Business processing method and device, computer equipment, storage medium and program product

Abstract

The embodiment of the invention provides a business processing method and device, computer equipment, a storage medium and a program product. The business processing method comprises the following steps: acquiring a first dyeing identifier associated with a first use case in business; the first dyeing identifier is associated with the first use case in a first operation process injected into the first use case, and an operation log generated by the first use case in the first operation process comprises the first dyeing identifier; obtaining a running log of the first use case associated with the first dyeing identifier; the running log is used for recording calling information of the entity function in the first use case in the first running process; and performing content identification processing on the running log to obtain an entity to which the entity function in the first use case belongs in the first use case and popularity information of the entity in the first running process. By adopting the embodiment of the invention, the popularity information of the entity in the use case in the running process of the use case can be efficiently determined.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a business processing method, a business processing device, a computer device, a computer readable storage medium, and a computer program product. BACKGROUND

[0002] In a software system with complex business, locating and optimizing performance bottlenecks is a challenging task. Analyzing the hotness information of entities in use cases in the software system is an effective means to locate and optimize performance bottlenecks of the software system. At present, traditional debugging and optimization methods often rely on manual analysis of running logs and codes to analyze the hotness information of entities in use cases in the running process of the software system. However, the efficiency of manually collecting running logs and manually analyzing running logs is low, and it is difficult to cope with the complexity of large-scale systems. Therefore, how to efficiently determine the hotness information of entities in use cases in the running process of the use cases has become a research hotspot. SUMMARY

[0003] The embodiments of the present application provide a business processing method, device, computer device, storage medium and program product, which can efficiently determine the hotness information of entities in use cases in the running process of the use cases.

[0004] In one aspect, the embodiments of the present application provide a business processing method, which comprises:

[0005] obtaining a first dyeing identifier associated with a first use case in a business; running the first use case to execute a first sub-business in the business; the first dyeing identifier is associated with the first use case by injecting the first dyeing identifier in a first running process of the first use case, and a running log generated by the first use case in the first running process contains the first dyeing identifier;

[0006] obtaining a running log of the first use case associated with the first dyeing identifier; the running log is used to record the calling information of an entity function in the first use case in the first running process;

[0007] performing content recognition processing on the running log to obtain an entity to which the entity function in the first use case belongs in the first use case, and the hotness information of the entity in the first running process; the hotness information of the entity in the first running process is used to indicate the importance of the entity in the first use case to the first running process of the first use case.

[0008] Correspondingly, the embodiments of the present application provide a business processing device, which comprises:

[0009] The acquisition unit is configured to acquire a first dyeing identifier associated with a first use case in a service; the first use case is configured to execute a first sub-service in the service; the first dyeing identifier is configured to be associated with the first use case by being injected into a first running process of the first use case; and a running log generated by the first use case in the first running process contains the first dyeing identifier.

[0010] The acquisition unit is configured to acquire a running log of the first use case associated with the first dyeing identifier; and the running log is configured to record calling information of an entity function in the first use case in the first running process.

[0011] The processing unit is further configured to perform content recognition processing on the running log to obtain an entity to which the entity function in the first use case belongs in the first use case and first heat information of the entity in the first running process; and the first heat information of the entity in the first running process is configured to indicate an importance of the entity in the first use case to the first running process of the first use case.

[0012] In an implementation manner, the running log includes a plurality of pieces of running log, and the first use case includes a plurality of entities; the processing unit is configured to perform content recognition processing on the running log to obtain an entity to which the entity function in the first use case belongs in the first use case and first heat information of the entity in the first running process, and specifically configured to perform the following steps:

[0013] performing content recognition processing on each piece of running log to obtain an entity to which an entity function recorded in each piece of running log belongs in the first use case and first heat information of the entity under the corresponding running log;

[0014] performing aggregation processing on the first heat information of each entity in the first use case under different running logs to obtain the heat information of each entity in the first use case in the first running process.

[0015] In an implementation manner, the processing unit is configured to perform content recognition processing on each piece of running log to obtain an entity to which an entity function recorded in each piece of running log belongs in the first use case and first heat information of the entity under the corresponding running log, and specifically configured to perform the following steps:

[0016] recognizing a log type of the running log;

[0017] acquiring a content recognition strategy matched with the log type of the running log;

[0018] performing content recognition processing on the running log according to the content recognition strategy to obtain an entity to which an entity function recorded in the running log belongs and first heat information of the entity under the running log.

[0019] In an implementation manner, the processing unit is configured to perform content identification processing on the running log according to a content identification strategy, to obtain an entity to which an entity function recorded in the running log belongs, and first heat information of the entity under the running log, and specifically configured to perform the following steps:

[0020] performing semantic understanding on log content of the running log according to a semantic understanding rule indicated by the content identification strategy, to obtain an entity function recorded in the running log, and heat characteristics of the entity function;

[0021] performing entity type identification on the entity function recorded in the running log, to obtain an entity to which the entity function recorded in the running log belongs;

[0022] determining the heat characteristics as the first heat information of the entity under the running log.

[0023] In an implementation manner, N target running logs in the plurality of running logs are used to record that a target entity function in the first use case is called at the same time, the target entity function belongs to a target entity, and N is an integer greater than 1; the processing unit is further configured to perform the following steps:

[0024] obtaining first heat contribution weights of the N target running logs to the target entity;

[0025] adjusting the first heat contribution weights of the N target running logs to the target entity, to obtain second heat contribution weights of the N target running logs to the target entity;

[0026] adjusting the first heat information of the target entity under the N target running logs according to the second heat contribution weights of the N target running logs to the target entity.

[0027] In an implementation manner, any entity in the first use case is represented as a reference entity; entities to which entity functions recorded in M reference running logs in the plurality of running logs belong are the reference entity, and M is an integer greater than 1; the processing unit is configured to perform aggregation processing on the first heat information of each entity in the first use case under different running logs, to obtain heat information of each entity in the first use case in the first running process, and specifically configured to perform the following steps:

[0028] obtaining heat influence information of an entity function recorded in each reference running log in the M reference running logs to the reference entity;

[0029] determining heat influence weights of the entity function recorded in each reference running log to the reference entity according to the heat influence information of the entity function recorded in each reference running log to the reference entity;

[0030] The first heat information of the reference entity under the M reference running logs is weighted and summed to obtain the heat information of the reference entity in the first running process according to the heat influence weight of the entity function recorded in each reference running log on the reference entity.

[0031] In an implementation manner, any entity in the first use case is represented as a reference entity; the entity to which the entity function recorded in M reference running logs in the plurality of running logs belongs is the reference entity, and M is an integer greater than 1; the processing unit is configured to aggregate the first heat information of each entity in the first use case under different running logs to obtain the heat information of each entity in the first use case in the first running process, and specifically configured to perform the following steps:

[0032] The first heat information of the reference entity under the M reference running logs is weighted and summed to obtain the heat information of the reference entity in the first running process according to the heat influence weight of the entity function recorded in each reference running log on the reference entity.

[0033] In an implementation manner, the first use case is further associated with a second dyeing identifier, the second dyeing identifier is associated with the first use case by being injected into a second running process of the first use case, and the running log generated in the second running process of the first use case contains the second dyeing identifier; the processing unit is further configured to perform the following steps:

[0034] The heat information of each entity in the first use case in the second running process is obtained.

[0035] The heat information of the same entity in the first use case in different running processes is summarized to obtain the comprehensive heat information of each entity in the first use case.

[0036] In an implementation manner, the business further includes a second use case, the second use case is run to execute a second sub-business in the business, and the first use case and the second use case belong to a use case set in the business; the processing unit is further configured to perform the following steps:

[0037] If the first entity is included in both the first use case and the second use case, the comprehensive heat information of the first entity in the first use case and the comprehensive heat information of the first entity in the second use case are summarized to obtain the comprehensive heat information of the first entity in the use case set;

[0038] If the first entity is included in the first use case and not included in the second use case, the comprehensive heat information of the first entity in the first use case is determined as the comprehensive heat information of the first entity in the use case set;

[0039] If the first entity is not included in the first use case and included in the second use case, the comprehensive heat information of the first entity in the second use case is determined as the comprehensive heat information of the first entity in the use case set.

[0040] In an implementation manner, when the processing unit is used to aggregate the comprehensive hotness information of the first entity in the first use case and the comprehensive hotness information of the first entity in the second use case to obtain the comprehensive hotness information of the first entity in the use case set, the processing unit is specifically used to perform the following steps:

[0041] obtain the use case weight of the first use case and the use case weight of the second use case;

[0042] perform weighted processing on the comprehensive hotness information of the first entity in the first use case by using the use case weight of the first use case to obtain the weighted hotness information of the first entity in the first use case;

[0043] perform weighted processing on the comprehensive hotness information of the first entity in the second use case by using the use case weight of the second use case to obtain the weighted hotness information of the first entity in the second use case;

[0044] accumulate the weighted hotness information of the first entity in the first use case and the weighted hotness information of the first entity in the second use case to obtain the comprehensive hotness information of the first entity in the use case set.

[0045] In an implementation manner, the comprehensive hotness information of each entity in the first use case includes comprehensive hotness information of each entity in the first use case under different hotness indexes; and the processing unit is further used to perform the following steps:

[0046] obtain a target hotness index selected under different hotness indexes;

[0047] output the comprehensive hotness information of each entity in the first use case under the target hotness index.

[0048] Correspondingly, an embodiment of the present application provides a computer device, which comprises:

[0049] a processor adapted to implement a computer program;

[0050] a computer readable storage medium, the computer readable storage medium storing a computer program, the computer program being adapted to be loaded and executed by the processor to perform the business processing method described above.

[0051] Correspondingly, an embodiment of the present application provides a computer readable storage medium, the computer readable storage medium storing a computer program, the computer program being read and executed by the processor of the computer device to cause the computer device to perform the business processing method described above.

[0052] Correspondingly, the embodiment of the present application provides a computer program product, which comprises a computer program stored in a computer readable storage medium. A processor of a computer device reads the computer program from the computer readable storage medium, and the processor executes the computer program, so that the computer device executes the business processing method.

