305results about How to "Lower deployment costs" patented technology

The invention discloses a multi-task electromagnetic model based on a hybrid expert network, and belongs to a wireless communication technology. The model comprises a preprocessing module, a feature extraction module, a task output module and a pre-training-fine tuning learning strategy. The preprocessing module carries out standardization processing on the multi-source electromagnetic signals; the feature extraction module is based on a Transform structure, introduces a hybrid expert network to replace part of a traditional feedforward neural network, and dynamically selects an expert sub-network through a task specific routing mechanism; the task output module configures a special structure according to different task targets; in the pre-training stage, a mask auto-encoder is used for pre-training large-scale label-free data, and a downstream task is subjected to full-amount fine adjustment through small-scale label data. According to the method, multi-task collaborative learning and differential expression are realized, and the recognition performance, robustness and processing efficiency of the model in a complex electromagnetic environment are improved.

The invention discloses an optical communication system and an optical communication method. An optical communication system includes a first device, an optical splitter, and a plurality of second devices. The first device is used for sending a first optical signal, the first optical signal is respectively transmitted to the plurality of second devices after being shunted by the optical splitter, and the first optical signal carries an analog radio frequency signal. The plurality of second devices are used for sending second optical signals respectively, the plurality of second optical signals are transmitted to the first device after passing through the optical splitter, and each second optical signal carries an analog radio frequency signal. The optical signal for communication between the first device and the second device bears the analog radio frequency signal, and the analog radio frequency signal does not need to be converted into a digital signal, packaged into an optical protocol frame format and then modulated into the optical signal. In this way, communication between the first device and the second device does not involve the optical protocol conversion process of optical protocol frame format encapsulation and de-encapsulation, the service delay of the system is reduced, the system structure is simpler, and power consumption and cost are reduced.

The invention discloses a chirp interval modulation backscatter communication method and a chirp interval modulation backscatter communication system based on variable-length scrambling codes, which mainly solve the problems of high power consumption of backscatter labels and high system deployment cost in the prior art. According to the scheme, a sending end sends an environment LoRa signal; the backscattering label modulates information to be sent to an environment LoRa signal in a time interval modulation mode, and modulates a variable-length scrambling code to an anchor symbol; a receiving end performs frequency shift compensation on the backscattering signal by adopting a frequency shift compensation method, demodulates the backscattering signal by combining a sliding window with a variable-length scrambling code, intercepts a backscattering signal sample through the sliding window, and performs de-spreading and fast Fourier transform (FFT) on the signal in the window; the position of an anchor symbol is determined by detecting the maximum value of an FFT peak value, information sent by the backscattering label is recovered from the position of the anchor symbol, and data demodulation is completed. On the premise of keeping demodulation reliability, the power consumption of the tag is effectively reduced, the deployment cost of a backscatter communication system is reduced, and the method can be used for ultra-low power consumption Internet of Things communication.

The invention relates to a quantum key management method and system for satellite internet biological characteristics, and the method comprises the following steps: carrying out the registration of biological characteristics and the initialization of communication protocol parameters for a user terminal which is accessed for the first time; for the registered user terminal, before initiating communication each time, binding the real-time biological characteristics with the quantum key, and carrying out encrypted transmission of the session key by using the bound quantum key, so as to carry out encrypted transmission of satellite internet transmission data by using the session key; and in the encryption transmission process of the satellite internet transmission data, according to a preset period, performing satellite link security level adjustment and quantum key dynamic updating. According to the invention, identity authentication and terminal authentication in a high-delay and narrow-bandwidth application scene can be realized, the security problems of identity counterfeiting, key leakage and the like are solved, and the method can be applied to satellite internet scenes such as emergency communication, field operation and the like, and has the advantages of strong compatibility, low deployment cost and the like.

The invention relates to the technical field of thermoelectric Internet of Things security, in particular to a thermoelectric Internet of Things terminal data credible situation awareness and protection platform, which comprises an integrated architecture for constructing a distributed probe acquisition layer, a behavior modeling and knowledge base, a dynamic trust evaluation engine, a situation awareness and decision center and a credible traceability module. Multi-dimensional trust evaluation is realized by fusing terminal behavior consistency analysis, service logic compliance verification and group behavior correlation detection, a self-adaptive weight adjustment mechanism is introduced to adapt to scene change, a hierarchical protection strategy is executed based on a dynamic credibility score, and non-interruption of network disconnection protection is guaranteed by relying on edge calculation. According to the method, the data credible sensing precision is improved, dynamic differential protection is realized, the operation and maintenance cost is reduced, multiple thermoelectric scenes are adapted, and a safety guarantee is provided for stable operation of a thermoelectric Internet of Things system.

