Information generation method and device, electronic equipment and storage medium

Abstract

The invention provides an information generation method and device, electronic equipment and a storage medium, relates to the field of cloud storage, and can be applied to a cloud platform. The method comprises the following steps: in response to received demand information, determining target identification information of target data according to the demand information; determining a target data packet corresponding to the target identification information from a pre-constructed data set; generating a use link diagram of the target data according to the target data packet; and generating a data analysis result corresponding to the target data according to the use link diagram. According to the invention, the readability of the information is improved, and the use experience of the user is improved.

Description

Technical Field

[0001] This disclosure relates to the field of cloud storage, and more particularly to an information generation method, apparatus, electronic device, and storage medium. Background Technology

[0002] In today's rapidly developing mobile internet era, mobile applications such as online audio and video streaming and download tools have become frequently used by users. Especially for applications with high monthly active users, the daily network request volume is enormous, and data consumption directly impacts user experience and product reputation. Therefore, it is necessary to provide efficient, accurate, and low-cost data management support for mobile applications. Summary of the Invention

[0003] This disclosure presents an information generation method, apparatus, electronic device, and storage medium.

[0004] According to a first aspect of this disclosure, an information generation method is provided, comprising: in response to receiving demand information, determining target identification information of target data based on the demand information; determining a target data packet corresponding to the target identification information from a pre-constructed data set; generating a usage link diagram of the target data based on the target data packet; and generating a data analysis result corresponding to the target data based on the usage link diagram.

[0005] According to a second aspect of this disclosure, an information generation apparatus is provided, comprising: an identifier determination module configured to determine target identifier information of target data based on the received demand information in response to receiving demand information; a data determination module configured to determine a target data packet corresponding to the target identifier information from a pre-constructed data set; a link diagram generation module configured to generate a usage link diagram of the target data based on the target data packet; and a result generation module configured to generate a data analysis result corresponding to the target data based on the usage link diagram.

[0006] According to a third aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a method as described in any implementation of the first aspect.

[0007] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing a computer to perform a method as described in any implementation of the first aspect.

[0008] According to a fifth aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in any implementation of the first aspect.

[0009] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0010] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 This is an exemplary system architecture diagram to which this disclosure can be applied; Figure 2 This is a flowchart of an embodiment of the information generation method according to the present disclosure; Figure 3 This is a flowchart of another embodiment of the information generation method according to this disclosure; Figure 4 This is a flowchart of yet another embodiment of the information generation method according to this disclosure; Figure 5 This is a flowchart of one embodiment of a method for generating a dataset; Figure 6 This is a flowchart of yet another embodiment of the information generation method according to this disclosure; Figure 7-1 This is a schematic diagram of a system for generating information based on this disclosure; Figure 7-2 This is an interactive flowchart of the system generated based on the information disclosed herein; Figure 7-3 This is a flowchart of the classification process requested in accordance with this disclosure; Figure 7-4 This is an application sequence diagram of the information generation method according to this disclosure; Figure 8 This is a schematic diagram of the structure of an embodiment of the information generation apparatus according to the present disclosure; Figure 9 This is a block diagram of an electronic device used to implement the information generation method of the embodiments of this disclosure. Detailed Implementation

[0011] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0012] It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other. This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0013] Figure 1 An exemplary system frame 100 is shown, to which embodiments of the information generation method or information generation apparatus of this disclosure may be applied.

[0014] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0015] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various applications for enabling information communication between the terminal devices 101, 102, and 103 and server 105 can be installed. These applications include cloud storage applications and instant messaging applications.

[0016] Terminal devices 101, 102, and 103 and server 105 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices, and can be implemented as multiple software programs or software modules, or as a single software program or software module; no specific limitation is made here. When server 105 is hardware, it can be implemented as a distributed server cluster composed of multiple servers, or as a single server. When server 105 is software, it can be implemented as multiple software programs or software modules, or as a single software program or software module; no specific limitation is made here.

[0017] Server 105 can provide various services through its built-in applications, taking cloud storage applications as an example. Users can operate through the cloud storage applications on terminal devices 101, 102, and 103 and send request information to server 105. Server 105 can receive the request information and run the cloud storage applications for processing, including: determining the target identifier information of the target data based on the request information; determining the target data packet corresponding to the target identifier information from a pre-built data set; generating a usage link diagram of the target data based on the target data packet; and generating data analysis results corresponding to the target data based on the usage link diagram.

[0018] It should be noted that the information generation method provided in this embodiment is generally executed by server 105, and correspondingly, the information generation device is generally located in server 105.

[0019] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0020] Continue to refer to Figure 2 The diagram illustrates a flow 200 of an embodiment of an information generation method according to the present disclosure. The information generation method includes the following steps: Step 201: In response to receiving the demand information, determine the target identifier information of the target data based on the demand information.

[0021] In this embodiment, the execution subject of the information generation method (e.g.) Figure 1 If the server 105 shown receives a request, it will determine the target identifier information of the target data based on the request information. The request information can be a piece of text or voice input directly by the user, representing the specific content of the information the user wants to obtain. The aforementioned executing entity can determine the identifier information of the target data, i.e., the target identifier information, based on the request information, where the target data is the information the user wants to obtain.

[0022] Target identification information can include the identity document (ID) of the user who inputs the requirement information. The aforementioned executing entity can determine the user identification information based on the identifier sent by the requirement information. Target identification information can also include the data identification information of the target data, i.e., the data identification information of the data required by the user. This data identification information can include information such as the data type and the corresponding time period. In this case, the aforementioned executing entity can generate the data identification information based on the attribute information (type, corresponding time period, etc.) of the user's required data (i.e., the target data). Target identification information can also include scenario identification information, i.e., the identification information corresponding to the business scenario. In this case, the aforementioned executing entity can perform semantic analysis on the requirement information to determine the corresponding business scenario based on the semantic analysis results, and thus determine the scenario identification information.

