486 results about "Price prediction" patented technology

The invention provides an electric power spot market price prediction method and system based on artificial intelligence, and relates to the technical field of electric power price prediction, and the method comprises the steps: calculating an impact factor weight through employing a deep reinforcement learning optimization fuzzy reasoning system based on historical transaction data and external association data; performing wavelet transform decomposition on the data to obtain a feature fluctuation sequence, and performing weighted fusion to generate a market feature fusion sequence; the method comprises the following steps: constructing a bidirectional feedback neural network by using a graph attention network to extract market transaction features and generate feature correction parameters, generating layered early warning signals in combination with a cascade forest algorithm to construct a dynamic market behavior feature map, and inputting an optimized feature map into a prediction model group comprising a plurality of prediction sub-models, and realizing information sharing and complementary learning through a cross-modal feature interaction module, generating an initial prediction result, performing constraint correction according to an intelligent clearing rule, correcting a correction prediction result, and finally outputting a predicted price of the electric power spot market.

The invention relates to an internet big data extraction method and device, equipment and a storage medium, and the method comprises the following steps: carrying out distributed crawler collection on an internet data source, obtaining original network data, and converting the original network data into a structured data matrix; and performing multi-level semantic analysis on the matrix, constructing a semantic feature map, and performing topic segmentation and classification to form a topic domain knowledge tree. Association rules in the knowledge tree are further mined, and an implicit knowledge network is constructed. Performing semantic decomposition and expansion on query conditions based on the network to generate expanded query data, and performing similarity matching with the knowledge network to obtain a candidate data set; and finally, performing multi-factor sorting and extraction on the candidate data, and outputting target data, thereby solving the technical problem that in the second-hand car market, due to wide data sources and fuzzy semantics, an existing system has relatively large deviation when performing price prediction and maintenance cost analysis.

The invention discloses a cabinet-level computing power server dynamic temperature regulation and load optimization method, and relates to the technical field of data centers, and the method comprises the following steps: collecting data, respectively building a thermodynamic model and a price prediction model, predicting a thermodynamic state, an electric power spot market price, and a renewable energy source supply condition, outputting and executing an optimal refrigeration system control plan by adopting an MPC algorithm; in combination with electric power spot market price data, cabinet-level computing power server energy consumption data and renewable energy supply data, the environment temperature of the cabinet-level computing power server and refrigeration system output are adjusted in real time through an MPC algorithm, and the future state of the cabinet-level computing power server can be accurately predicted. The MPC algorithm predicts the thermodynamic state of the cabinet-level computing power server and dynamically optimizes the temperature regulation strategy, the real-time performance is good, the temperature and the load can be adjusted in time, the stability and the energy efficiency of the cabinet-level computing power server are ensured, and safe and stable operation of the data center and the minimization of the energy consumption cost are ensured.

The invention aims to provide a hotel price prediction system combining reinforcement learning and a long short-term memory network, and relates to the technical field of neural networks, and the system comprises a data acquisition multi-dimensional time sequence feature extraction unit which is used for acquiring historical operation data of a hotel, carrying out the multi-scale decomposition of a multi-dimensional time sequence through wavelet transform, and obtaining a multi-dimensional time sequence feature extraction unit; obtaining a wavelet coefficient as a multi-dimensional time sequence feature; the long and short-term memory network processing unit is used for inputting the multi-dimensional time sequence features into a long and short-term memory network as input and outputting a hidden state; and the price distribution prediction unit is used for using the price prediction model according to the hidden state, generating predicted price distribution, defining a Markov decision process, using the predicted price distribution as a reinforcement learning model, and generating a final price prediction result according to the predicted price distribution. According to the invention, the precision of price prediction is improved, and the coping capability of the system for market fluctuation is greatly enhanced.

The invention relates to the technical field of electric power market price prediction, in particular to an electric power spot price prediction method and system based on similar day probability correction, and the method comprises the following steps: S1, electric power data collection: obtaining historical market information of a day-ahead spot market and day-ahead electric power spot price data; s2, feature engineering construction: performing feature engineering construction on the collected power data; s3, data preprocessing: forming a data set; s4, performing model training: performing training on the LightGBM model; s5, similar day matching: screening k historical days with the highest similarity with the prediction day; s6, performing interval probability distribution modeling of the bidding space and the day-ahead electricity price: establishing a probability mapping matrix of the bidding space interval and the day-ahead electricity price interval; and S7, prediction result correction and final electricity price prediction: generating a final day-ahead spot electricity price prediction sequence. According to the invention, the method is closer to the actual market situation when the prediction logic is constructed, and the stability and adaptability of electricity price trend modeling are enhanced.

