44 results about "Early warning score" patented technology

The invention belongs to the technical field of river and lake water quality monitoring, and discloses a river and lake water quality intelligent traceability monitoring system and method.Dissolved organic matter fluorescent fingerprints are analyzed in real time through a three-dimensional fluorescent monitoring unit, pollution markers are detected, and parameters such as COD, ammonia nitrogen and total phosphorus are synchronously detected through a full-spectrum monitoring unit; the video monitoring unit is used for collecting section images / videos of rivers and lakes, other sensors are used for collecting real-time values of chlorophyll, pH, conductivity, turbidity and dissolved oxygen in a water body, then the data processing module is used for carrying out normalization processing on multi-source data, and the analysis and early warning module is used for analyzing water quality early warning scores, spectrum early warning scores and video early warning scores. All scores are synthesized to obtain a comprehensive early warning score, traceability is started when comprehensive early warning is larger than or equal to the grade II, pollution sources are recognized based on three-dimensional fluorescence spectrum comparison and upstream patrol, and a river and lake water body monitoring-early warning-traceability-verification closed loop is achieved.

The invention discloses a supply chain performance path optimization and collaborative decision-making method based on an AI algorithm, and particularly relates to the field of supply chain performance path optimization, and the method comprises the steps: firstly obtaining order goods information and warehouse resource data, and judging whether a single warehouse can independently perform a performance or not through matching; if yes, constructing a comprehensive evaluation function including total transportation time, time redundancy, path complexity and resource utilization rate, and generating an optimal path from the warehouse to the delivery place; if multi-warehouse collaboration is needed, dynamically selecting a centralized point warehouse, generating a comprehensive early warning score and selecting an optimal centralized point by comprehensively calculating the cargo transfer cost and the historical performance risk value of each warehouse, and finally planning a transfer path from a supply warehouse to the centralized point and a performance path from the centralized point to a terminal to form a full-link collaboration scheme; according to the method, a traditional single-warehouse decision mode is broken through, global resource scheduling is realized through a double-path optimization mechanism, the transportation delay risk is remarkably reduced, and the supply chain operation efficiency is improved.

The invention provides an electric power operation ground wire intelligent positioning and early warning control method and system, and relates to the technical field of electric power operation safety monitoring, and the method comprises the steps: constructing a double-layer architecture cooperative intelligent positioning system, processing multi-source sensing data through employing a self-adaptive particle swarm optimization data fusion algorithm, and optimizing a spatial topological relation through a graph attention mechanism; extracting features from the time sequence state sequence by adopting multi-scale and cavity convolution, and constructing a space-time double-flow heterogeneous graph; and in combination with a power operation field knowledge graph and Monte Carlo tree search, risk early warning scoring is realized. According to the invention, the grounding wire position monitoring precision and the risk early warning accuracy are improved, and the power operation safety guarantee capability is enhanced.

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 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, and the method comprises the steps: obtaining a customs task instruction data set; performing cross-table retrieval on the acquired customs task instruction data set based on a hierarchical weighted table retrieval enhancement method to obtain a structured evidence packet of the data set; performing cross-modal understanding on the obtained structured evidence packet according to a multi-modal large model to obtain early warning middleware of the data set; and performing early warning scoring on the obtained early warning middleware to complete customs risk early warning based on cross-modal retrieval enhancement. The multi-modal large model is adopted, the space-channel decoupling attention gating module and the self-adaptive hierarchical feature selection module are adopted, the information fidelity is guaranteed, efficient fusion of multi-layer feature representation is achieved at the same time, feature representation with higher discriminative force is provided for multi-modal reasoning, and the multi-modal reasoning accuracy is improved. And the automatic identification and early warning level of prohibited goods and high-risk goods in the customs clearance process is improved.

The invention provides a pavement construction early warning method and device based on multi-dimensional data and electronic equipment, and relates to the technical field of intelligent monitoring, and the method comprises the steps: obtaining pavement vibration data, equipment inclination data and positioning environment data through ground nail equipment; inputting the road surface vibration data, the equipment inclination data and the positioning environment data into a data cleaning model, and performing false alarm filtering through the dynamic integral accumulation algorithm to obtain a construction early warning comprehensive score; and determining a construction early warning strategy based on the construction early warning comprehensive score. According to the invention, vibration monitoring, inclination angle sensing and geographical environment multi-dimensional data are fused, an intelligent construction early warning system is constructed, and the early warning accuracy is improved.

