Project detection method, device, equipment, medium and program product

Abstract

The invention provides a project detection method, device and equipment, a medium and a program product, which can be applied to the technical field of artificial intelligence. The item detection method comprises the following steps: in response to a received instruction for detecting a target item, obtaining respective index information and evaluation information of a plurality of functions included in the target item; a multi-dimensional detection model is utilized, the target project is processed according to the index information and the evaluation information of the multiple functions, scores of multiple dimensions of the target project and core contribution functions of the multiple dimensions are generated, the multiple dimensions comprise the performance dimension and the evaluation dimension of the target project, and the core contribution functions of the multiple dimensions are generated; the core contribution function of each dimension is the function with the highest contribution degree in the process of generating the score of each dimension; and generating detection information of the target project according to the scores of the plurality of dimensions and the core contribution functions corresponding to the plurality of dimensions.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence, specifically to a project detection method, apparatus, equipment, medium, and program product. Background Technology

[0002] In related technologies, different departments responsible for different functions of the target project can conduct separate tests on each function, obtain test results, and then summarize the test results of each function to obtain the overall test result for the project. However, these technologies suffer from several drawbacks. The testing processes for each function are independent, failing to consider the interrelationships between functions, resulting in poor test effectiveness for the target project. Furthermore, these technologies only include scores in the generated test results for the target project, leading to poor interpretability. Summary of the Invention

[0003] In view of the above problems, this application provides a project testing method, apparatus, equipment, medium and procedure product.

[0004] According to a first aspect of this application, a project detection method is provided, comprising: in response to receiving an instruction to detect a target project, acquiring indicator information and evaluation information for each of multiple functions included in the target project; using a multi-dimensional detection model, processing the target project based on the indicator information and evaluation information for each of the multiple functions, generating scores for multiple dimensions of the target project and core contributing functions for each of the multiple dimensions, wherein the multiple dimensions include a performance dimension and an evaluation dimension of the target project, the performance dimension score characterizes the operational effect of the target project, the evaluation dimension characterizes the user satisfaction of the target project, and the core contributing function of each dimension is the function with the highest contribution in the process of generating the score for each dimension; generating detection information for the target project based on the scores for the multiple dimensions and the core contributing functions corresponding to each of the multiple dimensions, wherein the detection information characterizes the multi-dimensional operational status of the target project.

[0005] According to embodiments of this application, a multi-dimensional detection model is used to process a target project based on the indicator information and evaluation information of multiple functions, generating scores for multiple dimensions of the target project and the core contributing functions of each dimension. This includes: determining whether there is a collaborative relationship between multiple functions; generating a first contribution of the collaborative relationship to each dimension when there is a collaborative relationship between multiple first functions; generating a second contribution of each function to each dimension based on the indicator information and evaluation information of each first function; and determining the score of each dimension based on the first contribution and the second contribution of each dimension.

[0006] According to an embodiment of this application, the method further includes: for a first function among multiple functions, determining a contribution score of the first function to each dimension based on a first contribution to each dimension including the collaborative relationship of the first function and a second contribution of the first function to each dimension, wherein the first function has a collaborative relationship with other functions among multiple functions; for a second function among multiple functions, determining a contribution score of the second function to each dimension based on the second contribution of the second function to each dimension, wherein the second function does not have a collaborative relationship with other functions among multiple functions; and determining the function with the highest contribution score to each dimension based on the contribution scores of the first function to each dimension and the contribution scores of the second function to each dimension as the core contributing function of each dimension.

[0007] According to an embodiment of this application, before processing the target project, the method further includes: determining whether there is sensitive redundant data in the indicator information and evaluation information; if there is sensitive redundant data in the indicator information or evaluation information, determining the risk level of the indicator information or evaluation information; and performing desensitization processing on the indicator information or evaluation information that matches the risk level according to the preset correspondence between the risk level and the desensitization processing and the risk level.

