An attribution collection method, apparatus, device and medium

Abstract

Embodiments of the present disclosure relate to an attribution collection method, device, equipment and medium, wherein the method comprises: when it is detected that a current workflow enters a preset node, a preset card point platform is called to obtain working data of a previous workflow; the card point platform is used to perform rule detection on the working data of the previous workflow based on at least one process rule corresponding to the previous workflow, to obtain a detection result; when the detection result is detection failure, the card point platform is used to respond to an attribution input operation, to display an attribution collection page based on a preset page address, and to obtain attribution data input by a user based on the attribution collection page. Through the setting of the card point platform, the present disclosure can not only quickly and efficiently perform rule detection on the working data of the historical workflow, but also save the time and effort of the collector. The collected person will not interrupt his normal work in the workflow, and the efficiency of attribution data collection is effectively improved.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to an attribution collection method, apparatus, device, and medium. Background Technology

[0002] To ensure quality and efficiency, corresponding standards or rules can be established for processes in various tasks. For example, in the development of applications, quality assurance (QA) personnel, business partners (BPs), and / or project officer coordinators (POCs) can formulate a series of rules for the development process.

[0003] In practice, the rules of a process may not always be followed due to inadequate communication or enforcement, leading to frequent non-compliance. Therefore, it is necessary to frequently collect and analyze attributions or causes. In related technologies, attribution collection is usually done manually, which is inefficient. Summary of the Invention

[0004] To address the aforementioned technical problems, this disclosure provides an attribution collection method, apparatus, device, and medium.

[0005] This disclosure provides an attribution collection method, the method comprising:

[0006] When the current workflow is detected to have entered a preset node, the preset checkpoint platform is invoked to retrieve the work data of the previous workflow;

[0007] The checkpoint platform performs rule detection on the work data of the previous workflow based on at least one workflow rule corresponding to the previous workflow, and obtains the detection result.

[0008] When the detection result is that the detection fails, the checkpoint platform responds to the attribution input operation, displays the attribution collection page based on the preset page address, and obtains the attribution data input by the user based on the attribution collection page.

[0009] This disclosure also provides an attribution collection device, the device comprising:

[0010] The calling module is used to call the preset checkpoint platform to obtain the work data of the previous workflow when the current workflow is detected to have entered a preset node.

[0011] The detection module is used to perform rule detection on the work data of the previous workflow based on at least one workflow rule corresponding to the previous workflow through the checkpoint platform, and obtain the detection result;

[0012] The data module is used to respond to the attribution input operation through the checkpoint platform when the detection result is a failure, display the attribution collection page based on the preset page address, and obtain the attribution data input by the user based on the attribution collection page.

[0013] This disclosure also provides an electronic device, the electronic device comprising: a processor; a memory for storing executable instructions of the processor; the processor being configured to read the executable instructions from the memory and execute the instructions to implement the attribution collection method as provided in this disclosure.

[0014] This disclosure also provides a computer-readable storage medium storing a computer program for performing the attribution collection method as provided in this disclosure.

[0015] Compared with the prior art, the technical solution provided in this disclosure has the following advantages: The attribution collection scheme provided in this disclosure, when detecting that the current workflow has entered a preset node, calls a preset checkpoint platform to obtain the work data of the previous workflow; the checkpoint platform performs rule detection on the work data of the previous workflow based on at least one process rule corresponding to the previous workflow, and obtains the detection result; when the detection result is a failure, the checkpoint platform responds to the attribution input operation, displays the attribution collection page based on a preset page address, and obtains the attribution data input by the user based on the attribution collection page. By adopting the above technical solution, by calling the checkpoint platform at a preset node of the current workflow to perform rule detection on the work data of the previous workflow, and obtaining attribution data through the attribution collection page displayed by the checkpoint platform when the detection fails, the checkpoint platform not only enables fast and efficient rule detection of work data in historical workflows, but also saves the time and effort of the collector by automatically obtaining the attribution data when the detection fails at a preset node of a workflow. The data collected will not be interrupted from the normal work of the person being collected, effectively improving the efficiency of attribution data collection. Attached Figure Description

[0016] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0017] Figure 1 A schematic flowchart of an attribution collection method provided in an embodiment of this disclosure;

[0018] Figure 2 A schematic diagram of a process rule provided for an embodiment of this disclosure;