The invention relates to an audio processing method and device, a storage medium, a controller and a vehicle, and the method comprises the steps: receiving an audio signal inputted by a sound source, determining a processing mode of the audio signal according to the type of the sound source, processing the audio signal based on the processing mode, and obtaining a processed audio signal, and finally, the processed audio signal is transmitted to an audio playing assembly of the target sound area for playing, and the audio playing assembly comprises loudspeakers installed at at least two different target positions, so that a three-dimensional sound field space is constructed in the target sound area. Therefore, the audio processing effect can be improved on the basis of saving the cost, and an independent sound area and three-dimensional audio experience are provided for a user.

The invention discloses an aero-engine vibration signal generation method under extreme imbalance, and belongs to the technical field of aero-engine fault signal generation. The method comprises the following steps: acquiring an original vibration signal at a target position of the aero-engine, and performing preprocessing operation; taking the preprocessed real vibration signal as a training sample, constructing a forward noise adding process, and training the improved one-dimensional diffusion generation network to learn a reverse denoising mapping relation; and inputting random Gaussian noise into the one-dimensional diffusion generation network after training convergence, and generating a target vibration signal matched with a real vibration signal feature through a reverse diffusion denoising process. Through targeted improvement of the diffusion generation network, the problem of data imbalance is effectively relieved, the model deployment cost is reduced, the signal generation capability of the model for minority types of faults is improved, and the method is suitable for fault signal generation scenes of aero-engines under complex working conditions.

The invention relates to a power distribution network fault distance measurement system and a monitoring method thereof, and the system comprises a distributed monitoring terminal network, a master station data processing center and a communication network which cooperate to achieve the collection, processing and positioning analysis of fault signals. According to the invention, through a fault branch identification strategy combining distributed power supply side cooperative monitoring and power flow direction analysis, the problem of fault branch identification of the power distribution network containing the distributed power supply is effectively solved; the relative time synchronization calibration technology is adopted, a high-precision satellite synchronization module is not needed, the satellite signal interference problem is avoided, meanwhile, the hardware deployment cost is reduced, the system adopts the dual-mode communication and standby power supply design, the system adapts to complex operation environments such as cities and underground, and the reliability is high; the monitoring terminal adopts an ARM + FPGA dual-core architecture, gives consideration to real-time performance and data processing capability, can be directly connected with existing power distribution network equipment, and is low in transformation difficulty and flexible in deployment; the parameter self-correction function enables the system to adapt to line parameter change and environmental influence, and the long-term distance measurement precision is stable.

The invention provides a cross-border trade supply chain risk early warning system based on digital twinning, and relates to the technical field of cross-border trade. The cross-border trade supply chain risk early warning system based on digital twinning comprises an entity layer, a data acquisition layer, a digital twinning modeling layer, a data processing layer, a risk early warning layer, a safety protection layer and an application output layer, and the entity layer is a monitoring object basis of the system and covers all key physical objects in a cross-border trade supply chain; comprising suppliers, logistics carriers, storage facilities, customs nodes and purchasers in the cross-border trade supply chain. The system integrates a multi-source data acquisition module, covers full-dimensional data of policies, logistics, finance, markets, Internet of Things perception and the like, ensures the comprehensiveness of risk factor extraction, improves the coverage of risk identification, greatly improves the accuracy and real-time performance of early warning, and solves the problem that a traditional early warning mode is inaccurate and lagged in evaluation.

The invention relates to the technical field of lasers, and provides a double-gain-medium Q-switched laser and a constant-power pulse width adjusting method.The laser comprises a first pumping source, a second pumping source, an optical fiber beam combiner, a beam shaping system and a laser resonant cavity, the laser resonant cavity comprises a reflecting mirror, a first gain medium, a second gain medium, a polarization selector, a quarter-wave plate, a Q switch and an output mirror which are arranged in sequence; the first gain medium is used for absorbing first pumping light, the second gain medium is used for absorbing second pumping light, and the pumping power of the first pumping source and the pumping power of the second pumping source can be independently controlled. By changing the pumping power of the two pumping sources, real-time adjustment of the pulse width of the laser under the constant output power is achieved, the structure is compact, and control is easy and convenient.

