Production risk identification method and device, medium and program product

Abstract

The invention discloses a production risk identification method and device, a medium and a program product, and relates to the field of financial science and technology. The method comprises the following steps: acquiring a risk identification object; obtaining risk influence factors of the risk identification object in a corresponding first time stage and a second time stage, the first time stage being a full life stage of a demand corresponding to the to-be-put-into-production demand item, and the second time stage being a put-into-production preparation stage of the to-be-put-into-production demand item; comparing the risk influence factor with a preset risk identification scene, and determining a target risk identification scene matched with the risk influence factor; wherein the risk identification scene is used for indicating a risk identification result and countermeasures; and determining a target identification result corresponding to the target risk identification scene and the target countermeasure as a production risk identification result. According to the production risk identification method, the comprehensiveness and the accuracy of a production risk identification result are improved, and meanwhile, the production risk identification efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of financial technology, and in particular to a method, device, medium and program product for identifying production risks. Background Art

[0002] Production risk identification is a comprehensive test of the software version quality before it is officially put into production, and it is also a predictive judgment of whether the production work is ready.

[0003] In related technologies, production risks are usually identified based on test quality reports provided by testers, or project risks reported by testers.

[0004] However, in the above process, the factors taken into consideration when identifying the production risk are not comprehensive enough, resulting in inaccurate results of the production risk identification. Summary of the Invention

[0005] The present invention provides a production risk identification method, device, medium and program product to solve the technical problem that the production risk identification results obtained by the production risk identification method in the related art are not accurate enough.

[0006] According to one aspect of the present invention, a method for identifying production risks is provided, the method comprising:

[0007] Obtaining a risk identification object; wherein the risk identification object includes an application set of demand items to be put into production;

[0008] Obtaining the risk impact factor of the risk identification object in the corresponding first time stage and second time stage; wherein the first time stage is the full life stage of the demand corresponding to the demand item to be put into production, and the second time stage is the production preparation stage of the demand item to be put into production;

[0009] Comparing the risk impact factor with a preset risk identification scenario to determine a target risk identification scenario that matches the risk impact factor; wherein the risk identification scenario is used to indicate the risk identification result and response measures;

[0010] The target identification results and target response measures corresponding to the target risk identification scenario are determined as the production risk identification results.

[0011] According to another aspect of the present invention, a device for identifying risks in a production process is provided, the device comprising:

[0012] A first acquisition module is configured to acquire a risk identification object; wherein the risk identification object includes an application set of demand items to be put into production;

[0013] A second acquisition module is configured to obtain the risk impact factor of the risk identification object in the corresponding first time stage and second time stage; wherein the first time stage is the full life cycle stage of the demand corresponding to the demand item to be put into production, and the second time stage is the production preparation stage of the demand item to be put into production;

[0014] A first determination module is configured to compare the risk impact factor with a preset risk identification scenario to determine a target risk identification scenario that matches the risk impact factor; wherein the risk identification scenario is used to indicate a risk identification result and a response measure;

[0015] The second determination module is used to determine the target identification result and target response measures corresponding to the target risk identification scenario as the production risk identification result.

[0016] According to another aspect of the present invention, an electronic device is provided, comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the production risk identification method described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and the computer program is used to enable a processor to implement the production risk identification method described in any embodiment of the present invention when executed.

[0021] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the method for identifying production risks according to any embodiment of the present invention is implemented.

[0022] The technical solution of the embodiment of the present invention, on the one hand, realizes the whole process monitoring of production risk by obtaining the risk impact factors of the risk identification object in the corresponding first time stage and second time stage, and the first time stage is the full life stage of the demand corresponding to the demand item to be put into production, and the second time stage is the production preparation stage of the demand item to be put into production, and identifies the production risk based on the risk impact factors that take into account multiple factors, thereby improving the comprehensiveness and accuracy of the production risk identification results. On the other hand, the risk impact factors can be compared with the preset risk identification scenarios to determine the target risk identification scenarios that match the risk impact factors, and then the production risk identification results are determined according to the target identification results and target response measures corresponding to the target risk identification scenarios, thereby realizing automatic identification of production risks, improving the efficiency of production risk identification, and reducing manpower input.

