Product requirement generation method and system of SaaS software based on data mining

Abstract

The application discloses a product demand generation method and system of SaaS software based on data mining, and a server method comprises the following steps: regularly acquiring and preprocessing operation behavior data and backend log data of a user on the SaaS software in a preset period to obtain user operation behavior time series data, wherein the operation behavior data is captured by front-end burying point code deployed in advance at key operation points of a front-end interface of the SaaS software; performing abnormal driving path analysis and behavior driving path analysis according to the user operation behavior time series data to obtain abnormal function points of logical abnormalities existing in the SaaS software and to-be-optimized function points of user abnormal behaviors existing in the SaaS software; and converting the abnormal function points and the to-be-optimized function points into to-be-processed product demand documents of the SaaS software. Therefore, by adopting the embodiment of the application, the SaaS software can fully meet the user demand. Meanwhile, a clear direction can be provided for iteration and optimization of the software, so that the iteration speed and quality of the software are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial management, and in particular relates to a product demand generation method and system of SaaS software based on data mining. BACKGROUND

[0002] In the field of industrial management, SaaS (Software as a Service) software is widely used in the production, management and service processes of enterprises. These software provide services through the Internet, which can help enterprises achieve efficient and convenient management. However, in the process of software development and iteration, enterprises need to continuously collect user needs and identify functional defects to ensure that the software can meet the needs of users and continuously improve.

[0003] In related technologies, SaaS industrial management software enterprises mainly rely on customer feedback to collect user needs and identify product defects in the system development process. Specifically, enterprises set up feedback channels in the software, and users can feed back to the enterprise through these channels when they encounter problems or have suggestions for improvement in the use process. Enterprises optimize and upgrade the software according to the feedback information of users.

[0004] However, customer feedback is not comprehensive. Users only feedback the obvious problems they encounter, and some potential needs or small defects may be ignored, resulting in the software not being able to fully meet the needs of users, reducing the iteration speed and quality of the software. SUMMARY

[0005] The embodiments of the present application provide a product demand generation method and system of SaaS software based on data mining. In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This part is not a general review, nor is it intended to determine the key / important elements or delineate the protection scope of these embodiments. Its only purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0006] In a first aspect, the embodiments of the present application provide a product demand generation method of SaaS software based on data mining, applied to a server, the method comprising:

[0007] Periodically obtaining and preprocessing user operation behavior data and backend log data on the SaaS software in a preset period to obtain user operation behavior time series data, the operation behavior data being captured by pre-deploying front-end tracking codes at key operation points of the front-end interface of the SaaS software;

[0008] According to the user operation behavior time sequence data, abnormal driving path analysis and behavior driving path analysis are performed, and logical abnormal function points existing in the SaaS software and to-be-optimized function points existing in user abnormal behaviors are obtained.

[0009] The abnormal function points and the to-be-optimized function points are converted into to-be-handled product requirement documents of the SaaS software.

[0010] Optionally, according to the user operation behavior time sequence data, the abnormal driving path analysis and the behavior driving path analysis include the following steps.

[0011] According to the user operation behavior time sequence data, a function abnormal scenario of a user when generating data based on the SaaS software is analyzed.

[0012] Based on the function abnormal scenario, an abnormal function point existing in the SaaS software is inferred.

[0013] According to the user operation behavior time sequence data, violation operation information of a user in the SaaS software is analyzed.

[0014] Based on the violation operation information, a to-be-optimized function point existing in user abnormal behaviors of the SaaS software is inferred.

[0015] Optionally, according to the user operation behavior time sequence data, the function abnormal scenario of the user when generating data based on the SaaS software includes the following steps.

[0016] Data cleaning and data formatting are performed on the user operation behavior time sequence data, to obtain target operation behavior data and target backend log data.

[0017] According to a business logic of the SaaS software, a plurality of key operation points when generating data are traversed.

[0018] From the target operation behavior data and the target backend log data, data records related to each key operation point are extracted, to obtain data records of each key operation point.

[0019] Based on the data records of each key operation point, operation flow information of the user in a data generation process is analyzed, to obtain a user operation flow of each key operation point.

[0020] According to the user operation flow of each key operation point and a preset abnormal scenario condition, an abnormal scenario of the user in each key operation point is determined as a function abnormal scenario of the user when generating data based on the SaaS software.

[0021] Optionally, based on the data records of each key operation point, the operation flow information of the user in the data generation process includes the following steps.

[0022] According to the time stamp in the data record of each key operation point, the data record of each key operation point is sorted in ascending order to obtain an operation sequence of each key operation point;

[0023] According to the operation sequence of each key operation point, an operation flowchart of the user in the data generation process is constructed to obtain an operation flowchart of each key operation point; wherein, the operation flowchart is used to represent the operation path of the user from start to end, the time point and operation content of each operation;

[0024] The operation path of the user from start to end, the time point and operation content of each operation are taken as operation flow information of the user in the data generation process.

[0025] Optionally, the preset abnormal scenario condition includes a mapping relationship between the abnormal scenario description information and the abnormal scenario;

[0026] According to the user operation flow of each key operation point and the preset abnormal scenario condition, the abnormal scenario appearing in each key operation point of the user is determined, including:

[0027] From the mapping relationship, the first abnormal scenario description information is obtained one by one;

[0028] It is checked whether there is a target key operation point user operation flow that meets the first abnormal scenario description information in the user operation flow of each key operation point;

[0029] If yes, the first abnormal scenario corresponding to the first abnormal scenario description information is obtained from the mapping relationship, to obtain the abnormal scenario corresponding to the target key operation point user operation flow;

[0030] If no, the step of obtaining the first abnormal scenario description information one by one from the mapping relationship is continuously executed until the abnormal scenario description information in the mapping relationship is completely traversed, and all the abnormal scenarios corresponding to the target key operation point user operation flow are taken as the abnormal scenario appearing in each key operation point of the user.

[0031] Optionally, the functional abnormal scenario includes a data interruption scenario and / or a data inconsistency scenario and / or an operation timeout scenario;

[0032] Based on the functional abnormal scenario, the abnormal function point with logical exception of the SaaS software is inferred, including:

[0033] In the case that the functional abnormal scenario is the data interruption scenario, the interruption point on the flow existing in the user operation flow of each key operation point is determined; based on the function function involved by the existing interruption point on the flow, the abnormal function point with logical exception of the SaaS software is determined; and / or,

[0034] In the case of the functional abnormal scenario being a data inconsistency scenario, analyzing whether there is data update delay or data loss between different modules involved in the user operation process of each key operation point, and whether the data sources are consistent, obtaining an analysis result; determining an abnormal function point of the SaaS software with logical abnormalities based on the function function involved in the analysis result; and / or,

[0035] In the case of the functional abnormal scenario being an operation timeout scenario, analyzing the performance parameters and operation complexity involved in the user operation process of each key operation point; determining an abnormal function point of the SaaS software with logical abnormalities based on the function function involved in the performance parameters and operation complexity.

