48 results about "Trend detection" patented technology

The invention relates to the technical field of tunnel disaster identification, in particular to a tunnel portal icing disaster identification and prediction method based on a multi-modal data fusion technology. According to the technical scheme, the method comprises the steps of multi-source data acquisition, heterogeneous data processing, feature level fusion, disaster recognition, space-time prediction and dynamic early warning. According to the invention, a multi-modal sensor is deployed to collect tunnel portal temperature, space structure and environmental parameters, intelligent processing and multi-stage feature fusion are carried out, ice layer distribution identification, icing trend detection and time-space prediction are realized by using a deep network, an ice melting device is dynamically activated in combination with a graded early warning mechanism, and vehicle early warning is linked. A sensing, analysis, prediction, disposal and calibration closed loop is formed, the accuracy of ice coagulation disaster detection, the prospective performance of prediction and the intelligence of disposal are remarkably improved, and the tunnel traffic safety and the long-term robustness of the system are guaranteed.

The invention relates to the technical field of numerical control system fault pre-diagnosis, and particularly discloses a fault monitoring method and system for a numerical control machine tool. The method comprises the steps that a spindle vibration signal, a cutting force signal and a spindle current signal are collected; outputting an alignment signal set; respectively executing adaptive filtering processing according to the alignment signal set; constructing a multi-dimensional feature vector; constructing a health index under the processing behavior period; then accumulated trend deviation analysis is executed through a CUSUM algorithm; and judging whether the tool wear trend is abnormal. The technical problem that in the prior art, static threshold judgment is carried out based on a single signal source, and especially in a numerical control machining scene with signal noise interference, accurate early warning of the early wear trend of a tool cannot be achieved is solved. Due to the fact that a period calibration mechanism fusing spindle vibration, cutting force and current signals, feature vector entropy weight optimization and a CUSUM trend detection mechanism are adopted, the pre-diagnosis level of the numerical control machine tool on cutter state changes is improved.

The invention relates to the technical field of health data processing, in particular to a cross-device health data fusion method, which comprises the following steps of monitoring a device connection state, identifying data missing and resending, calibrating multi-device time and performing data difference, calculating a device stability score to adjust confidence fusion data, extracting periodic health parameters and analyzing a change trend. And detecting the factor activation state and combining the sensitivity to obtain a risk score, and outputting a health processing result. According to the method, through combination of equipment connection state identification and data caching progress calculation, the precision of breakpoint data recovery is enhanced, a timestamp alignment and interpolation correction mechanism is adopted, continuous compensation of multi-source data is realized, the data weight is adjusted by using a stability score, the credibility of fused data is improved, and the reliability of the fused data is improved. In combination with behavior characteristic trend extraction and risk factor sensitivity determination, the accuracy and individualized distinguishing ability of chronic disease risk identification are optimized, and the individualized health management and disease prevention ability is enhanced.

The invention discloses a control method and system for a rotatable display screen, and particularly relates to the technical field of display screen control. Precise rotation control is realized through module initialization and physical zero calibration after system power-on and in combination with angle error calculation and feedback control; a zero offset trend detection and dynamic compensation algorithm is particularly introduced, the offset of the reference position of the encoder is effectively corrected, the rotation precision and the system stability in a multi-task control environment are ensured, and the use reliability of equipment and the user experience are remarkably improved through real-time feedback, error closed-loop adjustment and state locking output.

A heterogeneous industrial equipment-oriented adaptive data fusion and decision optimization system relates to the technical field of industrial automation and information, and comprises a cloud, and the cloud is in communication connection with a data acquisition module, a protocol labeling module, a protocol self-consistent module, a detection decision module, a fast response compensation module and a critical trend detection module; the data acquisition module is used for acquiring multi-source type data of each heterogeneous industrial device, and the protocol labeling module is used for constructing an industrial connection topological graph and performing initial communication protocol labeling on the industrial connection topological graph; the protocol self-consistent module is used for carrying out interactive feature analysis on each node in the industrial connection topological graph and carrying out automatic protocol matching on protocol abnormal nodes; the detection decision module detects the multi-source type data of each node in real time, and selects the rapid response compensation module to execute the rapid response compensation operation or the critical trend detection module to execute the critical trend detection operation according to a monitoring result, so that the production efficiency is remarkably improved, and the product quality is ensured.