[0023] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 This is a flow chart of a method for identifying production risks provided by an embodiment of the present invention;

[0026] Figure 2 is a schematic diagram of a first time period and a second time period provided by an embodiment of the present invention;

[0027] Figure 3 is a schematic diagram of obtaining risk impact factors in an embodiment of the present invention;

[0028] Figure 4 This is a flow chart of another method for identifying production risks provided by an embodiment of the present invention;

[0029] Figure 5 is a schematic diagram of the execution process of the risk identification task provided by an embodiment of the present invention;

[0030] Figure 6 This is a schematic diagram of a production risk identification result provided by an embodiment of the present invention;

[0031] Figure 7This is a structural diagram of a production risk identification device provided by an embodiment of the present invention;

[0032] Figure 8 It is a structural diagram of an electronic device for implementing the production risk identification method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0034] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the term "comprising" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The acquisition, storage, use, processing, etc. of data in the embodiments of the present invention comply with the relevant provisions of national laws and regulations.

[0035] To facilitate subsequent understanding, the terms involved in this embodiment are explained below.

[0036] Production risk: refers to factors that may occur before the version media is successfully deployed in the production environment and may hinder or threaten the smooth production of the version, or factors that may cause operational risks or adverse business impacts after the version is deployed.

[0037] Demand item: A demand item is the smallest unit that can be independently scheduled for production based on business demand objectives.

[0038] Application of requirement items: It is a system or platform responsible for assisting in the implementation of requirements and has a dedicated project team.

[0039] In the related art, production risk identification is performed based on the test quality report provided by the tester, or the project risk reported by the tester. This implementation method has the following problems: on the one hand, the risk content only considers the impact that the risk may have on the test at the current stage, and does not predict the production risk from the perspective of the entire life cycle of the demand; on the other hand, from the issuance of the test quality report to the pre-production stage, there is still a lot of pre-production preparation work (such as the preparation of production verification cases, the preparation of production plans, the determination of production strategies, the preparation of production handover pipelines, production drills, etc.). There are many links, trivial tasks and they are closely related to production. It is an area where production risks are prone to occur frequently. Ignoring any preparatory work before production or relying on manual identification of the completion status of each stage is not only inefficient, but also easy to miss production risks, thereby causing production interruptions, monitoring alarms, production problems, etc. in the production environment, bringing production safety hazards. Therefore, how to accurately and efficiently complete production risk identification before production is a problem that needs to be solved in this field.

[0040] This embodiment provides a method for identifying production risks. This method enables full-process monitoring of production risks. This method identifies production risks based on risk influencing factors that take into account multiple factors, thereby improving the comprehensiveness and accuracy of production risk identification results. Furthermore, the method determines the production risk identification results based on the target identification results and target response measures corresponding to the target risk identification scenario, enabling automated identification of production risks, improving efficiency, and reducing human effort.

[0041] Figure 1 This is a flow chart of a method for identifying production risks provided by an embodiment of the present invention. This embodiment is applicable to scenarios where production risks are identified. The method can be executed by a production risk identification device, which can be implemented in the form of hardware and / or software. The production risk identification device can be configured in an electronic device, such as a computer device or a server. Figure 1 As shown, the method includes the following steps 101 to 104.

[0042] Step 101: Obtain a risk identification object.

[0043] Among them, the risk identification object includes the application set of demand items to be put into production.

[0044] The demand items to be put into production in this embodiment may be demand items in the financial field. The application set of the demand items to be put into production may include at least one application. The application set of the demand items to be put into production obtained in step 101 may be an application list of the demand items to be put into production.

[0045] In one implementation, an application set of the requirement items input by the user may be determined as a risk identification object.

[0046] In another implementation, the risk identification object can be automatically acquired according to the set monitoring start time. This implementation will be described in detail in the subsequent embodiments.

[0047] Step 102: Obtain risk impact factors of the risk identification object in the corresponding first time period and second time period.

[0048] Among them, the first time stage is the full life stage of the demand corresponding to the demand item to be put into production, and the second time stage is the production preparation stage of the demand item to be put into production.

[0049] The first time period and the second time period corresponding to different risk identification objects are not exactly the same. Therefore, what is obtained in step 102 is the risk impact factor of the risk identification object in its corresponding first time period and second time period. In this embodiment, the risk impact factor refers to various factors that may cause production risks.