[0023] Step 202: Determine the target data packet corresponding to the target identification information from the pre-built data set.

[0024] In this embodiment, the execution entity determines the target data packet corresponding to the target identification information from a pre-built dataset. That is, the execution entity searches the pre-built dataset based on the target identification information to obtain the corresponding data, thus obtaining the target data packet.

[0025] The aforementioned execution entity first constructs a standardized set of search conditions based on user and data identifiers from the target identifier information. This set is in a key-value pair structure. Then, it converts the natural language-style requirement identifiers into search syntax supported by the target database. This involves transforming the natural language-style search conditions into search statements supported by the database, such as SQL (Structured Query Language) statements, along with invalid data filtering rules. These invalid data filtering rules can include data integrity checks, data validity checks, and permission compliance checks, ensuring search accuracy. Next, the execution entity uses the generated structured search statements to perform a search operation on the database, filtering invalid data through multiple layers to obtain a filtered target dataset. Finally, the execution entity encapsulates the filtered target data in a standard format and adds verification information and security identifiers, resulting in a standardized and verifiable target data package.

[0026] Step 203: Generate a usage link diagram of the target data based on the target data packet.

[0027] In this embodiment, the execution entity generates a usage link diagram of the target data based on the target data packet. The execution entity first verifies the target data packet; if the verification passes, it further parses the target data packet and extracts data in the target dimension based on the parsing results. This target dimension data is the data required to generate the usage link diagram. Finally, the usage link diagram is generated based on the target dimension data.

[0028] The aforementioned execution entity first extracts the outer metadata of the target data packet and the MD5 (Message-Digest Algorithm 5) checksum from the verification information. Then, it calculates the MD5 value of the received data packet and compares it with the MD5 checksum in the verification information. If they match, the verification passes, thus preventing packet loss or data tampering. Next, it determines the core data required for using the link graph, such as the source dimension, target dimension, and data traffic value. This is done by parsing the target data packet and extracting the data for the target dimension based on the parsing results. The source dimension (i.e., the left node of the link graph) can include time slices and traffic types, while the target dimension can include the business scenario name (e.g., offline download, video preview, login interface, etc.). Grouping by source dimension + target dimension, the total data traffic (MB, megabit) of each group is accumulated to form a traffic mapping relationship between time slices + traffic types and business scenarios, thus obtaining the source data for using the link graph. Furthermore, the aforementioned execution entity filters groups with a total traffic of less than 0.1MB to prevent minor traffic from interfering with the core view. Finally, the aforementioned execution entity will obtain the preset visualization style, layout rules, etc., load the source data, and generate a usage link diagram. The usage link diagram here can be a Sankey diagram. A Sankey diagram is a flowchart type that uses branch widths to correspond to data flow. Of course, the usage link diagram can also be other diagrams, but this embodiment does not specifically limit it.

[0029] Step 204: Generate data analysis results corresponding to the target data based on the link diagram.

[0030] In this embodiment, the aforementioned execution entity generates data analysis results corresponding to the target data based on the usage link diagram. The execution entity first generates multiple segments of data usage information based on the usage link diagram, and then analyzes and summarizes these multiple segments of data usage information to obtain the data analysis results.

[0031] The aforementioned execution entity first divides the time frame by time slices, for example, using 30-minute intervals as the smallest unit to ensure that the granularity of the time slots matches the needs of anomaly localization. Then, it calculates the total data traffic for each time slice, thus obtaining data usage information for multiple time periods. Next, combining this with the business scenario requirements corresponding to the demand information, the raw data usage information from multiple segments is summarized and analyzed to obtain data analysis results. These results can include multiple segments of differentiated information (each segment focusing on a single analytical perspective), ensuring highly targeted and non-redundant information. For example, segment 1 is time-segment traffic distribution information, focusing on the time dimension, including total traffic for each time slice, traffic type percentage, and high-consumption scenarios; segment 2 is business scenario-traffic detail information, focusing on the scenario dimension, including total traffic for each business scenario, uplink / downlink traffic percentage, and corresponding time-segment distribution; segment 3 is anomaly traffic-specific information, focusing on the anomaly dimension, including the anomaly scenario name, anomaly time period, anomaly traffic value / percentage, and whether it is a user-initiated operation scenario; segment 4 is traffic type-scenario association information, focusing on the type dimension, including the core scenarios corresponding to control flow / data flow and their traffic contribution percentage. Furthermore, each piece of information can be formatted as title + core fields + data example, thus ensuring the readability of the results.

[0032] The information generation method provided by this disclosure firstly determines target identifier information of target data based on received demand information; then, it determines the target data packet corresponding to the target identifier information from a pre-constructed data set; subsequently, it generates a usage link diagram of the target data packet; and finally, it generates data analysis results corresponding to the target data packet based on the usage link diagram. This method, through the targeted transformation from demand information to target identifier information, clarifies the core dimensions of retrieval, avoiding indiscriminate data scanning; combined with the structured storage of the pre-constructed data set, it achieves rapid matching of target data packets, reducing retrieval time and improving data retrieval efficiency; furthermore, the retrieval process focuses only on data packets corresponding to the target identifier information, filtering redundant and irrelevant data, improving the purity of the target data, avoiding conclusion bias caused by data noise in subsequent analysis stages, and reducing the computational power consumption of data processing; generating data analysis results based on the usage link diagram transforms abstract data into information that users can intuitively see, thereby improving information readability and enhancing the user experience. This method starts with demand information, the link diagram focuses on the core relationships of target data, and the analysis results specifically address user needs, avoiding data piling up, and the conclusions can be directly converted into actionable actions (such as product optimization solutions), thereby improving the efficiency of demand resolution.

