Method and system for quickly generating fuel contract price terms

Abstract

The invention discloses a method and system for quickly generating fuel contract price terms, and the method comprises the steps: obtaining fuel contract data, and carrying out the preprocessing of the fuel contract data, and obtaining fuel contract processing data; constructing and training a natural language description model based on the fuel contract processing data and a preset language model; extracting a contract text from the fuel contract data, analyzing the contract text, and determining semantic information of the contract text; extracting key information of the contract text from the semantic information, and determining a contract type according to the key information of the contract text; inputting the key information into a natural language description model to obtain natural language description of fuel contract price terms; and determining a description template of fuel contract price terms according to the contract type, and generating the fuel contract price terms based on the description template and the natural language description. Through the automation technology, a large amount of fuel contract data can be rapidly processed, the natural language description of the price terms is generated, the processing efficiency is improved, and time and human resources are saved.

Description

Technical Field

[0001] The present invention relates to the technical field of contract generation, and in particular to a method and system for rapidly generating price clauses of a fuel contract. Background Art

[0002] As global trade and business activities increase, the number and complexity of fuel contracts are also increasing. Traditional manual methods cannot meet the needs of large-scale contract processing, and more efficient methods are needed to process large amounts of contract data.

[0003] In the existing fuel contract price clause generation process, the tabular and structured price data is usually manually converted into natural language text descriptions. This method is not only inefficient, but also prone to errors, and it is difficult to meet the needs of large-scale and efficient contract processing. At the same time, manually generated text descriptions cannot provide effective support for machine-automated settlement. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for quickly generating fuel contract price terms, including: Obtain fuel contract data, and pre-process the fuel contract data to obtain fuel contract processing data; Build and train a natural language description model based on fuel contract processing data and a preset language model; Extract the contract text from the fuel contract data, parse the contract text, and determine the semantic information of the contract text based on the parsing results; Extract key information of the contract text from the semantic information, and determine the contract type based on the key information of the contract text; Input key information into the natural language description model, and output a natural language description of the fuel contract price terms; A description template for fuel contract price clauses is determined according to the contract type, and the fuel contract price clauses are generated based on the description template and the natural language description.

[0005] Further, the obtaining of fuel contract data and preprocessing of the fuel contract data to obtain fuel contract processing data includes: Obtain fuel contract data from the contract database and clean the collected fuel contract data to remove duplicate, missing or incomplete data; The cleaned fuel contract data is standardized to unify the data format and units in the fuel contract data.

[0006] Furthermore, the natural language description model is constructed and trained based on the fuel contract processing data and the preset language model, including: The preset language model is used as the basic model, and the fuel contract processing data is divided into a training set and a test set according to a certain ratio; The training set and test set are input into the basic model, and the basic model is trained and tested until the basic model meets the preset convergence conditions to obtain a natural language description model.

[0007] Furthermore, the contract text is extracted from the fuel contract data, and the contract text is parsed, and the semantic information of the contract text is determined according to the parsing result, including: The contract text is extracted from the fuel contract data through text extraction technology, and the contract text is segmented to split the contract text into words or phrases; Conduct semantic analysis on words or phrases to determine the meaning of words or phrases, and conduct syntactic analysis on words or phrases to determine the grammatical relationship of words or phrases; The semantic information of the contract text is determined based on the meaning and grammatical relationship of words or phrases.

[0008] Furthermore, extracting key information of the contract text from the semantic information and determining the contract type according to the key information of the contract text includes: Analyze the importance of semantic information, determine semantic information whose importance exceeds a preset value as key information of the contract text, and extract the key information of the contract text from the semantic information; The key information of the contract text is matched with the preset contract type, and the preset contract type with the highest total matching degree is used as the contract type of the contract text.

[0009] Furthermore, the step of analyzing the importance of semantic information and determining semantic information whose importance exceeds a preset value as key information of the contract text includes: Analyze the basic elements of semantic information related to fuel contracts and determine the evaluation indicators of the importance of semantic information based on the basic elements; Calculate the correlation between each evaluation indicator and the fuel contract, and determine the evaluation score corresponding to each evaluation indicator according to the correlation; The importance of the semantic information is obtained by comprehensively analyzing the evaluation scores of each evaluation indicator, and the semantic information whose importance exceeds a preset value is determined as the key information of the contract text.