The present invention proposes a potential risk personnel early warning system based on XXL scheduled scheduling, which includes: a custom early warning module, a rule verification module, a scheduling module and a matching module, wherein the custom early warning module is used to configure an early warning template containing data source rules, scoring rules and proportions; the rule verification module is used to determine whether the template configured in the custom early warning module complies with the configuration rules. If it does not comply, the custom early warning module is re-entered to modify the early warning template; if it complies, the early warning template is submitted and an early warning task is initiated; the scheduling module receives the early warning task, schedules the execution of the early warning task through the XXL Job scheduler, presents the execution result on the front-end page, and directly optimizes the early warning template on the front-end page to obtain the optimized early warning model; the matching module is used to match the input data one by one with the early warning model, calculate the early warning score and obtain the early warning result.

The application discloses a supply chain performance path optimization and collaborative decision-making method based on an AI algorithm, and particularly relates to the field of supply chain performance path optimization, and comprises the following steps: firstly, order goods information and warehouse resource data are acquired, and it is judged whether a single warehouse can independently perform the performance by matching; if yes, a comprehensive evaluation function containing total transportation time, time redundancy, path complexity and resource utilization rate is constructed, and an optimal path from the warehouse to a delivery place is generated; if multiple warehouses need to be cooperated, a centralized point warehouse is dynamically selected, the goods transfer cost and historical performance risk value of each warehouse are comprehensively calculated, a comprehensive early warning score is generated, and an optimal centralized point is selected, finally, the transfer path from the supply warehouse to the centralized point and the performance path from the centralized point to the terminal are planned, and a full-link collaborative scheme is formed; the application breaks through the traditional single-warehouse decision-making mode, realizes global scheduling of resources through a double-path optimization mechanism, significantly reduces the transportation delay risk, and improves the operation efficiency of the supply chain.

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 invention relates to the technical field of intelligent manufacturing, and discloses a logistics execution strategy determination method and device, electronic equipment and a storage medium, and the method comprises the steps: determining current storage data, current logistics data and current loading and unloading data from different types of system data in a current logistics supply chain; obtaining a plurality of simulation strategies according to the current storage data, the current logistics data and the current loading and unloading data; according to the simulation storage space utilization rate, the simulation logistics damage rate and the simulation loading and unloading safety value of each simulation strategy, an early warning score of each simulation strategy is obtained through calculation, and the simulation strategy with the minimum early warning score serves as an execution strategy. According to the invention, the logistics supply chain can be adjusted in real time foreseeably, so that the flexible management degree and the risk regulation and control degree of the logistics supply chain are improved.

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 relates to the technical field of monitoring management, and discloses a monitoring management method and system for equipment working conditions, which comprises the following steps: determining the authority importance value of each equipment department according to department information and a department authority model, determining the management authority of each equipment department based on the authority importance value, comparing an equipment monitoring parameter set with a standard equipment monitoring parameter set, dividing each equipment monitoring parameter in the equipment monitoring parameter set according to a comparison result, constructing an equipment monitoring space according to the equipment position of the equipment, mining the association result of an environmental data set and an equipment parameter set according to an association rule algorithm, determining the adjustment coefficient of an initial early warning score based on the association result, comparing a target early warning score with data in a historical target early warning score database, and judging whether to give an early warning according to a comparison result. The application effectively reduces the monitoring difficulty and the workload, thereby guaranteeing the monitoring efficiency.

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.

The invention 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; according to a preset process rule, performing business process element definition and process element attribute information addition on the preprocessed data to obtain a business process; determining an early warning score for the business process based on process elements and attribute information in the business process; under the condition that the early warning score triggers early warning and it is determined that the business and resources are not matched based on the business process, resource demand prediction is carried out on the business process based on the external influence factor, and a predicted resource demand for the business process is obtained; and based on the predicted resource demand for the business process, scheduling optimization is performed on the test resources required by the business process, and a resource scheduling optimization strategy for the business process is obtained.

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 present application relates to a kind of distribution network whole process operating state early warning method, comprising the following steps: collecting the operating characteristic data of each power grid node in distribution network whole process;Set evaluation time range, select multiple time nodes in evaluation time range to obtain the operating characteristic data of each power grid node;The operating characteristic data of each power grid node obtained from each time node is formed into feature vector;Based on each feature vector, the operating state information entropy value of each time node is calculated, and the operating state feature time sequence is synthesized by the operating state information entropy value of each time node calculated;Operating state feature time sequence is used as the input of neural network model pre-trained, and corresponding early warning score is output, and the early warning score indicates the probability of failure of distribution network in the given length of prediction time range after evaluation time range.

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