Portable blood cell real-time classified counting system and method based on big data algorithm

Abstract

The invention discloses a portable blood cell real-time classified counting system and method based on a big data algorithm, and relates to the technical field of biomedical engineering. Aiming at the problems of insufficient portability, poor real-time performance and difficulty in complex cell feature recognition of traditional blood cell classification counting equipment, the system realizes fusion acquisition of high-resolution cell images and dynamic spectral information through a multi-modal image acquisition module; according to the system, intelligent preprocessing and multi-scale feature extraction of cell images are completed by using a self-adaptive preprocessing unit and a lightweight feature extraction engine; according to the system, cell classification algorithm optimization based on meta-learning and clinical knowledge is realized by means of a big data enhancement classifier; according to the system, an intelligent interaction terminal and an ultra-low power consumption hardware architecture are combined, so that real-time visualization of a cell classification result and miniaturization integration of equipment are realized; and finally, in a portable scene, the accuracy, the real-time performance and the equipment usability of blood cell classification counting are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of biomedical engineering technology, and in particular to a portable real-time blood cell classification and counting system and method based on a big data algorithm. Background Art

[0002] In the field of clinical blood testing, blood cell classification and counting is one of the core links in disease diagnosis and treatment efficacy evaluation. Although traditional blood cell analysis equipment such as large flow cytometers and fully automatic blood cell analyzers have high detection accuracy, they generally have problems such as large equipment size, dependence on fixed laboratory environments, and high testing costs. They are difficult to meet the real-time detection needs in primary care, bedside diagnosis (POCT), and portable scenarios of field emergency. In addition, existing technologies face the following challenges in complex cell morphology recognition, multimodal data fusion, and low-power hardware integration: 1. Portability and real-time bottlenecks Traditional equipment requires complex optical systems and large computing units, making miniaturization impossible. Peripheral blood testing often relies on manual smear microscopy, which is not only time-consuming, requiring over 10 minutes to analyze a single sample, but also relies on the physician's experience and can result in significant subjective errors. The need for real-time testing in portable scenarios, such as rapid emergency triage and community-based chronic disease management, urgently requires solutions that overcome the limitations of device size and processing speed.

[0003] 2. Difficulties in Recognizing Complex Cell Features Blood cell morphology is highly heterogeneous. Abnormal cells, such as leukemia cells and atypical lymphocytes, exhibit subtle differences in nucleocytoplasmic structure and granule distribution compared to normal cells. Traditional classification algorithms based on handcrafted features, such as geometric morphology and grayscale statistics, struggle to effectively capture these underlying features. Furthermore, different staining methods, such as Wright's and Giemsa stains, can cause shifts in cell color and texture. Traditional models lack the ability to adaptively correct for these staining differences, resulting in classification errors exceeding 8%.

[0004] 3. Inefficient multimodal data fusion Existing portable devices often analyze single-modal images, such as RGB microscopy images, ignoring the value of spectral information in characterizing cellular composition. Dynamic spectral data, such as near-infrared (NIR) spectra, can reveal in-depth information about hemoglobin concentration and the absorption properties of intracellular substances. However, the complexity of algorithms for temporal and spatial alignment and feature fusion of multimodal data makes traditional methods difficult to implement in real time on edge devices.

[0005] 4. The contradiction between low power consumption and high performance Portable devices are limited by battery capacity and heat dissipation capabilities. Traditional deep learning models like ResNet have high computational complexity, consuming over 200mW per frame, making them inadequate for continuous detection. Lightweight model design requires a balance between accuracy and computing power. Existing lightweight networks like MobileNet are inadequate for extracting multi-scale features of blood cells, such as nuclear lobulation and granular detail, resulting in reduced feature representation capabilities.

[0006] 5. Lack of Integration of Small Sample Learning and Clinical Knowledge Sample labeling for rare blood cells, such as blasts and abnormal promyelocytes, is costly. Traditional supervised learning models generalize poorly in small sample sizes, with recognition accuracy rates below 70%. Clinical knowledge, such as nuclear-cytoplasmic ratio thresholds and staining sensitivity patterns, often exists in the form of rules, making it difficult to directly embed them into deep learning models. This results in algorithms lacking medical prior constraints and can easily lead to classification decisions that violate pathological logic. Summary of the Invention

[0007] To address the above issues, the present invention proposes a portable real-time blood cell classification and counting system and method based on big data algorithms. Through the innovation of multimodal image acquisition, lightweight feature extraction, meta-learning and clinical knowledge integration, and ultra-low power hardware architecture, it breaks through the bottlenecks of traditional technologies in portability, real-time performance, complex feature recognition, and small sample adaptability, and provides high-precision, easy-to-operate blood cell analysis solutions for primary care and bedside testing scenarios, significantly improving detection efficiency and diagnostic accuracy. In order to achieve the above technical effects, the present invention adopts the following technical solutions: A portable real-time blood cell classification and counting system based on big data algorithms, including: Multimodal image acquisition module; integrated low-power microscope camera with a resolution of ≥1920×1080 and a dynamic spectrum fusion light source system; Adaptive pre-processing unit; built-in edge computing chip with computing power ≥ 2TOPS; Lightweight feature extraction engine; adopts the deformable residual shrinkage network DRSNet architecture, including a dynamic cross-scale convolution group: 3×3 depthwise separable convolution, 5×5 deformable convolution, and 7×7 dilated convolution are deployed in parallel, and the weights of features at each scale are dynamically allocated through morphological attention gating; the nuclear-cytoplasm ratio guided attention mechanism: based on the nuclear-cytoplasm segmentation mask output by the preprocessing module, a spatial channel dual attention gate is constructed, applying a 1.8x gain to the nuclear region feature channel and a 1.5x gain to the plasma region feature channel. The red blood cell interference channel response is attenuated to below 0.3 times, and the white blood cell feature signal-to-noise ratio is increased to 25dB; Big data enhanced classifier; integrating meta-learning incremental training algorithm with clinical knowledge graph; Smart interactive terminal: equipped with a 3.5-inch retina screen and real-time fluorescent marking engine; Ultra-low power hardware architecture: Using three-dimensional stacked heterogeneous integration technology, the image sensor, edge computing chip, lithium battery, and micro heat sink are integrated into a 50×80×30mm³ body. The output end of the multimodal image acquisition module is connected to the input end of the adaptive preprocessing unit, the output end of the adaptive preprocessing unit is connected to the input end of the lightweight feature extraction engine, the output end of the lightweight feature extraction engine is connected to the input end of the big data enhancement classifier, the output end of the big data enhancement classifier is connected to the input end of the intelligent interactive terminal, and the output end of the intelligent interactive terminal is connected to the information input end of the ultra-low power consumption hardware architecture.

[0008] As a further technical solution of the present invention, the dynamic spectrum fusion light source system includes an LED array with a wavelength of 450-650nm and an adaptive spectrum modulation circuit. It can synchronously collect RGB three-channel and near-infrared NIR spectral images through time-sharing multiplexing technology under a light intensity of ≤5μW / mm². In combination with a nano-scale optical metasurface lens group with a dynamically adjustable focal length of 5-50mm and an aberration of ≤0.5μm, it can achieve in situ observation of peripheral blood red blood cell hemolysis rate ≤2% and white blood cell nuclear cytoplasm contrast improvement of 60%.

[0009] As a further technical solution of the present invention, the adaptive preprocessing unit includes: Generative Adversarial Network Noise Suppression Module: This module builds a noise image mapping model based on the CycleGAN architecture, generating adversarial samples that match the noise distribution of the input image in real time. Through feature-level subtraction, it improves the salt and pepper noise suppression rate to 98%, compresses the Gaussian noise standard deviation to ≤5, and retains ≥95%; Morphology-guided multi-scale enhancement module: Dynamically adjusts the enhancement strategy based on the prior knowledge of white blood cell morphology and uses adaptive structural element expansion in the nuclear region, increasing the nuclear-cytoplasmic grayscale difference from 30% in traditional methods to 55%. At the same time, dual-threshold CLAHE satisfies the relationship: nuclear region threshold = global threshold × 1.5, plasma region threshold = global threshold × 0.8, thus avoiding over-enhancement of background noise.

[0010] As a further technical solution of the present invention, the big data enhanced classifier includes: Small Sample Rapid Adaptation Module: Based on the MAML model-independent meta-learning algorithm, it constructs a fast weight update mechanism for rare blood cells, improving recognition accuracy from 70% to 92% through 5-shot learning. The Small Sample Rapid Adaptation Module includes a meta-training data preprocessing unit, a fast weight generator, a clinical prior regularization module, a lightweight meta-gradient optimizer, and a cross-task transfer evaluator. Dynamic classification boundary generator: Accesses the clinical knowledge base, dynamically adjusts the classification hyperplane based on real-time detected cell morphology parameters, and adaptively corrects feature offsets caused by differences in Wright staining / Giemsa staining, ensuring that the classification error under different staining methods is ≤1.5%; the dynamic classification boundary generator includes a clinical knowledge graph construction module, a real-time feature mapping engine, a staining difference adaptive regulator, a dynamic hyperplane adjuster, and a cross-modal validation calibrator.

[0011] As a further technical solution of the present invention, the meta-training data preprocessing unit includes a generative adversarial network (GAN) module, a domain adaptation module, a fully connected layer feature distribution adjustment module, and a training value dynamic adjustment module; The fast weight generator includes a weight initialization module, an accelerated convergence module and a weight generation module; The clinical prior regularization module includes a pathology rule regularization term parameter setting module, a clinical knowledge graph judgment module, a rule regularization term function generation module and a pathology classification module; The lightweight meta-gradient optimizer includes a feature channel screening module, an interference channel identification module, a data dimension deletion module, a dynamic learning rate adjustment module and a computing power improvement module; The cross-task migration evaluator includes a cell image classification module, a self-triggered meta-training module, a different staining simulation module, a chromosome recognition module and a chromosome feature offset module; The output end of the meta-training data preprocessing unit is connected to the input end of the fast weight generator, the output end of the fast weight generator is connected to the input end of the clinical prior regularization module, the output end of the clinical prior regularization module is connected to the input end of the lightweight meta-gradient optimizer; the output end of the lightweight meta-gradient optimizer is connected to the input end of the cross-task transfer evaluator.

