621results about How to "Avoid delay" patented technology

The invention provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

The invention provides a visual project management method and system based on task tracking, and relates to the technical field of project management, and the method comprises the steps: collecting project risk information through employing a context awareness technology, generating a risk propagation network, and calculating a risk diffusion coefficient; performing hierarchical analysis on project requirements based on the risk diffusion coefficient to obtain a task decomposition scheme, constructing a task transmission chain based on the task decomposition scheme and generating a visual task relation graph, and generating a task state transition rule and an alternative task path based on an early warning state of a task node, meanwhile, constructing a task elastic scheduling mechanism and storing the task elastic scheduling mechanism into a rule database; and identifying a preorder task node and a post-order task node on the task transmission chain, sending the task reconstruction instruction to the preorder task node and the post-order task node to realize dynamic task adjustment, updating the risk propagation network according to an adjustment result, and optimizing the risk diffusion coefficient.

The invention relates to the technical field of data processing, in particular to an intelligent logistics platform management system and method based on multi-source data. The method comprises the following steps: acquiring cold-chain logistics order data; performing multi-type cargo temperature control demand processing on the cold-chain logistics order data to generate cargo temperature control demand data; performing multi-node temperature control task decomposition on the cold-chain logistics order data through the cargo temperature control demand data to generate multi-node fine temperature control task data; performing cold chain abnormal feature analysis according to the multi-node fine temperature control task data to generate abnormal temperature control processing scheme data; and carrying out transportation-junction point temperature control correction according to the abnormal temperature control processing scheme data, and carrying out cold chain quality weighted scoring to generate logistics quality scoring data. According to the invention, through cold chain task decomposition and cooperation of multiple transportation nodes, intelligent cold chain logistics management is realized, and the temperature control management efficiency and cargo quality guarantee of cold chain logistics are improved.

The invention relates to the technical field of force feedback handles, in particular to a remote surgical robot control system based on a force feedback handle and a control method thereof. The method comprises the following steps: carrying out multi-dimensional force data acquisition on an end effector of the surgical robot, and carrying out contact force size, direction and time sequence feature extraction to obtain multi-dimensional force feature data; performing surgical operation safety baseline analysis according to the multi-dimensional force feature data to generate surgical operation safety baseline data; performing dynamic force feedback threshold calculation according to the surgical safety operation baseline data to obtain force feedback threshold range data; and performing adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain dynamic force feedback control parameters. Through real-time control instruction optimization and a closed-loop feedback strategy, the system can quickly respond to an operation intention, early warning is carried out on potential risks, and it is ensured that the operation process is safer and more reliable.

The invention discloses a low-voltage power distribution area topology identification method and system based on clustering analysis and a graph theory, and relates to the technical field of low-voltage power distribution area topology classification. The system can accurately identify the topological structure of each partition through the combination of a clustering analysis model and a graph theory algorithm, and automatically calculate a topological characteristic coefficient Tp; therefore, the complex topological structure of the power distribution area can be classified and analyzed more carefully and comprehensively. According to a traditional method, accurate classification is often difficult when a complex branch structure is faced, and a topology recognition module ensures the accuracy of a classification result through a calculation mode of a graph theory. Through the feedback management module, when the power performance risk coefficient Lpjx of the partition exceeds a safety threshold R, a fault early warning signal can be sent out in time, abnormal nodes are automatically identified, faults are isolated, meanwhile, load transfer is executed, the power demand is rapidly transferred to the normally operating partition, and the power failure range and time are reduced.

According to the coastal saline-alkali land underground water level adjusting system based on real-time monitoring and intelligent regulation and control, the system integrates the soil salinity sensor, the underground water level sensor and the central processing unit, future trend prediction is carried out by monitoring key parameters such as soil salinity and underground water level in real time in combination with a deep learning model, and the real-time monitoring and intelligent regulation and control are achieved. And precise intelligent regulation and control are realized. The system can effectively reduce soil salinization, prevent seawater from flowing backwards, improve the utilization efficiency of water resources and reduce the labor management cost at the same time. The method is characterized in that the application of multi-parameter comprehensive monitoring, a deep learning prediction model and a self-adaptive intelligent regulation and control algorithm provides a new technical solution for coastal saline-alkali land treatment.