[0019] Figure 3 A schematic diagram of an attribution input provided in an embodiment of this disclosure;

[0020] Figure 4 A schematic diagram illustrating a data import method provided in an embodiment of this disclosure;

[0021] Figure 5 A schematic diagram illustrating a rule detection method provided in an embodiment of this disclosure;

[0022] Figure 6 A schematic diagram illustrating an attribution collection method provided in an embodiment of this disclosure;

[0023] Figure 7 This is a schematic diagram of an attribution collection device provided in an embodiment of the present disclosure;

[0024] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0025] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0026] It should be understood that the steps described in the method embodiments of this disclosure may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this disclosure is not limited in this respect.

[0027] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0028] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0029] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0030] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0031] In related technologies, the collection of attribution data for situations that do not conform to process specifications is usually done manually by quality assurance personnel, business partners, and / or project coordinators. After obtaining the relevant data, they find the specific person in charge to fill in the attribution, and then organize and analyze it. The whole process is done manually, which not only consumes the energy and time of the collectors, but also easily interrupts the normal work of the person whose data is collected, resulting in low efficiency.

[0032] To address the aforementioned issues, this disclosure provides an attribution collection method, which will be described below with reference to specific embodiments.

[0033] Figure 1 This is a flowchart illustrating an attribution collection method provided in an embodiment of the present disclosure. The method can be executed by an attribution collection device, which can be implemented in software and / or hardware, and is generally integrated into an electronic device. Figure 1 As shown, the method includes:

[0034] Step 101: When the current workflow is detected to have entered a preset node, the preset checkpoint platform is called to obtain the work data of the previous workflow.

[0035] The attribution collection method of this disclosure can be applied to various scenarios, such as R&D scenarios and business scenarios. This disclosure will use the application in the R&D scenario as an example for illustration.

[0036] Here, a workflow can be understood as a user-executed process that requires rule checks. The specific workflow is not limited; for example, a workflow can include a release process or a communication process in a development scenario. The current workflow can be the workflow that the user has currently started, and the previous workflow can be a historical workflow that has been completed but has not yet undergone rule checks. There can be one or more previous workflows, and the specific number is not limited.

[0037] A preset node can be a custom node added in advance to the current workflow. The specific location of the preset node can be determined according to the actual situation. Preset nodes for different payroll processes can be the same or different. For example, when the current workflow is a release process, the preset node can be the initial release access confirmation node.

[0038] The checkpoint platform can be a pre-built platform that includes a variety of tools. The specific tools are not limited. For example, it can include online tools, communication tools, and compilation platforms. Checkpoints can be understood as adding some custom checks through these tools. Only after the checks are passed can the next step be continued. In this embodiment of the disclosure, the checkpoint platform can be used to perform rule-based detection and attribution data collection.

[0039] In this embodiment of the disclosure, the attribution collection device can start the current workflow in response to the user's start trigger operation. When the current workflow is detected to have entered a preset node, the device can obtain the work data of the previous workflow through an interface call to the checkpoint platform. The work data can be the running data in the workflow or the record data of other related processes.

[0040] Step 102: Use the checkpoint platform to perform rule detection on the work data of the previous workflow based on at least one workflow rule corresponding to the previous workflow, and obtain the detection result.

[0041] Rule checking can be used to check whether work data conforms to pre-set process specifications or rules. Process rules can be rules pre-entered into the checkpoint platform by rule-makers. Different workflows can correspond to different process rules, and a workflow can include at least one process rule, depending on the specific circumstances. The aforementioned rule-makers can be those who formulate and implement process rules. For example, in a development scenario, rule-makers can include quality assurance personnel, business partners, and / or project coordinators. Quality assurance personnel can perform quality verification before software launch, business partners can be personnel used to verify a specific function of the developed software, and project coordinators can be personnel used to circulate messages, understand the execution specifications, and optimize the process.

[0042] After the attribution collection device calls the checkpoint platform to obtain the work data of the previous workflow, the checkpoint platform determines at least one process rule of the previous workflow by searching locally. Specifically, it can search according to the process type or process identifier of the previous workflow, and there is no limit to the specific search. Then, it can perform rule detection on the work data of the previous workflow according to at least one process rule to obtain the detection result. For each process rule, the detection result can include two types: detection passed or detection failed.

