36results about How to "Avoid forecast errors" patented technology

The invention discloses a user behavior prediction method and device, relates to the technical field of information and is mainly intended to predict user behaviors more accurately. The method comprises: analyzing a user behavior log of a target user to obtain a user attribute vector and a user behavior index vector; inputting the user attribute vector and the user behavior index vector to a pre-trained behavior prediction decision tree model to obtain predicted behavior probability of the target user, wherein the behavior prediction decision tree model is acquired by using a preset decision tree algorithm to train a sample user attribute vector, a sample user behavior index vector and a sample user; determining predicted behaviors of the target user according to the predicted behavior probability. The user behavior prediction method and device are applicable to prediction of user behaviors.

The invention relates to a waiting time predicting method for a queuing machine. The waiting time predicting method comprises following steps of collecting historical arriving moments of customers, the line lengths at the arriving moments of the customers and waiting time in a plurality of historical service periods, generating a customer waiting time table, recording the arriving moments of the customers and the line lengths at the arriving moments of the customers when the customers arrive, acquiring information about the customers required to wait in line, counting the waiting time of the customers by virtue of the customer waiting time table, then acquiring information about one queued customer accepting the service, recording actual waiting time of the queued customer, recounting the waiting time of the rest queued customers, and carrying out cyclic operation until the last service period ends. The waiting time predicting method has the advantages that the accuracy is high, a prediction error is avoided, the customers can conveniently and reasonably arrange the time, and the time availability is improved; meanwhile, the waiting time can be corrected according to queuing information of the customers at any time, so that the customers cannot miss service chances, and the vacant number rate is decreased.

Disclosed is a centrifugal pump flow prediction method based on measurement of power and differential pressure. The centrifugal pump flow prediction method is characterized by including steps that 1, a whole centrifugal pump is respectively divided into a motor module, a mechanical model and a hydraulic power module on the basis of a 'gray box' construction theory; 2, a whole flow prediction mathematical model of the centrifugal pump is built on the basis of system analysis; 3, local establishment of a motor output power prediction mathematical model is performed on the basis of a motor inner loss model; 4, local establishment of a mechanical transmission part output power (or centrifugal pump input power) prediction mathematical model is performed on the basis of a mechanical loss model; 5, local establishment of a model among centrifugal pump input power, flow and rotating speed and a model among centrifugal pump differential pressure, flow and rotating speed is performed on the basis of a fluid flow loss model; 6, a centrifugal pump flow prediction model based on power (rotating speed and torque) and differential pressure is established by combination with a motor mathematical model, a mechanical mathematical model and a hydraulic power mathematical model; 7, the centrifugal pump flow prediction model is corrected by means of a compensation algorithm, and the flow prediction accuracy is improved.

The invention discloses a shopping mall building air conditioner cooling load prediction method based on GBDT, a storage medium and equipment, and the method comprises: collecting cooling load data, and carrying out the normalization processing to serve as the cooling load energy consumption prediction; establishing a load prediction model based on a gradient lifting decision tree algorithm; inputting the preprocessed data into a prediction model for training, selecting a grid search-cross validation mode, and optimizing the three hyper-parameters with the maximum influence on the performanceof the GBDT model; establishing a final cold load prediction model by completing parameter optimization of the prediction model, and obtaining a predicted cold load curve according to the parameters and the structure of the prediction model; and evaluating the prediction performance of the prediction model, adopting the prediction error for evaluation, enabling the deviation between the true valueand the prediction value to form the prediction error, and completing mall building air conditioner cooling load prediction. The method has good prediction precision, universality and applicability,and is especially suitable for large public buildings with periodically changing cold loads.

The invention relates to a soil type merging and multiple regression-based soil manganese content prediction method. The divide-and-conquer control of different spatial variation characteristics showed by soil trace elements in different soil types is involved; the spatial heterogeneity of the soil trace elements can be detected by analyzing a discrete degree of spatial distribution of effective manganese content of soil; the multicollinearity problem in local regression analysis can be diagnosed through the local regression analysis; and especially in a spatial prediction process, a comprehensive prediction model is built by measuring spatial distribution characteristics of the effective manganese content of the soil under the sampling densities of different soil samples.

