158results about How to "Achieve effectiveness" patented technology

The invention relates to the field of temperature control, in particular to an automatic control method and system for a constant-temperature test room. The method comprises the following steps: acquiring real-time indoor operation monitoring parameters and external environment multi-dimensional data of a constant-temperature test room; performing indoor operation state analysis on the real-time indoor operation monitoring parameters, and constructing a laboratory dynamic state space model; carrying out multi-time-point environment temperature fluctuation calculation on the external environment multi-dimensional data, and carrying out nonlinear correlation learning to generate indoor state-environment nonlinear correlation characteristics; carrying out dynamic temperature evolution simulation on the laboratory dynamic state space model based on the indoor state-environment nonlinear correlation characteristics, and carrying out dynamic temperature evolution prediction to obtain temperature prediction values of a plurality of regions; constant-temperature deviation compensation calculation is carried out on the temperature prediction values of the multiple areas, then intelligent temperature control compensation is carried out, and an intelligent temperature control strategy is constructed; according to the invention, efficient and accurate constant-temperature automatic control is realized.

The invention provides a security management and retrieval method based on Internet of Vehicles data, and the method comprises the steps: carrying out the cooperative training of a global key management model through a federated learning technology on the premise of not leaking the original data of all parties, and achieving a security key management scheme under the participation of multiple parties through a model parameter aggregation and updating mechanism; when data retrieval is needed, a user submits a retrieval request and identity authentication information, and after a system verifies the identity of the user, a ciphertext data identifier meeting conditions is matched in an attribute index according to a keyword and an authorization attribute of the request; the system acquires corresponding ciphertext data from a distributed database according to the ciphertext data identifier, and directly performs keyword matching calculation on the ciphertext data through a secure multi-party computing protocol under the condition of not decrypting the data; and according to the mined security threats and abnormal modes, a data encryption strategy and an access control strategy are dynamically adjusted, authority limitation or access blocking is performed on suspicious users, and the security protection capability of the system is improved.

The invention discloses an intelligent storage resource dynamic allocation method and system based on deep reinforcement learning, and relates to the technical field of storage management. The intelligent storage resource dynamic allocation method based on deep reinforcement learning comprises the following steps: environment data judgment; inventory management evaluation; calculating complex evaluation; and effective evaluation. According to the method, whether inventory allocation is carried out or not is judged through the obtained environment data and the preset environment data, then the storage utilization evaluation value is obtained according to the obtained tobacco inventory management data, whether storage optimization is carried out or not is judged, then the calculation complexity evaluation value is obtained according to the obtained calculation resource data, and whether calculation complexity optimization is carried out or not is judged. And finally, obtaining an optimized effective evaluation value according to the storage utilization evaluation value and the calculated complex evaluation value, thereby achieving the effect of improving the storage resource utilization efficiency in the dynamic allocation process of the intelligent storage resources, and solving the problem of low storage resource utilization efficiency in the dynamic allocation process of the intelligent storage resources in the prior art.

The invention discloses a multi-robot task scheduling method, system and device and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the role definition of a robot, collecting environment information in real time, and constructing a dynamic environment model; according to the dynamic environment model, identifying current tasks to be executed, evaluating urgency, complexity and resource requirements of each task, and forming a priority list; disassembling a high-priority task in the priority list into sub-tasks, and assigning a role and a task allocation scheme of the robot; a biologically inspired self-organizing algorithm is adopted, the robot exchanges information according to the state of the robot and task requirements, and a task allocation scheme is dynamically adjusted; the robot exchanges information according to the own state and the task demand, and dynamically adjusts the task allocation scheme, so that the flexibility and adaptability of task allocation are realized, the change of environment and state can be responded in time, and task delay caused by insufficient resources is avoided.

The invention discloses an efficient feeding intelligent warehouse management method and device based on the Internet of Things. The efficient feeding intelligent warehouse management method based on the Internet of Things comprises the following steps: acquiring data elements, and preprocessing the data elements; constructing a demand prediction model through a Fourier algorithm, an autoregression algorithm and a parallel movement method; determining a feeding sequence and a scheduling plan according to the demand prediction result; the inventory remaining amount is obtained, and a multi-level inventory early warning mechanism is set according to the inventory remaining amount; order emergency degrees are obtained, order priorities are determined according to the order emergency degrees, and order sequences are reordered; adjusting the scheduling plan according to the reordered order sequence; and the AGV and the robot are used for feeding according to the scheduling plan. Through the Fourier algorithm, the autoregression algorithm and the parallel movement method, the order demand in a period of time in the future is predicted, the effect of improving the feeding efficiency is achieved, and the problem of low feeding efficiency in the prior art is solved.