The invention discloses a software testing system based on virtual reality, which comprises a data acquisition module, a testing module and an output module, and is characterized in that the testing module is used for deploying a running exercise amount target of a user on the current day; the data acquisition module, the test module and the output module are in communication connection with one another, user picture information is called and analyzed within different time through the head inclination angle detection sub-module, and a favorable basis is provided for the test system to monitor the motion state of a user; a foot force application area detection sub-module is used for acquiring a specific foot force application area of the user in a monitoring time period, and judging whether the current motion state of the user is stable or not; through the step number change trend detection sub-module, the step number increase condition of the user in the monitoring time period is monitored, the humanization degree of the treadmill is greatly improved, the daily experience of the user using the treadmill is improved, and the treadmill has the advantages of being high in humanization degree and high in motion parameter monitoring precision.

The invention discloses a battery SOH dynamic prediction method based on federated learning and working condition adaptive fusion. The method comprises the steps that an end-side heterogeneous multi-modal feature extraction mechanism is adopted to extract physical features and working condition features of each battery from a battery changing cabinet end and a vehicle-mounted end; a double-branch fusion prediction network is constructed, and the double-branch fusion prediction network is trained through cooperation of a battery changing cabinet end global aggregation mechanism and a vehicle-mounted end self-adaptive mechanism; processing the extracted features by adopting two branch networks in the trained double-branch fusion prediction network and a gating fusion mechanism to obtain a predicted state of health (SOH) of each battery; the method comprises the following steps: constructing a predicted SOH sequence of each battery, constructing a multi-time-scale early warning module through a short-term mutation detection method and a long-term trend detection method, carrying out judgment operation on the predicted SOH sequence of each battery, judging whether each battery is in a risk state or not, and outputting an alarm when the battery is in the risk state; and the SOH prediction precision and the early warning capability of attenuation abnormity are improved.

The invention provides an event detection method and system for a mechanical overspeed test of a steam turbine generator unit, relates to the technical field of state monitoring and fault diagnosis of electric power equipment, and aims to solve the problems that existing mechanical overspeed test recognition is prone to false alarm and missing alarm, and positioning at a critical moment is not accurate. The method comprises the following steps: acquiring the active power of a generator and the rotating speed of a steam turbine, carrying out preprocessing and rolling smoothing processing to obtain a smoothing power and smoothing rotating speed sequence, determining a first key time point based on the smoothing power sequence, carrying out segmented backtracking and descending trend detection on the smoothing power sequence, and determining a second key time point. And carrying out descending track detection on the smooth rotating speed sequence, determining a third key time point, backtracking the smooth rotating speed sequence, determining a fourth key time point, determining a fifth key time point based on the original rotating speed, and judging whether a mechanical overspeed test is carried out or not. According to the invention, problems in the prior art are solved, and reliable and automatic identification and accurate event definition of the mechanical overspeed test are realized.

The invention discloses a time series data anomaly detection method and device, electronic equipment and a computer program product. The method comprises the following steps: acquiring a to-be-analyzed time sequence queue comprising a plurality of to-be-analyzed time sequence data; sliding a preset sliding window on the to-be-analyzed time sequence queue to obtain window data corresponding to the preset sliding window after each sliding; detecting a short-time trend of the window data according to a plurality of pieces of window data in the first window sequence; under the condition that the short-time trend shows that trend change occurs in the multiple pieces of window data in the first window sequence, the long-time trend of the window data is detected according to the multiple pieces of window data in a second window sequence, and the length of the second window sequence is larger than that of the second window sequence; and generating abnormal early warning information under the condition that the long-time trend shows that trend change occurs in the multiple pieces of window data in the second window sequence. The technical problem that in the prior art, the accuracy rate of an anomaly detection result obtained through trend detection is low is solved.

The present application relates to the technical field of lesion recognition, in particular to an automatic lesion recognition ultrasonic system for real-time monitoring, which comprises a delay path discrimination module, a gray level trend detection module, a texture feature screening module, a feature fusion sorting module and a region highlight labeling module. Based on continuous ultrasonic frames, the collected deep tissue echo path is analyzed, and the echo arrival time of each pixel point in the continuous frame is detected. The present application supports multi-type data fusion judgment through a comprehensive judgment process supported by multi-dimensional parameter collaborative screening, penetration behavior, gray level trend and texture aggregation. The regional abnormal priority sorting mode improves the hierarchy of lesion feature discrimination, provides partition directional recognition for local structural abnormalities and early micro-variation, and converts the feature judgment result into a high confidence region label by a weight aggregation method. The image output process automatically completes the real-time visual presentation of the high-risk area, improving the clarity and pertinence of the lesion region presentation.