[0053] In the embodiment of the present application, by injecting the dyeing identifier in the running process of the use case, the running log generated in the running process of the use case can be quickly obtained based on the injected dyeing identifier, so that the running log generated in the running process of the use case can be efficiently collected. Moreover, by performing the content recognition processing on the running log, the heat information of the entity to which the entity function recorded in the running log belongs can be quickly determined, so that the running log can be efficiently analyzed to determine the heat information of the entity in the use case in the running process. That is, the embodiment of the present application can improve the efficiency in the collection stage and the analysis stage of the running log, so that the heat information of the entity in the use case in the running process of the use case can be efficiently determined. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0055] Figure 1 is an architecture schematic diagram of a business processing system provided by the embodiment of the present application;

[0056] Figure 2 is a function schematic diagram of a business service background provided by the embodiment of the present application;

[0057] Figure 3 is a flow schematic diagram of a business processing method provided by the embodiment of the present application;

[0058] Figure 4 is another identification flow schematic diagram of a running log provided by the embodiment of the present application;

[0059] Figure 5 is a flow schematic diagram of another business processing method provided by the embodiment of the present application;

[0060] Figure 6 is a schematic diagram of entity heat summary between use cases provided by the embodiment of the present application;

[0061] Figure 7 is a structure schematic diagram of a business processing device provided by the embodiment of the present application;

[0062] Figure 8 is a structural schematic diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0064] In order to make the technical solutions provided by the embodiments of the present application more clearly understood, the technical terms related to the embodiments of the present application are introduced first here:

[0065] I. Business

[0066] Business refers to a task or transaction performed by a computer object. The computer object can include, but is not limited to, an application program, software, an applet, or a Web (World Wide Web, Global Wide Area Network, or World Wide Web) application program, etc. For example, the business can include a game business, an instant messaging business, or a resource delivery business, etc. In the game business, a game player can play a game through a computer object corresponding to the game business. In the instant messaging business, an instant messaging object can perform instant messaging through a computer object corresponding to the instant messaging business. In the resource delivery business, a resource delivery object can perform resource delivery through a computer object corresponding to the resource delivery business.

[0067] The business can include sub-businesses under different business types, and the sub-business can be understood as a business function in the business. For example, the resource delivery business can include a resource account registration sub-business and a resource transfer sub-business, etc. The resource account registration sub-business can be understood as a resource account registration function in the business, and the resource transfer sub-business can be understood as a resource transfer function in the business.

[0068] II. Use Case

[0069] A use case corresponds to a sub-business in a business. The use case can be used to describe the execution logic of the sub-business. Running the use case can be used to execute the corresponding sub-business, that is, the sub-business needs to be executed by calling the corresponding use case. For example, a resource delivery business can include a resource account registration sub-business and a resource transfer sub-business. The resource delivery business can include a resource account registration use case and a resource transfer use case. The resource account registration sub-business corresponds to the resource registration use case, and the resource transfer sub-business corresponds to the resource transfer use case. The resource account registration sub-business needs to call the resource account registration use case for execution, and the resource transfer sub-business needs to call the resource transfer use case for execution.

[0070] A use case can include one or more use case steps. Running each use case step of the use case can implement the running of the use case. Each use case step can be executed by calling one or more services, that is, the running process of the use case refers to the running process of each service involved in the use case, and each service can call each other. The service is executed by calling an entity function (or can be referred to as an entity method), that is, the running process of the use case can be understood as the running process of each entity function involved in the use case. In the running process of the use case, the calling link of the use case refers to the link composed of each entity function called in a complete running process of the use case.

[0071] III. Entity

[0072] An entity can be a service, a class, a method, etc. in a use case, and plays a specific role in the calling link of the use case. The embodiment of the present application takes the entity as a class as an example. When the entity is a class, the entity can be referred to as an entity class. A use case can include one or more entities, and an entity can include one or more entity functions.

[0073] IV. Graph ID

[0074] The graph ID is a kind of identifier, which can be injected and passed in the calling link of the use case. A running process of a use case can be associated with a graph ID. A graph ID can be associated with a use case by being injected into a running process of the use case. By injecting and passing the graph ID in the calling link of the use case, the running log generated in the running process of the use case can contain the graph ID injected in the running process, so that the log, the entity and the specific calling link of the use case can be associated through the graph ID, for tracing and positioning the entity.

[0075] The running log can be used to record the calling of the entity function in the use case during the running of the use case. The running log can include one or more levels of running logs, for example, the running log can include at least one level of running logs: entity function level running log, service level running log, full link calling log, etc. Among them, the entity function level running log refers to the log reported by the entity function, which can be used to record the calling of the entity function; the service level running log refers to the log reported by the service, which can be used to record the calling of the entity function in the service; the full link calling log refers to the log reported by the whole running process of the use case, which records the log containing the complete calling link information of the use case, and is used to trace and analyze the execution path and behavior of the use case. The full link calling log can be used to record the calling of the entity function in the whole process of the use case running.

[0076] The running log can also include one or more types of running logs, such as RPC (Remote Procedure Call) request log, data change log, and breakpoint log, etc.

[0077] Based on the introduction of the above technical terms, the embodiment of the present application provides a business processing method, which provides a technical scheme of full link calling log positioning and runtime entity class hot spot distribution visualization based on artificial intelligence model. The scheme realizes the calling link positioning and entity hot spot analysis of the complex business processing system by combining artificial intelligence model and full process dyeing identification. By associating the calling link, entity information and dyeing identification, the hot spot area and performance bottleneck of the entity in the business processing system can be accurately identified, and a visual hot spot map is provided to show the heat distribution of the entity in runtime. This provides a new method and tool for business processing system performance optimization and fault troubleshooting, improves the efficiency and reliability of the business processing system, and reduces the risk of failure. The scheme has wide application prospect and important practical value in large-scale software systems and distributed applications.

[0078] In the above introduction of the business processing method, the artificial intelligence model can be a natural language processing model, for example, GPT. Among them:

[0079] An artificial intelligence model is a model related to the field of artificial intelligence (AI). Artificial intelligence is a theory, method, technology, and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a similar way to human intelligence. Artificial intelligence is the design principle and implementation method of various intelligent machines, enabling machines to have perception, reasoning, and decision-making functions.

[0080] Artificial intelligence technology is a comprehensive discipline and involves a wide range of fields, including both hardware and software technologies. Artificial intelligence basic technologies generally include, for example, sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, pre-training model technology, operation / interaction systems, mechatronics, and the like. Among them, the pre-training model, also known as the large model or basic model, can be widely applied to downstream tasks in various directions of artificial intelligence after fine-tuning. Artificial intelligence software technologies mainly include computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning, and the like.

[0081] A natural language processing model refers to a model related to the field of natural language processing (NLP). Natural language processing is an important direction in the field of computer science and artificial intelligence. It studies various theories and methods that enable effective communication between humans and computers using natural language. Natural language processing involves natural language, i.e., the language used in daily life, and is closely related to linguistic studies. At the same time, it involves computer science and mathematics. The pre-training model, an important technology for training models in the field of artificial intelligence, is developed from a large language model (LLM) in the field of NLP. After fine-tuning, the large language model can be widely applied to downstream tasks. Natural language processing techniques generally include text processing, semantic understanding, machine translation, robot question answering, knowledge graph, and the like.

[0082] GPT (Generative Pre-Trained) is a natural language processing model based on the Transformer architecture, which has strong language generation and understanding capabilities.

[0083] The business processing system provided by the embodiments of the present application is introduced below, and the business processing system is suitable for implementing the business processing method provided by the embodiments of the present application.

[0084] AsFigure 1 As shown, the business processing system can include a business request front end 101 and a business service back end 102. The present embodiment does not limit the communication connection mode between the business request front end 101 and the business service back end 102. The business request front end 101 and the business service back end 102 can establish a direct communication connection through wired communication, or the business request front end 101 and the business service back end 102 can establish an indirect communication connection through wireless communication. Figure 1 As shown, the business processing system, taking the number of business request front ends 101 connected to the business service back end 102 as two for example, the number of business request front ends 101 connected to the business service back end 102 in the business processing system in actual operation can be one or more. The present embodiment does not limit the number of business request front ends connected to the business service back end 102.

[0085] I. Business request front end 101

[0086] The business request front end 101 can be a terminal running business client. The business client can include but is not limited to any one of the following: a business application, a business software, a business applet, or a business web application. The terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart voice interaction device, a smart watch, a vehicle-mounted terminal, a smart home appliance, an aircraft, etc., but is not limited thereto. The business request front end 101 can generate a business processing request (e.g., a palm resource delivery request) in response to a business operation (e.g., a palm resource delivery operation) of a business personnel. The business request front end 101 can send the business processing request to the business service back end 102 through the terminal.

[0087] II. Business service back end 102

[0088] The business service back end 102 can be a single physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms, etc. Basic cloud computing services.

[0089] The business service back end 102 can be used to provide background services for the business request front end 101. The background services provided by the business service back end 102 for the business request front end can include: responding to a business processing request, executing a use case corresponding to a sub-business (e.g., a palm payment sub-business) requested to be executed by the business processing request, to execute the sub-business requested to be executed by the business processing request.

[0090] In addition to providing background services for the business request front end 101, the business service background 102 also has the function of analyzing the hotness information of the entities in the use case. The function of analyzing the hotness information of the entities in the use case mainly involves three stages, which are the whole dyeing identification generation injection stage, the entity positioning hotness analysis stage, and the entity hotness distribution visualization stage. Among them:

[0091] (1) Whole dyeing identification generation injection stage:

[0092] The whole dyeing identification generation injection stage can be executed by the whole dyeing identification generation injection module (hereinafter referred to as the first module) of the business service background 102. As shown in Figure 2 In the whole dyeing identification generation injection stage, after receiving the business processing request sent by the business personnel through the business request front end 101, the first module can generate a dyeing identification at the entrance of the service (the entrance here can be the entrance of the SOA (Service-Oriented Architecture, Service-Oriented Architecture) microservice request), determine the use case to be called by the business processing request, and based on the framework context, the dyeing identification is transmitted in the running process of the use case (the running process of the use case specifically refers to the running process of service 1, service 2…service n). When printing and recording the running log of the use case, in addition to printing the calling situation of the entity function in the use case, the dyeing identification can be printed and recorded synchronously. It can be understood that the running log of the use case can contain the transmitted dyeing identification and the calling situation of the entity function in the use case. The dyeing identification has the ability to pass between multiple interfaces. In the running process, the first module can inject the dyeing identification when calling the interface, especially when calling the entity function in the use case, so that the running log that records the calling situation of the entity function can contain the dyeing identification. In particular, in the use case implemented in Java (a kind of object-oriented programming language), it can be implemented through the way of annotation, so as to print the dyeing identification into the running log when calling the interface or the entity function.

