A method and system for automatic pre-warning of natural gas marketing strategy execution

Abstract

The application discloses a kind of natural gas marketing strategy execution situation automatic early warning method and system, the method includes: S1: obtaining the natural gas marketing strategy file of a month, and the key word recognition is carried out to natural gas marketing strategy file, extracts natural gas basic data, and it is imported into audit informationization supervision and management platform marketing audit system;S2: obtaining natural gas enterprise financial management system Fmis, marketing system push natural gas system data;S3: according to the natural gas basic data and natural gas system data obtained, using natural gas marketing strategy logic calculation model, real-time calculation natural gas marketing income related quantity;S4: according to natural gas marketing income related quantity, using natural gas sales strategy execution situation suspiciousness multistage screening filtering model is checked gradually, obtains each level suspicious clue, carries out hierarchical classification automatic early warning, fills the blank of domestic about natural gas marketing intelligent audit.

Description

Technical Field

[0001] This invention relates to the fields of marketing management and big data auditing for natural gas enterprises, specifically to an automatic early warning method and system for the implementation of natural gas marketing strategies. Background Technology

[0002] Natural gas marketing, as a core business of oil and gas field enterprises, directly determines their economic benefits. With the continuous advancement of urbanization in my country, large-scale gas purchases are increasing, making natural gas marketing increasingly flexible under the influence of market, policy, and other factors. First, natural gas marketing is affected by seasonality, experiencing peak and off-peak periods throughout the year. During peak periods, supply falls short of demand, while during off-peak periods, vigorous promotions are implemented. Marketing strategies need to be adjusted multiple times throughout the year, increasing charges for exceeding consumption limits during peak periods and reducing charges during off-peak periods to optimize resource allocation and meet dynamic market demands. Second, natural gas marketing not only needs to maintain and expand market share but also needs to shoulder social responsibility, maximizing the economic and social benefits for natural gas enterprises. Therefore, strengthening natural gas marketing management, enhancing audits of natural gas marketing activities, and controlling operational risks are particularly important.

[0003] In the execution of natural gas marketing strategies, traditional auditing methods are still largely employed, primarily relying on manual monthly summarization, statistics, and calculations. Due to the large number of marketing customers and the centralized settlement method at the end of the month, the review and auditing work is difficult to guarantee effectively. At the same time, the marketing strategy is difficult to interpret, with numerous and widespread implementing units, and the multi-level interpretation process is prone to deviations. In addition, marketing settlement is a key position with relatively fixed personnel, resulting in significant operational and management risks.

[0004] Digital auditing has become the mainstream auditing method for companies and enterprises. While the oil and gas and State Grid industries have accumulated some experience in digital auditing, many other industries are still only at the level of viewing data through the system and have not conducted in-depth research and application. Intelligent auditing of natural gas marketing is still a blank slate.

[0005] Therefore, existing natural gas marketing strategies are complex to formulate, frequently adjusted, and involve high labor intensity for employees. The review and audit process is not sufficiently robust, and the multi-level interpretation process is prone to errors. The personnel are relatively fixed, resulting in significant operational and management risks. Traditional manual auditing cannot achieve comprehensive supervision of the execution of natural gas marketing strategies, and there is a lack of a system to automatically issue early warnings regarding the execution of natural gas marketing strategies. Summary of the Invention

[0006] The technical problem to be solved by this invention is that, given the complexity of existing natural gas marketing strategies, frequent adjustments, high labor intensity for employees, insufficient review and auditing safeguards, potential for deviations in multi-level interpretation processes, relatively fixed personnel, and significant operational and management risks, there is currently a lack of a system to automatically provide early warnings regarding the implementation of natural gas marketing strategies.

[0007] The purpose of this invention is to provide an automatic early warning method and system for the implementation of natural gas marketing strategies. Based on natural gas marketing auditing, this invention fills the gap in domestic intelligent auditing of natural gas marketing.

[0008] This invention is achieved through the following technical solution:

[0009] In a first aspect, the present invention provides an automatic early warning method for the implementation of a natural gas marketing strategy, the method comprising the following steps:

[0010] S1: Obtain the natural gas marketing strategy document for a certain month (usually a Word document), perform keyword recognition on the natural gas marketing strategy document, and extract basic natural gas data; maintain the natural gas basic data table for that month based on the natural gas basic data, and import it into the marketing audit system of the audit information supervision and management platform;

[0011] S2: Obtain natural gas system data pushed by the natural gas enterprise's financial management system (Fmis) and marketing system;

[0012] S3: Based on the natural gas basic data obtained in step S1 and the natural gas system data obtained in step S2, the natural gas marketing revenue related quantities are calculated in real time using the natural gas marketing strategy logic calculation model.

