51 results about "Combinatorial algorithms" patented technology

The invention relates to the technical field of test question generation, in particular to an intelligent test question generation method and system based on learning behavior analysis. The method comprises the following steps: acquiring interactive behavior data and score data of learners in a user online learning platform in real time; inputting the interactive behavior data and the score data into a pre-trained knowledge state analysis model to generate a user knowledge state matrix; based on the knowledge state matrix and in combination with a preset teaching target library, identifying a target knowledge point set which needs to be strengthened currently and a corresponding cognitive training type; according to the target knowledge point set needing to be strengthened and the corresponding cognitive training type, a test question element combination algorithm is called, question stems, interference items and question solving path prompts are dynamically assembled, and personalized test questions are generated. The method has the advantages that full-closed-loop intelligent teaching from behavior analysis of the user to targeted training is achieved, and personalized test questions adaptive to individual cognitive vulnerabilities are dynamically generated.

The invention discloses a GNSS (Global Navigation Satellite System) signal quality adaptive integrated navigation method, and relates to the technical field of vehicle navigation. IMU and GNSS positioning information and positioning states are read, integrated navigation is realized through an ESKF loose combination algorithm, a measurement covariance matrix is changed in real time according to the GNSS positioning state and a DOP factor, and finally, an error state value is estimated and fed back to an integrated navigation system. Position and speed information errors and attitude Euler angle errors under a navigation coordinate system are fed back to INS mechanical arrangement, angular acceleration zero offset errors of the IMU are fed back to the IMU, and updating of the state quantity is completed. A measurement covariance matrix is changed in real time according to a GNSS positioning state and a DOP factor by aiming at IMU and GNSS data through a loosely combined ESKF fusion algorithm, an error state is estimated and fed back to an integrated navigation system, and the positioning precision can be effectively improved.

The invention discloses a structure optimization design method based on a particle swarm optimization algorithm, aiming at a super high-rise building, an initial structure parameter matrix is generated by establishing an optimization model, setting a target function and taking structure layer displacement, an inter-layer displacement angle and top layer peak resultant acceleration as constraint conditions; a wind vibration response characteristic matrix is obtained by adopting a frequency domain calculation method in combination with structural dynamic characteristics and a wind tunnel test result, and an equivalent static wind load matrix and an acceleration response matrix are calculated. A combined algorithm of an optimization criterion method and a particle swarm algorithm is used for performing multiple iterations, wind load and acceleration response data are recalculated by initializing and updating related parameters of a particle swarm, calculating a fitness value, iteratively updating a position and a speed matrix, and finally an optimal solution matrix is output according to convergence judgment. Therefore, a final design scheme of the structure is determined, and comprehensive optimization of wind resistance and economic benefits of the structure is realized.

The invention belongs to the technical field of packaging jet printing, and discloses an external packaging carton two-dimensional code jet printing quality online monitoring and error correction process, which comprises the following steps: building a vision, laser, spectrum and pressure four-source sensing positioning system in a preprocessing stage, comprehensively collecting carton material and form related data, and forming a pre-calibration closed loop in cooperation with microsample jet printing verification; in the jet printing process, a high-speed imaging system of self-adaptive light supplement works synchronously, after an image is preprocessed through a combinatorial algorithm, various defects such as blooming and scratches are accurately recognized through a ResNet34-Transform-GAN fusion model, and then an LSTM neural network is used for predicting the development trend of the defects and conducting early warning; a three-level parameter configuration system of cloud decision, edge execution and twin simulation is utilized, an edge calculation unit uploads data such as materials, sizes and flatness collected in real time to the cloud, historical optimal parameters of the corresponding materials are called, and then dynamic adjustment is conducted in combination with the conveying speed.

The invention provides a method for identifying the characteristics of a metal foreign body in a GIS, and the method comprises the steps: building an electric field simulation model, and setting the metal foreign body; a multi-modal sensor is adopted to collect signals generated by partial discharge of the metal foreign matter so as to obtain a multi-modal atlas database; performing multi-domain feature extraction on the multi-modal atlas database, and generating a lightweight fingerprint feature vector through feature dimension reduction; the lightweight fingerprint feature vector sequentially passes through all steps of a combinatorial algorithm to solve identification items of the metal foreign matter; and selecting a globally optimal group of identification items to be combined into a metal foreign matter feature identification scheme. According to the invention, fine analysis and processing are carried out on partial discharge signals through a signal processing and pattern recognition algorithm, so that the detection and classification precision of metal foreign matters can be effectively improved; real-time monitoring and quick response under complex working conditions can be realized, a quantitative basis can be provided for fault early warning and equipment maintenance, and the operation reliability and safety of GIS equipment are further enhanced.

