19 results about "Analysis factors" patented technology

The invention relates to the technical field of equipment operation and maintenance management, in particular to a medical equipment operation and maintenance method and system.The method comprises the following steps that fault units are screened based on multi-source heterogeneous data, analysis factors and fault association identification abnormal intervals, distribution state identification key nodes are extracted, offset is identified, and a propagation track is extracted; and extracting a trend superposition efficiency layer to identify synchronous change, and outputting a fault behavior linkage response control instruction set. According to the method, through structured integration and labeling of multi-source heterogeneous data, a reference state model can be formed in the early stage of operation, abnormal fluctuation recognition and threshold screening are combined, potential risks are rapidly positioned, manual troubleshooting delay is avoided, key node recognition and influence area visualization are achieved, accurate processing is facilitated, and the method is suitable for large-scale popularization and application. Through time continuous offset analysis and track identification, the fault evolution trend can be captured, a quick response instruction is generated in combination with overlay analysis of operation factors and recovery efficiency, and the reliability and safety of equipment are improved.

The invention provides an industry rotation comprehensive analysis system based on data flow, which relates to the technical field of intelligent finance and comprises a control flow management module and a function module. The functional module comprises a data acquisition module, a streaming feature clustering compression module, a multi-model collaborative optimization module, a dynamic hierarchical visualization module, a semantic driving interaction module and a transparent verification module, and data are transmitted among the modules in a JSON format through a high-performance message queue RabbitMQ. And efficient data integration is realized. The system can process a time sequence of transaction data, a periodic report of financial data and a monthly index of macroscopic data in a unified manner, a standardized analysis factor is generated, and the problems that data formats are not uniform and timestamps are not aligned are solved. Compared with the low efficiency of processing hundreds of pieces of data per second in the prior art, the method supports processing thousands of pieces of data per second, and the processing speed is remarkably improved.

The invention provides a delay factor analysis method and device for a direct-fired coal pulverizing system, and aims to accurately quantify the independent influence degree of each to-be-analyzed factor such as the primary air volume and the coal feeding amount of a coal feeder on response delay of the direct-fired coal pulverizing system and provide reliable data support for formulating a targeted and accurate delay compensation strategy. The delay factor analysis method of the direct-fired pulverizing system comprises the following steps: calculating the amount of pulverized coal passing through a pulverized coal pipe in unit time of a single coal mill; after it is determined that the pulverized coal amount is in steady-state operation within the first preset time, a control variable method is adopted to apply step disturbance to any to-be-analyzed factor, and change data of the pulverized coal amount along with time is monitored in real time; in the change data, when the pulverized coal amount reaches the first pulverized coal amount for the first time and is maintained for second preset time, delay time caused by changes of the to-be-analyzed factors is determined, and the influence degree of the to-be-analyzed factors on response delay of the direct-fired pulverizing system is analyzed on the basis of the delay time.

The present invention relates to the technical field of detection systems for foundations, and particularly to an intelligent detection system for evaluating the bearing capacity of foundations. The system includes a drone, a visual detection module, a soil detection module, a control module, a bearing capacity analysis module, and a communication module. The visual detection module is installed on the drone and is used to detect and obtain relevant data on the foundation surface. The soil detection module is used to detect and obtain soil-related data. The control module obtains the analysis factors for the foundation bearing capacity. The bearing capacity analysis module obtains information on whether the foundation bearing capacity is qualified based on the analysis factors for the foundation bearing capacity and transmits it to the communication module. The communication module transmits the information on whether the foundation bearing capacity is qualified to the user terminal. The system integrates the visual detection module and the soil detection module, combines multiple data sources, and provides a more comprehensive and accurate evaluation of the foundation bearing capacity.