[0033] Furthermore, the collection, storage, use, processing, transmission, provision, and disclosure of any type of information, such as user personal information, involved in the technical solutions disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0034] Continue to refer to Figure 3 , Figure 3 A flow 300 of another embodiment of the information generation method according to the present disclosure is shown. The information generation method includes the following steps: Step 301: In response to receiving the request information, generate user identification information based on the sending identifier of the request information.

[0035] In this embodiment, the execution subject of the information generation method (e.g.) Figure 1 If server 105 receives a request, it generates user identification information based on the sending identifier of the request. If the aforementioned execution entity receives a request, it determines the target identification information of the target data based on the request. The request can be a piece of text or voice input directly by the user, representing the specific content of the information the user wants to obtain. The aforementioned execution entity can determine the identification information of the target data, i.e., the target identification information, based on the request information, where the target data is the information the user wants to obtain. The target identification information may include the identifier ID of the user who input the request information, and the aforementioned execution entity can determine the user identification information based on the sending identifier of the request information.

[0036] Step 302: Generate data identification information based on the attribute information of the target data.

[0037] In this embodiment, the aforementioned execution entity generates data identification information based on the attribute information of the target data. The target identification information may also include the data identification information of the target data, i.e., the data identification information of the data required by the user. Here, the data identification information may be information such as the data type and the corresponding time period. In this case, the aforementioned execution entity can generate data identification information based on the attribute information (type, corresponding time period, etc.) of the data required by the user (i.e., the target data).

[0038] By combining user identifiers and data identifiers to form target identifier information, it is possible to accurately map to specific target data packets in a preset data set, thereby improving the accuracy of target data location.

[0039] Step 303: Generate a set of search criteria based on user identification information and data identification information.

[0040] In this embodiment, the execution entity generates a set of search criteria based on user identifier information and data identifier information. The execution entity first constructs a standardized set of search criteria based on user identifier information, data identifier information, etc., in the target identifier information; the set is in a key-value pair structure.

[0041] Step 304: Generate a structured search statement based on the set of search criteria.

[0042] In this embodiment, the execution entity generates a structured search statement based on the search condition set. Then, the execution entity converts the natural language style search conditions in the search condition set into search statements supported by the data set, such as SQL statements and invalid data filtering rules. These invalid data filtering rules may include data integrity verification, data validity verification, and permission compliance verification, thereby ensuring the accuracy of the search.

[0043] Step 305: Use structured search statements to search the dataset and obtain the target data package.

[0044] In this embodiment, the execution entity uses a structured search statement to search the dataset and obtain the target data packet. Then, the execution entity uses the generated structured search statement to perform a search operation in the dataset, filtering out invalid data through multiple layers to obtain the filtered target data packet.

[0045] By transforming the set of conditions into standardized structured search statements, the ambiguity of natural language descriptions is eliminated, ensuring the accuracy of search results.

[0046] Step 306: Generate a usage link diagram of the target data based on the target data packet.

[0047] Step 307: Generate data analysis results corresponding to the target data based on the link diagram.

[0048] Steps 306-307 are basically the same as steps 203-204 in the aforementioned embodiments. For specific implementation methods, please refer to the aforementioned description of steps 203-204, which will not be repeated here.

[0049] from Figure 3 It can be seen from this that, with Figure 2 Compared to the corresponding embodiments, the information generation method in this embodiment first combines user identifiers and data identifiers to form target identifier information, which can then be accurately mapped to specific target data packets in a preset data set, improving the accuracy of target data location. In addition, by converting the condition set into standardized structured search statements, the ambiguity of natural language descriptions is eliminated, ensuring the accuracy of search results.

[0050] Continue to refer to Figure 4 , Figure 4 A flow 400 of yet another embodiment of the information generation method according to this disclosure is shown. The information generation method includes the following steps: Step 401: In response to receiving the request information, generate user identification information based on the sending identifier of the request information.

[0051] Step 402: Generate data identification information based on the attribute information of the target data.

[0052] Step 403: Generate a set of search criteria based on user identification information and data identification information.

[0053] Step 404: Generate a structured search statement based on the set of search criteria.

[0054] Steps 401-404 are basically the same as steps 301-304 in the aforementioned embodiments. For specific implementation methods, please refer to the aforementioned description of steps 301-304, which will not be repeated here.

[0055] Step 405: Use structured search statements to search the dataset to obtain the target dataset.

[0056] In this embodiment, the execution subject of the information generation method (e.g.) Figure 1 The server 105 shown will use structured search statements to search the dataset and obtain the target dataset. That is, the execution entity will first use the generated structured search statements to perform search operations in the dataset, and remove invalid data through multiple layers of filtering to obtain the filtered target dataset.

[0057] Step 406: Encapsulate the target dataset according to a preset format and add verification information to obtain the target data packet.

[0058] In this embodiment, the execution entity encapsulates the target dataset according to a preset format and adds verification information to obtain a target data packet. The execution entity also encapsulates the filtered target data according to a standard format and adds verification information and security identifiers to obtain a standardized and verifiable target data packet.

[0059] Structured search statements are generated based on explicit user and data identifiers, which can directly locate target records in the dataset, avoiding full scans or invalid filtering, reducing search time and improving data extraction efficiency. In addition, the added verification information can verify whether packet loss or tampering has occurred during transmission or storage, improving the accuracy of data verification.

[0060] Step 407: Verify the target data packet based on the verification information.

