Transportation plan adjustment method and system for robot process automation

Abstract

The invention provides a transportation plan adjustment method and system for robot process automation, and the method comprises the steps: carrying out the cross-platform data capture of a gasoline information page through a robot process automation technology, and obtaining the data information of the information page; based on a natural language processing technology and an optical character recognition technology, basic transportation information and reference gasoline data of gasoline are read from a transportation contract, and a transportation risk allocation coefficient is set; estimating resource loss data generated in the transportation process by using a loss calculation engine based on the basic transportation information, the current accurate gasoline data, the transportation risk allocation coefficient and the reference gasoline data; and adjusting the transportation plan by using the resource loss data and the current accurate gasoline data. The latest current accurate gasoline data are integrated into the transportation plan, so that the transportation plan can reflect the latest transportation plan, and disputes and complaints of clients are avoided.

Description

Technical Field

[0001] This invention belongs to the field of logistics and transportation technology, specifically relating to a method and system for adjusting transportation plans using robotic process automation. Background Technology

[0002] Gasoline prices fluctuate continuously due to multiple factors, including international crude oil supply and demand, geopolitical risks, market speculation, and domestic refined oil pricing mechanisms. Given the high proportion of gasoline prices in total transportation costs and the market mechanism's transmission effect on cost changes, fluctuations in gasoline prices directly impact freight rates, potentially increasing transport costs for shippers and compressing profit margins for carriers. To balance the interests of shippers and carriers, changes in gasoline prices need to be incorporated into transportation planning.

[0003] The current gasoline transportation management system cannot keep up with timely updates when gasoline data fluctuates frequently, causing the gasoline plan in the system to fail to reflect the latest transportation plan. Summary of the Invention

[0004] To overcome the shortcomings of the prior art, in a first aspect, this invention application proposes a method for adjusting transportation plans in robotic process automation, comprising:

[0005] Robotic process automation (RPA) technology is used to crawl data from the gasoline information page across platforms to obtain the data information of the page.

[0006] Based on the data information and the data fluctuation range, the gasoline data in the data information is verified using data verification rules to obtain the current accurate gasoline data;

[0007] Based on natural language processing and optical character recognition technologies, basic transportation information, benchmark gasoline data, and transportation risk sharing coefficients are read from transportation contracts.

[0008] Using a loss calculation engine, based on the basic transportation information, the current accurate gasoline data, the transportation risk sharing coefficient, and the baseline gasoline data, the resource loss data generated during transportation is estimated.

[0009] The transportation plan is adjusted using the resource depletion data and the current accurate gasoline data.

[0010] Preferably, the step of verifying the gasoline data in the data information based on the data information and the data fluctuation range, and obtaining the current accurate gasoline data using data verification rules, includes:

[0011] Compare the gasoline data in the data information with the data fluctuation range;

[0012] If the gasoline data does not exceed the data fluctuation range, then the gasoline data is marked as the current accurate gasoline data;

[0013] If the gasoline data exceeds the data fluctuation range, the gasoline data is marked as suspicious gasoline data. Based on the suspicious gasoline data, robotic process automation technology is used to re-collect data information until the gasoline data in the re-collected data information does not exceed the data fluctuation range. Then, the gasoline data in the re-collected data information is marked as the current accurate gasoline data.

[0014] Preferably, the process of setting the data fluctuation range includes:

[0015] Obtain historical data and market conditions for oil product usage;

[0016] Based on the aforementioned market conditions, set the data fluctuation range;

[0017] Based on the historical data and the data fluctuation amplitude, the data fluctuation range is determined.

[0018] Preferably, the resource loss data satisfies the following formula:

[0019]

[0020] In the above formula: F represents resource loss data, F0 represents basic transportation information, P1 represents current accurate gasoline data, P0 represents baseline gasoline data, and α represents transportation risk sharing coefficient.

[0021] Preferably, the method of using robotic process automation (RPA) technology to perform cross-platform data scraping on the gasoline information page to obtain the data information of the information page includes:

[0022] The elements in the robotic process automation technology are associated with the information page according to the preset element positioning rules to obtain the association relationship;

[0023] The pre-built exception handling strategy and preset trigger conditions are used as data collection constraints. According to the preset collection frequency, the correlation relationship and the collection constraints, the robotic process automation technology is used to crawl the gasoline information page across platforms to obtain the data information of the information page.

[0024] The anomaly handling strategy is constructed based on the acquired historical data, historical network anomaly information, and historical data fluctuation range.

[0025] Preferably, the pre-construction process of the exception handling strategy includes:

[0026] Based on the historical data structure of the acquired historical data information, it is determined whether the historical data structure of the historical data information has changed; when the historical data structure of the historical data information has changed or the historical network abnormal information is identified, the robot process automation technology will stop collecting data information from the information page; otherwise, the robot process automation technology will continue to collect data information from the information page, as the first abnormality handling sub-strategy.

[0027] Based on the historical data fluctuation range, determine whether the historical data information exceeds the historical data fluctuation range; when the historical data information exceeds the historical data fluctuation range, correct the historical data information to not exceed the historical data fluctuation range, otherwise do not correct the historical data information, as a second anomaly handling sub-strategy;

[0028] When historical city data or historical data information of gasoline is missing in the acquired historical data information, it will be recorded and a manual review process will be initiated; otherwise, it will be recorded but no manual review process will be initiated. This will be the third sub-strategy for handling anomalies.

[0029] The first exception handling sub-strategy, the second exception handling sub-strategy, and the third exception handling sub-strategy are used as exception handling strategies.

