Power spot transaction settlement data automatic processing and intelligent pushing method based on web automation and intelligent interaction technology fusion

Abstract

The application relates to a power spot transaction settlement data automatic processing and intelligent pushing method based on the integration of Web automation and intelligent interaction technology. The method comprises the following steps: acquiring static power settlement data by sending a data calling request to a power business system, and acquiring dynamic power settlement data by simulating page interaction operation in the power business system; determining power spot transaction settlement data according to the static power settlement data and the dynamic power settlement data, and extracting user account information configured in a target communication application program from the power spot transaction settlement data; pushing the power spot transaction settlement data to a target receiving account corresponding to the user account information by simulating user interface interaction operation in the target communication application program; and processing the power spot transaction settlement data by acquiring a business processing message returned by the target receiving account. The method can improve the efficiency of power spot transaction settlement processing.

Description

Technical Field

[0001] This application relates to the field of automation technology for electricity market transaction settlement, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies. Background Technology

[0002] With the continued advancement of market-oriented reforms in the energy sector, the scale and complexity of electricity trading have increased dramatically in order to improve the operational efficiency of the electricity market and promote the full participation of industrial and commercial users in the electricity market.

[0003] The current electricity spot market is characterized by multiple participants, high frequency, strong real-time requirements, and complex settlement rules. Transaction settlement data is scattered across multiple heterogeneous business systems, such as user transaction files, daily electricity accumulation verification, and daily electricity accounting and monitoring. Furthermore, most of these systems are restricted by security controls and do not have standardized data interfaces, making direct data integration impossible. Electricity spot transaction settlement data encompasses multiple dimensions of core information, including transaction entity ID, transaction period, transaction volume, settlement price, and deviation electricity volume. The data format exhibits significant heterogeneity (coexistence of tabular data and nested form data), and some data requires multiple page operations (clicking to query, switching tabs, and entering filter conditions) to obtain.

[0004] Currently, the mainstream processing mode in the industry still relies on manual operation: operators need to log in to various business systems, manually query and operate relevant data, then use Excel or simple tools to organize and calculate the data, perform system operations, and finally manually select recipients and copy and paste settlement information through the application to complete message push. This results in low efficiency in the settlement processing of electricity spot transactions. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies, which can improve the efficiency of electricity spot transaction settlement processing and improve the efficiency of electricity spot transaction settlement processing.

[0006] Firstly, this application provides a method for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies, including:

[0007] Static electricity settlement data is obtained by sending a data request to the electricity business system, and dynamic electricity settlement data is obtained by simulating page interaction operations in the electricity business system.

[0008] Based on the static electricity settlement data and the dynamic electricity settlement data, determine the electricity spot market transaction settlement data, and extract the user account information configured in the target communication application from the electricity spot market transaction settlement data;

[0009] By simulating user interface interaction operations in the target communication application, the electricity spot transaction settlement data is pushed to the target receiving account corresponding to the user account information;

[0010] Obtain the business processing message returned by the target receiving account, and process the electricity spot transaction settlement data according to the task type indicated by the business processing message.

[0011] In one embodiment, obtaining dynamic electricity settlement data by simulating page interaction operations in the power business system includes:

[0012] Identify the DOM nodes of the target business page in the power business system;

[0013] The node score of the DOM node is determined based on the matching degree between the actual node characteristics and the preset node characteristics.

[0014] When the node rating value is greater than or equal to a preset threshold, the page interaction operation is simulated on the DOM node to obtain the dynamic electricity settlement data;

[0015] If the node score is less than the preset threshold, the process returns to the step of identifying the DOM node of the target business page in the power business system.

[0016] In one embodiment, the actual node features include an actual node identifier, actual node attributes, actual node path, and actual node text; the preset node features include a preset node identifier, preset node attributes, preset node path, and preset node text; determining the node score value of the DOM node based on the matching degree between the actual node features and the preset node features includes:

[0017] Obtain the identifier matching degree between the actual node identifier and the preset node identifier, the attribute matching degree between the actual node attribute and the preset node attribute, the path matching degree between the actual node path and the preset node path, and the text matching degree between the actual node text and the preset node text;

[0018] The node score is obtained by weighted summing of the identifier matching degree, the attribute matching degree, the path matching degree, and the text matching degree.

[0019] In one embodiment, obtaining static electricity settlement data by sending a data request to the electricity business system includes:

[0020] Obtain the encryption function of the power business system and the associated function corresponding to the encryption function; the associated function is other functions that the encryption function needs to call when it runs.

[0021] Based on the encryption function and the association function, a reverse script is generated; the reverse script is a script file used to perform encryption processing.

[0022] The script file is invoked to encrypt the parameters of the request to be encrypted, thereby obtaining the encrypted parameters, and a data call request carrying the encrypted parameters is sent to the power business system.

[0023] The system receives data interface response data returned by the power business system in response to the data call request, decrypts the data interface response data, and obtains the static power settlement data.

[0024] In one embodiment, the step of pushing the electricity spot transaction settlement data to the target receiving account corresponding to the user account information by simulating user interface interaction operations in the target communication application includes:

[0025] Image recognition is performed on the user interface of the target communication application to locate the search control in the user interface;

[0026] The user account information is input into the search control to perform a search, and the target receiving account is determined from the search results;

[0027] Simulate a click operation on the message editing control corresponding to the target receiving account, and paste the electricity spot transaction settlement data into the message editing control;

[0028] The send control corresponding to the message editing control is simulated to click, and the electricity spot transaction settlement data is pushed to the target receiving account corresponding to the user account information.

[0029] In one embodiment, determining the electricity spot transaction settlement data based on the static electricity settlement data and the dynamic electricity settlement data includes:

[0030] Extract the relevant fields from the static electricity settlement data and the dynamic electricity settlement data;

[0031] Based on the associated fields, the static electricity settlement data and the dynamic electricity settlement data are integrated to obtain the integrated data;

[0032] Based on preset electricity spot settlement data cleaning rules, outliers and missing values ​​in the integrated data are processed to obtain the electricity spot transaction settlement data.

[0033] Secondly, this application also provides a device for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies, comprising:

[0034] The acquisition module is used to acquire static electricity settlement data by sending a data call request to the power business system, and to acquire dynamic electricity settlement data by simulating page interaction operations in the power business system.

[0035] The extraction module is used to determine electricity spot transaction settlement data based on the static electricity settlement data and the dynamic electricity settlement data, and to extract user account information configured in the target communication application from the electricity spot transaction settlement data;

[0036] The push module is used to push the electricity spot transaction settlement data to the target receiving account corresponding to the user account information by simulating user interface interaction operations in the target communication application.

[0037] The processing module is used to obtain the business processing message returned by the target receiving account and process the electricity spot transaction settlement data according to the task type indicated by the business processing message.

[0038] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0039] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0040] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0041] The aforementioned method, apparatus, computer equipment, computer-readable storage medium, and computer program product for automated processing and intelligent push of electricity spot transaction settlement data, based on the integration of Web automation and intelligent interaction technologies, acquires static electricity settlement data by sending data call requests to the electricity business system, and acquires dynamic electricity settlement data by simulating page interaction operations in the electricity business system; determines electricity spot transaction settlement data based on the static and dynamic electricity settlement data, and extracts user account information configured in the target communication application from the electricity spot transaction settlement data; pushes the electricity spot transaction settlement data to the target receiving account corresponding to the user account information by simulating user interface interaction operations in the target communication application; acquires business processing messages returned by the target receiving account, and processes the electricity spot transaction settlement data according to the task type indicated by the business processing messages. In this way, it can automatically acquire static and dynamic data scattered across multiple electricity business systems, integrate them into complete electricity spot transaction settlement data, and push the data to the target receiving account through simulated user interface interaction operations. Simultaneously, it can acquire feedback messages from the target receiving account and process them accordingly, forming a closed-loop data processing and push mechanism, thus improving the efficiency of electricity spot transaction settlement processing. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is an application environment diagram of a method for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies in one embodiment.