The invention relates to a large-scale new energy base scheduling method based on uncertainty of an electric power spot market, and the method comprises the steps: collecting historical data of spot market node marginal electricity price and wind-solar power generation power, and generating a day-ahead market decision scene set comprising the spot market electricity price and new energy unit output through a Monte Carlo sampling method; establishing a two-stage stochastic optimization model for the large new energy base to participate in the day-ahead market by considering the uncertainty of the spot market, and establishing a model for participating in the real-time market; calculating a day-ahead market bidding strategy, a thermal power generating unit start-stop plan and a wind turbine generator maintenance plan in combination with the uncertain day-ahead market decision scene set; and according to high-precision short-period price prediction and renewable energy power prediction values, a real-time market bidding strategy and a charging and discharging strategy of energy storage equipment are calculated, and profit maximization in a large-scale new energy base market environment is realized. The problem of low economic benefit during energy storage scheduling of a large-scale new energy base in an electric power spot market is solved.

The invention belongs to the technical field of intelligent heat supply, and provides a green power consumption heat storage and release matching control method and system based on a water heat storage boiler, and the method comprises the steps: firstly obtaining a heat load prediction sequence and an electric power spot market price prediction sequence of a heat supply system; secondly, regarding the heat supply network as a graph structure composed of nodes and pipelines, and establishing a pipe network dynamic model for describing the hydraulic and thermal characteristics of the heat supply pipe network; and finally, with the purpose of minimizing the comprehensive operation cost of the heat supply system in the whole scheduling period, and with the whole network heat balance, heat source operation, centralized heat storage device operation, heat supply network safety and user comfort as constraint conditions, solving to obtain a source network load storage cooperative control strategy. The purpose of integrated collaborative optimization of the heat source, the energy storage, the pipe network and the load is achieved, a scheduling strategy with better economical efficiency can be generated, and the problems that the absorption capacity is insufficient, and the overall operation economical efficiency of the system is not high are solved.

The invention relates to the field of financial stock price markets, in particular to a stock price prediction method based on a Transform-KAN model, which comprises the following steps: S1, selecting a proper data set on a Kaggle platform, processing missing values and abnormal values of the stock price data set, dividing the data set into a training set and a test set, carrying out maximum-minimum normalization processing on the training set and the test set; s2, generating a time window feature by using a time sequence feature extraction method, and converting the time window feature into an input format suitable for a Transform structure; s3, a Transform-KAN model is constructed for stock price prediction, a long-term dependency relationship of a time sequence is extracted by using a Transform, and an MLP layer of the Transform is replaced by using KAN (Kolmogorov-Arnold Networks), so that the nonlinear fitting capability of the model is enhanced; s4, defining a loss function and an optimizer, and setting hyper-parameters such as a learning rate and a batch size; s5, inputting the data into the network, training the model by using the training set, and storing the trained model; and S6, loading the stored model, predicting the test set, and evaluating by using various evaluation indexes.

The invention provides a multi-mode hybrid expert model electricity transaction data processing method and system, a storage medium and electronic equipment. The method comprises the steps of collecting multi-source heterogeneous data in an electricity market environment; extracting multi-modal features of the multi-source heterogeneous data, and performing dynamic weighted fusion to obtain fusion features, wherein the multi-modal features comprise time sequence features, meteorological features, image features, text features and market features; different expert models including a load prediction model, a price prediction model, a risk assessment model and a strategy optimization model are allocated for processing and analysis results are obtained according to the fusion features; and generating output data in combination with the analysis result, wherein the output data comprises a service instruction and an analysis report. According to the method, the power transaction prediction precision is remarkably improved, the reasoning delay is greatly reduced, the dynamic adaptive capacity and a closed-loop self-evolution mechanism are achieved, the decision interpretability and the system robustness are enhanced, and the strict requirements of the power market for high precision, real-time performance and stability can be met.