The invention provides a wind turbine generator system spare part demand prediction method, device and equipment. The method comprises steps that the service life information of wind turbine generatorsystem spare parts is acquired according to the historical consumption information of the wind turbine generator system spare parts; a cumulative probability distribution function of the service lifeof the wind turbine generator system spare parts is acquired according to the service life information and a service life distribution model; failure frequency of the wind turbine generator system spare parts is acquired according to the cumulative probability distribution function, and the demand quantity of the wind turbine generator system spare parts in an order period is acquired according to the failure frequency. The service life distribution model in the reliability field is applied to demand prediction of the wind turbine generator system spare parts, the multi-time failure possibility of the wind turbine generator system spare parts in the order period is considered, wind turbine generator system spare part demand prediction based on self wind turbine generator system spare partfailure rules is realized, and prediction result accuracy is relatively high.

The invention provides a photovoltaic output power prediction method and system and a storage medium. The method comprises the steps of obtaining photovoltaic output data and meteorological data in apreset historical time period; decomposing the photovoltaic output power sequence by adopting a wavelet packet decomposition algorithm to obtain 2n decomposition sequences, and respectively carryingout single-branch reconstruction on the 2n decomposition sequences to obtain 2n reconstruction sequences; combining the 2n reconstruction sequences with the corresponding meteorological data to obtain2n data sets; respectively inputting the 2n data sets into corresponding pre-trained long-short-term memory neural network units to obtain prediction results of the 2n long-short-term memory neural network units on photovoltaic output power in a preset future time period; and performing weighted summation on the prediction results output by the 2n long-short-term memory neural network units to obtain a comprehensive prediction result of the photovoltaic output power in the preset future time period. The prediction precision can be improved.

The invention relates to the technical field of information processing, in particular to a user behavior prediction method and device, a computer readable storage medium and terminal equipment. The user behavior prediction method provided by the invention comprises the following steps: acquiring voice evaluation information of evaluating a target service by a user, and converting the voice evaluation information into corresponding text information; analyzing the text information, and determining an evaluation object of the user for evaluating the target business and an evaluation result corresponding to the evaluation object; extracting a voice segment corresponding to the evaluation result in the voice evaluation information; carrying out voice analysis on the voice segment to obtain an emotion tag and a tone tag corresponding to the evaluation result; and according to the emotion tag, the mood tag, the evaluation object and the evaluation result, predicting the behavior of the user so as to comprehensively and accurately predict the user behavior by comprehensively analyzing the emotion, the mood, the evaluation object and the evaluation content, thereby avoiding the prediction error of the user behavior and improving the prediction accuracy of the user behavior.

The application discloses a tumor volume change tendency chart generating device after ablation. The generating device comprises an image acquiring module, a characteristic extracting module, a clinical parametric quantity acquiring module and a tendency chart generating module, wherein the image acquiring module is used for acquiring ultrasonic images of a target area, and acquiring images of a tumor-like lesion interest area; the characteristic extracting module is used for extracting image secretomics characteristics from the images of the tumor-like lesion interest area; the clinical parametric quantity acquiring module is used acquiring clinical parametric quantity information; and the tendency chart generating module is used for inputting the image secretomics characteristics and theclinical parametric quantity information into a forecasting model so as to obtain a tumor volume change tendency chart within a target time range. The generating device can accurately predict the tumor volume change trend after ablation. The application further discloses tumor volume change tendency chart generating equipment after ablation, and a storage medium, and the tumor volume change tendency chart generating equipment and the storage medium have the beneficial effects.