[0010] Furthermore, the key information of the contract text is matched with the preset contract type, and the preset contract type with the highest total matching degree is used as the contract type of the contract text, including: A number of preset contract types are preset, and each preset contract type has multiple tags corresponding to it; Match the key information of the contract text with multiple tags of each preset contract type, and calculate the matching degree between the key information of the contract text and the tags in each preset contract type in turn; The matching degree between the key information of the contract text and the label in each preset contract type is accumulated to obtain multiple total matching degrees, and the preset contract type with the highest total matching degree is used as the contract type of the contract text.

[0011] Furthermore, the step of determining a description template of the fuel contract price clause according to the contract type, and generating the fuel contract price clause based on the description template and the natural language description includes: Analyze the characteristics of the contract type and determine the description template of the fuel contract price clause based on the characteristics of the contract type; The information of each position in the description template is obtained, the natural language description is matched with the information of each position in the description template, and the successfully matched natural language description is filled into the corresponding position in the description template to generate the fuel contract price terms.

[0012] The present invention also provides a system for rapidly generating fuel contract price terms, comprising: An acquisition module is used to acquire fuel contract data and pre-process the fuel contract data to obtain fuel contract processing data; A modeling module, used to acquire fuel contract processing data and a preset language model to build and train a natural language description model; A parsing module is used to extract the contract text from the fuel contract data, parse the contract text, and determine the semantic information of the contract text according to the parsing result; An extraction module is used to extract key information of the contract text from the semantic information and determine the contract type according to the key information of the contract text; An output module is used to obtain key information and input it into a natural language description model, and output a natural language description of the fuel contract price terms; The generation module is used to obtain a description template for determining fuel contract price terms according to the contract type, and generate the fuel contract price terms based on the description template and the natural language description.

[0013] Compared with the prior art, the method and system for quickly generating fuel contract price clauses in the embodiment of the present invention have the following beneficial effects: The present invention can quickly process a large amount of fuel contract data and generate a natural language description of price terms by using automated methods and technologies. Compared with traditional manual processing methods, this method can greatly improve processing efficiency and save time and human resources. The present invention can reduce the impact of human error and subjective judgment on contract processing by using natural language processing and machine learning technology. Model training and automated processing can improve accuracy and reduce potential errors. The present invention can ensure the consistency and quality of contract data by using preprocessing and standardization methods, and the use of natural language description models can ensure that the generated price clause description is consistent with the contract data and follows the preset language model; The process in the present invention can adapt to the processing needs of fuel contracts of different sizes and complexities. By adjusting the training data and model parameters, the processing process can be expanded and customized to adapt to different contract types and needs; The present invention can trace and extract key information of the contract text by using natural language processing and information extraction technology, thereby increasing the traceability of the contract processing process and providing a basis for explaining the generation of contract terms. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic diagram of the flow structure of a method for quickly generating fuel contract price clauses in an embodiment of the present invention; Figure 2 It is a schematic diagram of the composition of a system for rapidly generating fuel contract price clauses in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0016] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0017] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0018] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0019] like Figure 1 As shown, in an embodiment of the present application, a method for quickly generating fuel contract price clauses is provided, including: S100: acquiring fuel contract data, and preprocessing the fuel contract data to obtain fuel contract processing data; S200: constructing and training a natural language description model based on the fuel contract processing data and a preset language model; S300: extracting a contract text from the fuel contract data, and parsing the contract text, and determining the semantic information of the contract text according to the parsing result; S400: extracting key information of the contract text from the semantic information, and determining the contract type according to the key information of the contract text; S500: inputting the key information into the natural language description model, and outputting a natural language description of the fuel contract price clause; S600: determining a description template of the fuel contract price clause according to the contract type, and generating the fuel contract price clause based on the description template and the natural language description.

[0020] Furthermore, fuel contract data is collected and pre-processed to ensure data quality and consistency; a natural language description model is constructed based on fuel contract processing data and a preset language model; natural language processing techniques, such as text parsing and information extraction, are used to parse the contract text, which can help determine the semantic information and keywords of the contract text; key information of the contract text, such as price, delivery terms, quantity, etc., is extracted from the parsed results, and the type or category of the fuel contract, such as long-term contracts, short-term contracts, fixed-price contracts, etc., is determined based on the extracted key information; the extracted key information is input into the natural language description model to generate a natural language description of the fuel contract price terms; based on the type of fuel contract, an applicable price terms description template is determined, and the final fuel contract price terms are generated based on the template and the natural language description.