[0061] In this embodiment, the execution entity verifies the target data packet based on the verification information. The execution entity first extracts the outer metadata of the target data packet and extracts the MD5 (Message-Digest Algorithm 5) checksum from the verification information. Then, it calculates the MD5 value of the received data packet and compares it with the MD5 checksum in the verification information. If they match, the verification passes; otherwise, it fails, thus preventing packet loss or data tampering.

[0062] Step 408: In response to determining that the target data packet has passed the verification, the target data packet is parsed, and the target dimension data is extracted based on the parsing result.

[0063] In this embodiment, if the execution entity determines that the target data packet passes the verification, it parses the target data packet and extracts the target dimension data based on the parsing result. The execution entity determines the core data required for using the link graph, such as the source dimension, target dimension, and data traffic value. It then parses the target data packet and extracts the target dimension data based on the parsing result. The source dimension (i.e., the left node of the link graph) can include time slices and traffic types, while the target dimension can include the business scenario name (e.g., offline download, video preview, login interface, etc.). The data is grouped by source dimension + target dimension, and the total data traffic (MB) of each group is accumulated to form a traffic mapping relationship between time slice + traffic type and business scenario, thereby obtaining the source data for using the link graph. Furthermore, the execution entity filters groups with a total traffic of less than 0.1MB to avoid minor traffic interference with the core view.

[0064] Step 409: Generate a usage link diagram based on the data of the target dimension.

[0065] In this embodiment, the aforementioned execution entity generates a usage link diagram based on the target dimension data. The execution entity acquires preset visualization styles, layout rules, etc., loads source data, and generates the usage link diagram. This usage link diagram can be a Sankey diagram; of course, it can also be other types of diagrams, and this embodiment does not specifically limit it.

[0066] Because the parsing process specifically extracts the core data dimensions required for the link diagram (such as time slices, traffic types, business scenarios, traffic values, etc.), redundant fields are filtered out, improving the accuracy of data extraction and avoiding irrelevant data from interfering with visualization modeling.

[0067] Step 410: Generate multi-segment data usage information based on the usage link diagram.

[0068] In this embodiment, the aforementioned execution entity generates multiple data usage information segments based on the usage link diagram. The execution entity first divides the data into time slices, for example, using 30-minute intervals as the smallest unit to ensure the time granularity matches the needs of anomaly localization. Then, it calculates the total data traffic corresponding to each time slice, thereby obtaining data usage information for multiple time periods.

[0069] Step 411: Analyze and summarize the multiple data segments using information to obtain the data analysis results.

[0070] In this embodiment, the aforementioned execution entity analyzes and summarizes multiple segments of data usage information to obtain data analysis results. The execution entity combines the business scenario requirements corresponding to the demand information, summarizing and analyzing multiple segments of raw data usage information to obtain data analysis results. These data analysis results can include multiple segments of differentiated information (each segment focuses on a single analytical perspective), ensuring the information is highly targeted and free of redundancy. For example, segment 1 is time-period traffic distribution information, focusing on the time dimension, including total traffic for each time slice, traffic type percentage, and high-consumption scenarios; segment 2 is business scenario-traffic detail information, focusing on the scenario dimension, including total traffic for each business scenario, uplink / downlink traffic percentage, and corresponding time slice distribution; segment 3 is abnormal traffic-specific information, focusing on the abnormal dimension, including the abnormal scenario name, abnormal time period, abnormal traffic value / percentage, and whether it is a user-initiated operation scenario; segment 4 is traffic type-scenario association information, focusing on the type dimension, including the core scenarios corresponding to control flow / data flow and traffic contribution percentage. Furthermore, each segment of information can adopt a format of title + core fields + data example, ensuring the readability of the results.

[0071] Based on the multi-segment data usage information extracted using the link diagram, it is broken down from differentiated perspectives such as time distribution, scenario details, anomaly-specific features, and type associations. This not only covers the basic attributes of the data (user, time period, traffic value) but also mines the core business association features (high-consumption scenarios, abnormal time periods, traffic type proportions). This ensures that the information dimensions are accurately matched with the needs of customer service handling, product optimization, etc., without redundant fields interfering with information utilization and improving information utilization.

[0072] from Figure 4 It can be seen from this that, with Figure 3Compared to the corresponding embodiments, the information generation method in this embodiment specifically extracts the core data dimensions (such as time slices, traffic types, business scenarios, and traffic values) required by the link diagram during the parsing process. This filters out redundant fields, improves the accuracy of data extraction, and avoids irrelevant data interfering with visualization modeling. In addition, based on the multi-segment data usage information extracted using the link diagram, it is split according to differentiated perspectives such as time distribution, scenario details, anomaly-specific features, and type associations. This not only covers the basic attributes of the data (users, time periods, and traffic values) but also mines core business association features (high-consumption scenarios, abnormal time periods, and traffic type proportions). This ensures that the information dimensions are accurately matched with the needs of customer service handling and product optimization, without redundant fields interfering with information utilization.

[0073] Continue to refer to Figure 5 , Figure 5 A flow 500 illustrating an embodiment of a method for generating a dataset includes: Step 501: For each network request, determine the request category corresponding to the network request based on the data size of the network request.

[0074] For each network request, the aforementioned execution entity uses a network interceptor to intercept it and determines the request category based on the data size of the network request. This request category can include control flow and data flow. Control flow requests typically have a single request body size of less than 1MB, such as login requests and purchase point reporting; while data flow requests typically have a single request body larger than 1MB, such as audio / video fragmentation and file upload / download. Therefore, the aforementioned execution entity determines the request category based on the data size of the network request; for example, requests with a data size less than 1MB are classified as control flow, and requests with a data size greater than 1MB are classified as data flow.

[0075] Step 502: Store the data corresponding to each request category according to the storage method corresponding to each request category to obtain the dataset corresponding to different request categories.