[0008] According to an embodiment of this application, after generating the detection information of the target project, the method further includes: saving the indicator information and evaluation information of each of the multiple functions included in the target project and the detection information of the target project as detection records to a database; in response to receiving an instruction to detect the target project again, obtaining the indicator information and evaluation information of each of the multiple functions included in the target project; determining the indicator change information and evaluation change information of each of the multiple functions included in the target project based on the indicator information and evaluation information of each of the multiple functions included in the target project, and the indicator information and evaluation information corresponding to each of the multiple functions included in the target project in the most recently saved detection record; and using an incremental detection model, adjusting the detection information in the most recently saved detection record based on the indicator change information and evaluation change information of each of the multiple functions, to generate the scores of multiple dimensions of the target project and the core contribution functions of each of the multiple dimensions.

[0009] According to an embodiment of this application, the indicator information includes key performance indicators; the method further includes: monitoring key performance indicators; and generating an instruction to detect the target item in response to the change value of the key performance indicators within a preset time period meeting a preset change condition.

[0010] The second aspect of this application provides a project detection device, comprising: an acquisition module, configured to, in response to receiving an instruction to detect a target project, acquire indicator information and evaluation information of multiple functions included in the target project; a detection module, configured to, using a multi-dimensional detection model, process the target project based on the indicator information and evaluation information of the multiple functions, generating scores for multiple dimensions of the target project and core contributing functions for each of the multiple dimensions, wherein the multiple dimensions include performance dimensions and evaluation dimensions of the target project, the performance dimension score characterizes the operational effect of the target project, the evaluation dimension characterizes the satisfaction of the target project users with the target project, and the core contributing function of each dimension is the function with the highest contribution in the process of generating the score of each dimension; and a generation module, configured to, based on the scores of the multiple dimensions and the core contributing functions corresponding to each of the multiple dimensions, generate detection information of the target project, wherein the detection information characterizes the multi-dimensional operational status of the target project.

[0011] A third aspect of this application provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0012] A fourth aspect of this application also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0013] The fifth aspect of this application also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method. Attached Figure Description

[0014] The above-mentioned contents, other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0015] Figure 1 The illustration shows an application scenario diagram of the project testing method, apparatus, device, medium, and program product according to embodiments of this application;

[0016] Figure 2 A flowchart illustrating a project detection method according to an embodiment of this application is shown schematically.

[0017] Figure 3 This schematic diagram illustrates the structural block diagram of a project testing apparatus according to an embodiment of this application;

[0018] Figure 4 A block diagram schematically illustrates an electronic device suitable for implementing a project testing method according to an embodiment of this application. Detailed Implementation

[0019] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0020] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0021] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0022] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0023] After a project with multiple functions is built and put into operation, it is necessary to continuously test each function to determine whether the project is running normally.

[0024] In related technologies, during the testing of a project that includes multiple functions, the department responsible for each function tests each function separately, obtains a score for each function, and transmits the scores of each function to the project management object. The project management object then compiles and summarizes the scores of each function to obtain the project's testing results.

[0025] However, in related technologies, each function of the project is tested independently, and the scores of each function obtained from the independent tests are statistically summarized as the test results of the project. This does not take into account the impact of the relationship between functions on the project's performance, resulting in a lack of testing of the project based on the relationship between functions.

[0026] Furthermore, in related technologies, the test results generated for projects mainly include scoring information on the project's operational performance. However, the scoring information on the project's operational performance cannot reflect the test results on the project's users and operational efficiency.

[0027] Based on this, embodiments of this application provide a project detection method. By using a multi-dimensional detection model, the method processes the indicator information and evaluation information of multiple functions included in the target project, generating scores that include dimensions representing the operational performance of the target project, and scores that include dimensions representing the user satisfaction with the target project. By outputting multi-dimensional detection results, the detection effect of the target project is improved. Furthermore, by setting the core contribution function of each dimension in the detection results, the transparency of the target project detection is improved, and the information richness of the detection results is enhanced.