[0044] Figure 2 This is a flowchart illustrating a method for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies in one embodiment.

[0045] Figure 3 This is a logic diagram of a method for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies in one embodiment;

[0046] Figure 4This is a flowchart illustrating the structure of a device for an automated processing and intelligent push method for electricity spot trading settlement data based on the integration of Web automation and intelligent interaction technologies, as shown in one embodiment.

[0047] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] The method for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server.

[0050] Terminal 102 obtains static electricity settlement data by sending a data call request to the electricity business system, and obtains dynamic electricity settlement data by simulating page interaction operations in the electricity business system; based on the static and dynamic electricity settlement data, terminal 102 determines the electricity spot transaction settlement data, and extracts the user account information configured in the target communication application from the electricity spot transaction settlement data; terminal 102 pushes the electricity spot transaction settlement data to the target receiving account corresponding to the user account information by simulating user interface interaction operations in the target communication application; terminal 102 obtains the business processing message returned by the target receiving account, and processes the electricity spot transaction settlement data according to the task type indicated by the business processing message.

[0051] The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, and IoT devices. The server 104 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server providing cloud computing services.

[0052] In one exemplary embodiment, such as Figure 2 As shown, a method for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies is provided. This method is applied to... Figure 1 Taking terminal 102 as an example, the explanation includes:

[0053] Step S202: Obtain static electricity settlement data by sending a data call request to the electricity business system, and obtain dynamic electricity settlement data by simulating page interaction operations in the electricity business system.

[0054] The power business system is an information system used to manage the settlement of electricity spot transactions, and it can run on one or more servers. For example, the power business system may include, but is not limited to, a user transaction file system, a spot daily electricity accumulation verification system, and a spot daily electricity accounting and monitoring system.

[0055] Among them, the data retrieval request can be a request sent by the data interface of the power business system to obtain data. This request can carry identity authentication information and query parameters.

[0056] Static electricity settlement data refers to structured data that does not rely on dynamic page rendering and can be directly obtained through data interfaces, such as information stored in tabular form, including transaction entity identification, transaction period, transaction volume, and settlement price.

[0057] Interactive page actions may include, but are not limited to, clicking, entering text, selecting from dropdown menus, and page navigation.

[0058] Dynamic electricity settlement data can refer to data that can only be obtained through page interaction, such as nested form data or pop-up detailed data that can only be displayed after clicking a query button, switching tabs, and entering filter conditions.

[0059] In practice, when obtaining static electricity settlement data, the terminal can construct a data call request containing identity authentication information, send the request to the data interface of the power business system, receive the response data returned by the power business system, and parse the static electricity settlement data from the response data.

[0060] When acquiring dynamic electricity settlement data, the terminal can launch a browser engine, access the power business system's page in the browser engine, identify interactive controls on the page, perform click, input, and other operations on the interactive controls, trigger the page's data loading, and extract the dynamic electricity settlement data from the loaded page.

[0061] As an optional implementation, acquiring static and dynamic electricity settlement data can be achieved through a hybrid data acquisition module. To address technical challenges such as multi-system dynamic data acquisition under non-open interfaces, page update adaptation, and acquisition failures caused by encrypted function parameters and dynamic signature verification, a collaborative optimization approach can be adopted. This involves sending data call requests to the power business system (e.g., via the requests library) and simulating page interaction (e.g., via the Selenium tool). Combined with reverse engineering techniques (e.g., JavaScript reverse engineering), this approach enables comprehensive and efficient acquisition of both static and dynamic electricity settlement data, while simultaneously overcoming system encryption protection limitations. Specifically, the following steps can be included:

[0062] (1) System initialization configuration. A dynamically adjustable configuration file is pre-built, which includes: system login information (such as username and password), collection frequency (e.g., can be set to 10 minutes / time or 30 minutes / time to adapt to real-time requirements), data storage path, number of retry attempts in case of exception, etc. At the same time, the configuration file has built-in reverse parsing execution function, script path and reverse environment configuration. The configuration file supports manual modification and automatic synchronization update, thereby reducing maintenance costs.

[0063] (2) Data Acquisition Authentication and Encryption Preprocessing. A simulated browser (e.g., using the Selenium tool) is used to launch and access the login pages of various power business systems, calling the login information in the configuration file and automatically inputting the username and password. For some power business system data acquisition requests that need encryption (e.g., dynamically generated tokens), reverse engineering techniques (e.g., JS reverse engineering) are introduced to parse the encryption logic. The specific implementation is as follows:

[0064] Tool Selection and Environment Setup: Obtain the encryption functions and their corresponding associated functions from the power business system. Associated functions are other functions that the encryption functions need to call during runtime. Specifically, appropriate reverse engineering environments and tools can be used (e.g., using the PyExecJS library in conjunction with a Node.js environment, and debugging with browser developer tools) to debug and analyze the encrypted files (such as encrypted JS files) of the target power business system, locate the encryption functions (such as encryptPassword, generateSignature, etc.), and extract the encryption logic and dependent associated function modules (such as JS modules), avoiding compatibility issues caused by directly cracking the encryption algorithm.

[0065] Reverse engineering of encrypted parameters: Based on the encryption function and associated function, a reverse script is generated. The reverse script is a separate script file (such as a separate .js file) used to perform encryption processing. The script file is called in the program, passing in the original parameters, encrypting the parameters to be encrypted, and obtaining the encrypted parameters (such as the dynamic token and the generated signature parameters), ensuring that the data request parameters sent to the system are compliant.

[0066] Dynamic parameter real-time update: To address the parameter validity period limitations in some systems (such as token expiration), the reverse script is periodically called in the data collection program to regenerate the encrypted parameters and update them in the data call request, thus avoiding data collection interruptions due to verification failure.

[0067] CAPTCHA processing: CAPTCHAs are verified using CAPTCHA recognition technology. Upon successful verification, session information is saved for subsequent data collection operations. Session encryption is also used to prevent access control leaks.

[0068] (3) Collaborative data collection logic. A collaborative strategy of "static priority, dynamic completion, and reverse adaptation" is adopted to optimize the timing of data call requests and simulated page interaction operations. Combined with reverse technology (such as JS reverse technology) to break through encryption restrictions and improve data collection efficiency.

[0069] Obtaining static electricity settlement data: A data call request carrying encrypted parameters and login session information is sent to the electricity business system (e.g., via an HTTP GET / POST request using the requests library). The system receives the data interface response data from the electricity business system in response to the data call request. For response data with encryption (e.g., JavaScript decryption of response data), the decryption logic is parsed using a reverse script to decrypt the data interface response data, obtaining the static electricity settlement data. Subsequently, the page data (table data and text comments) is parsed, converted to the target data format, and stored in the original database.

