298results about How to "Improve discrimination" patented technology

The invention provides an unmanned aerial vehicle multi-mode remote sensing image target detection method and device based on a hybrid Mamb-CNN network. The method comprises the steps of obtaining a visible light image and a thermal infrared image to be detected; inputting the visible light image and the thermal infrared image into a target detection network to obtain a remote sensing detection result; the target detection network is constructed based on a twinborn feature extraction network, the twinborn feature extraction network comprises a visible light branch network, an infrared branch network, a cross-modal fusion module, a neck layer and a detection head, the visible light branch network extracts visible light local features in a visible light image, the infrared branch network extracts infrared local features in a thermal infrared image, and the cross-modal fusion module carries out cross-modal fusion; the cross-modal fusion module fuses the visible light local features and the infrared local features to obtain multi-scale features, the neck layer is based on the multi-scale features to obtain global fusion features, and the detection head is based on the global fusion features to obtain remote sensing detection results, so that the precision and robustness of multi-modal target detection are enhanced.

The invention discloses a passive unsupervised domain adaptive medical image segmentation method and device based on pseudo labels, and relates to the technical field of computers, and the method comprises a supervised source domain model training stage, an unsupervised adaptive target domain model training stage and a segmentation stage. The method specifically comprises the following steps of: performing pixel-level self-marking on a source domain medical image by applying pseudo-tag pixel-level uncertainty estimation to generate a reliable pseudo-tag; and on this basis, a strong and weak enhanced teacher-student target domain model is used, and a high-quality pseudo tag is fully utilized to train a target domain medical image, so that the network adapts to representation specific to the target domain medical image. According to the method, unsupervised segmentation is carried out on the target domain data graph on the premise that the source domain model and the target domain unmarked data are known, source domain data do not need to be accessed, the features of the source domain model can be reasonably utilized, and the problem existing in SFUDA can be well relieved; and the accurate segmentation capability on the target domain is still maintained under the condition that less data is available compared with the UDA method.

The invention relates to a multi-article identification and distinguishing method, system and device and a medium. The method comprises the steps of obtaining multi-view image data of articles in a target area; performing object detection and instance segmentation on the multi-view image data by using a deep learning algorithm to obtain preprocessed image data; acquiring a first spatial position of the object in the target area by using a depth sensor, and performing spatial correction by combining the preprocessed image data to obtain depth information of the object in the target area; in combination with the preprocessed image data and depth information, performing secondary positioning on articles in the target area through a multi-modal data fusion algorithm, optimizing the distinguishing between the articles in the target area by using spatial correlation, confirming the identity information of each article in the target area, and generating an identification result; and matching the identification result with the article state in a preset database to generate a matching result, and feeding back the matching result to the user side. The method has the effect of improving the recognition precision of the borrowing machine.

The invention provides a remote sensing image target dynamic detection method based on a multi-kernel, and belongs to the field of remote sensing image detection.The method comprises the steps that a remote sensing image is collected, a YOLOv8 model is optimized, information extraction is conducted on the remote sensing image through a multi-kernel-based cross-stage feature fusion module in a backbone network of the optimized YOLOv8 model, and the remote sensing image is obtained; obtaining feature maps of different scales; performing dynamic up-sampling on the feature map by using a neck network, outputting an up-sampled feature map in response to the neck network, adding a dynamic detection head, and performing optimization and feature fusion on the up-sampled feature map by using an attention mechanism to obtain a fused feature map; and classifying the feature map to obtain a classified target detection result. According to the method, the problems of insufficient experimental effect on a multi-category data set, large number of small targets, large scale change and dense distribution of remote sensing image targets and detection conditions under a complex background in the prior art are solved.

The invention discloses a personalized federal confrontation cross-modal hashing method based on double prototypes, which mainly comprises the following steps of: clustering local class-level prototypes uploaded by a client at a server side to obtain a clustering prototype, averaging to obtain an unbiased prototype, and according to gradient change, obtaining an unbiased prototype; generating personalized hierarchical weights for the client network using the super network; the client constructs an adversarial learning process by using approximate hash codes and clustering prototypes of the two modal networks, keeps the consistency between an unbiased prototype and a local class-level prototype, and further embeds the unbiased prototype into a training process of a client model; after local training is completed, all network parameters and local class-level prototypes of the client side are uploaded to the server; and after the last communication round is completed, generating a to-be-retrieved hash code based on the final client parameter, and providing an original data retrieval result according to the original data position information corresponding to the hash code with the minimum distance.