The invention discloses a lithium battery remaining charging time prediction method for guaranteeing electrical safety. The lithium battery remaining charging time prediction method is characterized in that: detection data are obtained through a charging data detection step; a battery charge state model prediction step and a remaining charging time model prediction step are used for acquiring battery charge states and outputting the remaining charging time through model prediction; in a battery charge increment comparison step, the battery charge states in the battery charge state model prediction step are compared to obtain a result; and in a model retraining step, models in the battery charge state model prediction step and the remaining charging time model prediction step are iteratively updated according to the result. The lithium battery remaining charging time prediction method has the advantages that the prediction model adaptively matches the aging state of a lithium battery, calculation resources are reasonably distributed at the local end and the cloud end, the hardware dependence is low and the like, the electrical safety of lithium battery charging is ensured through accurate prediction of the remaining charging time, and the application is wide.

The invention relates to a commodity brand feature acquisition method and device, a sales volume prediction method and device and electronic equipment, and belongs to the technical field of artificialintelligence. The method comprises the steps of: obtaining historical daily average sales volume sequences corresponding to multiple commodity brands including a target commodity brand; converting the historical daily average sales sequence corresponding to each commodity brand into a sentence character string; forming a matrix array based on all the sentence character strings, wherein each row in the matrix array corresponds to one sentence character string; converting each sentence character string in the matrix array into a corresponding sentence vector based on a word vector model to obtain a semantic vector matrix; and clustering each sentence vector in the semantic vector matrix to obtain a label corresponding to the class where the clustered target commodity brand is located, the label being a brand feature corresponding to the target commodity brand. A high-dimensional sparse feature matrix brought by adopting one-hot coding is avoided, so that the time and space required fortraining the sales prediction model are reduced, and the prediction precision of the sales prediction model is improved.

The invention discloses a single-soldier guided rocket dynamic target prediction and guidance method, which can effectively reduce the normal overload of a rocket in the flight process and ensure therelatively high hit accuracy. The single-soldier guided rocket dynamic target prediction and guidance method comprises the following steps of: measuring the motion state of the target in a ground inertial coordinate system by ground fire control equipment, transforming into the inertial coordinate system of the rocket and transmitting to a computer on the rocket; using the computer on the rocket to estimate the flight time required for the rocket to hit the target and calculating the point of intersection of a guided missile and the target according to the motion state of the target, the initial position of the rocket in the inertial coordinate system of the rocket and the set speed of the single-soldier guided rocket; in the current guidance period, controlling the rocket to fly to the target point according to the set guidance law by taking the point of intersection of the guided missile and the target as the target point; when the current guidance period ends, obtaining the positioninformation of the target in the inertial coordinate system of the rocket by solving with the computer on the rocket, solving the remaining flight time of the rocket hitting the target, and updatingthe point of intersection of the guided missile and the target; and entering the next guidance period, and repeatedly executing till the rocket hits the target.

The invention discloses a content caching method based on deep learning. The method comprises the following steps: (1) collecting user request information of edge nodes to construct a time request sequence; (2) calculating content popularity according to the request sequence data; (3) carrying out maximum and minimum normalization processing; (4) converting a time sequence prediction problem into a supervised learning problem; (5) offline training a popularity prediction model based on the time convolutional network; (6) calling a popularity prediction model to predict popularity, performing weighted summation on predicted popularity data and historical popularity data based on index average, and calculating a content value; and (7) performing cache decision by using the LRU. Popularity distribution of the corresponding content can be predicted only according to the characteristic of the content request sequence, meanwhile, balance can be achieved between long-term memory and short-term sudden memory in combination with historical content popularity information, and good effects can be achieved on the aspects of prediction accuracy and cache hit rate improvement.

The invention discloses a method for predicting the plane distribution of thin sand bodies based on fractional-order Hilbert transform. According to the vertical division of multiple sand body development sections, the attribute plane map of each sand body development section is extracted, and the best order corresponding to the development of each sand body development section is selected based on the actual drilling conditions. Amplitude property plan for thin sand body plane distribution prediction. The invention makes full use of the advantages that fractional Hilbert transform can improve the vertical resolution and can realize the perfect correspondence between the event axis and the acoustic logging curve on the seismic section, effectively predict the plane distribution characteristics of the thin sand body, and improve the thin sand body. Accuracy of Sand Body Plane Distribution Prediction.