[0012] As a further technical solution of the present invention, the working method of the clinical knowledge graph construction module is as follows: through the multi-dimensional pathological feature knowledge base, 100,000+ clinical cell morphology annotation data are integrated to construct a four-dimensional knowledge graph containing nuclear-cytoplasmic ratio (N / C), granule density, number of nuclear lobes, and staining method sensitivity features. The association between cell type morphological parameters and staining methods is established through the graph neural network (GNN), with the number of nodes ≥ 200,000 and the number of edges ≥ 5 million, to achieve a priori modeling of the distribution of rare cell features. The real-time feature mapping engine works by using a federated learning module to aggregate test data from each terminal in real time, automatically updating the knowledge graph with every 100 newly annotated samples, and dynamically adjusting feature association weights through an attention mechanism to ensure that the knowledge base evolves synchronously with the distribution of actual clinical test data. The working principle of the staining difference adaptive regulator is: using the dual-modal feature offset detection module to detect the Mahalanobis distance between the sample features and the corresponding cell types in the knowledge graph, and to identify the feature offset caused by the staining difference in real time: In formula (1), is the feature mean of the cell type in the knowledge graph, is the covariance matrix; when When the staining difference correction process is triggered; a dynamic threshold compensation algorithm is used to automatically apply dynamic gain compensation to the nuclear region feature channel to address the common problem of low nuclear-cytoplasmic contrast in Wright staining; and Gaussian filtering is performed on the granularity feature channel to address the problem of excessively dark particles in Giemsa staining, reducing the parameter measurement error caused by staining differences from 8% in traditional methods to 1.2%; The working principle of the dynamic hyperplane adjuster is: based on the real-time feature vector through the adaptive classification boundary generation algorithm Based on the prior distribution of the knowledge graph, the classification hyperplane is dynamically adjusted through online learning of the support vector machine (SVM). The initial hyperplane is determined by the optimal classification boundary of the cell type in the knowledge graph. Every time 5 cell samples of the same type are detected, the stochastic gradient descent (SGD) is used to update the hyperplane normal vector w and intercept b: In formula (2), is the learning rate, The cross-modal validation calibrator is used to label samples and ensure that the hyperplane always fits the current detection data distribution; the hyperplane is forced to be pulled back to the feasible domain through the projected gradient descent method to avoid generating classification boundaries that violate pathological logic, reducing abnormal classification decisions by 70%; the working principle of the cross-modal validation calibrator is as follows: the color features of the RGB image and the texture features of the NIR image are extracted, and the mutual information of the classification results of the two types of features is calculated. When the mutual information is less than 0.6, it indicates that the difference between the modalities is large, triggering the feature space recalibration process, and fusing the two types of features through the knowledge graph prior knowledge, so that the cross-modal classification consistency is improved to more than 95%. The cross-modal validation calibrator is used to extract the color features of the RGB image and the texture features of the NIR image, and the mutual information of the classification results of the two types of features is calculated. When the mutual information is less than 0.6, it indicates that the difference between the modalities is large, triggering the feature space recalibration process, and fusing the two types of features through the knowledge graph prior knowledge, so that the cross-modal classification consistency is improved to more than 95%.

[0013] As a further technical solution of the present invention, the working method of the MAML model-independent meta-learning algorithm is as follows: S1. Feature extraction: using a pre-trained convolutional neural network to extract features of blood cell images , where x is the input blood cell image, is the grayscale matrix, It is the feature extraction function of the pre-trained convolutional neural network ResNet, and the output is the high-level semantic feature vector of the image; S2. Classifier Adaptation: For newly emerged blood cell types, we use MAML to quickly adjust the classifier parameters W and bias b, combining gradient masking, adaptive learning rate, and feature weighting strategies to improve classification performance and adaptation to new blood cell types. In formulas (3) and (4), W is the weight matrix of the classifier, with a shape of C×d, C is the dimension of the feature vector, which is used to map the feature vector to the classification space; a is the learning rate, which controls the step size of parameter update. When combined with the adaptive learning rate strategy, a can be dynamically adjusted during training; is the loss function The gradient of W and b indicates the direction of parameter update; Support set loss function, usually cross entropy loss, measures the classifier's prediction error for support set samples; S3, real-time classification: using adapted parameters Classify new blood cells in real time: In formula (5), The activation function converts the linear output into a probability distribution for multi-classification tasks. As a further technical solution of the present invention, the intelligent interactive terminal is configured to support: Dynamic marking of abnormal cells: When the edge computing unit outputs the white blood cell classification results in real time, pseudo-color markings are generated synchronously, with a marking delay of ≤10ms; Wireless collaborative diagnosis system: Establishes an encrypted connection with the cloud pathology platform via Bluetooth 5.0, uploads cell morphology feature vectors, supports remote expert consultation, synchronization delay ≤150ms, and local detection data anonymization processing efficiency ≥200 samples / second.

[0014] As a further technical solution of the present invention, the ultra-low power consumption hardware architecture is achieved through a dynamic computing power allocation algorithm: ① the computing power in low-load scenarios is reduced to 0.5TOPS, and the power consumption is ≤50mW; ② when detecting peak loads, NPU hardware acceleration is enabled, the single frame processing time is ≤12ms, and the continuous detection time is ≥10 hours; The working method of the dynamic computing power allocation algorithm is as follows: Modeling the Quantification Model of Cell Image Complexity: Defining the Blood Cell Image Feature Tensor , construct the morphological entropy function: In formula (6), represents the probability of occurrence of k-type cells, and the gradient norm reflects the complexity of the texture; when Trigger low power mode; calculate differential precision modulation function, design dynamic bit width allocation method, In formula (7), Controls the bit width adjustment rate; when the system detects the lobulated features of white blood cells , automatically increase the convolution kernel weight width to 8 bits: In formula (8), W is the original weight matrix with a shape of C×d; is the quantized weight matrix, which is the same shape as W and reduces storage and computation costs through discretization; The quantization range threshold defines the upper and lower bounds at which weights are clipped. ; Weights outside this range will be constrained to the boundary value; To quantize the step size and control the fineness of the discretization, is the number of quantization bits; the spatiotemporal joint optimization acceleration equation, when the NPU activation conditions are met When heterogeneous acceleration is enabled: In formula (9), Transformation coefficients Multiplexing factor for blood cell feature map; To calculate the total number of operations, the unit is ops; NPU main frequency, in Hz, represents the number of clock cycles that the NPU can execute per second; To calculate the complexity coefficient, characterize the average number of clock cycles per operation; NPU computing efficiency reflects the actual utilization rate of NPU. is the total amount of memory access, Memory bandwidth, which indicates the amount of data that can be transferred per second by the memory; is the memory access complexity coefficient, which represents the additional overhead of memory access; by solving the constrained optimization problem: In formula (10), the single frame processing time is guaranteed to be ; Establish a dynamic energy budget model: In formula (11), Enable probability of NPU controlled by sigmoid activation function; is the basic power consumption of the system; is the operating voltage of the NPU, which is usually positively correlated with the frequency; is the operating voltage of the NPU, which is usually positively correlated with the frequency; by solving the HamiltonJacobi-Bellman equation: In formula (12), the cost function , achieving optimal control under 10.5 hours of battery life. As a further technical solution of the present invention, S1, multimodal image acquisition and light source adaptation Peripheral blood is collected to make a single cell layer smear. A low-power microscope camera with a resolution of at least 1920×1080 and a dynamic spectral fusion light source of a 450-650nm LED array are used to simultaneously capture RGB and near-infrared images. In combination with a nanolens assembly, in situ observation of peripheral blood red blood cell hemolysis rates of no more than 2% and a 60% improvement in white blood cell nucleocytoplasm contrast is achieved. S2. Adaptive image preprocessing and noise removal CycleGAN generates adversarial examples to suppress salt and pepper noise and Gaussian noise, achieving a 98% suppression rate for salt and pepper noise and compressing the standard deviation of Gaussian noise to below 5. Simultaneously, combined with prior knowledge of white blood cell morphology, the threshold for nuclear-cytoplasmic region segmentation is enhanced, increasing the nuclear-cytoplasmic grayscale difference to 55%, preserving cell details while reducing background interference. S3. Lightweight network feature extraction and classification decision The Deformable Residual Shrinkage Network (DRSNet) was used to extract multi-scale features in parallel, including 3×3 depthwise separable convolutions, 5×5 deformable convolutions, and 7×7 dilated convolutions. The nuclear-to-cytoplasm ratio was used to guide the attention mechanism to enhance white blood cell features, increasing the signal-to-noise ratio of white blood cell features to 25dB. Combining the meta-learning MAML algorithm with the clinical knowledge graph, the recognition accuracy of rare blood cells was increased from 70% to 92% after just five training passes. The classification hyperplane was dynamically adjusted based on real-time cell morphology parameters to accommodate differences in staining methods, keeping the classification error within 1.5%. S4, real-time marker display and wireless collaborative diagnosis The edge computing unit outputs white blood cell classification results in real time, simultaneously generating pseudo-color markers with a marker delay of less than 10ms, and displays cell types and counts on a 3.5-inch retina screen. It also establishes an encrypted connection with the cloud-based pathology platform via Bluetooth 5.0, uploading cell morphology feature vectors to support remote expert consultations. The synchronization delay is less than 150ms, and the local test data anonymization processing efficiency reaches over 200 samples per second. S5, ultra-low power hardware and dynamic computing power management Using three-dimensional stacked heterogeneous integration technology, the image sensor, edge computing chip, lithium battery and micro heat sink are integrated into a 50×80×30mm³ body. The morphological entropy function Em is used to quantify the complexity of cell images and dynamically allocate computing power: in low-load scenarios, the computing power is reduced to 0.5TOPS, and the power consumption does not exceed 50mW. When detecting peak loads, NPU hardware acceleration is enabled, and the single-frame processing time does not exceed 12ms. Combined with a dynamic energy budget model to optimize the voltage and frequency combination, a continuous detection time of at least 10 hours is achieved.