The invention discloses a multi-scale feature fusion fault prediction method for a high-speed rail bearing. The method comprises the following steps: acquiring a fault vibration signal of the high-speed rail bearing through a vibration signal comparison technology; noise and interference signals of the high-speed rail bearing fault original vibration signals are removed through an adaptive filter; performing multi-level decomposition on the vibration signal by using wavelet packet decomposition, extracting sub-band signals of different frequency bands, calculating instantaneous frequency and energy distribution of the sub-band signals by using Hilbert-Huang transform, and forming a time-frequency-energy three-dimensional feature map; carrying out weight distribution on each feature dimension in the three-dimensional feature map by adopting a self-adaptive weighting algorithm; constructing a training data set through the time-frequency-energy three-dimensional feature map and the fault type label; and training the long short-term memory network LSTM by using the training data set, constructing a fault prediction model, and performing fault prediction on the high-speed rail bearing. According to the invention, accurate prediction of the high-speed rail bearing fault is realized, and the accuracy of fault detection is improved.

The invention relates to the technical field of signal encryption, in particular to an aviation data bus signal encryption method and system based on a field programmable gate array (FPGA), which comprises the following steps of: based on input three-dimensional tensor signal data, extracting slices from a time index of an aviation data bus according to a fixed interval, progressively sequencing amplitudes of signal channels, and then segmenting; according to the method, through processing logic of matrix decomposition and feature curve generation, the feature extraction and dimension reduction process is optimized, the distinction degree and the expression capacity of feature information are improved, the randomness and the anti-cracking capacity of a key are enhanced through a key generation mode combining a dynamic disturbance factor and an encryption parameter, and the safety of signal transmission is guaranteed; through combination of amplitude superposition and frame-by-frame storage, dynamism and high efficiency of a signal encryption process are realized, signal tampering and eavesdropping risks are reduced, and through feature reconstruction and point-by-point matching processing in a decryption stage, accurate restoration of signal amplitude is ensured, and data integrity and anti-interference capability are guaranteed.

The invention discloses a multi-factor network security authentication method and system based on an SDN (Software Defined Network). The method comprises the following steps: constructing a multi-dimensional authentication space comprising an identity feature dimension, a behavior feature dimension and an environment feature dimension in an SDN controller; decomposing the eigenvalues into deterministic sequence components and random sequence components by adopting a dynamic sequence decomposition method, and calculating the correlation degree of the eigenvalues among dimensions; according to the method, the stability index is calculated based on the feature stability evaluation function, the fusion authentication decision function is constructed to carry out authentication decision, safe and reliable network authentication is achieved through the technical means of multi-dimensional feature fusion analysis, dynamic sequence decomposition, self-adaptive decision and the like, and the method has high practical value.

The invention discloses an elevator fault prediction method and system based on sound and vibration fusion, and the method comprises the steps: carrying out the weighted fusion of a vibration signal and a sound signal according to a signal-to-noise ratio, and then combining with a feature matrix composed of a wavelet packet transformation normalized energy value, a time domain and a frequency domain, applying a Sodat swarm algorithm to an optimization support vector machine model, and carrying out the prediction of an elevator fault. And carrying out feature classification on the feature matrix by using the model. According to the invention, the accuracy of fault prediction can be improved, and the risk of sudden faults is reduced, so that the maintenance efficiency is optimized and the fault rate is reduced.

The invention relates to the technical field of single cell data processing, in particular to a database visualization method based on single cells. The method comprises the following steps: acquiring a single cell data set; standardizing the single cell data set to obtain standardized single cell data; performing high-dimensional feature extraction on the standardized single-cell data to obtain single-cell high-dimensional feature data; and performing nonlinear dimension reduction processing on the single-cell high-dimensional feature data to obtain single-cell dimension reduction data. Performing data clustering on the single cell dimension reduction data to obtain single cell clustering data; performing gene differential expression extraction on the single cell clustering data to obtain differential expression gene data; multi-modal data fusion analysis is carried out on single cell data through a data processing technology, a feature mapping technology and a visualization technology; and dynamic weight division is carried out on the single cell database, so that the effect of visualization of the single cell database is enhanced.

