient, the gas pressure, the firmness coefficient and the gas emission risk index, determining a risk grade according to the disaster early warning score, and completing gas disaster early warning. According to the invention, the accuracy of gas disaster early warning can be improved.

The invention discloses an overseas illegal worker early warning method and a related device, which are used for carrying out identification and early warning on workers possibly having illegal departure worker behaviors. The method comprises the following steps: acquiring user signaling data; obtaining overseas illegal work behavior characteristic values according to the user signaling data; obtaining an early warning score according to the overseas illegal worker behavior characteristic value; and carrying out early warning on illegal workers according to the early warning score.

1. The name of the design product: the graphical user interface of the early warning scoring system of electronic equipment. 2. The use of the design product: for an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. The design product only involves the graphical user interface, and the rear view, left view, right view, top view and bottom view are omitted. 6. The use of the graphical user interface: as an early warning scoring system in the medical field. 7. The human-computer interaction mode of the graphical user interface: the front view is the main interface, which displays the early warning scoring, user basic information, and displays the scoring time and the next scoring time; in the front view, clicking on any user's row jumps to the change state diagram 1, which displays the detailed information of the user; in the change state diagram 1, clicking the envelope-shaped button in the interface jumps to the change state diagram 2, and a window for sending a message reminder to other devices is popped up, in which the information can be edited by oneself, and clicking the send button. 8. Other circumstances that need to be explained: "XX" in the interface represents letters, numbers, characters and / or combinations thereof, and its content can be changed.

This invention relates to the field of integrated defense and early warning technology for clustered targets, and discloses a clustered target threat early warning method based on intuitionistic fuzzy game theory and consensus decision-making. This method collects raw data from multiple sensors and quantifies it into an intuitionistic fuzzy set to generate a clustered integrated decision matrix; calculates the degree of consensus and hesitation potential energy; when consensus is insufficient, it generates resource focusing instructions to drive sensors to adjust physical parameters and perform feedback correction to form a consensus closed loop; it constructs a strategic conflict model to perform game-theoretic linkage analysis to solve for optimal attribute weights and calculates a dynamic early warning threshold; finally, it calculates the final early warning score based on the MABAC algorithm, compares it with the dynamic threshold to trigger an alarm, and outputs a dynamic interpretation vector. This invention solves the problems of decision oscillation and poor adaptability to fixed thresholds in complex electromagnetic environments by using physical information synergy enhancement and strategic game threshold drift mechanisms, thereby improving the accuracy and interpretability of early warnings.

The invention provides an account risk early warning method and device for a financial management system. The method comprises the following steps: acquiring real-time transaction data of a financial account; the value of at least one account path index in the real-time transaction data forms a path feature vector; obtaining a user portrait corresponding to the financial account, and calculating a personal risk score according to the deviation between the path feature vector and the user portrait; when the individual risk score is greater than or equal to a second threshold value, determining a pre-stored risk cluster to which the path feature vector belongs, and calculating a correlation degree between the path feature vector and the pre-stored risk cluster to obtain a group risk score; and performing weighted calculation on the individual risk score and the group risk score to obtain a risk early warning score, and generating alarm information under the condition that the risk early warning score is greater than or equal to a third threshold value. The method solves the problem that in the prior art, account risk early warning rules are rigid, and the risk early warning accuracy is low due to the fact that newly-added risk types are difficult to deal with.

The invention relates to the technical field of disease and insect pest detection, and discloses a tobacco disease and insect pest early warning method based on multispectral sensing, comprising the following steps: step 1, acquiring a multispectral reflectivity orthoimage, selecting a health reference pixel set, and calculating a multispectral index and a robust deviation value; step 2, calculating a red edge symbiotic linkage score and a space abnormal pixel; step 3, calculating a time difference of first decline of the photochemical reflection index and the near-infrared red edge ratio to obtain a time sequence coupling fraction; step 4, calculating a comprehensive early warning score and generating early warning candidate plaques; 5, red edge position change, near-infrared red edge ratio change and centroid drift are judged according to the re-scanning data, and an early warning patch is determined; step 6, combining the red-edge blue-shift pixel ratio with time sequence coupling to obtain a vertical pixel ratio screening area; and step 7, outputting the position, the area and the comprehensive early-warning scoring result of the early-warning plaque. According to the invention, high-precision early warning and stable space identification of tobacco diseases and insect pests are realized.