The invention discloses a thin sand body plane distribution prediction method based on fractional order Hilbert transform. The thin sand body plane distribution prediction method comprises the following steps: firstly, calculating fractional order Hilbert transform of three-dimensional seismic data of 10 orders; and dividing the sand body development section into a plurality of sand body development small layer sections in the longitudinal direction, extracting an attribute plane graph of each sand body development small layer section, and optimizing an amplitude attribute plane graph corresponding to the optimal order based on the development condition of the actual drilling well in each sand body development small layer section to perform thin sand body plane distribution prediction. Thethin sand body plane distribution prediction method makes full use of the advantages that fractional order Hilbert transform can improve the longitudinal resolution, can realize perfect correspondence of the event and the acoustic logging curve on the seismic profile, effectively predicts the plane distribution characteristics of the thin sand body, and improves the accuracy of the plane distribution prediction of the thin sand body.

The invention discloses an MIMO (Multiple Input Multiple Output) mode switching method and device. An MIMO mode which is possibly used at present is judged according to RI (Rank Indication) and CQI (Channel Quality Indicator) information reported by UE (User Equipment) and an instantaneous BLER (Block Error Ratio) value recorded by an eNB (evolved Node B), thereby avoiding forecasting the throughput / system capacity of various MIMO modes, efficiently avoiding the forecasting error and reducing the realizing complexity; and simultaneously, the MIMO mode which is practically used at present is judged by increasing the quantity of to-be-applied records corresponding to the to-be-applied MIMO modes generated based on periodicity, thereby increasing the quantity of switching sample points for judging the MIMO mode, avoiding the influence of an instantaneous channel error on the mode switching property and fully demonstrating the advantages of self-adaption switching.

The present invention relates to the technical field of information processing, and in particular to a user behavior prediction method, a prediction device, a computer-readable storage medium, and a terminal device. The user behavior prediction method provided by the present invention includes: obtaining voice evaluation information of the user evaluation target service, and converting the voice evaluation information into corresponding text information; analyzing the text information, and determining the evaluation object and the evaluation object of the user evaluation target service Corresponding evaluation results; extract the voice segment corresponding to the evaluation result in the voice evaluation information; conduct phonetic analysis on the voice segment to obtain the emotion label and tone label corresponding to the evaluation result; according to the emotion label, tone label, evaluation object and evaluation result, Predict user behavior to comprehensively and accurately predict user behavior through all-round analysis of emotion, tone, evaluation object and evaluation content, avoid user behavior prediction errors, and improve the accuracy of user behavior prediction.

The embodiment of the invention discloses an image transformation system and method, a storage medium and electronic equipment. The image transformation system mainly comprises an image acquisition assembly used for acquiring a first image containing a target object and a second image containing a reference object; the image transformation component is used for inputting the first image and the second image into an image transformation model and obtaining a target image obtained by the image transformation model; wherein the image transformation model adopts a deformable attention mechanism to predict features of a target object and a reference object in a target image, the predicted features are utilized to further obtain the target image, and a partial region of the reference object in the target object is transformed into the target object. According to the invention, the transformation effect of image transformation can be effectively improved.

The invention discloses a method for deriving the operation situation of an energy storage device based on LSTM. The technical scheme adopted by the present invention includes: identifying model parameters according to historical operation data; establishing the Thevenin equivalent circuit model of the energy storage battery; estimating the SOC and SOH of the energy storage device according to the identification parameters and historical operation data; establishing a thermal model of the energy storage battery; Identify the model parameters and estimate the core temperature of the battery; according to the historical operating data and the operating state of the energy storage device, use LSTM to predict the changes of the voltage and current of the energy storage device, so as to construct the voltage consistency index of the energy storage battery pack; Based on the SOC, SOH and core temperature of the energy storage device, the SOC consistency index of the energy storage battery pack is constructed, so as to deduce the operation situation of the energy storage device. The invention deduces the operation state of the energy storage device through the operation data that is easy to measure by voltage, current and ambient temperature, and avoids the large prediction error when using LSTM to predict multiple operation states such as SOC and SOH at present.