[0030] Preferably, the data information includes: gasoline data, gasoline product name data, gasoline city data, gasoline price data, and gasoline price effective date data.

[0031] Preferably, after using robotic process automation (RPA) technology to perform cross-platform data scraping on the gasoline information page and obtain the data information of the information page, the process includes:

[0032] The gasoline data, gasoline product name data, gasoline city data, gasoline price data, and gasoline price effective date data in the aforementioned data information are subjected to format standardization conversion; the format standardization conversion includes: data type conversion, decimal place standardization conversion; and / or

[0033] When the gasoline data in the data information changes, the robotic process automation technology combined with optical character recognition technology or natural language processing technology is used to analyze the reason for the change in the gasoline data and the effective date of the change.

[0034] Preferably, after using robotic process automation (RPA) technology to perform cross-platform data scraping on the gasoline information page and obtain the data information of the information page, the method further includes:

[0035] The data information, data acquisition operation steps, and network status information during acquisition are recorded in detail.

[0036] The data, operation steps, and network status information are processed using end-to-end log processing technology to form an end-to-end data acquisition log.

[0037] The entire data collection logs are transformed into a visual audit chain using audit data transformation technology.

[0038] Secondly, this invention application also proposes a transportation planning adjustment system for robotic process automation, comprising:

[0039] The data acquisition module is used to use robotic process automation technology to crawl data from the gasoline information page across platforms and obtain the data information of the information page.

[0040] The gasoline data verification module is used to verify the gasoline data in the data information based on the data information and the data fluctuation range, and to obtain the current accurate gasoline data.

[0041] The reading module is used to read basic transportation information of gasoline, benchmark gasoline data, and set transportation risk sharing coefficients from transportation contracts based on natural language processing technology and optical character recognition technology.

[0042] The resource loss data calculation module is used to estimate the resource loss data generated during transportation based on the basic transportation information, the current accurate gasoline data, the transportation risk sharing coefficient, and the benchmark gasoline data, using the loss calculation engine.

[0043] The adjustment module is used to adjust the transportation plan using the resource depletion data and the current accurate gasoline data.

[0044] Preferably, the gasoline data verification module is specifically used for:

[0045] Compare the gasoline data in the data information with the data fluctuation range;

[0046] If the gasoline data does not exceed the data fluctuation range, then the gasoline data is marked as the current accurate gasoline data;

[0047] If the gasoline data exceeds the data fluctuation range, the gasoline data is marked as suspicious gasoline data. Based on the suspicious gasoline data, robotic process automation technology is used to re-collect data information until the gasoline data in the re-collected data information does not exceed the data fluctuation range. Then, the gasoline data in the re-collected data information is marked as the current accurate gasoline data.

[0048] Preferably, the system further includes a data fluctuation range setting module, used for:

[0049] Obtain historical data and market conditions for oil product usage;

[0050] Based on the aforementioned market conditions, set the data fluctuation range;

[0051] Based on the historical data and the data fluctuation amplitude, the data fluctuation range is determined.

[0052] Preferably, the resource loss data satisfies the following formula:

[0053]

[0054] In the above formula: F represents resource loss data, F0 represents basic transportation information, P1 represents current accurate gasoline data, P0 represents baseline gasoline data, and α represents transportation risk sharing coefficient.

[0055] Preferably, the acquisition module is specifically used for:

[0056] The elements in the robotic process automation technology are associated with the information page according to the preset element positioning rules to obtain the association relationship;

[0057] The pre-built exception handling strategy and preset trigger conditions are used as data collection constraints. According to the preset collection frequency, the correlation relationship and the collection constraints, the robotic process automation technology is used to crawl the gasoline information page across platforms to obtain the data information of the information page.

[0058] The anomaly handling strategy is constructed based on the acquired historical data, historical network anomaly information, and historical data fluctuation range.

[0059] Preferably, the system further includes an exception handling strategy construction module, used for:

[0060] Based on the historical data structure of the acquired historical data information, it is determined whether the historical data structure of the historical data information has changed; when the historical data structure of the historical data information has changed or the historical network abnormal information is identified, the robot process automation technology will stop collecting data information from the information page; otherwise, the robot process automation technology will continue to collect data information from the information page, as the first abnormality handling sub-strategy.

[0061] Based on the historical data fluctuation range, determine whether the historical data information exceeds the historical data fluctuation range; when the historical data information exceeds the historical data fluctuation range, correct the historical data information to not exceed the historical data fluctuation range, otherwise do not correct the historical data information, as a second anomaly handling sub-strategy;

[0062] When historical city data or historical data information of gasoline is missing in the acquired historical data information, it will be recorded and a manual review process will be initiated; otherwise, it will be recorded but no manual review process will be initiated. This will be the third sub-strategy for handling anomalies.

[0063] The first exception handling sub-strategy, the second exception handling sub-strategy, and the third exception handling sub-strategy are used as exception handling strategies.

[0064] Preferably, the data information includes: gasoline data, gasoline product name data, gasoline city data, gasoline price data, and gasoline price effective date data.

[0065] Preferably, the acquisition module includes:

[0066] The conversion submodule is used to perform format standardization conversion on the gasoline data, gasoline product name data, gasoline city data, gasoline price data, and gasoline price effective date data in the data information; the format standardization conversion includes: data type conversion, decimal place standardization conversion; and / or

[0067] The parsing submodule is used to analyze the reason for the change in gasoline data and the effective date of the change in gasoline data when the gasoline data in the data information changes, using the robotic process automation technology combined with optical character recognition technology or natural language processing technology.

