A method, device, equipment and medium for matching oilfield regulation data

Abstract

The present application relates to the field of data processing, and particularly relates to a kind of oilfield regulation data matching method, device, equipment and medium.The method comprises: receiving the regulation query request sent by client, wherein the regulation query request includes user characteristics and query content;Obtain the target file matched with query content, and obtain the operation regulation associated with query content from target file;From standard fusion database, obtain the target quantization value of regulation data contained in operation regulation;Match based on the target quantization value of regulation data and user characteristics, and send the regulation data with the highest matching degree as target data to client.The present application solves the problems of existing technology in the management of oilfield safety and environmental protection operation regulation, such as dependence on traditional methods, low retrieval efficiency, poor accuracy, difficulty in dynamic updating and optimization of storage strategy, etc.

Description

Technical Field

[0001] This invention relates to the field of data processing, and specifically to a method, apparatus, equipment, and medium for matching oilfield procedure data. Background Technology

[0002] Currently, the management of oilfield safety and environmental protection operating procedures relies on traditional document storage and manual retrieval methods, depending on simple keyword matching. This lack of intelligent processing and efficient quantitative analysis results in low retrieval efficiency and poor accuracy when dealing with massive amounts of data. Furthermore, oilfield safety and environmental protection standards information is characterized by a large volume of user information, diverse data types, and high information density. Traditional methods struggle to dynamically update and optimize storage strategies, cannot adjust retrieval parameters and thresholds according to actual needs, and cannot effectively cope with frequently changing oilfield operating standards and procedures. The main reason for this is the lack of advanced quantitative models and intelligent matching algorithms, preventing the system from flexibly adapting to data changes and dynamic adjustments to user needs. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a method, apparatus, equipment and medium for matching oilfield procedure data, in order to solve the problems of existing technologies in the management of oilfield safety and environmental protection operation procedures, such as reliance on traditional methods, low retrieval efficiency, poor accuracy, difficulty in dynamic updates and optimization of storage strategies.

[0004] In a first aspect, embodiments of the present invention provide a method for matching oilfield procedure data, the method comprising:

[0005] Receive a procedure query request sent by the client, wherein the procedure query request includes user characteristics and query content;

[0006] Obtain the target file that matches the query content, and obtain the operation procedure associated with the query content from the target file;

[0007] Obtain the target quantization value of the procedure data contained in the operating procedure from the standard fusion database;

[0008] The target quantification value of the procedure data is matched with the user characteristics, and the procedure data with the highest matching degree is sent to the client as the target data.

[0009] Furthermore, before obtaining the target quantization value of the procedure data contained in the operating procedure from the standard fusion database, the method further includes:

[0010] The pre-acquired safety and environmental protection standard data is deconstructed to obtain multiple different types of data content;

[0011] Analyze the correlation between different types of data content to obtain correlation analysis results, and use the correlation analysis results to construct the first subject area of ​​the resource standard document and the second subject area of ​​the standard document;

[0012] A standard fusion database is constructed based on the first subject area and the second subject area of ​​the resource standard document;

[0013] Different types of data content are quantized separately to obtain quantized values ​​corresponding to different types of data content, and the quantized values ​​corresponding to different types of data content are stored in the standard fusion database.

[0014] Furthermore, the pre-acquired safety and environmental protection standard data is deconstructed to obtain multiple different types of data content, including:

[0015] Get the preset data dimensions;

[0016] According to the preset data dimension, each data record in the safety and environmental protection standard data is traversed to obtain the corresponding field of the data dimension, and the field of the preset data dimension is used as the first data content;

[0017] Check each data record for a field that represents a change to the first data content, and use the field representing the change as the second data content.

[0018] Other fields in the safety and environmental protection standard data, excluding the first data content and the second data content, shall be regarded as other data content.

[0019] Based on the first data content, the second data content, and the other data content, different types of data content are constructed.

[0020] Furthermore, the analysis of the correlation between different types of data content yields correlation analysis results, and these results are used to construct a first subject area for the resource standard document and a second subject area for the standard document, including:

[0021] Identify potential correlations between the first data content, the second data content, and the other data content;

[0022] Content related to resource standards is selected from the first data content, the second data content, and the other data content respectively, and the first subject area of ​​the resource standard document is summarized.

[0023] Content related to the standard is selected from the first data content, the second data content, and the other data content respectively, and the second subject area of ​​the standard document is summarized.

[0024] Furthermore, the quantization of different types of data content to obtain quantized values ​​corresponding to different types of data content includes:

[0025] Obtain a Locality Sensitive Hash Function (LSHF), wherein the LSHF is used to quantify the data content in the safety and environmental protection standard data;

[0026] The first quantization value corresponding to each first data content is calculated using the locality-sensitive hash function.

[0027] The second quantization value corresponding to each second data content is calculated using the locality-sensitive hash function;

[0028] The third quantization value corresponding to each other data content is calculated using the locality-sensitive hash function.

[0029] Furthermore, obtaining the target quantization value of the procedure data contained in the operating procedure from the standard fusion database includes:

[0030] Obtain the business description corresponding to the operation procedure and the quantitative value of different types of data content corresponding to the operation procedure from the standard fusion database;

[0031] The sum of quantized values ​​is calculated based on the quantized values ​​of different types of data content corresponding to the aforementioned operating procedures;

[0032] The target quantification value is calculated by combining the business description and the quantification value.

[0033] Furthermore, the method also includes:

[0034] Request to query the testing procedure within the preset time period;

[0035] If the number of procedural query requests carrying the same query content reaches a preset threshold, a threshold update mechanism is triggered, and the quantitative difference threshold related to the query content is updated based on the threshold update mechanism.