[0028] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0029] In the technical solution of this application, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with relevant laws, regulations, and standards, and necessary measures have been taken to ensure that they do not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse.

[0030] In scenarios involving automated decision-making using personal information, the methods, devices, and systems provided in this application all offer users corresponding entry points for choosing to agree to or reject the automated decision-making results. If the user chooses to reject, the process proceeds to the expert decision-making stage. Here, "automated decision-making" refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests, or economic, health, and credit status through computer programs, and then making a decision. Here, "expert decision-making" refers to the activity of making decisions by personnel who specialize in a particular field, possess specialized experience, knowledge, and skills, and have reached a certain level of professional expertise.

[0031] Figure 1 The illustration shows an application scenario diagram of the project testing method, apparatus, device, medium, and program product according to embodiments of this application.

[0032] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0033] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0034] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0035] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0036] It should be noted that the project detection method provided in this application embodiment can generally be executed by server 105. Correspondingly, the project detection device provided in this application embodiment can generally be located in server 105. The project detection method provided in this application embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the project detection device provided in this application embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0037] It should be understood that Figure 1The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0038] The following will be based on Figure 1 The described scene, through Figure 2 The project detection method according to the embodiments of this application will be described in detail.

[0039] Figure 2 A flowchart illustrating a project detection method according to an embodiment of this application is shown schematically.

[0040] like Figure 2 As shown, the project detection method in this embodiment includes operations S210 to S230.

[0041] In operation S210, in response to receiving an instruction to detect the target item, the system obtains the indicator information and evaluation information of each of the multiple functions included in the target item.

[0042] Metrics information can be performance metrics that can be obtained through performance testing tools. For example, metrics information may include at least one of the following: performance metrics of the function, runtime, response time, and latency.

[0043] Evaluation information can include feedback from users who use the function, those who maintain or develop the model, and public opinion regarding the function. Evaluation information may include user surveys and expert ratings.

[0044] When operating S220, a multi-dimensional detection model is used to process the target project based on the indicator information and evaluation information of each of the multiple functions, generating scores for multiple dimensions of the target project and the core contribution functions of each dimension.

[0045] Multiple dimensions include the target project's performance dimension and evaluation dimension. The performance dimension score represents the target project's operational effectiveness, while the evaluation dimension represents the target project's user satisfaction with the target project.

[0046] A performance dimension score can be determined comprehensively based on multiple metrics. Alternatively, a performance dimension can include multiple sub-dimensions, with scores for each sub-dimension determined based on various metrics, and these scores used as the overall performance dimension score. For example, the performance dimension score could include the function's highest performance score, processing efficiency score, operational risk score, and compliance score.

[0047] The score for one evaluation dimension can be determined by comprehensively considering various evaluation information. Alternatively, an evaluation dimension can include multiple sub-dimensions, and the scores for each sub-dimension can be determined based on various evaluation information. The scores of the multiple sub-dimensions can then be used as the score for the overall evaluation dimension.

[0048] A multi-dimensional detection model can be a pre-trained neural network model. The input of a multi-dimensional detection model can include indicator information and evaluation information for each of the multiple functions. For example, if the target project includes n functions, and the sum of the number of indicator information and evaluation information included in each function is m, then the input of the multi-dimensional detection model can be an n*m matrix, which includes features corresponding to the m pieces of information for each of the n functions.

[0049] The core contributing function of a dimension is the function that contributes the most to the generation of the dimension's score. The core contributing function can also be called the primary attribution. For example, if the scores of each dimension of the target project are determined based on the scores of multiple functions in each dimension and the preset weights of multiple functions in each dimension, then the function with the highest weight can be selected as the core contributing function, or the function with the highest score in each dimension can be selected as the core contributing function, or the function with the highest weighted score in each dimension can be selected as the core contributing function.