[0068] Preferably, the system further includes a visual audit chain formation module, used for:

[0069] The data information, data acquisition operation steps, and network status information during acquisition are recorded in detail.

[0070] The data, operation steps, and network status information are processed using end-to-end log processing technology to form an end-to-end data acquisition log.

[0071] The entire data collection logs are transformed into a visual audit chain using audit data transformation technology.

[0072] Thirdly, this application also proposes an electronic device, comprising: at least one processor and a memory; wherein the memory and the processor are connected via a bus;

[0073] The memory is used to store one or more programs;

[0074] When the one or more programs are executed by the at least one processor, the method for adjusting transportation plans in robotic process automation is implemented.

[0075] Fourthly, this application also proposes a readable storage medium having an executable program stored thereon, which, when executed, implements the aforementioned method for adjusting a transportation plan for robotic process automation.

[0076] Compared with the closest prior art, the present invention application has the following beneficial effects:

[0077] This invention discloses a robotic process automation (RPA) method and system for adjusting transportation plans. The method employs RPA technology to cross-platformly crawl data from a gasoline information page, obtaining the page's data information. Based on this data and its fluctuation range, gasoline data is validated using data verification rules to obtain the current accurate gasoline data. Using natural language processing and optical character recognition (OCR) technologies, basic gasoline transportation information, baseline gasoline data, and a set transportation risk-sharing coefficient are retrieved from the transportation contract. A loss calculation engine is used to estimate resource loss data generated during transportation based on the basic transportation information, the current accurate gasoline data, the transportation risk-sharing coefficient, and the baseline gasoline data. The transportation plan is then adjusted using the resource loss data and the current accurate gasoline data. By dynamically acquiring the current accurate gasoline data and integrating it into the transportation plan, the system ensures that the transportation plan reflects the latest developments, preventing customer disputes and complaints. Attached Figure Description

[0078] Figure 1 A flowchart of a transportation plan adjustment method for robotic process automation provided in this invention application;

[0079] Figure 2 This is a screenshot showing the results of capturing real-time updates of oil price data in different regions in Embodiment 1 provided in this application of the present invention.

[0080] Figure 3 This is an interface diagram of the oil price data acquisition task at midnight and 1 a.m. daily in Embodiment 1 provided for this invention application;

[0081] Figure 4 This is a flowchart illustrating the formation of a full-link data acquisition log from oil price data in Embodiment 1 provided in this application of the present invention;

[0082] Figure 5 An architecture diagram of a transportation planning adjustment system for robotic process automation provided in this invention application;

[0083] Figure 6 This is a schematic diagram of the operation of an electronic device provided in this invention application. Detailed Implementation

[0084] The specific embodiments of this invention will be further described in detail below with reference to the accompanying drawings.

[0085] Example 1:

[0086] like Figure 1 As shown, this invention application proposes a method for adjusting transportation plans in robotic process automation, which may include the following steps:

[0087] Step 1: Use robotic process automation (RPA) technology to crawl data from the gasoline information page across platforms to obtain the data information of the page;

[0088] Step 2: Based on the data information and the data fluctuation range, verify the gasoline data in the data information using data verification rules to obtain the current accurate gasoline data;

[0089] Step 3: Based on natural language processing and optical character recognition technologies, read the basic transportation information of gasoline, benchmark gasoline data, and set the transportation risk sharing coefficient from the transportation contract;

[0090] Step 4: Using the loss calculation engine, based on the basic transportation information, the current accurate gasoline data, the transportation risk sharing coefficient, and the benchmark gasoline data, estimate the resource loss data generated during transportation;

[0091] Step 5: Adjust the transportation plan using the resource depletion data and the current accurate gasoline data.

[0092] In step 1 above, the step of using robotic process automation (RPA) technology to perform cross-platform data scraping on the gasoline information page to obtain the data information of the information page may include the following steps:

[0093] Step 1.1: Associate the elements in the robotic process automation technology with the information page according to the preset element positioning rules to obtain the association relationship;

[0094] Step 1.2: Use the pre-built anomaly handling strategy and preset triggering conditions as data collection constraints; according to the preset collection frequency, the aforementioned correlation, and the aforementioned data collection constraints, use robotic process automation technology to perform cross-platform data crawling on the gasoline information page to obtain the data information of the information page; wherein, the anomaly handling strategy is constructed based on the acquired historical data information, historical network anomaly information, and historical data fluctuation range.

[0095] The aforementioned Robotic Process Automation (RPA) technology is used to crawl data across platforms on the gasoline information page to obtain the data information. The core of RPA technology lies in its ability to achieve cross-platform data crawling, real-time monitoring, intelligent processing, and early warning mechanisms. By automating and replacing manual operations, it can achieve closed-loop management of gasoline-related data from collection to storage, significantly improving the efficiency and accuracy of data acquisition, while also responding quickly to market changes.

[0096] The data source configuration process for data scraping involves confirming the URL (Uniform Resource Locator) address of the gasoline information page provided by PetroChina's official website, or information related to transportation plans. An enterprise-level RPA technology framework is then selected, with UiPath (Machine Process Automation) as the core component. Leveraging UiPath's multi-protocol stack support architecture, interface connections with the gasoline business system are established, reducing system integration complexity. During data scraping, intelligent parsing of unstructured data such as PDFs, scanned documents, and images is supported, generating structured data for storage in the database. The gasoline information page can scrape data that can influence transportation plans, including gasoline price change dates, price data, fuel type, city where the gasoline is located, historical transportation information, and regional transportation load data.