[0015] Positive beneficial effects The present invention significantly improves the accuracy, real-time performance and usability of blood cell classification and counting through multimodal data fusion, lightweight algorithm architecture and innovative hardware design: integrating a low-power microscope camera and a dynamic spectrum fusion light source system, synchronously collecting high-resolution RGB images and near-infrared NIR spectrum data, and combining with a nano-scale optical metasurface lens group to achieve in situ observation of low hemolysis rate of peripheral red blood cells and high contrast between white blood cell nuclei and cytoplasm. The fusion of spectral and morphological features increases the signal-to-noise ratio of white blood cell features to 25dB, and the accuracy of abnormal cell feature recognition is 22% higher than that of traditional methods. Based on the MAML meta-learning algorithm, the recognition accuracy of rare blood cells after 5 learning cycles is increased from 70% to 92%. The clinical knowledge graph is integrated to construct a four-dimensional knowledge graph, and the classification hyperplane is dynamically adjusted to counteract staining differences. The classification error under different staining methods is ≤1.5%, an 85% reduction compared to traditional methods. The device features a built-in edge computing chip and a deformable residual shrinkage network (DRSNet) architecture, achieving a single-frame full-process processing time of ≤12ms and supporting continuous testing for ≥10 hours. A dynamic computing power allocation algorithm achieves a balance between low power consumption and high performance. Three-dimensional stacked heterogeneous integration technology integrates the device into a 50×80×30mm³ body, weighing ≤200g. A 3.5-inch retina display allows for real-time labeling of abnormal cells, and Bluetooth encrypted transmission supports remote consultation. Suitable for primary care and emergency medical care, the device enables "sample collection and testing immediately," reducing the cost of single-test consumables by over 70%. It supports direct peripheral blood testing, anonymizes edge data to ensure privacy and security, and uses a federated learning mechanism to continuously adapt the algorithm to the distribution of clinical data. This invention provides a high-precision, low-power, and easy-to-use real-time blood cell classification and counting solution, filling the gap in high-end portable blood testing equipment and promoting the development of clinical testing towards precision, immediacy, and accessibility, with significant medical value and socioeconomic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which: Figure 1 This is a schematic diagram of the overall module flow of the portable real-time blood cell classification and counting system based on big data algorithm of the present invention; Figure 2 This is a flow chart of a small sample rapid adaptation module of a portable real-time blood cell classification and counting system based on a big data algorithm of the present invention; Figure 3 This is a flow chart of a dynamic classification boundary generator of a portable real-time blood cell classification and counting system based on a big data algorithm of the present invention; Figure 4 These are the improved working steps of the portable real-time blood cell classification and counting method based on big data algorithm of the present invention. DETAILED DESCRIPTION

[0017] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0018] like Figures 1-4 As shown, a portable real-time blood cell classification and counting system based on big data algorithm is characterized by: Multimodal image acquisition module; integrated low-power microscope camera with a resolution of ≥1920×1080 and a dynamic spectrum fusion light source system; Adaptive pre-processing unit; built-in edge computing chip with computing power ≥ 2TOPS; Lightweight feature extraction engine; adopts the deformable residual shrinkage network DRSNet architecture, including a dynamic cross-scale convolution group: 3×3 depthwise separable convolution, 5×5 deformable convolution, and 7×7 dilated convolution are deployed in parallel, and the weights of features at each scale are dynamically allocated through morphological attention gating; the nuclear-cytoplasm ratio guided attention mechanism: based on the nuclear-cytoplasm segmentation mask output by the preprocessing module, a spatial channel dual attention gate is constructed, applying a 1.8x gain to the nuclear region feature channel and a 1.5x gain to the plasma region feature channel. The red blood cell interference channel response is attenuated to below 0.3 times, and the white blood cell feature signal-to-noise ratio is increased to 25dB; Big data enhanced classifier; integrating meta-learning incremental training algorithm with clinical knowledge graph; Smart interactive terminal: equipped with a 3.5-inch retina screen and real-time fluorescent marking engine; Ultra-low power hardware architecture: Using three-dimensional stacked heterogeneous integration technology, the image sensor, edge computing chip, lithium battery, and micro heat sink are integrated into a 50×80×30mm³ body. The output end of the multimodal image acquisition module is connected to the input end of the adaptive preprocessing unit, the output end of the adaptive preprocessing unit is connected to the input end of the lightweight feature extraction engine, the output end of the lightweight feature extraction engine is connected to the input end of the big data enhancement classifier, the output end of the big data enhancement classifier is connected to the input end of the intelligent interactive terminal, and the output end of the intelligent interactive terminal is connected to the information input end of the ultra-low power consumption hardware architecture.

[0019] This system innovatively designs portable real-time blood cell classification and counting from multiple dimensions, specifically addressing the bottlenecks of existing technologies in terms of accuracy, real-time performance, and portability, and achieving efficient, accurate, and intelligent blood cell analysis. Specific examples are as follows: 1. Solution to the problem of insufficient microscopic imaging quality The multimodal image acquisition module utilizes a low-power microscope camera and a dynamic spectral fusion light source system. This system integrates a 450-650nm wavelength LED array and an adaptive spectral modulation circuit. Under low light intensities of ≤5μW / mm², it uses time-sharing multiplexing to simultaneously capture three RGB channels and near-infrared (NIR) spectral images. Combined with a nanoscale optical metasurface lens system with a dynamically adjustable focal length of 5-50mm and an aberration of ≤0.5μm, this system controls the peripheral red blood cell hemolysis rate to ≤2% and improves the leukocyte nuclear-cytoplasmic contrast by 60%, enabling high-fidelity in situ cell morphology observation and providing a high-quality data source for subsequent feature extraction.

[0020] 2. Solution to image preprocessing efficiency and robustness issues The adaptive pre-processing unit builds a two-layer noise suppression and enhancement mechanism: The Generative Adversarial Network noise suppression module, based on the CycleGAN architecture, generates adversarial samples that match the noise distribution of the input image in real time. Through feature-level subtraction, it achieves a 98% salt and pepper noise suppression rate and compresses the Gaussian noise standard deviation to ≤5, while retaining ≥95% of cellular details. The morphology-guided multi-scale enhancement module introduces white blood cell morphology priors and uses adaptive structural element expansion on the nuclear region, which increases the nuclear-cytoplasm grayscale difference from 30% of the traditional method to 55%. Combined with the dual-threshold CLAHE (nuclear region threshold = global threshold × 1.5, plasma region threshold = global threshold × 0.8), it enhances target features while suppressing over-enhancement of background noise.

[0021] 3. Solving the Problems of Complex Cell Feature Extraction and Small Sample Classification The lightweight feature extraction engine and the big data enhanced classifier form a dual engine of "feature-enhanced intelligent decision-making": The lightweight feature extraction engine uses the Deformable Residual Shrinkage Network (DRSNet) architecture. By parallelizing a dynamic cross-scale convolution group consisting of 3×3 depthwise separable convolutions, 5×5 deformable convolutions, and 7×7 dilated convolutions, combined with a core-to-plasma ratio-guided attention mechanism (feature channels gain 1.8x in the nucleus region, 1.5x in the plasma region, and attenuation of the red blood cell interference channel to less than 0.3x), it improves the signal-to-noise ratio of white blood cell features to 25dB, significantly enhancing multi-scale feature expression capabilities. Big data enhanced classifier integrates MAML meta-learning algorithm and clinical knowledge graph: The small sample rapid adaptation module, based on the MAML algorithm, builds a fast weight update mechanism for rare blood cells. Through 5-shot learning, it improves recognition accuracy from 70% to 92%, solving the generalization problem of small samples. The dynamic classification boundary generator is connected to the four-dimensional clinical knowledge graph (integrating more than 100,000 annotated data to construct a four-dimensional feature association of nuclear-cytoplasmic ratio and granular density, with ≥ 200,000 nodes and ≥ 5 million edges), and the feature offset caused by staining difference is detected by Mahalanobis distance (D M >2), combined with dynamic threshold compensation and SVM online learning to adjust the classification hyperplane, the classification error is ≤1.5% under different staining methods, and abnormal classification decisions are reduced by 70%.

[0022] 4. Solutions to the problem of insufficient real-time interaction and remote diagnosis capabilities Intelligent interactive terminals build an integrated link for "local real-time labeling and cloud-based collaborative diagnosis": The edge computing unit outputs classification results in real time, simultaneously generates pseudo-color labels (delay ≤ 10ms), and clearly displays cell types and counts on a 3.5-inch retina screen; Based on Bluetooth 5.0, it encrypts and transmits cell morphology feature vectors to the cloud pathology platform, supports remote expert consultation (synchronization delay ≤ 150ms), and anonymizes local data at an efficiency of ≥ 200 samples / second, ensuring privacy and security while improving primary diagnosis efficiency.

[0023] 5. Solution to the problem of hardware power consumption and performance balance Ultra-low power hardware architecture achieves energy efficiency optimization through dynamic computing power allocation algorithm: 3D stacked heterogeneous integration technology integrates image sensors, 2TOPS edge computing chips, and lithium batteries into a 50×80×30mm³ body, weighing ≤200g; The dynamic computing power allocation mechanism is based on the morphological entropy function E m Quantifying image complexity (E m When the value is less than 2.3 bits, the low power consumption mode is triggered, the computing power is reduced to 0.5TOPS, and the power consumption is ≤50mW; E m When the bit rate is >4.1, NPU acceleration is enabled, and single-frame processing is ≤12ms. The dynamic energy budget model and the Hamilton Jacobi Bellman equation are combined to optimize the voltage-frequency combination, achieving continuous detection for ≥10 hours, solving the battery life bottleneck of portable devices. The system builds a closed loop of "data acquisition model update performance improvement": The real-time feature mapping engine uses a federated learning module to aggregate test data from various terminals. It automatically updates the clinical knowledge graph with every 100 newly annotated samples and dynamically adjusts feature association weights through an attention mechanism to ensure that the algorithm evolves in sync with the distribution of real clinical data. The cross-modal verification calibrator calculates the mutual information between the classification results of RGB color features and NIR texture features (recalibration is triggered when the mutual information is less than 0.6), combines the knowledge graph prior with multimodal features, and improves the cross-modal classification consistency to more than 95%, thereby enhancing the robustness of the algorithm.

[0024] Through the above innovations, this system breaks through the limitations of traditional blood cell testing equipment in portability, accuracy and real-time performance, and provides innovative solutions for primary care and bedside diagnosis scenarios that combine laboratory-level testing capabilities with rapid on-site response characteristics, significantly improving the clinical value and application scope of blood cell analysis.

[0025] The above content has described in detail the solutions of the present invention to the problems in the prior art. The following will further detail the specific embodiments of the present invention.

[0026] Furthermore, the dynamic spectrum fusion light source system includes an LED array with a wavelength of 450-650nm and an adaptive spectrum modulation circuit. It can synchronously collect RGB three-channel and near-infrared NIR spectral images through time-sharing multiplexing technology under a light intensity of ≤5μW / mm². Combined with a nano-scale optical metasurface lens group with a dynamically adjustable focal length of 5-50mm and an aberration of ≤0.5μm, it can achieve in situ observation of peripheral blood red blood cell hemolysis rate ≤2% and white blood cell nuclear cytoplasm contrast increased by 60%.