The application discloses a cutterhead mud cake detection method and device, electronic equipment and a storage medium, and belongs to the technical field of tunnel construction. The detection method comprises the following steps: reading historical tunneling data from a PLC controller and an industrial computer of a shield tunneling machine; training a parameter trend detection model by using mud cake related parameters in the historical tunneling data; the parameter trend detection model comprises a one-dimensional convolution layer, an LSTM layer and a full connection layer; obtaining mud cake related parameters of the shield tunneling machine in a target time period, inputting the mud cake related parameters in the target time period into the parameter trend detection model, and obtaining a change trend of the mud cake related parameters in the target time period; judging whether the change trend of the mud cake related parameters in the target time period conforms to cutterhead mud cake characteristics; if yes, it is determined that the shield tunneling machine is in a cutterhead mud cake state; and if no, it is determined that the shield tunneling machine is not in the cutterhead mud cake state. The application can improve the accuracy of detecting cutterhead mud cake.

The invention relates to a production line monitoring method and device, equipment, a storage medium and a program product. The method comprises the following steps: grouping production line data of a target process in a battery production line to obtain a plurality of data groups; for each data group, determining at least one initial change trend according to a plurality of continuous production line data with consistent change trends in the group; performing fitting processing on the plurality of production line data corresponding to each preliminary change trend, and determining at least one target change trend according to a fitting result; wherein the target change trend is used for monitoring the battery production line. According to the invention, the accuracy of trend detection can be improved, so that the problem of missing report is reduced.

The abnormality analysis text automatic generation method, system and equipment of DHI interpretation report belong to the cross field of breeding technology and data text generation technology. In order to solve the problems of large workload and low efficiency of manually writing abnormality analysis text in the current DHI interpretation report, the DHI key performance index data is first acquired, the position of the data value of this month in the corresponding abnormality degree type array is located, the performance index name and the corresponding abnormality degree value are directly spliced, and the description text of the performance index static abnormality is obtained. Meanwhile, the global movement, local movement and standardization processing are carried out on the data of this month and the historical data, a single-layer bidirectional GRU network is used as an encoder for processing, the hidden states of all time steps and the hidden state of the decoder t-1 step are processed by using an attention layer, and an LSTM long short-term memory network is used as a decoder to generate the description text of the performance index dynamic trend detection.

An adaptive data fusion and decision optimization system for heterogeneous industrial equipment, which relates to the field of industrial automation and information technology, includes a cloud. The cloud is communicatively connected to a data acquisition module, a protocol annotation module, a protocol self-consistency module, a detection and decision module, a rapid response compensation module, and a critical trend detection module. The data acquisition module is used to collect multi-source type data of each heterogeneous industrial equipment. The protocol annotation module is used to construct an industrial connection topology diagram and perform initial communication protocol annotation on the industrial connection topology diagram. The protocol self-consistency module is used to analyze the interaction characteristics of each node in the industrial connection topology diagram and automatically match the protocol for protocol-abnormal nodes. The detection and decision module performs real-time detection on the multi-source type data of each node, and according to the monitoring results, selects the rapid response compensation module to perform rapid response compensation operations or the critical trend detection module to perform critical trend detection operations, significantly improving production efficiency and ensuring product quality.

The present invention relates to the field of health data processing technology, specifically to a cross-device health data fusion method, including the following steps: monitoring device connection status, identifying data loss and reissuing it, calibrating multi-device time and performing data interpolation, calculating device stability score to adjust confidence fusion data, extracting periodic health parameters and analyzing change trends, detecting factor activation status and combining sensitivity to derive risk scores, and outputting health processing results. In the present invention, by combining device connection status identification with data cache progress calculation, the accuracy of breakpoint data recovery is enhanced, timestamp alignment and interpolation correction mechanism are adopted to achieve continuity compensation of multi-source data, and stability score is used to adjust data weights, thereby improving the credibility of fused data. By combining behavioral feature trend extraction with risk factor sensitivity determination, the accuracy of chronic disease risk identification and individual differentiation capabilities are optimized, and the ability of personalized health management and disease prevention is enhanced.