[0021] In an embodiment of the present application, a method for quickly generating fuel contract price terms is provided, wherein the fuel contract data is obtained and pre-processed to obtain fuel contract processing data, including: obtaining fuel contract data from a contract database, and cleaning the collected fuel contract data to remove duplicate, missing or incomplete data; and standardizing the cleaned fuel contract data to unify the data format and units in the fuel contract data.

[0022] Specifically, remove duplicate data: remove duplicate contract data by comparing the unique identifier of the contract data (such as contract number or contract date); handle missing data: check whether there are missing values ​​in the contract data, and you can choose to delete missing data, fill missing values, or use interpolation methods to estimate; handle outliers: check whether there are outliers or outliers in the contract data, and you can choose to delete outliers or use reasonable methods to handle them; unify data formats: check the data formats of various fields in the contract data, such as date, currency, quantity, etc., and unify them into a consistent format, for example, unify the date format to YYYY-MM-DD, and unify the currency format to a specific currency symbol and decimal places; unify units: for fields involving quantity or measurement, ensure the consistency of their units, for example, unify the fuel quantity to kilograms or tons, and unify the energy unit to joules or kilowatt-hours.

[0023] In an embodiment of the present application, a method for quickly generating fuel contract price terms is provided, wherein a natural language description model is constructed and trained based on fuel contract processing data and a preset language model, including: using the preset language model as a basic model, and dividing the fuel contract processing data into a training set and a test set according to a certain ratio; inputting the training set and the test set into the basic model, training and testing the basic model until the basic model meets the preset convergence conditions, and obtaining a natural language description model.

[0024] Specifically, the fuel contract processing data is divided into a training set and a test set according to a certain ratio (for example, 80% training set and 20% test set) to ensure that the data distribution and characteristics of the training set and the test set are similar; select a preset language model as the basic model, such as GPT-3, BERT, etc.; input the training set into the basic model, and train the model through back propagation and optimization algorithms (such as stochastic gradient descent). During the training process, the model will adjust the parameters according to the input training data and gradually optimize the performance of the model; according to the preset convergence conditions (such as reaching a certain accuracy or the loss function drops to a certain level), determine whether the model meets the preset convergence conditions. If the model meets the convergence conditions, it can be considered that the model has been trained and the training can be stopped. Otherwise, continue to optimize the model and perform more training iterations.

[0025] In an embodiment of the present application, a method for quickly generating fuel contract price terms is provided, which extracts contract text from fuel contract data, parses the contract text, and determines semantic information of the contract text based on the parsing result, including: extracting the contract text from the fuel contract data by text extraction technology, and performing word segmentation processing on the contract text to split the contract text into words or phrases; performing semantic analysis on the words or phrases to determine the meaning of the words or phrases, performing syntactic analysis on the words or phrases to determine the grammatical relationship of the words or phrases; and determining the semantic information of the contract text based on the meaning and grammatical relationship of the words or phrases.

[0026] Specifically, text extraction technology (such as regular expressions, natural language processing tools, etc.) is used to extract the contract text from the fuel contract data, and the contract text is segmented to split it into words or phrases; the segmented words or phrases are subjected to word meaning analysis to determine their meanings, and a dictionary or semantic knowledge base is used to obtain the meaning of the words and context-related information. Word meaning analysis can help understand the terms, keywords and entities in the contract text; the segmented words or phrases are subjected to syntactic analysis to determine their grammatical relationships. Syntactic analysis can help understand the grammatical relationships between different words or phrases in the contract text, such as subject-predicate relationships, verb-object relationships, etc.; based on the word meanings and grammatical relationships of words or phrases, the semantic information of the contract text can be inferred. For example, through word meaning analysis and syntactic analysis, the main topics, key terms, restrictions, etc. in the contract text can be determined.