[0050] The first time period and the second time period can cover the entire life cycle of the demand corresponding to the demand item to be put into production and the production preparation node of the demand item to be put into production. Therefore, this embodiment can realize the whole process monitoring of production risk.

[0051] Figure 2 Schematic diagram of the first time period and the second time period provided by the embodiment of the present invention. Figure 2 As shown in the figure above, there is no overlap between the first time period and the second time period. Figure 2 As shown in the figure below, the first time period and the second time period have some overlap. Figure 2 The implementation effect of this embodiment will not be affected by any implementation method in the embodiment.

[0052] Optionally, the risk impact factor includes at least one of the following: version release status of each application in the application set of the production requirement item, test acceptance exception reports, unclosed test loop issues, unscheduled production verification cases, unentered production windows, special production strategies, and incomplete production drills. This implementation of risk impact factors allows for more specific identification of production risks throughout the lifecycle of the production requirement item and during the production preparation phase, further improving the accuracy of production risk identification results.

[0053] In step 102, multiple platforms can be connected to automatically collect various information of the demand items to be put into production in the corresponding first time period and the content of the matters in the second time period, and screen out matters such as overdue completion, execution abnormalities, and adjustments and changes as risk influencing factors.

[0054] Figure 3FIG. 1 is a schematic diagram of obtaining risk impact factors in an embodiment of the present invention. Figure 3 As shown, first, connect to the project management platform to filter applications for the expected production requirements, collect content with clear production dates, clear release information, and clear affiliated departments, and obtain the version release status of each application in the application set of the production requirements. At the same time, extract content in the requirement information that adopts special production strategies, including those that do not produce business in the production version, those that produce business in steps and batches, and those that produce business in the production version. In addition, connect to the project management platform to obtain test quality reports and extract test acceptance exception reports such as those that have not been submitted, those that have been submitted but not approved, and those with test problems. Second, connect to the problem management platform to collect unclosed-loop testing issues such as integration testing, acceptance testing, and adaptability testing, and extract serious bottleneck problems that affect production. Third, connect to the technology management workbench to obtain production window time and business production verification schedule information, from which to obtain production verification case verification cases that have not been scheduled and production window entries that have not been entered. Fourth, extract the production drills related to the production risk identification objects, and collect content on unfinished production drills such as the environment is not ready, the version is not installed, the test verification is not completed, and there are drill problems.

[0055] Step 103: Compare the risk impact factor with the preset risk identification scenario to determine the target risk identification scenario that matches the risk impact factor.

[0056] Among them, the risk identification scenario is used to indicate the risk identification results and response measures.

[0057] In this embodiment, risk identification scenarios are pre-set, and the number of risk identification scenarios may be at least one.

[0058] Optionally, the risk identification scenarios in this embodiment may include at least one of the following: version change, production strategy change, test verification failure, production window not entered, and untimely production handover.

[0059] In one implementation, keywords are provided in the risk identification scenario. If a risk impact factor matches a keyword in a risk identification scenario, the risk identification scenario is determined as a target risk identification scenario. Alternatively, if the number of keywords matching a risk impact factor with a risk identification scenario exceeds a preset ratio, the risk identification scenario is determined as a target risk identification scenario.

[0060] In another implementation method, if the judgment logic is indicated in the risk identification scenario, step 103 can be implemented as follows: for each risk identification scenario, the risk impact factor is judged according to the judgment logic indicated by the risk identification scenario; if there is a risk impact factor that meets the risk identification result of the risk identification scenario, the risk identification scenario is determined as the target risk identification scenario.

[0061] Optionally, the judgment logic in the risk identification scenario of this embodiment represents the judgment method, judgment object, and judgment order, etc., and the judgment logic in the risk identification scenario can be determined based on the time sequence of completion of various preparations before the production date.

[0062] This implementation method can compare the risk impact factors with each risk identification scenario, and can efficiently and accurately determine the target risk identification scenario.

[0063] Optionally, after executing step 101 , a snapshot of the application set of the demand item to be put into production may be taken so as to be compared with the latest application set of the demand item to be put into production in step 103 .

