37results about How to "Solve the problem of inaccurate forecasts" patented technology

The invention provides a continental facies clay shale reservoir stratum gas content prediction method and device and relates to the technical field of natural gas exploration. The method comprises the steps that logging data and a core sample of the continental facies clay shale reservoir stratum are obtained, and relevant parameters of the continental facies clay shale reservoir stratum are determined; then the total organic carbon volume fraction, the effective porosity, the water saturation, the continental facies clay shale reservoir stratum temperature and the pore pressure of the continental facies clay shale reservoir stratum are determined; then the adsorbed gas quantity of the continental facies clay shale reservoir stratum is determined according to an adsorbed gas quantity prediction model of the continental facies clay shale reservoir stratum; the free gas quantity of the continental facies clay shale reservoir stratum is determined according to a free gas quantity prediction model of the continental facies clay shale reservoir stratum; and accordingly the total gas content of the continental facies clay shale reservoir stratum is determined according to the adsorbed gas quantity of the continental facies clay shale reservoir stratum and the free gas quantity of the continental facies clay shale reservoir stratum. By the adoption of the continental facies clay shale reservoir stratum gas content prediction method and device, the problem that in the prior art, various factors affecting the adsorption capacity of shale are not sufficiently considered, and accordingly the current continental facies clay shale reservoir stratum gas content prediction is inaccurate can be solved.

The present invention provides a device maintenance information processing method and device, a computer device and a storage medium. The method comprises the steps of: obtaining model input data of aplurality of devices according to faults having happened in each device in the devices, inputting the model input data of each device into a prediction model for training, employing a trained prediction model to perform prediction and obtain one prediction fault frequency and prediction maintenance cost of each device in a future observation time quantum, and determining total cost data of each device according to the prediction fault frequency and the prediction maintenance cost of each device. The prediction model is employed to perform prediction of the fault frequency and the maintenancecost of each device in the future observation time quantum to determine the total maintenance cost of each device, and therefore, the problem is solved prediction is inaccurate caused by predication of fault frequency and the maintenance cost of each device through people's experience or simple algorithms.

The invention discloses a method for predicting the gas emission quantity of a working face under the pressure relief condition. The method comprises the following steps: a, determining the gas emission compositions of a stope face, which comprise coal wall gas emission, falling coal gas emission, gas emission from residual coal in goaf or / and adjacent-layer gas emission; b, respectively calculating the emission quantities of all gas emission sources; c, according to the emission quantities of all the gas emission sources, calculated in the step b, predicting the gas emission quantity of the stope face, and building a gas emission quantity prediction mathematical model under the exploitation condition of an underlying remote protective layer. The method solves the problem of inaccurate gas emission prediction of the stope face under the pressure relief condition, so as to better guide safe mining of the working face.

The invention provides a data classification method and device, equipment and a computer readable storage medium. According to the data classification method and device, the equipment and the computerreadable storage medium provided by the invention, all minority sample sets are mixed to form a new sample set which serves as training samples to perform machine learning together with majority sample sets so as to obtain a first classification model, so that a problem of imbalance in sample quantity of the minority sample sets and the majority sample sets is solved; and all of the minority sample sets serve as new samples to perform training again so as to obtain a second classification model, the type of the data is predicted through combining the first classification model and the secondclassification model, and a problem of inaccurate prediction for the minority data is solved.

A trajectory prediction method, applied to automatic driving in the field of artificial intelligence, includes obtaining posture information and historical motion trajectory information of a target object (201); obtaining target information representing the behavioral intention of the target object based on the posture information (202) using the target information and the historical trajectory information as input to acquire the predicted trajectory of the target object by a trajectory prediction model (203). The method takes the behavior intention of the pedestrian as the input feature of the pedestrian trajectory prediction, which can reduce the prediction error of the pedestrian trajectory and improve the prediction performance of the pedestrian trajectory.