The invention discloses a high-rise fire intelligent positioning voice alarm method and system, and particularly relates to the technical field of voice alarm, and the method comprises the steps: taking the concentration of different types of smog generated by combustion of different materials, the brightness value of flame, and the spreading speed information of flame as the input of a fuzzy Bayesian neural network model; taking the fire hazard grade probability distribution as the output of a fuzzy Bayesian neural network model, determining the fire hazard grade of the fire hazard area according to the fire hazard grade probability distribution, collecting the crowd flow information and the area geographical location information in the monitoring area, and carrying out the comprehensive analysis of the crowd flow information and the geographical location information in the monitoring area. According to the invention, the fire hazard monitoring efficiency and accuracy are improved, the specific condition of the fire hazard is combined with the alarm level, and the flexibility and effectiveness of the fire hazard emergency response are realized.

The invention relates to the technical field of energy planning, and particularly discloses a source-storage-transmission combined planning method considering carbon emission responsibility allocation, and the method comprises the steps: building a carbon emission responsibility double-layer allocation strategy; based on a carbon emission responsibility double-layer allocation strategy, establishing a source-storage-transmission combined planning model considering carbon emission responsibility allocation; and solving the source-storage-transmission combined planning model considering the carbon responsibility allocation to obtain a system source-storage-network optimal combined planning scheme. According to the method, carbon emission responsibilities are scientifically shared according to the principle of'who causes, who undertakes, who governs and who benefits', the effect of the carbon emission responsibilities in the planning process is effectively considered, an energy storage system is additionally arranged, the system flexibility is improved, and renewable energy consumption is promoted.

The invention relates to the technical field of network content security, network space security and the like, and discloses a self-adaptive multi-mode false message detection method and model based on double features, which are realized by adopting the self-adaptive multi-mode false message detection model based on the double features. The self-adaptive tweet processing module is used for obtaining natural language content, visual content and mark information of a tweet; the data enhancement module obtains natural language content of the tweet and inputs the natural language content into a large language model of the data enhancement module to rewrite the text; the multi-modal feature construction module is used for obtaining multi-modal mode features and fact features; the false message detection module combines a multi-expert network aggregation tweet feature containing a gating network to realize classification of false messages, finally achieves the beneficial effect of accurately performing false message detection, and aims at improving the accuracy of an existing false message detection method in a real scene.

The invention discloses a method for regenerating a lithium iron phosphate positive electrode material from waste lithium iron phosphate battery black powder, and belongs to the technical field of waste lithium ion battery recovery. According to the method, the black powder of the waste lithium iron phosphate battery is subjected to wet leaching, a lithium iron phosphate precursor is prepared through a one-step hydrothermal method after deep impurity removal of a leaching solution, and finally modified lithium iron phosphate is prepared through high-temperature roasting. According to the method disclosed by the invention, the lithium iron phosphate positive electrode material can be regenerated only through a hydrothermal method, and lithium iron phosphate and carbon are uniformly compounded after carbonization roasting. The modified lithium iron phosphate is used as a lithium ion battery positive electrode material, the electrical property is excellent, the 0.1 C discharge specific capacity is 159.21 mAh / g, and the 1 C discharge specific capacity can reach 145.33 mAh / g. The main raw material involved in the invention comes from the waste lithium iron phosphate battery, so that the production cost of lithium iron phosphate can be effectively reduced, and waste battery resource circulation and environment-friendly utilization are realized.

The invention provides a multi-engine collaborative robot simulation method and system according to the claim 1, and the system comprises the steps: developing a simulation management system based on a UE5 three-dimensional simulation engine, and achieving the effective management and display of various information and data in a simulation process; a high-precision map is developed, a laser radar and other sensors are simulated to realize scene data acquisition and interaction, and the authenticity and credibility of a simulation environment are optimized for practical application simulation; based on Vortex dynamics modeling software, through a VHLInterface data interaction mode, robot and scene interaction dynamics modeling is carried out, and a robot simulation result is obtained; based on FastDDS communication middleware, data communication between the UE5 and the ROS2 is established, an ROS2 message format is compatible, and a hardware-in-the-loop simulation test of the controller is carried out. The technical problems that a multivariate control algorithm and a fine dynamic model are difficult to consider, data interaction of a simulation system is difficult, complexity and cost of the simulation system are high, and reliability is low are solved.