The present application relates to the technical field of geological disaster analysis, and discloses a county geological disaster risk analysis method and system, which comprises the following steps: dividing a slope unit of a geological disaster area, screening out susceptibility analysis factors in the slope unit, selecting a training set and a test set from the geological disaster area and a non-geological disaster area; establishing an initial analysis model of the training set, optimizing the hyperparameters of the initial analysis model, outputting the susceptibility classification result of the training set by using the optimized analysis model, analyzing the classification accuracy of the susceptibility classification result, and determining the susceptibility analysis result of the slope unit; calculating the risk index of the slope unit, and dividing the risk subarea in the geological disaster area by using the risk index; screening out vulnerability analysis factors in the slope unit, performing hierarchical analysis on the vulnerability analysis factors by using a judgment matrix; and calculating the vulnerability index of the slope unit. The present application can improve the accuracy of the geological disaster analysis result to match the real geological disaster risk situation.

The present invention provides an ecological environment monitoring feedback system, comprising a dynamic data acquisition module, which respectively acquires real-time data collected by detection parts in a water area and a region as dynamic analysis factors; a similar data comparison module, which compares the dynamic analysis factors corresponding to the water area with the dynamic analysis factors corresponding to the region, and generates a dynamic factor comparison table based on the comparison results; a marking data analysis module, which selects abnormal factors as marking factors in the dynamic factor comparison table, obtains abnormal diffusion rates through diffusion calculation based on the marking factors in the water area and the marking factors in the region within a certain period of time, and then determines the diffusion direction based on the abnormal diffusion rate, the diffusion direction including diffusion from the water area to the region or diffusion from the region to the water area; and an early warning decision output module, which indexes the abnormal processing scheme under the corresponding abnormal type in the decision database according to the abnormal diffusion rate and the diffusion direction. The present invention has the advantages of real-time monitoring of environmental dynamic factors and improved response speed and efficiency of environmental protection.

Systems and methods for determining whether an alert should be generated, a delay period should be configured, or both, for a message composed in a messaging application or received by it, are disclosed. The methods determine a relationship score between a sender electronic device and a recipient electronic device. One or more analysis factors, which are determined based on the relationship score, are used to analyze the content of the message to determine whether the content is appropriate based on the relationship score and the applied factors. If the content is not appropriate, then an alert warning the sender of the inappropriate content is generated. A transmission delay period that is dynamically changed based on the relationship score and factors is embedded into the message such that the message may not be delivered until the expiration of such delay period. Emergency-related content is transmitted without an alert or delay.

A high-resolution farmland image extraction method includes (1) merging existing medium spatial resolution farmland remote sensing data products as the initial farmland spatial range, optimizing the products by integrating vegetation index and harmonic time series analysis factors, and removing significant non-farmland areas; (2) using the results of the previous step as a sample library, constructing a spatial-spectral-temporal feature set based on the time series medium-resolution image, and re-extracting farmland through the random forest algorithm; (3) performing multi-scale segmentation on the high spatial resolution image, obtaining segmented vector patches, nesting the results of the previous step into the vector patches, extracting the patch mean and perimeter-area ratio, and processing them using the natural breakpoint method to obtain high-confidence high spatial resolution samples; (4) inputting the high-resolution samples obtained by migration into a semantic segmentation model to extract farmland. This method utilizes existing remote sensing products as prior information, effectively removes erroneous information in the products, reduces the cost of manual sample annotation, and has a high degree of automation.

This invention provides a method and system for optimizing the layout of petrochemical plants based on domino accident analysis. The method identifies all objects requiring domino accident analysis based on influencing factors; it determines the cumulative escalation probability of domino accidents by considering the synergistic coupling effect of equipment analysis on accident escalation probability, combined with the reliability and failure probability of safety barriers; it forms domino accident correlation evolution paths under multiple accident escalation vectors, and establishes a domino accident evolution physical model using node graph technology; it then dynamically simulates and calculates the escalation state and impact range of domino accidents; and it optimizes the layout based on a comprehensive analysis of the cumulative escalation probability, domino accident escalation state, and impact range data, identifying applicable safeguards. This approach overcomes the problems of unclear research criteria, incomplete analysis factors, and limited application scenarios in existing technologies. Based on domino accident consequence assessment modeling technology, it proposes plant facility layout optimization and blocking strategies.