[0027] In a specific embodiment, a dynamic spectral fusion light source system achieves breakthroughs in microscopic imaging performance through the coordinated regulation of multiple physical quantities. Its core lies in constructing a three-dimensional optimization system of "spectrum-optics-biocompatibility": integrating a 450-650nm wide-spectrum LED array and an adaptive spectral modulation circuit to cover the characteristic absorption bands of hemoglobin and cellular components, and sequentially exciting the RGB three channels and near-infrared spectrum acquisition within a single frame period through time-sharing multiplexing technology to achieve spatiotemporal alignment of multimodal optical signals; configuring a nanoscale optical metasurface lens group with a dynamically adjustable focal length of 5-50mm, and utilizing the subwavelength unit phase control characteristics of the metasurface structure to achieve high-precision focusing with an aberration of ≤0.5μm, breaking through the aberration limitations of traditional refractive lenses, while supporting dynamic observation switching from low-magnification panoramic views to high-magnification details; operating at an extremely low light intensity of ≤5μW / mm², it reduces the risk of light energy damage by more than 95% compared to traditional microscopic light sources, combined with spectral modulation to avoid ultraviolet band excitation, and controls the peripheral red blood cell hemolysis rate to ≤2%, ensuring the in situ integrity of cell morphology. The system achieves high-fidelity cell imaging through a process of light source timing control, dynamic focus adjustment, multimodal image fusion, and biocompatibility verification: the adaptive spectral modulation circuit activates the LED array in sequence and time-sharing, with the exposure time of each spectral channel ≤1ms and the total time for single-frame multimodal acquisition ≤4ms; the nanolens group dynamically adjusts the focal length and compensates for aberrations according to the sample thickness, ensuring that the resolution at different focal lengths is ≥2μm; the low-power microscope camera synchronously captures four-channel raw images and generates RGB-NIR multimodal image stacks after preprocessing; the optical power sensor monitors the light intensity in real time and feeds it back to the modulation circuit, combined with the temperature sensing module to ensure that the cells remain physiologically active during the detection process. This system achieves three key breakthroughs in blood cell testing: leukocyte nuclear-cytoplasmic contrast is increased from 30% to 60% compared to traditional methods, nuclear membrane edge clarity is improved by 40%, and NIR spectral channels assist in distinguishing eosinophils from basophils. Single-sample multimodal acquisition time is ≤100ms, and red blood cell hemolysis rate is ≤2%, significantly reducing detection errors. Multimodal images increase the signal-to-noise ratio of leukocyte characteristics to 25dB, improving the accuracy of abnormal cell identification from 75% to 97%, shortening the preprocessing phase, and reducing the overall detection process time by 30%. The dynamic spectral fusion light source system, through the deep coupling of spectral optimization, optical innovation, and biocompatible design, establishes the "cornerstone of precise imaging" for portable devices. This resolves the contradiction between light energy damage and image quality in traditional microscopic testing. Multimodal data output provides high-value input to intelligent algorithms, enabling portable blood cell testing devices to possess analytical capabilities comparable to laboratory-grade microscopes, laying a key technical foundation for precise diagnosis in primary care settings.

[0028] Furthermore, the adaptive preprocessing unit includes: Generative Adversarial Network Noise Suppression Module: This module builds a noise-image mapping model based on the CycleGAN architecture, generating adversarial samples that match the noise distribution of the input image in real time. Through feature-level subtraction, it improves the salt and pepper noise suppression rate to 98%, compresses the Gaussian noise standard deviation to ≤5, and retains ≥95%; Morphology-guided multi-scale enhancement module: Dynamically adjusts the enhancement strategy based on the prior knowledge of white blood cell morphology and uses adaptive structural element expansion in the nuclear region, increasing the nuclear-cytoplasmic grayscale difference from 30% in traditional methods to 55%. At the same time, dual-threshold CLAHE satisfies the relationship: nuclear region threshold = global threshold × 1.5, plasma region threshold = global threshold × 0.8, thus avoiding over-enhancement of background noise.

[0029] In a specific embodiment, the adaptive preprocessing unit constructs a cascade optimization system for improving the quality of blood cell images through the "noise adversarial-morphological enhancement" dual-engine architecture: a generator-discriminator game system is constructed based on the CycleGAN architecture, the generator learns the noise distribution characteristics and generates adversarial samples isomorphic to the input image noise, and the discriminator distinguishes real noise from generated noise in real time, and achieves a salt and pepper noise suppression rate of ≥98% and a Gaussian noise standard deviation of ≤5 while retaining ≥95% of cell detail features through feature-level subtraction operations; introduces prior knowledge of white blood cell morphology, and uses adaptive structural element expansion operations to increase the nuclear-cytoplasm grayscale difference from 30% of the traditional method to 55%, and designs a dual-threshold CLAHE algorithm to avoid over-enhancement of background noise through differentiated parameter settings of nuclear area threshold = global threshold × 1.5 and plasma area threshold = global threshold × 0.8. The system first decomposes the input image through a multi-scale feature extractor. The generator generates adversarial noise samples based on CycleGAN and achieves noise suppression through residual connections. The discriminator uses the PatchGAN structure to optimize the generator parameters. The pre-trained U-Net model is used for core-plasma segmentation, and the core region parameters are calculated to dynamically generate adaptive structural elements. The global threshold is calculated and the core-plasma region threshold is determined through the Otsu algorithm. An adaptive dilation operation is applied to the core region, and a dual-threshold CLAHE processing is performed on the plasma region. The enhanced core-plasma features are fused through the attention mechanism. This solution achieves a salt and pepper noise suppression rate of 98%, compresses the Gaussian noise standard deviation to ≤5, increases the nuclear-cytoplasmic grayscale difference to 55%, and compresses the background noise variance to ≤15, thereby improving the nuclear-cytoplasmic ratio measurement accuracy from ±0.15 to ±0.08 and the nuclear lobation number recognition accuracy from 82% to 95%. The preprocessed image improves the signal-to-noise ratio of white blood cell features to 25dB, and the classifier's F1-score for neutrophils and eosinophils is improved to 0.96 / 0.98 respectively. The single-frame processing time is ≤8ms and the power consumption is ≤15mW. The adaptive preprocessing unit solves the contradiction between noise removal and detail preservation in traditional preprocessing methods through the deep fusion of generative adversarial networks and morphological priors. It highlights the key nuclear-cytoplasmic features through differentiated enhancement strategies, provides a high-quality data foundation for intelligent classification, significantly improves detection accuracy and robustness while maintaining processing time and low power consumption, and promotes the expansion of blood cell analysis technology to bedside instant detection scenarios. Furthermore, the big data enhancement classifier includes: Small Sample Rapid Adaptation Module: Based on the MAML model-independent meta-learning algorithm, it constructs a fast weight update mechanism for rare blood cells, improving recognition accuracy from 70% to 92% through 5-shot learning. The Small Sample Rapid Adaptation Module includes a meta-training data preprocessing unit, a fast weight generator, a clinical prior regularization module, a lightweight meta-gradient optimizer, and a cross-task transfer evaluator. Dynamic classification boundary generator: Accesses the clinical knowledge base, dynamically adjusts the classification hyperplane based on real-time detected cell morphology parameters, and adaptively corrects feature offsets caused by differences in Wright staining / Giemsa staining, ensuring that the classification error under different staining methods is ≤1.5%; the dynamic classification boundary generator includes a clinical knowledge graph construction module, a real-time feature mapping engine, a staining difference adaptive regulator, a dynamic hyperplane adjuster, and a cross-modal validation calibrator.

[0030] In a specific embodiment, the big data enhanced classifier constructs an accurate decision-making system for intelligent classification of blood cells through a dual-engine architecture of "small sample fast learning-dynamic boundary adaptation": based on the MAML model-independent meta-learning algorithm, it breaks through the traditional deep learning's dependence on large-scale labeled data and constructs a two-stage framework of "pre-training-fast adaptation". In the meta-training stage, knowledge priors are acquired through multi-task learning. In the testing stage, only 5 labeled samples are needed to quickly update the classifier parameters, which increases the accuracy of rare blood cell identification from 70% to 92%. The architecture includes five core units: data preprocessing, fast weight generation, clinical prior constraints, gradient optimization and migration evaluation; it accesses a four-dimensional knowledge graph containing more than 100,000 clinical labeled data, and establishes the association between cell type-morphological parameters-staining method through a graph neural network. During real-time detection, the system detects feature offsets through Mahalanobis distance and triggers the staining difference correction process. Combined with SVM online learning, it dynamically adjusts the classification hyperplane, so that the classification error under different staining methods is ≤1.5%, and abnormal classification decisions are reduced by 70%. The system first uses a generative adversarial network to generate morphological variation samples, eliminates device bias through domain adaptation technology, adopts bilinear initialization and gradient accumulation strategies to quickly generate weight parameters adapted to new categories, converts pathological rules into regularization term constraint model learning space, compresses meta-training computational load through channel screening and dynamic learning rate adjustment, and verifies the model generalization ability under different staining conditions; integrates clinical cell morphology annotation data to construct a knowledge graph, uses a federated learning module to aggregate terminal data and dynamically adjust feature association weights, detects the Mahalanobis distance between sample features and the knowledge graph, and applies dynamic gain compensation or Gaussian filtering to feature channels; dynamically updates the classification hyperplane through SVM online learning based on real-time feature vectors and knowledge graph priors, calculates the mutual information of RGB and NIR feature classification results, and triggers feature space recalibration. This classifier only needs 5 labeled samples to achieve a 92% recognition accuracy for rare types. The computational complexity in the meta-training stage is compressed by 60%, making the processing time of edge devices ≤8ms. The classification error under different staining methods is controlled at ≤1.5%, and abnormal classification decisions are reduced by 70%. The probability of classification that violates pathological rules is reduced by 95% through knowledge graph constraints. The knowledge graph dynamic update mechanism driven by federated learning can improve the classification accuracy by 1-2% for every 1,000 samples updated. The fusion of RGB and NIR features increases the cross-modal classification consistency to more than 95%. The knowledge graph-assisted feature calibration mechanism ensures that the classification accuracy remains ≥90% under low signal-to-noise ratio conditions.The big data enhanced classifier breaks through the traditional blood cell classification's dependence on large-scale labeled data and the limitations of differences in staining methods through the deep integration of meta-learning and knowledge graphs. It achieves rapid learning under small sample conditions and maintains high accuracy in complex staining environments. At the same time, it ensures the medical rationality of classification decisions through clinical knowledge constraints. The system takes ≤12ms to process a single frame and consumes ≤20mW of power. It achieves blood cell classification capabilities comparable to those of professional laboratories on resource-constrained portable devices, providing accurate and reliable intelligent tools for primary medical care and bedside diagnosis.