[0064] Assuming that the risk identification scenario is a version change scenario, the implementation process of step 103 includes: by judging whether there is a change in the application version information of the item to be put into production, predicting whether there is a risk of cancellation of production or new production in the subsequent list to be put into production, at this time the risk identification is not over yet, and it is upgraded to compare with the snapshot of the risk identification object (referring to the application set of the item to be put into production stored after step 101). If there is no matching result, it is judged that there is a new object put into production in the risk identification object. On this basis, the completion of the pre-production preparations for the new object and the collection results of the test quality report are continued to be upgraded. If there is a problem, the production risk content is further upgraded until the risk identification step of the scenario or the identification step of the associated scenario is completed.

[0065] Step 104: Determine the target identification result and target response measures corresponding to the target risk identification scenario as the production risk identification result.

[0066] For example, the production risk identification results include: identification result 1 and response measure 1, identification result 2 and response measure 2. Users can make subsequent improvements based on the production risk identification results.

[0067] In the method of this embodiment, the production risk identification covers the end-to-end life cycle of the demand and the production preparation stage. Among them, for the factors affecting normal production, such as the release status of each application in the demand items to be put into production, unresolved problems left over from testing, unclosed problems in pre-production testing, unentered production verification cases, incomplete access to production, production strategy adjustments, production window time adjustments, unresolved production and demonstration problems, and unhanded-over production pipelines, it is necessary not only to confirm the possibility of risk existence, but more importantly, to carry out global connectivity problem assessments for problems at different stages, and accurately identify the content of production risks to avoid missing risk issues, triggering larger-scale production risks or events that have a major impact on the business, thereby improving the quality and efficiency of production risk identification.

[0068] The method for identifying production risks provided in this embodiment, on the one hand, realizes full-process monitoring of production risks by obtaining the risk impact factors of the risk identification object in the corresponding first time stage and second time stage, and the first time stage is the full life stage of the demand corresponding to the demand item to be put into production, and the second time stage is the production preparation stage of the demand item to be put into production. When identifying production risks, the risk impact factors are identified based on multiple factors, thereby improving the comprehensiveness and accuracy of the production risk identification results. On the other hand, the risk impact factors can be compared with the preset risk identification scenarios to determine the target risk identification scenarios that match the risk impact factors. Then, the production risk identification results are determined according to the target identification results and target response measures corresponding to the target risk identification scenarios, thereby realizing automatic identification of production risks, improving the efficiency of production risk identification, and reducing manpower input.

[0069] Figure 4 This is a flow chart of another production risk identification method provided by an embodiment of the present invention. Figure 1 Based on the illustrated embodiment and various optional implementations, the implementation process of how to obtain risk impact factors and how to determine the target risk identification scenario is described in detail. Figure 4 As shown, the production risk identification method provided by this embodiment includes the following steps 401 to 406.

[0070] Step 401: Create risk identification task information.

[0071] Among them, the risk identification task information is used to indicate the monitoring start time, monitoring end time, the production date of the demand item to be put into production, the production date range, and the production risk identification result push list.

[0072] Step 401 can be implemented by the commissioning risk identification configuration module. The commissioning risk identification configuration module creates risk identification task information. The risk identification task information is shown in Table 1 below.

[0073] Optionally, as shown in Table 1, the risk identification task information includes not only the monitoring start time, monitoring end time, the production date of the required item to be put into production, and the list of people to be notified of the production risk identification results, but also the task number, task name, creator, and task status. The content of the risk identification task information can be selected based on actual needs.

[0074] Table 1 Risk identification task information

[0075]

[0076] Through the above steps 401 and 402, by creating risk identification task information, it is possible to automatically start the risk identification task according to the risk identification task information, automatically obtain the risk identification object, and reduce manpower input through automation, thereby improving the efficiency of production risk identification.

[0077] Step 402: Based on the risk identification task information, the risk identification task is started at the monitoring start time, and a set of applications of the to-be-commissioned demand items that meet the commissioning date range are obtained as risk identification objects.

[0078] Among them, the risk identification object includes the application set of demand items to be put into production.

[0079] Optionally, step 402 can be implemented by a task scheduling module. The task scheduling module runs in a scheduled task state, scans the tasks to be started in the "Production Risk Identification Task Information Table", starts the risk identification task at the monitoring start time based on the risk identification task information, and obtains a set of applications of the to-be-launched requirement items that meet the production date range as risk identification targets.

