67results about How to "Realize deep integration" patented technology

The invention discloses a multi-modal information fusion and dynamic updating group behavior analysis method. The method comprises the steps of information integration and dynamic updating, multi-modal information obtaining and information preprocessing, feature extraction, feature representation conversion, alignment and fusion, and multi-modal information real-time processing and dynamic updating through a stream processing framework. A self-learning mechanism and a self-optimization mechanism are introduced; group portrait generation: extracting population statistical characteristics, geographic position distribution, behavior and emotional psychological characteristics, social relationships, social media and economic characteristics from the multi-modal data to generate a group portrait; analyzing group behaviors; and outputting visualization, and outputting group behavior prediction results in a visualized manner, including a behavior trend graph, a group clustering graph, an association rule graph and an abnormal behavior detection graph. According to the method, multi-modal data from various sources and forms are effectively integrated and analyzed, self-learning and self-optimization mechanisms are introduced, and finally group behavior analysis is performed.

The invention discloses a fault diagnosis method and system based on a multi-scale adaptive hypergraph convolutional neural network, and belongs to the technical field of fault diagnosis. The method comprises the following steps: acquiring original vibration data of mechanical equipment, and carrying out data preprocessing; constructing a fault diagnosis model based on the multi-scale adaptive hypergraph convolutional neural network; and inputting the vibration data after data preprocessing into the constructed fault diagnosis model, and outputting a fault diagnosis result. According to the method, a vibration signal is decomposed into different frequency bands, features under various scales are extracted respectively, prior knowledge is fused, local topological graphs are constructed through various weighting measures, and a plurality of local topological graphs jointly form a spatial hypergraph to serve as input of a model; a dual-channel graph convolutional network is introduced to extract features, weights of different channels are adaptively distributed through an attention mechanism, and deep fusion of the features is realized. Experiments on two reference data sets show that the method can effectively improve the fault diagnosis accuracy, and has good stability and generalization.

The invention discloses a 5G message video monitoring method based on artificial intelligence. The method comprises the following steps: S1, constructing a multi-modal data set; s2, multi-modal fusion features are generated by using a joint convolutional neural network; s3, generating a target behavior trend curve and a target behavior prediction result by using the 3D convolutional neural network; s4, generating a target threshold curve by using an adaptive algorithm based on variable domain search, and obtaining behavior abnormality warning information; s5, generating comprehensive alarm information by using a method of combining fuzzy logic reasoning and a Bayesian network; s6, cross-camera data space-time correlation reasoning is carried out by using the graph neural network; s7, constructing an event severity curve by using a multivariable regression analysis and analytic hierarchy process, and generating a severity level and an event response measure of the event; and S8, pushing alarm information and event response measures to related personnel or equipment through the 5G message platform. The invention provides an efficient video monitoring method in combination with artificial intelligence and a 5G network.

The invention relates to the technical field of medical systems, in particular to an intelligent gastrointestinal postoperative intestinal obstruction diagnosis and intervention decision-making system and method, and the system comprises a data preprocessing module, a multi-modal data fusion module, a gastrointestinal function recovery model construction module, a self-adaptive progressive reinforcement learning optimization module AHRL and a diagnosis and intervention recommendation module. The self-adaptive progressive reinforcement learning optimization module optimizes an intervention strategy layer by layer through a five-layer progressive algorithm from the steps of dynamic adjustment of feature weights, recursive state propagation, layered accumulation optimization, symptom similarity matrix generation, progressive weight reverse feedback and the like, deep fusion of key symptoms by multi-modal data is achieved, and the accuracy of the key symptoms is improved. And the intervention scheme is updated in real time according to the patient state change. In the intervention decision recommendation module, the system generates personalized diagnosis suggestions based on the optimized strategy, carries out real-time risk assessment, and provides continuous and dynamic intervention support for the patient.

The invention provides a task planning method for an intelligent world model with a body based on visual reasoning, and the method combines the language processing capability of a large language model and an advanced visual perception technology, so that an intelligent agent can pre-reasone an environment state and simulate possible actions through the world model under the condition of obtaining a small amount of environment visual information; therefore, effective decision making and rapid adaptation to constantly changing environment states are realized in a complex environment, and the task execution efficiency and the task completion success rate of the intelligent agent in a complex scene are remarkably improved.