[0070] Acquiring dynamic electricity settlement data: For dynamically rendered data and pop-up detail data (such as dynamic data rendered by JS), dynamic data is extracted by simulating page interaction operations in the power business system (such as using Selenium to simulate browser page jumps, clicking query buttons, entering filter conditions, opening pop-ups, etc.). If encryption verification (such as request parameter signature) is required during the loading of dynamic electricity settlement data, a reverse script is called to generate compliant encrypted parameters and inject them into the request of the simulated page interaction operation to ensure the normal loading of dynamic electricity settlement data. After extraction, the dynamic electricity settlement data is merged with static electricity settlement data, and missing fields are supplemented to ensure data integrity.

[0071] Step S204: Based on static electricity settlement data and dynamic electricity settlement data, determine electricity spot transaction settlement data, and extract user account information configured in the target communication application from the electricity spot transaction settlement data.

[0072] Among them, electricity spot trading settlement data can refer to a data set that has been integrated and processed and contains all the information required for electricity spot trading settlement. This data set can cover information from multiple dimensions such as trading entity identification, trading period, trading volume, settlement price, and deviation electricity.

[0073] Among them, the target communication application can refer to communication software used to push settlement data to relevant personnel, such as instant messaging applications used within an enterprise.

[0074] User account information can refer to information that identifies the recipient in the target communication application, such as user nickname, account identifier, etc.

[0075] In practice, the terminal can extract common fields from static and dynamic electricity settlement data, and integrate the two types of data based on these common fields to obtain electricity spot transaction settlement data containing complete information. The terminal can extract fields associated with the recipient from the electricity spot transaction settlement data, such as the transaction entity identifier and the department to which it belongs, and map these fields to user account information in the target communication application according to a preset mapping relationship.

[0076] Step S206: By simulating user interface interaction in the target communication application, the electricity spot transaction settlement data is pushed to the target receiving account corresponding to the user account information.

[0077] User interface interactions may include, but are not limited to, clicking controls, entering text, and pasting data. The target receiving account may refer to the recipient's account corresponding to the user's account information in the target communication application.

[0078] In practice, the terminal can launch the target communication application, locate the various functional controls in the user interface of the target communication application, click on the search control and enter user account information to search, determine the target receiving account from the search results, click on the message editing control and paste the electricity spot transaction settlement data into the message editing control, and click on the send control to complete the data push.

[0079] Step S208: Obtain the business processing message returned by the target receiving account, and process the electricity spot transaction settlement data according to the task type indicated by the business processing message.

[0080] Among these, business processing messages can refer to feedback information returned by the target receiving account in response to received electricity spot transaction settlement data. This feedback information may contain instructions for specific data processing. Task types may include, but are not limited to, data verification tasks, data supplementation tasks, and data confirmation tasks.

[0081] In practice, the terminal can monitor messages from the target receiving account in the target communication application, extract the text content of the messages, identify keywords indicating the task type from the text content, and trigger the corresponding data processing flow according to the task type. For example, when the task type is a data supplementation task, the terminal can re-execute the data collection process, obtain supplementary data, and push it again; when the task type is a data confirmation task, the terminal can mark the corresponding settlement data as confirmed.

[0082] The aforementioned method for automated processing and intelligent push of electricity spot transaction settlement data, based on the integration of Web automation and intelligent interaction technologies, obtains static electricity settlement data by sending data call requests to the electricity business system and obtains dynamic electricity settlement data by simulating page interaction operations in the electricity business system. Based on the static and dynamic electricity settlement data, the method determines the electricity spot transaction settlement data and extracts the configured user account information from the electricity spot transaction settlement data in the target communication application. By simulating user interface interaction operations in the target communication application, the method pushes the electricity spot transaction settlement data to the target receiving account corresponding to the user account information. The method then obtains the business processing message returned by the target receiving account and processes the electricity spot transaction settlement data according to the task type indicated in the business processing message. This approach automatically acquires static and dynamic data scattered across multiple electricity business systems, integrates them into complete electricity spot transaction settlement data, and pushes the data to the target receiving account through simulated user interface interaction operations. Simultaneously, it obtains feedback messages from the target receiving account and processes them accordingly, forming a closed-loop data processing and push mechanism that improves the efficiency of electricity spot transaction settlement processing.

[0083] In another embodiment, dynamic electricity settlement data is obtained by simulating page interaction operations in the power business system, including: identifying DOM nodes of the target business page in the power business system; determining the node score value of the DOM node based on the matching degree between the actual node characteristics and preset node characteristics; simulating page interaction operations on the DOM node to obtain dynamic electricity settlement data when the node score value is greater than or equal to a preset threshold; and returning to the step of identifying the DOM node of the target business page in the power business system when the node score value is less than the preset threshold.

[0084] In this context, a DOM node refers to a node element in the Document Object Model (DOM), which can represent various controls or data display areas on a page. The target business page can refer to a page in the power business system that contains the dynamic power settlement data to be acquired.

[0085] In practice, the terminal can load the target business page through the browser engine, obtain the DOM structure of the page, extract the information of each node from the DOM structure, and identify the DOM nodes in the page.

[0086] In this context, actual node features refer to the characteristic attributes possessed by the currently identified DOM nodes. Preset node features refer to the pre-stored characteristic attributes used to locate the nodes containing the target data.

[0087] The matching degree can refer to the similarity between the actual node features and the preset node features.

[0088] Among them, the node score can be used to quantitatively evaluate whether the current node is the target node.

[0089] In practice, the terminal can extract the actual node features of the DOM node, compare the actual node features with the pre-stored preset node features, calculate the matching degree of each feature dimension, and calculate the comprehensive node score based on the matching degree of each feature dimension.

[0090] The preset threshold can be used to determine whether a node is a critical value for the target node.

[0091] When the node rating is greater than or equal to a preset threshold, the terminal can determine that the DOM node is the target node, perform interactive operations such as clicking and input on the DOM node, trigger the page to load dynamic data, and extract dynamic electricity settlement data from the loaded page.

[0092] When a node's score is less than a preset threshold, the terminal can determine that the DOM node is not the target node, continue to identify other DOM nodes on the page, and repeat the process of feature matching and score calculation until a node with a score greater than or equal to the preset threshold is found.

[0093] The technical solution of this embodiment determines whether a node is a target node by calculating the node score value of the DOM node. It can adapt to the changes in node characteristics caused by page updates in the power business system. When some characteristics of a node in the page change, as long as the comprehensive score value still meets the threshold condition, the target node can be correctly located, thus improving the robustness of node identification.

[0094] In another embodiment, the actual node features include an actual node identifier, actual node attributes, actual node path, and actual node text; the preset node features include a preset node identifier, preset node attributes, preset node path, and preset node text; the node score value of the DOM node is determined based on the matching degree between the actual node features and the preset node features, including: obtaining the identifier matching degree between the actual node identifier and the preset node identifier, the attribute matching degree between the actual node attributes and the preset node attributes, the path matching degree between the actual node path and the preset node path, and the text matching degree between the actual node text and the preset node text; the identifier matching degree, attribute matching degree, path matching degree, and text matching degree are weighted and summed to obtain the node score value.

[0095] Among them, the actual node identifier can refer to the id attribute value of the DOM node; the preset node identifier can refer to the id attribute value of the target node that is stored in advance; the identifier matching degree can be used to quantify the similarity between the actual node identifier and the preset node identifier.

[0096] Among them, actual node attributes can refer to the class attribute value of DOM nodes; preset node attributes can refer to the class attribute value of pre-stored target nodes; attribute matching degree can be used to quantify the similarity between actual node attributes and preset node attributes.

[0097] The actual node path can refer to the path of a DOM node in the DOM tree, such as an XPath path; the preset node path can refer to the path of a target node that has been stored in advance; and the path matching degree can be used to quantify the similarity between the actual node path and the preset node path.