[0036] Secondly, embodiments of the present invention provide a device for processing safe and environmentally friendly data, the device comprising:

[0037] The receiving module is used to receive a procedure query request sent by the client, wherein the procedure query request includes user characteristics and query content;

[0038] The query module is used to obtain target files that match the query content and to obtain operation procedures associated with the query content from the target files;

[0039] The acquisition module is used to obtain the target quantization value of the procedure data contained in the operation procedure from the standard fusion database;

[0040] The matching module is used to match the target quantization value of the procedure data with the user characteristics, and send the procedure data with the highest matching degree as the target data to the client.

[0041] Thirdly, embodiments of the present invention provide a computer device, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method described in the first aspect or any corresponding embodiment thereof.

[0042] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that cause a computer to perform the method described in the first aspect or any of its corresponding embodiments.

[0043] The method provided in this application first receives a procedure query request from a client containing user characteristics and query content, breaking away from the traditional single retrieval mode. The introduction of user characteristics allows the system to better understand user needs, moving beyond simple keyword matching. In the step of obtaining the target file matching the query content and the associated operation procedure, precise target file location avoids blind searching through a large number of irrelevant documents, improving the targeting and efficiency of the retrieval. Filtering out the parts related to the query content from numerous operation procedures reduces interference from irrelevant information, improving retrieval accuracy. The target quantification value of the operation procedure is obtained from a standard fusion database, giving the operation procedure a numerical feature description. This allows for quantitative comparison of different operation procedures, helping to more accurately assess their matching degree with user needs. Simultaneously, the use of a standard fusion database changes the traditional document storage method, facilitating data management and updates, and providing a foundation for dynamic updates and optimized storage strategies. Finally, based on the target quantification value and user characteristics, the system matches the procedure data with the highest matching degree to the client, ensuring that users can quickly obtain the procedure that best suits their needs. This exact matching method greatly improves the accuracy and efficiency of retrieval, and as user requests change, the system can further optimize the storage strategy based on the matching results to better meet user needs. Attached Figure Description

[0044] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0045] Figure 1 This is a flowchart illustrating a method for matching oilfield procedure data according to some embodiments of the present invention;

[0046] Figure 2 This is a flowchart illustrating a method for matching oilfield procedure data according to some embodiments of the present invention;

[0047] Figure 3 This is a structural block diagram of an oilfield procedure data matching device according to an embodiment of the present invention;

[0048] Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] According to embodiments of the present invention, a method, apparatus, device, and medium for matching oilfield procedure data are provided. It should be noted that the steps shown in the flowcharts in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0051] Example 1

[0052] This embodiment provides a method for matching oilfield procedure data. Figure 1 This is a flowchart of a method for matching oilfield procedure data according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0053] Step S101: Receive a procedure query request sent by the client, wherein the procedure query request includes user characteristics and query content.

[0054] In this embodiment, when a user initiates a procedure query request on the client, the request contains two important pieces of information. First, user characteristics, which may include the user's identity, department, and usage scenario requirements. These characteristics are used to match the retrieved operating procedures to find the procedure that best meets the user's specific needs. Second, the query content, such as the user entering "oil well fire emergency measures," which specifies the topic of the procedure the user wants to query.

[0055] Step S102: Obtain the target file that matches the query content, and obtain the operation procedure associated with the query content from the target file.

[0056] In this embodiment, after receiving a query request, the system matches the query content against existing oilfield safety and environmental protection standard documents. For example, if the query content is "oil well fire emergency measures," the system will identify target files whose storage business description quantification value falls within the range relevant to the query content (e.g., file 1 with a storage business description quantification value between 120,000 and 130,000). Then, it filters out operation procedures associated with the query content from these target files. These operation procedures may have some correlation with the query content in terms of business description, target object, and data attributes.

[0057] Step S103: Obtain the target quantization value of the procedure data contained in the operating procedure from the standard fusion database.

[0058] In this embodiment of the application, obtaining the target quantization value of the procedure data contained in the operation procedure from the standard fusion database includes: obtaining the business description corresponding to the operation procedure and the quantization value of different types of data content corresponding to the operation procedure from the standard fusion database; calculating the sum of quantization values ​​based on the quantization values ​​of different types of data content corresponding to the operation procedure; and comprehensively calculating the target quantization value using the business description and the quantization values.

[0059] Specifically, the standards fusion database stores a large amount of oilfield safety and environmental protection standards data, including detailed information on various operating procedures. When the system needs to retrieve the business description of a specific operating procedure, it searches and locates the relevant information in the database. For example, for the operating procedure "Emergency Measures for Oil Well Fires," the business description might be "emergency handling steps and methods in the event of an oil well fire, including personnel evacuation and the use of fire extinguishing equipment." The system accurately finds and extracts the business description of this operating procedure through specific query statements or indexing mechanisms.

[0060] During the deconstruction process, safety and environmental protection standard data is divided into different types of data content, such as first data (fields corresponding to preset data dimensions), second data (fields that represent changes to the first data content), and other data (fields other than the first and second data).

[0061] Each type of data is quantified to obtain its own quantified value, which is then stored in the standard fusion database. For example, the first data for the "Oil Well Fire Emergency Measures" operating procedure might be quantified values ​​for dimensions such as the type of oil well to which it applies and the severity of the fire; the second data might be the relevant quantified values ​​for the most recent revision of the operating procedure; other data might include quantified values ​​for the time requirements for implementing the procedure, the resources required, etc.

[0062] The system retrieves and obtains the quantified values ​​of different types of data content from the database based on the identification information of the operating procedures.