The invention discloses a low-slow-small unmanned aerial vehicle detection method based on acoustic fusion features, relates to an unmanned aerial vehicle detection method, and belongs to the field of signal processing technologies and deep learning. The objective of the invention is to solve the problem that the traditional unmanned aerial vehicle detection method mainly depends on radar, vision, infrared and other technologies, but the detection accuracy of a low-speed small unmanned aerial vehicle is low in a complex urban environment. The method comprises the following steps: establishing an unmanned aerial vehicle audio signal data set in an urban environment; constructing an unmanned aerial vehicle detection network model; taking the unmanned aerial vehicle audio signal in the urban environment as the input of the unmanned aerial vehicle detection network model, and taking the detection result as the output of the unmanned aerial vehicle detection network model; the detection result is whether the unmanned aerial vehicle exists; obtaining a trained unmanned aerial vehicle detection network model; and inputting an audio signal in a to-be-detected urban environment into the trained unmanned aerial vehicle detection network model, and outputting a detection result by the trained unmanned aerial vehicle detection network model.

The invention discloses a vehicle re-identification method, device, equipment and product based on multi-modal pre-training. The method comprises the following steps: acquiring vehicle data of a target vehicle; wherein the vehicle data at least comprises one of vehicle image data or text description information of the target vehicle; performing feature extraction on the vehicle data by adopting a trained multi-modal vehicle re-identification model to obtain final features of the vehicle data, and outputting information of the target vehicle according to the final features; wherein the trained multi-modal vehicle re-identification model comprises a pre-trained visual converter model, a pre-trained text graph model and a visual converter main network. According to the method, multiple query modes and text information embedding can be supported, capturing of image text information and understanding of image semantics are improved, the robustness and discrimination of extracted features are enhanced through pre-training, and therefore the vehicle re-recognition performance is improved.

The invention discloses a method for acquiring an optimal grabbing pose of a plug seedling. The method depends on a two-finger clamping jaw of graded transplanting equipment. The two-finger clamping jaw is positioned above the hole tray; the method comprises the following steps: 1, representing a grabbing pose: # imgabs0 # 2, constructing a data set: shooting a hole tray image from a vertically downward visual angle; the shot images are segmented and zoomed according to plug grids, and pixel-level capture pose labeling is carried out on each image; 3, training a neural network: inputting a required original image into the real-time grabbing detection network, and outputting the grabbing confidence degree, the grabbing angle and the grabbing width of each pixel point in the image by the real-time grabbing detection network; according to the method, the optimal grabbing pose of each seedling in the plug is obtained by fusing machine vision and deep learning, the seedlings are grabbed based on the optimal grabbing poses, leaves and stalks of the seedlings can be effectively avoided, the damage rate in the plug seedling grabbing process is reduced, and the survival rate of the transplanted seedlings is increased.

The invention discloses a chemical raw material detection method and system based on an adversarial network, a medium and equipment, and relates to the technical field of power demand response. The method comprises the following steps: acquiring to-be-detected chemical raw material data; a chemical raw material detection model is constructed, the chemical raw material detection model is trained, and the training steps comprise: obtaining a known chemical raw material data set, learning chemical raw material data based on a superposition state of quantum bits to construct a hidden space, and expanding the chemical raw material data set by using an adversarial network with the hidden space; and training a chemical raw material detection model by using the expanded chemical raw material data set, and detecting the to-be-detected chemical raw material data by the detection model to obtain a chemical raw material identification result. According to the method, the chemical raw material data is expanded through the generative adversarial network based on the multi-quantum superposition state, the problem that the model generalization ability is poor due to insufficient training samples is effectively solved, and the accuracy of the detection result is improved.

The invention belongs to the field of drug design, and particularly discloses a virtual screening method of a small-molecule inhibitor targeting YTHDF1 protein, which comprises the following steps: acquiring a YTHDF1 protein structure, and carrying out homologous modeling to generate various conformation proteins; small molecules combined with the YTHDF1 protein are obtained, bait molecules are generated, and ligand molecules are formed; a ligand molecule is in butt joint with a binding site on the YTHDF1, and the compound is characterized through PLEC fingerprints; the method comprises the following steps: constructing a specificity scoring function by using a machine learning algorithm, carrying out scoring screening on a compound to be screened and a compound butted with YTHDF1 molecules, carrying out affinity prediction, and screening out the small-molecule inhibitor targeting YTHDF1. According to the virtual screening method provided by the invention, the YTHDF1 targeting molecules with high affinity can be accurately screened on a large scale, candidate molecules are provided for research and development of medicines for treating YTHDF1 related diseases, and a foundation is laid for clinical tests and medicine optimization.