[0098] Among them, actual node text can refer to the text content contained in the DOM node; preset node text can refer to the text content of the target node that is stored in advance; and text matching degree can be used to quantify the similarity between actual node text and preset node text.

[0099] The terminal can compare the actual node identifier with the preset node identifier to calculate the identifier matching degree. For example, when the actual node identifier and the preset node identifier are exactly the same, the identifier matching degree is 1; when the actual node identifier and the preset node identifier are partially the same, the identifier matching degree is 0.5; when the actual node identifier and the preset node identifier are completely different, the identifier matching degree is 0.

[0100] The terminal can compare the actual node attributes with the preset node attributes to calculate the attribute matching degree. For example, when the actual node attributes are exactly the same as the preset node attributes, the attribute matching degree is 1; when the actual node attributes contain the preset node attributes, the attribute matching degree is 0.6; and when the actual node attributes are completely different from the preset node attributes, the attribute matching degree is 0.

[0101] The terminal can compare the actual node path with the preset node path to calculate the path matching degree. For example, when the actual node path and the preset node path are exactly the same, the path matching degree is 1; when the actual node path and the preset node path have the same hierarchical structure but some node names are different, the path matching degree is 0.7; when the actual node path and the preset node path are completely different, the path matching degree is 0.

[0102] The terminal can compare the actual node text with the preset node text to calculate the text matching degree. For example, when the actual node text contains keywords from the preset node text, the text matching degree is 1; when the actual node text partially contains keywords from the preset node text, the text matching degree is 0.4; and when the actual node text does not contain keywords from the preset node text, the text matching degree is 0.

[0103] The terminal can set weight coefficients for identifier matching degree, attribute matching degree, path matching degree and text matching degree respectively, and then sum them up after multiplying each matching degree with the corresponding weight coefficient to obtain the node score value.

[0104] For example, in the process of acquiring dynamic electricity settlement data, a Document Object Model (DOM) node intelligent recognition algorithm is introduced to solve the problem of existing data acquisition technologies failing after page updates. It also adapts to JavaScript (JS) encrypted logic update scenarios. The node score value can be represented as:

[0105] S = α*S id + β*S class + γ*Sx path + δ*S text ;

[0106] Where S is the node rating of the DOM node, ranging from 0 to 1. 0.7 can be determined as greater than or equal to a preset threshold. This preset threshold can be adjusted through configuration to adapt to the page characteristics of different systems.

[0107] Among them, S id The identification matching degree between the actual node identifier and the preset node identifier is calculated as follows: 1 point for a complete match, 0.5 points for a partial match, and 0 points for no match. This is used to accurately locate nodes with fixed identifiers (IDs).

[0108] Among them, S class The attribute matching degree between the actual node attributes and the preset node attributes is calculated as follows: 1 point for a perfect match, 0.6 points for including the target class attribute, and 0 points for no match. The matching node has a fixed class attribute but a changing ID.

[0109] Among them, Sx path This represents the path matching degree between the actual node path and the preset node path. For example, a perfect match scores 1 point, a hierarchical match scores 0.7 points, and a mismatch scores 0 points. It is suitable for scenarios where the node hierarchy is fixed but the identity identifier (ID) and class (class) change. Here, xpath is Extensible Markup Language (XML Path Language).

[0110] Among them, S text The text matching degree between the actual node text and the preset node text is calculated as follows: 1 point is awarded for containing the target keyword, 0.4 points are awarded for partially containing the target keyword, and 0 points are awarded for not containing it. It is suitable for text nodes without identity identifiers (ID) or classes, such as pop-up titles and button text.

[0111] Where α, β, γ, and δ are weighting coefficients. For example, α can be set to 0.3, β to 0.25, γ to 0.3, and δ to 0.15. This weighting coefficient setting ensures that node identifiers and paths have a higher weight in the calculation of node scores, followed by node attributes, with node text having the lowest weight. This aligns with the characteristics of DOM node positioning, where identifiers and paths are highly stable, while text is prone to change. Optionally, the weighting can be optimized using a grid search algorithm, prioritizing the matching weight between the identifier (ID) and the Extensible Markup Language (XPath) path language, balancing matching accuracy and adaptation flexibility.

[0112] The node score is obtained by weighted summing the matching scores of the actual node identifier and the preset node identifier, the matching scores of the actual node attributes and the preset node attributes, the matching scores of the actual node path and the preset node path, and the matching scores of the actual node text and the preset node text. This allows for multi-dimensional matching of the target Document Object Model (DOM) node. If the node score is greater than or equal to a preset threshold (e.g., successful matching), page interaction is simulated on the DOM node to obtain dynamic electricity settlement data (e.g., data collection). If the node score is less than the preset threshold (e.g., S < 0.7, indicating a failed match), page element scanning is automatically triggered, returning to the step of identifying the DOM node of the target business page in the power business system (e.g., re-identifying the target node), and updating the node positioning parameters. If an update to the JavaScript (JS) encryption logic is detected (e.g., a change in encryption function name or addition of parameters), a JavaScript (JS) reverse script update warning is triggered, prompting manual re-debugging of the encryption logic. This eliminates the need to modify the main data collection program, achieving dual adaptation for page updates and encryption logic updates.

[0113] In the above embodiments, by calculating the node score through multi-dimensional feature matching, the matching degree of the node can be comprehensively evaluated. Even if the feature of a certain dimension changes, it can still be compensated by the feature of other dimensions, thereby improving the accuracy and stability of node identification.

[0114] Optionally, preliminary processing can be performed on the collected static and dynamic electricity settlement data (including data decrypted by JS) to unify the data format, such as standardizing dates to "YYYY-MM-DD HH:MM:SS" format, converting numeric types to floating-point types, and standardizing transaction entity identifiers (IDs) to fixed-length strings; duplicate data can also be removed, such as by using the drop_duplicates() function of the panel data analysis library (Python Data Analysis Library, pandas) to mark missing fields, providing support for subsequent data processing stages.

[0115] In another embodiment, static electricity settlement data is obtained by sending a data call request to the power business system, including: obtaining the encryption function and its corresponding associated function from the power business system; the associated function is another function that needs to be called when the encryption function runs; generating a reverse script based on the encryption function and the associated function; the reverse script is a script file used to perform encryption processing; calling the script file to encrypt the parameters to be encrypted to obtain the encrypted parameters; and sending a data call request carrying the encrypted parameters to the power business system; receiving the data interface response data returned by the power business system in response to the data call request; and decrypting the data interface response data to obtain the static electricity settlement data.

[0116] In this context, the encryption function refers to the function used by the power business system to encrypt request parameters during data transmission. The associated function refers to other functions that the encryption function needs to call during its execution; these associated functions can provide auxiliary functions or dependent data to the encryption function.

[0117] In practice, the terminal can access the power business system's page, obtain the script file loaded on the page, identify the function used for encryption processing from the script file, and extract the code of the encryption function and its associated functions.

[0118] Among them, reverse scripts can refer to script files that are extracted and encapsulated from the encryption logic of the power business system and can be executed locally. These script files can achieve the same encryption processing functions as the power business system.

[0119] The terminal can integrate the code of the encryption function and the associated function, remove the code that is not related to the encryption process, and encapsulate the integrated code into an independent script file. This script file can receive the parameters to be encrypted and return the encrypted parameters.

[0120] Here, "request parameters to be encrypted" refers to unencrypted parameters that need to be carried when sending a data retrieval request, such as query conditions and time ranges. "Encrypted parameters" refers to parameters that have undergone encryption processing.