[0036] Optionally, according to the user operation behavior time series data, analyzing the illegal operation information of the user in the SaaS software includes:

[0037] Data cleaning and data formatting are performed on the user operation behavior time series data to obtain final operation behavior data and final backend log data;

[0038] From the final operation behavior data and the final backend log data, data records related to the user operation behavior are extracted;

[0039] According to the time stamp in the data record, the data records related to the user operation behavior are sorted in ascending order to obtain the original operation sequence of the user;

[0040] Based on the original operation sequence of the user, an original operation flowchart of the user in the time sequence is established; wherein the original operation flowchart is used to represent the operation path, the time point of each operation and the operation content of the user from beginning to end;

[0041] According to the original operation flowchart of the user in the time sequence and the preset illegal operation condition, the illegal operation information of the user in the SaaS software is determined.

[0042] Optionally, the preset illegal operation condition includes an association relationship between the illegal operation type and the illegal operation description information;

[0043] According to the original operation flowchart of the user in the time sequence and the preset illegal operation condition, the illegal operation information of the user in the SaaS software is determined, including:

[0044] From the association relationship, the first illegal operation description information is obtained one by one;

[0045] Checking whether there is a first original operation satisfying the first illegal operation description information in the original operation flowchart of the user in the time sequence;

[0046] If so, obtain the first violation operation type corresponding to the violation operation description information from the association relationship, and obtain the first violation operation type corresponding to the first original operation;

[0047] If not, continue executing the step of traversing through the relationships to obtain the description information of the first violation operation one by one until all the description information of the violation operation in the relationships has been traversed. Then, treat all the first violation operation types as the user's violation operation information in the SaaS software.

[0048] Optionally, violation information includes frequent operations and / or abnormal exits and / or unauthorized input and / or permission violations;

[0049] Based on the information regarding violations, it is inferred that the SaaS software has functionalities requiring optimization due to abnormal user behavior, including:

[0050] If the violation information indicates frequent operations, obtain the functional functions involved in the frequent operations as functional points to be optimized in the SaaS software indicating abnormal user behavior; and / or,

[0051] If the violation information indicates an abnormal exit, obtain the functional functions involved in the abnormal exit, as these are considered optimization points for the SaaS software indicating abnormal user behavior; and / or,

[0052] If the violation information is due to improper input, obtain the functional functions involved in the improper input, as these are considered as functional points in the SaaS software that require optimization due to abnormal user behavior; and / or,

[0053] In cases where the violation information pertains to permission violations, the functional functions involved in the permission violations are considered as functional points to be optimized in SaaS software where abnormal user behavior exists.

[0054] Secondly, embodiments of this application provide a product requirements generation system for SaaS software based on data mining, the system comprising:

[0055] The data acquisition module is used to periodically acquire and preprocess user operation behavior data and backend log data on the SaaS software within a preset period to obtain user operation behavior time sequence data. The operation behavior data is captured by front-end embedding code deployed in advance at key operation points on the front-end interface of the SaaS software.

[0056] The functional path analysis module is used to perform anomaly-driven path analysis and behavior-driven path analysis based on the time sequence data of user operation behavior, so as to obtain the abnormal functional points of logical anomalies in the SaaS software and the functional points to be optimized for abnormal user behavior.

[0057] A product demand generation module is configured to convert the abnormal function points and the to-be-optimized function points into a to-be-processed product demand document of the SaaS software.

[0058] The technical scheme provided by the embodiments of the present application can have the following beneficial effects:

[0059] In the embodiments of the present application, on the one hand, by deploying the front-end burying point code at the key operation points of the front-end interface of the SaaS software, the operation behavior data of the user can be actively mined. This method can provide detailed and comprehensive user behavior records, and provide accurate data basis for actively analyzing the behavior mode of the user, so that the problems and potential needs in the software can be more comprehensively identified, and the SaaS software can fully meet the needs of the user. On the other hand, by performing abnormal driving path analysis and behavior driving path analysis according to the user operation behavior time sequence data, the logical abnormalities and the abnormal behaviors of the user existing in the software can be actively identified. The abnormal driving path analysis can find the logical problems encountered by the user in the data generation process, and the behavior driving path analysis can identify the unusual operation of the user in the use process. The analysis results are converted into the to-be-processed product demand document, which can provide a clear direction for the iteration and optimization of the software, thereby improving the iteration speed and quality of the software.

[0060] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0061] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the present application.

[0062] Figure 1 is a method flow diagram of a product demand generation method of a SaaS software based on data mining provided by the embodiments of the present application;

[0063] Figure 2 is a process schematic block diagram of an abnormal function point generation process provided by the embodiments of the present application;

[0064] Figure 3 is a process schematic block diagram of a to-be-optimized function point generation process provided by the embodiments of the present application;

[0065] Figure 4 is a process schematic block diagram of a product demand generation process of a SaaS software based on data mining provided by the embodiments of the present application;

[0066] Figure 5 is an abnormal driving UI interface diagram provided by the embodiments of the present application;

[0067] Figure 6 is a behavior-driven UI interface diagram provided by an embodiment of the present application;

[0068] Figure 7 is a user operation flow UI interface diagram provided by an embodiment of the present application;

[0069] Figure 8 is a structure schematic diagram of a product requirement generation system of a SaaS software based on data mining provided by an embodiment of the present application;

[0070] Figure 9 is a structure schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0071] The following description and drawings are illustrative of the specific embodiments of the present application and are not intended to limit the generality of the application.

[0072] It should be clear that the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0073] The following description refers to the accompanying drawings. Unless otherwise indicated, same numbers in different drawings indicate same or similar elements. The implementations described in the following example embodiments are not meant to represent all implementations consistent with the present application. Rather, they are merely examples of systems and methods consistent with some aspects of the present application as detailed in the appended claims.