[0093] That is, when calling the entity function of the entity, the dyeing identification should be injected into the entity function, so as to record and trace the dyeing identification in the execution process of the entity function, which can ensure the association between the calling of the entity function and the dyeing identification, and provide data support for subsequent entity hotspot analysis.

[0094] It should be noted that the generation method of the colored identifier also needs to be agreed in this module (i.e., the first module). It should also be noted that the reporting of the colored identifier is in the manner of use cases, so the colored identifier can be used to trace across use cases. The purpose of tracing the use case is to be able to sort out the entire call link of the use case, so as to determine the entities on the entire call link, so that the subsequent module can better trace the entities.

[0095] (2) Entity positioning heat analysis stage:

[0096] The entity positioning heat analysis stage can be executed by an entity positioning heat analysis module (hereinafter referred to as the second module) of the business service background 102. As shown in Figure 2 In the entity positioning heat analysis stage, for any use case (taking use case 1 as an example), the second module can read the running log set (or can be referred to as a running log file or a running log stream) of use case 1 in a running process (i.e., running process 1 injected by the associated colored identifier 1) based on a colored identifier (taking colored identifier 1 as an example) associated with the use case, and provide it as input to the GPT model (which can be referred to as a content recognition model) in the second module for processing. The GPT model in the second module can associate the log to a specific entity according to the content of the log. Through learning the model, different entities in the log can be understood and accurately matched. The GPT model in the second module can also identify the content of the running log, and determine the heat information of the entity according to the information of the calling times, the occurrence times, the calling time consumption, the calling time consumption proportion, and the calling error rate of the entity function identified. That is, the second module can analyze the running log generated in a running process of a use case by the GPT model in the module, locate the entity involved in the running process, and determine the heat information of the entity.

[0097] In addition, the second module can also concatenate the colored identifiers associated with the use case, and aggregate the heat information of the entity in different running processes (i.e., running processes injected by different colored identifiers (such as Figure 2 As shown in the colored identifier 1-colored identifier m) within an entity heat statistical period (for example, 5 minutes, 10 minutes, half an hour, 1 hour, 3 hours, etc.) to obtain the final heat information of the entity in the use case.

[0098] (3) Entity heat distribution visualization stage:

[0099] The entity heat distribution visualization stage can be executed by an entity heat distribution visualization module (hereinafter referred to as the third module) of the business service background 102. As shown in Figure 2As shown, in the entity heat distribution visualization phase, the third module aims to generate a visualized heat map based on the heat information of entities in use cases (e.g., the frequency of calls, the frequency of appearance in use cases, and other relevant information such as the time taken by entity function calls relative to the total time taken in the call chain). Through visualization, the heat distribution of entities during runtime can be intuitively understood, helping to analyze and optimize system performance and efficiency.

[0100] The third module can be developed around entity calls and provides freely configurable visualization options. It can be expanded and customized according to specific business needs and data characteristics. In one implementation, popularity analysis can be performed based on different popularity metrics (e.g., call count, occurrence frequency, call duration, call duration percentage); for example, a popularity metric can be selected, allowing for the visualization of an entity's popularity information under the chosen metric. In another implementation, popularity analysis can be performed based on different dimensions; for example, a single use case (e.g., ...) can be output. Figure 2 The popularity information of entities in use case 1) shown, and for example, can be applied to different use cases (e.g., Figure 2 The popularity information of the same entities between use cases 1 and 2 (as shown) is aggregated, and the popularity information of the entities after aggregation of multiple use cases is output. In another implementation, the third module can also support flexible visualization configuration, allowing customization of display styles, color mappings, chart types, etc., to meet the needs of different users.

[0101] The third module generates heatmaps based on entity popularity information. Heatmaps can be presented in various formats, such as matrix heatmaps, scatter plots, and bar charts. Through color mapping (e.g., different popularity information is mapped to different colors), the popularity distribution of entity classes during runtime can be visually displayed. Furthermore, the third module provides hotspot analysis functionality. Based on the visualization results of the heatmap, it analyzes the popularity of entity classes. For example, it can identify and highlight hotspot areas (i.e., entities with popularity information exceeding a popularity threshold, such as entities with call frequency or time exceeding the corresponding threshold), and identify and highlight entities with concentrated hotspots, helping to discover potential performance bottlenecks and optimization points. Additionally, the third module offers configuration options, allowing customization of the heatmap display. Users can select different indicators and dimensions for visualization according to their needs, and adjust parameters such as chart style and color mapping to obtain more informative and readable heatmaps.

[0102] Therefore, the business service background 102 realizes the positioning of entities in the use case running log based on the GPT model, the determination of the hotness information of the entities, and the visualization of the runtime entity hotspot information distribution, thereby providing the ability to comprehensively analyze and optimize the complex system, which can help the business processing system accurately identify performance bottlenecks and optimization points, improve the efficiency and reliability of the business processing system, and reduce the risk of failure.

[0103] It can be understood that the business processing system described in the embodiments of the present application is for more clearly illustrating the technical solutions of the embodiments of the present application, and does not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0104] The business processing method provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0105] The embodiments of the present application provide a business processing method, and the content introduced by the business processing method includes an entity hotness statistical manner for a running process of a use case. The business processing method can be executed by a computer device, for example, a business service background 102 in a business processing system. As shown in Figure 3 The business processing method can include but is not limited to the following steps S301-S303:

[0106] S301, obtaining a first dyeing identifier associated with a first use case in a business; running the first use case to execute a first sub-business in the business; the first dyeing identifier is associated with the first use case by being injected into a first running process of the first use case, and a running log generated by the first use case in the first running process contains the first dyeing identifier.

[0107] The business can include multiple use cases, and the first use case can be any use case in the business; that is, the hotness information of the entities in each use case in the business can be counted. Alternatively, the business can include multiple use cases, and the target use case is a use case selected from the multiple use cases included in the business, and the first use case can be any use case in the target use case; that is, the hotness information of the entities in each use case in the business does not need to be counted, but the hotness information of the entities in the selected use case in the business can be counted, and the use case to be analyzed can be flexibly selected; this is because in the business, the sub-businesses that are often more important are the focus of performance optimization and fault troubleshooting of the business processing system, and therefore, the use cases corresponding to these sub-businesses can be selected, and the hotness information of the entities in these use cases can be counted.

[0108] The use case in the business has a one-to-one correspondence with the sub-business in the business, that is, one use case corresponds to one sub-business, and running one use case is used to execute one sub-business in the business. For the first use case, the first use case corresponds to the first sub-business in the business, and running the first use case is used to execute the first sub-business in the business.

[0109] The running process of the use case can be injected with a dye mark, and a dye mark can be associated with the use case by injecting the running process of the use case. The running log generated by the use case in the running process can contain the dye mark injected by the use case in the running process. For the first dye mark associated with the first use case, the first dye mark can be associated with the first use case by injecting the first running process of the first use case, and the running log generated by the first use case in the first running process can contain the first dye mark.

[0110] Based on the introduction of the association between the use case and the dye mark, the first dye mark and the first use case are taken as an example to introduce the way of injecting the running process of the use case with the dye mark. The way of injecting the first running process of the first use case with the first dye mark can include: receiving a business processing request, the business processing request can be used to request to execute the first sub-business, the first sub-business is executed by the first running process of the first use case; generating the first dye mark corresponding to the business processing request; the first running process of the first use case can include the process of calling the entity function in the first use case through the interface and running the called entity function, and the injection of the first dye mark can be performed when the entity function is called, so that the running log recording the calling of the entity function in the first use case can contain the first dye mark.

[0111] The generation principle of the dye mark is uniqueness and randomness, and the purpose is to enable the dye mark to uniquely identify the execution process of the use case. The generation method of the first dye mark can include: converting the request information of the business processing request, the response information of the business processing request and the random information into an identifier to obtain the first dye mark. The request information of the business processing request can include but is not limited to at least one of the following: request time of the business processing request, and request content of the business processing request; the response information of the business processing request can include at least one of the following: interface information responding to the call of the business processing request, machine information (such as machine IP (Internet Protocol) address) responding to the business processing request, and thread information (such as thread ID) responding to the business processing request; the random information can include but is not limited to at least one of the following: time seconds, milliseconds, incremental ID (incremental number, for example, the incremental number is incremented by 1 for each dye mark generated), and random number.

[0112] It is worth noting that the hotness information of the entity in the use case in the business is counted according to the entity hotness counting period. In the entity hotness counting period, the use case can be run multiple times, and each running process is injected with a dyeing identifier. That is, the use case can be associated with multiple dyeing identifiers in the entity hotness counting period. For the first use case, the first use case can be associated with multiple dyeing identifiers in the current entity hotness counting period, and the first dyeing identifier can be any dyeing identifier associated with the first use case in the current entity hotness counting period.

[0113] In S302, the running log of the first use case associated with the first dyeing identifier is obtained; the running log is used to record the calling information of the entity function in the first use case in the first running process.

[0114] The running log of the first use case associated with the first dyeing identifier can be understood as the running log generated by the first use case in the first running process injected by the first dyeing identifier. The running log can be used to record the calling information of the entity function in the first use case in the first running process. The calling information can include but is not limited to at least one of the following: the name of the called entity function, the calling start time of the entity function, the calling end time of the entity function, the calling time consumption of the entity function (the calling time consumption refers to the time consumed by the entity function from the calling start to the calling end), and the calling result of the entity function (for example, the number of member variables changed by the entity function).

[0115] In S303, the content recognition processing is performed on the running log to obtain the entity function in the first use case belonging to the entity in the first use case, and the hotness information of the entity in the first running process; the hotness information of the entity in the first running process in the first use case is used to indicate the importance of the entity in the first use case to the first running process of the first use case.

[0116] After obtaining the running logs, content recognition processing can be performed on the running logs to obtain the entity to which the entity function in the first use case belongs in the first use case, and the heat information of the entity in the first running process. Specifically, the number of running logs can be multiple, and the first use case can include multiple entities; content recognition processing can be performed on each running log to obtain the entity to which the entity function recorded in each running log belongs in the first use case, and the first heat information of the entity under each running log; it should be noted that different entity functions can belong to the same entity, and the same entity function can be called multiple times in the first running process of the first use case, so the same entity can be recorded under different running logs; thus, the first heat information corresponding to each entity in the first use case under different running logs can be aggregated to obtain the heat information of each entity in the first use case in the first running process. The heat information of the entity in the first use case in the first running process can be used to indicate the importance of the entity in the first use case to the first running process of the first use case.