The invention relates to the technical field of order management, in particular to an AI-based game order management system which comprises a dependency recognition module, an affiliation matching module, a frequency curve module, a trend detection module and an order locking module. According to the method, the directivity relation between the fields is expressed by constructing the field dependency path diagram, accurate description of order structure features is achieved, the type attribution judgment accuracy is improved, the structure complexity quantification capability is enhanced through dependency edge number interval mapping, and the type distinguishing effect is enhanced; the combination frequency trend of the role level and the recharge amount in the period reveals the behavior active stage, the frequency amplification quantifies the behavior change intensity, and the device type newly-added ratio and frequency trend linkage analysis can identify the migration characteristics of the login path, capture the non-continuous behavior evolution, extract the user ID based on the field combination and evaluate the repetition rate. The identification coverage range and the response precision of potential abnormal aggregation behaviors are improved, and the risk identification capability in a complex order environment is enhanced.

The invention relates to the technical field of speech recognition and audio signal processing, in particular to an ultra-low power consumption speech wake-up system with adaptive threshold adjustment capability, which comprises an input sound processing module and an energy trend detection module for monitoring and receiving an audio signal x (t) from a microphone and outputting an envelope signal En (t); the adaptive control module updates the gain Gain of the programmable amplifier according to the envelope signal En (t); the programmable amplifier adopts the updated gain Gain to amplify the audio signal x (t) to obtain a signal x '' (t), and the signal x '' (t) passes through a band-pass filter to obtain a signal V 'BPF (t); the threshold judgment and wake-up output module adaptively adjusts a wake-up threshold Threshold according to the envelope signal En (t), and outputs a wake-up mark digital signal; the problem that an existing fixed sound pressure threshold value scheme cannot automatically adjust the wake-up threshold according to environmental noise changes is solved.

The invention discloses a production line visual management platform based on dynamic task data driving, and relates to the technical field of intelligent manufacturing and industrial informatization, and the platform comprises a dynamic task data collection module which is used for collecting the operation state data of production line stations, equipment, personnel and materials in real time; the task evolution modeling module is used for constructing a task evolution process according to the collected data; the task granularity self-adaptive refining module is used for dynamically adjusting the task management granularity according to the task state change; the abnormal fluctuation trend detection module is used for detecting abnormal fluctuation in the task state data; the visual management module is used for generating a production line visual interface based on the task evolution and the anomaly detection result; and the scheduling decision linkage module is used for optimizing a production line scheduling strategy according to the anomaly detection result and the task state. The problems of static data updating, task granularity fixing and anomaly response lagging existing in an existing production line visual management platform are solved.

The invention relates to the technical field of seismology, and particularly discloses a K value self-pickup method based on MK trend detection, which comprises the following steps of: preprocessing seismic oscillation data; performing curve feature recognition on the seismic oscillation Fourier spectrum by using an MK trend detection algorithm; picking up starting and ending frequencies of a seismic oscillation high-frequency descending section according to the Fourier spectrum ascending and descending characteristic curve; and extracting a descending frequency band according to the start-stop frequency, and performing fitting calculation to obtain a K value. According to the method, the value of the seismic oscillation high-frequency attenuation parameter K is calculated by extracting the curve rising and falling characteristics of the seismic oscillation Fourier spectrum and then identifying the starting and ending frequencies, so that the precision of picking up the starting and ending frequencies of the descending section of the Fourier spectrum can be remarkably improved, and batch calculation of the seismic oscillation high-frequency attenuation parameters is realized; the method has the characteristics of high flexibility, high accuracy, high efficiency, simplicity in operation and the like.

The invention discloses a real-time anomaly detection and intelligent early warning system and method based on artificial intelligence, and relates to the technical field of artificial intelligence and data security, and the method comprises the steps: obtaining historical data assets, and constructing a tensor potential energy function; guiding the generated detector particles to migrate through the tensor gradient of the tensor potential energy function; negative selection is carried out on the migrated detector particles, an initial detector is generated, cloning is carried out on the initial detector, a clone detector is generated, and the detector is migrated to a potential abnormal region based on the tensor gradient; when the clone detector stably resides in the potential abnormal region, recording an evolution trajectory of the clone detector, and constructing a trend memory unit; according to a trend memory unit, generating a trend detector and migrating to a detector particle insufficient coverage area based on a tensor gradient; and performing response matching on the to-be-detected data assets, the clone detector and the trend detector to complete anomaly detection early warning. And high detection precision and early warning response rate are kept in a complex data environment.