[0074] In the description of the present application, it should be understood that the terms "first", "second", etc. are only for the purpose of description, and cannot be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances. In addition, in the description of the present application, "multiple" means two or more, unless otherwise specified. "And / or", which describes the relationship between the associated objects, means that there can be three relationships, for example, A and / or B can mean that there are three cases of A alone, A and B together, and B alone. The character " / " generally represents that the associated objects before and after are in an "or" relationship.

[0075] At present, SaaS industrial management software enterprises mainly rely on customer feedback to collect user requirements and identify product defects in the system development process. Specifically, enterprises set up feedback channels in the software, and users can feed back to the enterprise through these channels when they encounter problems or have suggestions for improvement in the use process. Enterprises optimize and upgrade the software according to the feedback information of users.

[0076] The applicant of this application recognizes that customer feedback is incomplete. Users only report the obvious problems they encounter, while some potential needs or minor defects may be overlooked, resulting in the software not fully meeting user needs and reducing the speed and quality of software iteration.

[0077] To address the aforementioned issues, this application provides a product requirement generation method and system for SaaS software based on data mining, thereby resolving the problems existing in the related technical issues. In the embodiments of this application, on the one hand, by deploying front-end embedding code at key operation points in the front-end interface of the SaaS software, user operation behavior data can be proactively mined. This method can provide detailed and comprehensive user behavior records, providing an accurate data foundation for proactively analyzing user behavior patterns, enabling a more comprehensive identification of problems and potential needs in the software, and thus allowing the SaaS software to fully meet user needs. On the other hand, by performing anomaly-driven path analysis and behavior-driven path analysis based on the time-series data of user operation behavior, logical anomalies and abnormal user behaviors existing in the software can be proactively identified. Anomaly-driven path analysis can discover logical problems encountered by users during data generation, while behavior-driven path analysis can identify unusual operations by users during use. Converting these analysis results into product requirement documents to be processed can provide a clear direction for software iteration and optimization, thereby improving the iteration speed and quality of the software. The following describes this in detail using exemplary embodiments.

[0078] The following will be combined with the appendix Figure 1 - Appendix Figure 7 This application provides a detailed description of the product requirements generation method for SaaS software based on data mining, as provided in its embodiments. This method can be implemented using a computer program and can run on a product requirements generation system for SaaS software based on data mining and the von Neumann architecture. This computer program can be integrated into the application or run as a standalone utility application.

[0079] Please see Figure 1 This application provides a flowchart illustrating a method for generating product requirements for SaaS software based on data mining, applicable to the server side. For example... Figure 1 As shown, the method in this application embodiment includes the following steps:

[0080] S101 periodically acquires and preprocesses user operation behavior data and backend log data within a preset period to obtain user operation behavior time sequence data. The operation behavior data is captured by front-end embedding code deployed in advance at key operation points on the front-end interface of the SaaS software.

[0081] Among them, periodic acquisition is to automatically collect data according to a preset time interval (such as every day, every week, etc.), which is used to ensure the timeliness of the data. Preprocessing is to clean, format and preliminarily analyze the collected raw data, remove invalid or erroneous data, which can ensure the accuracy and consistency of the data. The preset period is a pre-set time range, such as every day, every week, every month, etc., which can be set by the user based on the actual business scenario. User operation behavior data on SaaS software is various operation records generated by users during the use of SaaS software, including clicking, inputting, submitting, etc. Backend log data is the log record generated by the backend system of SaaS software, including request processing, error information, performance indicators, etc. Front-end point code is the code deployed at the key operation point of the front-end interface of SaaS software, which is used to capture the user's operation behavior. Key operation points are operation points that have important influence on business logic and user experience during the use of SaaS software, such as login, submit form, click button, etc.

[0082] In some embodiments of the present application, front-end point code is deployed at the key operation points (such as login button, submit form button, etc.) of the front-end interface of SaaS software. The point code automatically records the timestamp, operation type, operation object, etc. information when the user triggers the operation. In the backend system of SaaS software, a log recording mechanism is set up to record detailed information of user operation, including request parameters, response time, error information, etc. According to a preset time interval (such as 1 o'clock in the morning every day), user operation behavior data and backend log data are automatically obtained from the backend server. Use data grabbing tools (such as timing task scripts) to extract data from log files or databases. The operation behavior data and the backend log data are preprocessed, which includes removing invalid, duplicate or erroneous data records, and also includes converting the data into a unified format.

[0083] In one possible implementation, there is a SaaS customer relationship management (CRM) system, and users perform customer information entry, follow-up record, sales opportunity management, etc. operations in the system. Front-end tracking code is deployed at key operation points (such as the "Add Customer" button, the "Submit Follow-up Record" button, etc.) of the front-end interface of the CRM system. When the user clicks the "Add Customer" button, the tracking code records the timestamp of the operation, the operation type (such as "click to add customer"), and the operation object (such as "customer information page"), and sends these information to the backend server. In the backend system of the CRM system, a log recording mechanism is set up to record detailed information of user operations, including request parameters, response time, error information, etc. For example, when the user submits customer information, the backend log records the parameters of the request (such as customer name, contact information, etc.), the response time (such as 100 milliseconds), and whether there is error information (such as none). At 1 a.m. every day, user operation behavior data and backend log data are automatically obtained from the backend server. The user operation behavior data and the backend log data are preprocessed to form complete user operation behavior time series data. For example: the user clicks the "Add Customer" button at 2023-10-11T08:00:00Z. The backend log shows that the customer information is successfully submitted at 2023-10-11T08:00:05Z, and the response time is 100 milliseconds.

[0084] S102, according to the user operation behavior time series data, performing abnormal driving path analysis and behavior driving path analysis to obtain abnormal function points of logical abnormalities and to-be-optimized function points of user abnormal behaviors existing in the SaaS software;

[0085] The abnormal driving path analysis is performed by checking the abnormal path in the user operation behavior time series data to identify the logical abnormalities existing in the software. The abnormal path is manifested as unexpected behavior or error encountered by the user when performing certain operations. For example, when the user submits customer information, the system returns an error, which causes the user to fail to complete the operation. Such abnormal path may indicate that there is a logical problem in the software when processing customer information submission.

[0086] The behavior driving path analysis is performed by checking the irregular behavior path in the user operation behavior time series data to identify the abnormal behavior of the user in using the software. These irregular behaviors imply potential optimization points of the software. For example, the user frequently clicks the "Back" button on a certain function page, which may indicate that the user experience of the function page is not good and needs to be optimized.

[0087] The abnormal function point of the logic exception is an abnormal function point in the SaaS software caused by a logic error or a design defect. The abnormal function point shows abnormal behavior during user operation and affects the normal operation of the software. The to-be-optimized function point of the user abnormal behavior is a user abnormal behavior in the SaaS software caused by user operation habits or software design problems.

