211 results about "Exponential smoothing" patented technology

The invention relates to the technical field of distributed data processing, in particular to a hydraulic engineering distributed flood prevention prediction system and method.The system comprises a real-time monitoring module for calculating the mean value and variance of data of a water level sensor every five minutes, and an anomaly detection module for comparing the mean value difference with a threshold value, recognizing a sudden change point and sending the sudden change point to a cloud server; the window adjustment module adjusts the collection frequency according to the abnormal result, the trend analysis module predicts the flood trend by applying moving average and exponential smoothing, and the early warning generation module evaluates the water level and generates an early warning signal. According to the method, the mean value and the variance of the water level monitoring data are calculated in real time and compared with the threshold values, so that the recognition capability of the abrupt change point is improved, the data collection frequency and the window size are adjusted according to the anomaly detection result, the adaptability and efficiency of data collection are enhanced, and the water level trend is predicted through the moving average and exponential smoothing technology; and in combination with a geographic information system, the affected area is accurately evaluated, so that the early warning accuracy and the decision support effectiveness are remarkably improved.

The invention relates to the technical field of pavement disease automatic detection, and particularly discloses a pavement disease automatic detection method and system based on deep learning, and the system comprises a municipal pavement data collection module, a pavement image quality analysis module, a pavement image re-collection module, a pavement disease evaluation feedback module and a database. Flight parameters (speed and height) are dynamically adjusted through an environmental quality index (EQI), the image acquisition quality is ensured, the detection precision in a complex environment is improved, a multi-disease coupling risk model is constructed, region weight and traffic volume dynamic weight are integrated, a disease risk level is output, and finally a disease development trend is predicted through a one-time exponential smoothing method. The system achieves the dual functions of current situation evaluation and trend early warning, predicts the disease evolution trend based on historical data, remarkably improves the scientificity and timeliness of municipal road risk management, reduces the maintenance cost, and prevents safety accidents.

The invention discloses a backup task scheduling method based on load prediction and an improved SAC algorithm, and relates to the technical field of data backup and intelligent scheduling. Aiming at the problems of response lagging, insufficient key task guarantee and low strategy exploration efficiency of a traditional scheduling method under a dynamic load, a task weight monitoring matrix is constructed, server performance, a network state and data importance are fused, and a task risk coefficient is calculated in combination with an exponential smoothing algorithm; carrying out sequence decomposition on a service load by adopting an Autoformer model, identifying a periodic mode through a weighted autocorrelation mechanism, and predicting a future load change trend; on this basis, an improved SAC scheduling framework is designed, a task weight guidance and temperature coefficient adaptive adjustment mechanism is introduced, and the task starting opportunity and execution duration are optimized in combination with an elastic time window. The invention provides an intelligent scheduling method with strong adaptive capability, which can autonomously optimize the task scheduling strategy according to the dynamic change of the system and effectively reduce the peak load of the server and the task backlog risk.

The invention relates to the technical field of wireless communication, in particular to a network security communication method and system applied to an unmanned equipment formation and a medium. The method comprises the following steps: firstly, according to a wind speed, a wind direction and a difference condition between an actual spatial position and a set spatial position of unmanned equipment, obtaining a self-noise representation degree at a to-be-analyzed moment; according to the noise interference degree, the initial smoothing coefficient of the to-be-analyzed moment is adjusted, and the adjusted smoothing coefficient of the unmanned equipment at the to-be-analyzed moment is obtained; filtering the actual spatial position by using the adjusted smoothing coefficient at each sampling moment to obtain filtered actual spatial position data; and performing communication transmission based on the filtered actual spatial position data. According to the method, the smoothing coefficient of each sampling moment is reasonably set, and exponential smoothing filtering is performed on the actual spatial position data of the unmanned equipment, so that the filtering effect is improved, the filtering data accuracy in wireless communication is improved, and the wireless communication safety is guaranteed.

The invention discloses an intelligent campus management system and method, and the method comprises the following steps: S1, collecting campus management data, and transmitting the campus management data to a cloud computing platform; s2, preprocessing the campus management data; s3, analyzing data by using an improved gating cycle unit and an exponential smoothing method; s4, performing curriculum scheduling optimization and classroom and laboratory resource allocation by adopting a super-gravitational search optimization algorithm; s5, automatically adjusting the air conditioner, the illumination, the access control authority and the monitoring strategy; s6, real-time data transmission is carried out, and the equipment response speed is optimized based on distributed calculation; s7, constructing a data sharing platform based on a block chain technology, and setting a data access permission through an intelligent contract; and S8, optimizing the campus management scheme based on reinforcement learning. The intelligent campus scheduling optimization and trend prediction system integrates the Internet of Things, artificial intelligence, a super gravitational search optimization algorithm, an improved gating loop unit and an exponential smoothing method, realizes intelligent campus scheduling optimization and trend prediction, and has the advantages of efficient optimization and intelligent management.