[0097] In step 1.2 above, when setting the data acquisition frequency, such as Figure 2 As shown, taking the process of capturing oil price data from the data information as an example, the oil price adjustment results in the data information will officially take effect at 24:00 on the day of the price adjustment announcement. PetroChina's official website will simultaneously update the oil price information. It can be set to run at 00:00, 1:00, 3:00, and 5:00 every day to capture oil prices for different cities, achieving real-time updates of oil price data for different regions. Figure 3 The image shows the interface for the daily oil price data retrieval task at midnight and 1 AM. Transportation information can be retrieved using RPA technology after scanning transportation contracts and confirmation slips; historical transportation information and regional transportation load data can be retrieved based on dispatch records of the cities where the gasoline is located.

[0098] The aforementioned data information includes: gasoline data, gasoline product name data, gasoline city data, gasoline price data, and gasoline price effective date data.

[0099] During data collection, an RPA process can be designed to automatically access pages or text files and extract data related to transportation, such as oil product name, oil type, city, transport vehicle, oil price, driver information, and effective date, from the page tables or text files.

[0100] In step 1.2 above, the pre-construction process of the exception handling strategy includes:

[0101] Step a: Based on the historical data structure of the acquired historical data information, determine whether the historical data structure of the historical data information has changed; when the historical data structure of the historical data information has changed or the historical network abnormal information is identified, terminate the robot process automation technology to collect data information from the information page; otherwise, the robot process automation technology continues to collect data information from the information page, as the first abnormality handling sub-strategy.

[0102] Step b: Based on the historical data fluctuation range, determine whether the historical data information exceeds the historical data fluctuation range; when the historical data information exceeds the historical data fluctuation range, correct the historical data information to not exceed the historical data fluctuation range, otherwise do not correct the historical data information, as the second anomaly handling sub-strategy;

[0103] Step c: When historical city data or historical data information of gasoline is missing in the acquired historical data information, record it and start the manual review process; otherwise, record it but do not start the manual review process. This is the third anomaly handling sub-strategy.

[0104] Step d: Use the first exception handling sub-strategy, the second exception handling sub-strategy, and the third exception handling sub-strategy as exception handling strategies.

[0105] The above-mentioned exception handling strategy mainly considers encountering data structure changes, network anomalies, and data fluctuation anomalies, recording the data acquisition time, data content, success status, and anomaly information. For severe anomalies such as data structure changes and network anomalies, the RPA process is terminated. Data structure changes can include anomalies such as missing regions or cities for oil products, missing oil product prices, or missing regional transportation load data. Missing regions or prices are considered severe anomalies, and can be recorded and a manual review process initiated to verify the price and region information. For anomalies such as price fluctuations exceeding the reasonable historical data fluctuation range, automatic correction can be performed. A hierarchical response mechanism is implemented, generating first, second, and third anomaly handling sub-strategies for different anomalies, ensuring that RPA can continuously and accurately extract data.

[0106] The above-mentioned process, after employing robotic process automation (RPA) technology to perform cross-platform data scraping on the gasoline information page and obtaining the data information of the information page, may include:

[0107] The gasoline data, gasoline product name data, gasoline city data, gasoline price data, and gasoline price effective date data in the aforementioned data information are subjected to format standardization conversion; the format standardization conversion includes: data type conversion, decimal place standardization conversion; and / or

[0108] When the gasoline data in the data information changes, the robotic process automation technology combined with optical character recognition technology or natural language processing technology is used to analyze the reason for the change in the gasoline data and the effective date of the change.

[0109] The aforementioned format standardization conversion mainly involves cleaning and standardizing the acquired data (numerical type verification, decimal place specification, etc.) to ensure format consistency. For example, taking oil prices as an example, if the decimal point is misplaced, the oil price data will change significantly. In this case, decimal place specification conversion is needed to convert the abnormal oil price data into normal oil price data; or, if the case of dates is inconsistent, it can be converted into a unified numerical form for expression. After format standardization conversion, the data information is stored in the database for easy retrieval or further processing.

[0110] The above-mentioned steps, after using robotic process automation (RPA) technology to perform cross-platform data scraping on the gasoline information page and obtaining the data information of the information page, also include the following steps:

[0111] Step A: Record in detail the data, the data acquisition operation steps, and the network status information during the acquisition;

[0112] Step B: The data, operation steps, and network status information are processed using end-to-end log processing technology to form an end-to-end data acquisition log;

[0113] Step C: The entire data collection logs are transformed into a visual audit chain using audit data transformation technology.

[0114] The aforementioned end-to-end data acquisition log (hereinafter referred to as the log) can record in detail the data captured each time, operation steps, network status, and other information, forming a visual audit chain for the data flow process. The visual audit chain can facilitate anomaly localization, and anomalies can be located to specific operation steps, improving data transparency. For example, if a network anomaly causes the robotic process automation technology to stop collecting data on a certain day, the log can be used to accurately locate a specific time period on that day as the network anomaly. The visual audit chain aims to facilitate anomaly localization.

[0115] For example, such as Figure 4As shown, the process of acquiring and logging oil price data is illustrated. First, the data source is determined, and then an RPA tool is used for data scraping. During scraping, the fields and frequency of the oil price data to be acquired can be set, and an exception handling strategy is configured. The RPA tool automatically generates data acquisition tasks and checks for data anomalies during the data acquisition process. After verifying the accuracy of the data, it stores the data in the database and records it as a normal log. For abnormal data, it records it as an exception log. Subsequently, when settling freight charges based on the latest oil price, this oil price data can be retrieved, and it can also be dynamically retrieved when making transportation plans.