The invention discloses a micro-plastic pollution treatment result evaluation method, which belongs to the technical field of pollution evaluation, and specifically comprises the following steps: automatically acquiring a ground image of a rural area by using an unmanned aerial vehicle, identifying a cultivated land area in combination with a cultivated land planning map, and respectively detecting plastic pollutants in the cultivated land area and a non-cultivated land area; counting the total plastic pollution amount N and the total plastic pollution amount m of the cultivated land area; drawing a time-varying broken line graph of N and m through multiple times of data collection, and further analyzing the cultivated land pollution condition and the pollution diffusion trend; meanwhile, the system can identify crop types in the cultivated land, calculate the pollution amount Pi of each type per unit area, and construct a pollution index Q of the cultivated land in combination with a polluted weight omega and a slope k; when the Q value exceeds a preset threshold value, the system automatically gives an alarm; the method is beneficial to preventing the pollution problem from worsening, and active intervention and effective control on micro-plastic pollution are realized.

The invention provides a network authority control method and device based on an IPv6 technology, equipment and a medium, and the method comprises the steps: obtaining an IPv6 address of a user in response to an access request of the user for an authority network; identifying identity information of the user based on the IPv6 address; distributing corresponding role information to the user based on the identity information, and obtaining permission information of the user according to the role information; and performing dynamic verification on the identity information and the role information of the user through a pre-trained zero-trust model, and performing permission control on the user passing the verification according to the permission information.

The invention provides an automatic intelligent detection system and method for a distribution network. The system comprises an intelligent sensor, a data acquisition and processing module and a distribution network monitoring center, the intelligent sensor carries out data acquisition on distribution network lines and equipment under the control of the data acquisition and processing module, and sends an acquisition result to the data acquisition and processing module; the data acquisition and processing module controls data acquisition of the intelligent sensor according to a control instruction sent by the distribution network monitoring center, and transmits an acquisition result of the intelligent sensor to the distribution network monitoring center for display and analysis; the distribution network monitoring center visually displays an acquisition result, controls the intelligent sensor to search for distribution network abnormity through operation of an operator, obtains abnormal information through analysis of acquired data, and cooperates with the data acquisition and processing module to realize positioning and continuous monitoring of abnormal points. The method can achieve the positioning and continuous monitoring of the abnormal points, and facilitates the guarantee of the normal operation of the distribution network.

The invention discloses an electric vehicle battery monitoring method and system based on dangerous smell monitoring, and the system comprises a smell monitoring system, a temperature monitoring system, a data processing platform, and an emergency management system. According to the electric vehicle battery monitoring method, a data processing platform configures an odor concentration threshold value corresponding to the safe odor concentration of a preset battery and a temperature threshold value corresponding to the safe temperature, and presets early warning grades based on historical battery data, the odor concentration threshold value and the temperature threshold value. The method comprises the following steps: acquiring current smell data and current temperature data of a battery in real time based on a smell chip and a temperature sensor, performing multi-modal data fusion on the current smell data and the current temperature data by an emergency safety management program to obtain a comprehensive index, and performing comprehensive calculation according to a comprehensive change rate to obtain a current early warning level; and a multi-modal data fusion technology and a grading early warning mechanism are adopted, so that the potential fault of the battery is pre-judged and effectively handled, and the use safety of the electric vehicle is guaranteed.

The invention belongs to the field of information analysis and management, and relates to an intelligent mass information analysis method based on big data, which comprises the following steps of: integrating keyword tags, theme tags, time tags and space tags of newly introduced data items on that day to generate index tags, and taking the index tags as a pointing basis for storing the index tags into a target database; the data management quality of a target database is greatly improved, retrieval data of all archived data items in the target database at a preset time point after the day is collected, all high-frequency data items and all low-frequency data items in the target database are recognized, index tags and storage positions are optimized for the high-frequency data items, and the data management efficiency is improved. According to the method, the low-frequency data special library transfer processing is performed for the low-frequency data items, the oversaturation state of the low-frequency data special library is identified, and the data cleaning operation is further performed, so that the retrieval efficiency and the storage resource utilization rate of the target database are improved, the storage cost is reduced, and the data management capability is enhanced.