[0013] S4: Based on the aforementioned natural gas marketing revenue, a multi-level screening and filtering model for suspicious points in the execution of the natural gas marketing strategy is used to conduct step-by-step checks, obtain clues for suspicious points at each level, and automatically issue warnings and push out clues for suspicious points at each level in the execution of the natural gas marketing strategy for the current month.

[0014] The keyword identification includes: matching the original keyword with the standard keyword stored in the keyword database; when the original keyword matches the standard keyword successfully, the corresponding keyword information is extracted and transformed.

[0015] This invention is an intelligent automatic early warning method for the implementation of natural gas marketing strategies. This early warning method designs an audit method for natural gas marketing by comprehensively interpreting, analyzing and summarizing marketing strategy documents, and analyzes and reviews the implementation of natural gas marketing strategies with a full sample. It quickly focuses on customers with abnormal settlement and clearing, and provides a strong guarantee for multi-level automatic early warning for full-coverage online supervision and audit of the implementation of natural gas marketing strategies.

[0016] Furthermore, the basic natural gas data in step S1 includes natural gas volume and price data;

[0017] Fill in the natural gas volume and price data into the "Period Price Fluctuation Ratio" and "Period Non-Residential Gas Volume Execution Price" forms as required, and import them into the marketing audit system of the audit information supervision and management platform.

[0018] Furthermore, in step S2, data pushed by the natural gas enterprise financial management system Fmis is collected in real time using data warehouse ETL technology (Extract, Transform, Load).

[0019] We independently collect and obtain data pushed by the marketing system in real time using Microsoft SQL Server Integration Services.

[0020] Furthermore, the logical calculation model for the natural gas marketing strategy in step S3 includes:

[0021] The first logical computation model has the following formula:

[0022]

[0023] R 合同.期 =P 合同.期 Q 合同.期

[0024]

[0025]

[0026] Where R is total revenue; R i P represents the total revenue of region i during the specified period; i Q represents the price in region i during the specified period; i The sales volume in region i during the specified period; i represents the peak-shaving gas, balancing gas, online gas purchases, and additional gas purchases under the contract during the specified period.

[0027] The second logical computation model includes:

[0028] The natural gas price settlement order is as follows: online transactions, additional gas settlement, and contract gas settlement.

[0029] The order of natural gas volume deduction is as follows: residential gas, non-residential gas (for city gas: peak-shaving gas > balance gas);

[0030] Theoretical equilibrium gas volume formula: Annual contract volume / Number of days in the year * 1.08 * Number of days in the corresponding period;

[0031] Theoretical peak-shaving gas volume formula: Monthly contracted volume - Theoretical equilibrium gas volume;

[0032] Among them: City gas customers:

[0033]

[0034] Industrial customers:

[0035]

[0036] Among them, R i P represents the total revenue of region i during the specified period; i Q represents the price in region i during the specified period; i B represents the sales volume in region i within the specified period. i The floating ratio of region i within the specified period; i represents the peak-shaving gas, balancing gas, online gas purchases, and additional gas purchases under the contract within the specified period;

[0037] Furthermore, the execution process of the multi-level filtering model in step S4 is as follows:

[0038] Step A: Based on the aforementioned natural gas marketing revenue, determine whether the cumulative monthly gas consumption and the monthly settlement amount are consistent. Perform first-level filtering. If the cumulative monthly gas consumption and the monthly settlement amount are consistent (i.e. equal), proceed to Step B. If the cumulative monthly gas consumption and the monthly settlement amount are inconsistent (i.e. unequal), record the clues of the inconsistency between the cumulative monthly gas consumption and the monthly settlement amount, and issue a first-level warning.

[0039] Step B: Determine whether the residential gas consumption structure is consistent with the actual residential gas consumption structure, and perform secondary filtering. If the residential gas consumption structure is consistent with the actual residential gas consumption structure (i.e. equal), proceed to step C; if the residential gas consumption structure is inconsistent with the actual residential gas consumption structure (i.e. not equal), record the clues of the inconsistency between the residential gas consumption structure and the actual residential gas consumption structure, and issue a secondary warning.

[0040] Step C: Determine whether the residential gas price is consistent with the actual residential gas price, and perform three-level filtering. If the residential gas price is consistent with the actual residential gas price (i.e., equal), proceed to step D; if the residential gas price is inconsistent with the actual residential gas price (i.e., not equal), record the clues of the inconsistency between the residential gas price and the actual residential gas price, and issue a three-level warning.

[0041] Step D: Determine if there is any phenomenon of non-natural gas enterprise financial management system Fmis settlement data, and perform four-level filtering. If it does not exist, proceed to step E; if it exists, record the existence of non-natural gas enterprise financial management system Fmis settlement data and issue a four-level warning.