[0117] It should be noted that, as shown in Figure 4 The running logs generated by the first use case in a running process can be aggregated by dyeing identification. It can be understood that although the sources of these running logs are different (for example, from log archives, field logs, device backup logs, and framework logs, etc.), the running logs generated by the first use case in a complete running process can be aggregated by dyeing identification and obtained completely; these running logs can restore all calling conditions in a complete running process of the first use case, and by performing content recognition processing on these running logs, the heat information of the entity in the first use case in a running process can be accurately obtained.

[0118] In the embodiment of the present application, by injecting the dyeing identifier in the running process of the use case, the running log generated in the running process of the use case can be quickly obtained based on the injected dyeing identifier, so that the running log generated in the running process of the use case can be efficiently collected; and by performing content recognition processing on the running log, the heat information of the entity to which the entity function recorded in the running log belongs can be quickly determined, so that the running log can be efficiently analyzed to determine the heat information of the entity in the use case in the running process. That is, the embodiment of the present application can improve the efficiency in the collection stage and the analysis stage of the running log, so that the heat information of the entity in the use case in the running process of the use case can be efficiently determined. Further, one running process of the use case is associated with one dyeing identifier, based on the dyeing identifier associated with the use case, the running log generated in the one running process of the use case can be obtained, by performing content recognition processing on the running log generated in the one running process of the use case, the heat information of the entity in the use case in the one running process of the use case can be obtained, so that based on one dyeing identifier, the complete call chain of the use case in the one running process can be traced back, and the heat information of the entity on the entire call chain can be accurately analyzed and determined.

[0119] The embodiment of the present application provides a business processing method, and the content introduced by the business processing method includes a content recognition manner of a running log, weight adjustment of a heat contribution weight of the running log, an entity heat aggregation manner between running logs, and an entity heat summary manner between use cases. The business processing method can be executed by a computer device, for example, a business service background 102 in a business processing system. As shown in the Figure 5 The business processing method can include but is not limited to the following steps S501-S504:

[0120] S501, obtaining a first dyeing identifier associated with a first use case in a business; running the first use case to execute a first sub-business in the business; the first dyeing identifier is associated with the first use case by being injected in a first running process of the first use case, and a running log generated in the first running process of the first use case contains the first dyeing identifier.

[0121] In the embodiment of the present application, the execution process of step S501 is the same as that of step S301 in the above-mentioned Figure 3 embodiment, and the specific execution process can be referred to the related description of step S301 in the above-mentioned Figure 3 embodiment, which will not be described here in detail.

[0122] S502, obtaining a running log of the first use case associated with the first dyeing identifier; the running log is used to record the call information of an entity function in the first use case in the first running process.

[0123] In the embodiments of this application, the execution process of step S502 is the same as that of step S302 in the above-described Figure 3 embodiments, and the specific execution process can be referred to the related description of step S302 in the above-described Figure 3 embodiments, which will not be described here again.

[0124] S503, content recognition processing is performed on each running log to obtain the entity to which the entity function recorded in each running log belongs in the first use case and the first hotness information of the entity under each running log.

[0125] The content recognition processing can be performed by a content recognition model, which can be, for example, a GPT model. The content recognition model can have the functions of identifying the log type of the running log, identifying the log level of the running log, identifying the entity to which the entity function recorded in the running log belongs, the first hotness information of the entity, and adjusting the hotness contribution weight of the running log to the entity. Through training of the content recognition model, the purpose is to enable the content recognition model to learn how to quickly locate which entity the current running log belongs to through the log content of the running log, and to classify, determine the level, de-duplicate and adjust the weight of the running log. The input of the training set is the code (including the code of the entity and the code of the entity function), and each code segment is associated with the corresponding weight, classification and log type (for example, RPC request log, data change log, breakpoint log, etc.). The output of the model is to automatically classify, de-duplicate and adjust the weight in the case of global log according to the given running log and function code. The process of content recognition processing will be introduced below in connection with the functions of the content recognition model.

[0126] In order to enable the content recognition model to work correctly, the format of the running log needs to be specified, and the style of each type of running log needs to be agreed. For example, specific markers or templates can be defined to represent RPC request logs, data change logs, breakpoint logs, etc. This can help the model to accurately identify different types of information in the running log and perform corresponding processing, that is, the content recognition model can have the function of identifying the log type of the running log. Based on this, the process of performing content recognition processing on each running log to obtain the entity to which the entity function recorded in each running log belongs in the first use case and the first hotness information of the entity under each running log can include: for any running log, identifying the log type of the running log, obtaining a content recognition strategy matching the log type of the running log; according to the content recognition strategy, performing content recognition processing on the running log to obtain the entity to which the entity function recorded in the running log belongs and the first hotness information of the entity under the corresponding running log.

[0127] In more details, the content recognition model can be configured to perform content recognition on the running log according to the content recognition strategy, to obtain an entity to which an entity function recorded in the running log belongs, and first heat information of the entity under the running log. The content recognition model can be configured to perform semantic understanding on the log content of the running log according to a semantic understanding rule indicated by the content recognition strategy, to obtain the entity function recorded in the running log and heat characteristics of the entity function; perform entity type recognition on the entity function recorded in the running log, to obtain the entity to which the entity function recorded in the running log belongs; and determine the heat characteristics as the first heat information of the entity under the running log. The content recognition model can be configured to perform content recognition on the running log, to quickly locate the entity to which the entity function recorded in the running log belongs and the heat information of the entity, so as to efficiently determine the heat information of the entity in the use case during the running of the use case.

[0128] The heat characteristics of the entity function can include heat characteristics of the entity function under different heat indicators, so that the first heat information of the entity can include first heat information of the entity under different heat indicators. The heat indicators can include, for example, the number of calls, the call time, the call time ratio, the number of changed member variables, and the like.

[0129] In addition, the content recognition model can also have the function of recognizing the log level of the running log. The content recognition model can be configured to recognize the log content of the running log, to determine the log level to which the running log belongs, for example, the debug level, the information level, the warning level, or the error level. Based on this, the running logs that do not meet the heat recognition level (for example, the warning level or the error level do not meet the heat recognition level) can be filtered, to avoid obtaining incorrect heat information of the entity after recognizing these running logs, and to affect the accuracy of the overall heat information.

[0130] In addition, the content recognition model can also have the function of adjusting the contribution weight of the running log to the entity. The reason for adjusting the contribution weight of the running log to the entity is introduced as follows: usually, the contribution weight of the running log to the entity is 1, that is, the heat information of the entity obtained by recognizing the running log is the final heat information of the entity under the running log; however, in the process of generating the running log, usually there are multiple running logs printed for the same entity function when the entity function is called, so that the entity function is called only once, but there are multiple running logs recording the calling of the entity function, if the contribution weight of the running log to the entity is not adjusted, and the heat information of the entity under these running logs is directly superimposed, it will cause the heat information of the entity to be greatly improved, while the real heat information of the entity is lower than the heat information obtained by recognition, so that the heat information of the entity obtained by content recognition is inaccurate. Therefore, for the case that multiple running logs are printed for the same entity function when the entity function is called, the contribution weight of the running log to the entity needs to be adjusted.

[0131] In detail, taking the first use case as an example, the process of adjusting the contribution weight of the running log to the entity can specifically include: in the multiple running logs generated in the first running process of the first use case, there are N target running logs for recording that the target entity function in the first use case is called at the same time, the target entity function belongs to the target entity, and N is an integer greater than 1; the first heat contribution weight of the N target running logs to the target entity can be obtained, which can be understood as the initial heat contribution weight, for example, it can be 1; the first heat contribution weight of the N target running logs to the target entity can be adjusted to obtain the second heat contribution weight of the N target running logs to the target entity; and the first heat information of the target entity under the N target running logs can be adjusted according to the second heat contribution weight of the N target running logs to the target entity.

[0132] The process of adjusting the first heat contribution weight of the N target running logs to the target entity to obtain the second heat contribution weight of the N target running logs to the target entity can include: the quotient between the first heat contribution weight of each target running log to the target entity and the total number N of target running logs can be determined as the second heat contribution weight of each target running log to the target entity. The process of adjusting the first heat information of the target entity under the N target running logs according to the second heat contribution weight of the N target running logs to the target entity can include: multiplying the second heat contribution weight of each target running log to the target entity by the first heat information of the target entity under the corresponding target running log.

[0133] For example, when entity function 1 in the target entity is called, 10 pieces of target running logs are printed. After content recognition processing is performed on the 10 pieces of target running logs, the hotness information of the target entity under each piece of target running log is 4, and since the hotness information of the target entity is generated by calling the same entity function 1, the correct hotness information of the target entity is 4. If the hotness information of the target entity under the 10 pieces of target running logs is directly accumulated, the hotness information of the target entity is 40, which is incorrect. Therefore, the hotness influence weight of each piece of target running log on the entity function needs to be adjusted to 1 / 10, and the hotness information of the target entity under each piece of target running log is adjusted to 4 / 10, so that the hotness information of the target entity under the 10 pieces of target running logs is accumulated to obtain the correct hotness information 4 of the target entity. It can be seen that the function of adjusting the hotness contribution weight of the running log on the entity based on the content recognition model can effectively avoid the error of the hotness information of the entity obtained by the content recognition processing in the case that the same entity function is called and multiple logs are printed for the entity function, so that the determination accuracy of the hotness information of the entity can be improved.

[0134] S504, the first hotness information of each entity in the first use case under different running logs is aggregated to obtain the hotness information of each entity in the first use case in the first running process.

[0135] After the content recognition processing is performed on each running log, the entity function recorded in each running log belongs to the entity in the first use case, and the first hotness information of the entity under the corresponding running log is obtained. Different running logs may correspond to the hotness information of the same entity. For example, the calling of entity function 1 and entity function 2 is recorded by running log 1 and running log 2 respectively, but entity function 1 and entity function 2 belong to the same entity, that is, running log 1 and running log 2 correspond to the hotness information of the same entity. In this case, the first hotness information of each entity in the first use case under different running logs can be aggregated to obtain the hotness information of each entity in the first use case in the first running process.