The embodiment of the invention discloses a parking space detection method, device and equipment and a storage medium, and the related embodiments can be applied to various scenes such as cloud technology, artificial intelligence and smart traffic, and are used for improving the accuracy of parking space acquisition and improving the parking efficiency. The method comprises the steps that a target feature map corresponding to a to-be-detected image is acquired, the target feature map comprises N anchor point frames, N is an integer larger than 1, the confidence score of each anchor point frame is calculated, the confidence score is used for indicating that the anchor point frame contains a probability value of a target object, and the probability value of each anchor point frame contains the target object; m target anchor point frames are determined from the N anchor point frames according to the confidence score, M is an integer larger than 1 and smaller than N, key point calculation is carried out on each target anchor point frame in the M target anchor point frames to obtain M key point coordinates, and the position of a parking space in the to-be-detected image is determined according to the M key point coordinates. And the position of the parking space in the to-be-detected image is pushed to the target terminal device.

The invention relates to a disease classification method fusing multi-modal features based on integrated learning and equipment. The classification method comprises the following steps: obtaining multi-modal magnetic resonance imaging and clinical text information of a same object on same equipment, and carrying out data preprocessing; extracting a whole-brain morphological feature map under each mode from the preprocessed multi-modal magnetic resonance imaging, correspondingly calculating image feature values in a brain region of interest, and extracting the clinical text information with inter-group differences to form text feature values; taking the image feature value of each mode and the text feature values as input of a corresponding optimal base classifier to obtain a plurality of rough classification results; and fusing the plurality of rough classification results to obtain a final classification result. Compared with the prior art, the invention has the advantages of high accuracy, high robustness and the like.

The invention discloses a prediction method for stress-strain response and strength of braided ceramic matrix composite. The method is characterized by comprising the following steps: preparing a beltmatrix fiber bundle with the same in-situ performance as woven CMCs, establishing a unit cell finite element model for the belt matrix fiber bundle, and performing finite element calculation on the unit cell finite element model to obtain the stress-strain response with matrix fiber bundles, and the maximum value of stress on stress-strain curve is strength of woven CMCs.. According to the prediction method for stress-strain response and strength of braided ceramic matrix composite, mechanical properties of fibers, a matrix and a fiber / matrix interface do not need to be used as input quantities; only the mechanical property of the yarn needs to be measured, a large number of component material performance test experiments can be omitted, prediction errors caused by component material performance test dispersity can be avoided, and therefore the efficiency of the prediction process and the accuracy of the prediction result can be greatly improved.

The invention provides a method and a system for identifying lithology in real time in a drilling process, and belongs to the technical field of petroleum and natural gas exploration and development. The method comprises the following steps: (1) collecting adjacent well data; (2) constructing a feature set and a label set: constructing the feature set and the label set by utilizing adjacent well data; (3) training a lithology identification basic model: training the lithology identification basic model by using the feature set and the label set; (4) correcting the lithology identification basic model in real time; and (5) lithology real-time identification: carrying out lithology real-time identification by using the corrected lithology identification basic model to obtain the type of lithology. According to the method, based on the data of the drilled adjacent well, the lithology identification basic model is established by using the random forest method, and the basic model is corrected by using the probability model, so that the prediction error of the model caused by the difference of geological and engineering conditions in new well deployment is avoided, and the accuracy and timeliness of lithology identification are greatly improved, and basis is provided for improving drilling speed, reducing drill bit abrasion and prolonging service life of downhole tools.