[0027] In an embodiment of the present application, a method for quickly generating fuel contract price terms is provided, wherein key information of a contract text is extracted from semantic information, and a contract type is determined based on the key information of the contract text, including: analyzing the importance of the semantic information, determining semantic information whose importance exceeds a preset value as key information of the contract text, and extracting the key information of the contract text from the semantic information; matching the key information of the contract text with a preset contract type, and using the preset contract type with the highest total matching degree as the contract type of the contract text.

[0028] Specifically, for each semantic information, an importance score can be assigned according to its importance and impact in the contract; according to the preset importance threshold, the semantic information with an importance exceeding the preset value is screened out and determined as the key information of the contract text. These key information may include the contract type, price terms, responsibilities of both parties, performance period, etc.; the extracted key information is matched with the preset contract type. The preset contract type can be a pre-defined list of contract types. Each contract type has its specific label. By matching it with the key information, the preset contract type that best matches the contract text can be determined; for each preset contract type, its matching degree with the key information of the contract text can be calculated; according to the matching degree analysis, the preset contract type with the highest total matching degree is selected as the contract type of the contract text, which means that the preset contract type is most matched with the key information of the contract text and may be most consistent with the actual type of the contract text.

[0029] In an embodiment of the present application, a method for quickly generating price clauses for a fuel contract is provided, wherein the importance of semantic information is analyzed and semantic information whose importance exceeds a preset value is determined as key information of the contract text, including: analyzing basic elements related to the semantic information and the fuel contract, and determining evaluation indicators of the importance of the semantic information based on the basic elements; calculating the correlation between each evaluation indicator and the fuel contract, and determining an evaluation score corresponding to each evaluation indicator based on the magnitude of the correlation; obtaining the importance of the semantic information by combining the evaluation scores of each evaluation indicator, and determining the semantic information whose importance exceeds a preset value as key information of the contract text.

[0030] Specifically, the basic elements of semantic information related to fuel contracts include: price terms: information on fuel prices, payment methods, price adjustment mechanisms, etc. included in the contract; delivery requirements: requirements involving fuel quantity, quality, delivery location and delivery time; quality standards: fuel quality requirements, inspection and testing methods, etc. stipulated in the contract; performance period: the time range for the start and end of fuel supply stipulated in the contract; liability and breach of contract terms: information involving the responsibilities, breach of contract liabilities and compensation of suppliers and buyers; termination and rescission terms: terms and conditions for how the contract is terminated or rescinded; dispute resolution: terms and mechanisms for dispute resolution in the contract; Based on these basic elements, evaluation indicators can be determined to evaluate the importance of semantic information; for each basic element, the correlation between it and the semantic information in the contract text can be calculated; based on the size of the correlation, the corresponding evaluation score of each basic element is determined; the evaluation scores of each basic element are combined to calculate the importance of the semantic information, and different weights can be given according to the importance of different basic elements by using weighted average or other comprehensive methods; based on the preset importance threshold, the semantic information whose importance exceeds the preset value is screened out and determined as the key information of the contract text.

[0031] In an embodiment of the present application, a method for quickly generating fuel contract price terms is provided, wherein the key information of a contract text is matched with a preset contract type, and the preset contract type with the highest total matching degree is used as the contract type of the contract text, including: presetting a number of preset contract types, and each preset contract type is provided with a plurality of tags; matching the key information of the contract text with a plurality of tags of each preset contract type, and calculating the matching degree of the key information of the contract text with the tags in each preset contract type in turn; accumulating the matching degree of the key information of the contract text with the tags in each preset contract type to obtain a plurality of total matching degrees, and using the preset contract type with the highest total matching degree as the contract type of the contract text.

[0032] Specifically, a number of preset contract types are pre-set, and multiple labels are set for each preset contract type, which may be keywords, phrases or specific elements related to the contract type; the key information of the contract text is matched with the label of each preset contract type, and a text matching algorithm (such as keyword-based matching, word vector similarity, etc.) is used to calculate the matching degree between the key information and the label; for each preset contract type, the key information is matched with its corresponding label and the matching degree of each label is obtained; the matching degree of the key information of the contract text and the label in each preset contract type is accumulated to obtain multiple total matching degrees, and the matching degrees of different labels are accumulated; based on the total matching degree evaluation, the preset contract type with the highest total matching degree is selected as the contract type of the contract text, which means that the preset contract type is most matched with the key information of the contract text and is the actual type that best matches the contract text.