The invention provides a coal mine rock burst early warning method and device and a coal mine early warning system.The method comprises the steps that related information of a coal mine is obtained, and the related information comprises one or more of a stress value, stress acceleration and stress amplification; the related information is evaluated, early warning scores corresponding to the related information are obtained, the stress values and the corresponding early warning scores are in positive correlation, the stress acceleration and the corresponding early warning scores are in positive correlation, and the stress amplification and the corresponding early warning scores are in positive correlation; calculating a weighted average value of all the early warning scores to obtain a comprehensive early warning score of the coal mine; the danger level of rock burst of the coal mine is determined according to the comprehensive early warning score, alarm information is generated according to the danger level, and the comprehensive early warning score and the danger level are in positive correlation. The method solves the problem of low early warning accuracy caused by the fact that coal mine stress early warning is based on a single stress index in the prior art.

The present application relates to the technical field of disease and pest detection, and discloses a tobacco disease and pest early warning method based on multispectral sensing, comprising the following steps: step 1, acquiring a multispectral reflectance orthoimage, selecting a healthy reference pixel set, and calculating multispectral indexes and robust deviation values; step 2, calculating red edge co-occurrence linkage scores and spatial abnormal pixels; step 3, calculating the first drop time difference of photochemical reflectance index and near-infrared red edge ratio to obtain a time series coupling score; step 4, calculating a comprehensive early warning score and generating a warning candidate patch; step 5, judging red edge position changes, near-infrared red edge ratio changes and centroid drift according to complex scanning data to determine an early warning patch; step 6, screening a region in combination with the proportion of red edge blue shift pixels and the proportion of time series coupling established pixels; and step 7, outputting the position, area and comprehensive early warning score result of the early warning patch. The present application realizes high-precision early warning and stable spatial recognition of tobacco diseases and pests.

This application discloses a mental health early warning method and system based on virtual avatar dialogue. First, a semi-structured dialogue list is compiled. Then, standard dialogue recordings based on the semi-structured dialogue list are acquired. Next, extended dialogue lists and manually compiled dialogue lists are generated based on the standard dialogue recordings. Then, extended dialogue recordings based on the extended dialogue lists and manually compiled dialogue lists are acquired. Next, a set of subjective indicator parameters is acquired, and an objective indicator parameter set is generated based on the standard dialogue recordings and the extended dialogue recordings. Finally, a comprehensive early warning score is calculated based on the set of subjective and objective indicator parameters. Compared with existing technologies, this application ensures the comprehensiveness and flexibility of subjective evaluation through semi-structured dialogue lists, extended dialogue lists, and manually compiled dialogue lists, while achieving objective evaluation through factors such as voice. Finally, an early warning assessment result is given based on both subjective and objective evaluations, effectively ensuring the accuracy of the assessment.

The invention relates to the technical field of network flow monitoring, and discloses a flow monitoring method, system, equipment and medium for an electric power communication backbone network, and the method comprises the steps: screening out a to-be-monitored service according to the risk score of each network service in the electric power communication backbone network and a service screening algorithm; generating reference data according to the digital twin network, and performing graph signal processing to obtain a graph Laplacian matrix; calculating a distance value between the real-time traffic data and the reference data according to the real-time traffic data and the graph Laplacian matrix; and obtaining an early warning score of each to-be-monitored service according to the distance value and the service weight, and obtaining a total early warning score of the power communication backbone network according to the early warning score of each to-be-monitored service. According to the method, on the basis of service screening, the digital twin network is utilized and graph structure characteristics are combined to carry out deviation evaluation, and the early warning score is optimally designed, so that the accuracy and timeliness of the early warning score are improved, and safe and reliable operation of the electric power communication network is ensured.

The application provides a server test service management method and electronic equipment, which can be applied to the technical field of computers. The server test service management method comprises the following steps: preprocessing service test data collected from a server test service system to obtain preprocessed data; defining service process elements and adding attribute information of the process elements to the preprocessed data according to a preset process rule to obtain a service process; determining an early warning score for the service process based on the process elements and the attribute information in the service process; when the early warning score triggers an early warning and it is determined based on the service process that there is a service and resource mismatch condition, predicting resource demand for the service process based on external influence factors to obtain predicted resource demand for the service process; and scheduling and optimizing test resources required by the service process based on the predicted resource demand for the service process to obtain a resource scheduling and optimization strategy for the service process.