[0076] The aforementioned execution entity stores the data corresponding to each request category according to the storage method corresponding to each request category, obtaining datasets corresponding to different request categories, and thus a data set. That is, the data set includes datasets corresponding to different request categories. Since the request categories in this embodiment include control flow and data flow, the data set includes datasets corresponding to control flow and datasets corresponding to data flow. The aforementioned execution entity stores the data corresponding to control flow according to the storage method corresponding to control flow, obtaining datasets corresponding to control flow; and stores the data corresponding to data flow according to the storage method corresponding to data flow, obtaining datasets corresponding to data flow.

[0077] In some optional implementations of this embodiment, step 502 includes: Step 5021: For a control flow request, in response to the determination that the number of control flow data corresponding to the control flow request has reached a preset number, the control flow data is aggregated and stored to obtain the control flow dataset corresponding to the control flow request.

[0078] For control flow requests, if the aforementioned execution entity determines that the number of control flow data corresponding to the control flow request has reached a preset number, it will aggregate and store the control flow data to obtain the control flow dataset corresponding to the control flow request. Specifically, for control flow, the memory accumulator accumulates information such as the number of requests, uplink / downlink bytes, and latency based on the interface identifier and return code. When the number of requests reaches 500, disk write is triggered, or when the accumulated time reaches 24 hours.

[0079] Step 5022: For a data stream request, in response to the determination that the data size of the data stream data corresponding to the data stream request has reached a preset size, the data stream data is aggregated and stored to obtain the data stream dataset corresponding to the data stream request.

[0080] For data stream requests, if the aforementioned execution entity determines that the data size of the data stream corresponding to the data stream request has reached a preset size, it will aggregate and store the data stream data to obtain the data stream dataset corresponding to the data stream request. That is, for data streams, traffic can be accumulated based on the business scenario plus the file URI (Uniform Resource Identifier) ​​prefix, and disk writes will be performed when the accumulated data size is greater than or equal to 50MB.

[0081] Based on the essential characteristics of control flow requests and data flow requests, differentiated aggregation storage strategies are set for number threshold triggering and size threshold triggering, respectively. This enables lightweight and efficient management of the two types of data, improves the optimization efficiency of storage resources, reduces the overhead of system operation, and thus improves data retrieval efficiency.

[0082] By classifying network requests into control flow and data flow based on data size, and designing targeted storage strategies, the efficiency, cost, and usability of data management are optimized from the root, improving the utilization of storage resources, enhancing data retrieval efficiency, and making the system more lightweight.

[0083] Continue to refer to Figure 6 , Figure 6 A flow 600 of another embodiment of the information generation method according to the present disclosure is shown. The information generation method includes the following steps: Step 601: In response to receiving the demand information, determine the target identifier information of the target data based on the demand information.

[0084] In this embodiment, the execution subject of the information generation method (e.g.) Figure 1 If the server 105 shown receives a request, it will determine the target identifier information of the target data based on the request. The target identifier information includes scenario identifier information. The scenario identifier information is the identifier information corresponding to the business scenario. At this time, the aforementioned execution entity can perform semantic analysis on the request information, thereby determining the corresponding business scenario based on the semantic analysis results, and then determining the scenario identifier information.

[0085] Step 602: Determine the dataset to be retrieved from the dataset based on the scene identification information.

[0086] In this embodiment, the aforementioned execution entity determines the dataset to be retrieved from the dataset based on the scenario identification information. This dataset can be either a control flow dataset or a data flow dataset. Specifically, the execution entity determines the dataset to be retrieved from the dataset based on the business scenario corresponding to the scenario identification information, thus determining whether the dataset to be retrieved is a control flow dataset or a data flow dataset. For example, if the scenario identification information is a traffic anomaly scenario, then the dataset to be retrieved can be determined to be a data flow dataset, because the data size of a single request body in a data flow dataset is relatively large, and traffic anomalies should be related to data in the data flow dataset. As another example, if the scenario identification information is a login anomaly scenario, then the dataset to be retrieved can be determined to be a control flow dataset, because the control flow dataset stores data with smaller single request bodies, typically login interface information, etc.

[0087] Step 603: Perform a search in the dataset to be searched to obtain the target data packet.

[0088] In this embodiment, the execution entity will search within the dataset to be searched to obtain the target data packet. After determining the dataset to be searched (control flow dataset or data flow dataset) from the dataset, the execution entity will search within the dataset to obtain the target data packet.

[0089] This allows for the classification and storage of data sets based on control flow and data flow, and the targeted filtering of datasets through scene identification, thereby improving the retrieval efficiency and accuracy of target data packets.

[0090] Step 604: Generate a usage link diagram of the target data based on the target data packet.

[0091] Step 605: Generate data analysis results corresponding to the target data based on the link diagram.

[0092] Steps 604-605 are basically the same as steps 203-204 in the aforementioned embodiment. For specific implementation methods, please refer to the aforementioned description of steps 203-204, which will not be repeated here.

[0093] Step 606: Determine the amount of data used within the preset time period based on the data analysis results.

[0094] In this embodiment, the execution entity determines the data usage within a preset time period based on the data analysis results. The time is divided into time periods, for example, using one hour as the smallest unit to break down the overall time range, and then the total data flow for each hour is calculated to obtain the cumulative data flow per hour.

[0095] Step 607: In response to determining that the data usage exceeds the historical threshold, an alarm message is generated.

[0096] In this embodiment, if the execution entity determines that the data usage exceeds the historical threshold, it will generate an alarm message. The execution entity will first statistically analyze the traffic data for each hour to obtain the historical threshold for each hour. Then, the execution entity will compare the current data traffic for each hour with the corresponding historical threshold. If it is determined that the current data traffic exceeds the historical threshold, an alarm will be triggered, and corresponding alarm information will be generated.