[0043] In some embodiments, if the current workflow is a pre-application release workflow, at least one workflow rule includes instrumentation testing and / or cross-checking; if the current workflow is an application release workflow, at least one workflow rule includes preview environment verification and / or low-traffic observation.

[0044] The criteria for instrumentation testing can be that the modified code is run and invoked in an offline environment before the application is released. The checkpoint effect is that if the test fails, attribution must be completed before the next code merge; otherwise, new code merges are prohibited. The criteria for cross-validation (review) can be that the code is verified by multiple people. The checkpoint effect is that if the test fails, attribution must be completed before the next code merge; otherwise, new code merges are prohibited. The criteria for preview environment verification can be that preview environment verification is performed before release to ensure normal functionality. The checkpoint effect is that if the test fails, attribution must be completed before the next deployment; otherwise, new deployments are prohibited. The criteria for small-scale observation can be that observation and monitoring are performed for a period longer than a preset time between canary releases and full releases. The preset time can be set to 5 minutes (for example only). The checkpoint effect is that if the test fails, attribution must be completed before the next deployment; otherwise, new deployments are prohibited. When any process rule is skipped or does not meet the criteria, the test result is determined to be a failure; otherwise, according to the specific process rule, the test result is considered a success when the criteria are met.

[0045] For example, Figure 2 This is a schematic diagram of a process rule provided in an embodiment of the present disclosure, such as... Figure 2 As shown in the figure, the diagram illustrates the addition of a process rule in the checkpoint platform. Each process rule can include a rule name, judgment criteria, and checkpoint effect. The figure exemplifies a process rule named "Small Traffic Observation". The judgment criteria is "Pipeline small traffic phase time greater than 5 minutes", which means that observation and monitoring will be carried out for more than 5 minutes between gray release and full release. The checkpoint effect is "Pipeline initialization check", which means that if the check fails, the attribution must be filled in before the next deployment; otherwise, a new deployment is prohibited. Figure 2 The examples provided are merely illustrative and not restrictive.

[0046] Step 103: When the detection result is "detection failed", the checkpoint platform responds to the attribution input operation, displays the attribution collection page based on the preset page address, and obtains the attribution data input by the user based on the attribution collection page.

[0047] Among them, attribution data can be the reason data determined by the user for the process rules that failed the detection.

[0048] The attribution input operation can be a trigger operation in the checkpoint platform for attribution input based on the result of failure. Specifically, it can include gesture control operations (such as click, long press, double click, etc.), voice control operations, or facial expression control operations on preset attribution input buttons, etc., and this disclosure embodiment does not limit this. The page address can be a pre-set web address of the attribution collection page, that is, a Uniform Resource Locator (URL). The attribution collection page can be an interactive page provided to users to enable them to quickly complete the attribution data entry. The attribution collection page can include functional components, etc., and is not limited thereto.

[0049] Specifically, the attribution collection device performs rule detection on the work data of the previous workflow based on at least one workflow rule corresponding to the previous workflow through the checkpoint platform. After obtaining the detection result, it determines the target workflow rule that failed the detection. The number of target workflow rules can be one or more. For each target workflow rule, the corresponding attribution data can be obtained through the checkpoint platform. Specifically, when obtaining attribution data through the checkpoint platform, the checkpoint platform can receive the user's attribution input operation for each target workflow rule, and then obtain a preset page address. Based on this page address, it displays the attribution collection page. The user can fill in or input the attribution data of the target workflow rule corresponding to the current attribution input operation on the attribution collection page. The checkpoint platform can then obtain the attribution data and return it to the attribution collection device for the collector to analyze the attribution data.

[0050] For example, Figure 3 This is a schematic diagram of an attribution input provided in an embodiment of the present disclosure, such as... Figure 3 As shown in the figure, a page 300 displays the detection results obtained after rule detection of the previous workflow is performed in the middle of the current workflow. Page 300 can include areas 301 and 302. Area 301 can display the nodes included in the current workflow. The release of access confirmation can be a preset node, that is, at the preset node, the checkpoint platform is called to perform rule detection on the work data of the previous workflow. The detection results can then be displayed in area 302. Area 302 exemplarily displays the detection results of three workflow rules corresponding to the previous workflow. The detection results of the first workflow rule and the third workflow rule are both passed, while the detection result of the second workflow rule is failed. An attribution input button 303 can then be displayed for the second workflow rule. When the user triggers the attribution input button 303, the checkpoint platform can receive the attribution input operation and then display the attribution collection page to obtain attribution data.