The invention relates to the technical field of measurement, in particular to an integrated sensing module for a dynamic weighing system, which comprises a physical domain, an environmental domain and a virtual domain, and realizes dynamic adjustment of parameters through cross-domain interaction so as to deal with high-frequency vibration and rapid temperature difference change in a complex environment. The physical domain is responsible for collecting multi-source data, the environment domain senses external interference in real time, and the virtual domain realizes time sequence feature extraction and dynamic model updating by combining the optimization capability of a recurrent neural network and reinforcement learning. According to the technical scheme, the real-time performance and adaptability of the system are remarkably improved, the environment change trend can be accurately predicted, the weighing strategy is adjusted in advance, and the error rate is controlled to be at an extremely low level. Meanwhile, by combining a negative feedback mechanism and objective function optimization, the sampling frequency is dynamically adjusted, and the energy consumption is reduced on the basis of guaranteeing the data processing efficiency. And deep fusion of wavelet decomposition, a dynamic normalization algorithm and a neural network model is adopted, so that the accuracy and stability of data processing are greatly enhanced.

The present application relates to the field of engineering detection technology, in particular to a deep compaction degree nondestructive testing method and system for large-thickness water-stable base course. A multi-source heterogeneous dataset of a to-be-detected area is obtained using an integrated detection device; a construction process compliance index and a process uniformity index of the to-be-detected area are obtained; each data source in the multi-modal nondestructive testing data is deeply corrected to obtain a deep correction compaction degree estimation set of each data source; the deep correction compaction degree estimation set of each data source is input into an adaptive weight fusion model to obtain a preliminary compaction degree distribution map; a fusion compaction degree distribution map of the to-be-detected area and a dynamic confidence score of each spatial point are calculated; the compaction quality of the to-be-detected area is determined, and a detection strategy optimization instruction is generated for the area with a confidence score lower than a confidence threshold. The present application can realize high-precision, high-confidence and engineering-actual nondestructive testing of deep compaction degree of large-thickness water-stable base course.

The application discloses a personnel trajectory drawing method and system based on an AI algorithm, and the method comprises the following steps: collecting positioning device signals and monitoring video data; performing dynamic Kalman filtering denoising processing on the positioning device signals to obtain first trajectory data, and performing self-adaptive light compensation enhancement processing on the monitoring video data to obtain first video data; performing trajectory prediction compensation on a signal interruption section based on an LSTM neural network; extracting personnel motion posture features and scene semantic features; constructing a space-time correlation graph by using a graph convolutional neural network to obtain enhanced trajectory data; performing multi-scale trajectory reconstruction based on an improved Transformer architecture to generate final personnel trajectory information; and outputting a visual trajectory report containing trajectory precision evaluation, abnormal behavior marking and electronic fence triggering events. The application realizes deep fusion of multi-source heterogeneous data and accurate reconstruction of trajectories, and significantly improves the continuity and reliability of personnel trajectory drawing in complex scenes.

The perception-assisted predictive handover method in the high-speed railway tunnel scene of the application belongs to the technical field of wireless communication and intelligent transportation, and the steps include: constructing a bistatic communication and perception integrated architecture, establishing a communication and perception signal model considering double selective channels, deducing a lower bound of Cramer-Rao based on joint time delay-Doppler parameter estimation to establish a perception-assisted location prediction model, designing a three-state predictive handover mechanism based on perception information, and proposing a perception-assisted dual deep Q network algorithm, jointly optimizing handover decision, clamp antenna configuration and power allocation. By integrating perception capability into handover decision, the application realizes predictive switching before signal degradation, significantly improving the handover success rate and communication throughput in the high-speed railway tunnel scene.

This invention relates to the field of medical image processing technology, specifically to an automatic segmentation and recognition method and system for cancer images based on the fusion of multimodal images. The method includes: acquiring a multimodal medical image set containing computed tomography (CT) images, magnetic resonance imaging (MRI) images, and positron emission tomography (PET) images; generating spatially aligned data blocks through three-dimensional spatial registration and voxel alignment; extracting fusion features using an improved encoder-decoder network optimized based on the characteristics and spatial dependencies of different modal channels; obtaining enhanced semantic feature maps through multi-level context-aware processing; identifying suspected cancer regions through a region candidate generation network; and simultaneously outputting pixel-level segmentation masks and cancer type probabilities through a segmentation network, ultimately generating a three-dimensional segmentation and recognition result. This method can accurately capture multimodal-specific features, clearly distinguish the boundaries between lesions and normal tissue, simplify the processing flow, reduce errors, and achieve accurate and efficient segmentation and recognition of cancer lesions.