[0116] In step 2 above, the step of verifying the gasoline data in the data information based on the data information and the data fluctuation range using data verification rules to obtain the current accurate gasoline data may include the following steps:

[0117] Step 2.1: Compare the gasoline data in the data information with the data fluctuation range;

[0118] Step 2.2: If the gasoline data does not exceed the data fluctuation range, then mark the gasoline data as the current accurate gasoline data;

[0119] Step 2.3: If the gasoline data exceeds the data fluctuation range, the gasoline data is marked as suspicious gasoline data; based on the suspicious gasoline data, the data information is re-collected using robotic process automation technology until the gasoline data in the re-collected data information does not exceed the data fluctuation range, then the gasoline data in the re-collected data information is marked as the current accurate gasoline data.

[0120] The above-mentioned data verification rules for gasoline data introduce a data fluctuation range, which is mainly based on historical experience. This range allows for timely identification and correction of abnormal gasoline data, effectively blocking abnormal gasoline data. For example, for regional transportation load data, the fluctuation range can be set to 10%. Data exceeding 10%, or even reaching 40%, can be identified as abnormal regional transportation load data and corrected to within 10%.

[0121] The above-mentioned process for setting the data fluctuation range includes:

[0122] Step e1: Obtain historical data and market conditions for oil usage;

[0123] Step e2: Based on the market conditions, set the data fluctuation range;

[0124] Step e3: Determine the data fluctuation range based on the historical data information and the data fluctuation amplitude.

[0125] Data validation rules can automatically verify data accuracy, primarily by setting a reasonable range of data fluctuations based on historical data and market conditions for oil usage. For example, for gasoline data, a fluctuation range of approximately 10% can be set for oil prices. If the oil price obtained by RPA exceeds this range, it is marked as suspicious data and further verification is required. The rules check whether the obtained oil price data meets the expected format requirements, whether it is a numeric type, whether the decimal places are correct, and whether it contains non-numeric characters. They also check whether the obtained oil price information is complete, including the prices of different grades of gasoline and diesel, as well as prices in different regions. If missing data is found, the data acquisition process must be re-executed or the data source must be checked for completeness. This effectively prevents obviously abnormal oil price data and ensures the accuracy of transportation plans.

[0126] In step 4 above, the resource loss data satisfies the following formula:

[0127]

[0128] In the above formula: F represents resource loss data, F0 represents basic transportation information, P1 represents current accurate gasoline data, P0 represents baseline gasoline data, and α represents transportation risk sharing coefficient.

[0129] The resource depletion data mentioned above, in the calculation formula, can be used to calculate regional transportation load data, gasoline freight costs, etc. For example, when calculating gasoline freight costs, the formula is expressed as follows:

[0130]

[0131] In the above formula: F is the gasoline settlement freight, F0 is the basic freight, P1 is the current oil price, P0 is the benchmark oil price, and α is the oil price risk sharing coefficient.

[0132] In summary, this invention utilizes RPA to dynamically acquire gasoline data from official websites or text files, achieving multi-dimensional value enhancement in three key areas: efficiency, accuracy, and scalability. It automates the entire process and enables cross-platform data acquisition, significantly improving data acquisition efficiency. RPA can simulate manual operation; for oil price data, it can automatically access the oil price page of the PetroChina official website at regular intervals, accurately capturing real-time price data through webpage element positioning technology. It also uses scanning to capture gasoline data affecting transportation and can synchronously update this data to the company's internal transportation planning system, completely replacing manual copying and pasting, achieving automated and intelligent data collection. This results in a leap in data quality and ensures accuracy. RPA can execute operations based on preset rules, avoiding issues such as transcription errors and format deviations encountered during manual data entry. Data validation rules intercept abnormal data in real time, ensuring zero data errors. This invention can also flexibly adapt to business changes; when the official website page structure is adjusted, only the element positioning rules in the RPA process need to be modified, without refactoring the underlying code. RPA can be combined with OCR (Optical Character Recognition) and NLP (Natural Language Processing) technologies to automatically parse unstructured information such as the reasons for price adjustments and the effective time in official oil price announcements.

[0133] The key innovations of this invention are mainly reflected in the following three key aspects:

[0134] 1. Webpage element identification: Accurately model the webpage containing gasoline data and identify gasoline data elements containing oil price information, such as HTML tags, tables, and text boxes. This is the foundation for data extraction. Only by accurately locating these elements can gasoline data be further obtained.

[0135] 2. Data Extraction and Transformation: The extracted webpage and text data are transformed to conform to specific formats and requirements, such as converting strings like prices, oil types, and dates into numerical formats for subsequent storage, analysis, and application. Simultaneously, it's necessary to handle missing or anomaly data to ensure data quality and integrity.

[0136] 3. Automatic Timing and Trigger Mechanism: The frequency and timing of automatic gasoline data acquisition can be set according to actual needs, such as hourly or daily acquisition. For example, a trigger condition can be set to automatically acquire the latest gasoline price information when gasoline prices change, ensuring the timeliness and accuracy of the acquired data. Simultaneously, a focus is placed on data accuracy verification and anomaly handling strategies. To ensure data reliability, the adopted data verification and anomaly handling methods are practical.