[0050] When operating S230, detection information for the target project is generated based on the scores of multiple dimensions and the core contribution functions corresponding to each dimension.

[0051] The detection information can characterize the multi-dimensional operation of the target project. The detection results of the target project can include the scores of multiple dimensions and the core contribution functions corresponding to each dimension.

[0052] By using a pre-trained multi-dimensional detection model, detection is performed based on the indicator information and evaluation information of each function within multiple functions of the target project. This enables the assessment of the synergistic gains and intrinsic connections between multiple functions based on the overall information of the project, improving the accuracy of the scores generated for the target project across multiple dimensions. Furthermore, by including the core contribution functions of each dimension in the detection results, attribution display for each dimension is achieved, allowing for adjustments and improvements to the target project based on the detection results. In addition, the multi-dimensional detection model generates indicator scores representing the direct results of the target project and evaluation scores representing its indirect benefits, based on the indicator information and evaluation information. These scores are used to demonstrate user or market satisfaction with the target project, ensuring the comprehensiveness and accuracy of the detection results.

[0053] The multi-dimensional detection model can be divided into three parts: collaborative relationship identification, contribution detection, and detection score determination. The collaborative relationship identification part detects the collaborative relationships between multiple functions. The contribution detection part determines the contribution of each function and each collaborative relationship to each dimension. The detection score module determines the detection score for each dimension based on the contribution.

[0054] According to embodiments of this application, a multi-dimensional detection model is used to process a target project based on the indicator information and evaluation information of multiple functions, generating scores for multiple dimensions of the target project and the core contributing functions of each dimension. This includes: determining whether there is a collaborative relationship between multiple functions; generating a first contribution of the collaborative relationship to each dimension when there is a collaborative relationship between multiple first functions; generating a second contribution of each function to each dimension based on the indicator information and evaluation information of each first function; and determining the score of each dimension based on the first contribution and the second contribution of each dimension.

[0055] The collaboration relationship identification component can determine whether there are collaboration relationships between multiple functions based on pre-trained identification rules and the function names of multiple functions. It can also obtain the execution order, input information, and output information of multiple functions in the target project, and use the collaboration relationship component to determine the collaboration relationships between multiple functions based on their execution order and input / output information.

[0056] The contribution detection section can generate a first contribution of the collaborative relationship or a second contribution of the function based on the collaborative relationship between functions or the indicator information and evaluation information of the function. The first contribution can represent the degree of influence of the collaborative relationship on the dimension score, and the second contribution can represent the degree of influence of a single function on the dimension score.

[0057] After obtaining the contribution of all collaborative relationships or functions to each dimension in multiple functions, the contribution of all collaborative relationships or functions to each dimension can be input into the detection score determination part. The detection score determination part can perform linear or non-linear combination of the contribution of collaborative relationships and functions to each dimension according to the combination rules learned in pre-training to generate the score for each dimension.

[0058] By using the collaborative relationship identification part in the multi-dimensional detection model, the collaborative relationships between multiple functions can be generated based on information from multiple functions, thereby obtaining the collaborative relationships between functions. The contribution detection part is used to generate the contribution of the collaborative relationships between functions, so that the detection score determination part can combine the collaborative relationships between multiple functions to generate scores for each dimension, thus improving the accuracy of the scores for each dimension.

[0059] Understandably, the contribution of a collaborative relationship can be used to represent the effect of at least two functions working together on various dimensions.

[0060] The multi-dimensional detection model may also include a core contribution function determination part, which is used to comprehensively determine the contribution score of each function in each dimension based on the contribution of the function and the contribution of the collaborative relationship including the function, and to determine the core contribution function of each dimension based on the contribution score.