The invention relates to the technical field of data communication, and discloses an intelligent agent high-speed bus implementation method and system based on an FPGA and a dynamic smoothing technology. The method comprises the following steps: constructing a single handshake message through a lightweight protocol, adjusting network indexes by adopting an exponential smoothing method, constructing a pipeline processing unit in an FPGA card by utilizing a VHDL, realizing key updating and encryption processing, constructing a three-layer adaptive structure connection subsystem, and optimizing algorithm parameters and resource allocation according to a running state. And a high-performance bus communication system is formed. On the premise of ensuring the communication security and reliability, the network transmission delay is remarkably reduced, and the data throughput is improved, so that the communication system can meet the requirements of emergency linkage and large data volume transmission in a scene (such as a rail transit station-level system) with a high real-time requirement.

The invention relates to the technical field of industrial automation control, and discloses a chemical production-oriented multi-stage cooperative intelligent control and equipment operation and maintenance data management platform, which comprises a multi-source data acquisition module for acquiring multi-source heterogeneous data and performing time alignment processing and quality verification; the working condition identification and feature extraction module is used for obtaining equipment operation state data by adopting weighted fusion and principal component analysis; the equipment health assessment module is used for calculating an equipment health degree score and predicting a degradation trend through exponential smoothing; the self-adaptive control module is used for designing a multivariable coordination controller and dynamically generating operation constraints of the multivariable coordination controller according to the equipment health degree score; the layered optimization control module is used for establishing an interlayer feedback mechanism; the collaborative decision-making module generates a collaborative decision-making trigger signal according to the equipment health degree information and generates a collaborative decision-making scheme; according to the invention, by establishing a collaborative decision-making mechanism, a decision-making scheme considering the production task completion degree, the equipment health degree and the maintenance cost is generated.

The invention belongs to the technical field of data processing, and particularly relates to a power supply load prediction method and system for a new energy vehicle, and the method comprises the steps: obtaining real-time operation parameters and a historical power supply load data set of the new energy vehicle; for any moment, determining the load fluctuation sensitivity according to the offset of the power supply load relative to the historical power supply load in the same period and the difference of the power supply load relative to the previous moment; determining the load demand potential by combining the difference between the residual electric quantity of the on-line controlled vehicle and the environment temperature; and determining a prediction weight based on the two indexes, extracting a long or short period data set in combination with a magnitude relationship between the prediction weight and a preset weight threshold, and realizing prediction by using an exponential smoothing method. According to the method, the problems of response lag and insufficient precision of a traditional prediction algorithm when a charging scene is highly dynamic are effectively solved, and high-precision real-time tracking of unsteady state load characteristics is realized.

The invention discloses an electric charge real-time settlement and intelligent deduction method for an intelligent electric meter, and belongs to the technical field of intelligent power grids, and the method comprises the steps: obtaining current waveform data through high-frequency sampling, generating a carrying feature vector through differential exponential smoothing, and achieving the dynamic classification of equipment load types based on the spectral characteristics and a lightweight convolutional neural network. Generating a dynamic rate parameter in combination with the power grid peak regulation demand index and the historical credit score of the user, and constructing a mixed charging bill; a cross-chain transaction message is formed by analyzing the hash value and embedding the hash value into a block chain timestamp, and a three-section settlement smart contract is activated; and monitoring load fluctuation data after fee deduction, generating an abnormal fluctuation mark, and performing optimization through federated learning. According to the method, comprehensive optimization of high-precision real-time charging, tamper-proof trusted transaction and adaptive anomaly monitoring is realized, and the peak regulation efficiency and the power utilization fairness of the power grid are remarkably improved.