The invention discloses a loop instruction processing method, device and equipment and a storage medium, and relates to the field of voice and chips. According to the specific implementation scheme, acomputer program is obtained, the computer program comprises a first loop body, the first loop body is generated according to a second loop body in to-be-compiled software codes, the first loop bodycomprises a plurality of first loop instructions, and the first loop instructions can be recognized by a hardware structure of computer equipment; under the condition that the first loop body is detected, loop parameters of the first loop body are determined according to the multiple first loop instructions; a plurality of first loop instructions are obtained according to the loop parameters of the first loop body; and the plurality of first loop instructions are executed. According to the embodiment of the invention, the first loop body is generated according to the second loop body in the to-be-compiled software code, and the first loop instruction included in the first loop body can be identified by the hardware structure of the computer equipment, so that the hardware structure can directly and accurately determine the loop parameter according to the first loop instruction.

The invention provides a flow prediction model training method and device, a flow prediction method and device, equipment and a medium. The flow prediction model training method comprises the steps: obtaining a flow data set, intercepting the flow data set through a sliding window operation, and obtaining the sample data features of the flow of the size of a sliding window and the sample labels corresponding to the sample data features of the flow; obtaining a next flow value after the sample data characteristics of the sample label flow; obtaining sample attribute characteristics corresponding to the sample label; processing the sample data characteristics of the flow by using a gated recurrent neural network of the model, and inputting the output memory state and the sample attribute characteristics into a full-connection network of the model for regression prediction; and according to the sample label and the prediction result, updating the parameter weight through a back propagation algorithm to obtain a trained flow prediction model. Therefore, a prediction result is associated with the attributes of different flows, and the problems of hysteresis quality and low accuracy of flow peak prediction are solved.

The embodiment of the invention provides a rock mass instability stage prediction method and device based on multi-feature fusion, and belongs to the technical field of rock mass instability prediction. The method comprises the following steps: acquiring an acoustic emission signal of a rock mass; carrying out mixed domain feature extraction on the obtained acoustic emission signal; obtaining a prediction error according to each characteristic parameter and a preset BP neural network model; establishing a D-S evidence theory identification framework, wherein the identification framework comprises a plurality of identification sub-frameworks in one-to-one correspondence with the rock mass instability stages; constructing a basic probability distribution function of the characteristic parameters according to the prediction error, and calculating trust values of the characteristic parameters and each stage of rock mass instability; and according to the obtained trust degree value, fusingall the feature parameters under the same recognition sub-framework through a D-S evidence theory, and performing rock mass instability stage prediction based on the fused feature parameters. Accurateprediction of each stage of rock mass instability is realized, and the problem of inaccurate prediction of single characteristic parameters is effectively solved.

The invention discloses a transformer noise calculation method and system based on a multi-line sound source model. The method comprises the steps that the relative position of a point to be predictedand a transformer is determined; according to the relative positions of the to-be-predicted points, selecting one or more multi-line sound source equivalent models corresponding to the side surfacesof the transformer having influence on the to-be-predicted points; inputting the position of a to-be-predicted point into the one or more multi-line sound source equivalent models to obtain noise of one or more affected transformer side surfaces at the position of the predicted point; superposing the one or more noises at the position of the prediction point to obtain comprehensive noise at the position of the point to be predicted; the method and the system are divided into a plurality of small cube modules through a finite element method, the small cube modules are equivalent to a pluralityof corresponding line sound sources, and a multi-line sound source model is established through a finite element-boundary element coupling method; noise generated by the transformer can be accuratelypredicted at different prediction positions, and the problem that noise prediction in a near-field area is inaccurate is solved.

The invention relates to an evaluation method, in particular to an evaluation method of single coal blending coking efficiency. First, single coal coke forming tests are performed as follows: performing coke oven testing on each kind of single coal, and simulating the actual coking production state; after coke is refined, determining experimental results including the mechanical strength and the post-reaction strength of the single coal coke; establishing a same coal test database, and comparing and analyzing data; then a database of the mechanical strength and the post-reaction strength of the single coal coke and blended coal refining coke is established as follows: testing or producing blended coal, and establishing a machine account; establishing a single coal and blended coal refiningcoke corresponding database; and calculating the mass of the single coal and data of the blended coal refining coke; and coke quality is predicted as follows: calculating the mechanical strength andthe post-reaction strength of the coke by use of a coal blending scheme and the mechanical strength and the post-reaction strength of the single coal for addition. The single coal blending efficiencyis improved, stable coke quality is ensured, and economic efficiency is improved.