[0061] According to an embodiment of this application, the project detection method further includes: for a first function among multiple functions, determining the contribution score of the first function to each dimension based on a first contribution degree of the first function to each dimension and a second contribution degree of the first function to each dimension, wherein the first function has a collaborative relationship with other functions among multiple functions; for a second function among multiple functions, determining the contribution score of the second function to each dimension based on a second contribution degree of the second function to each dimension, wherein the second function does not have a collaborative relationship with other functions among multiple functions; and determining the function with the highest contribution score to each dimension based on the contribution scores of the first function to each dimension and the contribution scores of the second function to each dimension as the core contributing function of each dimension.

[0062] For any dimension among multiple dimensions, the contribution score of the first function in that dimension can be determined by the sum of the first contribution of the collaborative relationship of the first function in that dimension and the second contribution of the first function in that dimension.

[0063] The collaborative relationship identification section can assign weights to collaborative relationships when identifying them. For any dimension across multiple dimensions, it can determine the contribution score of the first function in that dimension based on the first contribution and weight of the collaborative relationship in that dimension, as well as the second contribution of the first function in that dimension.

[0064] By combining the first contribution degree of the collaborative relationship of the first function and the second contribution degree of the first function, the independent contribution and collaborative contribution of the first function are combined to obtain the contribution score used to determine whether a function is a core contributing function, thereby improving the accuracy of the contribution score.

[0065] Before testing the target project, risk detection and anonymization processing can be performed on the indicator information and evaluation information of multiple functions.

[0066] According to embodiments of this application, the project detection method further includes: before processing the target project, determining whether there is sensitive redundant data in the indicator information and evaluation information. Then, if sensitive redundant data exists in the indicator information or evaluation information, determining the risk level of the indicator information or evaluation information. Then, based on the preset correspondence between risk level and desensitization processing, and the risk level, performing desensitization processing on the indicator information or evaluation information that matches the risk level.

[0067] Sensitive and redundant data refers to data in indicator information or evaluation information that is irrelevant to the project detection or involves privacy, such as personal information of the object in the evaluation information of the object using the function.

[0068] Pre-defined risk detection rules or pre-trained risk detection models can be used to detect indicator information or evaluation information and determine the risk level of sensitive and redundant data in the indicator information or evaluation information.

[0069] The preset risk level and desensitization processing correspondence includes desensitization processing operations corresponding to different risk levels. For example, for sensitive and redundant data with a high risk level, deletion or replacement with preset meaningless information can be used; for sensitive and redundant data with a medium risk level, partial masking or generalization operations can be used; and for sensitive and redundant data with a low risk level, anonymization desensitization operations can be used.

[0070] By anonymizing indicator or evaluation information before its use, the project testing process meets compliance requirements. Furthermore, based on the different risk levels of sensitive and redundant data, anonymization is performed on the data corresponding to the risk level, ensuring both the compliance and validity of the indicator and evaluation information.

[0071] In addition, the input to the pre-trained multi-dimensional detection model can also be historical detection results as well as information on changes in indicators and evaluations of multiple functions.

[0072] According to an embodiment of this application, after generating the detection information of the target project, the method further includes: saving the indicator information and evaluation information of each of the multiple functions included in the target project and the detection information of the target project as detection records to a database; in response to receiving an instruction to detect the target project again, obtaining the indicator information and evaluation information of each of the multiple functions included in the target project; determining the indicator change information and evaluation change information of each of the multiple functions included in the target project based on the indicator information and evaluation information of each of the multiple functions included in the target project, and the indicator information and evaluation information corresponding to each of the multiple functions included in the target project in the most recently saved detection record; and using an incremental detection model, adjusting the detection information in the most recently saved detection record based on the indicator change information and evaluation change information of each of the multiple functions, to generate the scores of multiple dimensions of the target project and the core contribution functions of each of the multiple dimensions.

[0073] By saving the input and output of the multi-dimensional detection model to a database, the project detection of the target project can be recorded.