The invention discloses a cross-city traffic prediction method and system based on multi-modal fusion and spatial expert routing, relates to the technical field of traffic prediction, and provides an adaptive modal selection mechanism based on a signal-to-noise ratio for the problems of missing, noise and uneven quality of multi-modal data in different cities. And a low-quality mode is dynamically suppressed in combination with comparative learning, and robust multi-mode fusion is realized. In order to solve the problems of large space structure difference and weak generalization ability among cities, a multi-modal guided space expert routing architecture is designed: modal sharing experts and routing experts are activated by using a multi-modal context, and local space dependence is adaptively modeled for different functional regions. The method supports any modal combination input, city specific fine tuning is not needed, and the zero sample cross-city prediction performance is significantly improved.

The invention relates to the technical field of Internet of Things communication, discloses an Internet of Things multi-Android client state synchronization method, and solves the problems of poor state consistency, high back-end dependence, large delay and insufficient compatibility in the prior art. According to the scheme, the method comprises the steps that after a client completes initialization, MQTT connection with a rear end is established, an MQTT state message pushed by the rear end is monitored, after the message is received, equipment state information is obtained through analysis, local state cache and a UI interface are updated, meanwhile, the client serves as a distribution node, a UDP synchronization message containing the equipment state and the IP of the distribution node is established, and the UDP synchronization message is sent to the client. The UDP synchronization message is broadcasted to the local area network, other clients in the local area network receive the UDP synchronization message and then verify whether the distribution node IP is the client in the local area network, if the verification is passed, the message is analyzed, the state updating timestamp and the local cache timestamp are compared, and the local state cache and the UI interface are updated only when the state updating timestamp is larger.

The invention relates to an out-of-levelness estimation method, device and equipment of an inclinometer and a medium, and belongs to the technical field of inclinometers. The method comprises the following steps: in a hardware environment without a gyroscope, acquiring initial attitude angle information through accelerometer data sampling and alignment; performing attitude matrix construction, navigation coordinate system projection, Kalman filter modeling, attitude error calculation and error compensation to equivalently realize a gyroscope-free zero-velocity correction effect, and obtaining a final attitude angle; a self-adaptive Kalman filter based on gravity acceleration module value constant constraint is provided, so that the influence of external interference on the measurement precision can be reduced; and finally, performing smooth filtering on the final attitude angles at a plurality of adjacent sampling moments, and outputting high-precision and high-stability unlevelness. According to the method, under the condition that the hardware cost is not increased, the measurement precision and stability of the inclinometer on the out-of-levelness are improved.

The invention discloses a wireless network positioning method, server and system, and relates to the technical field of wireless communication and positioning navigation. The method comprises the following steps: receiving a signal sent by a to-be-positioned target node through a plurality of positioning reference nodes and extracting an original positioning parameter sequence; then, improved Z-score exception elimination based on median and median absolute deviation and adaptive attenuation coefficient smoothing based on average inter-frame hopping are carried out on the sequence, and a stable estimated value and a measurement variance are output; and finally, according to the number of the effective positioning reference nodes, adaptively switching a CHAN algorithm or a weighted least square method to carry out position calculation. The improved Z-score filtering and dynamic weight fusion technology is adopted, the defects in the prior art are overcome, the problem that a fixed algorithm is poor in robustness when the reference node topology change is positioned is solved through an algorithm self-adaptive switching mechanism, the precision and adaptability are remarkably improved in a complex scene, and meanwhile the deployment and maintenance cost is reduced.

The application provides an intelligent substation anomaly detection method based on meta-learning technology, first, a multi-working condition task library is constructed for meta-training. Secondly, a double meta-knowledge learning framework is designed to learn task-level model initialization parameters suitable for rapid adaptation. Subsequently, online reconstruction error driven anomaly detection is deployed, and the adapted model is used to calculate data reconstruction error in real time, and combined with the adaptive threshold to accurately distinguish and alarm grading. Finally, the system supports a dynamic updating mechanism, which continuously collects verified samples and periodically triggers meta-updating. The application significantly reduces the data dependence and deployment cost in new scenarios, improves the model's generalization ability to multiple working conditions and sensitivity to rare anomalies through meta-knowledge sharing, and ensures the long-term effectiveness of the detection system through online learning mechanism, providing an efficient, adaptive and evolutionary security protection solution for intelligent substations.