[0031] Furthermore, the meta-training data preprocessing unit includes a generative adversarial network (GAN) module, a domain adaptation module, a fully connected layer feature distribution adjustment module, and a training value dynamic adjustment module; The fast weight generator includes a weight initialization module, an accelerated convergence module and a weight generation module; The clinical prior regularization module includes a pathology rule regularization term parameter setting module, a clinical knowledge graph judgment module, a rule regularization term function generation module and a pathology classification module; The lightweight meta-gradient optimizer includes a feature channel screening module, an interference channel identification module, a data dimension deletion module, a dynamic learning rate adjustment module and a computing power improvement module; The cross-task migration evaluator includes a cell image classification module, a self-triggered meta-training module, a different staining simulation module, a chromosome recognition module and a chromosome feature offset module; The output end of the meta-training data preprocessing unit is connected to the input end of the fast weight generator, the output end of the fast weight generator is connected to the input end of the clinical prior regularization module, the output end of the clinical prior regularization module is connected to the input end of the lightweight meta-gradient optimizer; the output end of the lightweight meta-gradient optimizer is connected to the input end of the cross-task migration evaluator. In a specific embodiment, the small sample rapid adaptation module constructs a "data enhancement-model compression-knowledge constraint" co-evolution system for the blood cell classifier through a five-cascade intelligent optimization architecture: morphological variation samples are generated through GAN and the diversity index is ≥0.92, domain adaptation eliminates equipment acquisition bias and the MMD distance is ≤0.05, the fully connected layer adjusts the feature distribution to increase the inter-class discreteness by 30%, the dynamic training value adjustment enhances data robustness and the noise tolerance is ≥20%; bilinear initialization compresses the number of convergence steps from 50 steps of the traditional method to 8 steps, and the gradient accumulation strategy makes the parameter update efficient. The rate is increased by 40%, and the weight generation module achieves a 5-shot learning accuracy of ≥92%; 128 pathology rules are converted into differentiable regularization terms, the knowledge graph judges the rationality of classification in real time and the logical consistency is ≥98%, and the rule function generation module constructs interpretable classification boundaries; channel screening compresses 60% of redundant parameters, and dynamic learning rate adjustment improves training stability by 50% and reduces computing power consumption to 1 / 3 of traditional MAML; cross-staining method simulation reduces the generalization error to ≤1.5%, and chromosome feature offset detection triggers self-triggered meta-training with an accuracy recovery speed of ≥95% / minute. The system first generates variant samples through pathological feature vector interpolation based on the StyleGAN2 architecture, adopts the maximum mean difference (MMD) metric and eliminates device bias through batch normalization parameter alignment, adjusts the feature distribution to improve inter-class discreteness, and enhances robustness through noise injection; uses a bilinear interpolation strategy to initialize weights, improves parameter update efficiency through a gradient accumulation strategy, and generates weights adapted to new categories based on the MAML meta-learning framework; converts clinical rules into differentiable loss functions, verifies classification results in real time and constructs constrained optimization, and provides prior probability guidance through knowledge graph reasoning; compresses redundant channels based on channel importance scores, locates noisy channels and reduces feature dimensions through the attention mechanism, adaptively adjusts the learning rate according to the gradient variance, and reduces computing power consumption through model quantization and pruning; tests the model's generalization ability under different staining conditions, and automatically starts the meta-training process when insufficient classification confidence is detected, simulates staining differences through color space transformation, and calculates feature offsets.This architecture improves the accuracy of GAN-generated samples by 22% under small sample conditions, domain adaptation increases the consistency between different microscopes to 97%, and dynamic training value adjustment maintains an accuracy of ≥85% under 20% noise; bilinear initialization and gradient accumulation strategy increase the convergence speed by 60%, and the 5-shot learning accuracy reaches 92%; pathology rule regularization reduces the probability of classification that violates medical logic by 95%, and the knowledge graph-assisted diagnosis accuracy of rare cell types reaches 98%; channel screening and quantization technology compresses model parameters by 60%, computing power consumption is reduced to 15mW, and single-frame inference time takes ≤8ms; cross-staining method classification error is ≤1.5%, and self-triggered meta-training enables accuracy recovery speed of 95% / minute. The small sample rapid adaptation module uses a five-level intelligent architecture to build a full-process optimization system from data enhancement to model deployment. It breaks through the traditional deep learning's reliance on large-scale labeled data, and achieves classification capabilities comparable to professional examiners under 5-shot conditions. At the same time, it ensures the rationality of decision-making through clinical knowledge constraints. Through lightweight model adaptation to portable devices, while maintaining a processing time of ≤8ms and ultra-low power consumption of ≤15mW, the blood cell classification accuracy is increased to 92%, and the abnormal cell detection rate is increased by 40%, providing a precise, fast and affordable blood cell analysis solution for primary medical scenarios.

[0032] Furthermore, the working method of the clinical knowledge graph construction module is as follows: integrating more than 100,000 clinical cell morphology annotation data through a multi-dimensional pathological feature knowledge base, constructing a four-dimensional knowledge graph containing nuclear-cytoplasmic ratio (N / C), granule density, number of nuclear lobes, and staining method sensitivity features, and establishing the association between cell type, morphological parameters, and staining method through a graph neural network (GNN). The number of nodes is ≥ 200,000 and the number of edges is ≥ 5 million, realizing a priori modeling of the distribution of rare cell features. The real-time feature mapping engine works by using a federated learning module to aggregate test data from each terminal in real time, automatically updating the knowledge graph with every 100 newly annotated samples, and dynamically adjusting feature association weights through an attention mechanism to ensure that the knowledge base evolves synchronously with the distribution of actual clinical test data. The working principle of the staining difference adaptive regulator is: using the dual-modal feature offset detection module to detect the Mahalanobis distance between the sample features and the corresponding cell types in the knowledge graph, and to identify the feature offset caused by the staining difference in real time: In formula (1), is the feature mean of the cell type in the knowledge graph, is the covariance matrix; when When the staining difference correction process is triggered; a dynamic threshold compensation algorithm is used to automatically apply dynamic gain compensation to the nuclear region feature channel to address the common problem of low nuclear-cytoplasmic contrast in Wright staining; and Gaussian filtering is performed on the granularity feature channel to address the problem of excessively dark particles in Giemsa staining, reducing the parameter measurement error caused by staining differences from 8% in traditional methods to 1.2%; The working principle of the dynamic hyperplane adjuster is: based on the real-time feature vector through the adaptive classification boundary generation algorithm Based on the prior distribution of the knowledge graph, the classification hyperplane is dynamically adjusted through online learning of the support vector machine (SVM). The initial hyperplane is determined by the optimal classification boundary of the cell type in the knowledge graph. Every time 5 cell samples of the same type are detected, the stochastic gradient descent (SGD) is used to update the hyperplane normal vector w and intercept b: In formula (2), is the learning rate, The cross-modal validation calibrator extracts the color features of RGB images and the texture features of NIR images, and calculates the mutual information of the classification results of the two types of features. When the mutual information is less than 0.6, it indicates that the difference between the modalities is large, which triggers the feature space recalibration process. The two types of features are fused through the prior knowledge of the knowledge graph, so that the cross-modal classification consistency is improved to more than 95%. In a specific embodiment, the dynamic classification boundary generator constructs a dynamic decision optimization system for blood cell classification through a three-level collaborative architecture of "knowledge graph - real-time adaptation - multimodal calibration": integrating more than 100,000 clinical annotation data to construct a four-dimensional knowledge graph containing nuclear-cytoplasmic ratio N / C, particle density, number of nuclear lobes, and staining method sensitivity features, and establishing the association relationship between cell type-morphological parameters-staining method through the graph neural network GNN, with the number of nodes ≥ 200,000 and the number of edges ≥ 5 million, to achieve prior modeling of the distribution of rare cell features; using a federated learning module to aggregate the detection data of each terminal in real time, automatically updating the knowledge graph for every 100 newly added annotated samples, and dynamically adjusting the feature association weights through the attention mechanism to ensure that the knowledge base and the actual clinical detection data distribution evolve synchronously; using a dual-modal feature offset detection module to detect the Mahalanobis distance D_M between the sample features and the corresponding cell type in the knowledge graph, when D_M>2 The staining difference correction process is triggered when the image is detected. Dynamic gain compensation is applied to the nuclear region feature channel to address the low nuclear-cytoplasmic contrast problem of Wright staining. Gaussian filtering is performed on the granularity feature channel to address the problem of excessively dark granules in Giemsa staining, reducing the parameter measurement error from 8% to 1.2%. Based on the real-time feature vector and the knowledge graph prior distribution, the classification hyperplane is dynamically adjusted through support vector machine (SVM) online learning. Stochastic gradient descent (SGD) is used to update the hyperplane parameters every time five similar samples are detected. The hyperplane is constrained within the feasible domain through projected gradient descent, reducing abnormal classification decisions by 70%. The color features of the RGB image and the texture features of the NIR image are extracted, and the mutual information of the classification results of the two types of features is calculated. When the mutual information is less than 0.6, the feature space recalibration process is triggered. The two types of features are fused through the knowledge graph prior knowledge, improving the cross-modal classification consistency to over 95%.The system first integrates clinically annotated data to construct a four-dimensional knowledge graph, using a graph neural network to learn relationships between nodes and model features for rare cells. A federated learning module regularly aggregates data to update the knowledge graph, while an attention mechanism adjusts feature weights based on real-time data. A bimodal feature offset detection module identifies staining differences, while a dynamic threshold compensation algorithm corrects for parameter measurement errors. An online support vector machine optimizes the hyperplane through incremental learning, using projected gradient descent to ensure that classification boundaries conform to pathological logic. Mutual information is calculated between cross-modal feature classification results, triggering knowledge graph-assisted feature fusion. This architecture increases the feature coverage of the four-dimensional knowledge graph for rare cells from 65% to 92%, achieving a mean average approach (MAP) of 0.96 for cell type prediction. Parameter measurement errors caused by staining differences are reduced to 1.2%, achieving 99% accuracy in automatic staining method identification. Abnormal classification decisions are reduced by 70%, and the accuracy of classifying newly variant cells returns to over 90% within 10 minutes. Cross-modal classification consistency is improved to 95%, maintaining classification accuracy at or above 90% under low signal-to-noise ratio conditions. The dynamic classification boundary generator breaks through the limitations of traditional fixed classifiers through the deep integration of knowledge graphs and real-time learning. It maintains high accuracy in complex clinical environments, has an adaptation time of ≤0.5 seconds, and supports automatic evolution, providing an accurate and reliable intelligent tool for immediate diagnosis in primary healthcare. As shown in Table 1, through the use of a four-dimensional knowledge graph and a dynamic classification algorithm, parameter measurement errors caused by staining differences were reduced from 8% to 1.2%, the proportion of abnormal classification decisions was reduced by 70%, and cross-modal classification consistency was increased to 95%, significantly improving classification reliability in complex scenarios. Based on the MAML meta-learning algorithm, the accuracy of rare cell identification was increased from 70% to 92% with only five labeled samples, breaking through the reliance of traditional supervised learning on large-scale data. The knowledge graph is automatically updated with every 100 new samples through federated learning, and online optimization is triggered every time five similar samples are detected at the classification boundary, enabling continuous model evolution without manual intervention. Feature offset detection and multimodal fusion time were reduced to 50ms and 12ms, respectively, reducing computing power consumption by 67%, adapting to the real-time and low-power requirements of portable devices.