[0074] If the database is not empty, meaning there is a historical record of detecting the target project, then when a command to detect the target project is received, the indicator information and evaluation information of each function of the target project in the most recently saved record can be compared with the indicator information and evaluation information of each function of the target project obtained this time. The changed information, including indicator change information and evaluation change information, can be identified. Based on the changed information, the multi-dimensional detection model can be used to adjust the detection results of the target function in the most recently saved record, thereby realizing incremental detection of the target project and reducing the amount of computation to generate the detection results of the target project.

[0075] In addition, a continuous monitoring mechanism can be set up to monitor the target project in real time.

[0076] According to embodiments of this application, the indicator information includes key performance indicators, which are a subset of predetermined indicator information. The project detection method further includes monitoring the key performance indicators. Then, in response to the change value of the key performance indicators within a preset time period meeting preset change conditions, an instruction to detect the target project is generated.

[0077] By monitoring key performance indicators in real time, it is possible to respond quickly to abnormal changes in key performance indicators, generate instructions to inspect the target project, and generate inspection results for the target project in a timely manner. This improves the transparency and response speed of project management and helps reduce the risk of project delays and cost overruns.

[0078] The embodiments of this application comprehensively consider multiple aspects of the target project, including performance, efficiency, user experience, risk management, and compliance, to accurately detect the overall impact and value of the target project. Furthermore, the multi-dimensional detection model quantifies the importance of each dimension through its score, detecting not only the direct results of the target project but also indirect benefits such as user satisfaction and market competitiveness, ensuring the comprehensiveness and accuracy of the detection results.

[0079] Based on the above-mentioned project testing methods, this application also provides a project testing device. The following will be combined with... Figure 3 The device is described in detail.

[0080] Figure 3 A schematic block diagram of a project testing apparatus according to an embodiment of this application is shown.

[0081] like Figure 3 As shown, the project detection device 300 of this embodiment includes an acquisition module 310, a detection module 320 and a generation module 330.

[0082] The acquisition module 310 is used to acquire the indicator information and evaluation information of the multiple functions included in the target project in response to receiving an instruction to detect the target project.

[0083] The detection module 320 is used to process the target project using a multi-dimensional detection model based on the indicator information and evaluation information of multiple functions, and generate scores for multiple dimensions of the target project and the core contribution functions of each dimension. The multiple dimensions include the performance dimension and the evaluation dimension of the target project. The score of the performance dimension represents the running effect of the target project, and the evaluation dimension represents the satisfaction of the users of the target project with the target project. The core contribution function of each dimension is the function that contributes the most in the process of generating the score of each dimension.

[0084] The generation module 330 is used to generate detection information of the target project based on the scores of multiple dimensions and the core contribution functions corresponding to each dimension. The detection information represents the multi-dimensional operation of the target project.

[0085] According to embodiments of this application, any multiple modules among the acquisition module 310, detection module 320, and generation module 330 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this application, at least one of the acquisition module 310, detection module 320, and generation module 330 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or any other reasonable means of integrating or packaging circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the acquisition module 310, detection module 320, and generation module 330 can be at least partially implemented as a computer program module, which can perform corresponding functions when the computer program module is run.

[0086] According to an embodiment of this application, the detection module 320 includes a detection score generation module, which is used to: determine whether there is a collaborative relationship between multiple functions; if there is a collaborative relationship between multiple first functions among the multiple functions, generate a first contribution of the collaborative relationship to each of the multiple dimensions; generate a second contribution of each of the multiple functions to each dimension based on the respective indicator information and evaluation information of the multiple first functions; and determine the score of each dimension based on the first contribution and the second contribution of each dimension.

[0087] According to an embodiment of this application, the detection module 320 further includes a core contribution function determination module, which is used to: for a first function among multiple functions, determine the contribution score of the first function to each dimension based on a first contribution degree of the first function to each dimension and a second contribution degree of the first function to each dimension, wherein the first function has a collaborative relationship with other functions among multiple functions; for a second function among multiple functions, determine the contribution score of the second function to each dimension based on a second contribution degree of the second function to each dimension, wherein the second function does not have a collaborative relationship with other functions among multiple functions; and determine the function with the highest contribution score to each dimension based on the contribution scores of the first function to each dimension and the contribution scores of the second function to each dimension, thus identifying the core contribution function of each dimension.