[0033] In an embodiment of the present application, a method for quickly generating fuel contract price clauses is provided, wherein a description template of the fuel contract price clauses is determined according to the contract type, and the fuel contract price clauses are generated based on the description template and the natural language description, including: analyzing the characteristics of the contract type, and determining the description template of the fuel contract price clauses based on the characteristics of the contract type; obtaining information at each position in the description template, matching the natural language description with the information at each position in the description template, and filling the successfully matched natural language description into the corresponding position in the description template to generate the fuel contract price clauses.

[0034] Specifically, for fuel contracts, the common price clause characteristics of different contract types can be analyzed. For example, fixed-price contracts may involve fixed prices, payment methods, and adjustment mechanisms; floating-price contracts may involve benchmark indices, price adjustment formulas, etc.; according to the analyzed contract type characteristics, determine the description template of the fuel contract price clause. The description template can be a structured template that contains placeholders or tags in a specific format for filling in specific information; analyze the description template to determine the specific information represented by each position. For example, the {price} position in the template may represent the fuel price, and the {payment method} position may represent the payment method, etc.; match the natural language description with the information at each position in the description template, and use natural language processing technology (such as keyword extraction, semantic similarity, etc.) to match. The successfully matched natural language description will be filled in the corresponding description template position; according to the successfully matched natural language description, fill it into the corresponding position in the description template to generate the fuel contract price clause.

[0035] like Figure 2 As shown, in an embodiment of the present application, a system for quickly generating fuel contract price clauses is provided, including: an acquisition module, used to acquire fuel contract data, and pre-process the fuel contract data to obtain fuel contract processing data; a modeling module, used to acquire and build a natural language description model based on the fuel contract processing data and a preset language model and train it; a parsing module, used to acquire a contract text extracted from the fuel contract data, and parse the contract text, and determine the semantic information of the contract text according to the parsing result; an extraction module, used to acquire key information of the contract text extracted from the semantic information, and determine the contract type according to the key information of the contract text; an output module, used to acquire the key information and input it into the natural language description model, and output a natural language description of the fuel contract price clause; a generation module, used to acquire a description template for determining the fuel contract price clause according to the contract type, and generate the fuel contract price clause based on the description template and the natural language description.

[0036] In summary, an embodiment of the present invention provides a method and system for quickly generating fuel contract price clauses, which includes: obtaining fuel contract data, and preprocessing it to obtain fuel contract processing data; constructing and training a natural language description model based on the fuel contract processing data and a preset language model; extracting the contract text from the fuel contract data, and parsing it to determine its semantic information; extracting the key information of the contract text from the semantic information, and determining the contract type based on the key information of the contract text; inputting the key information into the natural language description model to obtain a natural language description of the fuel contract price clause; determining the description template of the fuel contract price clause according to the contract type, and generating the fuel contract price clause based on it and the natural language description. The present invention can quickly process a large amount of fuel contract data and generate a natural language description of the price clauses through automation technology, thereby improving processing efficiency and saving time and human resources.

[0037] Finally, it should be noted that it is obvious that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0038] The above is only an example of implementation of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be regarded as falling within the scope of protection of the present invention and being restricted. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the platform described above can refer to the corresponding process in the aforementioned platform embodiment, and will not be repeated here.

[0039] The term "comprises" or any other similar term is intended to cover a non-exclusive inclusion such that a process, platform, article, or apparatus / platform that includes a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, platform, article, or apparatus / platform.

[0040] So far, the technical solutions of the present invention have been described in conjunction with the further embodiments shown in the accompanying drawings, but it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0041] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A method for rapidly generating fuel contract price terms, characterized in that: include: Obtain fuel contract data, and pre-process the fuel contract data to obtain fuel contract processing data; Build and train a natural language description model based on fuel contract processing data and a preset language model; Extract the contract text from the fuel contract data, parse the contract text, and determine the semantic information of the contract text based on the parsing results; Extract key information of the contract text from the semantic information, and determine the contract type based on the key information of the contract text; Input key information into the natural language description model, and output a natural language description of the fuel contract price terms; A description template for fuel contract price clauses is determined according to the contract type, and the fuel contract price clauses are generated based on the description template and the natural language description.