The invention discloses a multi-dimensional data analysis-based cross-institution risk assessment system for unhealthy assets, which relates to the technical field of unhealthy asset management and comprises a data management module, a trend analysis module, a factor identification module and a risk assessment module. Through cooperative operation of four modules of data management, trend analysis, factor identification and risk assessment, comprehensive perception and dynamic assessment of cross-institution non-performing assets are realized, so that multi-source heterogeneous data can be effectively integrated, key risk features can be extracted, and the value change trend of the non-performing assets can be identified, thereby accurately locking core risk factors, and improving the risk assessment efficiency. And the identification result is compared with the known risk factor, so that the quantitative evaluation of the risk level is realized, the financial institution is assisted in dynamically adjusting the risk prevention and control strategy, and the cross-institution risk linkage response capability and the asset management efficiency are further improved.

The invention relates to the technical field of computers, and provides an image search anomaly analysis method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a search term, a first description text and a second description text of a target image; comparing the search word with the first description text and the second description text to obtain a search comparison result; if the search comparison result indicates that the first description text does not contain the search word and the second description text contains the search word, determining that the processing parameter corresponding to the first description text is abnormal; and if the search comparison result indicates that the first description text contains the search word and the second description text does not contain the search word, determining that the processing parameter corresponding to the second description text is abnormal. According to the method, analysis factors are enriched, the analysis strength is enhanced, and the reliability of anomaly analysis is enhanced, so that the accuracy of image search anomaly analysis is improved.

Systems and methods for integrated multi-factor multi-label analysis include using one or more deep learning systems, such as neural networks, to analyze how well one or more entities are likely to benefit from a targeted action. Data associated with each of the entities is analyzed to determine a score for each of the proposed targeted actions using multiple analysis factors. The scores for each analysis factor are determined using a different multi-layer analysis network for each analysis factor. The scores for each analysis factor are then combined to determine an overall score for each of the proposed targeted actions. The entities and the proposed targeted actions with the highest scores are then identified and then used to determine which entities are to be the subject of which targeted actions.

The present invention relates to the field of radar imaging technology, and in particular to a sparse ISAR imaging method and system based on a tight frame and a projection operator. The method comprises constructing an ISAR imaging generalized analysis model for directly analyzing ISAR images based on an inverse synthetic aperture radar (ISAR) imaging system and utilizing a tight frame. Furthermore, an orthogonal projection operator is set based on the analysis factor and frame size of the ISAR imaging generalized analysis model, and the ISAR imaging generalized analysis model is decomposed and solved by the orthogonal projection operator to obtain a sparse ISAR imaging result. The method constructs a sparse ISAR imaging generalized analysis model based on a tight frame, which can directly analyze ISAR images, thereby reducing the number of model unknowns and increasing training and analysis efficiency. Furthermore, the method efficiently solves the sparse ISAR imaging model based on a simple iterative algorithm using a projection operator. Each iteration requires only a simple proximal mapping, and an acceleration strategy is introduced to increase the iterative convergence speed of the proximal mapping, thereby improving imaging efficiency while ensuring imaging quality.

The invention discloses an energy analysis method, system, product and medium. The method comprises the following steps: in an energy analysis simulation process, when a continuous oscillation locking characteristic of a key factor is monitored, firstly identifying a conflict factor with an antagonism relationship; afterwards, target deviation caused by compromise to other factors is quantified by simulating behaviors of each energy analysis factor in a preset compromise decreasing range, and a compromise function of each factor is constructed based on the target deviation; thirdly, combining all compromise functions into a total deviation objective function, and finding an optimal decision variable combination capable of minimizing total deviation by solving the function; and finally, adjusting system operation parameters according to the group of decision variables. By implementing the technical scheme provided by the invention, the operation stability of the energy analysis system is improved under the condition that data oscillation occurs in the energy system.