The invention relates to the field of bioengineering, in particular to a preparation method of cells and application of the cells in cell effectiveness detection. The invention provides a cell culture method which comprises the following steps: carrying out first stimulation, first culture, second stimulation, blocking, second culture and cleaning on pretreated cells to obtain the cells. The invention provides a method for determining the effectiveness of an immune cell preparation activated in vitro, according to the characteristics of the immune cell preparation activated in vitro, a two-step stimulation activation mode is adopted, the activation ratio of CD69 + CD44 + is used as a systematic internal control index of the method, and meanwhile, the mechanism of the immune cell preparation activated in vitro is combined to determine the effectiveness of the immune cell preparation activated in vitro. The Tc cell subpopulation secreting granzyme B is used as a method detection index, so that the indirect evaluation of the effectiveness of the immune cell preparation activated in vitro in human body is realized, and the method can be used for release detection of the immune cell preparation activated in vitro in human body.

The invention discloses a self-operated spring supporting device and an adjusting method, and relates to the technical field of well station pipeline technology and valve equipment integrity and maintenance. The device comprises a supporting outer cylinder and a supporting inner cylinder, the supporting inner cylinder is sleeved with the supporting outer cylinder, the supporting inner cylinder comprises an inner cylinder, a spring, a positioning groove, a bottom plate, a reinforcing hole and a reinforcing plate, the cross section of the bottom plate is round, the inner cylinder is welded to the top of the bottom plate, the cross section of the inner cylinder is annular, and the central axis of the inner cylinder coincides with the central axis of the bottom plate; the side face of the inner cylinder is provided with two positioning grooves which are symmetrically arranged, and the interior of the inner cylinder is fixedly connected with a spring. According to the method, the spring is arranged, the calculation formula is written, the pipeline specification, the wall thickness, spring parameters and other basic information are input, the pipeline weight and the deformation quantity of the supporting spring are automatically produced, supporting effectiveness and accuracy are achieved, and the purposes of bearing and vibration buffering are achieved. If the ground sinks, the compression of the spring can play a role in clearance compensation.

The invention provides an intelligent concrete detection method and system based on machine learning, and the method comprises the steps: carrying out the one-by-one parameter learning of each network module unit of a basic intelligent concrete detection network through a first sample concrete sample image data sequence, and generating a temporary intelligent concrete detection network; the learning efficiency and accuracy of concrete characteristics are remarkably improved, then key module units in a prior machine learning network and a temporary concrete intelligent detection network are fused through module interaction processing, and a plurality of fusion network module units are formed. And finally, migration parameter learning is performed on a second interaction network module unit in the fusion network module unit by using the second sample concrete sample image data sequence until the training convergence requirement is met, and a target concrete intelligent detection network for completing module parameter learning is generated, so that the intelligent level of concrete detection is improved, and the detection efficiency is improved. And the generalization capability and the detection precision of the concrete intelligent detection network are obviously enhanced.

The invention discloses a liposome precursor composition, which is prepared from the following ingredients in percentage by mass: 40 to 53 percent of polyhydric alcohol, 35 to 45 percent of panthenol, 10 to 15 percent of lecithin and 2 to 4 percent of ceramide NP. According to the liposome precursor composition disclosed by the invention, the problem that ceramide is not matched with a common formula system is effectively improved by adding panthenol with a specific content and combining the combined action of lecithin and polyhydric alcohol, and the stability, the water solubility and the permeability on the skin surface of a ceramide system are remarkably enhanced; the ceramide can play a better role in the epidermal layer and the corium layer of the skin. The invention also discloses a preparation method of the liposome precursor composition, in the method, chemical penetration enhancers which are not welcomed by cosmetics such as azone and the like are not used, and any unwelcomed solubilizers or surfactants are not introduced, so that the safe and effective application of the ceramide NP is realized.

The invention relates to the field of medical data management, and provides a block chain-based medical data acquisition method, which comprises the following steps of: registering a personal account according to personal basic information uploaded by a user, performing verification when the user logs in so as to determine whether short message secondary verification needs to be performed or not; the method comprises the following steps: acquiring medical data of a user through a sensor group, performing fragmentation encryption processing on the acquired medical data by using a public key, storing the data in each storage node in a distributed manner by means of a block chain technology, extracting encrypted fragments, performing decryption to obtain decrypted medical data, analyzing the decrypted medical data to generate an analysis report, and displaying the analysis report. Therefore, safety management and effective application of the medical data of the user can be realized.