[0042] Step E: Determine whether the sales settlement volume is consistent with the settlement quantity of the natural gas company's financial management system FMIS. Perform five-level filtering. If the sales settlement volume is consistent with the settlement quantity of the natural gas company's financial management system FMIS (i.e., equal), proceed to step F. If the sales settlement volume is inconsistent with the settlement quantity of the natural gas company's financial management system FMIS (i.e., unequal), record the clues of the inconsistency between the sales settlement volume and the settlement quantity of the natural gas company's financial management system FMIS, and issue a five-level warning.

[0043] Step F: Determine if the customer is a customer subject to late payment penalties and perform a six-level filtering. If not, proceed to step G; if so, record the customer as a customer subject to late payment penalties and issue a six-level warning.

[0044] Step G: Determine the difference between non-co-managed sales revenue and co-managed sales revenue, and perform seven-level filtering and seven-level early warning.

[0045] Furthermore, the natural gas marketing revenue related quantities in step S4 include monthly settlement volume, actual residential gas consumption structure, actual residential gas price, monthly contract settlement volume, and default payment amount.

[0046] Secondly, the present invention also provides an automatic early warning system for the implementation of natural gas marketing strategies, the system comprising a first acquisition module, a second acquisition module, a calculation module, a multi-level filtering module, and an early warning module;

[0047] The first acquisition module is used to acquire the natural gas marketing strategy document (usually a Word document) for a certain month, and to perform keyword recognition on the natural gas marketing strategy document to extract basic natural gas data; maintain the natural gas basic data table for that month based on the natural gas basic data, and import it into the marketing audit system of the audit information supervision and management platform;

[0048] The second acquisition module is used to acquire natural gas system data pushed by the natural gas enterprise's financial management system (Fmis) and marketing system;

[0049] The processing module is used to calculate the relevant amount of natural gas marketing revenue in real time based on the acquired natural gas basic data and natural gas system data, using a natural gas marketing strategy logic calculation model.

[0050] The multi-level screening and filtering module is used to perform a step-by-step check based on the natural gas marketing revenue related amount, using a multi-level screening and filtering model for suspicious points in the execution of the natural gas sales strategy, to obtain clues to suspicious points at each level.

[0051] The early warning module is used to automatically issue early warnings and push out clues at all levels regarding the implementation of the natural gas marketing strategy for the current month, based on the clues at each level.

[0052] Furthermore, the second acquisition module utilizes data warehouse ETL technology (Extract, Transform, Load) to collect data pushed by the natural gas enterprise financial management system FMIS in real time; and utilizes Microsoft SQL Server Integration Services to independently collect data pushed by the marketing system in real time.

[0053] Thirdly, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned automatic early warning method for the execution status of a natural gas marketing strategy.

[0054] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned automatic early warning method for the execution status of a natural gas marketing strategy.

[0055] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0056] 1. This invention is an intelligent automatic early warning method for the implementation of natural gas marketing strategies. This early warning method designs an audit method for natural gas marketing by comprehensively interpreting, analyzing and summarizing marketing strategy documents, and analyzes and reviews the implementation of natural gas marketing strategies with a full sample. It quickly focuses on customers with abnormal settlement and clearing, and provides a strong guarantee for multi-level automatic early warning for full-coverage online supervision and audit of the implementation of natural gas marketing strategies.

[0057] 2. First, this invention automates the calculation and settlement of natural gas sales revenue in the marketing operations of the Southwest Oil and Gas Field Company for the first time, reducing the number of people required for similar audit projects from 8-10 to 2-3, effectively saving audit resources and greatly improving work efficiency. Second, this invention can automatically collect data periodically, automatically calculate and compare, quickly focus on settlement and settlement of abnormal customers, and then automatically analyze audit doubts from multiple dimensions to achieve accurate push of audit clues. Third, this invention also enables rapid querying, statistics, and analysis of a variety of natural gas marketing business data, such as customer type, customer volume, customer residential gas consumption structure, customer contract gas consumption, customer actual gas consumption, and natural gas sales output value, overcoming the shortcomings of traditional audits that can only be manually sampled and reviewed, which helps the audit to conduct comprehensive verification and analysis and serves the prediction of the company's development trend. Fourth, this invention does not need to change with the frequent changes in natural gas marketing strategies, only requires simple parameter maintenance, successfully avoiding the risk of calculation deviation caused by multi-level understanding, and further promoting the standardization of marketing calculations. Fifth, it promotes the development of marketing field from ex-post audit to in-process audit, enabling the Southwest Oil and Gas Field Company to release marketing strategies at any time and internal audits to follow up in real time, effectively preventing and mitigating related risks. Attached Figure Description

[0058] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0059] Figure 1 This is a flowchart of an automatic early warning method for the execution of a natural gas marketing strategy, according to Embodiment 1 of the present invention.