[0063] The quantified values ​​of different types of data content reflect the characteristics and importance of operational procedures in various aspects. By calculating the sum of the quantified values, a comprehensive numerical index can be obtained to assess the overall value of the operational procedure or its relative importance with other operational procedures. For example, a higher sum of quantified values ​​may indicate that the operational procedure is significant in multiple aspects or is more closely matched to a specific query requirement.

[0064] The system adds up the quantized values ​​of different data types corresponding to the obtained operating procedures. Assuming the quantized value of the first data is A, the quantized value of the second data is B, and the quantized value of the other data is C, then the sum of the quantized values ​​= A + B + C. In actual calculations, different weights are assigned to different data types based on their importance, resulting in a weighted sum. For example, if the first data is considered more important, it can be given a higher weight coefficient, and the calculation method becomes: sum of quantized values ​​= αA + βB + γC, where α, β, and γ are different weight coefficients.

[0065] The business description provides the specific content and meaning of the operating procedures, playing a crucial role in understanding and evaluating them. When calculating target quantifiable values, the business description can serve as an important reference factor, combining with the quantifiable values ​​to more comprehensively reflect the characteristics and value of the operating procedures. For example, keywords and semantic information contained in the business description can be correlated with the quantifiable values ​​to further adjust and optimize the calculation results of the target quantifiable values.

[0066] Multiple methods can be employed for comprehensive calculation. This application involves textual analysis of the business description to extract key features, and then fusing these features with quantified values ​​for calculation. For example, machine learning algorithms can be used, taking the feature vectors and quantified values ​​of the business description as input, and training a model to predict the target quantified value. The final target quantified value is a numerical value that comprehensively considers the business description of the operational procedures and the quantified values ​​of different types of data content. It can be used for subsequent data analysis, matching degree calculation, and decision support processes.

[0067] Step S104: Match the target quantification value of the procedure data with the user characteristics, and send the procedure data with the highest matching degree as the target data to the client.

[0068] In this embodiment, the target quantification value of the procedure data is a comprehensive value obtained by quantifying the business description of the operating procedure and different types of data content. It reflects the characteristics and importance of the operating procedure in multiple aspects, including but not limited to the applicable scenario, the complexity of the operating steps, and the level of safety risk. For example, for an operating procedure for "emergency measures for oil well fires," its target quantification value may comprehensively include the quantification values ​​of fire severity, emergency response time requirements, and the number of required equipment and personnel involved in the business description of the procedure.

[0069] User characteristics include information such as user identity, department, and usage scenario requirements. These characteristics can reflect users' specific needs and preferences regarding security procedures. For example, frontline operators may focus more on specific operating procedures and safety precautions, while managers may emphasize compliance and overall management requirements. Users in different departments may have different needs for specific types of procedures; for instance, the security department may pay more attention to risk assessment and emergency response sections.

[0070] The system matches the target quantized values ​​of the procedure data with user characteristics to determine which procedures best meet the user's needs. This matching process can employ various methods, such as calculating the similarity or correlation between the two. For example, user characteristics can be converted into numerical vectors, and then vector similarity can be calculated between them and the target quantized values ​​of the procedure data. Methods such as cosine similarity and Euclidean distance can be used to measure the degree of similarity between the two.

[0071] The system can also weight and adjust the target quantification values ​​of the procedure data based on user characteristics to highlight aspects that the user is concerned about. For example, if the user is a security personnel, the system can increase the weight of quantification values ​​related to risk assessment to improve the match with the user's needs.

[0072] By matching the target quantification value of the procedure data with user characteristics, the system can obtain a matching score between each procedure and user needs. The system will select the procedure with the highest matching score as the target data. For example, if there are multiple operating procedures for "emergency measures for oil well fires," the system will sort these procedures according to their matching scores and select the procedure with the highest score as the target data that best meets the user's needs.

[0073] Once the procedure data with the highest matching degree is identified, the system will send it to the client for user viewing and use. The target data sent may include detailed content of the procedure, business description, target quantification values, and information on the degree of matching with user characteristics, so that users can better understand why the procedure is recommended as the one that best meets their needs.

[0074] The method provided in this application first receives a procedure query request from a client containing user characteristics and query content, breaking away from the traditional single retrieval mode. The introduction of user characteristics allows the system to better understand user needs, moving beyond simple keyword matching. In the step of obtaining the target file matching the query content and the associated operation procedure, precise target file location avoids blind searching through a large number of irrelevant documents, improving the targeting and efficiency of the retrieval. Filtering out the parts related to the query content from numerous operation procedures reduces interference from irrelevant information, improving retrieval accuracy. The target quantification value of the operation procedure is obtained from a standard fusion database, giving the operation procedure a numerical feature description. This allows for quantitative comparison of different operation procedures, helping to more accurately assess their matching degree with user needs. Simultaneously, the use of a standard fusion database changes the traditional document storage method, facilitating data management and updates, and providing a foundation for dynamic updates and optimized storage strategies. Finally, based on the target quantification value and user characteristics, the system matches the procedure data with the highest matching degree to the client, ensuring that users can quickly obtain the procedure that best suits their needs. This exact matching method greatly improves the accuracy and efficiency of retrieval, and as user requests change, the system can further optimize the storage strategy based on the matching results to better meet user needs.

[0075] Example 2

[0076] In this embodiment of the application, before obtaining the target quantization value of the procedure data contained in the operating procedure from the standard fusion database, such as Figure 2 As shown, the method also includes:

[0077] Step S201: Deconstruct the pre-acquired safety and environmental protection standard data to obtain multiple different types of data content.

[0078] In this embodiment of the application, the pre-acquired safety and environmental protection standard data is deconstructed to obtain multiple different types of data content, including the following steps A1-A5:

[0079] Step A1: Obtain the preset data dimensions.