The hot plate welding device with the heat insulation structure comprises a welding box, a middle partition plate, a heat insulation cover and a driving structure, hot molds are fixed to the upper surface and the lower surface of the middle partition plate respectively, the driving structure drives the middle partition plate to reciprocate in the front-back direction, and the heat insulation cover is fixedly connected with the welding box and made of heat insulation materials. The heat insulation covers and the hot molds are arranged in a one-to-one correspondence mode, the heat insulation covers cover the exteriors of the corresponding hot molds and are in sliding connection with the upper surface and the lower surface of the middle partition plate, an openable door body is arranged in front of the heat insulation covers in the moving direction of the middle partition plate, when the door body is opened, the hot molds can stretch out of the heat insulation covers, and when the door body is closed, the hot molds can stretch out of the heat insulation covers. A heat insulation cavity is defined by the heat insulation cover and the middle partition plate. The heat insulation cover is used in the welding box, so that an effective heat insulation effect can be achieved, heat of a hot mold is controlled in a fixed space and is discharged to a fixed position in order, and the working risk that a user of the hot plate welding machine needs to be in a high-temperature working environment for a long time is reduced.

The embodiment of the invention provides an image retrieval method, device and equipment for target image information. The method comprises the following steps: acquiring to-be-retrieved target image information; analyzing and extracting the target image information to obtain target image feature information; matching the target image feature information in a preset image data structured index database, and retrieving to obtain a retrieval result containing matched target image data and structured description information associated with the target image data; outputting a retrieval result; wherein the image data structured index database is obtained through the following processes: obtaining multi-modal original data containing images, videos and text descriptions of the same target object; performing content analysis and feature extraction on the multi-modal original data to obtain corresponding structured description information; and storing the structured description information as an index structure supporting feature-based matching retrieval according to a preset storage format. According to the embodiment of the invention, the deep semantic content of the image can be efficiently understood and accurately matched.

The utility model discloses a slab staggering beam column joint reinforcing structure, which relates to the technical field of constructional engineering transformation and comprises a column body, a first slab staggering beam and a second slab staggering beam, the height position of the first slab staggering beam is lower than that of the second slab staggering beam, and a first thickening layer and a second thickening layer are fixedly arranged on the lower end face of the first slab staggering beam and the lower end face of the second slab staggering beam respectively. Stirrup frames are installed on the outer sides of the column body, the first slab staggering beam, the second slab staggering beam, the first thickening layer and the second thickening layer, a filling layer is arranged on the inner side of each stirrup frame, and the bottom face, on the outer side of the first slab staggering beam, of each stirrup frame is lower than the bottom face, on the outer side of the second slab staggering beam, of each stirrup frame; the stress state of the staggered beams is changed by increasing the section thickness of the staggered beams and reinforcing the stirrups, so that the staggered beams of the underground garage are changed from staggered layers to continuous, the stress state of column tops is changed from eccentric stress to axial stress, and the unbalanced bending moment of short columns caused by staggered layers is eliminated.

The invention discloses a preparation method of flame-retardant regenerated carbon fiber modified engineering plastic particles. The preparation method comprises the following steps: S1, crushing; s2, under the nitrogen atmosphere condition, heating and cracking the carbon fiber broken blocks to obtain regenerated carbon fibers; s3, adding the regenerated carbon fiber and a flame retardant into an active agent solution, and performing uniform dispersion, surface treatment, filtration and drying to obtain activated regenerated carbon fiber; s4, dissolving resin into a solvent, adding the activated regenerated carbon fibers and a flame retardant, heating and stirring, and evaporating the solvent to obtain a pasty mixture; s5, processing and cutting the pasty mixture to obtain flame-retardant regenerated carbon fiber master batches; s6, drying the engineering plastic particles; and S7, mixing the engineering plastic particles with an auxiliary agent, adding the mixture from a main feed of a twin-screw extruder, adding the flame-retardant regenerated carbon fiber master batch through a side feed, and performing extrusion granulation through the extruder to obtain the modified engineering plastic particles. The modified engineering plastic particles have excellent flame retardance and mechanical properties.

The present disclosure provides a data processing method and device, an electronic device, and a storage medium. The method comprises: in response to receiving a request to annotate a first data set, obtaining a pre-stored second data set, the second data set comprising at least one annotated picture, selecting a target picture from at least one picture according to the first data set, selecting a picture to be annotated from the first data set, determining an annotated area of ​​the picture to be annotated, pasting the target picture according to the annotated area, performing a fusion process on the pasted pictures, generating an annotated picture, displaying the annotated picture, selecting a target picture from the annotated pictures so as to paste the target picture to the annotated area of ​​the picture to be annotated, and performing a fusion process on the pasted pictures to obtain an annotated picture, thereby achieving the technical effect of saving manpower and improving the efficiency of annotation, achieving the technical effect of improving the accuracy and reliability of annotation, and achieving the technical effect of comprehensiveness and effectiveness of annotation.