Methods and systems for media trend detection and maintenance are provided herein. A set of media items each having common media characteristics is identified. A set of pose values is determined for each respective media item of the set of media items. Each pose value is associated with a particular predefined pose for objects depicted by the set of media items. A set of distance scores is calculated. Each distance score represents a distance between the respective set of pose values determined for a media item and a respective set of pose values determined for an additional media item. A coherence score is determined for the set of media items based on the calculated set of distance scores. Responsive to a determination that the coherence score satisfies one or more coherence criteria, a determination is made that the set of media items corresponds to a media trend of a platform.

The invention provides a measurement data change trend detection method and system and a computer storage medium. The measurement data change trend detection method comprises the steps that historical measurement data and new measurement data are integrated; sequentially selecting a plurality of groups of preset quantity of data from the integrated historical measurement data and the new measurement data according to the time sequence, and judging whether a second extreme value in each group of preset quantity of data is behind the first extreme value or not; if it is judged that the second extreme value in the preset number of data of the current group is behind the first extreme value, intercepting interval data from the first extreme value to the second extreme value in the preset number of data of the current group; and combining all the interval data according to the time sequence, and judging whether the change trend of the combined interval data is abnormal or not. According to the technical scheme, the change trend of the measurement data can be effectively detected.

The invention provides an etching end point detection method, a semiconductor structure and a manufacturing method thereof, and the etching end point detection method can accurately determine an etching end point based on the direct detection of the concentration of a first reaction product in an etching process. Compared with trend detection of the concentration of the first reaction product through a spectrum mode and the like, the accuracy of the etching end point determined in the method is higher. Meanwhile, according to the manufacturing method of the semiconductor structure based on the etching end point detection method, the etching end point can be automatically detected in the etching process of the wafer to be etched, etching is stopped, excessive etching or insufficient etching is avoided, the accuracy of the size of the formed semiconductor structure and the stability of the etching process are guaranteed, and the yield is improved. Therefore, the manufacturing yield of the semiconductor structure is improved.

This invention discloses a method for analyzing soil pollution change trends, comprising: acquiring a dataset of soil monitoring points at multiple time points within a target area; delineating core areas and time periods where pollutants in the soil exhibit significant upward or downward trends and spatially form a pattern; spatiotemporally matching the core areas and time periods with human activity factors and natural factors, constructing spatial statistical models, and obtaining the contribution rates of human activity factors and natural factors to the pollution change trend; this invention effectively integrates the inconsistency problem of spatiotemporal dynamic monitoring data, overcomes the data comparability barrier caused by changes in monitoring points, and realizes a closed-loop analysis from trend detection to cause analysis, significantly improving the accuracy and practicality of soil pollution source tracing and risk management.

The invention relates to the technical field of lesion recognition, in particular to an automatic lesion recognition ultrasonic system for real-time monitoring, which comprises a delay path judgment module, a gray scale trend detection module, a texture feature screening module, a feature fusion sorting module and a region highlight labeling module. The acquired deep tissue echo path is analyzed, and the echo arrival time of each pixel point in continuous frames is detected; according to the method, multi-type data fusion judgment is supported through a comprehensive judgment process of multi-dimensional parameter collaborative screening, penetration behavior, gray trend and texture aggregation, hierarchy of lesion feature judgment is improved through a regional anomaly priority sorting mode, and partition directional recognition is provided for local structure anomaly and early tiny variation; the weight aggregation method converts a feature judgment result into high-confidence region labeling, real-time visual presentation of a high-risk region is automatically completed in the image output process, and the presentation definition and pertinence of a lesion region are improved.