The invention discloses an urban user power utilization efficiency collaborative optimization analysis method and system based on the Internet of Things, and relates to the technical field of power analysis, and the technical scheme is characterized in that a multi-source heterogeneous data pool containing timestamp alignment is constructed; generating a user electricity consumption characteristic matrix through a space-time dynamic weight fusion algorithm; calculating a frequency modulation potential value through an entropy-TOPSIS combinatorial algorithm based on the user power consumption characteristic matrix; inputting the frequency modulation potential value into an LSTM-GAN confrontation generative network, and dynamically generating a three-dimensional efficiency parameter vector containing a user optimization degree, a good power grid contribution degree and an environmental benefit index; and based on the three-dimensional efficiency parameter vector, an optimization target of a user and a power grid is coordinated through a Stackelberg game model, and a dynamic electricity price strategy and a load adjustment strategy are output. The method has the advantages of being low in response delay, high in energy efficiency optimization rate, good in environmental benefit and the like when being used for coping with complex power consumption scenes in urban areas.

The invention discloses a cable material diameter detection method based on an eddy current impedance effect, and particularly relates to the field of cable nondestructive testing, which comprises the following steps: S1, synchronously acquiring the resistance and inductance of a coil under a low-frequency signal and the resistance and inductance of the coil under a high-frequency signal by adopting a double-frequency excitation eddy current sensor; the environment temperature and the lift-off distance between the sensor and the cable are measured; s2, denoising is carried out through a combinatorial algorithm, and temperature compensation is carried out on the resistance and inductance of a sensor coil and the conductivity of a cable core wire; s3, calculating a material sensitive factor under low frequency, calculating a line diameter sensitive factor under high frequency, and correcting when the lift-off distance deviates; s4, calculating a material deviation rate to judge the material, fitting a linear regression model, and combining with conductivity to correct the skin depth to calculate the line diameter; and S5, according to the material and the wire diameter, comprehensively determining progressive output, and triggering a corresponding alarm in case of abnormality. The problems of parameter coupling and incomplete temperature compensation are solved, and the detection precision and the environmental adaptability are improved.

The invention relates to the technical field of unmanned driving, provides a point cloud data de-noising method and system based on intelligent driving, and proposes that when a grey wolf algorithm is used for optimizing by using scanning factors alpha i and t in a hiking algorithm, different alpha i and t are adopted for optimization in the processes of local optimization and jumping out of a local optimal point, so that the optimal point of the hiking algorithm is obtained. The three-dimensional point cloud recognition method comprises the following steps: firstly, carrying out clustering on three-dimensional point clouds through a DBSCAN algorithm, thus forming a hiking combinatorial algorithm, then optimizing a DBSCAN clustering algorithm of the three-dimensional point clouds by using the hiking combinatorial algorithm to obtain an optimal neighborhood radius Eps parameter and an optimal minimum point number MinPts parameter, and correcting all three-dimensional point cloud data marked as noise points through the DBSCAN algorithm, thereby eliminating noise and improving the recognition precision of the three-dimensional point clouds.

The invention discloses a 5G and Beidou fused vehicle positioning navigation system. The system comprises a multi-source heterogeneous positioning module; a high-precision map and a dynamic traffic information base; a self-adaptive multi-layer map matching module; the dynamic path planning module considers steering limitation and real-time position feedback; and a central information fusion and decision unit. According to the invention, multi-source heterogeneous positioning data such as Beidou / GPS, 5G positioning, high-precision IMU, a vehicle-mounted atomic clock and a barometric altimeter are fused, and a tight combination algorithm based on ESKF or a factor graph is adopted, so that the system can realize accurate positioning in an environment with good satellite signals, partial shielding and even serious loss. Continuous, smooth and high-precision vehicle state estimation can be output, particularly when the number of visible satellites is insufficient, double-satellite positioning can be achieved by using the vehicle-mounted atomic clock to predict the clock error and combining the elevation constraint of the barometric altimeter, and the usability and robustness of positioning are improved.