The invention discloses a dual-mode communication method and system based on intelligent decision making. The method comprises the following steps: acquiring historical operation data and actual operation data of HPLC (High Performance Liquid Chromatography) and HRF (High Performance Framework); creating a reinforcement learning model which comprises a state space, an action space and a reward function; the state space is a combined vector of a dual-mode channel quality index, the action space comprises HPLC priority, HRF priority and dual-mode self-adaption, and the reward function is a weighted calculation formula of throughput, power consumption and packet loss rate; and performing iterative training on the reinforcement learning model based on the historical operation data to obtain an intelligent optimization model, inputting the actual operation data into the intelligent optimization model to obtain an optimal action, and adjusting the dual-mode communication parameters according to a corresponding preset parameter adjustment rule. According to the invention, communication mode selection and parameter adjustment are more suitable for actual environment requirements, the communication efficiency is improved, the energy consumption and data loss risk are effectively reduced, the system architecture is simplified, and the deployment cost and response delay are reduced.

This invention discloses a heterogeneous acceleration system and method for analog modulation recognition of large-scale multi-channel signals, comprising: a CPU host processing end and a GPU device computing end; the CPU host processing end is used for asynchronous pipelined processing of data reading, task scheduling, and result distribution, realizing parallel execution of data reading, GPU computing, and result distribution on the time axis; the GPU device computing end completes the entire process of signal preprocessing and deep learning model inference within the GPU memory after the original signal is transmitted to the video memory via the PCIe bus, and does not exchange data with the CPU host processing end until the recognition result is generated. This invention eliminates data transport and I / O blocking through a three-stage asynchronous pipeline architecture and a closed-loop processing link in the entire video memory, and combines parallel preprocessing operators, inference acceleration engines, multi-stream scheduling mechanisms, and dynamic video memory pools to achieve high throughput and low latency recognition of large-scale multi-channel analog modulation signals.

The application belongs to the field of artificial intelligence model compression and edge deployment in intelligent operation and maintenance and fault diagnosis of power equipment, and discloses a transformer cooperative distillation incremental learning method and system based on time sequence consistency, which comprises the following steps: a teacher model is used to screen a no-label sample set to obtain a pseudo-label sample set, the pseudo-label sample set is combined with an original label sample set to obtain a distillation training sample set; a multi-mechanism cooperative distillation training scheme containing soft and hard label joint distillation, time sequence consistency distillation and multi-task distillation is constructed, and a student model is subjected to distillation training through the multi-mechanism cooperative distillation training scheme; and a cooperative distillation incremental learning scheme in which a cloud end continuously updates a teacher model to adapt to new multi-source monitoring data and a student model after edge distillation generates a pseudo-label to expand the distillation training sample set is constructed. The application can be used for realizing high-precision and low-cost training and continuous updating of a student model in a resource-limited terminal or an online scene.

The invention discloses a battery state and early fault diagnosis system based on multi-source data fusion and artificial intelligence. The battery state and early fault diagnosis system comprises a data acquisition module, a feature extraction module, an artificial intelligence processing module and an early warning and execution module. The data acquisition module is responsible for synchronously acquiring voltage, current and temperature data from a battery pack in real time to form a multi-source data set for describing the real-time running state of the battery; the feature extraction module receives an original data stream from the data acquisition module. According to the method, the limitation of a traditional method based on a simple model or threshold judgment is broken through, the strong nonlinear mapping capability of the artificial intelligence model is utilized, and the deep correlation with the internal health state and the early fault of the battery is accurately mined from the high-dimensional features; according to the method, the estimation precision of the health state of the battery is remarkably improved, early weak fault symptoms which cannot be perceived by a traditional method can be identified, the crossing from post-event alarm to pre-event early warning is realized, and the safety of the system is greatly improved.

The application relates to the technical field of industrial visual inspection and edge computing, and provides a distributed storage and hardware cooperative deployment method and system of an industrial quality inspection multi-task visual model, which comprises the following steps: constructing a multi-task visual model; storing model weight files of a shared backbone network in a first storage partition in a read-only mode; storing task head weight files of a plurality of lightweight task heads in the form of independent files in a second storage partition in a distributed mode; determining a target task identifier corresponding to a current product to be inspected; loading the shared backbone network and a task head corresponding to the target task identifier by edge computing hardware; inputting a product image into the shared backbone network and extracting general visual features of the product image; inputting the general visual features into the task head corresponding to the target task identifier in parallel to obtain classification results of the task heads; and determining the quality state of the current product to be inspected according to the classification results. Thus, systematic cost reduction is achieved.