[0097] By quantifying data usage and comparing it with historical thresholds through data analysis results to trigger alarms, the system achieves accurate identification and timely warning of abnormal data usage behavior, improving the accuracy of anomaly detection and the timeliness of risk response.

[0098] from Figure 6 It can be seen from this that, with Figure 2 Compared to the corresponding embodiments, the information generation method in this embodiment improves the retrieval efficiency and accuracy of target data packets by using scene identifiers to target and filter datasets based on the data set architecture classified and stored according to control flow and data flow, according to the data analysis results quantifying data usage and triggering alarm logic by comparing with historical thresholds. This achieves accurate identification and timely warning of abnormal data usage behavior, improves the accuracy of anomaly detection, and also improves the timeliness of risk response.

[0099] Continue to refer to Figure 7-1 , Figure 7-1 A schematic diagram 700 of an information generation system according to the present disclosure is shown, which includes: a cloud platform 710, a client 702, and a server 703.

[0100] The cloud platform 701 receives request information and determines the target identifier information of the target data based on this information. Request information can be text or voice input directly by the user, representing the specific content of the information the user wants to obtain. The aforementioned execution entity can determine the identifier information of the target data, i.e., the target identifier information, based on the request information. The target identifier information can include the user ID that inputs the request information; the execution entity can determine the user ID based on the sending identifier of the request information. Target identifier information can also include the data identifier information of the target data, i.e., the data identifier information of the data required by the user. This data identifier information can include information such as the data type and the corresponding time period. In this case, the execution entity can generate the data identifier information based on the attribute information (type, corresponding time period, etc.) of the data required by the user (i.e., the target data). Target identifier information can also include scenario identifier information, i.e., the identifier information corresponding to a business scenario. In this case, the execution entity can perform semantic analysis on the request information to determine the corresponding business scenario based on the semantic analysis results, and thus determine the scenario identifier information.

[0101] Client 702 is used to determine the target data packet corresponding to the target identifier information from a pre-built dataset. The execution entity first constructs a standardized set of search conditions based on user identifiers, data identifiers, etc., in the target identifier information. This set is in key-value pair format. Then, the natural language-style requirement identifiers are converted into search syntax supported by the target database. This involves converting the natural language-style search conditions in the search condition set into search statements supported by the dataset, such as SQL (Structured Query Language) statements, along with invalid data filtering rules. These invalid data filtering rules can include data integrity checks, data validity checks, and permission compliance checks, thereby ensuring search accuracy. Next, the execution entity uses the generated structured search statements to perform search operations in the dataset, filtering invalid data through multiple layers to obtain the filtered target dataset. Finally, the execution entity encapsulates the filtered target data in a standard format and adds verification information and security identifiers, resulting in a standardized and verifiable target data packet.

[0102] The server-side 703 function is used to generate a usage link diagram of the target data based on the target data packet; and to generate corresponding data analysis results based on the usage link diagram. The execution entity first extracts the outer metadata of the target data packet and the MD5 checksum from the verification information. Then, it calculates the MD5 value of the received data packet and compares it with the MD5 checksum in the verification information. If they match, the verification passes, thus preventing packet loss or data tampering. Next, it determines the core data required for the usage link diagram, such as the source dimension, target dimension, and data traffic value. It parses the target data packet and extracts the data for the target dimension based on the parsing results. The source dimension (i.e., the left node of the usage link diagram) can include time slices and traffic types, while the target dimension can include business scenario names (e.g., offline download, video preview, login interface, etc.). Grouping by source dimension + target dimension, the total data traffic (MB) of each group is accumulated to form a traffic mapping relationship between time slice + traffic type and business scenario, thus obtaining the source data for the usage link diagram. Furthermore, the execution entity filters groups with a total traffic of less than 0.1MB to prevent minor traffic from interfering with the core view. Finally, the aforementioned execution entity acquires preset visualization styles, layout rules, etc., loads source data, and generates a usage link diagram, which can be a Sankey diagram. The execution entity first generates multiple segments of data usage information based on the usage link diagram, and then analyzes and summarizes these segments to obtain the data analysis results.

[0103] Cloud platform 701 is also used to receive and display data analysis results.

[0104] Continue to refer to Figure 7-2 , Figure 7-2 An interactive flow of an information generation system according to this disclosure is shown, including: First, the client intercepts network requests using a network interceptor. Then, a classification engine categorizes these requests into control flow and data flow. Different methods are used to aggregate and store different types of requests. The system also reports battery level and network type to the scheduler.

[0105] Specifically, the reporting scheduler monitors system power consumption and network type: it only triggers when the device is idle and the network is Wi-Fi; for control flow, it reports aggregation results every 500 entries or 1 day, and for data flow, it reports every 50MB. The reported content is only the dimension + cumulative value, without URL or header, and the single packet is <2KB, reducing traffic by 95% compared to traditional full logs.

[0106] The server receives the aggregated data through the receiving channel and writes the aggregation results directly to the message queue, using user + date + scenario as the primary key. Real-time water level refers to a real-time, dynamic quantitative monitoring indicator of data usage (such as traffic consumption) for users / business scenarios based on aggregated data reported by clients. Its core is to reflect the real-time changes and trends in data usage, used to promptly identify abnormal fluctuations.

[0107] Specifically, the server receives data in real time through a receiving interface and calculates the user's hourly cumulative traffic using a scrolling window. If the traffic exceeds a baseline, an alarm is triggered and pushed to relevant personnel; a red alarm pop-up appears on the operations platform. If the traffic does not exceed the baseline, cold storage is implemented. Cold storage refers to a storage model that persistently preserves aggregated traffic data with low access frequency and long storage periods using low-cost, high-capacity storage media and management strategies.