[0080] Specifically, preset data dimensions are the angles and standards used to divide the data before deconstructing safety and environmental protection standard data. These dimensions can be determined based on the characteristics of the safety and environmental protection standard data and actual application needs. For example, they may include dimensions such as standard type, scope of application, indicator name, indicator value, and publication time. Obtaining preset data dimensions provides a clear direction and basis for subsequent data deconstruction.

[0081] Step A2: Traverse each data record in the safety and environmental protection standard data according to the preset data dimensions to obtain the corresponding fields of the data dimensions, and use the fields of the preset data dimensions as the first data content.

[0082] Specifically, the system will examine each record in the safety and environmental protection standards data one by one. According to preset data dimensions, it will extract the corresponding data fields from each record. For example, if the preset data dimensions include standard types, then for a given record, the value of its corresponding standard type field will be extracted. These extracted fields corresponding to the preset data dimensions are used as the first data content. The first data content reflects the basic characteristics and information of the safety and environmental protection standards data within the preset data dimensions.

[0083] Step A3: Check if there is a field in each data record that represents a change to the first data content, and use the field used to represent the change as the second data content.

[0084] Specifically, for each data record, after determining the primary data content, a further check is performed to determine if there are fields indicating changes to the primary data content. These fields may include the data revision number, update description, revision date, etc. If such fields exist, they are extracted as the secondary data content. The secondary data content reflects the dynamic changes in safety and environmental protection standard data, which is crucial for tracking data evolution and updates.

[0085] Step A4: Select the fields in the safety and environmental protection standard data other than the first data content and the second data content as other data content.

[0086] Specifically, after determining the first and second data contents, the remaining fields in the safety and environmental protection standards data are treated as other data contents. These other data contents may include some auxiliary information, remarks, etc. Although they do not belong to the preset data dimensions or changes to the first data contents, they may still have some reference value in certain situations.

[0087] Step A5: Construct different types of data content based on the first data content, the second data content, and other data content.

[0088] Specifically, the first data content, the second data content, and other data content are combined to construct different types of data content. These different types of data content can provide more detailed and comprehensive information for subsequent data analysis, storage, and processing. For example, the first data content can be used for categorized storage and retrieval, the second data content can be used for data update management, and other data content can provide additional information support.

[0089] Step S202: Analyze the correlation between different types of data content, obtain the correlation analysis results, and use the correlation analysis results to construct the first subject area of ​​the resource standard document and the second subject area of ​​the standard document.

[0090] In this embodiment of the application, the correlation between different types of data content is analyzed to obtain correlation analysis results. The correlation analysis results are then used to construct a first subject area of ​​the resource standard document and a second subject area of ​​the standard document. This includes: identifying potential correlation factors between the first data content, the second data content, and other data content; filtering out content related to the resource standard from the first data content, the second data content, and other data content respectively, and summarizing the first subject area of ​​the resource standard document; and filtering out content related to the standard from the first data content, the second data content, and other data content respectively, and summarizing the second subject area of ​​the standard document.

[0091] Specifically, the first data content consists of fields corresponding to preset data dimensions in the safety and environmental protection standards data, reflecting the basic characteristics of the data. For example, it may include the standard's scope of application, indicator names, and numerical values. The second data content consists of fields that represent changes to the first data content, such as revision numbers and update descriptions, reflecting the dynamic changes in the data. Other data content consists of fields other than the first and second data content, and may include some auxiliary information or remarks.

[0092] First, analyze the common characteristics, interrelationships, and dependencies among these different types of data content. For example, observe whether certain keywords appear frequently in different types of data content, or whether there are similarities in specific data structures or formats. Consider business logic connections; for instance, a change in a metric value in the first data content might be related to a revision note in the second data content. You can also analyze potential relationships in terms of data sources, generation time, etc.

[0093] Secondly, filter resource standard-related content from different types of data: For the first data content, examine fields related to resource standards, such as indicators and descriptions related to resource utilization, protection, and management. In the second data content, look for information related to changes in resource standards; for example, the reasons for revising resource standards may be related to changes in resource conditions. From other data content, filter out information that may provide supplementary explanations of resource standards, such as background information on specific resources or relevant case studies.

[0094] Next, based on the selected content related to resource standards, the first subject area of ​​the resource standard documents is summarized. This can be achieved by extracting common themes, key concepts, or classification methods. For example, if the selected content mainly involves the management and protection of water resources, then the first subject area can be summarized as "water resource standards," and further subdivided into different sub-themes, such as water resource utilization efficiency and water pollution prevention.

[0095] Next, for the first set of data, identify fields related to standard development, implementation, and evaluation, such as the standard's level and applicable scope. In the second set of data, focus on information related to standard revisions and updates, such as the scope and impact of revisions. From other data, filter out supplementary information that may be relevant to the standard, such as implementation cases or feedback.

[0096] Based on the selected standard-related content, a second subject area for the standard documents is summarized. This can be categorized according to the type, field, or purpose of the standard. For example, if the selected content covers safety standards from different industries, the second subject area can be summarized as "Industry Safety Standards," and further divided into sub-subject areas such as petrochemical safety standards and mining safety standards.

[0097] Step S203: Construct a standard fusion database based on the first subject area of ​​the resource standard document and the second subject area of ​​the standard document.

[0098] In this embodiment, the overall classification architecture of the database is designed based on a first subject domain (resource standard document subject domain) and a second subject domain (standard standard document subject domain). For example, two main database partitions can be set up, each corresponding to one of the two subject domains. Under each subject domain, sub-topics can be further subdivided to form a hierarchical structure for better organization and management of data.