The invention discloses a storage, calculation and training AI server load fluctuation self-balancing method based on a recursive game network. The method comprises the following steps: S1, recording real-time load data of each task; s2, constructing a macroscopic hierarchical recursive game network model based on the overall resource pool of the AI server; s3, generating an overall resource allocation strategy through multi-agent competition calculation of the macroscopic hierarchical recursive game network model; s4, constructing a micro-hierarchy recursive game network model based on the resource allocation strategy generated by the macro-hierarchy; s5, sub-task resources are optimally allocated; s6, issuing the adjusted resource quota to the microscopic hierarchical recursive game network model in real time; s7, carrying out resource pre-allocation on the upcoming high-load task; and S8, enabling resource allocation to gradually tend to an optimal state. According to the method, the resource utilization efficiency, the system response capability and the task completion stability of the AI server in a multi-task load fluctuation scene are remarkably improved.

The invention discloses a distributed unmanned aerial vehicle detection system, and belongs to the technical field of unmanned aerial vehicle detection planning, and the system specifically comprises the steps: obtaining all to-be-allocated tasks and the number of unmanned aerial vehicles of an unmanned aerial vehicle base station; obtaining importance grade scores of the to-be-allocated tasks, sorting the to-be-allocated tasks according to the importance grade scores to obtain a first sequence, and formulating planning routes of all unmanned aerial vehicles based on the first sequence; any unmanned aerial vehicle is selected and flies according to the corresponding planned route, the consumed electric quantity and the remaining electric quantity of the unmanned aerial vehicle are obtained in real time, whether the electric quantity of the unmanned aerial vehicle meets the task allocation requirement or not is judged, and if yes, the unmanned aerial vehicle is controlled to continue to fly along the current planned route; if not, a target unmanned aerial vehicle is selected from all the remaining unmanned aerial vehicles, the planned route of the target unmanned aerial vehicle is corrected, and a final planned route is obtained, and efficient unmanned aerial vehicle management and route planning are achieved.

The invention discloses a valve fault data detection method and system, and relates to the technical field of industrial automation control and fault diagnose.The method comprises the steps that historical data of operation of a valve are obtained, the collected historical data are analyzed through a deep learning method, and time sequence features are extracted; a fault scoring model is established according to the extracted time sequence features, the fault of the valve is warned in advance, and meanwhile the state of the valve is monitored in real time through the latest real-time data flow; and the prediction result of the fault scoring model is verified through the real-time data flow, and the valve abnormity scoring standard is dynamically adjusted according to the real-time data and the output of the fault scoring model. According to the method, the fault prediction model is constructed by collecting the time sequence data of the valve rod displacement and combining the deep learning technology, the valve operation state can be monitored in real time, abnormal features can be accurately recognized, and delay and errors of manual detection are avoided.

The invention relates to a time slot allocation method and device of a dynamic TDMA protocol and a storage medium. The method comprises the following steps: carrying out time slot allocation on a network allocation frame in a broadcast time slot according to the priority size of node packet sending, resending a time slot request frame in an arbitration time slot when a certain node applies for sending high-priority data, and after a plurality of nodes send the time slot request frame in the arbitration time slot, sending the time slot request frame in the arbitration time slot. And the main node calculates to obtain an arbitration value corresponding to each node according to the weight corresponding to the priority category of each data to be sent and the weight of the number of frames to be sent, and inserts the new time slot allocation table into the next stage according to the arbitration values from large to small to continue to send the data. By adopting the method, when temporary high-priority data is inserted, high-priority emergencies can be responded in time.

The invention discloses an intelligent scheduling and order dispatching management method and system, and relates to the technical field of intelligent scheduling, and the method comprises the steps: collecting original data, and carrying out the preprocessing; performing feature extraction on the preprocessed original data to obtain task priority related features and employee condition related features; based on historical data, constructing an intelligent scheduling model, inputting task priority related features into the intelligent scheduling model, calculating task priority scores, and performing task priority ranking according to the task priority scores; on the basis of task priority ranking and employee condition related characteristics, optimal matching personnel are automatically selected for task allocation; and monitoring execution condition data of the task in real time, and optimizing the intelligent scheduling model by utilizing the execution condition data. According to the method, the intelligent scheduling model is constructed, the health and skills of employees are considered, efficient and reasonable distribution is ensured, the model is optimized in real time, and the task execution quality and efficiency are improved.