The invention belongs to the technical field of groundwater reserve prediction, and particularly relates to a physical logic driven groundwater reserve intelligent prediction method and system. Comprising the following steps: performing complementation and physical verification on multi-source heterogeneous observation data related to groundwater reserves in a target area to obtain a physical verification complete data set conforming to physical logic; performing space-time decoupling causal contribution analysis on the physical verification complete data set to generate a causal contribution matrix; according to the causal contribution matrix, constructing and training a physical constraint emergence type space-time prediction model; applying a physical anchoring adversarial migration strategy to adapt the trained emergence type prediction model to the target new region to obtain a region adaptive prediction model; and inputting the future scene conditions into the regional adaptation prediction model for simulation, and generating a groundwater reserve prediction result. According to the method, intelligent prediction with high precision, high robustness and physical interpretability on groundwater reserves can be realized, and scientific decision support is provided for water resource management.

This invention discloses a multi-robot task scheduling method and apparatus for wind turbine blades based on a multi-level Stackelberg game model, relating to the field of wind turbine blade manufacturing technology. The method includes: constructing a Stackelberg game framework, assigning roles according to process dominance, with the edge-cutting robot as the first-level leader, the grinding robot as the second-level leader, and the roller-coating robot as the follower; the edge-cutting robot formulates an initial scheduling strategy based on an auction mechanism, and the grinding robot optimizes the strategy through a same-level auction, the combination of these two strategies forming the first-level game; the roller-coating robot determines the optimal solution based on the combined strategy of the first two, forming a second-level game with the grinding robot; a multi-level Stackelberg game model is established based on work efficiency, energy consumption, load balance, and conflict penalty terms; the game equilibrium is solved using backward induction, finally obtaining the optimal scheduling scheme for each robot. Using this invention can improve the overall work efficiency of wind turbine blade post-processing.

The present application relates to the technical field of three-dimensional packing, and more particularly to a three-dimensional packing method based on a graph neural network, a medium and an equipment, which solves the constraint perception defects of relying only on the spatial geometric state by constructing a heterogeneous relationship graph containing multiple complex constraints such as mutual exclusion, pressure bearing and temperature control, and generating relationship perception embedding vectors and global graph embedding vectors by using a graph neural network coding; the deep fusion of space and constraint information is realized by simultaneously fusing container space occupation, global constraint profiles and personalized constraint features of goods through decision state representation; the latest constraint topology and space conditions are ensured for each packing action by cyclically executing the packing action and dynamically updating the state, so that a complete packing scheme meeting multiple constraints is finally output, and the compliance and space utilization of the packing scheme are improved.

The invention belongs to the technical field of aircraft control, and particularly relates to a simulation test system and method for an aircraft obstacle avoidance algorithm. The invention discloses a simulation test system and method for an aircraft obstacle avoidance algorithm. The system comprises a virtual environment modeling device, a dynamics resolving device, an obstacle avoidance logic processing device, a physiological feature modeling device, a stress parameter mapping device, a comprehensive evaluation feedback device and a data monitoring device. According to the method, kinematic parameters are mapped into physiological stress parameters such as gravity overload and motion sickness, and an obstacle avoidance track is fed back and corrected according to a physiological comfort threshold, so that an obstacle avoidance path conforms to the tolerance limit of a human body on the premise of ensuring safety. According to the method, deep fusion of control engineering and aeronautical medicine is realized, obstacle avoidance assessment extends to the field of physiological tolerance from physical safety, the scientificity of simulation assessment of the manned aircraft is improved, and the risk of personnel disability caused by violent maneuver in real flight is reduced.

The invention relates to the technical field of chemical water treatment, in particular to a cloud side-end cooperative intelligent control method and platform for a chemical water treatment black light factory, and the method comprises the steps: constructing a digital twin model fusing equipment topology and operation parameters, and simulating and optimizing the optimal operation state of a system in an unattended and multi-unit cooperative scene; the edge sensing terminal is used for collecting water quality, energy consumption and equipment operation parameters of each process unit under dynamic working condition disturbance in real time, and the actual operation state of the system is accurately recognized through feature extraction and data fusion analysis; by comparing actual and optimal operation states, extracting deviation parameters, performing multi-dimensional analysis, identifying an operation instability mode and key influence factors, generating a self-adaptive cloud side-end cooperative control strategy and driving an intelligent execution device to realize closed-loop regulation, the response capability, the operation stability and the energy efficiency level of the system to complex disturbance are improved, and the energy efficiency of the system is improved. And unmanned, intelligent and collaborative optimization operation of the chemical water treatment process is effectively supported.