The invention provides an infrared weak and small target detection method based on a semantic guidance fusion network. An encoder extracts image semantic features through a convolutional neural network; shallow semantic features are guided by using spatial distribution of deep semantic features through a semantic-guided adaptive fusion module, and target pixels are segmented through a segmentation detection head. According to the method, the problem of deep semantic feature target space information loss caused by downsampling and the problem that target backgrounds are similar and difficult to distinguish due to complex backgrounds can be relieved, so that model false alarms are reduced while space detail information is enhanced. The feature maps of different semantic levels are effectively fused, and it is ensured that the fused feature maps have rich semantic information and accurate spatial positioning information. And the detection performance of the model in a complex scene is improved.

The invention relates to a firmware identification method of IoT (Internet of Things) equipment, in particular to an online IoT equipment firmware identification method based on representation learning, which comprises the following steps: firstly simulating firmware of target IoT equipment to obtain the running state of the target IoT equipment, and then collecting an embedded webpage as analysis input through a GET request of HTTP (Hyper Text Transport Protocol); a fusion feature is generated through a deep feature fusion extraction module, and a hard mining cosine triple center loss mechanism is introduced to optimize the fusion feature; in the model training stage, a full-connection neural network is used as a classifier, and features are mapped to corresponding labels; after training is completed, feature representation learned by the framework is analyzed through a clustering algorithm, and similar samples are recognized and clustered; and according to a clustering result, selecting a representative page to query the real Internet of Things equipment, and determining a firmware version through a voting mechanism. The method provided by the invention is obviously superior to the most advanced scheme in the aspect of firmware identification, and the identification accuracy has extremely high superiority.

The invention discloses a character string data read optimization method based on a B + tree and a learning index. A learning index generated based on a machine learning (ML) model focuses on integer data of a fixed size, but is lack of consideration for character strings of variable lengths and is poor in performance in character string data. In order to solve the problem, the invention provides a character string slicing method which can convert a character string with a variable length into an integer with a fixed size for prediction and extract a common prefix from a character string key to reduce a model fitting error. According to the method, the characteristics of character string data are fully considered, complex multi-dimensional linear regression is simplified into one-dimensional linear regression, and the model calculation overhead is reduced. Besides, the invention provides a self-adaptive strategy to select a proper index to manage the calculation cost, the method for dynamically selecting the index nodes can utilize the advantages of the two index types to the maximum extent, and the calculation overhead is effectively reduced while the index efficiency is ensured.

The invention discloses a pathology grading method and system based on HER2 immunohistochemical pictures, belongs to the technical field of immunohistochemical (IHC), bioinformatics and medical diagnosis, is used for carrying out pathology grading on a test sample, and comprises the following steps: collecting and processing a plurality of original HER2 immunohistochemical pictures of the test sample, and obtaining a sample picture set for pathology grading; reading a sample picture set and converting an RGB color space in the sample picture set into an HSV color space; determining the staining intensity and range of the cell membrane based on the immunohistochemical picture in the sample picture set converted to the HSV color space; extracting and determining the membrane rate based on the characterization of the dyeing intensity and range of the cell membrane, and carrying out test treatment on the membrane rate to obtain the membrane rate of the test set; the film rate is the ratio of the pixel where the film is located to the background pixel; evaluating the test sample based on the corrected membrane rate data, and performing pathological grading on HER2 expression in the test sample based on a grading system. The invention further discloses a system, electronic equipment and a computer readable storage medium.

The present application relates to the technical field of apple tree pest image processing, and specifically to a pest segmentation detection method for apple trees, the method comprising: obtaining an apple tree pest image; obtaining feature points in the apple tree pest image at various scales; obtaining auxiliary directions according to the gradient changes of each feature point in different directions, screening suspected pest feature points according to the number of auxiliary directions of all feature points; obtaining the pest probability of the suspected pest feature point according to the grayscale change difference and local density of each suspected pest feature point in different auxiliary directions; screening pest feature points according to the pest probability of the suspected pest feature point in the apple tree pest image at all scales, and identifying the location of the pest in the apple tree pest image to be detected according to all pest feature points. The present application can improve the accuracy of pest segmentation detection.