The invention relates to a humanoid robot whole body motion control method and system based on guide learning and remapping data. According to the method, guide learning is adopted as a motion control algorithm framework of the humanoid robot; the method comprises the following steps: firstly, acquiring human body weight mapping data, and performing coordinate transformation on the human body weight mapping data to serve as a feedforward action in guide learning; constructing a neural network, inputting an observation vector of the robot into the neural network, carrying out reinforcement learning on the neural network, outputting rotation angle data, needing to be finely adjusted, of each joint of the robot, carrying out exponential smoothing processing on the rotation angle data, and outputting a smoothed signal as a feedback signal; and the feedforward action and the feedback signal are combined through PD control to generate a final rotation angle required by each joint of the robot, and then the rotation angle is converted into an output torque of a motor and output, so that whole-body motion control of the humanoid robot is completed. Compared with the prior art, the method has the advantages that the action accuracy and the real-time fine adjustment requirement are both considered, and the control robustness is high.

The embodiment of the invention provides an end-to-end speech synthesis method, device and equipment based on multi-modal emotion fusion. The method is applied to the technical field of speech synthesis. Extracting acoustic features through an acoustic encoder; the interaction weights of the text features and the acoustic features are dynamically distributed by using a double attention mechanism, and the collaboration of the emotion information and the voice information is enhanced; generating an emotion intensity curve by using a bidirectional long-short-term memory network, a time convolutional network, a self-attention mechanism and an exponential smoothing technology; inputting the fused features and the emotion intensity curve into a variance adapter for feature enhancement and expansion, and outputting the fused features and the emotion intensity curve to a Mel decoder for parallel decoding to obtain a Mel spectrum of the synthesized speech; the vocoder is used to map the Mel spectrum features into the sound waveform, and the final emotional voice is generated, so that the emotional expressive force and naturalness of the generated voice are improved, and the emotional intensity can be adjusted according to different application scenes.

The invention discloses a maximum correlation entropy adaptive dynamic estimation method containing a distributed photovoltaic power distribution network, and belongs to the technical field of power systems. The invention discloses a maximum correlation entropy adaptive dynamic estimation method for a distributed photovoltaic power distribution network, and the method comprises the steps: building a two-stage three-phase distributed photovoltaic physical model containing a feedback control link, and forming a photovoltaic system state equation containing a differential link; establishing a power distribution network state equation based on a Holt's two-parameter exponential smoothing method; the kernel width parameter of the maximum correlation entropy is adjusted in real time according to the measurement error statistical characteristics; a maximum correlation entropy criterion is adopted as a target function, and a robust Kalman gain updating formula is obtained in combination with a Gaussian kernel function and an adaptive weight matrix; and integrating the robust Kalman gain updating formula into the integral framework of the volume Kalman filtering to complete the dynamic state estimation of the power distribution network. By adopting the maximum correlation entropy adaptive dynamic estimation method containing the distributed photovoltaic power distribution network, the problem that an existing dynamic state estimation method is insufficient in estimation precision in the distributed photovoltaic and non-Gaussian noise power distribution network can be solved.

The invention relates to a real-time overrun early warning method based on gas concentration characteristic change, and belongs to the technical field of coal mine safety monitoring and early warning. The method comprises the following steps: collecting gas concentration time sequence data in real time through gas sensor networks deployed on a working face and a tunneling face; grading concentration intervals, wherein the concentration intervals comprise a threshold interval, an alarm color and an alarm score; the method comprises the following steps of: preprocessing collected gas concentration time sequence data, and then respectively calculating three alarm scores by adopting an amplification change bandwidth judgment method, an exponential smoothing difference method and a sliding window Z score method to correspond to alarm scores in concentration interval grading; voting fusion: at the same sampling moment, synchronously aligning the calculated three alarm scores, and then determining final early warning information according to a voting fusion rule; and outputting early warning information in a visualized manner. According to the method, the concentration interval dynamic grading and voting fusion strategy is combined, refined recognition and grading early warning of the gas risk situation are achieved, and the timeliness and accuracy of alarm are remarkably improved.

The invention discloses an AUV (Autonomous Underwater Vehicle) pose detection method and system based on underwater light vision, a medium and a program product, and belongs to the field of underwater robots. The method comprises the following steps: judging the turbidity of a water body by analyzing the blue channel characteristics of an image, dynamically adjusting the graying weight, and generating a preprocessed image by adopting blue channel enhancement; constructing triple constraint conditions of contour number, spacing and area, and combining a variable step size strategy to realize threshold adaptive optimization; wick pixel coordinates are extracted through contour circularity screening, a leak detection grading mechanism is established, and feature point compensation is performed based on exponential smoothing filtering prediction and historical frame data; and finally, performing P3P pose calculation according to confidence levels. The method can be suitable for various water areas, blue light source feature extraction and feature point matching tasks are well achieved, and the stability and accuracy of AUV pose detection are improved.