This invention belongs to the interdisciplinary field of medical image analysis and deep learning, and discloses a method and apparatus for pathological slide image segmentation using multimodal supervised contrastive learning. The method generates biological prior feature maps through decoupling, extracts visual and semantic features by combining the master encoder and prior encoder, and achieves feature alignment using a cross-modal attention mechanism. Supervised contrastive learning is introduced to enhance intra-class compactness and inter-class separability. A multi-scale decoupled decoder is designed, with structural similarity loss and boundary-aware loss respectively supervising structural and detail branches. Finally, dynamic weighted fusion and pseudo-label calibration strategies are used to optimize segmentation performance, significantly improving the accuracy and robustness of pathological image segmentation.

This invention relates to a method and system for collaborative optimization of AI services based on zero-cost agents. The method includes: acquiring the model structural features of the AI ​​service to be evaluated; using a pre-set non-trained evaluation model to quantitatively analyze the information transmission characteristics of the model structural features to obtain performance scores for each candidate model version; based on the performance scores and combined with the real-time status of computing resources, determining the deployment strategy of the AI ​​service instance on the target computing node through a deployment algorithm that balances queuing latency benefits and communication latency benefits; monitoring the service request load in real time; selecting the target version from the candidate model versions based on load changes and the performance scores; and performing online replacement of the deployed AI service. This invention, through a proxy model constructed with quantitative encoding, communication-queuing collaborative awareness deployment, and load awareness model replacement, realizes a link collaboration mechanism, which can effectively reduce end-to-end latency and improve resource utilization efficiency and service stability.

This invention relates to an in-memory computing circuit in the field of integrated circuits, and particularly to a multifunctional in-memory computing circuit and chip based on a 9T1C-SRAM cell. The multifunctional in-memory computing circuit includes five NMOS transistors N1-N5, four PMOS transistors P1-P4, and a capacitor C0. P1, P2, N1, and N2 form a latch structure; N3 controls row-direction read / write operations via word line WL and bit line BL; P3, P4, N4, and N5 are used to implement Boolean logic write-back, row-direction read / write, and multiply-accumulate functions. This invention's multifunctional in-memory computing circuit simultaneously supports Boolean logic write-back, signed multiply-accumulate (MAC) operations, and bidirectional row and column read / write, achieving deep integration of logical operations, arithmetic operations, and data movement within the same memory cell.

The invention provides a supply chain multimodal transport intelligent data system and method, and belongs to the technical field of supply chain management. The invention provides a supply chain multimodal transport intelligent data system. The system comprises a unified data base module, a core calculation engine module, an intelligent decision engine module, a hierarchical alarm response module and a closed-loop optimization module. The unified data base module is used for accessing multi-source heterogeneous data and cleaning, standardizing and fusing the multi-source heterogeneous data to form a global unified data view; the core calculation engine module is used for performing data processing and feature extraction on the global unified data view; the intelligent decision engine module is used for carrying out prediction analysis on the extracted feature data and generating a prediction result and a risk score; the grading alarm response module is used for comparing the risk score with a preset threshold value, triggering an alarm and executing grading response; and the closed-loop optimization module is used for returning feedback data generated by execution and intervention to the unified data base module.

This invention provides a power terminal data authentication method and system based on dynamic binding of network identifiers. First, it acquires a set of terminal operation and associated network topology data of the target power terminal within a preset time window. Dynamic network identifier generation parameters, including characteristics such as terminal state fluctuations, are generated from the terminal operation data. Based on these dynamic network identifier generation parameters and the network topology data, dynamic network identifier allocation is performed, generating a dynamic network identifier sequence associated with a sub-time period. This dynamic network identifier sequence is dynamically bound to the terminal operation data, generating a power terminal data set with time-series authentication tags. The tags are used to verify data integrity and source legitimacy. The tagged data is dynamically authenticated and matched according to preset rules, generating authentication results and policy adjustment parameters to adjust the generation parameter logic. Finally, the generation parameters are updated based on the results and parameters and synchronized to the terminal, triggering the data authentication iteration for the next time window.