The invention relates to the technical field of electric power spot market real-time price prediction, and discloses an electric power spot market real-time price prediction method based on low-sample high-dimension data, and the method comprises the steps: extracting historical data from a database; carrying out feature extraction from historical data, and carrying out standardization processing on the extracted features; constructing a real-time electricity price prediction model by adopting a generative neural network model, and inputting the extracted features of the historical data into the real-time electricity price prediction model to obtain a first predicted electricity price; inputting the first predicted electricity price into a heuristic predictor for adjustment to obtain a second predicted electricity price; adjusting factors for adjusting the first predicted electricity price by the heuristic predictor comprise similarity, a trend factor, a seasonal factor and a special event adjusting factor, and fusing the adjusting factors to comprehensively adjust the first predicted electricity price; existing electricity price prediction cannot be reasonably quantified due to external factors in the early stage of development of an electric power spot market, so that the prediction result is inaccurate.

The invention discloses a building engineering cost management system and method based on big data. The method comprises the steps of obtaining engineering stage information according to building engineering process information; obtaining stage cost information according to the engineering stage information, establishing a price prediction model through the stage cost information by using a big data technology, and obtaining primary optimal stage cost information; transaction companies are selected and acquired according to the optimal cost by using a big data technology, the transaction risk of each transaction company is evaluated, and the cost information of the optimal stage is acquired; obtaining optimal engineering cost information according to the optimal stage cost information, establishing a BIM model, updating the BIM model in real time, and adjusting the optimal stage cost information; obtaining actual engineering cost information, comparing the optimal engineering cost information with the actual engineering cost information to obtain cost difference information, and adjusting the cost information of the optimal stage; under the condition that the engineering cost quality is guaranteed, the cost input is reduced to the maximum extent, and the maximum benefits of enterprises are guaranteed.

The invention provides a stock price prediction method based on a corrected emotion index and stock market historical transaction data, and relates to the application of computer artificial intelligence in the financial field, and the method specifically comprises the steps: selecting a historical transaction data set of a to-be-predicted stock and a corresponding stock comment data set; the method comprises the following steps: selecting a financial corpus data set to carry out pre-training on a BERT (Bidirectional Encoder Representation Transformers) model, so as to obtain an emotion classification model; inputting the stock comment data into an emotion classification model to obtain an emotion index data set; constructing a correction model for correcting the abnormal investment emotion index to obtain a corrected emotion index data set; the method comprises the following steps of: establishing a long short term memory (LSTM) model used for predicting a stock price; and merging the stock historical price data set and the corrected emotion index data set, and inputting the merged data set into the LSTM model to obtain a price trend prediction value. By correcting the abnormal emotion data, the abnormal value of the emotion data is reduced, and the accuracy of stock data prediction is effectively improved.

Systems, apparatuses, methods, and computer program products are disclosed for rental optimization of real estate. An example method includes detecting, by an event monitoring engine, an occurrence of a trigger event. The example method further includes defining, by the event monitoring engine and based on a trigger event attribute set, an area of interest, and identifying, by a prospect engine, a rentable unit within the area of interest that corresponds to a trigger event type attribute. The example method further includes generating, by the prospect engine and based on the trigger event attribute set, a rental price prediction for the rentable unit, if the rental price prediction satisfies a predefined rental price threshold, generating, by the prospect engine, a personalized rental recommendation for the rentable unit, and outputting, by communications hardware, a rental prompt based on the personalized rental recommendation.

Disclosed is a method for optimizing an open-pit mine production plan based on time-series prediction of copper price. The method includes collecting historical copper price based on dates, generating a processed multi-factor dataset using linear normalization and linear interpolation manners, and generating a correlation matrix, focusing on the correlation matrix using a Graph Convolutional Network (GCN) model, and decomposing a trend item sequence and a seasonal item sequence through Autoformer mechanism, predicting copper price through a time-series prediction model, and importing copper price as parameters into a production plan mathematical model to generating the production plan. The present disclosure is capable of focusing on the correlation between different factors in a multi-factor dataset including historical price information, effectively improving the accuracy of copper price prediction, and obtaining solutions with a higher accuracy and better fit for an actual production situation.

The invention relates to the technical field of bulk commodities, in particular to a bulk commodity transaction price prediction method, which comprises the following steps: acquiring multi-modal data of bulk commodities in the current period; the multi-modal data comprises meteorological data, macroeconomic index quantized values and news event analysis parameters processed by natural languages; the news event analysis parameters comprise market emotion indexes and policy change influence factors; performing data preprocessing on the multi-modal data of the bulk commodities in the current period to obtain preprocessed multi-modal data; inputting the preprocessed multi-modal data into a trained price prediction model to obtain a final prediction price; the price prediction model is a hybrid neural network model fused with an LSTM-Transformer (LSTM-Transformer). According to the method, the accuracy of bulk commodity price prediction is improved, and the calculation efficiency is high.