The invention discloses an intelligent cell gate fault early warning system based on a combinatorial algorithm, and relates to the technical field of data processing, and the system comprises a standard periodic signal determination module which is used for interacting with a power transmission module of an intelligent cell gate and determining a standard periodic signal; the periodic reference signal determination module is used for identifying and identifying a signal inflection point window aiming at a standard periodic signal and determining a periodic reference signal; the fault decision training module is used for supervising and training the fault decision module; and the fault detection module is used for performing periodic matching judgment and fault trend prediction based on the signal inflection point window. The technical problems that a traditional intelligent unit gate fault detection method in the prior art is lagged in response, insufficient in precision and poor in multi-scene environment adaptability are solved, the real-time performance and precision of fault detection are improved through periodic signal matching, fault trend prediction and transfer learning, and the fault detection efficiency is improved. And meanwhile, the adaptability of the multi-scene environment is improved.

The invention relates to the technical field of heat dissipation, and provides a neural network control method of a heat exchange module. The method comprises the steps that heat exchange performance parameter information of a heat exchange module is obtained in real time; wherein the heat exchange performance parameter information comprises heat exchange performance parameter values of a plurality of heat exchange performance parameters; performing combination processing on each heat exchange performance parameter in the heat exchange performance parameter information based on a heat exchange performance parameter combination algorithm to obtain a plurality of heat exchange performance parameter combinations; for each heat exchange performance parameter combination, inputting the heat exchange performance parameter combination into a corresponding target control parameter prediction model to obtain a corresponding control parameter; and the heat exchange module is controlled to operate based on all the control parameters. According to the method, the independent target control parameter prediction model is constructed for each control parameter, so that mutual interference and error transmission of the control parameters in the prediction process are reduced, and the accuracy and reliability of the control parameters are ensured.

The embodiment of the invention provides an MCU anti-theft authentication method and system and a storage medium, and belongs to the technical field of automobile controller safety authentication. The method comprises the following steps: setting a first identification code and a second identification code according to a unique identification code of a vehicle, and sending the first identification code and the second identification code to an MCU; combining the first identification code and the second identification code according to a combination algorithm to obtain a first combination value; calculating a hash value of the first combination value according to a hash algorithm as a third identification code and sending the third identification code to the MCU; performing first authentication according to the first identification code, the second identification code and the third identification code, and judging whether the first authentication is successful or not; under the condition that the first authentication is judged to be successful, performing second authentication and judging whether the second authentication is successful or not; and under the condition of judging that the second authentication is successful, judging that the anti-theft authentication is successful. The MCU anti-theft authentication method is high in safety and reliability.

The invention provides an antenna automatic control method adaptive to different antenna deployment modes. The antenna automatic control method comprises the steps that S1, a remote information processor reads circuit level data of an external antenna; s2, the communication module diagnoses the circuit level data of the external antenna based on an antenna in-situ identification algorithm, and judges the working state of the external antenna; step S3, based on a single-pole double-throw switch combination algorithm, evaluating a judgment result in the step S2, calculating an optimal switching scheme, and sending a corresponding switching instruction; s4, the single-pole double-throw switch receives the switching instruction and executes switching operation, the communication module verifies the switching effect through the step S2, and if switching fails, the switching instruction is sent again; and S5, setting a signal instability threshold, and comparing the signal receiving intensities before and after switching. According to the invention, a plurality of TBOX parts do not need to be varied to adapt to the selection of different antenna deployment, the flexibility of antenna deployment is improved, and the platform compatibility difficulty is reduced.

The invention provides a USB device authentication method and system based on a windows system, and the method comprises the steps: a server side analyzes a USB device obtaining instruction to obtain first information, and processes the first information based on a preset encryption algorithm to obtain each first identifier; processing each first identifier and the first information based on a first combinatorial algorithm to obtain a target white list file; when a target client monitors that a second USB device is inserted into the USB interface of the target client, obtaining second information and processing the second information based on a preset encryption algorithm to obtain a second identifier; authenticating the second USB device according to the second identifier and the target white list file; therefore, the white list file is created and stored through the server, compared with a physical disk, the method is more convenient, large-scale online deployment can be achieved, carrying is not needed, meanwhile, the convenience of updating the white list file and the safety of the system are greatly improved, and the risk of data leakage is reduced.