[0099] Design corresponding data tables for each subject area and sub-topic. The fields of the data tables should be determined based on the characteristics of the subject area and data requirements. For the first subject area of ​​a resource standard document, data tables may include "Resource Type Table" (recording the types and characteristics of different resources) and "Resource Management Indicator Table" (storing indicator data related to resource management), etc. For the second subject area of ​​a standard document, there may be "Standard Type Table" (distinguishing different types of standards) and "Standard Scope Table" (recording the objects and fields to which the standard applies), etc.

[0100] Collect data related to oilfield safety and environmental protection standards. This data may come from various sources, such as standard documents issued by government departments, internal operating procedures of enterprises, and industry research reports. Ensure the accuracy and completeness of the data by performing preliminary data cleaning and verification.

[0101] Based on the definitions of the first and second subject areas, the collected data is categorized. Each piece of data is then assigned to a specific subject area and subtopic. For example, if a piece of data pertains to management standards for petroleum resources, it is mapped to the relevant subtopic under the first subject area of ​​the resource standards document.

[0102] Import the categorized data into the corresponding tables in the standard fusion database. Batch import can be performed using data import tools or by writing scripts. During the import process, pay attention to data format conversion and compatibility to ensure the data is correctly stored in the database.

[0103] To improve database query performance, create indexes on fields in key data tables. For example, create indexes on the "Resource Name" field in the "Resource Type Table" and the "Standard Name" field in the "Standard Type Table". Indexes can speed up data retrieval, which is especially important when dealing with large amounts of data.

[0104] Establish relationships between different data tables to enable cross-table queries and data analysis. For example, establish a relationship between the "Resource Management Indicators Table" and the "Standards Application Scope Table" to determine the applicable standard scope for a specific resource management indicator.

[0105] Validate the data imported into the database, checking its integrity, accuracy, and consistency. Validation can be performed using methods such as random sampling and data comparison to ensure the data meets expected quality standards. Optimize database performance based on actual usage. This may include adjusting indexing strategies, optimizing query statements, and adding database caching. Regularly monitor database performance metrics, such as query response time and throughput, to promptly identify and resolve performance issues.

[0106] Step S204: Quantize different types of data content to obtain quantized values ​​corresponding to different types of data content, and store the quantized values ​​corresponding to different types of data content in the standard fusion database.

[0107] In this embodiment of the application, different types of data content are quantized to obtain quantized values ​​corresponding to different types of data content, including: obtaining a locality-sensitive hash function, wherein the locality-sensitive hash function is used to quantize the data content in the safety and environmental protection standard data; calculating a first quantized value corresponding to each first data content using the locality-sensitive hash function; calculating a second quantized value corresponding to each second data content using the locality-sensitive hash function; and calculating a third quantized value corresponding to each other data content using the locality-sensitive hash function.

[0108] Specifically, a locality-sensitive hash function (LSH) is a function that can convert input of arbitrary length (such as text descriptions) into a fixed-length output (quantized value). In the processing of safety and environmental protection standard data, it is used to quantize the data content for better data storage, retrieval, and analysis. LSH functions can be obtained by calling relevant algorithm libraries or by implementing a specific hash algorithm yourself.

[0109] The first data content consists of fields corresponding to preset data dimensions within the safety and environmental protection standards data. For example, this might include key information such as the standard's scope of application, indicator names, and numerical values. For each data item in the first data content, data preprocessing operations are performed. This might include removing punctuation, converting text to lowercase, and performing word segmentation to better extract features and perform hash calculations.

[0110] Key features are extracted from the preprocessed initial data content. These features can be keywords, phrases, or specific data attributes. For example, if the initial data content is a description of oil well safety standards, keywords such as "oil well," "safety," and "equipment" might be extracted as features.

[0111] The extracted features are input into a locality-sensitive hash function to obtain the corresponding quantized value. This quantized value is a fixed-length numerical value that represents a specific feature of the first data content. By repeating this process for different first data content items, a first quantized value corresponding to each first data content can be obtained.

[0112] The second data content consists of fields representing changes made to the first data content. For example, it might include the data revision number, update description, etc. Similar to the processing of the first data content, the second data content undergoes data preprocessing to remove unnecessary characters and formatting, and extract key features. These features may be related to the type and extent of data changes. The extracted features are then input into a locality-sensitive hash function to obtain a second quantized value corresponding to the second data content. This quantized value reflects the change characteristics of the second data content.

[0113] The third data content consists of fields in the safety and environmental protection standards data other than the first and second data content. Preprocessing and feature extraction are performed on the third data content, extracting appropriate features based on its characteristics. These features may include key content such as auxiliary information and notes. The extracted features are input into a locality-sensitive hash function to obtain the third quantized value corresponding to the third data content. This quantized value provides a numerical representation of the third data content, facilitating subsequent data analysis and processing.

[0114] Example 3

[0115] Here is a complete quantization example:

[0116] First, obtain three pre-set thresholds: the first threshold, the second threshold, and the third threshold.

[0117] The first threshold specifically addresses the quantitative differences in business descriptions. This means that this threshold plays a crucial role in assessing the similarity or difference between different business descriptions. If the quantitative difference between two business descriptions is greater than 5, they can be considered to have significant differences in their business descriptions. For example, one business description is "Emergency Measures for Oil Well Fires" with a quantitative value of 100, and another is "Oil Well Leakage Prevention Procedures" with a quantitative value of 94. The difference between the two is 6, which is greater than the first threshold of 5, indicating that these two business descriptions have a relatively significant difference in the quantitative representation of their business content.

[0118] The second threshold is used to measure the quantitative differences in other data. "Other data" here may refer to various auxiliary data besides the business description, such as the data source, generation time, and relevant personnel information. When the quantitative difference in other data is greater than 3, it indicates that these data have significant differences in the corresponding aspects. For example, for two different safety and environmental protection standard documents, if the sum of the quantitative values ​​of other data in one document is 50 and that of the other is 54, the difference is 4, which is less than the second threshold of 3, indicating that the two documents are relatively similar in terms of these other data.