The invention discloses a fabricated building construction quality monitoring system based on the Internet of Things, which comprises a supervision module, an information acquisition module, an information reminding module, a cloud computing module, a BIM management module and a material supply module, and is characterized in that a designer sends the size and material information of a prefabricated part to a manufacturer of the prefabricated part through the BIM management module; the supervision module receives monitoring information sent by the information acquisition module, transportation information sent by the transportation module, storage information sent by the component storage module and material information sent by the material supply module, and sends the received information to the cloud computing module, and an analysis module of the cloud computing module analyzes and calculates the received information to obtain an analysis result. And the influence of the influence factors of the prefabricated part in each process before hoisting on the qualified rate of the prefabricated part is calculated, and the production of the prefabricated part is adjusted according to the predicted qualified rate, so that the quality supervision strength of the prefabricated part is improved, and the quality monitoring of the prefabricated building is enhanced.

The invention relates to a business volume prediction model construction method and device, computer equipment, a storage medium, and a business volume prediction method. The method comprises the following steps: acquiring historical business volume data; constructing a historical sample set based on the historical business volume data; performing discrete processing and complete up-sampling processing on the historical sample set to generate a training sample set; training a preset initial random neural network through the training sample set and a dynamic sampling algorithm, and constructing a business volume prediction model. According to the invention, the business volume prediction model is constructed through dynamic sampling and the random neural network, and the dynamic sampling balances the training samples in a peak period and a non-peak period according to a prediction error in the model training process, so that the imbalance problem of too few samples in the peak period is solved, the obtained business volume prediction model can effectively solve the problem of inaccurate business volume prediction in the peak period, and a more accurate basis for decision making in the peak period is provided.

The invention belongs to the technical field of iron and steel production, in particular to a method for predicting quality indexes of metallurgical coke, which includes: establishing a coke quality data collecting production data; establishing a data correspondence; screening database data; establishing a regression equation of coke quality, and obtaining the partial regression coefficientsm 1, m 2, m 3,... mn and constant b with coke quality as dependent variable y, coal quality index and coal test index, and single coal quality as independent variable x. The application of the prediction equation of coke quality index, the blended coal quality index corresponding to the coke quality index, the single coal quality index and the coal test index can be added or updated. If the data area selected by the LINEST function formula is adjusted, the formula will be updated. According to the actual production situation, the coke quality reacted strength CS or M40 and its related data will be selected. The accuracy of coke quality index prediction is greatly improved due to its fast calculation, convenient update and flexible use.

The invention provides a method and a system for predicting the condensation performance of an umbrella-shaped flexible tensioned membrane condenser. The prediction method comprises the following steps: establishing a triangular mesh model of the umbrella-shaped flexible tensioned membrane condenser; carrying out shape finding design on a reflective film of the umbrella-shaped flexible tensioned membrane condenser to obtain geometric position coordinates of each triangular grid in the reflective film of the umbrella-shaped flexible tensioned membrane condenser after shape finding; randomly generating incident points of light rays on the reflective film, calculating a reflection equation of the incident light rays after being reflected by the film by utilizing a Fresnel principle and combining sunlight non-parallelism so as to obtain reflected light rays, and obtaining the position of the light rays falling on a receiving surface after being reflected by the reflective film of the umbrella-shaped flexible tensioned membrane condenser; and predicting the condensation performance of the umbrella-shaped flexible tensioned membrane condenser according to the reflected light received by the receiving surface. According to the technical scheme provided by the invention, the problem of inaccurate evaluation of the condensation performance of the umbrella-shaped flexible tensioned membrane condenser can be solved.