The invention discloses a container sealing supervision device which is characterized in that a video of an environment behind a vehicle is shot in real time through an intelligent lock with a camera and uploaded to a server side, image processing and similarity detection are carried out in combination with a deep learning algorithm of the server side, and whether the intelligent lock is mounted at a correct position of the tail of the vehicle or not can be accurately judged; the server side adopts a bilateral filtering algorithm to carry out denoising processing on the image and carries out similarity detection on the image through a twin neural network, so that misjudgment caused by environmental noise or background change can be effectively avoided, and the accuracy of a container opening process is improved. When it is detected that the mounting position of the intelligent lock is abnormal or the similarity between the shot image and the reference image is too low, the server side can send abnormal prompt information to the customer service side in time, so that a worker can quickly respond and deal with the abnormal situation, and real-time monitoring and effective management of the container transportation process are achieved.

The invention provides an enterprise space appeal matching method based on an AI model, and the method comprises the following steps: collecting the multi-source sensing data of an enterprise space, and building a structured time-space information matrix through an access control, energy consumption, position and personnel label sensing terminal; constructing a causal relationship graph by using a structural causal modeling algorithm, and identifying key causal factors directly related to the spatial appeal; causal invariant characterization is extracted based on a multi-environment invariant risk minimization method, and an intervention effect model is established in combination with controllable intervention variables such as partition adjustment, capacity limitation and use strategies; generating an anti-fact sample of the candidate space unit by using an anti-fact reasoning mechanism, and calculating a matching adaptation score; establishing a matching optimization model according to constraint conditions such as topology, capacity, safety and moving lines; according to the method, high-precision and interpretable matching distribution under the conditions of complex space resources and multi-dimensional appeals is realized, and the method has the effects of flexible deployment, high matching stability and sustainable optimization.

The application discloses a power device, a preparation method and a vehicle, and relates to the technical field of power devices. The power device comprises a substrate, a central region of the substrate forms a cell structure, and a part of the substrate away from the central region forms a voltage-withstanding terminal; an edge position of the voltage-withstanding terminal is provided with a step part; and a first passivation layer is deposited on the step part, and ions are implanted in the first passivation layer to modulate a carrier distribution in the substrate at the voltage-withstanding terminal. The step part is arranged at the edge position of the voltage-withstanding terminal, the three-dimensional structure of the substrate is fully utilized, and the area of the voltage-withstanding terminal is reduced. The first passivation layer is deposited on the step part, and charged ions are implanted in the first passivation layer, so that the charged ions are pinned in the first passivation layer and cannot move with an applied electric field, and the purpose of modulating the carrier distribution in the substrate is achieved, and the effectiveness and stability of the voltage-withstanding of the voltage-withstanding terminal are effectively realized.

The invention provides an intelligent medical illusion detection method and system based on multi-modal data enhancement, and relates to the technical field of medical illusion detection. The method comprises the steps of collecting multi-modal medical data, and constructing an original multi-modal data set; performing data preprocessing on the original multi-modal data set, and generating a plurality of enhanced modal samples through semantic alignment and information fusion among modals by adopting a cross-modal data enhancement technology; modal feature vectors of the modal samples are extracted; according to the modal feature vector, determining a multi-modal consistency index; fusing the modal feature vectors, and determining an abnormal score; obtaining a semantic distance between the modal sample and a knowledge graph; determining illusion confidence; and according to the illusion confidence, judging whether medical illusion appears or not. According to the method and the device, the illusion content in the medical data can be detected according to the consistency of the multi-modal data, the abnormal score and the semantic distance between the multi-modal data and the knowledge graph, and the accuracy and the reliability of the medical data are improved.

The application discloses a kind of based on GiST spatial index multi-resolution fragmentation DEM database construction method, comprising: S1, with the resolution of fragmentation DEM image tile as hierarchical division standard;S2, existing DEM image tile is hierarchically divided according to S1 hierarchical division standard, to minimum outer rectangle as unit independent storage;S3, based on DEM image tile, the hierarchical structure rule of R tree is used to establish parent-child node relationship, and the initial R tree multi-resolution skeleton is constructed;S4, on the basis of initial R tree multi-resolution skeleton, uniform different hierarchical spatial relationship description rule is formulated, and according to the rule, the spatial correlation between DEM image tile is described, and the preliminary establishment of multi-resolution DEM database based on GiST spatial index is completed;S5, the DEM image tile to be stored is stored into database, and the database is improved.The application improves the performance of DEM data storage.