The invention discloses a geological disaster slope reinforcing structure with a high-stability load, and belongs to the technical field of geological disaster protection. The structure comprises a protective net arranged at the slope bottom, and a plurality of cement beads, fixed pulley assemblies and slope top fixing columns which are arranged between the protective net and the slope top; the cement ribs are mounted through the elastic anchoring assemblies and can generate elastic displacement along the slope surface when being pulled; the suspended rope is connected with the top of the protective net, bypasses the fixed pulley and is fixed on the cement rib; the buried rope is connected with the top of the protective net, penetrates through the bottom of the cement rib from the lower portion and is finally connected to the fixing column. When the protective net bears loads and tends to turn over, the buried ropes are tensioned and drive the cement protruding edges to move, the force direction is converted through the fixed pulleys, the suspended ropes are promoted to generate reverse tension, and therefore the rollover force is counteracted. Dynamic dispersion and feedback compensation of the load on the whole slope are achieved through linkage cooperation of micro-displacement of the cement protruding edges and the rope system.

A kind of new water level measurement environmental monitoring operation system of port, including data acquisition module, data processing module, data display module, early warning control module and operation equipment;The data acquisition module is electrically connected with data processing module, the data processing module is respectively connected with data display module, early warning control module, the early warning control module is connected with operation equipment communication;The data acquisition module includes weather monitoring part and radar liquid level meter;The weather monitoring part is used to collect the wind speed, temperature, humidity, PM2.5 concentration data of wharf site;The radar liquid level meter is used to collect the real-time distance data with water surface, and the real-time water level of wharf is calculated in combination with the preset "design highest water level point" and "transfer station layer height distance".The utility model aims at overcoming the problem that environmental monitoring data is dispersed in field production operation, early warning is not timely, and is disjointed with operation process, realizes the real-time acquisition, processing, display, early warning of water level and multidimensional environmental data, and links control operation equipment, improves the safety and reliability of wharf production operation.

The invention discloses a strengthening process method and system for 3D printing interruption repair, and belongs to the technical field of 3D printing. The method comprises the following steps: polishing an initial top layer of a printed part; thermal properties of the printing material are obtained, and thermal activation parameters are determined according to the thermal properties; performing thermal activation treatment on the polished initial top layer according to thermal activation parameters; and performing continuous printing on the initial top layer after thermal activation treatment. An oxide layer and a rough structure can be removed through polishing, and precise butt joint on the geometric level is achieved; the initial top layer is subjected to thermal activation treatment, macromolecular chain segments on the surface of the old material are unfrozen and enter a high-elastic state or a viscous flow state, during continuous beating, new and old materials are subjected to mutual diffusion and molecular entanglement at the joint face, an original straight physical interface is converted into a wavy transition area with a certain thickness, and metallurgical-grade or macromolecular-grade deep fusion is achieved. And the bonding strength of a continuous bonding interface is remarkably improved, residual thermal stress is effectively reduced, and secondary warping and cracking are prevented.

The present application belongs to the technical field of unmanned aerial vehicle navigation, in order to solve the problem that the existing complex tunnel environment unmanned aerial vehicle path planning fails to mine BIM semantic information, etc., an unmanned aerial vehicle navigation method and system based on semantic BIM and multi-sensor fusion are proposed, the tunnel original road BIM model is subjected to lightweight processing, the geometric entity extraction and attribute set are adopted to analyze the component data of the tunnel BIM model; the three-dimensional grid map is used as a global map template, the current collected point cloud data is used to adjust the unmanned aerial vehicle attitude, the unmanned aerial vehicle accurate pose is obtained, and then a dynamic environment map is constructed; based on the static semantic cost, the dynamic obstacle space-time prediction cost and the channel passing cost, a path search algorithm is used for path planning. The present application combines priori semantics and real-time obstacle avoidance requirements for path optimization, realizing high-precision and high-safety flight inspection of the unmanned aerial vehicle in the complex tunnel environment.

This application belongs to the field of industrial internet security technology and relates to a method for constructing an industrial internet security risk knowledge graph, aiming to solve the problems of missing industrial features, poor dynamism, and risk assessment being detached from process in existing technologies. The method includes: acquiring and preprocessing multi-source heterogeneous data to generate equipment association identifiers, vulnerability association identifiers, and process node identifiers, forming a standardized dataset; extracting initial triples based on the standardized dataset and performing a two-stage entity merging; validating the merged triples according to process rules to obtain valid triples; writing the valid triples into a graph database and writing runtime sequence data into a time-series database; establishing a mapping between entities and runtime sequence data through equipment association identifiers to generate an industrial internet security risk knowledge graph. This application achieves unified data association, accurate entity fusion, and graph-time-series collaborative storage, improving the accuracy of graph construction and risk assessment capabilities.