[0060] Figure 2 This is a flowchart of an automatic early warning method for the execution of a natural gas marketing strategy, according to Embodiment 2 of the present invention.

[0061] Figure 3 This is a block diagram of an automatic early warning system for the implementation of a natural gas marketing strategy according to the present invention.

[0062] Figure 4 This is a schematic diagram of the customer basic information data form of the present invention.

[0063] Figure 5 This is a schematic diagram of a price fluctuation ratio data form during the period of this invention.

[0064] Figure 6 This is a schematic diagram of the non-residential gas volume execution price data form for the present invention.

[0065] Figure 7 This is a flowchart of the hierarchical early warning process of the multi-level screening and filtering model of this invention. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are only for explaining this invention and are not intended to limit this invention.

[0067] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known structures, circuits, materials, or methods have not been specifically described in order to avoid obscuring the invention.

[0068] Throughout this specification, references to "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "an embodiment," "an example," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0069] Example 1

[0070] like Figure 1 , Figure 7 As shown, the present invention provides an automatic early warning method for the implementation of a natural gas marketing strategy, the method comprising the following steps:

[0071] S1: Obtain the natural gas marketing strategy document for a certain month (usually a Word document), and perform keyword recognition on the natural gas marketing strategy document to extract basic natural gas data; the basic natural gas data includes natural gas volume and price data; maintain and fill in the natural gas volume and price data into the "Period Price Fluctuation Ratio" and "Period Non-Residential Gas Volume Execution Price" forms as required, and import them into the marketing audit system of the audit information supervision and management platform;

[0072] S2: Obtain natural gas system data pushed by the natural gas enterprise's financial management system (Fmis) and marketing system. Specifically, data pushed by the natural gas enterprise's financial management system (Fmis) is collected in real time using data warehouse ETL technology (Extract, Transform, Load), including 313 key fields from 10 main data tables such as user name, responsibility center code, responsibility center name, supplier code, and supplier name. Data pushed by the marketing system is collected in real time independently using Microsoft SQL Server Integration Services, including 167 key fields from 4 main data tables such as customer name, unit, residential gas volume for the current month, online market transaction volume for the current month, online market transaction gas price for the current month, offline market transaction volume for the current month, and offline market transaction gas price for the current month.

[0073] S3: Based on the basic natural gas data obtained in step S1 and the natural gas system data obtained in step S2, the natural gas marketing revenue-related quantities are calculated in real time using the natural gas marketing strategy logic calculation model; the natural gas marketing strategy logic calculation model includes:

[0074] The first logic computation model (i.e., the basic logic computation model) has the following formula:

[0075]

[0076] R 合同.期 =P 合同.期 Q 合同.期

[0077]

[0078]

[0079] Where: R represents total revenue; R i P represents the total revenue of region i during the specified period; i Q represents the price in region i during the specified period; i The sales volume in region i during the specified period; i represents the peak-shaving gas, balancing gas, online gas purchases, and additional gas purchases under the contract during the specified period.

[0080] The second logical computation model (i.e., other logical computation models) includes:

[0081] The natural gas price settlement order is as follows: online transactions, additional gas settlement, and contract gas settlement.

[0082] The order of natural gas volume deduction is as follows: residential gas, non-residential gas (for city gas: peak-shaving gas > balance gas);

[0083] Theoretical equilibrium gas volume formula: Annual contract volume / Number of days in the year * 1.08 * Number of days in the corresponding period;

[0084] Theoretical peak-shaving gas volume formula: Monthly contracted volume - Theoretical equilibrium gas volume;

[0085] The basic formula for calculating sales revenue is as follows:

[0086] Among them: City gas customers:

[0087]

[0088] Industrial customers:

[0089]

[0090] Among them, R i P represents the total revenue of region i during the specified period; i Q represents the price in region i during the specified period; i B represents the sales volume in region i within the specified period. i The floating ratio of region i within the specified period; i represents the peak-shaving gas, balancing gas, online gas purchases, and additional gas purchases under the contract within the specified period;

[0091] S4: Based on the aforementioned natural gas marketing revenue, a multi-level screening and filtering model for suspicious points in the execution of the natural gas sales strategy is used to conduct a step-by-step inspection, obtaining clues for suspicious points at each level. This results in tiered and categorized automatic early warnings and push notifications regarding suspicious points in the execution of the natural gas marketing strategy for the current month. For example... Figure 7 As shown, the execution process of the multi-level filtering model is as follows:

[0092] Step A: Based on the natural gas marketing revenue, determine whether the cumulative monthly gas consumption and the monthly settlement amount are consistent. Perform first-level filtering. If the cumulative monthly gas consumption and the monthly settlement amount are consistent (i.e., equal), proceed to Step B. If the cumulative monthly gas consumption and the monthly settlement amount are inconsistent (i.e., unequal), record the clues indicating the inconsistency and issue a first-level warning. The cumulative monthly gas consumption is read from Step S2, and the monthly settlement amount is calculated based on the data from Step S2.