[0033] The data show that the method of the present invention has achieved significant breakthroughs in classification accuracy, adaptability and engineering performance through the deep collaboration of knowledge graphs and dynamic algorithms, providing key technical support for the clinical application of portable blood cell detection equipment.

[0034] Furthermore, the working method of the MAML model-independent meta-learning algorithm is as follows: S1. Feature extraction: extracting features of blood cell images using a pre-trained convolutional neural network , where x is the input blood cell image, is the grayscale matrix, It is the feature extraction function of the pre-trained convolutional neural network ResNet, and the output is the high-level semantic feature vector of the image; S2. Classifier Adaptation: For newly emerged blood cell types, we use MAML to quickly adjust the classifier parameters W and bias b, combining gradient masking, adaptive learning rate, and feature weighting strategies to improve classification performance and adaptation to new blood cell types. In formulas (3) and (4), W is the weight matrix of the classifier, with a shape of C×d, C is the dimension of the feature vector, which is used to map the feature vector to the classification space; a is the learning rate, which controls the step size of parameter update. When combined with the adaptive learning rate strategy, a can be dynamically adjusted during training; is the loss function The gradient of W and b indicates the direction of parameter update; Support set loss function, usually cross entropy loss, measures the classifier's prediction error for support set samples; S3, real-time classification: using adapted parameters Classify new blood cells in real time: In formula (5), Activation function, converts linear output into probability distribution for multi-classification tasks. In a specific embodiment, the technical essence of the MAML model-independent meta-learning algorithm is to use the meta-learning concept to optimize the initial parameters of the model in the blood cell classification scenario, so that the model can quickly adapt and accurately classify when facing new blood cells. This algorithm is applicable to various models that can be optimized by gradient descent. Combined with the blood cell classification model, it breaks through the limitations of traditional machine learning in the classification of new blood cells and realizes efficient and intelligent blood cell analysis. In the technical implementation process, the first step is feature extraction. Using the pre-trained convolutional neural network ResNet, feature extraction operations are performed on the input blood cell image, and the blood cell image is converted into a high-level semantic feature vector, laying the foundation for subsequent classification. Next, the classifier adaptation stage is entered. For the newly emerging blood cell types, the MAML algorithm is used to quickly adjust the classifier parameters W and bias b. During this period, the gradient mask, adaptive learning rate and feature weighting strategy are combined according to formula (4) and formula (5) to support the set loss function. The gradient is used as a guide to dynamically adjust the learning rate , update the classifier parameters to enhance the classification performance and adaptability of new blood cells. Finally, in the real-time classification stage, the adapted parameters are used , the blood cell image feature vector is processed by the Softmax activation function and converted into a probability distribution. According to formula (5), the new blood cells can be classified in real time and accurately. From the technical effect, the MAML algorithm significantly improves the efficiency and accuracy of classifying new blood cells. In the case of few samples, compared with traditional methods, it can adapt to new blood cell types more quickly and reduce classification errors. For example, in the identification of some rare blood cell types, the accuracy of traditional methods may be only 50%, but after adopting the MAML algorithm, the accuracy can be increased to more than 80%. At the same time, the algorithm effectively reduces the dependence on a large amount of labeled data, significantly shortens the training time, enhances the generalization ability of the model in the blood cell classification task, and provides more efficient and accurate technical support for clinical blood testing. As shown in Table 2, the feature extraction stage compresses a 512×512 high-dimensional image into a 256-dimensional low-dimensional feature vector using a pre-trained ResNet. This preserves high-level semantic information about nuclear morphology and cytoplasmic texture, providing a compact and discriminative input for subsequent classification. In the classifier adaptation stage, for example, the MAML algorithm rapidly updates classifier parameters under a low-sample condition of 10 samples per class for five new rare blood cell categories. The adaptive learning rate α is dynamically decayed from 0.01 to 0.005 during training, reducing the loss function L_s from 2.3 to 0.8, demonstrating the model's ability to rapidly adapt to the features of new categories. In the real-time classification stage, the softmax function is used to convert the linear output into a probability distribution. The classification confidence for the new blood cell category A reaches 0.92, significantly exceeding the confidence of traditional algorithms, which is typically below 0.7 under low-sample conditions, validating MAML's classification reliability in small-sample conditions. In summary, the table data demonstrates the key parameters and optimization effects of the MAML algorithm throughout the entire process, from feature extraction to classification. It highlights its rapid adaptability and classification accuracy for new blood cell types even with limited sample size, providing an efficient meta-learning solution for portable real-time blood cell classification systems. Furthermore, the intelligent interactive terminal is configured to support: Dynamic marking of abnormal cells: When the edge computing unit outputs the white blood cell classification results in real time, pseudo-color markings are generated synchronously, with a marking delay of ≤10ms; Wireless collaborative diagnosis system: Establishes an encrypted connection with the cloud pathology platform via Bluetooth 5.0, uploads cell morphology feature vectors, supports remote expert consultation, synchronization delay ≤150ms, and local detection data anonymization processing efficiency ≥200 samples / second.

[0035] In a specific embodiment, the technical essence of the intelligent interactive terminal lies in leveraging edge computing and wireless communication technologies to optimize the interactive process and data sharing mechanism for blood cell testing, thereby improving the timeliness and security of clinical diagnosis. This technology aims to address the issues of delayed abnormal cell marking, untimely data transmission for remote consultations, and inadequate data privacy protection in traditional blood cell testing, providing medical professionals with more efficient and accurate diagnostic support. During technical implementation, the dynamic abnormal cell marking function utilizes an edge computing unit, operating simultaneously with the output of white blood cell classification results. Based on the classification results, the edge computing unit rapidly generates pseudo-color labels, with the entire marking process strictly controlled to ≤10ms. The wireless collaborative diagnosis system establishes an encrypted connection with the cloud pathology platform via Bluetooth 5.0. The local device packages and uploads cell morphology feature vectors, using the AES encryption algorithm to ensure data transmission security. To anonymize local test data, a hash function is used to replace sensitive information, achieving efficient processing of ≥200 samples per second, ensuring data privacy during transmission and storage, and synchronization latency of ≤150ms. In terms of technical effectiveness, the dynamic labeling of abnormal cells allows doctors to immediately spot abnormal cells, increasing diagnostic response speed by 60% and significantly improving diagnostic efficiency. The wireless collaborative diagnosis system enables efficient remote expert consultations, breaking through geographical limitations and providing high-level diagnostic support to primary healthcare institutions, increasing diagnostic accuracy by approximately 15%. The improved efficiency of anonymizing local test data not only protects patient privacy but also provides a reliable data foundation for big data analysis, promoting the rational use and research development of medical data.

[0036] Furthermore, the ultra-low power consumption hardware architecture is achieved through a dynamic computing power allocation algorithm: ① The computing power in low-load scenarios is reduced to 0.5TOPS, and the power consumption is ≤50mW; ② When detecting peak loads, NPU hardware acceleration is enabled, with a single frame processing time of ≤12ms and a continuous detection time of ≥10 hours; The working method of the dynamic computing power allocation algorithm is as follows: Modeling the cell image complexity quantification model: defining the blood cell image feature tensor , construct the morphological entropy function: In formula (6), represents the probability of occurrence of k-type cells, and the gradient norm reflects the complexity of the texture; when Trigger low power mode; calculate differential precision modulation function, design dynamic bit width allocation method, In formula (7), Controls the bit width adjustment rate; when the system detects the lobulated features of white blood cells , automatically increase the convolution kernel weight width to 8 bits: In formula (8), W is the original weight matrix with a shape of C×d; is the quantized weight matrix, which is the same shape as W and reduces storage and computation costs through discretization; The quantization range threshold defines the upper and lower bounds at which weights are clipped. ; Weights outside this range will be constrained to the boundary value; To quantize the step size and control the fineness of the discretization, is the number of quantization bits; the spatiotemporal joint optimization acceleration equation, when the NPU activation conditions are met When heterogeneous acceleration is enabled: In formula (9), Transformation coefficients Multiplexing factor for blood cell feature map; To calculate the total number of operations, the unit is ops; NPU main frequency, in Hz, represents the number of clock cycles that the NPU can execute per second; To calculate the complexity coefficient, characterize the average number of clock cycles per operation; NPU computing efficiency reflects the actual utilization rate of NPU. is the total amount of memory access, Memory bandwidth, which indicates the amount of data that can be transferred per second by the memory; is the memory access complexity coefficient, which represents the additional overhead of memory access; by solving the constrained optimization problem: In formula (10), the single frame processing time is guaranteed to be ; Establish a dynamic energy budget model: In formula (11), Enable probability of NPU controlled by sigmoid activation function; is the basic power consumption of the system; is the operating voltage of the NPU, which is usually positively correlated with the frequency; is the operating voltage of the NPU, which is usually positively correlated with the frequency; by solving the HamiltonJacobi-Bellman equation: In formula (12), the cost function , achieving optimal control under 10.5 hours of battery life. In a specific embodiment, the ultra-low power consumption hardware architecture realizes the intelligent coordinated optimization of computing resources and power consumption in the blood cell detection scenario through a dynamic computing power allocation algorithm. Its technical essence lies in constructing a multi-dimensional quantization model and a constraint optimization system to accurately match the complexity of the detection task and the hardware energy efficiency. The image feature complexity is quantified by the morphological entropy function, combined with differentiable precision modulation and spatiotemporal joint optimization, and the computing power allocation and quantization accuracy are dynamically adjusted. While ensuring the detection accuracy, the adaptive power consumption management of "low load energy saving and high load acceleration" is achieved, breaking through the technical bottleneck of traditional hardware architecture in portable devices where computing power and battery life are difficult to balance. The technical implementation process is driven by the complexity of blood cell images. The texture complexity of the image feature tensor is calculated through the morphological entropy function. When the complexity is lower than the threshold, the low power consumption mode is triggered, the computing power is reduced to 0.5TOPS, and the power consumption is ≤50mW; if complex features are detected, the convolution kernel weight bit width is increased to enhance the feature extraction capability. When the detection load reaches its peak, processing time is calculated using a spatiotemporal joint optimization equation. Constrained optimization is combined to ensure single-frame processing time is ≤11.4ms, while enabling NPU hardware acceleration to meet real-time detection requirements. By optimizing the voltage-frequency combination using an energy budget model and the Hamilton-Jacobi-Bellman equation, a continuous detection time of ≥10 hours is achieved while ensuring detection accuracy, addressing the battery life pain point of portable devices. This technology significantly improves the energy efficiency and practicality of the hardware architecture. In low-load scenarios, power consumption is reduced by over 70% compared to traditional solutions. Under peak load, single-frame processing time is ≤12ms, and the detection frame rate reaches ≥83FPS. The continuous detection time exceeds 10 hours, a 50% improvement over similar products. Dynamic bit width adjustment and NPU acceleration maintain complex feature detection accuracy above 98%, a 5% improvement over fixed bit width solutions. This achieves the optimal balance between power consumption, performance, and battery life for portable blood cell testing equipment, promoting the practical development of point-of-care testing technology. Table 3 focuses on the core performance of the ultra-low-power hardware architecture, comparing parameters in low-load and peak-load scenarios to visually demonstrate the results of the dynamic computing power allocation algorithm optimization. From operating modes to energy consumption and storage, the data clearly demonstrates the architecture's remarkable success in balancing power consumption and performance.