[0088] According to an embodiment of this application, the device further includes a desensitization module, which is used to: determine whether there is sensitive redundant data in the indicator information and evaluation information before processing the target project; determine the risk level of the indicator information or evaluation information if there is sensitive redundant data in the indicator information or evaluation information; and perform desensitization processing on the indicator information or evaluation information that matches the risk level according to the preset correspondence between the risk level and the desensitization processing and the risk level.

[0089] According to an embodiment of this application, the detection module 320 is further configured to: after generating detection information for the target project, save the indicator information and evaluation information of each of the multiple functions included in the target project and the detection information of the target project as detection records in the database; in response to receiving an instruction to detect the target project again, obtain the indicator information and evaluation information of each of the multiple functions included in the target project; determine the indicator change information and evaluation change information of each of the multiple functions included in the target project based on the indicator information and evaluation information of each of the multiple functions included in the target project, and the indicator information and evaluation information corresponding to each of the multiple functions included in the target project in the most recently saved detection record in the database; and adjust the detection information in the most recently saved detection record using an incremental detection model based on the indicator change information and evaluation change information of each of the multiple functions, thereby generating scores for multiple dimensions of the target project and the core contribution functions of each of the multiple dimensions.

[0090] According to an embodiment of this application, the indicator information includes key performance indicators; the device further includes a monitoring module, which is used to: monitor the key performance indicators; and generate an instruction to detect the target item in response to the change value of the key performance indicators within a preset time period meeting a preset change condition.

[0091] Figure 4 A block diagram schematically illustrates an electronic device suitable for implementing a project testing method according to an embodiment of this application.

[0092] like Figure 4 As shown, an electronic device 400 according to an embodiment of this application includes a processor 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage portion 408 into a random access memory (RAM) 403. The processor 401 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 401 may also include onboard memory for caching purposes. The processor 401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0093] RAM 403 stores various programs and data required for the operation of electronic device 400. Processor 401, ROM 402, and RAM 403 are interconnected via bus 404. Processor 401 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 402 and / or RAM 403. It should be noted that the programs may also be stored in one or more memories other than ROM 402 and RAM 403. Processor 401 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0094] According to embodiments of this application, the electronic device 400 may further include an input / output (I / O) interface 405, which is also connected to a bus 404. The electronic device 400 may also include one or more of the following components connected to the input / output (I / O) interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output (I / O) interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0095] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0096] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include ROM 402 and / or RAM 403 and / or one or more memories other than ROM 402 and RAM 403 described above.

[0097] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code is used to enable the computer system to implement the item detection method provided in the embodiments of this application.

[0098] When the computer program is executed by the processor 401, it performs the functions defined in the system / apparatus of this application embodiment. According to the embodiments of this application, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0099] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via communication section 409, and / or installed from removable medium 411. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0100] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by processor 401, it performs the functions defined in the system of this application embodiment. According to embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0101] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0102] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0103] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

Claims

1. A method for project testing, characterized in that, The method includes: In response to receiving an instruction to detect a target item, the system acquires the indicator information and evaluation information of each of the multiple functions included in the target item. Using a multi-dimensional detection model, the target project is processed based on the respective indicator information and evaluation information of the multiple functions to generate scores for multiple dimensions of the target project and the core contributing functions of each of the multiple dimensions. The multiple dimensions include the performance dimension and the evaluation dimension of the target project. The score of the performance dimension represents the operating effect of the target project, and the evaluation dimension represents the satisfaction of the users of the target project with the target project. The core contributing function of each dimension is the function with the highest contribution in the process of generating the score of each dimension. Based on the scores of the multiple dimensions and the core contribution functions corresponding to each of the multiple dimensions, detection information of the target project is generated, and the detection information represents the multi-dimensional operation of the target project.