This invention discloses a clean air leak detection system and method based on the Internet of Things (IoT), relating to the field of industrial process monitoring. The method includes: preprocessing the acquired raw monitoring signal to obtain a standardized signal; performing robust statistical analysis on the standardized signal based on a sliding window to obtain the time-varying baseline center and the time-varying baseline fluctuation intensity; correcting the standardized signal according to the time-varying baseline center to obtain a residual signal; performing wavelet multi-resolution decomposition on the residual signal to obtain approximation coefficients and detail coefficients distributed at different scales; performing threshold shrinkage processing on the detail coefficients and reconstructing a trend enhancement signal using the processed detail coefficients and approximation coefficients; adaptively setting detection parameters based on the time-varying baseline fluctuation intensity, and using a sequential cumulative sum algorithm to detect persistent shift trends in the trend signal. Through adaptive baseline estimation, wavelet denoising, and sequential cumulative sum detection, high sensitivity and low false alarm detection of weak leak trends are achieved.

The invention discloses an access method and system on an industrial data link, and relates to the field of industrial data processing, and the method comprises the steps: collecting the time sequence monitoring data of industrial equipment, deploying a trend detector at the edge side, and dynamically adjusting the anchoring frequency on the link according to the data sudden change intensity and the abnormal confidence; when a transient event is captured, an event snapshot containing a time window and an associated point location is generated, the event snapshot is linked preferentially through an independent channel, and the event snapshot falls back to a conventional anchoring rhythm after abnormities subside. When historical data is archived in a roll-by-roll mode, connection records containing inter-roll associated indexes are generated in roll switching to be linked, a cross-roll index directory corresponding to a hash root on the chain is maintained, and an anchoring time period window on the chain is inquired before roll cutting of an archiving system so as to align a boundary. According to the method, the problems of transient abnormal tracing blind areas and cross-volume chain breakage caused by existing fixed anchoring are solved, the data tracing continuity and the key event capturing capability are improved, and reliable support is provided for industrial data auditing and fault tracing.

Apparatus (10) for predicting machining dimensions, comprising: a trend detection means (11) for detecting trend information for each of a plurality of workpieces (40) on which machining is carried out, wherein the trend information indicates a state trend of a machining tool (20) during a machining period from a start to an end of the machining performed by the machining tool (20); feature calculating means (14) for calculating a feature based on the trend information using the state trend in each of a plurality of sections included in the machining period; a measured value detecting means (15) for detecting a measured value of a dimension of each of the plurality of workpieces (40) after machining; and a predicting means (18) for predicting, when a new target workpiece (41) is machined, a dimension of the new target workpiece (41) after machining; characterized by a section specifying means (16) for specifying a section having a calculated feature with a highest degree of relevance as a specific section of the plurality of sections, where the relevance degree is a relevance degree for the measured value; and wherein the predicting means (18) predicts the dimension of the new target workpiece (41) after machining based on the feature calculated using the state trend in the specific portion.

The application belongs to the technical field of pipeline leakage detection, and particularly relates to a pipeline pressure leakage signal enhancement method, which comprises the following steps: obtaining upstream and downstream pipeline pressure signals; performing trend detection and trend elimination on the upstream and downstream pipeline pressure signals to obtain the upstream and downstream pipeline pressure signals after trend elimination; performing multi-scale characterization on the prominence degree of interval sub-signal amplitude in the upstream and downstream pipeline pressure signals after trend elimination to obtain the prominence degree characterization function of each interval sub-signal in the upstream and downstream signals and the optimal filtering parameter; and multiplying the upstream and downstream pipeline pressure signals filtered by the optimal filtering parameter with the prominence degree characterization function of each interval sub-signal in the upstream and downstream signals to obtain the enhanced upstream and downstream pipeline pressure signals. The method solves the problem of weak leakage signal enhancement and ensures the optimization of the leakage signal enhancement effect.

The invention provides a remote sensing image target trend detection method which can be applied to the technical field of remote sensing image processing and analysis. The method comprises the steps that an input image is acquired and preprocessed; based on spectral features and geographic position prior information, a semantic segmentation network is adopted to separate a target area from the preprocessed image; constructing a cost measurement function by using an optimal transmission theory, solving an optimal transmission plan, and obtaining feature mapping after image smoothing; based on feature mapping, texture features and motion features are extracted, and spatial-temporal features of the target area are obtained through fusion; capturing a space-time dependency relationship in the multi-temporal remote sensing image feature sequence by adopting a time sequence analysis model; constructing a target trend prediction model based on the feature mapping, the spatio-temporal features and the spatio-temporal dependency relationship; and outputting a trend prediction result by using the target trend prediction model. The invention further provides a remote sensing image target trend detection device, equipment and a medium.