The invention discloses an online car-hailing driver fatigue driving pre-judgment method and system. The invention relates to the technical field of online car hailing. And collecting driver departure data O and driver order receiving data M which are arranged according to a time sequence. O = [O1, O2,..., ON]; m = [M1, M2,..., MN]; o1, O2,..., ON and M1, M2,..., MN are historical data of driver departure data O and driver order receiving data M respectively. Executing exponential smoothing according to the trend and the seasonality of the driver departure data O and the driver order receiving data M in the time sequence; by adopting the Holt-Wittes seasonal model, the method can accurately capture the periodicity and tendency characteristics of the driving and order receiving behaviors of the driver, and significantly improves the prediction accuracy. The automatic data processing and prediction process greatly shortens the prediction period, improves the operation efficiency, and enables the platform to respond to the potential risk of fatigue driving of the driver more quickly.

The invention provides an optical access network PON port fault prediction and operation and maintenance support method and system, and the method comprises the steps: collecting real-time multi-index sequences of optical power, bit error rate, temperature, frame loss rate, business load and the like, automatically extracting a long-term trend based on variational mode decomposition, constructing a residual sequence, generating a state transition map through employing a Markov transition field algorithm, and carrying out the fault prediction and operation and maintenance support of the PON port. And future operation state distribution prediction is realized through the LSTM neural network. And after an abnormal threshold is dynamically adjusted in combination with quantile regression, a time sequence prediction result and a threshold response are fused to output a comprehensive fault probability score, and an early warning signal is triggered through exponential smoothing and continuity check, so that the progressive fault detection accuracy and advancement can be improved, and an efficient and steady prediction and early warning basis is provided for intelligent operation and maintenance of the PON port.

The invention discloses a rotary table angle data filtering method based on an adaptive cut-off frequency filter, relates to the technical field of signal data filtering, and aims to solve the problem of numerical value jumping caused by high-frequency noise interference on rotary table angle data. The method comprises the following steps: firstly, setting a minimum cut-off frequency and a speed coefficient for an adaptive cut-off frequency filter, obtaining rotary table angle data, calculating the change speed of filtered standard data, and dynamically adjusting the cut-off frequency of the filter; filtering the data for multiple times by using an exponential smoothing formula; and finally, updating the dynamic cut-off frequency according to a filtering result to obtain filter parameters. The filter is a low-pass filter, has the advantages of being easy and convenient to adjust parameters, high in adaptability and high in calculation efficiency, can effectively suppress noise, and meanwhile keeps sensitivity to fast change data. Experiments prove that the method has good performance in different speed data processing, and is suitable for an inertial navigation equipment turntable system.

The invention discloses a financial futures market condition analysis method and system based on multi-agent collaboration. The method comprises the steps that structured data including market data, news information and social media emotion are collected in real time; carrying out technical index technology and trend analysis and supporting resistance potential identification by adopting indexes including a moving average line, a relative strength index RSI, an exponential smoothing different and similar moving average line MACD and a Boforest belt, and outputting a technical analysis report; a basic analysis report is output by analyzing the financial statement, the macroeconomic data and the industry dynamics; the method comprises the following steps: analyzing market emotion, news emotion and social media public opinions by adopting a natural language processing method and an emotion analysis method to obtain market emotion indexes; and generating executable prediction and investment suggestions by integrating the technical analysis report, the basic analysis report and the market emotion indexes. According to the method, the problems of limitation of a single model, data islands, insufficient real-time performance and the like in the prior art are solved.

The invention relates to the technical field of data processing, in particular to an energy storage coordination control method and system based on artificial intelligence, and the method comprises the steps: calculating the distribution deviation degree of power generation in a power generation sequence, and calculating the gradient change degree of the power generation according to the distribution deviation degree; calculating the optimal cut-off distance of the generating capacity; according to the optimal truncation distance, clustering the generating capacity by using a density peak clustering algorithm to obtain a plurality of clusters, calculating a mean value of Euclidean distances between all data points in the clusters and a cluster center, and marking the data points greater than the mean value in the clusters as abnormal points; eliminating abnormal points in the generating capacity sequence, filling data at the elimination position, and obtaining a generating capacity predicted value at the next moment by using an exponential smoothing method; similarly to the method for obtaining the power generation prediction value, obtaining the power consumption prediction value; and adjusting the power grid frequency according to the difference between the power generation predicted value and the power consumption predicted value. The method has the effect of improving the accuracy of the power grid frequency regulation result.