2. The method according to claim 1, characterized in that, The process of utilizing a multi-dimensional detection model to process the target project based on the respective indicator and evaluation information of the multiple functions, generating scores for multiple dimensions of the target project and the core contribution functions of each dimension, includes: Determine whether there is a collaborative relationship between the multiple functions; In the case where there is a collaborative relationship among multiple first functions among the multiple functions, a first contribution of the collaborative relationship to each of the multiple dimensions is generated; Based on the respective indicator information and evaluation information of the plurality of first functions, a second contribution degree of each of the plurality of functions to each dimension is generated; The score for each dimension is determined based on the first contribution and the second contribution of each dimension.

3. The method according to claim 2, characterized in that, The method further includes: For the first function among the plurality of functions, the contribution score of the first function to each dimension is determined based on the first contribution degree of the first function to each dimension and the second contribution degree of the first function to each dimension, wherein there is a collaborative relationship between the first function and other functions among the plurality of functions. For the second function among the multiple functions, the contribution score of the second function to each dimension is determined based on the second contribution degree of the second function to each dimension. The second function has no collaborative relationship with the other functions among the multiple functions. Based on the contribution scores of the first function to each dimension and the contribution scores of the second function to each dimension, the function with the highest contribution score to each dimension is determined as the core contributing function for each dimension.

4. The method according to any one of claims 1 to 3, characterized in that, Before processing the target item, the method further includes: Determine whether there is any sensitive redundant data in the indicator information and evaluation information; If there is sensitive redundant data in the indicator information or evaluation information, determine the risk level of the indicator information or evaluation information. Based on the preset correspondence between risk levels and desensitization processing, and the risk level itself, the indicator information or evaluation information is desensitized in accordance with the risk level.

5. The method according to any one of claims 1 to 3, characterized in that, After generating the detection information of the target item, the method further includes: The indicator information and evaluation information of each of the multiple functions included in the target project, as well as the detection information of the target project, are saved as detection records in the database; In response to receiving another instruction to test the target item, obtain the indicator information and evaluation information of each of the multiple functions included in the target item; Based on the indicator information and evaluation information of each of the multiple functions included in the target project, and the indicator information and evaluation information of each of the multiple functions included in the target project in the most recently saved detection record in the database, determine the indicator change information and evaluation change information of each of the multiple functions included in the target project. Using an incremental detection model, the detection information in the most recently saved detection record is adjusted based on the indicator change information and evaluation change information of each of the multiple functions, thereby generating scores for multiple dimensions of the target project and the core contribution functions of each of the multiple dimensions.

6. The method according to claim 5, characterized in that, The indicator information includes key performance indicators; the method further includes: Monitor the aforementioned key performance indicators; In response to the fact that the change value of the key performance indicator within a preset time period meets the preset change conditions, the instruction to detect the target item is generated.

7. A project testing device, characterized in that, The device includes: The acquisition module is used to acquire the indicator information and evaluation information of each of the multiple functions included in the target project in response to receiving an instruction to detect the target project; The detection module is used to process the target project using a multi-dimensional detection model based on the respective indicator information and evaluation information of the multiple functions, and generate scores for multiple dimensions of the target project and the core contributing functions of each of the multiple dimensions. The multiple dimensions include the performance dimension and the evaluation dimension of the target project. The score of the performance dimension represents the running effect of the target project, and the evaluation dimension represents the satisfaction of the users of the target project with the target project. The core contributing function of each dimension is the function with the highest contribution in the process of generating the score of each dimension. The generation module is used to generate detection information of the target project based on the scores of the multiple dimensions and the core contribution functions corresponding to each of the multiple dimensions. The detection information represents the multi-dimensional operation of the target project.

8. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1-6.

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

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1-6.

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