To provide a warning score output system and a warning score output method which allow a medical worker to correctly and easily grasp the priority of a treatment for a patient from a list image.SOLUTION: A warning score output system comprises: a warning score acquisition unit which acquires a warning score based on biological information of a patient; a sorting unit which sorts a plurality of patients based on the warning score; an image formation unit which forms a list image in which the plurality of patients are arrayed in the order of being sorted by the sorting unit; and an output unit which outputs the list image. The image formation unit forms a treatment status region in which a treatment status for each patient can be described in the list image.SELECTED DRAWING: Figure 3

1. Name of the product in this design: Graphical User Interface of an Early Warning Scoring System for Electronic Devices. 2. Intended use of this design: for use in an electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. This design relates only to the graphical user interface; the rear view, left view, right view, top view, and bottom view are omitted. 6. Uses of the graphical user interface: as an early warning scoring system in the medical field. 7. Human-computer interaction method of graphical user interface: The main view interface is used to display early warning score, user basic information, score time, next score time and other icon buttons. 8. Other situations requiring explanation: Other explanation: "XX" in the interface represents letters, numbers, text and / or combinations thereof, and its content is variable; the numbers in the interface are variable.

1. Name of the product in this design: Graphical User Interface of an Early Warning Scoring System for Electronic Devices. 2. Intended use of this design: for use in an electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. This design relates only to the graphical user interface; the rear view, left view, right view, top view, and bottom view are omitted. 6. Uses of the graphical user interface: as an early warning scoring system in the medical field. 7. Human-computer interaction method of graphical user interface: The main view displays user list information, early warning score, vital sign information table, etc., and related operations can be performed by clicking icon buttons such as those in the menu bar. 8. Other situations requiring explanation: Other explanation: "XX" in the interface represents letters, numbers, text and / or combinations thereof, and its content is variable.

The present application relates to the technical field of coal mine safety, a gas disaster cloud early warning method and system based on multidimensional data driving, comprising: confirming discrete data based on multiple historical coal mine data, confirming an influence coefficient based on the discrete data and a pre-constructed correlation analysis algorithm, confirming a gas state monitoring mechanism, obtaining a coal mining working area, confirming gas pressure based on the coal mining working area, a pressure sensor and a hole sealing air bag, confirming a firmness coefficient based on the coal mining working area and a crushing testing machine, confirming a gas emission risk index based on the coal mining working area, a gas flow meter and a gas concentration sensor, calculating a disaster early warning score according to the pressure influence index, the hardness influence index, the emission speed influence coefficient, the gas pressure, the firmness coefficient and the gas emission risk index, confirming a risk level according to the disaster early warning score, and completing gas disaster early warning. The present application can improve the accuracy of gas disaster early warning.

The present application relates to the technical field of power equipment detection, and discloses a micro offset early warning method and system for an electric energy meter detection device, a medium and a product. The early warning method comprises the following steps: randomly decomposing an error value of a detection unit into three errors subject to a normal distribution; obtaining prior and posterior distributions of the three errors according to a Bayesian method, and calculating a system offset mean value and a 95% confidence interval of the offset; calculating a fusion early warning score of the detection unit based on the system offset mean value; and triggering a micro offset early warning according to a judgment result of the system offset mean value, the confidence interval and the fusion early warning score. The present application can effectively identify a micro systematic offset far below a maximum allowable error, scientifically distinguish between equipment failure and individual dispersion of an electric meter, solve the problem that a traditional method cannot find a gradual drift, realize a leap from "threshold management" to "trend management", and provide reliable technical support for predictive maintenance of an electric energy meter calibration device and high-precision metering guarantee.

The invention relates to an inpatient-oriented hospitalization medical behavior acquisition, analysis and supervision system. The system comprises an admission recording module, a disease course recording module, a nursing recording module and a doctor advice management module; based on voice recognition, natural language processing and other technologies, a structured medical record document is automatically generated, and manual errors and repeated labor are reduced; the illness state of a patient can be evaluated in real time, disease course records are automatically generated, and critical value automatic early warning is achieved; the nursing recording module monitors vital signs through an early warning scoring engine, automatically calculates early warning total time division and synchronously and actively pushes alarm information to a doctor on duty or a doctor in bed management, a traditional one-way communication mode of'instruction-response 'is broken through, information is ensured to be direct, and omission is avoided; the doctor's advice management module supports voice issuing and intelligent auditing; the system can also automatically generate a whole-process record covering department transfer, shift change and hospital discharge, and seamless transmission of medical information is guaranteed; through the linkage of the modules, the medical service quality, safety and digitization level are effectively improved.