[0093] The formula for determining the consistency between cumulative monthly gas consumption and monthly settlement amount is as follows:

[0094]

[0095] Step B: Determine whether the residential gas consumption structure matches the actual residential gas consumption structure, and perform secondary filtering. If the residential gas consumption structure matches the actual residential gas consumption structure (i.e., equal), proceed to step C; if the residential gas consumption structure does not match the actual residential gas consumption structure (i.e., not equal), record the clues of the discrepancy between the residential gas consumption structure and the actual residential gas consumption structure, and issue a secondary warning. The residential gas consumption structure is read from step S2, and the actual residential gas consumption structure is calculated based on the data from steps S1 and S2.

[0096] The formula for judging the consistency between the residential gas consumption structure and the actual residential gas consumption structure is as follows:

[0097]

[0098] Step C: Determine whether the residential gas price is consistent with the actual residential gas price. Perform three-level filtering. If the residential gas price is consistent with the actual residential gas price (i.e., equal), proceed to step D. If the residential gas price is inconsistent with the actual residential gas price (i.e., unequal), record the clues of the inconsistency between the residential gas price and the actual residential gas price, and issue a three-level warning. The residential gas price is read from step S2, and the actual residential gas price is calculated based on the data from steps S1 and S2.

[0099] The formula for judging the consistency between residential gas prices and actual residential gas prices is as follows:

[0100]

[0101] Step D: Determine if there is any phenomenon of non-natural gas enterprise financial management system Fmis settlement data, and perform four-level filtering. If it does not exist, proceed to step E; if it exists, record the existence of non-natural gas enterprise financial management system Fmis settlement data and issue a four-level warning.

[0102] Step E: Determine whether the sales settlement volume is consistent with the settlement quantity of the natural gas company's financial management system FMIS. Perform five-level filtering. If the sales settlement volume is consistent with the settlement quantity of the natural gas company's financial management system FMIS (i.e., equal), proceed to step F. If the sales settlement volume is inconsistent with the settlement quantity of the natural gas company's financial management system FMIS (i.e., unequal), record the clues of the inconsistency between the sales settlement volume and the settlement quantity of the natural gas company's financial management system FMIS, and issue a five-level warning.

[0103] The formula for judging the consistency of sales revenue deviation settlement is as follows:

[0104] if

[0105] Q 月度 ≤0.95Q 月合同

[0106] but

[0107]

[0108] Where, k 居民 For residential gas consumption structure; R 偏差结算 The amount to be paid for breach of contract.

[0109] Step F: Determine if the customer is a customer subject to late payment penalties and perform a six-level filtering. If not, proceed to step G; if so, record the customer as a customer subject to late payment penalties and issue a six-level warning.

[0110] Step G: Determine the difference between non-co-managed sales revenue and co-managed sales revenue, and perform seven-level filtering and seven-level early warning.

[0111] The derived formulas used for non-co-managed and co-managed premises are as follows:

[0112] Online gas volume distribution formula (monthly settlement unit):

[0113]

[0114] Formula for the distribution of additional gas volume during the period (calculated on a monthly basis):

[0115]

[0116] Liquidation formula:

[0117] R 清算差异.月 =R 清算收入.月 -R 复算收入.月

[0118]

[0119] Among them, R 复算 The audited revenue (excluding liquidation portion) for the specified month.

[0120] Specifically: Keyword identification includes: matching the original keywords with standard keywords stored in the keyword database; when the original keywords successfully match the standard keywords, extracting and converting the corresponding keyword information.

[0121] Furthermore, the natural gas marketing revenue related quantities in step S4 include monthly settlement volume, actual residential gas consumption structure, actual residential gas price, monthly contract settlement volume, and default payment amount.

[0122] This invention is an intelligent automatic early warning method for the implementation of natural gas marketing strategies. This early warning method designs an audit method for natural gas marketing by comprehensively interpreting, analyzing and summarizing marketing strategy documents, and analyzes and reviews the implementation of natural gas marketing strategies with a full sample. It quickly focuses on customers with abnormal settlement and clearing, and provides a strong guarantee for multi-level automatic early warning for full-coverage online supervision and audit of the implementation of natural gas marketing strategies.