The invention relates to the technical field of project management, and particularly discloses a construction project progress management platform, which comprises a data acquisition module, a data processing module, a dynamic adjustment module and a collaborative application module, the data acquisition module acquires real-time data of a construction site; the data processing module is used for processing the collected data; the dynamic adjustment module establishes a dynamic optimization model and a resource scheduling model; the collaborative application module supports a construction party, a supervisor and an owner to check the progress and feedback in real time; according to the invention, the actual data of the construction site can be monitored and acquired in real time, the construction plan and resource allocation are dynamically adjusted through the established model, and the construction efficiency and management level are improved.

The invention relates to the technical field of circuit breaker service life evaluation, in particular to a high-voltage circuit breaker residual service life intelligent evaluation system, which comprises an operation data acquisition module for acquiring operation data of a high-voltage circuit breaker; the degradation degree feature extraction module is used for extracting degradation degree features from the processed operation data; the residual life calculation module is used for constructing a residual life prediction model by utilizing the degradation degree characteristics and adopting a generalized regression neural network algorithm, and calculating the residual life of the residual life prediction model; the early warning and maintenance strategy module is used for formulating a predictive maintenance strategy of the high-voltage circuit breaker based on the residual life calculation result in combination with the residual life probability density function and the expected loss rate of the circuit breaker, and balancing the economical efficiency and the reliability; according to the method, the degradation process of the circuit breaker is reflected in real time, potential faults of equipment are predicted in time, and delay and inaccuracy caused by traditional experience judgment and regular inspection are avoided.

The embodiment of the invention provides a self-adaptive detection and detection system for low-altitude flight inspection, and aims to improve the efficiency and safety of unmanned aerial vehicle inspection operation. Firstly, a plurality of unmanned aerial vehicle description information sets, unmanned aerial vehicle cluster description information and flight inspection task description information are acquired; the information covers performance parameters and task requirements of the unmanned aerial vehicles in different inspection scenes, then the information is loaded into a target adaptive inspection decision model for knowledge learning in advance, and target estimation confidence is generated by analyzing the information so as to evaluate the success probability of the unmanned aerial vehicle cluster in executing a specific inspection task. And when the target estimation confidence meets the set requirement, the corresponding unmanned aerial vehicle cluster is intelligently scheduled to execute the inspection task, so that efficient completion of the task is ensured, and the adaptability and flexibility of the unmanned aerial vehicle inspection operation are effectively improved through intelligent evaluation and dynamic scheduling.

The invention relates to the technical field of big data, and particularly discloses a business data processing method and device, computer equipment and a readable storage medium. The method comprises the following steps: in response to a resource registration request for target service data, grouping the target service data to obtain a plurality of grouped data; the target business data comprises any business data in the business data uploaded by the plurality of resource transaction channel ends; storing the grouping information of the plurality of grouped data to a clearing task queue list corresponding to the target business data; issuing the task processing message to enable a plurality of servers subscribing the task processing message to obtain grouping information from the clearing task queue list in parallel, and executing a registration subtask on the grouping data according to the grouping information; and according to the sub-task execution states of the grouped data returned by the plurality of servers, determining resource registration operation results of the plurality of grouped data. By adopting the method, the accuracy of business data processing can be improved.

The invention provides a dynamic spreading factor anti-interference method based on annealing Q learning. The method comprises the following steps: determining an information transmission rate according to a spreading factor; determining system throughput and an optimization target according to the signal-to-noise ratio of the receiving end; determining a state space, an action space, a state transition probability matrix and rewards in the decision process; calculating a reward obtained by the selection action according to a broadband spectrum sensing result; the interference condition at the current moment is obtained through broadband spectrum sensing; deciding a spreading factor of the next time slot according to the Q value table; the user selects the action of the next time slot according to the decision information and updates the state; the award obtained by selecting the corresponding action is calculated according to the broadband spectrum sensing result; selecting the spreading factor of the next time slot according to the Q value table, and updating the Q value of the selection action in the state; and the user makes the most anti-interference strategy in a dynamic interference environment. According to the method, the system throughput is maximized, and time delay and overhead caused by channel switching are avoided.