[0136] In the aggregation process, for the case that different entity functions belong to the same entity, the hotness influence of the entity function on the entity can be considered, or the hotness influence of the entity function on the entity can not be considered; wherein the hotness influence of the entity function on the entity can be determined based on the importance of the entity function to the entity, and the higher the importance of the entity function to the entity, the greater the hotness influence of the entity function on the entity, and the lower the importance of the entity function to the entity, the smaller the hotness influence of the entity function on the entity. The aggregation processes in the two cases are different, specifically:

[0137] In the process of aggregating the influence of the entity function on the heat of the entity, the first heat information of each entity in the first use case under different running logs is aggregated to obtain the heat information of each entity in the first use case in the first running process, which can include: any entity in the first use case can be represented as a reference entity; the entity to which the entity function recorded in the M reference running logs in the multiple running logs belongs is the reference entity, and M is an integer greater than 1; the heat influence information of the entity function on the reference entity recorded in each reference running log in the M reference running logs can be obtained; the heat influence weight of the entity function on the reference entity recorded in each reference running log is determined according to the heat influence information of the entity function on the reference entity recorded in each reference running log; the first heat information of the reference entity under the M reference running logs is weighted and summed according to the heat influence weight of the entity function on the reference entity recorded in each reference running log to obtain the heat information of the reference entity in the first running process.

[0138] The heat influence information of the entity function on the reference entity can be determined according to at least one of the following information: the number of occurrences of the entity function in the first use case, and the number of times the entity function is called in the call information recorded in the historical running logs. According to the heat influence information of the entity function on the reference entity recorded in each reference running log, the heat influence weight of the entity function on the reference entity recorded in each reference running log can be determined, which can include: the quotient between the heat influence information of the entity function on the reference entity recorded in each reference running log and the total heat influence information can be determined as the heat influence weight of the entity function on the reference entity recorded in each reference running log; the total heat influence information can be the sum of the heat influence information of the entity function on the reference entity recorded in the M reference running logs.

[0139] For example, entity function 1 and entity function 2 belong to reference entity 1. Reference running log 1 records the calling of entity function 1, and the content recognition processing of reference running log 1 obtains the first heat information of reference entity 1 under reference running log 1 as 4; reference running log 2 records the calling of entity function 2, and the content recognition processing of reference running log 2 obtains the first heat information of reference entity 1 under reference running log 2 as 6; the heat influence information of entity function 1 on reference entity 1 is 6, and the heat influence information of entity function 2 on reference entity 1 is 4, the heat influence weight of entity function 1 on reference entity 1 can be calculated as 0.6, and the heat influence weight of entity function 2 on reference entity 1 can be calculated as 0.4. After aggregation, the heat information of reference entity 1 in the first use case is 4*0.6+6*0.4=4.8.

[0140] In this way, the influence of the entity function on the heat of the entity is considered in the aggregation process, so that the heat feature of the entity function can more accurately represent the heat information of the entity, and the accuracy of the heat information of the identified entity can be improved.

[0141] In the aggregation process, the influence of the entity function on the heat of the entity is not considered, and the first heat information of each entity in the first use case under different running logs is aggregated to obtain the heat information of each entity in the first use case in the first running process, which can include: any entity in the first use case can be represented as a reference entity; the entity to which the entity function belongs recorded in the M reference running logs in the plurality of running logs is the reference entity, and M is an integer greater than 1; the first heat information of the reference entity under the M reference running logs is accumulated to obtain the heat information of the reference entity in the first running process.

[0142] For example, entity function 1 and entity function 2 both belong to reference entity 1. Reference running log 1 records the calling condition of entity function 1, and the content recognition processing of reference running log 1 obtains the first heat information of reference entity 1 under reference running log 1 as 4; reference running log 2 records the calling condition of entity function 2, and the content recognition processing of reference running log 2 obtains the first heat information of reference entity 1 under reference running log 2 as 6. After aggregation processing, the heat information of reference entity 1 in the first use case = 4+6 = 10.

[0143] In this way, the influence of the entity function on the heat of the entity is not considered in the aggregation process, and such aggregation processing method is simpler and has lower performance requirements for the business service background, which can improve the identification efficiency of the heat information of the entity.

[0144] Generally speaking, the heat information of the entity in the use case in a running process has little reference value, because the performance of the business service background, the network and other factors may affect the heat information of the entity in a running process. Therefore, it is necessary to count the heat of the entity in the use case in the entity heat statistical period, that is, in a period of time, which has reference value, so as to reduce the influence of the performance of the business service background, the network and other factors on the heat information of the entity in a running process. That is, after determining the heat information of the entity in the first use case in the first running process of the first use case, the heat information of the entity in the first use case in each running process in the current entity heat statistical period can be summarized to obtain the comprehensive heat information of the entity in the first use case in the current entity heat statistical period.

[0145] In detail, the first use case can also be associated with a second coloring identifier, the number of the second coloring identifier can be one or more, and the second coloring identifier can be other coloring identifier associated with the first use case in the current entity heat statistics period except the first coloring identifier. The second coloring identifier is associated with the first use case by injecting the second running process of the first use case, and the running log generated by the first use case in the second running process contains the second coloring identifier. Based on this, the heat information of each entity in the first use case in the second running process can be obtained; the heat information of the same entity in the first use case in different running processes can be aggregated to obtain the comprehensive heat information of each entity in the first use case.

[0146] It should be noted that the aggregation here means that the heat information of the same entity in different running processes is accumulated. For example, the first use case is run twice in the current entity heat statistics period, the heat information of entity 1 in the first running process is 3, and the heat information of entity 1 in the second running process is 4, then after aggregation, the comprehensive heat information of entity 1 in the first use case = 3 + 4 = 7. It should be noted that the determination method of the heat information of each entity in the first use case in the second running process is the same as the determination method of the heat information of each entity in the first use case in the first running process. For details, please refer to the determination method of the heat information of each entity in the first use case in the first running process, which will not be repeated here.

[0147] As described above, the first heat information of the entity can include the first heat information of the entity under different heat indicators; after the aggregation processing of the first heat information of the entity under each running log in the first running process, the heat information of the entity in the first running process can include the heat information of the entity under different heat indicators in the first running process; after the aggregation processing of the heat information of the entity in the first use case in different running processes of the first use case, the comprehensive heat information of the entity in the first use case can include the comprehensive heat information of the entity under different heat indicators in the first use case. Based on this, when outputting the comprehensive heat information of the entity in the first use case, the target heat indicator under different heat indicators can be selected; the comprehensive heat information of each entity in the first use case under the target heat indicator can be output. For example, the selected heat indicators include the call frequency and the call time, and the call frequency and the call time of each entity in the first use case can be output.

[0148] In addition, the comprehensive heat information of each entity in the first use case under the target heat indicator can be output in the form of a heat map. The heat map can include but is not limited to any one of the following: matrix heat map, scatter plot, column chart, etc.

[0149] In order to more intuitively show the heat information of each entity in the first use case under the second heat index, the output form of the heat map can include at least one of the following: ① different comprehensive heat information can be displayed differently, and the same comprehensive heat information can be displayed the same; for example, different comprehensive heat information can be represented as different colors, and the same comprehensive heat information can be represented as the same color; for another example, different comprehensive heat information can be represented as different brightness or different transparency of the same color, and the same comprehensive heat information can be represented as the same brightness or the same transparency of the same color; for another example, the comprehensive heat information is represented by the same two-dimensional or three-dimensional shape (for example, a circle, a sphere, a rectangle), and different comprehensive heat information can be represented by different sizes of shapes (for example, circles with different radii, rectangles with the same length but different widths), and the same comprehensive heat information can be represented by the same size of shape (for example, circles with the same radius, rectangles with the same length and width). ② The comprehensive heat information greater than the heat threshold value can be displayed differently, for example, the comprehensive heat information greater than the heat threshold value can be highlighted, and for another example, the entity corresponding to the comprehensive heat information greater than the heat threshold value can be highlighted. ③ Entities with similar comprehensive heat information can be displayed in clusters, for example, entities with comprehensive heat information of 0-3 can be displayed in a first cluster (low heat cluster), entities with comprehensive heat information of 4-6 can be displayed in a second cluster (medium heat cluster), and entities with comprehensive heat information of 7-10 can be displayed in a third cluster (high heat cluster).

[0150] In addition, the comprehensive heat information of entities in different use cases can be combined, and different use cases that can be combined usually belong to the same use case set. In an implementation mode, the business types of the sub-businesses corresponding to the use cases in the use case set are usually similar, for example, the three sub-businesses corresponding to the three use cases in the use case set are password resource delivery sub-business, face swiping resource delivery sub-business and palm swiping resource delivery sub-business, all of which belong to resource delivery type, which is conducive to combination analysis between similar use cases. In another implementation mode, the use cases in the use case set can be determined by self-definition, that is, the use cases for which the heat of entities needs to be combined and analyzed can be determined by self-definition, so that the heat information of entities in the use cases can be flexibly combined and analyzed. In another implementation mode, the use cases in the use case set can be all use cases included by the business, and such use case combination mode is more universal.

[0151] The process of combining the comprehensive hotness information of the entities in different use cases can include: the business can further include a second use case, running the second use case can be used to execute a second sub-business in the business, the first use case and the second use case belong to a use case set in the business; if the first entity is included in both the first use case and the second use case, the comprehensive hotness information of the first entity in the first use case and the comprehensive hotness information of the first entity in the second use case are summarized to obtain the comprehensive hotness information of the first entity in the use case set; if the first entity is included in the first use case and not included in the second use case, the comprehensive hotness information of the first entity in the first use case is determined as the comprehensive hotness information of the first entity in the use case set; if the first entity is not included in the first use case and included in the second use case, the comprehensive hotness information of the first entity in the second use case is determined as the comprehensive hotness information of the first entity in the use case set.

[0152] The process of combining the comprehensive hotness information of the first entity in the first use case and the comprehensive hotness information of the first entity in the second use case to obtain the comprehensive hotness information of the first entity in the use case set can include: obtaining a use case weight of the first use case and a use case weight of the second use case; performing weighted processing on the comprehensive hotness information of the first entity in the first use case by using the use case weight of the first use case to obtain weighted hotness information of the first entity in the first use case; performing weighted processing on the comprehensive hotness information of the first entity in the second use case by using the use case weight of the second use case to obtain weighted hotness information of the first entity in the second use case; and accumulating the weighted hotness information of the first entity in the first use case and the weighted hotness information of the first entity in the second use case to obtain the comprehensive hotness information of the first entity in the use case set.