[0119] The third threshold addresses the difference in comprehensive quantitative values. Comprehensive quantitative values ​​are typically calculated by combining quantitative values ​​from multiple different aspects, reflecting an overall quantitative status. If the difference in comprehensive quantitative values ​​is greater than 8, it is considered that there is a significant difference overall. For example, if two safety and environmental protection standard documents have comprehensive quantitative values ​​of 80 and 90 respectively, the difference is 10, which is greater than the third threshold of 8, indicating that the two documents have significant differences in overall quantitative performance.

[0120] Suppose we have the following three business descriptions: Business Description 1: Emergency Response Plan for Large Oil Well Fires. Business Description 2: Leakage Prevention Guidelines for Small Oil Wells. Business Description 3: Maintenance Manual for Aging Oil Well Equipment.

[0121] Each business description is processed, including removing punctuation, converting to lowercase, and word segmentation. Business description 1, after processing, becomes "Emergency Response Plan for Large Oil Well Fires." Business description 2, after processing, becomes "Guidelines for Leakage Prevention in Small Oil Wells." Business description 3, after processing, becomes "Maintenance Manual for Aging Oil Well Equipment."

[0122] Extract keywords from the pre-processed business description, such as "oil well", "fire", "emergency", "leakage", "prevention", "maintenance", "large", "small", "old", "treatment", "solution", "guideline", "manual", etc.

[0123] Choose a locality-sensitive hash function; here, we use MD5. Apply the MD5 function to each keyword to obtain a 16-byte locality-sensitive hash value. For example, "oil well" might get 0x2b8c..., "fire" might get 0x5d7a..., and so on.

[0124] Suppose we decide to use the first 8 bits (32 bits) of the locality-sensitive hash value to represent the quantization value of each keyword. Convert the 16-byte locality-sensitive hash value to a 32-bit value. For example, "oil well" is 0x2b8c, converted to the decimal value 182212; "fire" is 0x5d7a, converted to the decimal value 239242, and so on.

[0125] Assign a weight to each keyword, for example, "oil well" with a weight of 0.2, "fire" with a weight of 0.15, and "emergency" with a weight of 0.12, etc. Calculate a comprehensive quantitative value for each business description.

[0126] Business Description 1: Emergency Response Plan for Large Oil Well Fires.

[0127] The quantification value for "Large-scale" is assumed to be 150,000, with a weight of 0.05. The quantification value for "Oil Well" is 182,212, with a weight of 0.2. The quantification value for "Fire" is 239,242, with a weight of 0.15. The quantification value for "Emergency" is 120,000, with a weight of 0.12. The quantification value for "Handling" is 100,000, with a weight of 0.1. The quantification value for "Plan" is 80,000, with a weight of 0.08.

[0128] Overall quantitative value:

[0129] 150000×0.05+182212×0.2+239242×0.15+120000×0.12+100000×0.1+80000×0.08=49221.24.

[0130] Business Description 2: Guidelines for Preventing Leaks in Small Oil Wells.

[0131] The quantification value for "Small" is assumed to be 130,000, with a weight of 0.05. The quantification value for "Oil Well" is 182,212, with a weight of 0.2. The quantification value for "Leak" is 160,000, with a weight of 0.15. The quantification value for "Prevention" is 140,000, with a weight of 0.12. The quantification value for "Guideline" is 90,000, with a weight of 0.08.

[0132] Overall quantitative value:

[0133] 130000×0.05+182212×0.2+160000×0.15+140000×0.12+90000×0.08=42272.24.

[0134] Business Description 3: Maintenance Manual for Old Oil Well Equipment.

[0135] The quantification value for "Aged" is assumed to be 140,000, with a weight of 0.05. The quantification value for "Oil Well" is 182,212, with a weight of 0.2. The quantification value for "Equipment" is 110,000, with a weight of 0.15. The quantification value for "Maintenance" is 130,000, with a weight of 0.12. The quantification value for "Manual" is 70,000, with a weight of 0.08.

[0136] Overall quantitative value:

[0137] 140000×0.05+182212×0.2+110000×0.15+130000×0.12+70000×0.08=41342.24.

[0138] Observe the distribution of quantified values, assuming most of them fall within the range of 0 to 1,000,000. Set the first threshold as a 20% difference in quantified values ​​and the second threshold as a 15% difference in quantified values. First threshold = average comprehensive quantified value × 20%. Assume the average comprehensive quantified value is (49221.24 + 42272.24 + 41342.24) / 3 = 44278.57. Then the first threshold is 44278.57 × 20% = 8855.71. Second threshold = average comprehensive quantified value × 15% = 44278.57 × 15% = 6641.79.

[0139] Based on the quantified value Q1 of the first data (assuming the first data is the sum of the quantified values ​​of the main keywords in the business description), the data is categorized and stored in different safety and environmental protection standard files. For example, assume that the comprehensive quantified value of the business description is set to be stored in file A if it is between 40,000 and 45,000, and stored in file B if it is between 45,000 and 50,000.

[0140] Before storing new data, check whether the difference in quantization values ​​between the existing data in the target file meets the threshold requirement. If the difference in quantization values ​​between the new data and the existing data is less than the threshold, it can be stored in the file; if the difference is greater than the threshold, the storage location needs to be reconsidered or the threshold needs to be adjusted.