The invention relates to the field of intelligent breeding, in particular to a breeding monitoring method and system for an intelligent agricultural orchard, and the method comprises the steps: obtaining poultry breeding data in an orchard range in real time; judging whether abnormal conditions are met or not according to the poultry breeding data; when the poultry breeding data is not abnormal, acquiring environmental data and behavior data of poultry; judging whether the behavior data meets abnormal behaviors or not according to the environment data; when the behavior data meets the abnormal behavior, marking a target poultry corresponding to the abnormal behavior; quantifying the behavior data according to the judgment result, and speculating the reason of the current abnormal behavior in combination with the quantified behavior data and the environment data; and generating a coping strategy according to the reason of the current abnormal behavior based on the speculation result. And the multi-dimensional data fusion capability is realized, and the adaptability of the system to complex conditions is remarkably improved. Misjudgment possibly caused by a single data source is avoided, and the accuracy of an abnormal behavior analysis result is ensured.

The invention relates to the technical field of equipment control, in particular to a self-adaptive switching method and system of a current transformer. The method comprises the following steps: collecting primary side parameters and secondary side parameters of a current transformer to obtain primary side collection parameters of the current transformer and secondary side collection parameters of the current transformer; performing current transformer state segmentation initialization according to the current transformer primary side acquisition parameters and the current transformer secondary side acquisition parameters so as to generate a current transformer operation state classification result; switching result judgment is conducted on the current transformers according to the current transformer operation state classification result, and when the current transformer operation state classification result is a current transformer critical state section, parallel buffering is conducted on the current transformers, and critical current transformer switching data are generated. According to the invention, through accurate state classification, a dynamic switching mechanism, time sequence optimization and visual display, the switching sensitivity and stability of the current transformer are improved.

The invention relates to the technical field of medical nursing, in particular to an analysis system based on a data model in the field of neurology nursing. The system comprises a neurology nursing behavior characteristic analysis module, a neurology nursing risk assessment module, a nursing demand and characteristic change rule analysis module and a neurology nursing decision support module. Neurology patient behavior dimension data and neurology nursing record dimension data can be obtained to carry out illness state nursing behavior feature analysis and neurology nursing risk assessment calculation, and a neurology illness state nursing risk assessment value is obtained; the neurology patient nursing demand is obtained, feature change rule mining analysis is carried out, meanwhile, nursing decision support analysis is carried out on the corresponding neurology patient nursing process, a neurology patient nursing decision support suggestion scheme is generated, and corresponding neurology patient nursing decision intervention measures are carried out. The nursing efficiency can be remarkably improved.

The invention discloses a multi-laser 3D printing path planning method and system based on collaborative optimization, and relates to the technical field of multi-laser 3D printing path planning and optimizing.The method comprises the steps that a 3D model to be printed is subjected to equal-area division; adjusting a real-time path based on a collision avoidance rule load balance; and finishing self-adaption and summarizing the data. According to the multi-laser 3D printing path planning method based on collaborative optimization, through the step of equal area division, the overall working efficiency of the system is improved, through the step of refining hierarchical division, the system can more accurately process a complex geometric structure, the printing safety and smoothness are improved, and through real-time load balance and path adjustment, the multi-laser 3D printing path planning method based on collaborative optimization can be used for realizing multi-laser 3D printing. And delay caused by too large task load of some laser heads is avoided. Through monitoring and automatic adjustment, the system achieves optimization of a self-adaptive working path, the overall printing efficiency is improved, and better effects are achieved in the aspects of fluency, safety and printing efficiency.

The invention relates to the technical field of electronic digital data processing, in particular to a memory bank initialization failure processing method which comprises the following steps: judging whether a memory bank fails to be read or not, if an error memory signal is detected, indicating that the memory bank fails to be read, and marking the memory bank as a memory bank to be detected; running state data, including memory bank performance data and aging condition data, of the to-be-tested memory bank is collected, and a memory bank performance evaluation index and a memory bank aging degree evaluation index are obtained through comprehensive analysis; and comprehensively analyzing to obtain a memory bank exception evaluation index, judging whether the memory bank has a fault according to the memory bank exception evaluation index, and processing the memory bank according to a judgment result. According to the method for processing the initialization failure of the memory bank, provided by the invention, data loss can be prevented, data security can be guaranteed, and system stability and performance optimization can be improved, so that dissatisfaction and troubles caused by system problems of users can be reduced, and the satisfaction degree of the users can be improved.