[0121] The terminal can call the generated reverse script file, pass the request parameters to be encrypted as input to the reverse script file, the reverse script file performs encryption processing and returns the encrypted parameters, the terminal encapsulates the encrypted parameters into a data call request and sends the data call request to the power business system.

[0122] Among them, data interface response data can refer to the response data returned by the power business system in response to the data call request, and the response data may be encrypted.

[0123] The terminal can receive data interface response data returned by the power business system, identify the encryption identifier in the data interface response data, determine the decryption method based on the encryption identifier, call the decryption method to decrypt the data interface response data, and extract static power settlement data from the decrypted data.

[0124] In the above embodiments, the terminal can obtain the encryption function of the power business system and generate a reverse script, thereby generating encryption parameters that meet the requirements of the power business system locally, breaking through the encryption protection restrictions of the power business system, and realizing the acquisition of static power settlement data.

[0125] In another embodiment, power spot transaction settlement data is pushed to the target receiving account corresponding to the user account information by simulating user interface interaction operations in the target communication application. This includes: performing image recognition on the user interface of the target communication application to locate the search control in the user interface; simulating a click operation on the search control, inputting user account information into the search control to perform a search, and determining the target receiving account from the search results; simulating a click operation on the message editing control corresponding to the target receiving account to paste the power spot transaction settlement data into the message editing control; and simulating a click operation on the send control corresponding to the message editing control to push the power spot transaction settlement data to the target receiving account corresponding to the user account information.

[0126] Image recognition can refer to identifying the location and type of various controls in a user interface using image processing technology. Search controls can refer to input boxes or buttons in a user interface used to search for contacts or groups.

[0127] In practice, the terminal can obtain a screenshot of the user interface of the target communication application, perform image processing on the screenshot, identify various control areas in the screenshot, and determine the area where the search control is located based on the shape, color, position and other features of the control area, thereby locating the position coordinates of the search control.

[0128] The terminal can control the mouse to move to the location of the search control and perform a click operation based on the coordinates of the search control, thereby activating the search control. Then, it can simulate keyboard input, enter user account information into the search control, trigger the target communication application to perform a search, identify the item that matches the user account information from the search results list, and determine that the account corresponding to the item is the target receiving account.

[0129] Among them, the message editing control can refer to the text box used in the target communication application for editing message content.

[0130] The terminal can perform image recognition on the user interface of the target communication application, locate the position coordinates of the message editing control corresponding to the target receiving account, control the mouse to move to that position and perform a click operation to activate the message editing control, then copy the electricity spot transaction settlement data to the clipboard, simulate the keyboard paste operation, and paste the electricity spot transaction settlement data into the message editing control.

[0131] The send control can refer to the button in the target communication application used to send messages.

[0132] The terminal can perform image recognition on the user interface of the target communication application, locate the position coordinates of the sending control, control the mouse to move to that position and perform a click operation, triggering the target communication application to send the electricity spot transaction settlement data in the message editing control to the target receiving account.

[0133] In the above embodiments, the terminal uses image recognition technology to locate various controls in the target communication application and completes data push by simulating mouse and keyboard operations. It can adapt to the interface changes of the target communication application. Even if the position of the control changes, the control can still be repositioned through image recognition, which improves the stability of data push.

[0134] In practical applications, the target communication application can be the enterprise-level communication software ELink. To address the technical issues of the target communication application lacking automated interaction and two-way interaction capabilities, this is achieved by simulating user interface interaction operations (such as keyboard and mouse simulation technology) within the target communication application, combined with image recognition improvements. This automates the entire process of pushing electricity spot transaction settlement data to the target receiving account corresponding to the user's account information (such as targeted push), obtaining business processing messages returned by the target receiving account (such as feedback instruction retrieval), and status recognition, forming a two-way interactive closed loop. The specific implementation steps are as follows:

[0135] (1) Initialization of the interaction environment: Configure the core parameters of the target communication application, including the application installation path, contact dictionary (such as {"user nickname": "department"}, which supports manual addition, deletion and modification, and automatic synchronization and updating), message template (such as customized according to the subject type, containing standardized fields of the power spot transaction settlement data and report links), interaction frequency, number of abnormal retries, etc.; at the same time, check the running status of the target communication application, and start it automatically if it is not started to ensure normal interaction.

[0136] (2) Tool Selection and Collaboration: Keyboard and mouse simulation core tools are used, such as the Python Graphical User Interface Automation (pyautogui) tool library; image recognition tools, such as the Open Source Computer Vision Library for Python (opencv-python) tool library, are used for status recognition and control positioning; and window control positioning tools, such as the Python Windows Automation (pywinauto) tool library, are used for login window and pop-up window positioning. These three tools work together to improve the fixed-coordinate operation mode of traditional keyboard and mouse simulation, enhance adaptability, and avoid operation failures caused by changes in the application window position.

[0137] (3) Targeted message push:

[0138] a. Recipient matching: Obtain personalized electricity spot transaction settlement data and reports from the aforementioned data processing steps, extract the user account information (such as contacts) configured in the target communication application from the electricity spot transaction settlement data, query the contact dictionary, match the corresponding recipient nickname and department, if no match is found, record the exception and suspend push notifications, and manually supplement the contact information later.

[0139] b. Recipient Location and Selection: Perform image recognition on the user interface of the target communication application, such as using an open-source computer vision library (opencv-python) to identify the "search box" icon on the main interface and locate the search control in the user interface; simulate click operations on the search control, such as using a graphical user interface automation tool library (pyautogui) to simulate mouse clicks on the search box and input user account information (such as the recipient's nickname) into the search control to perform a search; then identify the relevant identifiers of the recipient's detailed information to obtain department information and compare them, determine the target receiving account from the search results, and simulate click operations to select it, avoiding selection errors caused by clicking at fixed coordinates.

[0140] c. Message Editing and Sending: The system calls the message template corresponding to the subject type and simulates click operations on the message editing control for the target receiving account. For example, it uses a graphical user interface automation tool library (pyautogui) to simulate mouse clicks on the message editing box, pasting electricity spot transaction settlement data (such as personalized settlement data and report links) into the message editing control (batch pasting is supported to avoid repetitive operations). After editing, it simulates click operations on the sending control corresponding to the message editing control (such as simulating mouse clicks on the "Send" button) to push the electricity spot transaction settlement data to the target receiving account corresponding to the user account information.

[0141] (4) Obtaining feedback business processing messages (such as feedback instructions):

[0142] a. Instruction monitoring: Periodically scan the message interface of the target communication application, such as scanning the message interface of the group interface once every minute using a graphical user interface automation tool library (pyautogui), and identifying the latest message information and locating unread feedback business processing messages through image recognition (such as the open-source computer vision library opencv-python).

[0143] b. Instruction Extraction: Simulate mouse clicks on the latest message, and use optical character recognition tools, such as Python Tesseract Optical Character Recognition (pytesseract), to recognize the message text content and extract the task type indicated by the business processing message (such as the feedback instruction keyword "execute a certain task").

[0144] c. Instruction Processing: Obtain the business processing message returned by the target receiving account, and process the electricity spot transaction settlement data according to the task type indicated by the business processing message. For example, based on the extracted instruction keywords, automatically trigger the corresponding task business processing logic.