[0141] Calculate the cosine similarity between the first and second sets of data to construct a similarity network. Assume the first set of data represents the set of quantified values ​​of the main keywords for business description 1, and the second set represents the set of quantified values ​​of the main keywords for business description 2. By calculating the cosine similarity between the two sets of quantified values, the degree of similarity between them can be determined. If the similarity is high, connections are established in the similarity network. Based on the connections in the similarity network, the K-means algorithm is used to cluster the business descriptions into different topic domains. For example, they can be divided into topic domains such as fire emergency response, leakage prevention, and equipment maintenance. Then, the Mahalanobis distance between data points within each topic domain, as well as the Mahalanobis distance between data points in different topic domains, is calculated. Mahalanobis distance measures the distance between data points in a high-dimensional space, reflecting the distribution and differences of the data.

[0142] Based on the calculated Mahalanobis distance and a preset threshold, the degree of correlation between different topic domains is analyzed. If the Mahalanobis distance is less than the threshold, it indicates a high degree of correlation between the two topic domains; if the Mahalanobis distance is greater than the threshold, it indicates a low degree of correlation between the two topic domains.

[0143] Quantitative values ​​and correlation analysis information are stored in a standard fusion database for subsequent queries and analysis. The database can store information such as the comprehensive quantitative value, keyword quantitative value, subject domain, and degree of correlation with other subject domains for each business description.

[0144] In this embodiment of the application, the method further includes the following steps B1-B2:

[0145] Step B1: Detect procedure query requests within a preset time period.

[0146] In this embodiment, the first step is to define the preset time period, such as one month, one week, or other suitable time intervals. The choice of this time period depends on the actual application scenario and the system's performance requirements. If the time period is too short, unnecessary detection and processing may be triggered frequently; if the time period is too long, the system may not respond promptly to user requests.

[0147] Within a preset time period, whenever a new procedure query request arrives, the system records relevant information about the request, including user characteristics, query content, and the request timestamp. For example, over a week, the system continuously receives procedure query requests from different clients and stores this information in a temporary database or data structure for subsequent analysis and processing.

[0148] Step B2: If the number of procedural query requests carrying the same query content reaches a preset threshold, a threshold update mechanism is triggered to update the quantitative difference threshold related to the query content based on the threshold update mechanism.

[0149] In this embodiment, the system statistically analyzes the procedure query requests received within a preset time period and categorizes them according to the query content. For each different query content, the system calculates the number of times it appears. For example, within a week, the system detects that multiple clients have sent procedure query requests regarding "emergency measures for oil well fires," and the system counts the number of these requests.

[0150] The number of requests for each query is compared to a preset threshold. This preset threshold can be determined based on system performance and business needs, and is generally adjusted through experience or experimentation. For example, a preset threshold of 10 requests might be set. If the number of query requests for "oil well fire emergency measures" reaches 10 within a week, the condition for triggering the threshold update mechanism is considered met.

[0151] When the number of requests for a particular query reaches a preset threshold, the system triggers a threshold update mechanism. This mechanism aims to dynamically adjust the quantitative difference threshold based on current user demand, thereby improving system performance and responsiveness. For example, if the number of requests for "oil well fire emergency measures" reaches the preset threshold, the system immediately initiates the threshold update mechanism.

[0152] Update the quantitative difference threshold related to the query content based on the threshold update mechanism:

[0153] Update the first threshold (quantified difference in business description):

[0154] The first threshold specifically addresses the quantitative differences in business descriptions. When updating the first threshold, the system can consider multiple factors, such as the importance of the current query content, the changing trends of the business descriptions, and the analysis results of historical data. For example, if frequent query requests for "oil well fire emergency measures" indicate that this business description is of high importance, the system can appropriately lower the first threshold to more accurately distinguish the quantitative differences in different business descriptions. For instance, if the original first threshold was a quantitative difference greater than 5, the updated threshold might become a quantitative difference greater than 4.

[0155] Update the second threshold (the difference in quantized values ​​of other data):

[0156] The second threshold is used to measure the quantitative differences of other data. When updating the second threshold, the system also needs to consider various factors, such as the type of other data, the frequency of change, and their relevance to the query content. For example, if the analysis finds that changes in other data have a significant impact on the query results, the system can adjust the second threshold appropriately. Suppose the original second threshold was a quantitative difference greater than 3, it might be updated to a quantitative difference greater than 2.

[0157] This embodiment also provides a device for processing safe and environmentally friendly data. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0158] Example 4

[0159] This embodiment provides a safe and environmentally friendly data processing device, such as... Figure 3 As shown, it includes:

[0160] The receiving module 301 is used to receive a procedure query request sent by the client, wherein the procedure query request includes user characteristics and query content;

[0161] The query module 302 is used to obtain the target file that matches the query content and to obtain the operation procedure associated with the query content from the target file;

[0162] The acquisition module 303 is used to obtain the target quantization value of the procedure data contained in the operating procedure from the standard fusion database;

[0163] The matching module 304 is used to match the target quantification value of the procedure data with user characteristics, and send the procedure data with the highest matching degree as the target data to the client.

[0164] In this embodiment, the device further includes: a construction module, used to deconstruct pre-acquired safety and environmental protection standard data to obtain multiple different types of data content; analyze the correlation between different types of data content to obtain correlation analysis results, and use the correlation analysis results to construct a first subject domain of the resource standard document and a second subject domain of the standard standard document; construct a standard fusion database based on the first subject domain of the resource standard document and the second subject domain of the standard document; quantify different types of data content to obtain quantized values ​​corresponding to different types of data content, and store the quantized values ​​corresponding to different types of data content in the standard fusion database.

[0165] In this embodiment, the construction module is used to obtain a preset data dimension; traverse each data record in the safety and environmental protection standard data according to the preset data dimension to obtain the corresponding fields of the data dimension, and use the fields of the preset data dimension as the first data content; check whether there is a field in each data record that represents a change to the first data content, and use the field used to represent the change as the second data content; use other fields in the safety and environmental protection standard data other than the first data content and the second data content as other data content; and construct different types of data content based on the first data content, the second data content, and other data content.