The invention discloses a mass data distributed storage management method, and relates to the technical field of data distributed storage management, and the method comprises the steps: based on mass data collection, analyzing data income overhead, utilizing an exponential smoothing method, predicting a data trend vector, and generating a structured data set; analyzing the hardware function of the distributed storage server according to the predicted data trend vector, and quantifying a storage capability vector; performing association matching based on the quantized data demand vector and the quantized storage capability vector, and generating a distributed storage server node meeting the data income overhead; and based on the data type, performing data fragmentation and the state of the distributed storage server meeting the data income overhead, establishing a minimum storage cost management model, and generating a mass data distributed storage management scheme. The method has the advantages that distributed storage server nodes can be efficiently matched, storage resource configuration is optimized, storage cost is reduced, and dynamic adjustment and efficient management are achieved.

A method and system are provided for rapidly determining equi-depth histogram (ED histogram) boundaries for single photon arrival times without storing complete photon timestamp histories. A plurality of control values (CVs) is initialized for a plurality of binners that classify photon events as early or late relative to the respective CVs. Upon detecting photon arrivals, early and late arrival counts are used to update the CVs using an improved stepping strategy. The disclosed stepping strategy includes scaling a base step size, smoothing with exponential smoothing, temporally decaying the smoothed step size, and further smoothing to produce the step size for updating the CV. The disclosed stepping approach enables the CVs to rapidly converge to the desired quantile locations with low bias and variance compared to previous methods. The optimized stepping strategy is robust to noise and background light, providing accurate distance estimates while reducing bandwidth requirements.

The invention discloses a reservoir bank landslide displacement prediction method based on dynamic weight fusion and multi-modal decomposition. The method comprises the following steps: S1, data acquisition and preprocessing; s2, optimal input feature optimization: constructing a multi-source heterogeneous feature set based on a displacement evolution mechanism, and screening optimal input features by using MIC and RF algorithms; s3, multi-factor and multi-scale decomposition and reconstruction: reconstructing the accumulated displacement of the landslide into a trend term, a periodic term and a random term by adopting an MEMD algorithm, and decomposing the optimal input characteristics into high-frequency and low-frequency components; s4, trend term displacement prediction modeling: realizing trend term displacement prediction by using a double exponential smoothing algorithm DES; s5, periodic term and random term parallel prediction: constructing LSSVM, XGBoost and GRU multi-parallel prediction models, and realizing periodic term and random term displacement prediction; s6, multi-model dynamic empowerment fusion; and S7, cumulative displacement prediction. The displacement evolution law is revealed through multi-mode decomposition, the model precision and generalization ability are effectively improved in combination with a dynamic fusion mechanism, and the problem that traditional single-model prediction is insufficient in stability is solved.

The invention relates to the technical field of instrument testing, in particular to an on-line instrument automatic inspection and calibration method and system, and the method comprises the following steps: obtaining measurement errors, humiture and electromagnetic interference data in real time, carrying out the normalized calculation of an error impact factor distribution value, monitoring an error change rate, and smoothly adjusting the weight according to an index. And analyzing the multi-channel Pearson correlation structure coupling correction matrix, screening the key error source optimization adjustment amount, and judging the error correction stability. According to the method, through real-time measurement error and environmental factor calculation, high-influence error sources are accurately screened, error analysis pertinence is improved, error weights are dynamically adjusted, correction parameters are made to change in a self-adaptive mode along with measurement conditions, compensation accuracy is improved, key error interaction relations are analyzed and screened in combination with multiple measurement channels, non-key factor interference is reduced, and the method is suitable for large-scale popularization and application. The error adjustment calculation is optimized, the correction convergence speed is accelerated, the environmental fluctuation influence is reduced, and the data consistency and stability are improved.

The invention discloses a water quality index multi-model short-term prediction method and system. The method comprises the following steps: acquiring water quality index time sequence data and preprocessing; respectively inputting the preprocessed data into a local linear regression model (LWLR), a dynamic linear model (DLM) and a simple exponential smoothing model (ESE) for prediction; and carrying out weighted fusion on prediction results to obtain a short-term prediction value. According to the method, the historical data of the water quality indexes are used as input, the capability of predicting future changes in a short term is achieved, and the prediction precision and adaptability can be improved.