[0145] Furthermore, a high-precision time-series task scheduler can be built based on the time management module. The time management module calculates time offsets, and a recursive time window detection algorithm is used to achieve second-level task triggering accuracy. For timed task scenarios, a periodic task heartbeat monitoring module is developed to automatically activate an anti-bounce mechanism to avoid duplicate execution when tasks are triggered with second-level precision. Ultimately, a fully automated interactive pipeline is formed, from command recognition and interface control to result feedback, enabling 24 / 7 unattended, precise task scheduling.

[0146] In another embodiment, determining electricity spot transaction settlement data based on static electricity settlement data and dynamic electricity settlement data includes: extracting related fields from the static electricity settlement data and dynamic electricity settlement data; integrating the static electricity settlement data and dynamic electricity settlement data based on the related fields to obtain integrated data; and processing outliers and missing values ​​in the integrated data based on preset electricity spot settlement data cleaning rules to obtain electricity spot transaction settlement data.

[0147] Among them, the associated fields can refer to the common fields used to establish relationships in static electricity settlement data and dynamic electricity settlement data, such as transaction entity identifiers and transaction dates.

[0148] The terminal can analyze the data structure of static electricity settlement data and dynamic electricity settlement data, identify fields with the same meaning in the two types of data, and determine the field as the associated field.

[0149] The terminal can merge data records with the same associated field values ​​in static electricity settlement data and dynamic electricity settlement data based on the values ​​of the associated fields, merging the fields in the two types of data into the same data record to form integrated data.

[0150] Among them, the data cleaning rules for electricity spot settlement can refer to the data cleaning rules formulated for the characteristics of electricity spot transaction settlement data. These rules may include the criteria for identifying outliers and the methods for filling missing values.

[0151] The terminal can detect outliers in the integrated data according to the preset power spot settlement data cleaning rules, replace or delete the detected outliers, and fill or mark the detected missing values ​​in the integrated data. The data after cleaning is the power spot transaction settlement data.

[0152] In one example, the terminal can calculate the mean and standard deviation of a numerical field in the integrated data. For each value in that field, it checks if the absolute value of the difference between that value and the mean is greater than three times the standard deviation. If it is, the value is marked as an outlier. The terminal can also compare the value with a pre-stored industry mean and check if the absolute value of the difference is greater than twice the industry standard deviation. If both conditions are met, the value is confirmed as an outlier, and the value is either replaced with the mean or the data record is deleted.

[0153] In another example, for missing values ​​in the integrated data, if the missing field is a critical field, such as transaction volume or settlement price, the terminal can calculate the imputed value using linear interpolation based on the adjacent valid values ​​before and after the missing value. Specifically, the terminal can obtain the valid value before and after the missing value, as well as the timestamps corresponding to these two valid values. Based on the relative position of the timestamp corresponding to the missing value between the two valid timestamps, the interpolation weight is calculated. The two valid values ​​are then weighted and summed according to the interpolation weight to obtain the imputed value. To improve the accuracy of imputed values, the terminal can also add a correction coefficient to the calculation result, which can be determined based on the historical fluctuation characteristics of the data.

[0154] In the above embodiments, the terminal integrates static and dynamic data by extracting related fields, and performs data cleaning based on cleaning rules specifically formulated for the characteristics of electricity spot settlement data. This effectively handles outliers and missing values ​​in the data and improves data quality.

[0155] In practical applications, to address the technical challenges of processing heterogeneous data in electricity spot market settlement, targeted improvements can be made to data processing tools, such as the Python Data Analysis Library (pandas), to build a dedicated processing model for electricity spot market settlement. This enables full automation of the data cleaning, standardization, verification, and accounting processes. The specific implementation steps are as follows:

[0156] (1) Data import and preliminary integration: Read the preprocessed data obtained from the SQLite original database (such as the static power settlement data and dynamic power settlement data extracted above), and extract the related fields (such as transaction date, main identity identifier ID and other key fields) from the static power settlement data and dynamic power settlement data through merging operations (such as using the merge() function of the panel data analysis library pandas). Based on the related fields, integrate the heterogeneous data from different power business systems to obtain the integrated data (such as forming a unified data frame data set) to ensure data consistency.

[0157] (2) Data cleaning: Based on the preset electricity spot settlement data cleaning rules, an automated cleaning algorithm is designed to address issues such as outliers, missing values, and format errors in the integrated data.

[0158] a. Improved 3σ outlier handling: For core numerical fields (such as transaction volume, settlement price, and deviation electricity volume), an improved 3σ principle is used to identify outliers. The core formula is as follows:

[0159] If it is, then it is determined to be an outlier.

[0160] in, For the i-th data sample (such as the trading volume and settlement price of a certain trading entity during a certain period);

[0161] Where μ is the overall mean of the data in this dimension, which can be calculated using the mean() function of the panel data analysis library pandas, reflecting the overall level of the currently collected data;

[0162] Where σ is the overall standard deviation of the data in this dimension, which can be calculated using the std() function of the panel data analysis library pandas, reflecting the dispersion of the currently collected data;

[0163] in, This is the industry average for electricity spot market data in this dimension. It is calculated based on long-term operating data of the electricity spot market, and is adapted to industry characteristics.

[0164] in, This represents the industry standard deviation of electricity spot market data for this dimension. If pre-stored in a benchmark database, it reflects the normal fluctuation range of data within the industry.

[0165] b. Missing Value Handling (Field-Specific Adaptation, Improved Linear Interpolation): Differentiated handling strategies are designed for different types of missing fields to avoid the limitations of general missing value handling methods, thereby obtaining electricity spot transaction settlement data.

[0166] Missing key fields (such as transaction volume, settlement price, entity identifier ID, and transaction date) are filled using an improved linear interpolation method. The core formula is as follows:

[0167] ;

[0168] in, For missing data values ​​(such as missing transaction volume or settlement price);

[0169] in, , For valid data adjacent to the missing data (such as the transaction volume of the hour before and the hour after the missing period).

[0170] in, The timestamp corresponding to the missing data (e.g., in the format "YYYY-MM-DD HH:MM:SS").

[0171] in, , The timestamps corresponding to adjacent valid data;

[0172] in, The correction coefficient, with a value range of 0.01 to 0.05, is adjusted according to the data dimension to compensate for the error of the linear interpolation method in the scenario of fluctuation in electricity spot data and improve the accuracy of filling.

[0173] Handling missing non-critical fields (such as notes and non-core auxiliary fields): mark them as "none" to avoid introducing invalid data.

[0174] For the cleaned, verified, and integrated electricity spot transaction settlement data, we strictly follow established standardized business processes and conduct fully online operations within our dedicated internal business management system. We complete accurate data entry and information queries according to the field specifications and data entry requirements of each business module, ensuring the completeness and accuracy of all data items without any missing data. Based on the node settings of each business process, we sequentially complete workflow operations (such as data sharing, daily verification, daily data reading, and process push).

[0175] In another embodiment, to address technical issues such as the lack of anomaly detection and handling mechanisms and poor system adaptability in the automated processing and push process, the system also includes intelligent anomaly control over all stages of electricity spot settlement. The specific steps are as follows:

[0176] (1) Construction of Operational Anomaly Features. An operational anomaly feature library covering the entire process of electricity spot settlement, including data acquisition, processing, and interaction, is pre-organized and constructed, ensuring that each anomaly type corresponds to a unique feature identifier, detection rules, and processing strategy. Specifically, the anomaly types included in the operational anomaly feature library are as follows:

[0177] Abnormalities in the data collection process: such as data collection failure due to network communication abnormalities, failure to authenticate when logging into the business system, data missing rate exceeding the preset threshold (e.g., greater than 20% of the total records), and node score value less than the preset threshold (e.g., S < 0.7) and still unable to be re-identified after performing page scanning, etc.