The application discloses a design method of an organic amine phase change absorption system for capturing carbon dioxide, and comprises the following steps: constructing and optimizing a three-dimensional molecular configuration of a phase separation agent to be predicted; performing molecular information analysis based on the molecular structure, thereby calculating an analysis factor related to the phase separation performance of the phase separation agent; inputting the analysis factor into a trained theoretical prediction model to obtain a phase separation performance prediction result of the phase separation agent; and selecting a suitable phase separation agent according to the prediction result and process conditions to construct the organic amine phase change absorption system. Based on the designed analysis factor and the theoretical prediction model, the phase separation behavior of the absorption system formed by the organic amine and different solvents can be accurately predicted, and a suitable phase separation agent can be screened according to the phase separation behavior, so that the organic amine phase separation system is rationally designed, thereby improving the problem that the screening and construction of the phase change absorption agent need to be completed through a large number of repeated experiments, and providing strong support for the efficient design and construction of the phase change absorption agent.

The invention relates to the technical field of model establishment, and discloses an emotion recognition model establishment method and system based on multi-modal data, and the method comprises the steps: obtaining a plurality of pieces of multi-modal data, outputting a modal data analysis value of sub-multi-modal data corresponding to each piece of multi-modal data based on a sub-multi-modal data model, and obtaining a plurality of modal data analysis value sequences; randomly distributing the modal data analysis value sequence to the modal data analysis value point diagram, and calculating modal data analysis factors of the modal data analysis value sequence; carrying out numerical value sorting on all modal data analysis factors, determining a plurality of candidate sub-multi-modal data, and taking the candidate sub-multi-modal data as a candidate sub-multi-modal data feature set; according to the method, the candidate sub-multi-modal data feature set is divided to obtain the model establishment set, the multi-modal data emotion recognition model is established, and the multi-modal data is utilized to construct the more accurate emotion recognition model, so that the real emotion state of the employee can be reflected more comprehensively and accurately, and a reliable decision basis is provided for enterprise management.

Systems and methods for integrated multi-factor multi-label analysis include using one or more deep learning systems, such as neural networks, to analyze how well one or more entities are likely to benefit from a targeted action. Data associated with each of the entities is analyzed to determine a score for each of the proposed targeted actions using multiple analysis factors. The scores for each analysis factor are determined using a different multi-layer analysis network for each analysis factor. The scores for each analysis factor are then combined to determine an overall score for each of the proposed targeted actions. The entities and the proposed targeted actions with the highest scores are then identified and then used to determine which entities are to be the subject of which targeted actions.

The invention provides a quantitative identification method for water resource bearing capacity driving factor influence and a related device, and relates to the field of hydrology and ecology. The electronic equipment divides the to-be-analyzed region into a plurality of sub-regions, takes each sub-region as a target sub-region, and determines a plurality of adjacent sub-regions from the plurality of sub-regions; dividing the target sub-region and the plurality of adjacent sub-regions into a plurality of region sets according to respective water resource bearing capacity indexes; and according to the overall discrete index of the plurality of adjacent sub-regions and the target sub-region for the water resource bearing capacity and the local discrete index of the to-be-analyzed factor in each region set, obtaining the correlation degree of the to-be-analyzed factor for the water resource bearing capacity. In this way, compared with the mode that the whole research area is regarded as a statistical whole to be analyzed, the problem that geographic local differences are ignored due to the fact that global stationarity hypothesis is relied on is solved.

The invention provides a firework and cracker production and sales whole process risk factor analysis model construction method, which comprises the steps of establishing a business process preliminary analysis framework and business process analysis factors, and listing risk subsystems and risk factors; a TF-IDF algorithm is used to extract the risk factors, and through similarity calculation, seed factors required by model construction are obtained; performing iterative divergence on the seed factors by adopting literature data, and performing divergence by an expert group Delphi method to achieve risk factor expansion so as to form a convergence factor set; adopting a DM-AHP hybrid algorithm to carry out secondary Delphi method operation on the confluence factor set, and endowing each element in the factor set with a weight; establishing hierarchical holographic modeling, and displaying related factors of the risk system for the factor set endowed with the weight data; risk factors of a risk system are analyzed from a mesoscopic level, modeling is performed by using a main logic diagram in a microscopic level, and a risk factor analysis subsystem is established. According to the method, risk factors are analyzed from the whole firework and firecracker production and sales process, and overall system analysis is facilitated.