The invention provides a multi-branch sequence recommendation method based on dynamic channel fusion. The method aims at solving the problems that in sequence recommendation, data have a large amount of noise and are sparse, and existing model prediction is smooth. The method comprises the following steps: on one hand, enabling a user sequence to pass through an embedding layer, introducing position information by utilizing RoPE rotation position coding, and extracting long-term dependency of a user in the sequence through a multi-feature channel feature network to obtain a new embedded Eglobal, a short-term interest and long-term dependency interactive embedded Ecross, an embedded Eema after exponential smoothing filtering and an embedded Efreq after frequency domain modulation; and on the other hand, in order to fully fuse the characteristics of different channels, a traditional hyper-parameter fusion mode is abandoned, and a pooling network layered adaptive fusion mode is adopted. Firstly, four channel features are divided into two groups including a self-attention group and a trend group, features of different channels are learned through Squeeze average pooling, learned parameters are activated through Excitation by adopting sigmoid to obtain channel weights, and feature representations of different groups are sent into a gating network for weighted summation loss calculation.

The present invention relates to the field of clean energy and provides a method for short-term wind power prediction for wind turbines. The method comprises the following steps: using cloud computing technology to collect real-time wind power data and meteorological data of the wind turbine within a preset time period, and using remote sensing technology to collect spatial feature data of the terrain surrounding the wind turbine; preprocessing the real-time wind power data, meteorological data, and data spatial feature data; performing extension representation on the preprocessed real-time wind power data, meteorological data, and data spatial feature data based on extenics to construct a wind power prediction model; combining terrain features and wind turbine structural parameters with modeling software to establish a wind farm model; extracting main characteristic factor data from the wind power prediction model, performing extension cluster analysis, and determining characteristic factor weights; and performing short-term wind power prediction using a time series exponential smoothing algorithm and multiple regression prediction, combined with the wind farm model and characteristic factor weights. This method improves prediction accuracy.

The invention relates to an asynchronous federated learning method and system, and the method comprises the steps: firstly, obtaining the prediction training time of a client through employing an exponential smoothing method in combination with anomaly detection and mutation detection according to the federated learning reality conditions of data isomerism, system isomerism and the like; secondly, utilizing a dynamic threshold segmentation algorithm and Monte Carlo simulation to obtain a two-stage waiting time threshold; and finally, the server selects a local model uploaded by the client to perform parameter aggregation in combination with the training time predicted by the client and a two-stage waiting time threshold, and finally forms a self-adaptive federated learning resource scheduling strategy. According to the method, client node resources can be used to the maximum extent at low cost, the global model training efficiency and precision are improved, and the multi-scene adaptability of federal learning is effectively enhanced. Experimental results show that compared with a classical method, the method has higher accuracy.

The invention belongs to the technical field of audio recognition, and particularly relates to a string music tremolo automatic detection method which comprises the following steps: S1, preprocessing and framing an input audio, and extracting time sequence features; s2, obtaining an emotion intensity score through an emotion intensity evaluation function; s3, mapping the emotion intensity score into an expected tremor speed, and obtaining a stable tremor speed through exponential smoothing and amplitude limiting; s4, generating a dynamic threshold value changing along with time; and S5, the main tremolo detector calculates the criterion in parallel, compares the criterion with a dynamic threshold value on a synchronization time axis to determine frame-level tremolo, connects adjacent frames to obtain a tremolo segment, and drives threshold value self-adaption by emotional intensity, so that the detection criterion is synchronized with a playing situation, wrong division and over-division are reduced, and the boundary consistency and the stability and accuracy of tremolo estimation are improved.

The invention relates to the technical field of terminal data acquisition, and particularly discloses a terminal data acquisition system and method based on 5G edge collaboration and a storage medium, the system comprises a data acquisition module, a type judgment module, a transfer cost confirmation module and a dynamic transfer module; according to the method, the historical garbage throwing data, the traffic flow and the pedestrian flow of the garbage throwing points are comprehensively analyzed, the required throwing number of the garbage cans can be accurately analyzed, then the throwing points to be added and the excessive throwing points are accurately screened out, and the requirement trend index is calculated through the secondary accumulation and the primary exponential smoothing method; a demand putting coefficient is determined through a linear regression algorithm in combination with a circulation value, the actual demand of a garbage putting point can be more scientifically reflected, finally, dynamic transferring is performed under the condition that the garbage can number constraint condition is met by adopting a linear programming algorithm and taking the minimum total transferring cost as a target function, and scientific allocation of the garbage can putting number is achieved.