The invention relates to the technical field of electric power spot transaction, in particular to an electric power spot market day-ahead declaration optimization strategy generation method, device and equipment, and the method comprises the steps: carrying out the preprocessing of the obtained operation boundary data and historical transaction data of an electric power spot market of a target region, and obtaining the processed data; based on the processed data and historical electricity price prediction data, optimizing a pre-trained initial electricity price prediction model to obtain an electricity price prediction model; generating at least one power day-ahead declaration optimization strategy according to the obtained power demand information of the user and day-ahead electricity price prediction data obtained based on a power price prediction model; and selecting a target power day-ahead declaration optimization strategy from the at least one power day-ahead declaration optimization strategy based on an analysis result obtained by analyzing each power day-ahead declaration optimization strategy in the at least one power day-ahead declaration optimization strategy. The optimal declaration scheme is provided for the user on the premise that market rules and constraint conditions are met.

The invention provides a real-time commodity price prediction system based on multi-source data fusion, and the system comprises a data integration module which is used for obtaining multi-source heterogeneous data, carrying out the alignment processing of the multi-source heterogeneous data according to the commodity types and timestamps, and generating a standardized fusion data set; the dynamic weight distribution module is used for dynamically calculating the weight coefficient of each data source according to the relevance between each data source in the standardized fusion data set and the commodity category; the model integration module is used for performing weighted fusion on the price trend prediction result output by the time sequence prediction model and the game correction output by the market game model based on the weight coefficient to generate initial price prediction; and the optimization output module is used for performing periodic rule correction and strategy collaborative analysis on the initial price prediction according to the real-time market dynamic data to obtain a predicted commodity price trend. By adopting the system, the commodity price trend can be dynamically predicted based on multi-source big data, an enterprise is helped to optimize a purchasing strategy, and the inventory overstock risk is reduced.

The invention discloses an intelligent energy-saving scheduling system and method based on an industrial computing power data center, and the method comprises the steps: collecting all kinds of operation data in the data center and external environment data in real time, and carrying out the preprocessing and cleaning of the data; using a machine learning algorithm to predict future load demand and energy price fluctuation; in combination with load prediction and energy price prediction, a multi-objective optimization algorithm is adopted to generate a resource scheduling plan; dynamically adjusting the working state of the cooling system according to the load change and the environment temperature and humidity data; executing a task according to the scheduling plan, feeding back according to the real-time monitoring data and an execution result, and adjusting a scheduling strategy; according to the intelligent energy-saving scheduling system and method based on the industrial computing power data center, by introducing an intelligent algorithm and multi-source data analysis, the energy utilization efficiency of the data center is improved, the operation cost is reduced, and optimal scheduling is achieved under the condition of dynamic load and energy price fluctuation.

The invention relates to the technical field of power system power purchase decision making, in particular to an agent quotation method and device in an out-of-area power purchase auxiliary decision making and a medium, and the method comprises the steps: setting a transaction scene and a declaration strategy, and determining a sending end and receiving end agent modeling range and an output data item; acquiring historical data according to the modeling range and the output data item, and preprocessing the historical data; performing agent modeling by using an actor commentator algorithm RNN-Actor-Critic improved on the basis of a recurrent neural network, and simulating a transaction by using an agent on the basis of a transaction scene; according to the change trend of agent boundary data, prediction boundary data of a transaction target time period is calculated, then an extreme gradient is utilized to promote an Xgboost model, and the predicted boundary data is input to calculate an expected transaction price; and calculating the declaration price based on the expected transaction price, and finally outputting the predicted transaction price, the predicted boundary data, the prediction model and the predicted declaration price.

The invention relates to a block chain-based green electricity transaction prediction method and system. When the green electricity price is predicted, the real-time green electricity transaction data needing to be acquired not only comprises the price data and the transaction volume data, but also comprises the time data, the weather data and the environment data, and the price prediction value of each time point can be predicted more accurately by considering the influence of the time data, the weather data and the environment data on the green electricity price; moreover, when the price prediction values are output through the prediction model, the fluctuation rate of each price prediction value and the probability distribution of each price prediction value in each fluctuation rate interval can be predicted, and the fluctuation size of each prediction value and the fluctuation probability of each prediction value in different fluctuation intervals can be known through the fluctuation rates and the probability distribution. Therefore, effective transaction suggestions can be given.