The invention discloses a five-axis numerical control machine tool machining error on-line compensation system based on deep learning, relates to the technical field of machine tool error compensation, and solves the problems that position correction difference is caused by neglecting a complex coupling relation between rotating shafts and linear shafts of different types of machine tools, and the error compensation accuracy is high. And deep learning models for machine tools with different requirement types are not designed, so that the model prediction precision is influenced. Determining the coupling relation of each axis error through the HTM homogeneous transformation matrix of the machine tool kinematic chain; aiming at kinematic chain structure differences of different types of machine tools, a differential correction strategy is designed. And constructing a zero drift time sequence model in combination with a historical drift trend term and a random error term predicted by the LSTM. Geometric error terms strongly related to machining errors are screened through grey correlation analysis, then the weight of the geometric error terms is quantified through random forest, the accuracy of key error recognition through a combinatorial algorithm is improved, and the model prediction precision is improved by training fusion models for machine tools with different requirement types.

The invention belongs to the technical field of biological carbon sequestration, and discloses a carbon sequestration pathway selection method and system based on assimilation pathway evaluation.The method comprises the steps that all reactions with carbon dioxide, carbonate and a one-carbon compound as substrates are obtained, carbon dioxide and bicarbonate radicals are used as carbon sources, and a plurality of carbon sequestration reactions are obtained through screening; carrying out pathway calculation on the carbon sequestration reaction by utilizing a combinatorial algorithm in combination with a simple flux equilibrium analysis technology, and determining a core carbon sequestration reaction according to a calculation result; based on the enzyme catalysis efficiency, simplicity, energy and reduction equivalent consumption and metabolite conversion relation in the core carbon sequestration reaction, the carbon sequestration pathway is evaluated, and the corresponding carbon sequestration pathway is selected according to the evaluation result. According to the invention, a plurality of novel carbon dioxide assimilation approaches with prospects are obtained, and a valuable alternative scheme is provided for bioconversion of industrial carbon dioxide in the future.

The invention relates to an osteoarthritis AI (osteoarthritis) prediction method based on NETs (neutrophile granulocyte extracellular traps) related key genes, and aims to develop a model universally applicable to gene diagnosis or molecular diagnosis by analyzing and disclosing a large sample data set through an advanced algorithm by utilizing ML (makeup language) to reveal genetic regulation of neutrophile granulocyte extracellular traps (NETs) in joint tissues. Therefore, the optimal algorithm needs to be compared and screened by using multiple algorithms during modeling, the gene (molecule) diagnosis efficiency is improved by combining the algorithms on the basis, key genes which can best reflect OA characteristics and are driven by NETs (key gene characteristics related to the NETs) are selected, and the disease-related genes and the relationship between the disease-related genes and disease phenotypes are identified, so that the disease-related genes are identified. Therefore, understanding of interaction of biological components is deepened, and diagnosis and treatment strategies are improved.

The application provides a special-shaped character detection and recognition method, system, device and storage medium, and relates to the field of special-shaped character detection and recognition.The method comprises the following steps: (1) constructing a training set based on an existing product image, and importing the training set into a rotating target detection model for training; (2) inputting a product image to be detected into the trained rotating target detection model to output string information M of different regions in the product image to be detected; and (3) based on the string information M of different regions, adopting an optimal combination algorithm to combine characters in front and back, and forming a plurality of independent strings.The application uses a rotating target detection model for character recognition, and adopts an optimal combination algorithm for character combination on the basis of character recognition, and then detects special-shaped strings on medical devices and other products.The application solves the problem of low character detection accuracy caused by irregular string shapes in a complex scene.

The invention discloses a mechanical arm scanning track planning method based on an improved ant colony algorithm, which comprises the following steps: step 1, dividing a to-be-measured workpiece into n primitives, setting n measuring points on the to-be-measured workpiece, and setting a complete set T of the measuring points as follows: T = {T1, T2, T3,..., Tm}; 2, solving a set coverage problem by using an improved greedy algorithm to obtain an optimal selection method of a scanning point position set; 3, solving a scanning track by using an improved ant colony algorithm and a combined algorithm based on a BFS strategy; step 4, optimizing the scanning track by using a particle swarm algorithm to obtain an overall scanning track with the shortest scanning time; and step 5, obtaining an overall scanning track with the shortest scanning time by using a particle swarm algorithm. According to the method, a scanning track is solved by using an improved ant colony algorithm and a BFS-based strategy, a larger area is selected to continue to carry out strategy path search, and a smaller area is solved by using the ant colony algorithm to obtain a loop. And opening the loop, combining the loop to an original path, and solving the whole path through the BFS to obtain an optimal path.