[0088] It should be noted that the exception-driven path analysis and the behavior-driven path analysis are two different analysis methods, and are respectively used for identifying the logic exception and the user abnormal behavior in the software. The exception-driven path analysis focuses on the logic error or the design defect in the software, and the behavior-driven path analysis focuses on the irregular behavior of the user in using the software.

[0089] In some embodiments of the present application, the specific process of performing the exception-driven path analysis and the behavior-driven path analysis according to the user operation behavior time sequence data includes: analyzing a function exception scenario of the user in generating data based on the SaaS software according to the user operation behavior time sequence data; inferring an abnormal function point of the SaaS software existing a logic exception based on the function exception scenario; analyzing violation operation information of the user in the SaaS software according to the user operation behavior time sequence data; and inferring a to-be-optimized function point of the SaaS software existing a user abnormal behavior based on the violation operation information.

[0090] The function exception scenario is an unexpected or error situation encountered by the user in generating data using the SaaS software, for example, the system returns an error when the user submits customer information, so that the user cannot complete the operation. The violation operation information is an operation that does not conform to the software use specification or the business logic during the use of the SaaS software by the user.

[0091] In some embodiments of the present application, the specific process of analyzing the function exception scenario of the user in generating data based on the SaaS software according to the user operation behavior time sequence data includes: performing data cleaning and data formatting on the user operation behavior time sequence data to obtain target operation behavior data and target backend log data; traversing a plurality of key operation points in generating data according to the business logic of the SaaS software; extracting data records related to each key operation point from the target operation behavior data and the target backend log data to obtain data records of each key operation point; analyzing operation flow information of the user in the data generation process based on the data records of each key operation point to obtain user operation flow of each key operation point; and determining an exception scenario of the user in each key operation point as the function exception scenario of the user in generating data based on the SaaS software according to the user operation flow of each key operation point and a preset exception scenario condition.

[0092] Specifically, the specific process of analyzing the operation flow information of the user in the data generation process based on the data record of each key operation point includes: sorting the data record of each key operation point in ascending order according to the time stamp in the data record of each key operation point, to obtain an operation sequence of each key operation point; constructing an operation flowchart of the user in the data generation process according to the operation sequence of each key operation point, to obtain an operation flowchart of each key operation point; wherein the operation flowchart is used to represent the operation path of the user from start to finish, the time point of each operation, and the operation content; and taking the operation path of the user from start to finish, the time point of each operation, and the operation content as the operation flow information of the user in the data generation process.

[0093] The preset abnormal scenario condition includes a mapping relationship between the abnormal scenario description information and the abnormal scenario. The mapping relationship is shown in Table 1, for example.

[0094] Table 1

[0095]

[0096] It should be noted that the mapping relationship is dynamically updated, and the user can dynamically manage it. There can be tens of thousands of actual mapping relationships.

[0097] Specifically, the specific process of determining the abnormal scenario of the user in each key operation point according to the user operation flow of each key operation point and the preset abnormal scenario condition includes: obtaining the first abnormal scenario description information from the mapping relationship one by one; checking whether there is a target key operation point in the user operation flow of each key operation point that meets the first abnormal scenario description information; if yes, obtaining the first abnormal scenario corresponding to the first abnormal scenario description information from the mapping relationship, to obtain the abnormal scenario corresponding to the user operation flow of the target key operation point; if no, continue to execute the step of obtaining the first abnormal scenario description information from the mapping relationship one by one, until the abnormal scenario description information in the mapping relationship is completely traversed, and all the abnormal scenarios corresponding to the user operation flow of the target key operation point are taken as the abnormal scenario of the user in each key operation point.

[0098] It should be noted that the user operation flow of each key operation point has flow description information, and the semantic similarity between the flow description information and the traversed first abnormal scenario description information can be analyzed to determine whether it meets the first abnormal scenario description information.

[0099] The functional abnormal scenario includes a data interruption scenario and / or a data inconsistency scenario and / or an operation timeout scenario.

[0100] In some embodiments of the present application, the specific process of inferring the abnormal function points of the SaaS software with logical abnormalities based on the function abnormality scenario includes: in the case of the function abnormality scenario being a data interruption scenario, determining the interruption points on the flow existing in the user operation flow of each key operation point; determining the abnormal function points of the SaaS software with logical abnormalities based on the function functions involved by the existing interruption points on the flow; and / or in the case of the function abnormality scenario being a data inconsistency scenario, analyzing whether there is data update delay or data loss between different modules involved by the user operation flow of each key operation point and whether the data sources are consistent to obtain an analysis result; determining the abnormal function points of the SaaS software with logical abnormalities based on the function functions involved by the analysis result; and / or in the case of the function abnormality scenario being an operation timeout scenario, analyzing the performance parameters and operation complexity involved by the user operation flow of each key operation point; determining the abnormal function points of the SaaS software with logical abnormalities based on the function functions involved by the performance parameters and operation complexity.

[0101] For example Figure 2 As shown, Figure 2 is a process schematic block diagram of an abnormal function point generation process provided by the present application. First, the collected user operation behavior time series data is cleaned, including removing error data, filling missing values, etc., and then formatted into a unified format. Then, according to the business logic of the SaaS software, all key operation points (KPs) are identified and traversed, which are the steps that the user must go through when generating data. The data records related to each key operation point are extracted from the cleaned and formatted data. According to the extracted data records, an operation flowchart is constructed, which arranges the operation steps in chronological order and shows the operation path. By traversing the user operation flow of each key operation point through a mapping table, possible abnormal scenarios are identified. It is identified whether there is a data interruption in the user operation flow, i.e. the user stops operation at a certain step, causing the data generation flow to fail to complete. Check whether there is a problem in the data synchronization mechanism between different modules, such as data update delay or data loss, and whether the data sources are consistent. Analyze the performance parameters and operation complexity in the user operation flow to identify whether there is an operation timeout situation. For the data interruption scenario, determine the flow interruption points and find the corresponding function functions. For the data inconsistency scenario, check the data synchronization mechanism and data sources and find the corresponding function functions. For the operation timeout scenario, analyze the performance parameters and operation complexity and find the corresponding function functions. All identified abnormal function points are summarized to obtain all abnormal function points.