[0166] In this embodiment of the application, the construction module is used to identify potential correlation factors among the first data content, the second data content, and other data content; to filter out content related to the resource standard from the first data content, the second data content, and other data content respectively, and to summarize the first subject area of ​​the resource standard document; and to filter out content related to the standard from the first data content, the second data content, and other data content respectively, and to summarize the second subject area of ​​the standard document.

[0167] In this embodiment, the construction module is used to obtain a Locality Sensitive Hash Function (LSHF), wherein the LSHF is used to quantify the data content in the safety and environmental protection standard data; calculates a first quantized value corresponding to each first data content using the LSHF; calculates a second quantized value corresponding to each second data content using the LSHF; and calculates a third quantized value corresponding to each other data content using the LSHF.

[0168] In this embodiment of the application, the acquisition module 303 is used to acquire the business description corresponding to the operation procedure and the quantitative value of different types of data content corresponding to the operation procedure from the standard fusion database; calculate the sum of quantitative values ​​based on the quantitative values ​​of different types of data content corresponding to the operation procedure; and calculate the target quantitative value by comprehensively using the business description and the quantitative value.

[0169] In this embodiment of the application, the device further includes: an update module, used to detect procedure query requests within a preset time period; if the number of procedure query requests carrying the same query content reaches a preset threshold, a threshold update mechanism is triggered, and the quantitative difference threshold related to the query content is updated based on the threshold update mechanism.

[0170] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 Take a processor 10 as an example.

[0171] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0172] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0173] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device as shown by a landing page for an app. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0174] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0175] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0176] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0177] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for matching oilfield procedure data, characterized in that, The method includes: Receive a procedure query request sent by the client, wherein the procedure query request includes user characteristics and query content; Obtain the target file that matches the query content, and obtain the operation procedure associated with the query content from the target file; Obtain the target quantization value of the procedure data contained in the operating procedure from the standard fusion database; The target quantification value of the procedure data is matched with the user characteristics, and the procedure data with the highest matching degree is sent to the client as the target data.

2. The method according to claim 1, characterized in that, Before obtaining the target quantization value of the procedure data contained in the operating procedure from the standard fusion database, the method further includes: The pre-acquired safety and environmental protection standard data is deconstructed to obtain multiple different types of data content; Analyze the correlation between different types of data content to obtain correlation analysis results, and use the correlation analysis results to construct the first subject area of ​​the resource standard document and the second subject area of ​​the standard document; A standard fusion database is constructed based on the first subject area and the second subject area of ​​the resource standard document; Different types of data content are quantized separately to obtain quantized values ​​corresponding to different types of data content, and the quantized values ​​corresponding to different types of data content are stored in the standard fusion database.

3. The method according to claim 2, characterized in that, The process of deconstructing the pre-acquired safety and environmental protection standard data yields multiple different types of data content, including: Get the preset data dimensions; According to the preset data dimension, each data record in the safety and environmental protection standard data is traversed to obtain the corresponding field of the data dimension, and the field of the preset data dimension is used as the first data content; Check each data record for a field that represents a change to the first data content, and use the field representing the change as the second data content. Other fields in the safety and environmental protection standard data, excluding the first data content and the second data content, shall be regarded as other data content. Based on the first data content, the second data content, and the other data content, different types of data content are constructed.

4. The method according to claim 3, characterized in that, The analysis examines the correlations between different types of data content to obtain correlation analysis results. These results are then used to construct a first subject area for the resource standard document and a second subject area for the standard document, including: Identify potential correlations between the first data content, the second data content, and the other data content; Content related to resource standards is selected from the first data content, the second data content, and the other data content respectively, and the first subject area of ​​the resource standard document is summarized. Content related to the standard is selected from the first data content, the second data content, and the other data content respectively, and the second subject area of ​​the standard document is summarized.

5. The method according to claim 3, characterized in that, The process of quantizing different types of data content to obtain quantized values ​​corresponding to different types of data content includes: Obtain a Locality Sensitive Hash Function (LSHF), wherein the LSHF is used to quantify the data content in the safety and environmental protection standard data; The first quantization value corresponding to each first data content is calculated using the locality-sensitive hash function. The second quantization value corresponding to each second data content is calculated using the locality-sensitive hash function; The third quantization value corresponding to each other data content is calculated using the locality-sensitive hash function.

6. The method according to claim 1, characterized in that, The step of obtaining the target quantization value of the procedure data contained in the operating procedure from the standard fusion database includes: Obtain the business description corresponding to the operation procedure and the quantitative value of different types of data content corresponding to the operation procedure from the standard fusion database; The sum of quantized values ​​is calculated based on the quantized values ​​of different types of data content corresponding to the aforementioned operating procedures; The target quantification value is calculated by combining the business description and the quantification value.

7. The method according to claim 1, characterized in that, The method further includes: Request to query the testing procedure within the preset time period; If the number of procedural query requests carrying the same query content reaches a preset threshold, a threshold update mechanism is triggered, and the quantitative difference threshold related to the query content is updated based on the threshold update mechanism.

8. A safe and environmentally friendly data processing device, characterized in that, The device includes: The receiving module is used to receive a procedure query request sent by the client, wherein the procedure query request includes user characteristics and query content; The query module is used to obtain target files that match the query content and to obtain operation procedures associated with the query content from the target files; The acquisition module is used to obtain the target quantization value of the procedure data contained in the operation procedure from the standard fusion database; The matching module is used to match the target quantization value of the procedure data with the user characteristics, and send the procedure data with the highest matching degree as the target data to the client.

9. A computer device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

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