[0108] Users can perform operations through the operation center's entry point, sending commands (carrying user ID, time range, etc.) to the server's configuration center with one click. The configuration center can retrieve relevant logs from the client's raw log pool. In other words, after receiving the logs, the client retrieves the local SQLite+ file cache, packages and encrypts the raw logs, and sends them back to the server for case review.

[0109] Continue to refer to Figure 7-3 , Figure 7-3 A flow diagram of a classification process according to the requests of this disclosure is shown, including: Step 7301, intercept the request; Step 7302: Determine if the requested data is less than 1MB. If yes, proceed to step 7303; otherwise, proceed to step 7306. Step 7303: Request a single packet smaller than 1MB and of type API (Application Programming Interface) or JSON; Step 7304, mark it as Tag=C control flow; Step 7305: Write to the control flow accumulator; Step 7306: Request a single packet greater than or equal to 1MB or an audio / video stream; Step 7307, mark the data stream with Tag=D; Step 7308: Write to the data stream accumulator; Step 7309: Determine whether the reporting conditions are met. If yes, proceed to step 7310; otherwise, proceed to step 7311. For control flow: The memory accumulator accumulates the number of requests, uplink / downlink bytes, and time consumption according to the dimension of "interface identifier + return code". When the cumulative total reaches 500 requests or 24 hours, it triggers disk write.

[0110] For data streams: Accumulate traffic based on the dimension of "business scenario + file URI prefix", and write to disk when the cumulative amount is ≥50MB or when the application goes to the background.

[0111] Step 7310, write binary data to disk; Step 7311, continue collecting.

[0112] Continue to refer to Figure 7-4 , Figure 7-4 An application sequence of the information generation method according to this disclosure is shown, including: This application scenario is a customer service scenario. A user calls customer service saying, "I used 2GB of data last night." Customer service enters the user's UID and time period on the operations platform, clicks "Retrieve," and sends a retrieval command to the configuration center. The configuration center then transmits the command to the application via MQTT (Message Queuing Telemetry Transport). The application retrieves an SQLite file, obtains a compressed raw log package from the raw log pool, and encrypts it via HTTPS (Hypertext Transfer Protocol Secure) before sending it back to the configuration center. The configuration center automatically parses the data and generates a Sankey diagram. The operations platform displays the generated Sankey diagram to the customer service personnel, allowing them to visually understand the usage of relevant data. For example, the customer service representative can see "1.8GB downloaded offline from 02:00 to 02:30," enabling minute-level identification of the cause and informing the user of the results.

[0113] Further reference Figure 8 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of an information generation apparatus, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0114] like Figure 8 As shown, the information generation device 800 of this embodiment includes: an identifier determination module 801, a data determination module 802, a link diagram generation module 803, and a result generation module 804. Specifically, the identifier determination module 801 is configured to determine target identifier information of target data based on received demand information; the data determination module 802 is configured to determine the target data packet corresponding to the target identifier information from a pre-constructed data set; the link diagram generation module 803 is configured to generate a usage link diagram of the target data based on the target data packet; and the result generation module 804 is configured to generate data analysis results corresponding to the target data based on the usage link diagram.

[0115] In this embodiment, the specific processing of the identifier determination module 801, data determination module 802, link diagram generation module 803, and result generation module 804 in the information generation device 800, and the resulting technical effects thereon, can be found in the following references: Figure 2 The relevant descriptions of steps 201-204 in the corresponding embodiments will not be repeated here.

[0116] In some optional implementations of this embodiment, the target identification information includes: user identification information and data identification information; and the identification determination module 801 is further configured to: generate user identification information based on the sending identifier of the demand information; and generate data identification information based on the attribute information of the target data.

[0117] In some optional implementations of this embodiment, the data determination module 802 includes: a condition generation submodule, configured to generate a set of search conditions based on user identification information and data identification information; a statement generation submodule, configured to generate a structured search statement based on the set of search conditions; and a search submodule, configured to perform a search in the data set using the structured search statement to obtain the target data packet.

[0118] In some optional implementations of this embodiment, the retrieval submodule is further configured to: use structured retrieval statements to search the dataset to obtain the target dataset; encapsulate the target dataset according to a preset format and add verification information to obtain the target data packet.

[0119] In some optional implementations of this embodiment, the data set includes: datasets corresponding to different request categories; and the information generation device 800 further includes a data set generation module, comprising: a category determination submodule, configured to determine the request category corresponding to each network request based on the data size of the network request, wherein the request categories include: control flow requests and data flow requests; and a data storage submodule, configured to store the data corresponding to each request category according to the storage method corresponding to each request category, thereby obtaining datasets corresponding to different request categories.

[0120] In some optional implementations of this embodiment, the data storage submodule is further configured to: for control flow requests, in response to determining that the number of control flow data corresponding to the control flow request reaches a preset number, aggregate and store the control flow data to obtain the control flow dataset corresponding to the control flow request; for data flow requests, in response to determining that the data size of the data flow data corresponding to the data flow request reaches a preset size, aggregate and store the data flow data to obtain the data flow dataset corresponding to the data flow request.

[0121] In some optional implementations of this embodiment, the target identification information includes: scene identification information; and the data determination module 802 is further configured to: determine the dataset to be retrieved from the dataset according to the scene identification information, wherein the dataset to be retrieved is a control flow dataset or a data flow dataset; and perform a search in the dataset to be retrieved to obtain the target data packet.

[0122] In some optional implementations of this embodiment, the link graph generation module 803 is further configured to: verify the target data packet according to the verification information; in response to determining that the target data packet passes the verification, parse the target data packet, extract the target dimension data according to the parsing result, and generate a link graph based on the target dimension data.