The application belongs to the technical field of risk early warning, and provides a customs risk early warning method and system based on cross-modal retrieval enhancement, which comprises the following steps: obtaining a customs task instruction data set; performing cross-table retrieval on the obtained customs task instruction data set based on a hierarchical weighted table retrieval enhancement method to obtain a structured evidence package of the data set; performing cross-modal understanding on the obtained structured evidence package based on a multi-modal large model to obtain an early warning intermediate of the data set; performing early warning scoring on the obtained early warning intermediate to complete the customs risk early warning based on cross-modal retrieval enhancement. The application adopts a multi-modal large model, and through a space-channel decoupling attention gate module and an adaptive hierarchical feature selection module, the application guarantees information fidelity and realizes efficient fusion of multi-layer feature representation, provides more discriminative feature representation for multi-modal reasoning, and improves the automatic identification and early warning level of prohibited goods and high-risk goods in the customs clearance process.

The present application relates to the technical field of tobacco monitoring, and discloses a method, medium and system for intelligently monitoring bubble-shaped green spots in the airing of cigar tobacco, which comprises: obtaining image information of bubble-shaped green spots in the airing of each cigar tobacco, and establishing a bubble-shaped green spot feature set according to the characteristics thereof. Subsequently, the surface images of each cigar tobacco in the airing area are obtained, and feature matching is performed. When the surface image features match the bubble-shaped green spot features, the tobacco is determined to be a sick tobacco. Based on the number and grade of sick tobacco, the early warning score of the airing area is calculated. At the same time, the illumination of each tobacco, and the environmental temperature and humidity of the airing area are obtained, so as to adjust the early warning score. Finally, according to the adjusted early warning score, the early warning grade of the airing area is determined. The present application combines image analysis and environmental monitoring, aiming to effectively warn and manage tobacco diseases.

This invention discloses a supercritical CO2 equipment early warning and self-healing system based on multimodal trend fusion, comprising a sensing and computing layer, a hybrid model fusion diagnostic layer, a multi-level hierarchical self-healing control layer, and a shadow-following evaluation module. The sensing and computing layer collects operational data and constructs a private knowledge base. The hybrid model fusion diagnostic layer constructs a multi-dimensional residual feature space, generates early warning scores, and outputs early warning information. The multi-level hierarchical self-healing control layer executes a three-level strategy, optimizing control parameters through a cost function. The three-level strategy includes performance degradation self-healing, equipment fault self-healing, and emergency protection self-healing. This invention constructs a sensing and computing layer, a hybrid model fusion diagnostic layer, and a multi-level hierarchical self-healing control layer. It generates early warning scores through multi-dimensional residual trend analysis and matching with the private knowledge base, executes a three-level self-healing strategy, and introduces a shadow-following model to quantify the net self-healing benefit and dynamically optimize decision thresholds, thereby achieving early warning prediction for supercritical CO2 equipment.

This application provides a method for early warning and control of microbial contamination in eye drop production, applied in the fields of pharmaceutical engineering and automation control technology. The method includes: collecting droplet sequences from filling needles, extracting necking time deviation, abnormal interface oscillation amplitude, and detachment volume deviation, and encapsulating these as abnormal physical indicators; collecting differential impedance of the high-purity water pipeline wall, the ratio of low-frequency response of adherent particles, and fluid pulsation attenuation to construct a biofilm formation risk index; merging these two data points in time to generate a current contamination risk score; combining wind speed disturbances, pressure differential jumps, and personnel trajectories, locating the contamination source and predicting the microbial diffusion path through reverse flow field tracing and forward simulation; and generating a comprehensive early warning score through spatiotemporal fusion, triggering tiered dynamic control measures such as maintaining production, issuing early warnings, suspending filling, flushing pipelines, regional lockdown, or adjusting airflow. This invention achieves real-time perception, precise source tracing, and proactive prevention and control of microbial contamination.

The invention provides a well drilling jamming intelligent prediction method based on working condition self-adaption and engineering experience verification. Belongs to the technical field of drilling engineering safety monitoring, utilizes underground and ground real-time parameters, adopts a sliding time window to automatically extract pressure, torque, rotating speed, mechanical drilling speed and other characteristics, distinguishes drilling, pulling-out, descending, reaming and other states through a working condition identification rule, and calls a special prediction model for different working conditions to obtain a primary drilling jamming risk probability. Under the working condition including the rotating speed and the torque, the static torque in the rotating speed jumping stage and the dynamic torque in the stable stage are obtained respectively, a torque difference index is constructed and compared with an experience threshold value, a primary result is verified, corrected and subjected to weighted fusion, and continuously-updated early warning scores and graded early warning signals are generated. According to the method, real-time performance, accuracy and engineering interpretability are taken into consideration, and automatic identification and intelligent early warning of the jamming risk in the whole drilling process can be achieved.