[0153] The process of determining the comprehensive hotness information of the first entity in the first use case as the comprehensive hotness information of the first entity in the use case set can include: obtaining a use case weight of the first use case, performing weighted processing on the comprehensive hotness information of the first entity in the first use case by using the use case weight of the first use case to obtain weighted hotness information of the first entity in the first use case, and determining the weighted hotness information of the first entity in the first use case as the comprehensive hotness information of the first entity in the use case set. Similarly, the process of determining the comprehensive hotness information of the first entity in the second use case as the comprehensive hotness information of the first entity in the use case set can include: obtaining a use case weight of the second use case, performing weighted processing on the comprehensive hotness information of the first entity in the second use case by using the use case weight of the second use case to obtain weighted hotness information of the first entity in the second use case, and determining the weighted hotness information of the first entity in the second use case as the comprehensive hotness information of the first entity in the use case set.

[0154] For example, as Figure 6As shown, use case 1 includes entity 1, entity 2 and entity 3; the comprehensive hotness information of entity 1 in use case 1 is 4, the comprehensive hotness information of entity 2 in use case 1 is 3, and the comprehensive hotness information of entity 3 in use case 1 is 5. Use case 2 includes entity 2, entity 3 and entity 4; the comprehensive hotness information of entity 2 in use case 2 is 2, the comprehensive hotness information of entity 3 in use case 2 is 5, and the comprehensive hotness information of entity 4 in use case 2 is 6. The use case weight of use case 1 is 0.6, and the use case weight of use case 2 is 0.4. By aggregating the hotness of entities in use case 1 and use case 2, the hotness information of entity 1 is 0.6*4=2.4, the hotness information of entity 2 is 0.6*3+0.4*2=2.6, the hotness information of entity 3 is 0.6*5+0.4*5=5, and the hotness information of entity 4 is 0.4*6=2.4.

[0155] In addition to outputting the hotness information of entities according to use cases, the hotness information of entities can also be output according to a use case set. Similar to the comprehensive hotness information of each entity in the first use case, the comprehensive hotness information of entities in a use case set can include the hotness information of entities in the use case set under different hotness indicators. Based on this, when outputting the comprehensive hotness information of entities in a use case set, a target hotness indicator selected under different hotness indicators can be obtained; and the comprehensive hotness information of each entity in the use case set under the target hotness indicator is output. For example, the selected hotness indicators include the number of calls and the call duration, and then the number of calls and the call duration of each entity in the use case set can be output.

[0156] In addition, the comprehensive hotness information of each entity in the use case set under the target hotness indicator can be output in the form of a hot spot map. The hot spot map can include but is not limited to any one of the following: a matrix heat map, a scatter plot, a column chart, etc.

[0157] In order to more intuitively present the hotness information of each entity in the use case set under the second hotness indicator, the output form of the hot spot map can include at least one of the following: ① different comprehensive hotness information can be displayed differently, and the same comprehensive hotness information can be displayed the same. ② The comprehensive hotness information greater than the hotness threshold value can be displayed differently. ③ Entities with similar comprehensive hotness information can be displayed in clusters.

[0158] In the embodiments of the present application, the running log generated in the running process of the use case can be quickly obtained based on the injected dyeing identifier, so that the running log generated in the running process of the use case can be efficiently collected. Furthermore, the heat information of the entity to which the entity function recorded in the running log belongs can be quickly determined through content recognition processing of the running log, so that the running log can be efficiently analyzed to determine the heat information of the entity in the use case in the running process. That is, the embodiments of the present application can improve the efficiency in the collection stage and analysis stage of the running log, so that the heat information of the entity in the use case in the running process of the use case can be efficiently determined. Further, when the heat information of the same entity under different running logs is aggregated, the influence of the entity function in the running log on the heat of the entity can be considered, so that the heat information of the entity can be more accurate. When the heat information of the same entity under different running logs is aggregated, the influence of the entity function in the running log on the heat of the entity can not be considered, and the heat information of the same entity under different running logs can be directly accumulated, so that the efficiency of determining the heat information of the entity can be higher. Further, the heat information of the entity in the use case can be output in a visual form under different heat indicators and dimensions, so that the heat distribution of the entity in the use case can be clearly and intuitively displayed. The heat of the entity between similar use cases can be combined, so as to help analyze the heat information of the entity in the similar use cases, and the heat information of the entity in the combined use case can be visually output after combination, so that the heat distribution of the entity after combination of the use case can be clearly and intuitively displayed.

[0159] The above describes the method of the embodiments of the present application in detail. In order to better implement the above-mentioned scheme of the embodiments of the present application, the device of the embodiments of the present application is provided accordingly.

[0160] Please refer to Figure 7 , Figure 7 is a structural schematic diagram of a business processing device provided by the embodiments of the present application. The business processing device can be arranged in the computer device provided by the embodiments of the present application. The computer device can be the business service background 102 in the business processing system shown in Figure 1 . The business processing device shown in Figure 7 may be a computer program running in the computer device. The business processing device can be used to execute part or all of the steps in the method embodiments shown in Figure 3 or Figure 5 . Please refer to Figure 7 , the business processing device can include the following units:

[0161] The acquisition unit 701 is configured to acquire a first dyeing identifier associated with a first use case in a service; the first use case is used to execute a first sub-service in the service; the first dyeing identifier is associated with the first use case by being injected into a first running process of the first use case; and a running log generated by the first use case in the first running process contains the first dyeing identifier;

[0162] The acquisition unit 701 is configured to acquire a running log of the first use case associated with the first dyeing identifier; and the running log is used to record calling information of an entity function in the first use case in the first running process.

[0163] The processing unit 702 is further configured to perform content recognition processing on the running log to obtain an entity to which an entity function in the first use case belongs in the first use case, and heat information of the entity in the first running process; and the heat information of the entity in the first running process is used to indicate an importance of the entity in the first use case to the first running process of the first use case.

[0164] In an implementation manner, the running log is multiple, and the first use case contains multiple entities; and the processing unit 702 is configured to, when performing the content recognition processing on the running log to obtain the entity to which the entity function in the first use case belongs in the first use case, and the heat information of the entity in the first running process, specifically perform the following steps:

[0165] performing content recognition processing on each running log to obtain an entity to which an entity function recorded in each running log belongs in the first use case, and first heat information of the entity under the corresponding running log;

[0166] performing aggregation processing on the corresponding first heat information of each entity in the first use case under different running logs to obtain the heat information of each entity in the first use case in the first running process.

[0167] In an implementation manner, the processing unit 702 is configured to, when performing the content recognition processing on each running log to obtain the entity to which the entity function recorded in each running log belongs in the first use case, and the first heat information of the entity under the corresponding running log, specifically perform the following steps:

[0168] recognizing a log type of the running log;

[0169] acquiring a content recognition strategy matched with the log type of the running log;

[0170] performing content recognition processing on the running log according to the content recognition strategy to obtain an entity to which an entity function recorded in the running log belongs, and first heat information of the entity under the running log.

[0171] In an implementation manner, the processing unit 702 is configured to perform content identification processing on the running log according to the content identification strategy, to obtain an entity to which an entity function recorded in the running log belongs, and first heat information of the entity under the running log, and specifically configured to perform the following steps:

[0172] performing semantic understanding on the log content of the running log according to the semantic understanding rule indicated by the content identification strategy, to obtain an entity function recorded in the running log and heat characteristics of the entity function;

[0173] performing entity type identification on the entity function recorded in the running log, to obtain an entity to which the entity function recorded in the running log belongs;

[0174] determining the heat characteristics as the first heat information of the entity under the running log.

[0175] In an implementation manner, N target running logs in the plurality of running logs are used to record that the target entity function in the first use case is called at the same time, the target entity function belongs to a target entity, and N is an integer greater than 1; the processing unit 702 is further configured to perform the following steps:

[0176] obtaining first heat contribution weights of the N target running logs to the target entity;

[0177] adjusting the first heat contribution weights of the N target running logs to the target entity, to obtain second heat contribution weights of the N target running logs to the target entity;

[0178] adjusting the first heat information of the target entity under the N target running logs according to the second heat contribution weights of the N target running logs to the target entity.

[0179] In an implementation manner, any entity in the first use case is represented as a reference entity; an entity to which an entity function recorded in M reference running logs in the plurality of running logs belongs is the reference entity, and M is an integer greater than 1; the processing unit 702 is configured to perform aggregation processing on the first heat information of each entity in the first use case under different running logs, to obtain heat information of each entity in the first use case in the first running process, and specifically configured to perform the following steps:

[0180] obtaining heat influence information of an entity function recorded in each reference running log in the M reference running logs to the reference entity;

[0181] determining heat influence weights of the entity function recorded in each reference running log to the reference entity according to the heat influence information of the entity function recorded in each reference running log to the reference entity;

[0182] According to the entity function recorded in each reference running log, the first heat information of the reference entity under M reference running logs is weighted and summed to obtain the heat information of the reference entity in the first running process.

[0183] In an implementation manner, any entity in the first use case is represented as a reference entity; the entity to which the entity function recorded in M reference running logs in the plurality of running logs belongs is the reference entity, and M is an integer greater than 1; the processing unit 702 is configured to aggregate the first heat information of each entity in the first use case under different running logs to obtain the heat information of each entity in the first use case in the first running process, and specifically configured to perform the following steps:

[0184] The first heat information of the reference entity under M reference running logs is accumulated to obtain the heat information of the reference entity in the first running process.

[0185] In an implementation manner, the first use case is further associated with a second dyeing identifier, the second dyeing identifier is associated with the first use case by being injected into a second running process of the first use case, and the running log generated by the first use case in the second running process contains the second dyeing identifier; the processing unit 702 is further configured to perform the following steps:

[0186] Obtain the heat information of each entity in the first use case in the second running process;

[0187] The heat information of the same entity in the first use case in different running processes is summarized to obtain the comprehensive heat information of each entity in the first use case.

[0188] In an implementation manner, the business further includes a second use case, the second use case is run to execute a second sub-business in the business, and the first use case and the second use case belong to a use case set in the business; the processing unit 702 is further configured to perform the following steps:

[0189] If the first entity is included in both the first use case and the second use case, the comprehensive heat information of the first entity in the first use case and the comprehensive heat information of the first entity in the second use case are summarized to obtain the comprehensive heat information of the first entity in the use case set;

[0190] If the first entity is included in the first use case and not included in the second use case, the comprehensive heat information of the first entity in the first use case is determined as the comprehensive heat information of the first entity in the use case set;

[0191] If the first entity is not included in the first use case and included in the second use case, the comprehensive heat information of the first entity in the second use case is determined as the comprehensive heat information of the first entity in the use case set.