The invention discloses an unmanned aerial vehicle security situation awareness method based on multi-source heterogeneous data fusion, and belongs to the technical field of unmanned aerial vehicles, and the method comprises the steps: obtaining the multi-source heterogeneous data of a target unmanned aerial vehicle in real time, and carrying out the preprocessing of the multi-source heterogeneous data; performing feature extraction on the preprocessed multi-source heterogeneous data according to data types to generate multi-source heterogeneous features; performing conversion processing on the multi-source heterogeneous features through a heterogeneous feature self-combination algorithm to generate fusion features, and introducing CLS nodes to generate single time step diagram structure data; cLS node vector representation is extracted from the single time step graph structure data through a graph neural network; paying attention to key data through an attention mechanism for CLS node vector representation to generate a comprehensive feature vector; and performing security situation awareness classification on the comprehensive feature vector through a CatBoost algorithm to generate a security situation awareness result. The dynamic change of the flight environment can be responded in real time, and the flight safety and the situation prediction capability of the unmanned aerial vehicle are enhanced.

In the embodiment of the present invention, image information including an object to be detected is obtained, an image opening operation is performed on the image information to highlight the object to be detected, and the object highlighting image information is obtained; a straight line segment detection operation is performed on the object highlighting image information using a straight line segment detection algorithm to determine all straight line segment objects in the object highlighting image information; a closed suppression combination algorithm is used to perform a straight line segment reconstruction operation on all straight line segment objects to determine the object to be detected in the image information. It can be seen that the present invention can automatically perform image processing and object detection on image information to determine overlapping irregular non-closed objects in the image information. Compared with the algorithms in the prior art, the present invention has higher detection accuracy and detection efficiency for overlapping irregular non-closed objects, and can greatly reduce the investment of manpower and material resources, reduce the error rate, and improve the efficiency of drawing review.

The invention provides a track fusion method and system based on Kalman filtering and a convex combination theory, and the method comprises the steps: data collection: collecting ship AIS data information collected by a base station at a certain place; data processing: carrying out data cleaning, data conversion and data aggregation processing on the acquired ship AIS data information; track data generation: assuming that a ship track is in uniform linear motion, generating track data according to an object motion expression, and considering loss caused by external influence to obtain an observation model; kalman filtering processing: processing the generated track data by using a Kalman filtering algorithm, and sequentially ending the prediction of the track information of all the sensors; and performing convex combination fusion, inputting predicted trajectory information after Kalman filtering iteration of each sensor is finished, and performing flight path fusion by adopting a convex combination algorithm. The method has technical advancement in the aspects of structure processing, track association, track state estimation fusion and the like, and the effect and accuracy of track fusion can be improved.

The present invention discloses a method for extracting the periodic and trend characteristics of InSAR ground subsidence considering spatial heterogeneity. This method obtains long-time series synthetic aperture radar image data, uses the PS-InSAR technology to conduct high-precision monitoring of ground subsidence. On this basis, principal component analysis (PCA) is adopted to reduce the dimension of deformation information, and a combined algorithm integrating spatio-temporal spectral clustering and independent component analysis (ICA) of blind source signals is proposed to efficiently identify different types of subsidence characteristics, extract the periodic characteristics, trend characteristics, and random characteristics of different categories of ground subsidence, and can conduct a more comprehensive, detailed, and accurate analysis of ground subsidence, providing a more reliable and scientific basis for subsidence monitoring, risk assessment, and prevention and control decision-making, and having broad application prospects.

The invention provides a user-defined vehicle customer questionnaire analysis processing method and device, and the method comprises the steps: collecting a vehicle customer questionnaire in a user-defined manner, investigating questionnaire information, and obtaining the questionnaire relation of a person, a vehicle and a question; vehicle questionnaire information is collected, and vehicle questionnaires of different survey channels are collected to form a data set through a data aggregation channel; obtaining a vehicle questionnaire data set, and storing the vehicle questionnaire data set into an aggregation Entering a service analysis layer, and analyzing the questionnaire information according to three service algorithms of brand, vehicle type and vehicle series; entering a question analysis layer, and performing algorithm analysis on the questionnaire information; entering a locating algorithm analysis layer, and carrying out locating algorithm analysis on the questionnaire information; entering a combinatorial algorithm analysis layer, and performing combinatorial algorithm analysis on the questionnaire information; and entering a value application layer, performing value mining according to the questionnaire data, entering customer insight, and portraying the automobile questionnaire customer according to different business systems of brands, automobile models and automobile series.