[0178] Processing errors: such as the percentage of identified outliers exceeding the preset range (e.g., the percentage of outliers in a single batch of data is greater than 5%) or business processing logic failing to execute.

[0179] Interaction errors: such as failure to push to the target communication application, inability to recognize the obtained feedback business processing messages (such as feedback instructions), or failure to match contact information in the dictionary.

[0180] (2) Automatic anomaly detection:

[0181] The system analyzes log data generated by each functional module to monitor the operational status of the entire electricity spot settlement process in real time and extracts abnormal features to be matched. The extracted abnormal features are compared with preset feature identifiers in the operational abnormality feature library; if a match is found, the corresponding type of operational abnormality is determined, and subsequent automated abnormality processing logic is triggered.

[0182] (3) Automated hierarchical handling of anomalies:

[0183] Based on the identified anomaly type, a corresponding differentiated processing strategy is automatically triggered. The core processing logic is as follows:

[0184] Data collection exception handling: For data collection failures, an automated retry mechanism is implemented (e.g., a maximum of 5 retries with a 3-second interval between each retrieval). If the data collection still fails after reaching the maximum number of retries, a general or severe warning is triggered. For node matching failures, the page elements are automatically rescanned to re-identify the target document object model (DOM) nodes and update the positioning parameters. For cases with excessively high data missing rates, protective measures such as pausing the data collection operation and triggering a severe warning are implemented.

[0185] Exception handling in the processing stage: If there are too many outliers, the data cleaning process will be re-executed, and an alert will be triggered if it fails again; if the business processing fails, the data processing program will be re-executed, and warning information will be recorded in real time.

[0186] Interaction process exception handling: Automated retries are executed for push failures (e.g., a maximum of 3 retries with a 5-second interval). If the retries are exhausted and the attempt still fails, an alert is triggered.

[0187] (4) Adaptive page update handling:

[0188] Combining the aforementioned intelligent DOM node recognition algorithm, when an update to the power business system page causes anomalies in data collection, the system automatically triggers node re-identification and location parameter update logic. This adaptive adjustment mechanism eliminates the need for manual program modification to adapt to the page structure, significantly reducing system maintenance costs and improving the solution's versatility.

[0189] Ultimately, through the coordinated efforts of hybrid data acquisition, intelligent data processing, two-way interaction with the target communication application, and full-process anomaly control, the entire process of electricity spot transaction settlement data processing and push was automated and intelligent.

[0190] For the convenience of those skilled in the art, Figure 3 An exemplary logic diagram is provided for a method of automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies. The specific process includes:

[0191] Process Initiation Phase: The process is triggered by the "Start" node, first entering the "Interactive Environment Initialization" and "Task Heartbeat Monitoring" nodes. This phase establishes the basic environment for subsequent task scheduling and interaction with target communication applications (such as enterprise-level communication software ELink).

[0192] The hybrid data acquisition module workflow is as follows: Based on the initial configuration, the system enters separate branches to acquire static and dynamic electricity settlement data. Static data is acquired by sending data request requests to the power business system (e.g., using the requests library combined with JavaScript reverse engineering). Simultaneously, dynamic data is acquired by simulating page interactions within the power business system (e.g., using the browser automation testing framework Selenium) and applying a Document Object Model (DOM) node intelligent recognition algorithm. The acquired data is then fed into the "Data Preprocessing" node for initial processing. This combination of static and dynamic acquisition ensures the integrity of the data collection process, with no isolated acquisition steps.

[0193] The data intelligence processing module workflow is as follows: Preprocessed data flows directly to the processing stage, passing through the "Data Reading" and "Data Integration" nodes sequentially. Subsequently, the integrated data enters the data cleaning branch, where improved outlier handling and improved linear interpolation missing value handling are performed. Data that meets the cleaning standards passes through the "Data Calculation" and "Output Standardized Settlement Data" nodes, ultimately triggering the "Internal System Business Process Processing" node. The output of this processing module directly serves as the data source for subsequent push stages, and the steps are logically interconnected.

[0194] The target communication application facilitates bidirectional interactive flow: the generated electricity spot transaction settlement data is transmitted to the interaction module. The system sequentially executes the "targeted recipient matching," "message editing," and "message sending" nodes, pushing the data to the target receiving account. Simultaneously, the system continuously monitors and obtains the business processing messages returned by the target receiving account, sending them back to the task scheduling center to trigger corresponding data reprocessing or instruction confirmation, forming a complete bidirectional interactive closed loop.

[0195] The entire process of anomaly control is managed by a central anomaly control mechanism that serves as the monitoring hub in the flowchart, maintaining real-time communication with the aforementioned data collection, processing, and interaction modules. This module encompasses nodes such as "anomaly feature organization," "real-time anomaly detection," "processing and retrying," and "page update adaptation." When an anomaly occurs at any module node in the process, the anomaly control mechanism immediately responds and triggers retry or adaptive adjustment logic to ensure the continuity of the overall business flow and the stability of data transfer.

[0196] Therefore, the technical solution of this application embodiment includes:

[0197] A hybrid data collection technology based on requests, selenium, and js reverse engineering is optimized: To address the shortcomings of current single data collection tools, the collaborative logic of the three is optimized. Requests, combined with js reverse engineering, is responsible for efficient static data collection (reducing resource consumption), while selenium is responsible for dynamic / pop-up data collection (making up for the shortcomings of static data collection). At the same time, a DOM node intelligent recognition algorithm is introduced to adapt to the dynamic updates of business system pages through multi-dimensional attribute matching, solving the problems of poor adaptability and incomplete data collection of existing single data collection technologies.

[0198] A dedicated processing technology for electricity spot settlement data based on optimized pandas: To address the poor adaptability of existing general data processing methods, this technology optimizes pandas' data cleaning and accounting algorithms, and designs a dedicated processing model and formula in conjunction with electricity spot trading settlement rules to achieve accurate processing of heterogeneous data. It is lightweight and highly adaptable.

[0199] ELink bidirectional intelligent interaction technology based on keyboard and mouse simulation + image recognition: Addressing the lack of automation in existing ELink interaction technologies, this technology improves the fixed coordinate operation mode of traditional keyboard and mouse simulation by combining image recognition to achieve adaptive positioning of controls, enabling bidirectional interaction of ELink directional push, feedback command acquisition, and status recognition, breaking through the limitation of existing push technologies that can only send in one direction.

[0200] End-to-end intelligent management and control technology: To address the lack of existing anomaly control, it constructs anomaly characteristics for the entire process of electricity spot settlement, designs and optimizes anomaly detection and retry algorithms, realizes automatic anomaly identification and intelligent retry, adapts to page updates to solve the problem of poor adaptability, and is lightweight and easy to deploy.

[0201] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0202] Based on the same inventive concept, this application also provides an apparatus for implementing the above-mentioned method for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies. The solution provided by this apparatus is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more embodiments of the apparatus for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies provided below can be found in the limitations of the method for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies described above, and will not be repeated here.

[0203] In one exemplary embodiment, such as Figure 4 As shown, a device for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies is provided, comprising:

[0204] The acquisition module 410 is used to acquire static power settlement data by sending a data call request to the power business system, and to acquire dynamic power settlement data by simulating page interaction operations in the power business system.