[0192] In an implementation manner, when the processing unit 702 is used for aggregating the comprehensive hotness information of the first entity in the first use case and the comprehensive hotness information of the first entity in the second use case to obtain the comprehensive hotness information of the first entity in the use case set, the processing unit 702 is specifically used for performing the following steps:

[0193] obtaining the use case weight of the first use case and the use case weight of the second use case;

[0194] performing weighted processing on the comprehensive hotness information of the first entity in the first use case by using the use case weight of the first use case to obtain the weighted hotness information of the first entity in the first use case;

[0195] performing weighted processing on the comprehensive hotness information of the first entity in the second use case by using the use case weight of the second use case to obtain the weighted hotness information of the first entity in the second use case;

[0196] performing accumulation on the weighted hotness information of the first entity in the first use case and the weighted hotness information of the first entity in the second use case to obtain the comprehensive hotness information of the first entity in the use case set.

[0197] In an implementation manner, the comprehensive hotness information of each entity in the first use case includes comprehensive hotness information of each entity in the first use case under different hotness indexes; and the processing unit 702 is further used for performing the following steps:

[0198] obtaining a target hotness index selected under different hotness indexes;

[0199] outputting the comprehensive hotness information of each entity in the first use case under the target hotness index.

[0200] According to one embodiment of the present application, Figure 7 The units in the business processing apparatus shown can be respectively or all combined into one or several other units to constitute, or some of the units can be further split into a plurality of units with smaller functions to constitute, which can realize the same operation without affecting the realization of the technical effects of the embodiments of the present application. The units are divided based on logical functions, and in actual application, the functions of one unit can also be realized by multiple units, or the functions of multiple units can be realized by one unit. In other embodiments of the present application, the business processing apparatus can also include other units, and in actual application, these functions can also be realized by other units and can be realized by multiple units in cooperation.

[0201] According to another embodiment of the present application, the business processing apparatus can be realized by running a program capable of performing the operations of the above-mentioned embodiments on a general-purpose computing device such as a computer including a central processing unit (CPU), a random access memory (RAM), a read-only memory (ROM), and the like processing elements and storage elements.Figure 3 or Figure 5 the computer program of each step involved in part or all of the method shown in Figure 7 the business processing apparatus shown in FIG. 1, and to implement the business processing method of the embodiments of the present application. The computer program can be recorded on, for example, a computer readable storage medium, and loaded into the above-mentioned computing device through the computer readable storage medium, and run therein.

[0202] In the embodiments of the present application, by injecting the dyeing identifier in the running process of the use case, the running log generated in the running process of the use case can be quickly obtained based on the injected dyeing identifier, so that the running log generated in the running process of the use case can be efficiently collected; and by performing content recognition processing on the running log, the heat information of the entity to which the entity function recorded in the running log belongs can be quickly determined, so that the running log can be efficiently analyzed to determine the heat information of the entity in the use case in the running process. That is, the embodiments of the present application can improve the efficiency in the collection stage and the analysis stage of the running log, so that the heat information of the entity in the use case in the running process of the use case can be efficiently determined.

[0203] Based on the above-mentioned method and device embodiments, the embodiments of the present application provide a computer device. Please refer to Figure 8 , Figure 8 is a structural schematic diagram of a computer device provided by the embodiments of the present application. Figure 8 The computer device shown in FIG. 8 at least includes a processor 801, an input interface 802, an output interface 803 and a computer readable storage medium 804. Wherein, the processor 801, the input interface 802, the output interface 803 and the computer readable storage medium 804 can be connected through a bus or other means.

[0204] The computer readable storage medium 804 can be stored in the memory of the computer device, and the computer readable storage medium 804 is used to store a computer program, the computer program includes computer instructions, and the processor 801 is used to execute the computer program stored by the computer readable storage medium 804. The processor 801 (or CPU (Central Processing Unit, Central Processing Unit)) is the computing core and control core of the computer device, which is suitable for implementing the computer program, and specifically suitable for loading and executing the computer program to realize the corresponding method process or corresponding function.

[0205] The embodiment of the present application further provides a computer readable storage medium (Memory). The computer readable storage medium is a memory device in a computer device, and is used for storing programs and data. It can be understood that the computer readable storage medium herein can include an internal storage medium in the computer device, and of course can include an extended storage medium supported by the computer device. The computer readable storage medium provides a storage space, and the storage space stores an operating system of the computer device. Moreover, the storage space also stores a computer program suitable for being loaded and executed by the processor. It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory (Non-Volatile Memory), such as at least one disk memory; and optionally can be at least one computer readable storage medium located away from the aforementioned processor.

[0206] The computer device can be Figure 1 The service processing system shown in the service processing system. In a specific implementation, the computer program stored in the computer readable storage medium 804 can be loaded and executed by the processor 801 to implement the corresponding steps in the method shown above. Figure 3 Or Figure 5 The computer program in the computer readable storage medium 804 is loaded and executed by the processor 801 to implement the following steps:

[0207] Obtain a first dyeing identifier associated with a first use case in the business; run the first use case to execute a first sub-business in the business; the first dyeing identifier is associated with the first use case by injecting the first use case in a first running process, and the running log generated by the first use case in the first running process contains the first dyeing identifier;

[0208] Obtain a running log of the first use case associated with the first dyeing identifier; the running log is used to record the calling information of the entity function in the first use case in the first running process;

[0209] Perform content recognition processing on the running log to obtain an entity to which the entity function in the first use case belongs in the first use case, and heat information of the entity in the first running process; the heat information of the entity in the first running process is used to indicate the importance of the entity in the first use case to the first running process of the first use case.

[0210] In an implementation manner, the number of running logs is multiple, and the first use case contains multiple entities; when the computer program in the computer readable storage medium 804 is loaded and executed by the processor 801 to perform content recognition processing on the running log to obtain the entity to which the entity function in the first use case belongs in the first use case, and the heat information of the entity in the first running process, the specific implementation is as follows:

[0211] The content recognition processing is performed on each running log to obtain an entity to which an entity function recorded in each running log belongs in the first use case and first heat information of the entity under the corresponding running log.

[0212] The first heat information of each entity in the first use case under different running logs is aggregated to obtain the heat information of each entity in the first use case in the first running process.

[0213] In an implementation manner, the computer program in the computer readable storage medium 804 is loaded by the processor 801 and is used to perform the following steps when performing the content recognition processing on each running log to obtain an entity to which an entity function recorded in each running log belongs in the first use case and first heat information of the entity under the corresponding running log:

[0214] The log type of the running log is recognized.

[0215] The content recognition strategy matched with the log type of the running log is obtained.

[0216] According to the content recognition strategy, the content recognition processing is performed on the running log to obtain an entity to which an entity function recorded in the running log belongs and first heat information of the entity under the running log.

[0217] In an implementation manner, the computer program in the computer readable storage medium 804 is loaded by the processor 801 and is used to perform the following steps when performing the content recognition processing on the running log according to the content recognition strategy to obtain an entity to which an entity function recorded in the running log belongs and first heat information of the entity under the running log:

[0218] According to the semantic understanding rule indicated by the content recognition strategy, the semantic understanding is performed on the log content of the running log to obtain an entity function recorded by the running log and heat characteristics of the entity function.

[0219] The entity type recognition is performed on the entity function recorded by the running log to obtain an entity to which the entity function recorded by the running log belongs.

[0220] The heat characteristics are determined as the first heat information of the entity under the running log.

[0221] In an implementation manner, N target running logs in the plurality of running logs are used to record that the target entity function in the first use case is called at the same time, the target entity function belongs to a target entity, and N is an integer greater than 1; the computer program in the computer readable storage medium 804 is loaded by the processor 801 and is further used to perform the following steps:

[0222] Obtain a first hotness contribution weight of N target running logs on a target entity;

[0223] Adjust the first hotness contribution weight of N target running logs on the target entity, to obtain a second hotness contribution weight of N target running logs on the target entity;

[0224] Adjust the first hotness information of the target entity in the N target running logs according to the second hotness contribution weight of the N target running logs on the target entity.

[0225] In an implementation manner, any entity in the first use case is represented as a reference entity; an entity to which an entity function recorded in M reference running logs in the plurality of running logs belongs is the reference entity, and M is an integer greater than 1; the computer program in the computer readable storage medium 804 is loaded and executed by the processor 801 to aggregate the first hotness information of each entity in the first use case under different running logs, to obtain the hotness information of each entity in the first use case in the first running process, and specifically used to execute the following steps:

[0226] Obtain the hotness influence information of the entity function recorded in each reference running log in the M reference running logs on the reference entity;

[0227] According to the hotness influence information of the entity function recorded in each reference running log on the reference entity, determine the hotness influence weight of the entity function recorded in each reference running log on the reference entity;

[0228] According to the hotness influence weight of the entity function recorded in each reference running log on the reference entity, perform weighted summation on the first hotness information of the reference entity under the M reference running logs, to obtain the hotness information of the reference entity in the first running process.

[0229] In an implementation manner, any entity in the first use case is represented as a reference entity; an entity to which an entity function recorded in M reference running logs in the plurality of running logs belongs is the reference entity, and M is an integer greater than 1; the computer program in the computer readable storage medium 804 is loaded and executed by the processor 801 to aggregate the first hotness information of each entity in the first use case under different running logs, to obtain the hotness information of each entity in the first use case in the first running process, and specifically used to execute the following steps:

[0230] Accumulate the first hotness information of the reference entity under the M reference running logs to obtain the hotness information of the reference entity in the first running process.

[0231] In an implementation manner, the first use case is further associated with a second dyeing identifier, the second dyeing identifier is associated with the first use case by being injected into a second running process of the first use case, and a running log generated by the first use case in the second running process contains the second dyeing identifier; the computer program in the computer readable storage medium 804 is loaded by the processor 801 and is further used for executing the following steps:

[0232] obtaining the heat information of each entity in the first use case in the second running process;

[0233] summarizing the heat information of the same entity in different running processes of the first use case to obtain the comprehensive heat information of each entity in the first use case.

[0234] In an implementation manner, the business further includes a second use case, the second use case is run to execute a second sub-business in the business, and the first use case and the second use case belong to a use case set in the business; the computer program in the computer readable storage medium 804 is loaded by the processor 801 and is further used for executing the following steps:

[0235] if the first entity is contained in both the first use case and the second use case, summarizing the comprehensive heat information of the first entity in the first use case and the comprehensive heat information of the first entity in the second use case to obtain the comprehensive heat information of the first entity in the use case set;

[0236] if the first entity is contained in the first use case and not contained in the second use case, determining the comprehensive heat information of the first entity in the first use case as the comprehensive heat information of the first entity in the use case set;

[0237] if the first entity is not contained in the first use case and contained in the second use case, determining the comprehensive heat information of the first entity in the second use case as the comprehensive heat information of the first entity in the use case set.