[0123] In practice:

[0124] Taking the online audit of the implementation of a natural gas marketing strategy of an oil and gas field company in February 2020 as an example, the implementation process of the automatic early warning method for the implementation of the natural gas marketing strategy in February 2020 is as follows:

[0125] Step S1: Obtain and interpret the natural gas marketing strategy document of a certain oil and gas field enterprise in February 2020, collect and organize all natural gas marketing strategies related to February 2020, extract key information, and convert it into gas volume and gas price data.

[0126] Simultaneously, maintain the gas volume and price data tables for February. Fill the converted gas volume and price data into the "Period Price Fluctuation Ratio" and "Period Non-Residential Gas Volume Execution Price" forms as required, and import them into the marketing audit system of the audit information supervision and management platform.

[0127] Step S2: Collect and summarize relevant system data. Data pushed from the natural gas company's financial management FMIS system and marketing system is collected using data warehouse ETL technology (Extract, Transform, Load) and Microsoft SQL Server Integration Services tools.

[0128] Step S3: Based on the natural gas basic data obtained in step S1 and the natural gas system data obtained in step S2, perform logical calculations using the natural gas marketing strategy logical calculation model to calculate the relevant amount of natural gas marketing revenue in real time.

[0129] Step S4: Based on the aforementioned natural gas marketing revenue, a multi-level screening and filtering model for suspicious points in the execution of the natural gas marketing strategy is used to conduct a step-by-step inspection, obtaining clues for suspicious points at each level. This results in tiered and categorized automatic early warnings and push notifications regarding suspicious points in the execution of the natural gas marketing strategy for the current month. In other words, by applying Occam's Razor, the causes of problems are analyzed in depth, and filtering is performed step-by-step, classifying and controlling problems from multiple levels, including system data sources, data collection, system mapping, settlement methods, clearing methods, and strategy execution.

[0130] The results showed that: Questions regarding the implementation of the February natural gas marketing strategy were categorized and pushed out at different levels. The system automatically screened out the questions at each level. In February, the system pushed out 122 questions regarding discrepancies between monthly gas consumption (cumulative) and monthly settlement volume; 92 questions regarding discrepancies between residential gas consumption structure and actual consumption; 4 questions regarding discrepancies between residential gas prices and audit recalculations; 1 question regarding discrepancies between sales settlement quantity and FMIS system quantity; 50 questions regarding sales settlement quantity not found in the FMIS system; 293 questions regarding other sales revenue discrepancies (non-co-managed); and 50 questions regarding other sales revenue discrepancies (co-managed).

[0131] Based on the above results, we will progressively correct and improve the issues in data collection and system mapping that were previously identified through system alerts, and conduct in-depth investigations into issues such as price strategy interpretation deviations and execution errors identified by system alerts.

[0132] Therefore, based on the above implementation, this invention is simple to apply, highly effective, and fills a gap in the field of intelligent auditing of natural gas marketing. First, it is the first time that the calculation and settlement of natural gas sales revenue has been automated in the marketing operations of the Southwest Oil and Gas Field Company, reducing the previous requirement of 8-10 people for similar audit projects to 2-3 people, effectively saving audit resources and greatly improving work efficiency. Second, this invention can automatically collect data periodically, automatically calculate and compare, quickly focus on settlement and settlement of abnormal customers, and then automatically analyze audit doubts from multiple dimensions, achieving accurate push of audit clues. Third, this invention also enables rapid querying, statistics, and analysis of a wide range of natural gas marketing business data, such as customer type, customer volume, customer residential gas consumption structure, customer contract gas consumption, customer actual gas consumption, and natural gas sales output value, overcoming the shortcomings of traditional auditing which can only perform manual sampling reviews, facilitating comprehensive audit verification and analysis, and serving the company's development forecasting. Fourth, this invention does not need to change with frequent changes in natural gas marketing strategies; it only requires simple parameter maintenance, successfully avoiding the calculation deviation risk caused by multi-level understanding, and further promoting the standardization of marketing calculations. Fifth, it has promoted the development of marketing audit from ex-post audit to in-process audit, enabling the Southwest Oil and Gas Field Company to release marketing strategies at any time and follow up with internal audits at any time, effectively preventing and resolving related risks.

[0133] Example 2

[0134] like Figure 1 , Figure 2 and Figures 4 to 7 As shown, the difference between this embodiment and Embodiment 1 is that the automatic early warning method for the execution of a natural gas marketing strategy in Embodiment 1 is applied to the automation of natural gas sales revenue calculation and settlement. Figure 2 As shown, based on Example 1, it also includes:

[0135] Step S5, Verification of Suspicious Points. Verify the revised and improved audit findings against on-site investigation to further investigate the audit issues.