The invention discloses a global and local feature fusion-based semi-supervised video target segmentation method and a global and local feature fusion-based semi-supervised video target segmentation system. According to the implementation scheme, the method comprises the following steps: firstly, acquiring required data sets including image and video data sets and corresponding segmentation labels, expanding the image data set, and sampling the video data set to obtain a training data set for training a segmentation model; and secondly, designing a segmentation model, constructing a loss function to train the segmentation model, and finally performing video target segmentation by using the trained segmentation model. According to the invention, a local-global branch fusion module is designed to carry out global context modeling and local detail enhancement on features used for non-local matching, so that the distinguishing capability of the model for distinguishing similar features is improved; therefore, the network can effectively solve the problem of wrong feature matching caused by similar interferents in a semi-supervised video target segmentation task. On this basis, a target scale self-adaptive down-sampling layer is further combined, and information of the same target under different scales is captured through the layer, so that the self-attention calculation complexity is reduced while the feature distinguishability is improved, and the balance between the model segmentation performance and the reasoning prediction efficiency is realized.

The invention provides a soil image soil type identification method based on multi-scale feature fusion, and the method comprises the steps: S1, obtaining a sample soil image, and carrying out the preprocessing of the sample soil image; s2, constructing an MAFL-SoilMobileNet model based on multi-scale feature fusion and a multi-scale dilatation attention mechanism, and inputting the sample image into the MAFL-SoilMobileNet model for training; s3, acquiring a soil image to be identified, preprocessing the soil image to be identified, and inputting the soil image to be identified into the trained MAFL-SoilMobileNet model to obtain a soil type identification result; according to the method, the key features of soil type identification in the soil image can be extracted, the distinction degree of the soil type identification features is enhanced, dynamic attention to a small number of soil type samples can be improved, and the discriminative feature information learning ability of an identification model can be improved.

The invention provides an emotion recognition method based on electroencephalogram feature fusion and a double-stage attention mechanism, and the method comprises the following steps: A, electroencephalogram signal processing: carrying out the preprocessing of an electroencephalogram signal; and B, double-stage attention feature fusion: in each selected frequency band, adopting a double-stage attention mechanism to fuse the electroencephalogram features, and generating fusion features for emotion classification. And C, double-branch feature extraction: performing double-branch 3D convolution processing on the fused features, extracting multi-scale space-spectral time features, and splicing the multi-scale space-spectral time features along a channel dimension to form uniform features. And D, classification and output: inputting the unified features into a classifier, and generating an emotion category prediction result through a flattening layer and a full connection layer. According to the method, the difference entropy, the power spectrum density and the difference entropy asymmetry feature are fused through unified three-dimensional feature representation, a double-stage attention mechanism is introduced, and high-accuracy emotion recognition is achieved.

The invention discloses a fully automatic relay testing machine, comprising a frame, a withstand voltage insulation test station is arranged on the top of the frame, an electrical parameter comprehensive test station is arranged on the frame below the withstand voltage insulation test station, a test station lifting device is arranged on a workbench below the electrical parameter comprehensive test station, the workbench is arranged on the frame, a circulating feeding device is arranged below the test station lifting device, a defective product grabbing and storing device is arranged at the output end of the test station lifting device, the defective product grabbing and storing device is arranged above the circulating feeding device, and a qualified product transfer storage device is arranged at one end of the defective product grabbing and storing device away from the test station lifting device. The present invention will facilitate the required data testing of relays by arranging the withstand voltage insulation test station and the electrical parameter comprehensive test station, and will facilitate the identification and sorting of defective products and qualified products of relays in combination with the defective product grabbing and storing device and the qualified product transfer storage device, thereby ensuring product quality, and will achieve the effect of automatic transportation by utilizing the circulating feeding device.

The invention discloses a small sample class increment gait recognition method in a multi-activity scene, and provides a gait recognition framework based on hybrid prototype enhancement in order to solve the problems existing in small sample class increment gait recognition in the multi-activity scene. First, the framework generates a hybrid prototype by introducing an ancillary activity tag, which prototype is more generalizable than a common prototype. And secondly, the prototype is adjusted through a selective prototype enhancement module based on an attention mechanism, so that the characterization capability and the discrimination capability of the prototype are improved. And finally, experimental verification is carried out on the public gait data set USC-HAD and the gait data set CDUT-AG collected by the user, and the result shows that the proposed method achieves the best effect on the small sample class increment gait recognition problem under the multi-activity scene, the problem of disastrous forgetting is effectively solved, and the effectiveness of the proposed method is verified.