This invention relates to the field of food processing technology, specifically to an automated pasta processing production line, including an automatic dough mixer, a dough conveyor, a dough pressing machine, a dough pressing elevator, a dough pressing and slitting machine, a square steamed bun production line, and a round steamed bun production line. The round steamed bun production line includes a dough conveyor, an elevator, a steamed bun forming machine, a steamed bun shaping machine, and a round steamed bun plating machine. The square steamed bun production line includes a dough block conveyor, a bidirectional stacking machine, a dough pressing machine, and a conveyor. The automated pasta processing production line is automatically controlled by a control system, which includes a centralized scheduling module, a raw material supply module, an intelligent dough mixing module, a rolling and conveying module, an adaptive forming and processing module, an intelligent plating and loading module, and a proofing and steaming module. This invention integrates functions such as automatic quantitative flour application, automatic constant temperature quantitative water addition, automatic dough mixing, a multi-functional forming system, automatic plating, automatic loading, and automatic proofing and steaming, thus improving the automation level of the equipment.

This invention provides a method for identifying ground faults in distribution network lines based on a time-series large model, comprising: Step S1: data acquisition and spatiotemporal alignment; Step S2: multi-scale sliding window segmentation; Step S3: calculation of physical enhancement features; Step S4: construction of a training sample set; Step S5: construction of a time-series large model with physical information enhancement; Step S6: model training; Step S7: real-time acquisition and identification of ground faults. This invention achieves high-precision, low-false-alarm ground fault detection, accurately identifies fault types and faulty lines, and effectively solves the identification challenges of extreme scenarios such as intermittent arc grounding and high-resistance grounding.

The invention discloses a wireless network coverage automatic planning method and system based on line characteristics. The method comprises the following steps: establishing an interval initial line segmentation model according to discrete line mileage information; detecting and judging whether the distance between the adjacent road sections of the same type is smaller than a preset engineering threshold value or not, and if yes, combining the two adjacent physically separated road sections to obtain an optimized line segmentation model; traversing the optimized line segmentation model, and calculating the deployment mileage of all wireless devices of each road section based on a preset layout rule according to the road section type; on the basis of all wireless device deployment mileages of each road section, a wireless network device entity is automatically generated in a CAD drawing, and an annular wireless network topology is automatically constructed. By deeply integrating the line feature data, the design rule and the CAD platform, a complex manual design process is converted into an efficient, accurate and intelligent automatic process, and the quality and efficiency of wireless network design of linear engineering such as railways are improved.

The invention relates to the technical field of artificial intelligence, and provides a visual information injection method and device based on a multi-modal large model, and the method comprises the steps: inputting a visual object into a visual encoder in a visual fusion model, and obtaining a visual feature sequence outputted by the visual encoder; inputting the visual feature sequence into a visual steering rotation matrix generator corresponding to at least one layer of the large model in the visual fusion model to obtain a visual steering rotation matrix generated by the visual steering rotation matrix generator based on the learnable query vector sequence and the visual feature sequence; and determining a second parameter vector of the corresponding layer based on the visual steering rotation matrix and the first parameter vector of the corresponding layer in the large model, and realizing visual information injection by using the second parameter vector and the text feature sequence. On the premise that the length of the input sequence is not increased and the structure of the large model is not damaged, deep fusion of vision and language is achieved, and the efficiency and performance of the large model in multi-modal tasks such as image question answering and video understanding are improved.

The embodiment of the present application relates to the technical field of electro-acoustic products, and discloses a loudspeaker module and electronic equipment. The loudspeaker module comprises an upper cover and a lower cover which jointly form a containing cavity; a support plate is arranged in the containing cavity, the support plate divides the containing cavity into a first cavity and a second cavity; a first diaphragm in the first cavity and a second diaphragm in the second cavity are further arranged in the containing cavity; a armature is further arranged in the containing cavity, the armature penetrates through the support plate and is fixed on the support plate, a magnetic piece and a coil are arranged on the armature, the coil magnetizes the armature in a powered state, and the magnetized armature reciprocates under the magnetic force of the magnetic piece; a first connecting rod and a second connecting rod are fixed on the armature, the first connecting rod is in the first cavity, and the first connecting rod drives the first diaphragm to vibrate along with the reciprocation of the armature; the second connecting rod is in the second cavity, and the second connecting rod drives the second diaphragm to vibrate along with the reciprocation of the armature, so that the integration of the loudspeaker sound unit is realized.