The invention discloses a brain-like edge calculation-driven multi-mode intelligent security method and system, and aims to improve the security capability in a complex dynamic scene. According to the method, multi-modal data of videos, audios, sensors and the like are collected, preprocessing is carried out by utilizing a brain-like edge computing gateway, dynamic behavior characteristics are extracted, secondary confirmation is carried out through a lightweight visual large model, and then formal early warning is generated. And the system executes intelligent linkage operation according to the early warning information risk level, and records false alarm information for model optimization. According to the invention, brain-like calculation and multi-modal fusion are adopted to improve the recognition precision, edge calculation reduces delay, an intelligent closed loop realizes differentiated treatment, a low-power design and a modular architecture reduce the cost, and the system has a self-learning capability. The defects of an existing security and protection system in the aspects of dynamic behavior recognition, response speed, disposal intelligence, cost control and the like are effectively overcome. The system is suitable for scenes such as a campus and a community, and provides an accurate, efficient and intelligent security solution.

The invention relates to the technical field of information resource security management, and particularly discloses a security management method and system based on computer information resources and a storage medium, and the method comprises the steps: obtaining user identification information of a user, extracting a reference evaluation information set, analyzing a user behavior abnormal evaluation value, and judging a user behavior tag. If the user behavior label is judged to be in a potential risk mode, communication end current limiting is carried out, abnormal secondary verification is started, a user behavior abnormal verification evaluation value is analyzed, user behavior label re-judgment is carried out, and user permission locking measures are determined. Potential risks can be perceived in time, the situation that potential safety hazards are gradually expanded under the condition that the potential safety hazards are not perceived is avoided, and the user permission locking accuracy is improved. For a potential risk mode, communication end current limiting is carried out and secondary verification is started, so that network activities of abnormal users are effectively limited, security risks are reduced, the risk degree of user behaviors is comprehensively and accurately judged, and the stability of the system and the security of information resources are guaranteed.

The invention relates to the technical field of photovoltaic power generation, and discloses a phase-locked loop-free photovoltaic power generation system multi-power quality cooperative improvement method and system, and the method comprises the steps: processing a collected current signal of a three-phase grid-connected point of a photovoltaic power generation system to obtain a current fundamental component and a current harmonic component of the grid-connected point, and determining a fundamental reactive power; determining the capacity required by harmonic suppression and the capacity required by reactive compensation according to the current harmonic component of the grid-connected point and the fundamental reactive power; obtaining the residual capacity of the photovoltaic power generation system, and carrying out an optimization algorithm to determine the optimal compensation coefficient of harmonic suppression and reactive compensation; and transmitting the optimal compensation coefficient of harmonic suppression and reactive compensation to a photovoltaic inverter control algorithm to complete multi-power quality collaborative improvement of the photovoltaic power generation system. The technical problems of large delay, slow control response speed, need of a phase-locked loop and poor power quality management target collaboration in the prior art are solved.

The invention relates to the technical field of holders, and discloses a self-stabilization holder searchlight tracking system, which comprises a holder autonomous stabilization subsystem, a tracking control subsystem and a searchlight subsystem, and is characterized in that the holder autonomous stabilization subsystem comprises a servo motor, a servo motor connecting arm, a servo controller and an attitude position sensor; one end of the servo motor connecting arm is fixedly connected with the servo motor, the other end of the servo motor connecting arm is fixedly provided with a fixing structure, and the top of the fixing structure is provided with a data transmission interface. According to the self-stabilizing pan-tilt searchlight tracking system, stable operation of a searchlight at different angles and in complex environments is achieved through the pan-tilt autonomous stabilizing system, the system can automatically adjust the direction and angle of the searchlight according to the position of a target, it is ensured that continuous tracking is conducted in the target moving process, and efficient illumination is provided; the design improves the precision of target tracking and the stability of illumination, and is suitable for various application scenes such as night monitoring, security patrol, search and rescue and the like.