[0051] This disclosure takes a research and development scenario as an example. Research and development work may include a variety of process rules, such as the code needing to include unit measurement, and the release and launch needing to observe small traffic volumes. In the event of a violation of process rules, this disclosure can quickly collect attributions and analyze them through a checkpoint platform in order to further complete or optimize the process.

[0052] The attribution collection scheme provided in this disclosure, when detecting that the current workflow has entered a preset node, calls a preset checkpoint platform to obtain the work data of the previous workflow; the checkpoint platform performs rule detection on the work data of the previous workflow based on at least one process rule corresponding to the previous workflow, and obtains the detection result; when the detection result is a failure, the checkpoint platform responds to the attribution input operation, displays an attribution collection page based on a preset page address, and obtains the attribution data input by the user based on the attribution collection page. By adopting the above technical solution, by calling the checkpoint platform at a preset node of the current workflow to perform rule detection on the work data of the previous workflow, and obtaining attribution data through the attribution collection page displayed on the checkpoint platform when the detection fails, the checkpoint platform not only enables fast and efficient rule detection of work data in historical workflows, but also saves the time and effort of the collector by automatically obtaining attribution data when the detection fails at a preset node of a workflow, and ensures that the work data being collected will not be interrupted while the person being collected is already in the workflow, effectively improving the efficiency of attribution data collection.

[0053] In some embodiments, the attribution collection method may further include: importing the work data of the previous workflow into the checkpoint platform after the previous workflow is completed.

[0054] Before performing step 101 above, the attribution collection device can also automatically import the work data of the previous workflow into the checkpoint platform after the previous workflow is completed, and / or manually collect the relevant work data and then manually import it into the checkpoint platform, so as to prepare for subsequent rule detection of the work data of the previous workflow.

[0055] For example, Figure 4 This is a schematic diagram illustrating a data import method provided in an embodiment of the present disclosure, such as... Figure 4 As shown in the diagram, the above workflow is taken as an example of the application release process. The application release process can be executed on the launch platform. The launch platform can record the running data, that is, the release pipeline related data in the diagram, and automatically import it into the checkpoint platform as data source 1 of the work data; and / or, manually collect other data from the previous workflow and then manually import it into the checkpoint platform as data source 2 of the work data. Subsequently, the checkpoint platform can perform rule detection on the work data of data source 1 and / or data source 2.

[0056] In the above solution, after a workflow is completed, the work data can be automatically or manually imported into the checkpoint platform so that when subsequent workflows are performed, the work data of the previous workflow can be used for rule detection and attribution collection, thereby improving the efficiency of rule detection and attribution collection.

[0057] In some embodiments, when the detection result is a failure, the attribution collection method may further include: in response to ignoring the trigger operation, returning to a preset node to continue executing the current workflow; and calling the checkpoint platform at the preset node of the next workflow to obtain attribution data for the previous workflow.

[0058] The "ignore trigger operation" can be a trigger operation in the checkpoint platform to temporarily ignore the result of a failed detection. Specifically, it can include gesture control, voice control, or facial expression control operations on a preset attribution ignore button, etc., and this embodiment does not limit this. The previous workflow can be the next workflow to be executed in relation to the current workflow.

[0059] The attribution collection device performs rule detection on the work data of the previous workflow based on at least one workflow rule corresponding to the previous workflow through the checkpoint platform. After obtaining the detection result, it determines that the detection result is a target workflow rule that fails the detection. The number of target workflow rules can be one or more. The checkpoint platform can receive ignore trigger operations for all target workflow rules. At this time, it indicates that the current workflow is urgent and needs to be executed immediately. It can exit the checkpoint platform and return to the preset node to continue executing the current workflow. Then, when the next workflow enters the preset node, the checkpoint platform is called again to obtain the attribution data for the previous workflow. The specific acquisition method is the same as in the above embodiment. It can also perform rule detection and attribution data collection on the work data of the current workflow.

[0060] In the above solution, when the detection result of the work data of a workflow is that the detection fails, the user can temporarily ignore the need for attribution collection according to the actual situation and continue to execute the current workflow. The attribution data will be filled in when the subsequent workflow is executed. This is more in line with the actual work needs, improves the flexibility of attribution data collection, and avoids delaying urgent workflows due to attribution collection.