[0102] In some embodiments of the present application, the specific process of analyzing the user's illegal operation information in the SaaS software according to the user operation behavior time series data includes: data cleaning and data formatting of the user operation behavior time series data to obtain final operation behavior data and final backend log data; extracting data records related to user operation behavior from the final operation behavior data and the final backend log data; sorting the data records related to user operation behavior in ascending order according to the time stamp in the data records to obtain the user's original operation sequence; based on the user's original operation sequence, establishing the user's original operation flowchart in time sequence; wherein the original operation flowchart is used to represent the user's operation path from start to end, the time point of each operation and the operation content; determining the user's illegal operation information in the SaaS software according to the user's original operation flowchart in time sequence and the preset illegal operation condition. For example, the user's original operation flowchart in time sequence is shown in Table 2. Figure 7

[0103] The preset illegal operation condition includes the association relationship between the illegal operation type and the illegal operation description information. For example, the association relationship is shown in Table 2.

[0104] Table 2

[0105]

[0106] It should be noted that the association relationship is dynamically updated and can be dynamically managed by the user. There may be tens of thousands of mapping relationships in practice.

[0107] Specifically, the specific process of determining the user's illegal operation information in the SaaS software according to the user's original operation flowchart in time sequence and the preset illegal operation condition includes: obtaining the first illegal operation description information from the association relationship one by one; checking whether the user's original operation flowchart in time sequence contains the first original operation that meets the first illegal operation description information; if yes, obtaining the first illegal operation type corresponding to the illegal operation description information from the association relationship to obtain the first illegal operation type corresponding to the first original operation; if not, continue to execute the step of obtaining the first illegal operation description information from the association relationship one by one until the illegal operation description information in the association relationship is completely traversed. In the case of ending, all the first illegal operation types are taken as the user's illegal operation information in the SaaS software.

[0108] It should be noted that the original operation flowchart has flow description information, and the semantic similarity between the flow description information and the traversed first illegal operation description information can be analyzed to determine whether it meets the first illegal operation description information.

[0109] ​The violation operation information includes frequent operation and / or abnormal exit and / or violation input and / or permission violation.

[0110] In some embodiments of the present application, based on the violation operation information, the specific process of inferring the function point of the SaaS software that exists user abnormal behavior to be optimized includes: in the case of frequent operation, obtaining the function function involved in the frequent operation as the function point of the SaaS software that exists user abnormal behavior to be optimized; and / or, in the case of abnormal exit, obtaining the function function involved in the abnormal exit as the function point of the SaaS software that exists user abnormal behavior to be optimized; and / or, in the case of violation input, obtaining the function function involved in the violation input as the function point of the SaaS software that exists user abnormal behavior to be optimized; and / or, in the case of permission violation, obtaining the function function involved in the permission violation as the function point of the SaaS software that exists user abnormal behavior to be optimized.

[0111] For example Figure 3 As shown, the collected user operation behavior data is cleaned and formatted. The cleaned data is sorted according to the time stamp to generate an operation sequence arranged in chronological order. The operation flowchart is constructed according to the sorted operation sequence. From the association relationship, the first violation operation description information is obtained one by one to identify the violation information (such as frequent operation, abnormal exit, violation input and permission violation) existing in the operation flowchart. According to the identified violation operation information, the function functions related to these operations are extracted. The user frequently performs the operation, and the related function function is extracted. The user abnormal exit condition is identified, and the related function function is extracted. The user violation input condition is identified, and the related function function is extracted. The user permission violation condition is identified, and the related function function is extracted. All extracted function functions are summarized to form a list of function points to be optimized.

[0112] S103, converting the abnormal function point and the function point to be optimized into a product requirement document to be processed of the SaaS software.

[0113] The SaaS software is Software as a Service (Software as a Service), a mode of providing software service through network. Users access software functions through the Internet without installing and maintaining software. The product requirement document to be processed records the product requirements to be processed by the development team, usually including function requirements, optimization requirements, repair requirements, etc.

[0114] In some embodiments of this application, requirement management tools (such as Jira and Trello) and document editing tools (such as Confluence and Markdown editors) are used to convert the identified abnormal and unoptimized functional points into detailed product requirement documents.

[0115] Specifically, the document content includes: a functional description to describe the problem or optimization point in detail; the scope of impact to explain the user range affected by the problem or optimization point; prioritization of requirements based on the severity of the problem and user feedback; and solution suggestions to provide possible solutions or optimization directions.

[0116] For example Figure 4 As shown, Figure 4 This application provides a schematic flowchart of a product requirements generation process for SaaS software based on data mining. The process involves periodically collecting user behavior data and backend log data from the SaaS software. The collected data undergoes preprocessing, including data cleaning and formatting. Then, anomaly-driven path analysis and behavior-driven path analysis are implemented through anomaly and behavior analysis. Anomaly-driven path analysis identifies functional anomaly scenarios and infers abnormal functionalities; behavior-driven path analysis analyzes violation information and infers functionalities requiring optimization. These analysis results are used to generate detailed product requirements documents, guiding the development team in corresponding repair and optimization work to improve software performance and user experience. The anomaly-driven path displayed on the development team's client interface is shown in the diagram. Figure 5 As shown, behavior-driven, for example Figure 6 As shown.

[0117] In this application embodiment, on the one hand, by deploying front-end embedding code at key operation points in the front-end interface of SaaS software, user operation behavior data can be proactively mined. This method can provide detailed and comprehensive user behavior records, providing an accurate data foundation for proactively analyzing user behavior patterns, enabling a more comprehensive identification of problems and potential needs in the software, thereby allowing the SaaS software to fully meet user needs. On the other hand, by performing anomaly-driven path analysis and behavior-driven path analysis based on the time-series data of user operation behavior, logical anomalies and abnormal user behaviors in the software can be proactively identified. Anomaly-driven path analysis can discover logical problems encountered by users during data generation, while behavior-driven path analysis can identify unusual operations by users during use. Converting these analysis results into product requirement documents to be processed can provide a clear direction for software iteration and optimization, thereby improving the iteration speed and quality of the software.

[0118] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of this application.

[0119] Please see Figure 8 This illustration shows a schematic diagram of a product requirement generation system for SaaS software based on data mining, provided in an exemplary embodiment of this application. This product requirement generation system for SaaS software based on data mining can be implemented as all or part of an electronic device through software, hardware, or a combination of both. System 1 includes a data acquisition module 10, a functional path analysis module 20, and a product requirement generation module 30.