The invention relates to an optimal scheduling method and system for participation of actual energy storage and virtual energy storage in a power market, and a medium. The method comprises the steps of collecting user historical load, historical spot transaction price and local medium and long term transaction time-of-use electricity price data; predicting the user total load, the virtual energy storage capacity and the local day-ahead electric power spot transaction price in the next day; constructing a hybrid energy storage model based on the actual energy storage physical property parameters; the minimum user operation cost is set as an optimization target, and constraint conditions are set according to the actual energy storage capacity, the virtual energy storage capacity, the battery charging and discharging efficiency, the transformer capacity and the like; and a genetic algorithm is adopted to obtain a 24-hour actual energy storage and virtual energy storage collaborative scheduling strategy in the next day. According to the method, through accurate load, schedulable capacity and price prediction, the optimization algorithm is adopted to carry out collaborative optimization of actual energy storage and virtual energy storage, and the economic benefit and flexibility of the whole system are remarkably improved.

The invention relates to the field of integrated energy system management, and discloses an IES load-energy price coupling prediction method and system based on mixed attention, and the method comprises the steps: determining a multi-dimensional management feature through a multi-dimensional management feature extraction unit according to integrated energy management information; wherein the comprehensive energy management information comprises renewable energy output information, multi-energy load information and energy price information in a time sequence; according to the multi-dimensional management features, through a mixed attention unit, determining fusion management features of the multi-dimensional management features under the channel attention weight and the time attention weight; and according to the fusion management features, through a fusion output unit, determining a renewable energy output prediction value, a multi-energy load prediction value and an energy price prediction value. The multi-dimensional management feature extraction unit comprises a multi-column convolutional neural network unit and a long-short term memory network unit. According to the invention, the integrated energy system can be managed and dispatched to plan and operate under the optimal target.

The invention specifically discloses a lithium carbonate price prediction method and device based on a large language model, and a medium, and relates to the technical field of financial prediction and intelligent decision support. The method comprises the following steps: firstly, collecting and processing numerical type and text type data, constructing a causal graph network by the numerical type data, and extracting factual abstracts by the text type data to construct a news event library; then designing a three-layer progressive prompt template, and driving the large language model to generate a prediction result with explanation; then, implementing a dual-verification self-optimization reflection mechanism, and triggering an reflection generation improvement strategy when misjudgment is carried out or an error exceeds a threshold value; using a KTO algorithm to construct asymmetric loss function fine tuning model parameters; and finally, deploying the optimized model to a production system. According to the method, the dynamic adaptability and robustness of the model are improved through a'prediction-reflection-optimization 'closed-loop optimization architecture and a KTO algorithm driven by a foreground theory, prediction precision and interpretability are considered, and multi-dimensional decision support is provided for lithium carbonate price prediction.

The invention relates to an ELM-based power market settlement price prediction method and device and a storage medium, and is applied to the technical field of power spot price prediction, and the method comprises the steps: predicting the real-time power price change in a future power market in a short period through employing a machine learning technology (ELM), carrying out the training and modeling through employing an ELM algorithm, and carrying out the calculation of the real-time power price change. Weights of an output layer and a hidden layer are optimized by minimizing an output error, in order to reduce a power market fluctuation risk caused by weather conditions and randomness of renewable energy sources, risk parameters are added in consideration of risk factors besides single-side prediction training of an artificial neural network, and a relatively stable price is calculated based on a given risk level. The method has the advantages of rapid convergence, high precision, strong generalization ability and the like, the adaptability and reliability of the model are improved by simplifying the training process and enhancing the improvement, and an effective prediction tool is provided for the electricity market.

The invention discloses an investment recommendation method and device, equipment, a medium and a product. Relates to the technical field of finance and artificial intelligence, and comprises the following steps: if an investment recommendation request sent by a target user is detected, determining an investment demand, candidate financial products, investment analysis data and user information data of the target user, and preprocessing the investment analysis data and the user information data; determining a risk tolerance category and a financial product proportion condition of the target user, and performing feature extraction processing on the preprocessed investment analysis data to obtain corresponding analysis features; and inputting the analysis features into a preset price prediction model to obtain prediction information of each candidate financial product, and determining a target financial product based on a preset optimization algorithm according to the prediction information, the investment demand, the financial product proportion condition and the risk tolerance category to perform investment recommendation on the target user. According to the method, personalized investment recommendation can be performed for the user, and the investment decision-making efficiency is improved.