[0061] The attribution collection method of this disclosure will be further illustrated by a specific example below. For example, Figure 5 This is a schematic diagram of a rule detection method provided in an embodiment of the present disclosure, such as... Figure 5As shown in the diagram, using a research and development scenario as an example, the process of rule detection before attribution collection is illustrated. Specifically, this includes: rule makers can configure rules on the checkpoint platform; R&D personnel can execute various workflows, with three shown in the diagram: release, communication, and others. Each workflow has checkpoints, i.e., the aforementioned preset nodes. At each checkpoint, the checkpoint platform is invoked to perform rule detection on the historical workflow data; the historical workflow data can be automatically or manually provided by data statisticians for data statistics, and then the data is entered into the checkpoint platform for subsequent rule detection.

[0062] For example, Figure 6 This is a schematic diagram of an attribution collection method provided in an embodiment of the present disclosure, such as... Figure 6 As shown in the diagram, the attribution collection process is illustrated using a research and development scenario as an example. Specifically, it includes the following steps: When developers are performing a release process on the platform, a preset node (initialization check in the diagram) is added to the release process. Upon reaching this initialization check, rule detection is triggered, calling the checkpoint platform to perform rule detection on historical workflow data. For example, rule one in the diagram is used to detect data from data source a, and rule two in the diagram is used to detect data from data source b. A detection report including the detection results is then obtained. Violation cases x and y in the diagram both indicate failed detections. The platform can then return the detection results to the release platform. If all detection results are passed, the release process can continue. Otherwise, the checkpoint platform's preset page address is received, displaying the attribution collection page to the developers. The developers are then guided to actively fill in attribution data based on the failed detections. The release process can then be resumed.

[0063] This solution adds a checkpoint to workflows that require rule checks by providing a checkpoint platform. When the workflow is in progress, the tools or platforms used can automatically trigger the checkpoint platform to check the rules of historical workflows. If the check fails, staff can be quickly guided to fill in attribution data through the checkpoint platform, thus automating attribution data collection. Rule makers can quickly enter rule and workflow data through the checkpoint platform and collect and analyze attribution data. Staff can choose to fill in the attribution data at a convenient time without affecting other normal work, effectively improving the efficiency of attribution data collection.

[0064] Figure 7 This is a schematic diagram of an attribution collection device provided in an embodiment of this disclosure. The device can be implemented by software and / or hardware and is generally integrated into an electronic device. Figure 7 As shown, the device includes:

[0065] The module 701 is used to call the preset checkpoint platform to obtain the work data of the previous workflow when the current workflow is detected to have entered a preset node.

[0066] The detection module 702 is used to perform rule detection on the work data of the previous workflow based on at least one workflow rule corresponding to the previous workflow through the checkpoint platform, and obtain the detection result;

[0067] The data module 703 is used to respond to the attribution input operation through the checkpoint platform when the detection result is a failure, display the attribution collection page based on the preset page address, and obtain the attribution data input by the user based on the attribution collection page.

[0068] Optionally, the checkpoint platform is used for rule-based detection and attribution data collection.

[0069] Optionally, the device further includes an import module for:

[0070] After the previous workflow is completed, the work data of the previous workflow is imported into the checkpoint platform.

[0071] Optionally, if the current workflow is a pre-application release workflow, the at least one workflow rule includes instrumentation testing and / or cross-checking; if the current workflow is an application release workflow, the at least one workflow rule includes preview environment verification and / or low-traffic observation.

[0072] Optionally, the device further includes an ignore module, used to: when the detection result is a detection failure,

[0073] In response to ignoring the trigger operation, return to the preset node and continue executing the current workflow;

[0074] At a preset node in the next workflow, the checkpoint platform is invoked to obtain attribution data for the previous workflow.

[0075] The attribution collection device provided in this disclosure can execute the attribution collection method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects of executing the method.

[0076] This disclosure provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the attribution collection method provided in any embodiment of this disclosure.