[0137] Those skilled in the art should know that:

[0138] Currently, gasoline data is primarily obtained through manual entry into the system. This method presents significant data risks and efficiency bottlenecks, making it difficult to achieve real-time intelligent settlement of freight costs and real-time updates to transportation plans. Manually entering gasoline data embeds high-risk, low-efficiency, and poorly controlled manual operations into core business processes. In a market environment characterized by frequent fluctuations in crude oil futures and regional price wars, its technological vulnerability will directly translate into business losses through a chain of transmission, including data distortion, delayed decision-making, customer churn, and profit collapse.

[0139] First, there is the issue of data accuracy. Manual data entry may result in incorrect numbers or decimal point positions, causing serious deviations in gasoline data, leading to cost accounting errors, settlement disputes, and ultimately affecting the company's cost analysis, transportation plans, and pricing strategies.

[0140] Second, data timeliness is lagging. Manual data entry requires manual collection and processing of gasoline data before input, making it impossible to automate real-time data acquisition and updates. This results in gasoline data within the system lagging behind actual market demand. When gasoline data fluctuates frequently, manual updates are insufficient to reflect the latest market changes. Lagging gasoline data affects management's judgment and decision-making regarding market trends, leading to a failure to adjust transportation plans or cost budgets in a timely manner when gasoline data changes, ultimately harming company profits.

[0141] Third, operational efficiency bottlenecks: Manual data entry requires dedicated personnel to regularly collect, verify, and input gasoline data, increasing labor costs and workload. The data entry process is relatively cumbersome, involving multiple steps such as data collection, organization, input, and verification, which takes a long time and reduces work efficiency.

[0142] Fourth, data security vulnerabilities: Manual data entry carries the risk of human tampering, impacting business decisions and operations. Auditing and tracing are challenging, making it difficult to accurately determine the source and history of data changes, hindering data traceability and accountability.

[0143] Initially, RPA technology focused on specific industries and scenarios, such as data entry in the financial industry and customer service support in the telecommunications industry. It was primarily used by enterprises to automate repetitive tasks like clicking mice, typing text, and reading and writing files, thereby improving work efficiency and reducing employee workload. This gradually gained market recognition and adoption. As the technology matured, RPA gradually combined with advanced technologies such as artificial intelligence and machine learning, enabling it to handle more complex business processes and tasks. Its application scope expanded to multiple industries, including finance, insurance, manufacturing, and retail, and it became deeply integrated with various technologies. On one hand, RPA's integration with AI (Artificial Intelligence) technologies became closer. Technologies such as Natural Language Processing (NLP), Optical Character Recognition (OCR), and machine learning enabled RPA to handle unstructured data and complex logical judgments, greatly expanding its application scenarios. On the other hand, hyperautomation technology became a new trend. RPA integrated with Business Process Management (BPM), low-code platforms, and other technologies to achieve end-to-end process automation, covering a wider range of business scenarios. Meanwhile, the RPAaaS (Robotic Process Automation as a Service) model has emerged, providing RPA technology as a cloud service to users. It has advantages such as flexibility, scalability and cost-effectiveness, which has further promoted the popularization and application of RPA.

[0144] Therefore, by fully integrating PRA technology, this invention can achieve the following effects:

[0145] By leveraging RPA technology, efficient, accurate, and compliant gasoline data management can be achieved, reducing enterprise operating costs, improving user experience, and ultimately enhancing market competitiveness. The introduction of RPA technology enables technological upgrades, constructing a closed-loop technology system of "automated data collection and trusted data storage," solving the problem of manual data entry, and achieving a transformation from "manual-driven" to "data-driven" approaches. This helps enterprises respond quickly in a market where gasoline data fluctuates frequently.

[0146] Simulating manual operation, it can strictly execute tasks according to preset data collection rules, automatically retrieving gasoline data from the designated PetroChina website without human intervention. This automated data collection avoids errors caused by human negligence, reduces error rates, achieves zero-error data entry, and ensures data standardization and accuracy. It supports real-time monitoring of oil price changes, automatically updating relevant systems with the latest gasoline data to ensure timeliness and consistency. Dynamic gasoline data acquisition improves work efficiency, freeing employees from repetitive data entry tasks so they can focus on high-value tasks such as data analysis and customer service optimization. RPA's lightweight deployment requires no additional hardware investment and can seamlessly integrate with existing systems, reducing costs. Detailed logs are recorded for each data collection, including complete operation logs such as time and source, ensuring the compliance and traceability of the gasoline data collection process. This enables full lifecycle recording and risk control of operational behavior, meeting audit requirements. Direct database connection reduces manual intervention, lowers the risk of data tampering, and enhances compliance and audit capabilities. The dynamic gasoline data acquisition process is transparent in real time, integrating the latest gasoline data into freight calculations and transportation plans, avoiding customer disputes and complaints caused by inaccurate transportation plans.

[0147] Example 2:

[0148] like Figure 5 As shown, the present invention also provides a transportation planning adjustment system for robotic process automation, comprising:

[0149] The data acquisition module is used to use robotic process automation technology to crawl data from the gasoline information page across platforms and obtain the data information of the information page.

[0150] The gasoline data verification module is used to verify the gasoline data in the data information based on the data information and the data fluctuation range, and to obtain the current accurate gasoline data.

[0151] The reading module is used to read basic transportation information of gasoline, benchmark gasoline data, and set transportation risk sharing coefficients from transportation contracts based on natural language processing technology and optical character recognition technology.

[0152] The resource loss data calculation module is used to estimate the resource loss data generated during transportation based on the basic transportation information, the current accurate gasoline data, the transportation risk sharing coefficient, and the benchmark gasoline data, using the loss calculation engine.