[0123] In some optional implementations of this embodiment, the result generation module 804 is further configured to: generate multiple data usage information segments based on the usage link diagram; analyze and summarize the multiple data usage information segments to obtain data analysis results.

[0124] In some optional implementations of this embodiment, the information generation device 800 further includes: a usage determination module, configured to determine the data usage within a preset time period based on data analysis results; and an alarm generation module, configured to generate alarm information in response to determining that the data usage exceeds a historical threshold.

[0125] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0126] Figure 9 A schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0127] Figure 9A schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0128] like Figure 9 As shown, device 900 includes a computing unit 901, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 902 or a computer program loaded from storage unit 908 into random access memory (RAM) 903. RAM 903 may also store various programs and data required for the operation of device 900. The computing unit 901, ROM 902, and RAM 903 are interconnected via bus 904. Input / output (I / O) interface 905 is also connected to bus 904.

[0129] Multiple components in device 900 are connected to I / O interface 905, including: input unit 906, such as keyboard, mouse, etc.; output unit 907, such as various types of monitors, speakers, etc.; storage unit 908, such as disk, optical disk, etc.; and communication unit 909, such as network card, modem, wireless transceiver, etc. Communication unit 909 allows device 900 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0130] The computing unit 901 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 901 performs the various methods and processes described above, such as information generation methods. For example, in some embodiments, the information generation method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed on device 900 via ROM 902 and / or communication unit 909. When the computer program is loaded into RAM 903 and executed by the computing unit 901, one or more steps of the information generation method described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform information generation methods by any other suitable means (e.g., by means of firmware).

[0131] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0132] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0133] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0134] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0135] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0136] Cloud computing refers to a technological system that enables access to elastic and scalable shared physical or virtual resources via a network. These resources can include servers, operating systems, networks, software, and storage devices, and can be deployed and managed in an on-demand, self-service manner. Cloud computing technology can provide efficient and powerful data processing capabilities for applications such as artificial intelligence and blockchain, as well as for model training.

[0137] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and Virtual Private Server (VPS) services, such as high management difficulty and weak business scalability.

[0138] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0139] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. An information generation method, comprising: In response to receiving demand information, the target identifier information of the target data is determined based on the demand information; The target data packet corresponding to the target identification information is determined from a pre-constructed dataset; Generate a usage link diagram of the target data based on the target data packet; The data analysis results corresponding to the target data are generated based on the link diagram.

2. The method according to claim 1, wherein, The target identification information includes: user identification information and data identification information; and The step of determining the target identifier information of the target data based on the demand information includes: The user identification information is generated based on the sending identifier of the demand information; The data identification information is generated based on the attribute information of the target data.

3. The method according to claim 2, wherein, Determining the target data packet corresponding to the target identification information from a pre-built dataset includes: A set of search criteria is generated based on the user identification information and the data identification information; Generate a structured search statement based on the set of search criteria; The target data packet is obtained by searching the data set using the structured search statement.

4. The method according to claim 3, wherein, The process of using the structured search statement to search the data set and obtain the target data packet includes: The target dataset is obtained by using the structured search statement to perform a search in the dataset. The target dataset is encapsulated according to a preset format and verification information is added to obtain the target data packet.

5. The method according to claim 1, wherein, The data set includes: datasets corresponding to different request categories; and The dataset is generated through the following steps: For each network request, the request category corresponding to the network request is determined based on the data size of the network request, wherein the request category includes: control flow request and data flow request; The data corresponding to each request category is stored according to the storage method corresponding to each request category, thus obtaining the dataset corresponding to each different request category.

6. The method according to claim 5, wherein, The step of storing the data corresponding to each request category according to the storage method corresponding to each request category to obtain the dataset corresponding to the different request categories includes: For the control flow request, in response to determining that the number of control flow data corresponding to the control flow request has reached a preset number, the control flow data is aggregated and stored to obtain the control flow dataset corresponding to the control flow request. For the data stream request, in response to determining that the data size of the data stream data corresponding to the data stream request has reached a preset size, the data stream data is aggregated and stored to obtain the data stream dataset corresponding to the data stream request.

7. The method according to claim 6, wherein, The target identification information includes: scene identification information; and Determining the target data packet corresponding to the target identification information from a pre-built dataset includes: The dataset to be retrieved is determined from the dataset based on the scene identification information, wherein the dataset to be retrieved is the control flow dataset or the data flow dataset; The target data packet is obtained by performing a search on the dataset to be searched.

8. The method according to claim 4, wherein, The step of generating a usage link graph of the target data based on the target data packet includes: The target data packet is verified according to the verification information; In response to determining that the target data packet has passed the verification, the target data packet is parsed, and data of the target dimension is extracted based on the parsing result; The usage link graph is generated based on the data of the target dimension.

9. The method according to claim 1, wherein, The step of generating the data analysis results corresponding to the target data based on the link diagram includes: Based on the aforementioned usage link diagram, generate multiple segments of data usage information; The data segments are analyzed and summarized using information to obtain the data analysis results.

10. The method according to any one of claims 1-9, further comprising: The data usage within a preset time period is determined based on the data analysis results. In response to determining that the data usage exceeds a historical threshold, an alarm message is generated.

11. An information generation apparatus, comprising: The identifier determination module is configured to determine the target identifier information of the target data based on the received demand information in response to receiving demand information. The data determination module is configured to determine the target data packet corresponding to the target identification information from a pre-constructed data set; The link graph generation module is configured to generate a usage link graph of the target data based on the target data packet; The result generation module is configured to generate data analysis results corresponding to the target data based on the usage link diagram.

12. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method of any one of claims 1-10.

13. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-10.

14. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-10.

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