[0120] The data acquisition module 10 is used to periodically acquire and preprocess user operation behavior data and backend log data on the SaaS software within a preset period to obtain user operation behavior time sequence data. The operation behavior data is captured by front-end embedding code deployed in advance at key operation points on the front-end interface of the SaaS software.

[0121] The functional path analysis module 20 is used to perform anomaly-driven path analysis and behavior-driven path analysis based on the time sequence data of user operation behavior, so as to obtain the abnormal functional points of logical anomalies in the SaaS software and the functional points to be optimized for abnormal user behavior.

[0122] Product requirement generation module 30 is used to convert abnormal function points and function points to be optimized into unprocessed product requirement documents for SaaS software.

[0123] It should be noted that the product requirement generation system for SaaS software based on data mining provided in the above embodiments is only illustrated by the division of the above functional modules when executing the product requirement generation method for SaaS software based on data mining. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the product requirement generation system for SaaS software based on data mining provided in the above embodiments and the product requirement generation method embodiments for SaaS software based on data mining belong to the same concept, and the implementation process is detailed in the method embodiments, which will not be repeated here.

[0124] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0125] In the embodiments of the present application, on the one hand, by deploying front-end point code at key operation points of the front-end interface of the SaaS software, user operation behavior data can be actively mined. This method can provide detailed and comprehensive user behavior records, provide accurate data basis for actively analyzing user behavior patterns, and enable more comprehensive identification of problems and potential needs in the software, thereby enabling the SaaS software to fully meet user needs. On the other hand, by performing abnormal driving path analysis and behavior driving path analysis according to user operation behavior time series data, logical abnormalities and user abnormal behaviors existing in the software can be actively identified. Abnormal driving path analysis can find logical problems encountered by users in the data generation process, and behavior driving path analysis can identify unusual operations of users in the use process. Converting these analysis results into product requirement documents to be processed can provide a clear direction for iteration and optimization of the software, thereby improving the iteration speed and quality of the software.

[0126] The present application also provides a computer readable medium having program instructions stored thereon, which, when executed by a processor, implement the product requirement generation method of the SaaS software based on data mining provided by each of the method embodiments.

[0127] The present application also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the product requirement generation method of the SaaS software based on data mining of each of the method embodiments.

[0128] Please refer to Figure 9 A structural schematic diagram of an electronic device is provided for the embodiments of the present application. As shown in Figure 9 The electronic device 1000 can include at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0129] The communication bus 1002 is used to realize the connection and communication between the components.

[0130] The user interface 1003 can include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 can also include a standard wired interface and a wireless interface.

[0131] The network interface 1004 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0132] The processor 1001 can include one or more processing cores. The processor 1001 connects various parts within the entire electronic device 1000 through various interfaces and lines, and performs various functions of the electronic device 1000 and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Alternatively, the processor 1001 can be implemented in at least one of a hardware form of a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic array (PLA). The processor 1001 can integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes an operating system, a user interface, and an application program; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 1001, but can be realized by a separate chip.

[0133] The memory 1005 can include a random access memory (RAM) and can also include a read-only memory (ROM). Alternatively, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 1005 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 1005 can alternatively be at least one storage system located away from the aforementioned processor 1001. As shown in the figure, the memory 1005 as a computer storage medium can include an operating system, a network communication module, a user interface module, and a product requirement generation application of SaaS software based on data mining. Figure 9

[0134] In Figure 9 ​In the illustrated electronic device 1000, the user interface 1003 is mainly used to provide an input interface for the user and to obtain the user's input data; while the processor 1001 can be used to call the product requirement generation application of data mining-based SaaS software stored in the memory 1005, and specifically perform the following operations:

[0135] Regularly acquire and preprocess user operation behavior data and backend log data within a preset period to obtain user operation behavior time sequence data. The operation behavior data is captured by front-end embedding code deployed in advance at key operation points on the front-end interface of the SaaS software.

[0136] Based on the time-series data of user operation behavior, anomaly-driven path analysis and behavior-driven path analysis are performed to identify the abnormal functional points of logical anomalies in the SaaS software and the functional points to be optimized for abnormal user behavior.

[0137] Convert abnormal and unoptimized functional points into product requirement documents for SaaS software.

[0138] In one embodiment, when the processor 1001 performs exception-driven path analysis and behavior-driven path analysis based on user operation behavior timing data, it specifically performs the following operations:

[0139] Based on the time-series data of user operation behavior, analyze the functional anomaly scenarios when users generate data based on SaaS software;

[0140] Based on the functional anomaly scenario, it is inferred that there are abnormal functional points in the SaaS software with logical errors;

[0141] Analyze user violation information in SaaS software based on time-series data of user operation behavior;

[0142] Based on the information about the violation, it is inferred that there are functionalities in the SaaS software that require optimization due to abnormal user behavior.

[0143] In one embodiment, when the processor 1001 analyzes abnormal functional scenarios when users generate data based on SaaS software using time-series data of user operation behavior, it specifically performs the following operations:

[0144] Perform data cleaning and formatting on the time-series data of user operation behavior to obtain the target operation behavior data and the target backend log data;

[0145] Based on the business logic of the SaaS software, we trace through multiple key operation points when generating data.

[0146] Extract data records related to each key operation point from the target operation behavior data and the target backend log data to obtain data records of each key operation point;

[0147] Based on the data records of each key operation point, analyze the operation flow information of the user in the data generation process to obtain the user operation flow of each key operation point;

[0148] According to the user operation flow of each key operation point and the preset abnormal scene condition, determine the abnormal scene of the user in each key operation point as the functional abnormal scene of the user based on the SaaS software in the data generation process.

[0149] In one embodiment, when the processor 1001 executes the data records based on each key operation point to analyze the operation flow information of the user in the data generation process, it specifically performs the following operations:

[0150] According to the time stamp in the data record of each key operation point, sort the data records of each key operation point in ascending order to obtain the operation sequence of each key operation point;

[0151] According to the operation sequence of each key operation point, construct an operation flowchart of the user in the data generation process to obtain the operation flowchart of each key operation point; wherein the operation flowchart is used to represent the operation path of the user from start to end, the time point and operation content of each operation;

[0152] The operation path of the user from start to end, the time point and operation content of each operation are used as the operation flow information of the user in the data generation process.