[0153] The adjustment module is used to adjust the transportation plan using the resource depletion data and the current accurate gasoline data.

[0154] Furthermore, the gasoline data verification module is specifically used for:

[0155] Compare the gasoline data in the data information with the data fluctuation range;

[0156] If the gasoline data does not exceed the data fluctuation range, then the gasoline data is marked as the current accurate gasoline data;

[0157] If the gasoline data exceeds the data fluctuation range, the gasoline data is marked as suspicious gasoline data. Based on the suspicious gasoline data, robotic process automation technology is used to re-collect data information until the gasoline data in the re-collected data information does not exceed the data fluctuation range. Then, the gasoline data in the re-collected data information is marked as the current accurate gasoline data.

[0158] Furthermore, the system also includes a data fluctuation range setting module, used for:

[0159] Obtain historical data and market conditions for oil product usage;

[0160] Based on the aforementioned market conditions, set the data fluctuation range;

[0161] Based on the historical data and the data fluctuation amplitude, the data fluctuation range is determined.

[0162] Furthermore, the resource loss data satisfies the following formula:

[0163]

[0164] In the above formula: F represents resource loss data, F0 represents basic transportation information, P1 represents current accurate gasoline data, P0 represents baseline gasoline data, and α represents transportation risk sharing coefficient.

[0165] Furthermore, the acquisition module is specifically used for:

[0166] The elements in the robotic process automation technology are associated with the information page according to the preset element positioning rules to obtain the association relationship;

[0167] The pre-built exception handling strategy and preset trigger conditions are used as data collection constraints. According to the preset collection frequency, the correlation relationship and the collection constraints, the robotic process automation technology is used to crawl the gasoline information page across platforms to obtain the data information of the information page.

[0168] The anomaly handling strategy is constructed based on the acquired historical data, historical network anomaly information, and historical data fluctuation range.

[0169] Furthermore, the system also includes an exception handling strategy construction module, used for:

[0170] Based on the historical data structure of the acquired historical data information, it is determined whether the historical data structure of the historical data information has changed; when the historical data structure of the historical data information has changed or the historical network abnormal information is identified, the robot process automation technology will stop collecting data information from the information page; otherwise, the robot process automation technology will continue to collect data information from the information page, as the first abnormality handling sub-strategy.

[0171] Based on the historical data fluctuation range, determine whether the historical data information exceeds the historical data fluctuation range; when the historical data information exceeds the historical data fluctuation range, correct the historical data information to not exceed the historical data fluctuation range, otherwise do not correct the historical data information, as a second anomaly handling sub-strategy;

[0172] When historical city data or historical data information of gasoline is missing in the acquired historical data information, it will be recorded and a manual review process will be initiated; otherwise, it will be recorded but no manual review process will be initiated. This will be the third sub-strategy for handling anomalies.

[0173] The first exception handling sub-strategy, the second exception handling sub-strategy, and the third exception handling sub-strategy are used as exception handling strategies.

[0174] Furthermore, the data information includes: gasoline data, gasoline product name data, gasoline city data, gasoline price data, and gasoline price effective date data.

[0175] Furthermore, the acquisition module includes:

[0176] The conversion submodule is used to perform format standardization conversion on the gasoline data, gasoline product name data, gasoline city data, gasoline price data, and gasoline price effective date data in the data information; the format standardization conversion includes: data type conversion, decimal place standardization conversion; and / or

[0177] The parsing submodule is used to analyze the reason for the change in gasoline data and the effective date of the change in gasoline data when the gasoline data in the data information changes, using the robotic process automation technology combined with optical character recognition technology or natural language processing technology.

[0178] Furthermore, the system also includes a visual audit chain formation module, used for:

[0179] The data information, data acquisition operation steps, and network status information during acquisition are recorded in detail.

[0180] The data, operation steps, and network status information are processed using end-to-end log processing technology to form an end-to-end data acquisition log.

[0181] The entire data collection logs are transformed into a visual audit chain using audit data transformation technology.

[0182] Example 3:

[0183] like Figure 6 As shown, the present invention also provides an electronic device, which may be a computer device, a microcontroller device, a smart mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, processor, and transceiver component are connected via a bus; the memory can be used to store executable programs, and an exemplary executable program may include instructions; the processor is used to execute the instructions stored in the memory. The memory can also be used to store data, which can be accessed and / or modified when instructions are executed.

[0184] The processor may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and it is suitable for implementing one or more instructions. Specifically, it is suitable for loading and executing one or more instructions in the storage medium to realize the corresponding method flow or corresponding function, so as to realize the steps of the transportation plan adjustment method for robot process automation in the above embodiment.

[0185] Example 4:

[0186] Based on the same inventive concept, this invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory). This readable storage medium is a memory device within an electronic device used to store programs and data. It is understood that the storage medium here can include both built-in storage media within the electronic device and extended storage media supported by the electronic device. The storage medium provides storage space, which stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more executable programs (including program code). It should be noted that the storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. Loading and executing one or more instructions stored in the storage medium by the processor can implement the steps of the robotic process automation transportation plan adjustment method described in the above embodiments.

[0187] Those skilled in the art will understand that embodiments of this invention can be provided as methods, systems, or computer program products. Therefore, this invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this invention 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.

[0188] This invention application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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.

[0189] 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 1The function specified in one or more boxes.

[0190] 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.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading the present invention, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims pending approval.