[0238] In an implementation manner, when the computer program in the computer readable storage medium 804 is loaded by the processor 801 and executes the summarizing of the comprehensive heat information of the first entity in the first use case and the comprehensive heat information of the first entity in the second use case to obtain the comprehensive heat information of the first entity in the use case set, the following steps are specifically used for executing:

[0239] obtaining the use case weight of the first use case and the use case weight of the second use case;

[0240] weighting the comprehensive heat information of the first entity in the first use case by using the use case weight of the first use case to obtain the weighted heat information of the first entity in the first use case;

[0241] The comprehensive hotness information of the first entity in the second use case is weighted by using the use case weight of the second use case, to obtain weighted hotness information of the first entity in the second use case.

[0242] The weighted hotness information of the first entity in the first use case and the weighted hotness information of the first entity in the second use case are accumulated, to obtain the comprehensive hotness information of the first entity in the use case set.

[0243] In an implementation manner, the comprehensive hotness information of each entity in the first use case includes comprehensive hotness information of each entity in the first use case under different hotness indexes; the computer program in the computer readable storage medium 804 is loaded by the processor 801 and is further used for executing the following steps:

[0244] obtaining a target hotness index selected under different hotness indexes;

[0245] outputting the comprehensive hotness information of each entity in the first use case under the target hotness index.

[0246] In the embodiment of the application, by injecting the dyeing identifier in the running process of the use case, the running log generated in the running process of the use case can be quickly obtained based on the injected dyeing identifier, so that the running log generated in the running process of the use case can be efficiently collected; and by performing content recognition processing on the running log, the hotness information of the entity to which the entity function recorded in the running log belongs can be quickly determined, so that the running log can be efficiently analyzed to determine the hotness information of the entity in the use case in the running process. That is, the embodiment of the application can improve the efficiency in the collection stage and the analysis stage of the running log, so that the hotness information of the entity in the use case in the running process of the use case can be efficiently determined.

[0247] The embodiment of the application further provides a computer program product, which includes a computer program stored in a computer readable storage medium. A processor of a computer device reads the computer program from the computer readable storage medium, and the processor executes the computer program, so that the computer device executes the processing method described above.

[0248] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed in the application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software mode depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0249] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined target, and can be implemented entirely or partially by using software, hardware (such as a processing circuit or a memory), or a combination thereof. Similarly, one processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be a part of an integral module or unit that includes the functions of the module or unit.

[0250] In the above embodiments, all or part can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. Computer instructions can be stored in a computer-readable storage medium or transmitted by a computer-readable storage medium. Computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (for example, coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (for example, infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server, data center, etc. that includes one or more available media sets. The available media can be magnetic media (for example, floppy disk, hard disk, magnetic tape), optical media (for example, DVD), or semiconductor media (for example, solid state disk (SSD)), etc.

[0251] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A business processing method, characterized in that, include: Obtain the first coloring identifier associated with the first use case in the business; Running the first use case is used to execute the first sub-business in the business; The first coloring identifier is associated with the first use case by being injected into the first execution process of the first use case, and the execution log generated by the first use case in the first execution process contains the first coloring identifier; Obtain the execution log of the first use case associated with the first coloring identifier; the execution log is used to record the call information of the entity functions in the first use case during the first execution process; The operation log is processed by content recognition to obtain the entity to which the entity function in the first use case belongs in the first use case, and the popularity information of the entity in the first operation process. The popularity information of the entity in the first use case in the first operation process is used to indicate the importance of the entity in the first use case to the first operation process of the first use case.

2. The method as described in claim 1, characterized in that, The number of operation logs is multiple, and the first use case contains multiple entities; the content recognition processing of the operation logs to obtain the entity to which the entity function in the first use case belongs, and the popularity information of the entity in the first operation process, includes: Each of the aforementioned operation logs is processed for content recognition to obtain the entity to which the entity function recorded in each of the aforementioned operation logs belongs in the first use case, and the first popularity information of the entity under the corresponding operation log; The first popularity information corresponding to each entity in the first use case under different running logs is aggregated to obtain the popularity information of each entity in the first use case during the first running process.

3. The method as described in claim 2, characterized in that, The step of performing content recognition processing on each of the running log entries to obtain the entity to which the entity function recorded in each running log entry belongs in the first use case, and the first popularity information of the entity under the corresponding running log entry, includes: Identify the log type of the runtime log; Obtain a content recognition strategy that matches the log type of the running log; According to the content recognition strategy, the operation log is processed to obtain the entity to which the entity function recorded in the operation log belongs, and the first popularity information of the entity under the operation log.

4. The method as described in claim 3, characterized in that, The step of performing content recognition processing on the runtime log according to the content recognition strategy to obtain the entity to which the entity function recorded in the runtime log belongs, and the first popularity information of the entity under the runtime log, includes: According to the semantic understanding rules indicated by the content recognition strategy, the log content of the operation log is semantically understood to obtain the entity functions recorded in the operation log and the popularity features of the entity functions; Entity type identification is performed on the entity functions recorded in the runtime log to obtain the entity to which the entity functions recorded in the runtime log belong; The heat feature is determined as the first heat information of the entity in the operation log.

5. The method as described in claim 2, characterized in that, Among multiple runtime logs, N target runtime logs are used to record that the target entity function in the first use case was called at the same time. The target entity function belongs to the target entity, and N is an integer greater than 1. The method also includes: Obtain the first popularity contribution weight of the N target operation logs to the target entity; The first popularity contribution weight of the N target operation logs to the target entity is adjusted to obtain the second popularity contribution weight of the N target operation logs to the target entity; Based on the second popularity contribution weight of the N target operation logs to the target entity, the first popularity information of the target entity in the N target operation logs is adjusted.

6. The method as described in claim 2, characterized in that, Any entity in the first use case is represented as a reference entity; the entity to which the entity function recorded in M ​​of the multiple runtime logs belongs is the reference entity, where M is an integer greater than 1; the aggregation processing of the first popularity information corresponding to each entity in the first use case under different runtime logs to obtain the popularity information of each entity in the first use case in the first runtime process includes: Obtain the influence information of the entity function on the popularity of the reference entity recorded in each of the M reference running logs; Based on the influence information of the entity function on the popularity of the reference entity recorded in each reference running log, determine the influence weight of the entity function on the popularity of the reference entity recorded in each reference running log. Based on the influence weight of the entity function on the popularity of the reference entity recorded in each reference operation log, the first popularity information of the reference entity under the M reference operation logs is weighted and summed to obtain the popularity information of the reference entity in the first operation process.

7. The method as described in claim 2, characterized in that, Any entity in the first use case is represented as a reference entity; the entity to which the entity function recorded in M ​​of the multiple runtime logs belongs is the reference entity, where M is an integer greater than 1; the aggregation processing of the first popularity information corresponding to each entity in the first use case under different runtime logs to obtain the popularity information of each entity in the first use case in the first runtime process includes: The first popularity information of the reference entity under the M reference operation logs is accumulated to obtain the popularity information of the reference entity in the first operation process.

8. The method as described in claim 1, characterized in that, The first use case is also associated with a second coloring identifier, which is established by injecting the second coloring identifier into the second execution process of the first use case. The execution log generated by the first use case in the second execution process contains the second coloring identifier. The method further includes: Obtain the popularity information of each entity in the first use case during the second running process; The popularity information of the same entity in the first use case is summarized in different running processes to obtain the comprehensive popularity information of each entity in the first use case.

9. The method as described in claim 8, characterized in that, The service also includes a second use case, which is used to execute a second sub-service within the service. The first and second use cases belong to a set of use cases within the service. The method further includes: If both the first use case and the second use case contain the first entity, then the comprehensive popularity information of the first entity in the first use case and the comprehensive popularity information of the first entity in the second use case are summarized to obtain the comprehensive popularity information of the first entity in the use case set. If the first use case contains the first entity and the second use case does not contain the first entity, then the comprehensive popularity information of the first entity in the first use case is determined as the comprehensive popularity information of the first entity in the use case set. If the first use case does not contain the first entity but the second use case contains the first entity, then the comprehensive popularity information of the first entity in the second use case is determined as the comprehensive popularity information of the first entity in the use case set.

10. The method as described in claim 9, characterized in that, The step of summarizing the comprehensive popularity information of the first entity in the first use case and the comprehensive popularity information of the first entity in the second use case to obtain the comprehensive popularity information of the first entity in the use case set includes: Obtain the use case weight of the first use case and the use case weight of the second use case; The comprehensive popularity information of the first entity in the first use case is weighted using the use case weight of the first use case to obtain the weighted popularity information of the first entity in the first use case. The comprehensive popularity information of the first entity in the second use case is weighted using the use case weight of the second use case to obtain the weighted popularity information of the first entity in the second use case. The weighted popularity information of the first entity in the first use case and the weighted popularity information of the first entity in the second use case are summed to obtain the comprehensive popularity information of the first entity in the use case set.

11. The method as described in claim 8, characterized in that, The comprehensive popularity information for each entity in the first use case includes the comprehensive popularity information for each entity under different popularity indicators; the method further includes: Obtain the target popularity index selected under the different popularity indices; Output the comprehensive popularity information of each entity in the first use case under the target popularity index.

12. A business processing apparatus, characterized in that, include: The acquisition unit is used to acquire the first coloring identifier associated with the first use case in the business; Running the first use case is used to execute the first sub-business in the business; The first coloring identifier is associated with the first use case by being injected into the first execution process of the first use case, and the execution log generated by the first use case in the first execution process contains the first coloring identifier; The acquisition unit is further configured to acquire the execution log of the first use case associated with the first coloring identifier; the execution log is used to record the call information of the entity functions in the first use case during the first execution process; The processing unit is configured to perform content recognition processing on the running log to obtain the entity function in the first use case, the entity to which the entity function in the first use case belongs in the first use case, and the popularity information of the entity in the first running process. The popularity information of the entity in the first use case in the first running process is used to indicate the importance of the entity in the first use case to the first running process of the first use case.

13. A computer device, characterized in that, The computer device includes: A processor is a tool for implementing computer programs. A computer-readable storage medium storing a computer program adapted to be loaded by the processor and executed as described in any one of claims 1-11.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and executed as described in any one of claims 1-11.

15. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the business processing method as described in any one of claims 1-11.

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