This invention discloses a parallel optimization system for massive video processing. The system adopts a four-layer integrated parallel and collaborative processing architecture, comprising, from top to bottom: a parallel task layer for video stream access, grouping, splitting, encapsulation, and queue management; a resource abstraction layer for unified hardware modeling, status acquisition, topology construction, and capability assessment; a parallel scheduling layer for task-hardware matching, load balancing, priority scheduling, and dynamic adjustment; and an execution optimization layer for data stream localization, parallel read / write, cache optimization, and zero-copy processing. The parallel task layer, resource abstraction layer, parallel scheduling layer, and execution optimization layer form a complete parallel processing link: task input, resource awareness, accurate scheduling, and optimized execution. This invention implements concurrent rate limiting and smooth access for the input video stream to prevent traffic surges; and sets synchronization points and timing control for parallel tasks to ensure orderly output.

This disclosure provides a QoS parameter configuration method, a relay terminal, a communication system, and a storage medium. The method includes: receiving first QoS confirmation information sent by a next-hop node; and sending second QoS confirmation information to an upstream node. The second QoS confirmation information includes: QoS parameters between the upstream node and the target terminal, and QoS parameters between the relay terminal itself and the upstream node. This disclosure allows the relay terminal to add QoS parameters between the upstream node and the target terminal to the QoS confirmation information. The upstream node can then determine whether the previously sent end-to-end QoS parameters between itself and the target terminal meet the requirements, thus satisfying the QoS demands corresponding to the end-to-end QoS parameters. For U2U multi-hop scenarios, a QoS parameter configuration scheme is proposed, which can fully utilize terminal resources, ensure service quality, and improve user experience.

The application relates to the technical field of data processing, and discloses a receiving multi-channel phase fast calibration compensation method, which comprises the following steps: sending original sampled data into a digital down-conversion module, sending filtered data into a peak value searching module, calculating a signal phase, calculating corresponding sine and cosine values, and outputting calibrated data after the down-converted data are multiplied with the sine and cosine values calculated in step S4 through a phase compensation module; the receiving multi-channel phase fast calibration compensation method can perform real-time phase calibration compensation, can perform phase calibration compensation on multiple different frequency points respectively, and greatly improves the calibration precision and the calibrated performance; the method can enable a large phased array system to be quickly deployed, greatly saves the time spent in calibrating the phase, can also reduce the uncertainty caused by human measurement errors, and greatly improves the stability of the system.

The invention relates to a method for identifying left and right manipulators for multi-finger touch screen interaction, which comprises the following steps of: S1, acquiring touch data through an API (Application Program Interface), and identifying the number of fingers and action types based on the touch data; s2, all the fingers are rearranged, all the adjacent two fingers serve as a group, N-1 two-finger groups are obtained, and N is the number of the fingers; s3, for each two-finger group, constructing a spatial triangle based on the positions and orientations of different fingers at the same moment, extracting spatial features based on the spatial triangle, constructing a time triangle based on the positions and orientations of the same finger at different moments, and extracting time features based on the time triangle; s4, respectively inputting the time features and the space features of the two-finger groups into first classifiers with independent values to obtain a plurality of first classification results in one-to-one correspondence with the number of the two-finger groups; and S5, fusing the first classification results of all the two-finger groups to obtain a left and right hand classification result. Compared with the prior art, the method has the advantages that the accuracy can be improved under the conditions of not depending on an external sensor, not needing calibration and not needing to access a privileged API (Application Program Interface).

The application relates to the technical field of data transmission control, and discloses a DICOM image routing method and system based on port multiplexing. By binding a shared DICOM port and listening to a connection request, multiple backend DICOM receiving nodes can share a unified access port, a command set and a data set are selectively parsed according to a command type, a structured session context is constructed, routing judgment is upgraded from a traditional coarse-grained mode based on a source IP and a source port to a fine-grained mode based on protocol semantics, a routing decision module adopts a multi-field rule matching and index acceleration mechanism, can improve rule screening efficiency and reduce matching delay in a scene with more concurrent connections, and a routing optimization module dynamically evaluates in combination with running state information and transmission performance information of candidate target DICOM receiving nodes, so that a communication bottleneck can be avoided, in which a fixed transmission path is long-term concentrated on part of nodes.