[0153] In one embodiment, when the processor 1001 executes the data records based on each key operation point to analyze the operation flow information of the user in the data generation process, it specifically performs the following operations:

[0154] From the mapping relationship, obtain the first abnormal scene description information one by one;

[0155] Check whether there is a target key operation point in the user operation flow of each key operation point that meets the first abnormal scene description information;

[0156] If so, from the mapping relationship, obtain the first abnormal scene corresponding to the first abnormal scene description information to obtain the abnormal scene corresponding to the user operation flow of the target key operation point;

[0157] If no, continue to execute the step of traversing the first abnormal scene description information from the mapping relationship one by one, until the abnormal scene description information in the mapping relationship is completely traversed, the abnormal scene corresponding to the user operation flow of all target key operation points is taken as the abnormal scene of the user in each key operation point.

[0158] In one embodiment, the processor 1001 specifically performs the following operations when executing the function abnormal scene based on the function abnormal scene to infer the abnormal function point with logical exception of the SaaS software:

[0159] In the case of the function abnormal scene being the data interruption scene, determining the interruption point on the flow existing in the user operation flow of each key operation point; determining the abnormal function point with logical exception of the SaaS software based on the function function involved by the existing interruption point on the flow; and / or,

[0160] In the case of the function abnormal scene being the data inconsistency scene, analyzing whether there is data update delay or data loss and whether the data source is consistent between the data synchronization mechanism of different modules involved by the user operation flow of each key operation point to obtain an analysis result; determining the abnormal function point with logical exception of the SaaS software based on the function function involved by the analysis result; and / or,

[0161] In the case of the function abnormal scene being the operation timeout scene, analyzing the performance parameters and operation complexity involved by the user operation flow of each key operation point; determining the abnormal function point with logical exception of the SaaS software based on the function function involved by the performance parameters and operation complexity.

[0162] In one embodiment, the processor 1001 specifically performs the following operations when executing the function abnormal scene based on the function abnormal scene to analyze the illegal operation information of the user in the SaaS software according to the user operation behavior time series data:

[0163] Data cleaning and data formatting are performed on the user operation behavior time series data to obtain final operation behavior data and final backend log data;

[0164] From the final operation behavior data and the final backend log data, data records related to the user operation behavior are extracted;

[0165] According to the time stamp in the data record, the data records related to the user operation behavior are sorted in ascending order to obtain the original operation sequence of the user;

[0166] Based on the original operation sequence of the user, an original operation flowchart of the user in the time sequence is established; wherein, the original operation flowchart is used to represent the operation path, the time point and the operation content of each operation of the user from beginning to end;

[0167] According to the original operation flowchart of the user in the time sequence and the preset violation operation condition, the violation operation information of the user in the SaaS software is determined.

[0168] In one embodiment, the processor 1001 specifically performs the following operations when performing the determination of the violation operation information of the user in the SaaS software according to the original operation flowchart of the user in the time sequence and the preset violation operation condition:

[0169] The first violation operation description information is obtained from the association relationship one by one;

[0170] It is checked whether there is a first original operation satisfying the first violation operation description information in the original operation flowchart of the user in the time sequence;

[0171] If yes, the first violation operation type corresponding to the violation operation description information is obtained from the association relationship, and the first violation operation type corresponding to the first original operation is obtained.

[0172] If no, the step of obtaining the first violation operation description information from the association relationship one by one is continuously performed until the violation operation description information in the association relationship is completely traversed, and all the first violation operation types are taken as the violation operation information of the user in the SaaS software.

[0173] In one embodiment, the processor 1001 specifically performs the following operations when performing the inference of the function point to be optimized of the SaaS software existing user abnormal behavior based on the violation operation information:

[0174] In the case of the violation operation information being a frequent operation, the function function involved in the frequent operation is obtained as the function point to be optimized of the SaaS software existing user abnormal behavior; and / or,

[0175] In the case of the violation operation information being an abnormal exit, the function function involved in the abnormal exit is obtained as the function point to be optimized of the SaaS software existing user abnormal behavior; and / or,

[0176] In the case of the violation operation information being a violation input, the function function involved in the violation input is obtained as the function point to be optimized of the SaaS software existing user abnormal behavior; and / or,

[0177] In the case of the violation operation information being a permission violation, the function function involved in the permission violation is obtained as the function point to be optimized of the SaaS software existing user abnormal behavior.

[0178] In the embodiments of the present application, on the one hand, by deploying front-end point code at key operation points of the front-end interface of the SaaS software, user operation behavior data can be actively mined. This method can provide detailed and comprehensive user behavior records, provide accurate data basis for actively analyzing user behavior patterns, and enable more comprehensive identification of problems and potential needs in the software, thereby enabling the SaaS software to fully meet user needs. On the other hand, by performing abnormal driving path analysis and behavior driving path analysis according to user operation behavior time sequence data, logical abnormalities and user abnormal behaviors existing in the software can be actively identified. Abnormal driving path analysis can discover logical problems encountered by users in the data generation process, and behavior driving path analysis can identify unusual operations of users in the use process. These analysis results can be converted into product requirement documents to be processed, and can provide a clear direction for iteration and optimization of the software, thereby improving the iteration speed and quality of the software.

[0179] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program for generating product requirements of the SaaS software based on data mining can be stored in a computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. The storage medium of the program for generating product requirements of the SaaS software based on data mining can be a disk, an optical disk, a read-only memory, or a random access memory.

[0180] The above only discloses preferred embodiments of the present application, and of course cannot limit the scope of the rights of the present application. Therefore, equivalent changes made in accordance with the claims of the present application are still within the scope of the present application.

Claims

1. A product requirement generation method for a data mining-based SaaS software, characterized by, Applied to the server side, the method includes: Regularly acquire and preprocess user operation behavior data and backend log data within a preset period to obtain user operation behavior time sequence data. The operation behavior data is captured by front-end embedding code deployed in advance at key operation points on the front-end interface of the SaaS software. Based on the user operation behavior time-series data, anomaly-driven path analysis and behavior-driven path analysis are performed to obtain the abnormal functional points of logical anomalies in the SaaS software and the functional points to be optimized for abnormal user behaviors. Anomaly-driven path analysis identifies logical anomalies in the software by examining abnormal paths in the user operation behavior time-series data. Behavior-driven path analysis identifies abnormal user behaviors during software use by examining non-routine behavior paths in the user operation behavior time-series data. The step of performing anomaly-driven path analysis and behavior-driven path analysis based on the user operation behavior time-series data includes: Based on the time-series data of user operation behavior, analyze the functional anomaly scenarios when the user generates data based on the SaaS software; based on the functional anomaly scenarios, infer that the SaaS software has abnormal functional points with logical anomalies; based on the time-series data of user operation behavior, analyze the user's violation information in the SaaS software; based on the violation information, infer that the SaaS software has functional points that need optimization due to abnormal user behavior; wherein, The step of analyzing the abnormal functional scenarios when the user generates data based on the SaaS software, according to the time-series data of the user's operation behavior, includes: The user operation behavior time-series data is cleaned and formatted to obtain target operation behavior data and target backend log data. Based on the business logic of the SaaS software, multiple key operation points during data generation are identified. Data records related to each key operation point are extracted from the target operation behavior data and target backend log data to obtain the data record for each key operation point. Based on the data record for each key operation point, the user's operation flow information during data generation is analyzed to obtain the user operation flow for each key operation point. Based on the user operation flow for each key operation point and preset abnormal scenario conditions, the abnormal scenarios that occur when the user generates data based on the SaaS software are determined as functional abnormal scenarios when the user generates data. The abnormal function points and the function points to be optimized are converted into a product requirement document to be processed for the SaaS software.