The invention provides a prediction method for original rock temperatures of a high-ground-temperature tunnel under the unexcavated condition. According to the prediction method, a heating conductingeffect of different elevations and different ground surface temperatures, which are formed by geological evolution, a ground temperature gradient of stratum rocks and a rock temperature difference ofdifferent points is utilized; the original rock temperature of each measured point in a corresponding range of the high-ground-temperature tunnel is relatively accurately calculated under the assistance of two independent development formulas; a problem of inaccurate prediction of a conventional method is solved; by an original rock temperature distribution diagram, heat damage prevention measurescan be taken in advance; the labor hygiene requirement of an operator is met; technical support is provided for heat damage governance and scientific construction of the high-ground-temperature tunnel; and safe and rapid construction of the high-ground-temperature tunnel is ensured.

The invention discloses an urban block vitality prediction method, a storage medium and a terminal, and belongs to the technical field of urban planning. A city block vitality prediction method comprises the following steps: obtaining vector boundaries of blocks in a to-be-detected area from road network data, and taking areas in the boundaries as basic units; obtaining built environment data of blocks in the to-be-detected area from the basic units as independent variables; acquiring crowd activity intensity data of the blocks in the to-be-detected area, and quantifying the crowd activity intensity data to acquire an urban vitality value; matching the basic units, the block built environment data and the crowd activity intensity data to construct a training database; adopting a GBDT algorithm to train the training database to obtain an urban block vitality prediction model; and predicting the block vitality of the to-be-detected area based on the city block vitality prediction model. According to the method, the vitality of the city constructed in the future is accurately predicted through the constructed environment indexes of the blocks, so that a basis is provided for a planning and design strategy.

The invention provides a time advance prediction method and device and a communication system, and relates to the technical field of communication, and the method comprises the steps: obtaining a plurality of pieces of measurement report (MR) data sent by at least one terminal in a terminal set to a base station; grouping the multiple pieces of MR data according to the user session ID to obtain multiple groups of MR data; sorting the multiple pieces of MR data in each group of MR data according to the sending time of each piece of MR data; after sorting, for one piece of MR data missing the timing advance in one group of MR data, determining whether another piece of MR data and another piece of MR data exist before and after the piece of MR data respectively, wherein the other piece of MR data and the another piece of MR data do not missing the timing advance; and if the other piece of MR data and the other piece of MR data exist, predicting the missing time advance of one piece of MR data by adopting a linear interpolation method.

The invention discloses a crack detection method based on Alford rotation. The method includes: step 1, obtaining radial components and tangential components of multi-component seismic data, wherein the radial component is a component along the direction connecting the shot point and the receiver point, and the tangential component is a component perpendicular to the direction along the line The component of the direction of the line between the shot point and the receiver point; step 2, based on the radial component and the tangential component, using Alford rotation to scan according to the rotation angle and time difference to obtain the correlation coefficient spectrum of the fast and slow waves; step 3, based on the The correlation spectrum determines the development information of subsurface fractures. The application of the present invention can effectively solve the problem of inaccurate prediction in the case of large angle intervals in conventional fracture prediction, and has positive effects on subsequent elimination of anisotropic influence and improvement of imaging quality.

The invention discloses a MA-DBN-based GIS decomposed gas content prediction method, which comprises the following steps of firstly, acquiring the SO2, SO2F2, SO2F2 / SO2, air temperature, humidity andaltitude data in a period of time monitored by a state grid for the GIS equipment; secondly, constructing an MA model, taking the collected gas data as the input of the model, and predicting the gas data in the future time period; and finally, constructing a DBN model, taking the predicted gas data as the input of the DBN model, and correcting the DBN model to obtain the accurately predicted gas data in the future time period. The method solves the problem of inaccurate MA algorithm model prediction, has the advantages of being high in convergence speed and not prone to falling into local optimum, can eliminate the fault before the fault of a GIS switch device happens, and reduces the economic losses caused by the faults of the GIS equipment.