The invention relates to the technical field of real estate, and discloses an ant colony algorithm and neural network combined house price data prediction method, which comprises the following steps: S1, multi-source data acquisition, S2, data preprocessing, S3, ant colony algorithm parameter optimization, S4, neural network dual-stage training and S5, adaptive optimization feedback. According to the house price data prediction technology combining the ant colony algorithm and the neural network, neural network model parameters are optimized by adopting the ant colony algorithm; in the house price prediction model, the parameters are combined to form a path, and ants traverse different parameter combination paths; along with iteration, pheromones on a better parameter combination path are continuously accumulated and enhanced, and ants are guided to explore more; in this way, the parameter space can be fully traversed, local optimum caused by parameter random initialization of a traditional neural network is avoided, and therefore the model can obtain a better parameter combination, and the prediction precision is remarkably improved.

The invention belongs to the field of financial science and technology and data analysis, and particularly relates to a financial transaction emotion analysis and price trend prediction method based on data fusion, which is mainly used for solving the problems of complexity and real-time demand of stock price prediction in a financial market. According to the method, a fundamental analysis module, an emotion evaluation module and a price prediction module are integrated, multiple data sources are integrated, an advanced machine learning algorithm is adopted, and the real dynamic state of the market is more comprehensively and accurately captured. The fundamental analysis module is used for systematically analyzing financial data, announcement information and other related indexes of the stock and providing deep understanding of a target stock; the emotion evaluation module analyzes text data in social media and financial forums and combines various emotion analysis methods to generate dynamic indexes reflecting market emotions; the price prediction module carries out modeling based on data of different time scales such as minute, hour and day lines, and the prediction precision is improved through a layer-by-layer feedback mode.

The invention discloses an electric power price prediction method and device based on time dimension, equipment and a storage medium, and relates to the technical field of electric power systems, and the method comprises the steps: carrying out the preprocessing of a multi-source data set comprising historical electric power transaction data, a load demand curve, energy production data and fuel cost, performing feature extraction on the obtained processed data set to obtain each data feature, and determining a training set and a verification set based on the data features by using Bernoulli distribution; constructing an initial power price prediction model based on a multi-head attention mechanism, a convolutional layer, a preset activation function, a bidirectional long-short-term memory network and integration of a low-rank adaptation technology, and training and verifying the initial power price prediction model by using the training set and the verification set respectively; and performing multi-scale prediction by using the obtained target power price prediction model to obtain prediction results of the power price in different time dimensions. And the real-time, day-ahead and weekly prediction accuracy of the electric power price is improved.

The invention relates to the technical field of intelligent pricing, in particular to an intelligent pricing management system based on machine learning and data analysis, and aims to improve the purity and consistency of data and lay an accurate data foundation for subsequent analysis through cleaning and format unified processing of market original data and elimination of redundant information. Through a non-dominated sorting genetic algorithm, the influence weight of each data point is analyzed, comprehensive quantitative modeling of price influence factors is realized, a model basis with better interpretation and adaptability is provided for price prediction, and market changes and competition patterns are dynamically captured by combining a linear regression model, so that the price prediction accuracy is improved. And the price is ensured to be dynamically and accurately adjusted along with the market through a fine adjustment strategy, the dynamic matching capability between the price and the market fluctuation is realized, the adaptability of the strategy under different market conditions is verified and optimized through a multi-scene simulation test and market feedback monitoring, and the high efficiency and stability of the price adjustment strategy in practical application are ensured.

The invention provides a government purchase commodity transaction price prediction method and device based on a CMPSOLSTMTLM model, and belongs to the technical field of data analysis. The method comprises the following steps: acquiring purchase price historical data of the same commodity in an administration and acquisition contract, preprocessing and normalizing the purchase price historical data, and dividing the purchase price historical data into a training set and a test set; constructing a government purchase commodity transaction price prediction model; adopting an improved algorithm CMPSO based on particle swarm optimization to carry out parameter optimization on the government purchase commodity transaction price prediction model; and segmenting data of the test set, inputting the segmented data into the trained government purchase commodity transaction price prediction model for prediction, and carrying out reverse normalization on an output result to obtain a commodity price prediction result. According to the method, the advantages of the LSTM and the Transform model are combined, and the particle swarm optimization algorithm is used for parameter adjustment and optimization, so that the price prediction precision and efficiency are improved.