[0205] Extraction module 420 is used to determine electricity spot transaction settlement data based on the static electricity settlement data and the dynamic electricity settlement data, and to extract user account information configured in the target communication application from the electricity spot transaction settlement data;

[0206] The push module 430 is used to push the electricity spot transaction settlement data to the target receiving account corresponding to the user account information by simulating user interface interaction operations in the target communication application.

[0207] The processing module 440 is used to obtain the business processing message returned by the target receiving account and process the electricity spot transaction settlement data according to the task type indicated by the business processing message.

[0208] In one embodiment, the acquisition module 410 is specifically used to identify the DOM node of the target business page in the power business system; determine the node score value of the DOM node based on the matching degree between the actual node characteristics of the DOM node and the preset node characteristics; if the node score value is greater than or equal to a preset threshold, simulate the page interaction operation on the DOM node to obtain the dynamic power settlement data; if the node score value is less than the preset threshold, return to the step of identifying the DOM node of the target business page in the power business system.

[0209] In one embodiment, the actual node features include an actual node identifier, an actual node attribute, an actual node path, and actual node text; the preset node features include a preset node identifier, a preset node attribute, a preset node path, and preset node text; the acquisition module 410 is specifically used to acquire the identifier matching degree between the actual node identifier and the preset node identifier, the attribute matching degree between the actual node attribute and the preset node attribute, the path matching degree between the actual node path and the preset node path, and the text matching degree between the actual node text and the preset node text; the identifier matching degree, the attribute matching degree, the path matching degree, and the text matching degree are weighted and summed to obtain a node score value.

[0210] In one embodiment, the acquisition module 410 is specifically used to acquire the encryption function of the power business system and the associated function corresponding to the encryption function; the associated function is other functions that need to be called when the encryption function runs; generate a reverse script based on the encryption function and the associated function; the reverse script is a script file used to perform encryption processing; call the script file to encrypt the parameters to be encrypted to obtain the encrypted parameters, and send a data call request carrying the encrypted parameters to the power business system; receive the data interface response data returned by the power business system in response to the data call request, decrypt the data interface response data, and obtain the static power settlement data.

[0211] In one embodiment, the push module 430 is specifically used to perform image recognition on the user interface of the target communication application, locate the search control in the user interface; simulate a click operation on the search control, input the user account information into the search control to perform a search, and determine the target receiving account from the search results; simulate a click operation on the message editing control corresponding to the target receiving account, paste the electricity spot transaction settlement data into the message editing control; and simulate a click operation on the sending control corresponding to the message editing control, push the electricity spot transaction settlement data to the target receiving account corresponding to the user account information.

[0212] In one embodiment, the extraction module 420 is specifically used to extract the associated fields from the static electricity settlement data and the dynamic electricity settlement data; to integrate the static electricity settlement data and the dynamic electricity settlement data according to the associated fields to obtain integrated data; and to process the outliers and missing values ​​in the integrated data based on preset electricity spot settlement data cleaning rules to obtain the electricity spot transaction settlement data.

[0213] The modules in the aforementioned automated processing and intelligent push device for electricity spot trading settlement data based on the integration of Web automation and intelligent interaction technologies can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0214] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 5As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interfaces. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for automated processing and intelligent push of electricity spot trading settlement data based on the integration of Web automation and intelligent interaction technologies. The display unit of the computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0215] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0216] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0217] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0218] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0219] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0220] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0221] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0222] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies, characterized in that, The method includes: Static electricity settlement data is obtained by sending a data request to the electricity business system, and dynamic electricity settlement data is obtained by simulating page interaction operations in the electricity business system. Based on the static electricity settlement data and the dynamic electricity settlement data, determine the electricity spot market transaction settlement data, and extract the user account information configured in the target communication application from the electricity spot market transaction settlement data; By simulating user interface interaction operations in the target communication application, the electricity spot transaction settlement data is pushed to the target receiving account corresponding to the user account information; Obtain the business processing message returned by the target receiving account, and process the electricity spot transaction settlement data according to the task type indicated by the business processing message.

2. The method according to claim 1, characterized in that, The step of obtaining dynamic electricity settlement data by simulating page interaction operations in the power business system includes: Identify the DOM nodes of the target business page in the power business system; The node score of the DOM node is determined based on the matching degree between the actual node characteristics and the preset node characteristics. When the node rating value is greater than or equal to a preset threshold, the page interaction operation is simulated on the DOM node to obtain the dynamic electricity settlement data; If the node score is less than the preset threshold, the process returns to the step of identifying the DOM node of the target business page in the power business system.

3. The method according to claim 2, characterized in that, The actual node features include actual node identifier, actual node attributes, actual node path, and actual node text; The preset node features include preset node identifier, preset node attributes, preset node path, and preset node text; The step of determining the node score value of the DOM node based on the matching degree between the actual node features of the DOM node and the preset node features includes: Obtain the identifier matching degree between the actual node identifier and the preset node identifier, the attribute matching degree between the actual node attribute and the preset node attribute, the path matching degree between the actual node path and the preset node path, and the text matching degree between the actual node text and the preset node text; The node score is obtained by weighted summing of the identifier matching degree, the attribute matching degree, the path matching degree, and the text matching degree.

4. The method according to claim 1, characterized in that, The step of obtaining static electricity settlement data by sending a data request to the power business system includes: Obtain the encryption function of the power business system and the associated function corresponding to the encryption function; the associated function is other functions that the encryption function needs to call when it runs. Based on the encryption function and the association function, a reverse script is generated; the reverse script is a script file used to perform encryption processing. The script file is invoked to encrypt the parameters of the request to be encrypted, thereby obtaining the encrypted parameters, and a data call request carrying the encrypted parameters is sent to the power business system. The system receives data interface response data returned by the power business system in response to the data call request, decrypts the data interface response data, and obtains the static power settlement data.

5. The method according to claim 1, characterized in that, The step of pushing the electricity spot transaction settlement data to the target receiving account corresponding to the user account information by simulating user interface interaction operations in the target communication application includes: Image recognition is performed on the user interface of the target communication application to locate the search control in the user interface; The user account information is input into the search control to perform a search, and the target receiving account is determined from the search results; Simulate a click operation on the message editing control corresponding to the target receiving account, and paste the electricity spot transaction settlement data into the message editing control; The send control corresponding to the message editing control is simulated to click, and the electricity spot transaction settlement data is pushed to the target receiving account corresponding to the user account information.

6. The method according to claim 1, characterized in that, The step of determining electricity spot transaction settlement data based on the static electricity settlement data and the dynamic electricity settlement data includes: Extract the relevant fields from the static electricity settlement data and the dynamic electricity settlement data; Based on the associated fields, the static electricity settlement data and the dynamic electricity settlement data are integrated to obtain the integrated data; Based on preset electricity spot settlement data cleaning rules, outliers and missing values ​​in the integrated data are processed to obtain the electricity spot transaction settlement data.

7. A device for automated processing and intelligent push of electricity spot transaction settlement data based on the integration of Web automation and intelligent interaction technologies, characterized in that, The device includes: The acquisition module is used to acquire static electricity settlement data by sending a data call request to the power business system, and to acquire dynamic electricity settlement data by simulating page interaction operations in the power business system. The extraction module is used to determine electricity spot transaction settlement data based on the static electricity settlement data and the dynamic electricity settlement data, and to extract user account information configured in the target communication application from the electricity spot transaction settlement data; The push module is used to push the electricity spot transaction settlement data to the target receiving account corresponding to the user account information by simulating user interface interaction operations in the target communication application. The processing module is used to obtain the business processing message returned by the target receiving account and process the electricity spot transaction settlement data according to the task type indicated by the business processing message.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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