Claims

1. A method for adjusting transportation plans in robotic process automation, characterized in that, include: Robotic process automation (RPA) technology is used to crawl data from the gasoline information page across platforms to obtain the data information of the page. Based on the data information and the data fluctuation range, the gasoline data in the data information is verified using data verification rules to obtain the current accurate gasoline data; Based on natural language processing and optical character recognition technologies, basic transportation information, benchmark gasoline data, and transportation risk sharing coefficients are read from transportation contracts. Using a loss calculation engine, based on the basic transportation information, the current accurate gasoline data, the transportation risk sharing coefficient, and the baseline gasoline data, the resource loss data generated during transportation is estimated. The transportation plan is adjusted using the resource depletion data and the current accurate gasoline data.

2. The method according to claim 1, characterized in that, The step of verifying the gasoline data in the data information based on the data information and the data fluctuation range, and obtaining the current accurate gasoline data using data verification rules, includes: Compare the gasoline data in the data information with the data fluctuation range; If the gasoline data does not exceed the data fluctuation range, then the gasoline data is marked as the current accurate gasoline data; If the gasoline data exceeds the data fluctuation range, the gasoline data is marked as suspicious gasoline data. Based on the suspicious gasoline data, robotic process automation technology is used to re-collect data information until the gasoline data in the re-collected data information does not exceed the data fluctuation range. Then, the gasoline data in the re-collected data information is marked as the current accurate gasoline data.

3. The method according to claim 2, characterized in that, The process of setting the data fluctuation range includes: Obtain historical data and market conditions for oil product usage; Based on the aforementioned market conditions, set the data fluctuation range; Based on the historical data and the data fluctuation amplitude, the data fluctuation range is determined.

4. The method according to claim 1, characterized in that, The resource loss data satisfies the following formula: In the above formula: F represents resource loss data, F0 represents basic transportation information, P1 represents current accurate gasoline data, P0 represents baseline gasoline data, and α represents transportation risk sharing coefficient.

5. The method according to claim 1, characterized in that, The method employs robotic process automation (RPA) technology to perform cross-platform data scraping on the gasoline information page, obtaining the data information of the page, including: The elements in the robotic process automation technology are associated with the information page according to the preset element positioning rules to obtain the association relationship; The pre-built exception handling strategy and preset trigger conditions are used as data collection constraints. According to the preset collection frequency, the correlation relationship and the collection constraints, the robotic process automation technology is used to crawl the gasoline information page across platforms to obtain the data information of the information page. The anomaly handling strategy is constructed based on the acquired historical data, historical network anomaly information, and historical data fluctuation range.

6. The method according to claim 5, characterized in that, The pre-construction process of the exception handling strategy includes: Based on the historical data structure of the acquired historical data information, it is determined whether the historical data structure of the historical data information has changed; when the historical data structure of the historical data information has changed or the historical network abnormal information is identified, the robot process automation technology will stop collecting data information from the information page; otherwise, the robot process automation technology will continue to collect data information from the information page, as the first abnormality handling sub-strategy. Based on the historical data fluctuation range, determine whether the historical data information exceeds the historical data fluctuation range; when the historical data information exceeds the historical data fluctuation range, correct the historical data information to not exceed the historical data fluctuation range, otherwise do not correct the historical data information, as a second anomaly handling sub-strategy; When historical city data or historical data information of gasoline is missing in the acquired historical data information, it will be recorded and a manual review process will be initiated; otherwise, it will be recorded but no manual review process will be initiated. This will be the third sub-strategy for handling anomalies. The first exception handling sub-strategy, the second exception handling sub-strategy, and the third exception handling sub-strategy are used as exception handling strategies.

7. The method according to any one of claims 1-6, characterized in that, The data information includes: gasoline data, gasoline product name data, gasoline city data, gasoline price data, and gasoline price effective date data.

8. The method according to claim 7, characterized in that, After obtaining the data information of the gasoline information page by cross-platform data crawling using robotic process automation (RPA) technology, the process includes: The gasoline data, gasoline product name data, gasoline city data, gasoline price data, and gasoline price effective date data in the aforementioned data information are subjected to format standardization conversion; the format standardization conversion includes: data type conversion, decimal place standardization conversion; and / or When the gasoline data in the data information changes, the robotic process automation technology combined with optical character recognition technology or natural language processing technology is used to analyze the reason for the change in the gasoline data and the effective date of the change.

9. The method according to claim 5, characterized in that, After obtaining the data information of the gasoline information page by cross-platform data crawling using robotic process automation (RPA) technology, the process further includes: The data information, data acquisition operation steps, and network status information during acquisition are recorded in detail. The data, operation steps, and network status information are processed using end-to-end log processing technology to form an end-to-end data acquisition log. The entire data collection logs are transformed into a visual audit chain using audit data transformation technology.

10. A transportation planning adjustment system for robotic process automation, characterized in that, include: The data acquisition module is used to use robotic process automation technology to crawl data from the gasoline information page across platforms and obtain the data information of the information page. The gasoline data verification module is used to verify the gasoline data in the data information based on the data information and the data fluctuation range, and to obtain the current accurate gasoline data. The reading module is used to read basic transportation information of gasoline, benchmark gasoline data, and set transportation risk sharing coefficients from transportation contracts based on natural language processing technology and optical character recognition technology. The resource loss data calculation module is used to estimate the resource loss data generated during transportation based on the basic transportation information, the current accurate gasoline data, the transportation risk sharing coefficient, and the benchmark gasoline data, using the loss calculation engine. The adjustment module is used to adjust the transportation plan using the resource depletion data and the current accurate gasoline data.

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