The invention discloses a behavior perception recommendation method, a terminal and a computer readable storage medium, and the method comprises the steps: obtaining attention scores of a next behavior and a current behavior of a target object, and adding the attention scores as heterogeneous behavior information into an input layer of a perception recommendation model; introducing a target behavior at the output side of the perception recommendation model, and training the perception recommendation model according to the target behavior to obtain a trained perception recommendation model; and predicting a target behavior of the target object according to the trained perception recommendation model to obtain a next behavior prediction result corresponding to the target behavior, and recommending the obtained prediction result to a client where the target object is located. According to the method, the attention mechanism is applied to the input layer, and the attention score between the next behavior and the current behavior is used as heterogeneous behavior information, so that the problem that the isomerism cannot be distinguished is solved; meanwhile, a real next behavior is introduced to the output side, so that the problem of inaccurate isomerism prediction is solved.

The invention provides a CT image data processing method and device and a storage medium. The method includes: acquiring multiple CT images each including a group of layer images of a human body part;selecting a preset number of layer images arranged at intervals from the layer images of the CT images as training images of the CT images, and synthesizing the training images; inputting the synthesized image into multiple pre-established network models separately for training; fusing the respective trained network models to obtain a fused network model; training the fused network model; inputting the target CT image into the trained fused network model, and outputting a pathological prediction result of the target CT image. The method selects multiple training images from different positions in the respective CT images, synthesizes the training images to obtain a synthesized image, trains the basic network models by the synthesized image, improves the accuracy of network training, and integrates the trained network models to obtain a fused network models, thereby improving robustness and prediction accuracy.

The invention provides a continental facies clay shale reservoir stratum gas content prediction method and device and relates to the technical field of natural gas exploration. The method comprises the steps that logging data and a core sample of the continental facies clay shale reservoir stratum are obtained, and relevant parameters of the continental facies clay shale reservoir stratum are determined; then the total organic carbon volume fraction, the effective porosity, the water saturation, the continental facies clay shale reservoir stratum temperature and the pore pressure of the continental facies clay shale reservoir stratum are determined; then the adsorbed gas quantity of the continental facies clay shale reservoir stratum is determined according to an adsorbed gas quantity prediction model of the continental facies clay shale reservoir stratum; the free gas quantity of the continental facies clay shale reservoir stratum is determined according to a free gas quantity prediction model of the continental facies clay shale reservoir stratum; and accordingly the total gas content of the continental facies clay shale reservoir stratum is determined according to the adsorbed gas quantity of the continental facies clay shale reservoir stratum and the free gas quantity of the continental facies clay shale reservoir stratum. By the adoption of the continental facies clay shale reservoir stratum gas content prediction method and device, the problem that in the prior art, various factors affecting the adsorption capacity of shale are not sufficiently considered, and accordingly the current continental facies clay shale reservoir stratum gas content prediction is inaccurate can be solved.

The invention discloses a road congestion prediction method, system and device based on checkpoint data, wherein the method includes the following steps: obtaining the dynamic characteristics of the road in the previous time period through the checkpoint data; combining the dynamic characteristics with the preset road congestion prediction The model predicts the road congestion level for the next time period. The present invention obtains the dynamic characteristics of the road through the bayonet data, and combines the dynamic characteristics and the road congestion prediction model to predict the congestion situation of the road in the next time period, which can effectively and accurately predict the traffic congestion situation, and solves the problem of relying on artificial prediction to cause prediction. Inaccurate problem; and the present invention can predict the traffic congestion in a large area, improves the prediction efficiency, provides the management efficiency of the traffic management department and shortens the travel time of the car owner and reduces the travel cost, and can be widely used in intelligent transportation technology field.

The invention discloses a flue gas oxygen content load prediction method and device based on sample migration. The method comprises the following steps: acquiring equipment data of a first participant and a second participant under a joint learning architecture; wherein the first participant is a participant who puts forward a prediction demand, and the second participant is other participants except the first participant; training a prediction classifier by using the equipment data of the first participant and the equipment data of the second participant; according to the prediction classifier, determining weight data of the equipment data of the first participant about the equipment data of the second participant; based on the equipment data and the weight data of the second participant, training a predictive gradient lifting model; and utilizing the predictive gradient lifting model to predict the flue gas oxygen content load of the first participant equipment. According to the method, the problem of inaccurate flue gas oxygen content load prediction caused by data distribution difference of energy equipment generated under different processes is solved, and the cost of an energy equipment sensor is saved.