[0077] Figure 8 This is a schematic diagram of an electronic device provided in an embodiment of the present disclosure. See below for details. Figure 8The diagram illustrates a structural schematic suitable for implementing the electronic device 800 in the embodiments of this disclosure. The electronic device 800 in the embodiments of this disclosure may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 8 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0078] like Figure 8 As shown, the electronic device 800 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage device 808 into a random access memory (RAM) 803. The RAM 803 also stores various programs and data required for the operation of the electronic device 800. The processing device 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0079] Typically, the following devices can be connected to I / O interface 805: input devices 806 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 807 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 808 including, for example, magnetic tapes, hard disks, etc.; and communication devices 809. Communication device 809 allows electronic device 800 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 8 An electronic device 800 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0080] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 809, or installed from a storage device 808, or installed from a ROM 802. When the computer program is executed by a processing device 801, it performs the functions defined in the attribution collection method of embodiments of this disclosure.

[0081] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0082] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0083] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0084] The aforementioned computer-readable medium carries one or more programs. When the electronic device executes one or more of these programs, the electronic device causes the following: when it detects that the current workflow has entered a preset node, it calls a preset checkpoint platform to obtain the work data of the previous workflow; the checkpoint platform performs rule detection on the work data of the previous workflow based on at least one workflow rule corresponding to the previous workflow, and obtains a detection result; when the detection result is a failure, the checkpoint platform responds to the attribution input operation by displaying an attribution collection page based on a preset page address, and obtains the attribution data input by the user based on the attribution collection page.

[0085] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0086] 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 disclosure. 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 the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can 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.

[0087] The units described in the embodiments of this disclosure can be implemented in software or in hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0088] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0089] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0090] It is understood that before using the technical solutions disclosed in the various embodiments of this disclosure, users should be informed of the types, scope of use, and usage scenarios of the information involved in this disclosure in an appropriate manner in accordance with relevant laws and regulations, and user authorization should be obtained.

[0091] The above description is merely a preferred embodiment of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features disclosed in this disclosure that have similar functions.

[0092] Furthermore, while the operations are described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or in a sequential order. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0093] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. An attribution collection method, characterized in that, include: When the current workflow is detected to have entered a preset node, the preset checkpoint platform is called to obtain the work data of the previous workflow. The previous workflow is a historical workflow that has been completed but has not yet been checked for rules. The checkpoint platform performs rule detection on the work data of the previous workflow based on at least one workflow rule corresponding to the previous workflow, and obtains the detection result. When the detection result is a failure, the checkpoint platform responds to the attribution input operation, displays the attribution collection page based on the preset page address, and obtains the attribution data input by the user based on the attribution collection page. The attribution data is the reason data determined by the user for the process rule of the failure.

2. The method according to claim 1, characterized in that, The checkpoint platform is used for rule-based detection and attribution data collection.

3. The method according to claim 1, characterized in that, The method further includes: After the previous workflow is completed, the work data of the previous workflow is imported into the checkpoint platform.

4. The method according to any one of claims 1, characterized in that, If the current workflow is a pre-application release workflow, the at least one workflow rule includes instrumentation testing and / or cross-checking; if the current workflow is an application release workflow, the at least one workflow rule includes preview environment verification and / or low-traffic observation.

5. The method according to claim 1, characterized in that, When the detection result is a failure, the method further includes: In response to ignoring the trigger operation, return to the preset node and continue executing the current workflow; At a preset node in the next workflow, the checkpoint platform is invoked to obtain attribution data for the previous workflow.

6. An attribution collection device, characterized in that, include: The calling module is used to call the preset checkpoint platform to obtain the work data of the previous work process when the current work process is detected to have entered a preset node. The previous work process is a historical work process that has been completed but has not yet been checked by rules. The detection module is used to perform rule detection on the work data of the previous workflow based on at least one workflow rule corresponding to the previous workflow through the checkpoint platform, and obtain the detection result; The data module is used to respond to the attribution input operation through the checkpoint platform when the detection result is a failure. It displays the attribution collection page based on a preset page address and obtains the attribution data input by the user based on the attribution collection page. The attribution data is the reason data determined by the user for the process rule of the failure.

7. The apparatus according to claim 6, characterized in that, The checkpoint platform is used for rule-based detection and attribution data collection.

8. The apparatus according to claim 6, characterized in that, The device further includes an import module for: After the previous workflow is completed, the work data of the previous workflow is imported into the checkpoint platform.

9. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store the processor's executable instructions; The processor is configured to read the executable instructions from the memory and execute the instructions to implement the attribution collection method according to any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program for executing the attribution collection method according to any one of claims 1-5.

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