The application relates to the technical field of wireless sensing, and discloses a human posture estimation and non-contact vital sign monitoring method and system based on CSI. WiFi channel state information (CSI) is collected, and dynamic CSI feature vectors are obtained through preprocessing. Features are mapped to human key point feature space through Transformer cross attention, spatial reasoning is performed through graph convolutional neural network fusion of human skeleton topology constraints, and human posture estimation results are output. Respiratory rate and heart rate are extracted based on multi-band frequency domain analysis, and vital sign monitoring results are output after being optimized in combination with posture information. Without cameras and wearable devices, non-contact human perception that is privacy-safe and wall-penetrating can be realized, and the method has the advantages of high precision, strong generalization and low-cost deployment, and is suitable for smart home, medical monitoring, emergency rescue and other scenes.

The invention provides a mobile robot unknown static target surrounding control method based on image vision, which comprises the following steps: acquiring an image containing an unknown static target, and extracting pixel coordinates of target feature points in an image plane; based on the internal reference of the monocular camera, converting the pixel coordinate into a normalized image plane coordinate, and constructing an image error signal according to the normalized image plane coordinate and an expected steady-state coordinate corresponding to the expected surrounding radius; calculating a forward linear velocity control quantity and a steering angular velocity control quantity of the mobile robot; the control quantity is output through a closed-loop control structure so as to synchronously realize two control targets: driving an image error signal to converge to an expected steady-state coordinate, and ensuring that a target feature point is always in an effective view range of a monocular camera in the whole surrounding control process; and outputting the forward linear velocity control quantity and the steering angular velocity control quantity obtained by calculation to a motion execution mechanism of the mobile robot, and driving the mobile robot to execute fixed-distance surrounding control on the unknown static target at an expected surrounding radius.

The utility model provides a kind of nurse station monitoring call alarm, the utility model relates to medical instrument technical field, including substrate and alarm component, substrate is provided with presser along side end edge, substrate is fixed with bottom lining on the same side of presser, bottom lining extends to the end of presser with the elastic clamp plate end of relative interior angle size, so that the plane of presser relative to substrate is in the state of warping;Alarm component is set on bottom lining;The utility model has the beneficial effect that structure is based on press touch switch to realize the monitoring of patient bed state, i.e. When patient leaves bed, the pop-up signal of press touch switch is sent to nursing station terminal, which is convenient for relevant nursing staff to respond in time, and the alarm structure is simple and convenient to lay out, the overall layout and use cost are low, and it can be installed in existing medical system bed end, which improves the practicability and applicability of the alarm.

The utility model discloses an intelligent bird repelling device for insulator, include: power module, with main control module electric connection, be used for to main control module power supply, vibration detection module, with main control module electric connection, be used for detecting the vibration signal of bird pecking insulator when, bird module, including sounding unit and light unit, main control module and bird module electric connection, be used for according to vibration signal drive sounding unit and light unit to respectively produce different frequency sound and rhythm change's light to drive bird. The utility model discloses through vibration detection module to detect the vibration signal of bird pecking insulator when, get main control module can according to vibration signal drive sounding unit and light unit respectively produce different frequency sound and rhythm change's light to drive bird, can effectively improve the bird repelling effect, reduce deployment and maintenance cost, through vibration detection module can prevent the false triggering of bird module, reduce operating power consumption, improve the life.

This utility model discloses an online pH detection and automatic dosing device for boiler water tanks, relating to the field of water quality regulation technology. It includes a chemical tank with an electromagnetic diaphragm metering pump fixedly connected to it. An inlet pipe is connected to the inlet end of the electromagnetic diaphragm metering pump, and the inlet pipe is vertically fixed to the inside of the chemical tank. A float is fitted onto the outside of the inlet pipe, and a magnetic block is fixedly connected to the bottom of the float. A Hall sensor is fixedly connected to the bottom of the chemical tank at a position corresponding to the magnetic block. The device also includes a controller and a pH sensor, all of which are electrically connected to the controller. This online pH detection and automatic dosing device for boiler water tanks can automatically and quantitatively add chemicals based on pH, making chemical addition more convenient and accurate.