The invention relates to a soil type merging and multiple regression-based soil manganese content prediction method. The divide-and-conquer control of different spatial variation characteristics showed by soil trace elements in different soil types is involved; the spatial heterogeneity of the soil trace elements can be detected by analyzing a discrete degree of spatial distribution of effective manganese content of soil; the multicollinearity problem in local regression analysis can be diagnosed through the local regression analysis; and especially in a spatial prediction process, a comprehensive prediction model is built by measuring spatial distribution characteristics of the effective manganese content of the soil under the sampling densities of different soil samples.

The invention discloses a single-soldier guided rocket dynamic target prediction and guidance method, which can effectively reduce the normal overload of a rocket in the flight process and ensure therelatively high hit accuracy. The single-soldier guided rocket dynamic target prediction and guidance method comprises the following steps of: measuring the motion state of the target in a ground inertial coordinate system by ground fire control equipment, transforming into the inertial coordinate system of the rocket and transmitting to a computer on the rocket; using the computer on the rocket to estimate the flight time required for the rocket to hit the target and calculating the point of intersection of a guided missile and the target according to the motion state of the target, the initial position of the rocket in the inertial coordinate system of the rocket and the set speed of the single-soldier guided rocket; in the current guidance period, controlling the rocket to fly to the target point according to the set guidance law by taking the point of intersection of the guided missile and the target as the target point; when the current guidance period ends, obtaining the positioninformation of the target in the inertial coordinate system of the rocket by solving with the computer on the rocket, solving the remaining flight time of the rocket hitting the target, and updatingthe point of intersection of the guided missile and the target; and entering the next guidance period, and repeatedly executing till the rocket hits the target.

The invention provides a photovoltaic output power prediction method and system and a storage medium. The method comprises the steps of obtaining photovoltaic output data and meteorological data in apreset historical time period; decomposing the photovoltaic output power sequence by adopting a wavelet packet decomposition algorithm to obtain 2n decomposition sequences, and respectively carryingout single-branch reconstruction on the 2n decomposition sequences to obtain 2n reconstruction sequences; combining the 2n reconstruction sequences with the corresponding meteorological data to obtain2n data sets; respectively inputting the 2n data sets into corresponding pre-trained long-short-term memory neural network units to obtain prediction results of the 2n long-short-term memory neural network units on photovoltaic output power in a preset future time period; and performing weighted summation on the prediction results output by the 2n long-short-term memory neural network units to obtain a comprehensive prediction result of the photovoltaic output power in the preset future time period. The prediction precision can be improved.

The invention provides an image defogging system based on an adversarial neural network, and the invention comprises the following steps: S1, selecting an image data set of RGBD, and making a defogging data set through employing an atmospheric scattering model; s2, normalizing the size of the picture in the data set to a * a; s3, building an adversarial neural network defogging model, wherein the model is divided into two parts: a generative network and a discrimination network; s4, training the adversarial neural network model by using the data set; and S5, storing the trained model, and inputting a foggy image to obtain a clear image. The invention does not need to manually extract features, effectively avoids intermediate variable prediction errors, realizes end-to-end defogging, and is simple and wide in applicability.

A centrifugal pump flow prediction method based on power and differential pressure measurement, characterized in that the method includes the steps: 1. Based on the 'grey box' construction theory, the centrifugal pump is divided into a motor module, a mechanical model and a hydraulic module; 2. Based on the system analysis, construct the mathematical model of the overall flow prediction of the centrifugal pump; 3. Based on the internal loss model of the motor, locally construct the mathematical model of the motor output power prediction; 4. Based on the mechanical loss model, locally construct the output power of the mechanical transmission components (or centrifugal pump input power) prediction mathematical model; 5. Based on the fluid flow loss model, locally build a model between the input power of the centrifugal pump, the flow rate, and the rotational speed, and the model of the pressure difference, flow rate, and rotational speed of the centrifugal pump; 6. Combining the mathematical model of the motor, the mechanical Mathematical model and hydraulic mathematical model, establish a centrifugal pump flow prediction model based on power (speed and torque) and pressure difference; 7. Use compensation algorithm to correct the centrifugal pump flow prediction model to improve the flow prediction accuracy.