[0136] Step S6: Identify Issues. Classify and categorize the identified audit issues, clarifying their type, nature, and impact. Upon verification, a total of 5 issues were confirmed in February, including 1 instance of inadequate implementation of pricing strategies, 1 instance of inconsistent methods for allocating gas usage exceeding the contract, and 3 instances of inconsistent order in which online transaction volume was deducted from contracted gas volume. The total impact amounted to RMB 1,287,400 (excluding tax).

[0137] Step S7: Push audit issues. Submit the audit findings to leadership for approval through the system.

[0138] Step S8, Leadership Approval. Following the audit workflow, leaders at all levels conduct quality control over key information, enabling the online approval process.

[0139] Step S9: Generate audit working papers. Based on audit practice, after review at various levels, audit working papers are generated and sent to the audited entity.

[0140] Specifically, in step S1 of this invention, natural gas volume and price data are maintained and filled into the "Period Price Fluctuation Ratio" and "Period Non-Residential Gas Volume Execution Price" forms as required. Specifically, parameter maintenance aims to meet changes in natural gas marketing strategies by adjusting key parameters such as user type, applicable period, user identifier, and fluctuation ratio without altering the formula. This method allows for quick and centralized interpretation of marketing strategy documents, accurate matching of each policy's key points, and facilitates rapid onboarding for maintenance staff, breaking down barriers of multi-level misunderstandings and significantly reducing the difficulty of review and approval. This parameter maintenance includes three master data forms: customer basic information, period price fluctuation ratio, and period non-residential gas volume execution price. Details of their main parameters can be found in [link to relevant documentation]. Figure 4 , Figure 5 and Figure 6 .

[0141] This invention further promotes full-scale auditing of natural gas marketing through digital methods such as automated data collection, automatic problem classification and grading, and automatic generation of audit working papers, filling the gap in intelligent marketing auditing within the oil and gas industry.

[0142] Example 3

[0143] like Figure 3 As shown, the difference between this embodiment and Embodiment 1 is that the present invention also provides an automatic early warning system for the execution of natural gas marketing strategies. The system includes a first acquisition module, a second acquisition module, a calculation module, a multi-level filtering module, and an early warning module.

[0144] The first acquisition module is used to acquire the natural gas marketing strategy document (usually a Word document) for a certain month, and to perform keyword recognition on the natural gas marketing strategy document to extract basic natural gas data; maintain the natural gas basic data table for that month based on the natural gas basic data, and import it into the marketing audit system of the audit information supervision and management platform;

[0145] The second acquisition module is used to acquire natural gas system data pushed by the natural gas enterprise's financial management system (Fmis) and marketing system;

[0146] The processing module is used to calculate the relevant amount of natural gas marketing revenue in real time based on the acquired natural gas basic data and natural gas system data, using a natural gas marketing strategy logic calculation model.

[0147] The multi-level screening and filtering module is used to perform a step-by-step check based on the natural gas marketing revenue related amount, using a multi-level screening and filtering model for suspicious points in the execution of the natural gas sales strategy, to obtain clues to suspicious points at each level.

[0148] The early warning module is used to automatically issue early warnings and push out clues at all levels regarding the implementation of the natural gas marketing strategy for the current month, based on the clues at each level.

[0149] The execution process of each module can be carried out according to the steps in Example 1, and will not be described in detail in this example.

[0150] Meanwhile, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the aforementioned automatic early warning method for the execution status of a natural gas marketing strategy.

[0151] Meanwhile, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned automatic early warning method for the execution status of a natural gas marketing strategy.