The application discloses a programmable Josephson quantum voltage standard AC output characteristic comparison test system and method. The application generates synchronous step waves through homologous triggering of two sets of PJVS systems, directly collects difference signals of the two step waves by using a differential sampler, and realizes high-precision comparison of AC output characteristics of the two sets of PJVS systems by combining difference correction and effective value calculation. The application saves a complex intermediate transmission standard, simplifies the comparison process, and improves test efficiency and result accuracy. The application considers theoretical differences caused by parameters such as a PJVS junction parameter, a step number, a bias combination algorithm and a working microwave frequency difference, and further improves test precision by correction, and is suitable for comparison scenes of quantum voltage standard systems of different manufacturers and different parameter configurations.

The invention discloses a regional photovoltaic output data enhancement and ultra-short-term power prediction method based on a dynamic graph attention network combinatorial algorithm, and the method comprises the following steps: firstly, decomposing a photovoltaic power sequence into a plurality of subsequences with stable trends through employing a variation mode decomposition model optimized by a Bayesian algorithm, combining and constructing a feature matrix; secondly, constructing a combined model based on a dynamic graph attention network and Transform, representing a space-time dynamic association relationship of a feature matrix, and then predicting and reconstructing each sub-sequence at a missing moment to obtain a complete photovoltaic data set; and finally, introducing a period-trend component decoupling prediction model based on a variational inference principle, decomposing the photovoltaic power into a periodic component and a trend component, respectively predicting the periodic component and the trend component, and reconstructing the periodic component and the trend component, thereby improving the photovoltaic prediction precision. According to the invention, the authenticity and prediction precision of photovoltaic data can be effectively improved, and powerful support is provided for new energy consumption and safe operation of a power system.

The invention discloses a foundation pit engineering emergency disposal system combined with safety system evaluation and a response method, the system comprises a multi-source data acquisition module, a safety system evaluation module, an intelligent emergency disposal module and a recovery optimization module, and each module forms a closed-loop architecture. The multi-source data acquisition module acquires full-cycle data; the safety system evaluation module calculates a comprehensive weight and a risk value through a combinatorial algorithm, and quantifies a safety level; the intelligent emergency disposal module starts a grading scheme based on an evaluation result and optimizes a decision based on a BIM model; and the recovery optimization module optimizes the parameters of the feedback system. According to the response method, efficient disposal is achieved through data collection, grade judgment, grading response and redisk updating. The method improves the risk identification efficiency and judgment accuracy, optimizes the resource allocation, reduces the disposal cost, adapts to a complex foundation pit scene, and guarantees the safety of construction and the surrounding environment.

The invention provides a collaborative optimization method for working parameters of a wheel loader, which comprises the following steps of: firstly, dividing the running distance of the loader in a stepping manner to ensure that the distance of each step is equal, then accurately calculating a single-stepping cost function and a stepping time length in each stepping state by adopting a dynamic programming-Brench combinatorial algorithm and combining an initial weight factor, and finally calculating the working parameters of the wheel loader according to the single-stepping cost function and the stepping time length. And accumulating and calculating the total running time of the loader. And if the time length is different from the preset time length, dynamically adjusting the weight factor through a rooting algorithm until a preset condition is met. According to the process, collaborative optimization of the driving speed and the energy management strategy is achieved, the charging and discharging rate and frequency of the battery are effectively reduced, and therefore battery aging is slowed down. Compared with an existing electro-hydraulic parallel hybrid drive system, the energy utilization efficiency is remarkably improved, the service life of a battery is greatly prolonged, and the overall economical efficiency and environmental protection performance of the loader are improved.

The invention provides a map model construction method and device. The method comprises the following steps: in the advancing process of the mapping mobile equipment, acquiring multiple pieces of inertial navigation data, multiple pieces of point cloud data of a target environment and multiple images of the mapping mobile equipment; according to the inertial navigation data corresponding to each piece of point cloud data, processing each piece of point cloud data by using a combinatorial algorithm so as to convert each piece of point cloud data into a global coordinate system and integrate the point cloud data to obtain an initial map model; performing ground segmentation on the initial map model to obtain multiple pieces of ground point cloud data; plane fitting is carried out on the multiple pieces of ground point cloud data to obtain the ground, and the ground is marked in the initial map model; performing target detection on each image to obtain a detection result of each image, and screening out an image of which the detection result is a non-static object from the plurality of images; and deleting the point cloud data corresponding to the image of which the detection result is the non-static object in the initial map model to obtain a final map model.