2. A method for rapidly generating fuel contract price terms according to claim 1, characterized in that: The obtaining of fuel contract data and preprocessing of the fuel contract data to obtain fuel contract processing data includes: Obtain fuel contract data from the contract database and clean the collected fuel contract data to remove duplicate, missing or incomplete data; The cleaned fuel contract data is standardized to unify the data format and units in the fuel contract data.

3. A method for rapidly generating fuel contract price terms according to claim 2, characterized in that: The natural language description model is constructed and trained based on the fuel contract processing data and the preset language model, including: The preset language model is used as the basic model, and the fuel contract processing data is divided into a training set and a test set according to a certain ratio; The training set and test set are input into the basic model, and the basic model is trained and tested until the basic model meets the preset convergence conditions to obtain a natural language description model.

4. A method for rapidly generating fuel contract price terms according to claim 1, characterized in that: The step of extracting the contract text from the fuel contract data, parsing the contract text, and determining the semantic information of the contract text according to the parsing result includes: The contract text is extracted from the fuel contract data through text extraction technology, and the contract text is segmented to split the contract text into words or phrases; Conduct semantic analysis on words or phrases to determine the meaning of words or phrases, and conduct syntactic analysis on words or phrases to determine the grammatical relationship of words or phrases; The semantic information of the contract text is determined based on the meaning and grammatical relationship of words or phrases.

5. A method for rapidly generating fuel contract price terms according to claim 4, characterized in that: The step of extracting key information of the contract text from the semantic information and determining the contract type according to the key information of the contract text includes: Analyze the importance of semantic information, determine semantic information whose importance exceeds a preset value as key information of the contract text, and extract the key information of the contract text from the semantic information; The key information of the contract text is matched with the preset contract type, and the preset contract type with the highest total matching degree is used as the contract type of the contract text.

6. A method for rapidly generating fuel contract price terms according to claim 5, characterized in that: The analyzing the importance of the semantic information and determining the semantic information whose importance exceeds a preset value as key information of the contract text includes: Analyze the basic elements of semantic information related to fuel contracts and determine the evaluation indicators of the importance of semantic information based on the basic elements; Calculate the correlation between each evaluation indicator and the fuel contract, and determine the evaluation score corresponding to each evaluation indicator according to the correlation; The importance of the semantic information is obtained by comprehensively analyzing the evaluation scores of each evaluation indicator, and the semantic information whose importance exceeds a preset value is determined as the key information of the contract text.

7. A method for rapidly generating fuel contract price terms according to claim 5, characterized in that: The key information of the contract text is matched with the preset contract type, and the preset contract type with the highest total matching degree is used as the contract type of the contract text, including: A number of preset contract types are preset, and each preset contract type has multiple tags corresponding to it; Match the key information of the contract text with multiple tags of each preset contract type, and calculate the matching degree between the key information of the contract text and the tags in each preset contract type in turn; The matching degree between the key information of the contract text and the label in each preset contract type is accumulated to obtain multiple total matching degrees, and the preset contract type with the highest total matching degree is used as the contract type of the contract text.

8. A method for rapidly generating fuel contract price terms according to claim 5, characterized in that: The step of determining a description template of the fuel contract price clause according to the contract type, and generating the fuel contract price clause based on the description template and the natural language description includes: Analyze the characteristics of the contract type and determine the description template of the fuel contract price clause based on the characteristics of the contract type; The information of each position in the description template is obtained, the natural language description is matched with the information of each position in the description template, and the successfully matched natural language description is filled into the corresponding position in the description template to generate the fuel contract price terms.

9. A system for rapidly generating fuel contract price terms, characterized in that: include: An acquisition module is used to acquire fuel contract data and pre-process the fuel contract data to obtain fuel contract processing data; A modeling module, used to acquire fuel contract processing data and a preset language model to build and train a natural language description model; A parsing module is used to extract the contract text from the fuel contract data, parse the contract text, and determine the semantic information of the contract text according to the parsing result; An extraction module is used to extract key information of the contract text from the semantic information and determine the contract type according to the key information of the contract text; An output module is used to obtain key information and input it into a natural language description model, and output a natural language description of the fuel contract price terms; The generation module is used to obtain a description template for determining fuel contract price terms according to the contract type, and generate the fuel contract price terms based on the description template and the natural language description.

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