[0037] Furthermore, S1. Multimodal image acquisition and light source adaptation Peripheral blood is collected to make a single cell layer smear. Using a low-power microscope camera with a resolution of at least 1920×1080 and a dynamic spectral fusion light source with a 450-650nm LED array, RGB and near-infrared images are simultaneously captured. Combined with a nano-lens assembly, in situ observation is achieved with a red blood cell hemolysis rate of no more than 2% and a 60% improvement in the nucleocytoplasmic contrast of white blood cells. S2. Adaptive image preprocessing and noise removal CycleGAN generates adversarial examples to suppress salt and pepper noise and Gaussian noise, achieving a 98% suppression rate for salt and pepper noise and compressing the standard deviation of Gaussian noise to below 5. Simultaneously, combined with prior knowledge of white blood cell morphology, the threshold for nuclear-cytoplasmic region segmentation is enhanced, increasing the nuclear-cytoplasmic grayscale difference to 55%, preserving cell details while reducing background interference. S3. Lightweight network feature extraction and classification decision The Deformable Residual Shrinkage Network (DRSNet) was used to extract multi-scale features in parallel, including 3×3 depthwise separable convolutions, 5×5 deformable convolutions, and 7×7 dilated convolutions. The nuclear-to-cytoplasm ratio was used to guide the attention mechanism to enhance white blood cell features, increasing the signal-to-noise ratio of white blood cell features to 25dB. Combining the meta-learning MAML algorithm with the clinical knowledge graph, the recognition accuracy of rare blood cells was increased from 70% to 92% after just five training passes. The classification hyperplane was dynamically adjusted based on real-time cell morphology parameters to accommodate differences in staining methods, keeping the classification error within 1.5%. S4, real-time marker display and wireless collaborative diagnosis The edge computing unit outputs white blood cell classification results in real time, simultaneously generating pseudo-color markers with a marker delay of less than 10ms, and displays cell types and counts on a 3.5-inch retina screen. It also establishes an encrypted connection with the cloud-based pathology platform via Bluetooth 5.0, uploading cell morphology feature vectors to support remote expert consultations. The synchronization delay is less than 150ms, and the local test data anonymization processing efficiency reaches over 200 samples per second. S5, ultra-low power hardware and dynamic computing power management Using three-dimensional stacked heterogeneous integration technology, the image sensor, edge computing chip, lithium battery and micro heat sink are integrated into a 50×80×30mm³ body. The morphological entropy function Em is used to quantify the complexity of cell images and dynamically allocate computing power: in low-load scenarios, the computing power is reduced to 0.5TOPS, and the power consumption does not exceed 50mW. When detecting peak loads, NPU hardware acceleration is enabled, and the single-frame processing time does not exceed 12ms. Combined with a dynamic energy budget model to optimize the voltage and frequency combination, a continuous detection time of at least 10 hours is achieved.

[0038] Although specific embodiments of the present invention have been described above, those skilled in the art will appreciate that these specific embodiments are merely illustrative, and that those skilled in the art may omit, substitute, and modify the details of the methods and systems described above without departing from the principles and spirit of the present invention. For example, combining the above method steps to perform substantially the same functions in substantially the same manner to achieve substantially the same results falls within the scope of the present invention. Accordingly, the scope of the present invention is limited solely by the appended claims.

Claims

1. A portable real-time blood cell classification and counting system based on big data algorithms, characterized by: Multimodal image acquisition module; integrated low-power microscope camera with a resolution of ≥1920×1080 and a dynamic spectrum fusion light source system; Adaptive pre-processing unit; built-in edge computing chip with computing power ≥ 2TOPS; Lightweight feature extraction engine; It uses the Deformable Residual Shrinkage Network (DRSNet) architecture, which includes a dynamic cross-scale convolution group: 3×3 depthwise separable convolution, 5×5 deformable convolution, and 7×7 dilated convolution are deployed in parallel, and the weights of features at each scale are dynamically allocated through morphological attention gating. Nucleus-to-cytoplasm ratio-guided attention mechanism: Based on the nucleus-to-cytoplasm segmentation mask output by the preprocessing module, a spatial channel dual attention gate is constructed, applying a 1.8x gain to the nucleus region feature channel and a 1.5x gain to the plasma region feature channel. The red blood cell interference channel response is attenuated to below 0.3x, thereby increasing the white blood cell feature signal-to-noise ratio to 25dB. Big data enhanced classifier; integrating meta-learning incremental training algorithm with clinical knowledge graph; Smart interactive terminal: equipped with a 3.5-inch retina screen and real-time fluorescent marking engine; Ultra-low power hardware architecture: Using three-dimensional stacked heterogeneous integration technology, the image sensor, edge computing chip, lithium battery, and micro heat sink are integrated into a 50×80×30mm³ body. The output end of the multimodal image acquisition module is connected to the input end of the adaptive preprocessing unit, the output end of the adaptive preprocessing unit is connected to the input end of the lightweight feature extraction engine, the output end of the lightweight feature extraction engine is connected to the input end of the big data enhancement classifier, the output end of the big data enhancement classifier is connected to the input end of the intelligent interactive terminal, and the output end of the intelligent interactive terminal is connected to the information input end of the ultra-low power consumption hardware architecture.

2. The portable real-time blood cell classification and counting system based on big data algorithm according to claim 1, characterized in that: The dynamic spectral fusion light source system includes an LED array with a wavelength of 450-650nm and an adaptive spectral modulation circuit. It can synchronously collect RGB three-channel and near-infrared (NIR) spectral images through time-sharing multiplexing technology under a light intensity of ≤5μW / mm². Combined with a nano-scale optical metasurface lens group with a dynamically adjustable focal length of 5-50mm and an aberration of ≤0.5μm, it can achieve in situ observation of peripheral blood red blood cell hemolysis rate ≤2% and white blood cell nuclear cytoplasm contrast improvement of 60%.

3. The portable real-time blood cell classification and counting system based on big data algorithm according to claim 1 is characterized by: The adaptive preprocessing unit comprises: Generative Adversarial Network Noise Suppression Module: This module builds a noise image mapping model based on the CycleGAN architecture, generating adversarial samples that match the noise distribution of the input image in real time. Through feature-level subtraction, it improves the salt and pepper noise suppression rate to 98%, compresses the Gaussian noise standard deviation to ≤5, and retains ≥95%; Morphology-guided multi-scale enhancement module: Dynamically adjusts the enhancement strategy based on the prior knowledge of white blood cell morphology and uses adaptive structural element expansion in the nuclear region, increasing the nuclear-cytoplasmic grayscale difference from 30% in traditional methods to 55%. At the same time, dual-threshold CLAHE satisfies the relationship: nuclear region threshold = global threshold × 1.5, plasma region threshold = global threshold × 0.8, thus avoiding over-enhancement of background noise.

4. The portable real-time blood cell classification and counting system based on big data algorithm according to claim 1, characterized in that: The big data enhanced classifier includes: Small Sample Rapid Adaptation Module: Based on the MAML model-independent meta-learning algorithm, it builds a fast weight update mechanism for rare blood cells, improving the recognition accuracy from 70% to 92% through 5-shot learning; The small sample rapid adaptation module includes a meta-training data preprocessing unit, a fast weight generator, a clinical prior regularization module, a lightweight meta-gradient optimizer, and a cross-task transfer evaluator; Dynamic classification boundary generator: Accesses the clinical knowledge base, dynamically adjusts the classification hyperplane based on real-time detected cell morphology parameters, and adaptively corrects feature offsets caused by differences in Wright staining / Giemsa staining, ensuring that the classification error under different staining methods is ≤1.5%; the dynamic classification boundary generator includes a clinical knowledge graph construction module, a real-time feature mapping engine, a staining difference adaptive regulator, a dynamic hyperplane adjuster, and a cross-modal validation calibrator.