The invention discloses a GCB arc contact electrical life monitoring method based on multi-source signal fusion sensing, and relates to the technical field of power system monitoring, and the method comprises the steps: obtaining multi-source signal data in the action process of a circuit breaker; performing preprocessing and feature extraction on the multi-source signal data to generate feature parameters of each signal; constructing a multi-information fusion feature set based on the feature parameters; constructing a multi-layer perceptron model, and training the multi-layer perceptron model by using the multi-information fusion feature set; and inputting the preprocessed multi-source signal data to be detected into the multi-layer perceptron model, outputting a classification result of the ablation degree of the GCB arc contact, and completing the monitoring of the electrical life of the GCB arc contact. According to the invention, through comprehensive utilization of the multi-source signal data and construction of the multi-information fusion feature set, high-precision classification of the ablation degree of the circuit breaker arc contact is realized, and the accuracy of electrical life monitoring is effectively improved.

The invention discloses an emotion recognition method based on voice recognition, and belongs to the technical field of voice processing. According to the invention, three observation windows with different lengths are arranged to slide on a to-be-recognized voice signal so as to obtain three pronunciation segment sets; calculating a pronunciation energy value at each moment in the pronunciation segment set, and constructing a pronunciation energy vector; calculating a pronunciation energy intensity value according to a ratio of the pronunciation energy vector to a daily average pronunciation energy value, and forming a pronunciation intensity weight vector; extracting a pronunciation spectrum frequency continuous value at each moment, and constructing a pronunciation spectrum frequency weight vector; and carrying out feature extraction on the pronunciation energy vector by adopting a dual-channel feature enhancement module, and carrying out feature enhancement in combination with the pronunciation spectrum frequency weight vector and the pronunciation intensity weight vector to finally obtain a voice emotion feature. And finally, carrying out classification processing on the three voice emotion features by utilizing a classifier so as to identify emotion types. According to the method, the accuracy of emotion recognition is remarkably improved through multi-dimensional feature extraction and enhancement.

The present invention relates to the field of artificial intelligence technology, and discloses a network security situation awareness method based on artificial intelligence, including obtaining network security data; using a fully connected neural network based on dynamic population evolution optimization to extract features from network security data to obtain a first feature; using an autoencoder neural network based on contrast entropy to reduce the first feature to obtain a second feature; using a fractional-order neural network based on complexity perception to construct a classifier model, and using the second feature to train the classifier model; and using the trained classifier model to classify network security situation awareness. The present invention improves the ability to distinguish complex network security situations, can achieve more accurate classification effects, and enhances the applicability of the model in network security data and the accuracy of classification.

The invention relates to the technical field of coal mine separation, in particular to a transmission type intelligent dry separator and an identification method. The transmission type intelligent dry separator comprises a conveying belt, a camera, a linear light source and a recognition system, the conveying belt is a light-transmitting belt and used for conveying to-be-separated minerals, the camera is located on the upper side of the conveying belt, the position, corresponding to the camera, of the conveying belt is a recognition area, and the linear light source is located in the recognition area. The camera is used for shooting an image of the identification area. According to the mineral sorting device, the camera is arranged above the conveying belt, the first linear light source is located below the recognition area and is opposite to the camera, and the conveying belt is a light-transmitting belt, so that light of the first linear light source penetrates through the conveying belt to light minerals to be sorted in the recognition area on the conveying belt; the camera shoots the to-be-sorted minerals in the recognition area, the recognition system receives the images shot by the camera and obtains the light transmittance of each to-be-sorted mineral from the images, and the distinguishing degree is higher.

The invention relates to the technical field of data processing, in particular to a fitness personal privacy data encryption protection method, which comprises the following steps: acquiring fitness personal privacy data of a plurality of users; determining a plurality of user categories according to the fitness personal privacy data of the plurality of users; according to preset hash function processing strategies corresponding to the user types, the fitness personal privacy data corresponding to the user types are processed, hash codes corresponding to the fitness personal privacy data of the users are obtained, and the preset hash function processing strategies corresponding to the user types are different; and transmitting the hash code to the target terminal to realize encryption protection of the private data of the fitness individual. According to the method and the device, the fitness individual privacy data corresponding to different user categories can be processed according to different preset hash function processing strategies to obtain hash codes with different lengths, so that the probability of hash collision is reduced, and the accuracy and the reliability of the fitness individual privacy data subjected to encryption protection are further improved.