The invention relates to the technical field of digital video devices, in particular to a digital video device control method and device. The method comprises the following steps: acquiring digital monitoring data, performing real-time scene acquisition and dynamic region optimization according to the data, and generating optimized dynamic region data; video frames are read based on the dynamic region optimization data, and abnormal behavior analysis and behavior intention evaluation are carried out; calculating the complexity of monitoring behaviors by combining video frame data and behavior intention data, and evaluating resource consumption increase; the processing bottleneck is evaluated according to the resource consumption growth data, and the processing capability attenuation analysis is carried out, so that the monitoring device is adjusted, and the system performance is optimized; according to the method, the resource allocation and processing capability of the video device is dynamically adjusted through real-time analysis and processing of monitoring data. According to the invention, the digital video device is adjusted, so that the prediction identification of the digital video device is more accurate and efficient.

The invention relates to a container environment safety protection method and device based on CVE, and the method comprises the steps: building an App service and an Agent service, and separating front and rear end management; constructing a CVE library, and according to the CVE library, performing security scanning on the container mirror image through an App service and an Agent service; monitoring the environment in the container in real time through the Agent service monitoring container; according to the CVE library, performing baseline compliance scanning on the Docker and Kubernetes components through an App service and an Agent service; the security scanning result, the real-time monitoring result and the baseline compliance scanning result are sent to Kafka; and processing the container environment according to the data in the Kafka, and storing the processed data into a CVE library for subsequent analysis and query. Compared with the prior art, the safety protection of the container environment is realized by constructing the own CVE library, and the distributed processing of the scanning tasks is realized by allocating the scanning tasks to different services, so that the cost is reduced while the overall scanning efficiency is improved.

The invention discloses an automobile road noise active control method and device based on an Elman neural network. The method comprises the following steps: acquiring vibration acceleration data and in-vehicle noise data when a vehicle runs; the vibration acceleration data and the in-vehicle noise data are used for training an Elman neural network; taking a predicted noise signal output by the Elman neural network as a reference signal, and inputting the reference signal into a filter to obtain a filtered signal; playing the filtering signal in the target noise reduction area, and synchronously collecting residual noise as an error signal; adjusting the weight parameter of the filter through the error signal, and processing the reference signal through the filter with the updated weight parameter to obtain an updated filtering signal; the steps of generating the error signal and updating the weight parameter are repeatedly executed until the average noise reduction amount in the vehicle is lower than a preset condition, and a final filtering signal is obtained; and playing the final filtering signal to counteract the noise in the vehicle. According to the invention, the acoustic feedback problem and the noise transmission time delay problem caused by directly using the noise signal as the reference signal can be avoided.

The invention provides a mine emergency rescue command system, and aims to solve the problems of untimely emergency response, incomplete information, low command and dispatch efficiency and the like in the prior art. The system comprises a mine environment data acquisition module, a wireless transmission module, a data processing module, a risk early warning module, a command and dispatch module and a rescue auxiliary module. The environment data acquisition module acquires gas concentration, temperature and humidity, dust concentration and personnel position information in a mine in real time through a sensor; the wireless transmission module transmits the data to a ground command center; the data processing module analyzes and generates a comprehensive safety score by using an algorithm; when the danger value exceeds the threshold value, the risk early warning module automatically triggers an alarm; and the commanding and dispatching module remotely dispatches rescue workers according to the real-time information, and generates an optimal rescue path by using the rescue auxiliary module. The system has real-time performance, intellectualization, information integration and remote command capability, and can quickly respond to mine accidents, improve rescue efficiency and reduce accident loss.

A dyeing-free imaging method for a plasmon tissue section based on a generative adversarial network relates to the technical field of biomedical detection, and comprises the following steps: 1) recording the tissue section placed on a plasmon glass slide by using a high-resolution bright field microscope and passing through a Hamp; e, dividing the dyed tissue slices into a training set and a verification set in proportion; 2) amplifying the number of paired images in the training set to a plurality of times through data enhancement; 3) constructing a GAN model, and training the GAN model by using the paired image after data enhancement as a data set; and 4) based on the trained GAN model, performing virtual staining of the staining-free imaging image of the plasmon tissue section. According to the invention, the generated virtual dyeing image is visually and statistically compared with the real Hamp; the E-dyed image has high similarity, and provides pathological interpretable images and more effective discrimination information for pathologists. The time is remarkably shortened, the cancer diagnosis efficiency is improved, and a rapid and effective tissue slice analysis means is provided for clinic.