5. The portable real-time blood cell classification and counting system based on big data algorithm according to claim 4 is characterized by: The meta-training data preprocessing unit includes a generative adversarial network (GAN) module, a domain adaptation module, a fully connected layer feature distribution adjustment module, and a training value dynamic adjustment module; The fast weight generator includes a weight initialization module, an accelerated convergence module and a weight generation module; The clinical prior regularization module includes a pathology rule regularization term parameter setting module, a clinical knowledge graph judgment module, a rule regularization term function generation module and a pathology classification module; The lightweight meta-gradient optimizer includes a feature channel screening module, an interference channel identification module, a data dimension deletion module, a dynamic learning rate adjustment module and a computing power improvement module; The cross-task migration evaluator includes a cell image classification module, a self-triggered meta-training module, a different staining simulation module, a chromosome recognition module and a chromosome feature offset module; The output end of the meta-training data preprocessing unit is connected to the input end of the fast weight generator, the output end of the fast weight generator is connected to the input end of the clinical prior regularization module, the output end of the clinical prior regularization module is connected to the input end of the lightweight meta-gradient optimizer; the output end of the lightweight meta-gradient optimizer is connected to the input end of the cross-task transfer evaluator.

6. The portable real-time blood cell classification and counting system based on big data algorithm according to claim 4, characterized in that: The clinical knowledge graph construction module works by integrating more than 100,000 clinical cell morphology annotation data through a multi-dimensional pathology feature knowledge base to construct a four-dimensional knowledge graph containing nuclear-cytoplasmic ratio (N / C), granule density, number of nuclear lobes, and staining method sensitivity features. A graph neural network (GNN) is used to establish the association between cell type morphological parameters and staining methods, with a node count of ≥ 200,000 and an edge count of ≥ 5 million, to achieve a priori modeling of the distribution of rare cell features. The real-time feature mapping engine works by using a federated learning module to aggregate test data from each terminal in real time, automatically updating the knowledge graph with every 100 newly annotated samples, and dynamically adjusting feature association weights through an attention mechanism to ensure that the knowledge base evolves synchronously with the distribution of actual clinical test data. The working principle of the staining difference adaptive regulator is: using the dual-modal feature offset detection module to detect the Mahalanobis distance between the sample features and the corresponding cell types in the knowledge graph, and to identify the feature offset caused by the staining difference in real time: In formula (1), is the feature mean of the cell type in the knowledge graph, is the covariance matrix; when When , the staining difference correction process is triggered; A dynamic threshold compensation algorithm is used to address the low nuclear-cytoplasmic contrast commonly seen in Wright's staining, automatically applying dynamic gain compensation to the nuclear region feature channel. To address the overly dark granular coloring seen in Giemsa staining, a Gaussian filter is used to smooth the granularity feature channel, reducing the parameter measurement error caused by staining differences from 8% in traditional methods to 1.2%. The working principle of the dynamic hyperplane adjuster is: based on the real-time feature vector through the adaptive classification boundary generation algorithm Based on the prior distribution of the knowledge graph, the classification hyperplane is dynamically adjusted through online learning of the support vector machine (SVM). The initial hyperplane is determined by the optimal classification boundary of the cell type in the knowledge graph. Every time 5 cell samples of the same type are detected, the stochastic gradient descent (SGD) is used to update the hyperplane normal vector w and intercept b: In formula (2), is the learning rate, The cross-modal validation calibrator extracts the color features of RGB images and the texture features of NIR images, and calculates the mutual information of the classification results of the two types of features. When the mutual information is less than 0.6, it indicates that the difference between the modalities is large, which triggers the feature space recalibration process. The two types of features are fused through the prior knowledge of the knowledge graph, so that the cross-modal classification consistency is improved to more than 95%.

7. The portable real-time blood cell classification and counting system based on big data algorithm according to claim 4, characterized in that: The MAML model-agnostic meta-learning algorithm works as follows: S1. Feature extraction: Extract features of blood cell images using a pre-trained convolutional neural network , where x is the input blood cell image, is the grayscale matrix, It is the feature extraction function of the pre-trained convolutional neural network ResNet, and the output is the high-level semantic feature vector of the image; S2. Classifier Adaptation: For newly emerged blood cell types, we use MAML to quickly adjust the classifier parameters W and bias b, combining gradient masking, adaptive learning rate, and feature weighting strategies to improve classification performance and adaptation to new blood cell types. In formulas (3) and (4), W is the weight matrix of the classifier, with a shape of C×d, C is the dimension of the feature vector, which is used to map the feature vector to the classification space; a is the learning rate, which controls the step size of parameter update. When combined with the adaptive learning rate strategy, a can be dynamically adjusted during training; is the loss function The gradient of W and b indicates the direction of parameter update; Support set loss function, usually cross entropy loss, measures the classifier's prediction error for support set samples; S3, real-time classification: using adapted parameters Classify new blood cells in real time: In formula (5), Activation function, which converts linear output into probability distribution for multi-classification tasks.

8. The portable real-time blood cell classification and counting system based on big data algorithm according to claim 1 is characterized by: The intelligent interactive terminal is configured to support: Dynamic marking of abnormal cells: When the edge computing unit outputs the white blood cell classification results in real time, pseudo-color markings are generated synchronously, with a marking delay of ≤10ms; Wireless collaborative diagnosis system: Establishes an encrypted connection with the cloud pathology platform via Bluetooth 5.0, uploads cell morphology feature vectors, supports remote expert consultation, synchronization delay ≤150ms, and local detection data anonymization processing efficiency ≥200 samples / second.

9. The portable real-time blood cell classification and counting method based on big data algorithm according to claim 1 is characterized by: The ultra-low power hardware architecture is achieved through a dynamic computing power allocation algorithm: ① The computing power in low-load scenarios is reduced to 0.5TOPS, and the power consumption is ≤50mW; ② When detecting peak loads, NPU hardware acceleration is enabled, with a single frame processing time of ≤12ms and a continuous detection time of ≥10 hours; The working method of the dynamic computing power allocation algorithm is as follows: Modeling the Quantification Model of Cell Image Complexity: Defining the Blood Cell Image Feature Tensor , construct the morphological entropy function: In formula (6), represents the probability of occurrence of k-type cells, and the gradient norm reflects the complexity of the texture; when Trigger low power mode when Differentiable precision modulation function calculation, design of dynamic bit width allocation method, In formula (7), Controls the bit width adjustment rate; when the system detects the lobulated features of white blood cells , automatically increase the convolution kernel weight width to 8 bits: In formula (8), W is the original weight matrix with a shape of C×d; is the quantized weight matrix, which is the same shape as W and reduces storage and computation costs through discretization; The quantization range threshold defines the upper and lower bounds at which weights are clipped. ; Weights outside this range will be constrained to the boundary value; To quantize the step size and control the fineness of the discretization, is the number of quantization bits; the spatiotemporal joint optimization acceleration equation, when the NPU activation conditions are met When heterogeneous acceleration is enabled: In formula (9), Transform coefficients Multiplexing factor for blood cell feature map; To calculate the total number of operations, the unit is ops; NPU main frequency, in Hz, represents the number of clock cycles that the NPU can execute per second; To calculate the complexity coefficient, characterize the average number of clock cycles per operation; NPU computing efficiency reflects the actual utilization rate of NPU. is the total amount of memory access, Memory bandwidth, which indicates the amount of data that can be transferred per second by the memory; is the memory access complexity coefficient, which represents the additional overhead of memory access; By solving a constrained optimization problem: In formula (10), the single frame processing time is guaranteed to be ; Establish a dynamic energy budget model: In formula (11), Enable probability of NPU controlled by sigmoid activation function; is the basic power consumption of the system; is the operating voltage of the NPU, which is usually positively correlated with the frequency; is the operating voltage of the NPU, which is usually positively correlated with the frequency; by solving the HamiltonJacobi-Bellman equation: In formula (12), the cost function , achieving optimal control with a battery life of 10.5 hours.

10. A portable real-time blood cell classification and counting method based on a big data algorithm, characterized by: A portable real-time blood cell classification and counting method based on a big data algorithm according to any one of claims 1 to 8 is characterized in that it includes the following steps: S1. Multimodal image acquisition and light source adaptation Peripheral blood is collected to make a single cell layer smear. A low-power microscope camera with a resolution of at least 1920×1080 and a dynamic spectrum fusion light source of a 450-650nm LED array are used to simultaneously capture RGB and near-infrared images. Combined with a nanolens assembly, in situ observation of peripheral blood red blood cell hemolysis rates of no more than 2% and a 60% increase in white blood cell nucleocytoplasm contrast is achieved. S2. Adaptive image preprocessing and noise removal CycleGAN generates adversarial examples to suppress salt and pepper noise and Gaussian noise, achieving a 98% suppression rate for salt and pepper noise and compressing the standard deviation of Gaussian noise to below 5. Simultaneously, combined with prior knowledge of white blood cell morphology, the threshold for nuclear-cytoplasmic region segmentation is enhanced, increasing the nuclear-cytoplasmic grayscale difference to 55%, preserving cell details while reducing background interference. S3. Lightweight network feature extraction and classification decision The Deformable Residual Shrinkage Network (DRSNet) was used to extract multi-scale features in parallel, including 3×3 depthwise separable convolutions, 5×5 deformable convolutions, and 7×7 dilated convolutions. The nuclear-to-cytoplasm ratio was used to guide the attention mechanism to enhance white blood cell features, increasing the signal-to-noise ratio of white blood cell features to 25dB. Combining the meta-learning MAML algorithm with the clinical knowledge graph, the recognition accuracy of rare blood cells was increased from 70% to 92% after just five training passes. The classification hyperplane was dynamically adjusted based on real-time cell morphology parameters to accommodate differences in staining methods, keeping the classification error within 1.5%. S4, real-time marker display and wireless collaborative diagnosis The edge computing unit outputs white blood cell classification results in real time, simultaneously generating pseudo-color markers with a marker delay of less than 10ms, and displays cell types and counts on a 3.5-inch retina screen. It also establishes an encrypted connection with the cloud-based pathology platform via Bluetooth 5.0, uploading cell morphology feature vectors to support remote expert consultations. The synchronization delay is less than 150ms, and the local test data anonymization processing efficiency reaches over 200 samples per second. S5, ultra-low power hardware and dynamic computing power management Using three-dimensional stacked heterogeneous integration technology, the image sensor, edge computing chip, lithium battery and micro heat sink are integrated into a 50×80×30mm³ body. The morphological entropy function Em is used to quantify the complexity of cell images and dynamically allocate computing power: in low-load scenarios, the computing power is reduced to 0.5TOPS, and the power consumption does not exceed 50mW. When detecting peak loads, NPU hardware acceleration is enabled, and the single-frame processing time does not exceed 12ms. Combined with a dynamic energy budget model to optimize the voltage and frequency combination, a continuous detection time of at least 10 hours is achieved.

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