2. The method of claim 1, wherein, The analysis of the user's operational process information during data generation, based on the data records of each key operation point, includes: Based on the timestamps in the data records of each key operation point, sort the data records of each key operation point in ascending order to obtain the operation sequence of each key operation point; constructing an operation flowchart of the user in a data generation process according to the operation sequence of each key operation point, to obtain an operation flowchart of each key operation point; wherein the operation flowchart is used to represent the operation path of the user from start to finish, the time point and operation content of each operation; taking the operation path of the user from start to finish, the time point and operation content of each operation as operation flow information of the user in the data generation process.

3. The method of claim 1, wherein, The preset abnormal scenario condition includes a mapping relationship between abnormal scenario description information and an abnormal scenario. The determination of the abnormal scenario of the user in each key operation point according to the user operation flowchart of each key operation point and the preset abnormal scenario condition comprises: obtaining first abnormal scenario description information from the mapping relationship one by one; checking whether there is a target key operation point user operation flowchart that meets the first abnormal scenario description information in the user operation flowchart of each key operation point; if yes, obtaining a first abnormal scenario corresponding to the first abnormal scenario description information from the mapping relationship, to obtain an abnormal scenario corresponding to the target key operation point user operation flowchart; if no, continue to execute the step of obtaining first abnormal scenario description information from the mapping relationship one by one, until all the target key operation point user operation flowcharts are obtained as the abnormal scenario of the user in each key operation point when the abnormal scenario description information in the mapping relationship is completely traversed.

4. The method of claim 1, wherein, The functional abnormal scenario includes a data interruption scenario and / or a data inconsistency scenario and / or an operation timeout scenario. The inference of the abnormal function point with logical exception of the SaaS software based on the functional abnormal scenario comprises: in the case of the functional abnormal scenario being a data interruption scenario, determining an interruption point on the flowchart existing in the user operation flowchart of each key operation point; determining the abnormal function point with logical exception of the SaaS software based on the function function involved by the existing interruption point on the flowchart; and / or, in the case of the functional abnormal scenario being a data inconsistency scenario, analyzing whether there is data update delay or data loss in the data synchronization mechanism between different modules involved by the user operation flowchart of each key operation point and whether the data source is consistent, to obtain an analysis result; determining the abnormal function point with logical exception of the SaaS software based on the function function involved by the analysis result; and / or, in the case of the functional abnormal scenario being an operation timeout scenario, analyzing the performance parameters and operation complexity involved by the user operation flowchart of each key operation point; determining the abnormal function point with logical exception of the SaaS software based on the function function involved by the performance parameters and operation complexity.

5. The method of claim 1, wherein, The analysis of the illegal operation information of the user in the SaaS software according to the user operation behavior time series data comprises: performing data cleaning and data formatting on the user operation behavior time series data to obtain final operation behavior data and final backend log data; Extract data records related to user operation behavior from the final operation behavior data and the final backend log data; Based on the timestamps in the data records, the data records related to the user's operation behavior are sorted in ascending order to obtain the user's original operation sequence; Based on the user's original operation sequence, an original operation flowchart of the user in the time series is established; wherein, the original operation flowchart is used to represent the user's operation path from start to finish, the time point of each operation, and the operation content; Based on the user's original operation flowchart in the time series and the preset violation operation conditions, the user's violation operation information in the SaaS software is determined.

6. The method of claim 5, wherein, The preset violation conditions include the relationship between violation type and violation description information; The step of determining the user's violation information in the SaaS software based on the user's original operation flowchart in the time series and preset violation conditions includes: From the aforementioned relationships, the description information of the first violation operation is obtained by traversing through them one by one; Check whether there is a first original operation in the user's original operation flowchart on the time series that satisfies the first violation operation description information; If so, obtain the first violation operation type corresponding to the violation operation description information from the association relationship, and obtain the first violation operation type corresponding to the first original operation; If not, continue executing the step of traversing through the association to obtain the first violation operation description information one by one until all violation operation description information in the association has been traversed. Then, treat all first violation operation types as the user's violation operation information in the SaaS software.

7. The method of claim 1, wherein, The violation information includes frequent operations and / or abnormal exits and / or unauthorized input and / or permission violations; The optimization points for the SaaS software that are inferred to have abnormal user behavior based on the violation information include: If the violation information indicates frequent operations, the function involved in the frequent operations is obtained as the functional point to be optimized in the SaaS software where there is abnormal user behavior. And / or, If the violation information indicates an abnormal exit, the function involved in the abnormal exit is retrieved and identified as a functional point to be optimized in the SaaS software due to abnormal user behavior; and / or, If the violation information is an illegal input, the function related to the illegal input is obtained as a function point to be optimized in the SaaS software where there is abnormal user behavior; and / or, If the violation information is a permission violation, the function involved in the permission violation is obtained as a function point to be optimized in the SaaS software where there is abnormal user behavior.

8. A product requirement generation system based on data mining SaaS software implemented using the method of any one of claims 1-7, characterized in that, The system includes: The data acquisition module is used to periodically acquire and preprocess user operation behavior data and backend log data on the SaaS software within a preset period to obtain user operation behavior time sequence data. The operation behavior data is captured by front-end embedding code deployed in advance at key operation points on the front-end interface of the SaaS software. a function path analysis module configured to perform abnormal driving path analysis and behavior driving path analysis according to the user operation behavior time sequence data, to obtain abnormal function points of logical abnormalities and function points to be optimized of user abnormal behaviors existing in the SaaS software; a product demand generation module configured to convert the abnormal function points and the function points to be optimized into product demand documents to be processed of the SaaS software.

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