[0152] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0153] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0154] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0155] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0156] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for automatic pre-alarm of natural gas marketing strategy execution, characterized in that, The method includes the following steps: S1: Obtain the natural gas marketing strategy document for a certain month, perform keyword recognition on the natural gas marketing strategy document, and extract basic natural gas data; maintain the natural gas basic data table for that month based on the natural gas basic data, and import it into the marketing audit system of the audit information supervision and management platform; S2: Obtain natural gas system data pushed by the natural gas enterprise's financial management system (Fmis) and marketing system; S3: Based on the natural gas basic data obtained in step S1 and the natural gas system data obtained in step S2, the natural gas marketing revenue related quantities are calculated in real time using the natural gas marketing strategy logic calculation model. S4: Based on the aforementioned natural gas marketing revenue, a multi-level screening and filtering model for suspicious points in the execution of the natural gas marketing strategy is used to conduct step-by-step checks, obtain clues for suspicious points at each level, and automatically issue warnings and push out clues for suspicious points at each level in the execution of the natural gas marketing strategy for the month. The execution process of the multi-level filtering model in step S4 is as follows: Step A: Based on the natural gas marketing revenue, determine whether the cumulative monthly gas consumption is consistent with the monthly settlement amount, and perform first-level filtering. If the cumulative monthly gas consumption is consistent with the monthly settlement amount, proceed to step B; if the cumulative monthly gas consumption is inconsistent with the monthly settlement amount, record the clues of the inconsistency and issue a first-level warning. Step B: Determine whether the residential gas consumption structure is consistent with the actual residential gas consumption structure, and perform secondary filtering. If the residential gas consumption structure is consistent with the actual residential gas consumption structure, proceed to step C; if the residential gas consumption structure is inconsistent with the actual residential gas consumption structure, record the clues of the inconsistency and issue a secondary warning. Step C: Determine whether the residential gas price is consistent with the actual residential gas price, and perform three-level filtering. If the residential gas price is consistent with the actual residential gas price, proceed to step D; if the residential gas price is inconsistent with the actual residential gas price, record the clues of the inconsistency and issue a three-level warning. Step D: Determine if there is any phenomenon of non-natural gas enterprise financial management system Fmis settlement data, and perform four-level filtering. If it does not exist, proceed to step E; if it exists, record the existence of non-natural gas enterprise financial management system Fmis settlement data and issue a four-level warning. Step E: Determine whether the sales settlement volume is consistent with the settlement quantity of the natural gas company's financial management system FMIS. Perform five-level filtering. If the sales settlement volume is consistent with the settlement quantity of the natural gas company's financial management system FMIS, proceed to step F. If the sales settlement volume is inconsistent with the settlement quantity of the natural gas company's financial management system FMIS, record the clues of the inconsistency between the sales settlement volume and the settlement quantity of the natural gas company's financial management system FMIS, and issue a five-level warning. Step F: Determine if the customer is a customer subject to late payment penalties and perform a six-level filtering. If not, proceed to step G; if so, record the customer as a customer subject to late payment penalties and issue a six-level warning. Step G: Determine the difference between non-co-managed sales revenue and co-managed sales revenue, and perform seven-level filtering and seven-level early warning.

2. The automatic early warning method for the implementation of a natural gas marketing strategy according to claim 1, characterized in that, The basic natural gas data in step S1 includes natural gas volume and price data; Fill in the natural gas volume and price data into the "Period Price Fluctuation Ratio" and "Period Non-Residential Gas Volume Execution Price" forms as required, and import them into the marketing audit system of the audit information supervision and management platform.

3. The automatic early warning method for the implementation of a natural gas marketing strategy according to claim 1, characterized in that, In step S2, data pushed by the natural gas enterprise financial management system Fmis is collected in real time using data warehouse ETL technology; We independently collect and obtain data pushed by the marketing system in real time using Microsoft SQL Server Integration Services.

4. The automatic early warning method for the implementation of a natural gas marketing strategy according to claim 1, characterized in that, The natural gas marketing revenue related quantities in step S4 include monthly settlement volume, actual residential gas consumption structure, actual residential gas price, monthly contract settlement volume, and default payment amount.

5. An automatic early warning system for implementing the automatic early warning method for the execution of a natural gas marketing strategy as described in any one of claims 1 to 4, characterized in that, The system includes a first acquisition module, a second acquisition module, a calculation module, a multi-level filtering module, and an early warning module; The first acquisition module is used to acquire the natural gas marketing strategy document for a certain month, and to perform keyword recognition on the natural gas marketing strategy document to extract basic natural gas data; maintain the natural gas basic data table for that month based on the natural gas basic data, and import it into the marketing audit system of the audit information supervision and management platform; The second acquisition module is used to acquire natural gas system data pushed by the natural gas enterprise's financial management system (Fmis) and marketing system; The calculation module is used to calculate the relevant amount of natural gas marketing revenue in real time based on the acquired natural gas basic data and natural gas system data, using a natural gas marketing strategy logic calculation model. The multi-level screening and filtering module is used to perform a step-by-step check based on the natural gas marketing revenue related amount, using a multi-level screening and filtering model for suspicious points in the execution of natural gas marketing strategies, to obtain clues for suspicious points at each level. The early warning module is used to automatically issue early warnings and push out clues at all levels regarding the implementation of the natural gas marketing strategy for the current month, based on the clues at each level.

6. The automatic early warning system according to claim 5, characterized in that, The second acquisition module uses data warehouse ETL technology to collect data pushed by the natural gas enterprise financial management system FMIS in real time; and uses Microsoft SQL Server Integration Services to independently collect data pushed by the marketing system in real time.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements an automatic early warning method for the execution status of a natural gas marketing strategy as described in any one of claims 1 to 4.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements an automatic early warning method for the implementation status of a natural gas marketing strategy as described in any one of claims 1 to 4.

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