[0080] Furthermore, the task scheduling module may call the data storage module to take a snapshot of the risk identification object.

[0081] Step 403: Between the monitoring start time and the monitoring end time, the risk identification task is used to periodically execute the step of obtaining the risk impact factor of the risk identification object in the corresponding first time period and second time period.

[0082] Among them, the first time stage is the full life stage of the demand corresponding to the demand item to be put into production, and the second time stage is the production preparation stage of the demand item to be put into production.

[0083] Optionally, step 403 can be implemented through a risk impact factor collection module. In step 403, the risk identification task can call the risk impact factor collection module to regularly execute the step of obtaining the risk impact factors of the risk identification object in the corresponding first time period and second time period, thereby collecting application release information of the demand item to be put into production, test quality reports, unclosed-loop issues of integration testing and adaptability testing, production verification arrangements, production drills, special production strategies, and production windows.

[0084] In this implementation, the collected initial risk impact factors can be filtered and then aggregated to obtain risk impact factors. Filtering refers to screening items that affect production according to certain rules, ultimately filtering out only information such as test failures, unclosed test issues, and incomplete or problematic production drills. Aggregation refers to statistically analyzing all filtered content based on the requirement application dimension to facilitate the subsequent generation of production risk identification results. For example, the aggregated results may include: the risk impact factor corresponding to requirement item 1 application 11, the risk impact factor corresponding to requirement item 1 application 12, ..., the risk impact factor corresponding to requirement item 1 application n, and so on.

[0085] Step 404: Between the monitoring start time and the monitoring end time, the risk identification task is used to periodically compare the risk impact factor with the preset risk identification scenario to determine the target risk identification scenario that matches the risk impact factor.

[0086] Among them, the risk identification scenario is used to indicate the risk identification results and response measures.

[0087] Optionally, step 404 can be implemented using a risk identification standard comparison module. In step 404, the risk identification task can call the risk identification standard comparison module to periodically compare the risk impact factors with pre-set risk identification scenarios to determine a target risk identification scenario that matches the risk impact factors. If content matching a pre-set scenario appears, the pre-set scenario is determined as the target risk identification scenario. Optionally, the risk identification task can call the risk identification standard comparison module to compare the risk impact factors with pre-set risk identification scenarios each time a risk impact factor is automatically collected to determine a target risk identification scenario that matches the risk impact factor.

[0088] Figure 5 This is a schematic diagram of the execution process of the risk identification task provided by the embodiment of the present invention. Figure 5 As shown, between the monitoring start time and the monitoring end time, the risk identification task periodically executes the step of obtaining the risk impact factor of the risk identification object in the corresponding first time period and second time period. Optionally, after each risk impact factor is collected, the risk identification task executes the step of comparing the risk impact factor with a preset risk identification scenario to determine a target risk identification scenario that matches the risk impact factor.

[0089] Through the above steps 403 and 404, the risk identification task automatically and regularly executes the step of obtaining the risk impact factors of the risk identification object in the corresponding first time period and the second time period, and automatically executes the step of comparing the risk impact factors with the preset risk identification scenarios to determine the target risk identification scenarios that match the risk impact factors. Through automation, manpower input is reduced and the efficiency of production risk identification is improved.

[0090] Step 405: Determine the target identification result and target response measures corresponding to the target risk identification scenario as the production risk identification result.

[0091] The implementation process and technical principle of step 405 are similar to those of step 104 and will not be repeated here.

[0092] Optionally, in steps 402 through 405, after the risk identification task is initiated, the database is automatically initialized with the dataset of production risk impact factors acquired from each platform. A snapshot of the application set of the initially acquired requirements to be put into production is retained. Upon subsequent receipt of datasets from the risk impact factor acquisition module, the corresponding information is automatically updated. Then, based on the execution results of the risk identification standard comparison module, the production risk identification process data is automatically aggregated to form a production risk identification result, which is then stored in the data storage module for subsequent traceability.

[0093] Step 406: Send the production risk identification result to the user terminal corresponding to the user included in the risk identification result push list.

[0094] Among them, the production risk identification results also include at least one of the following: the number of preset risk identification scenarios, the users included in the risk identification result push list, the identification time of the production risk identification results, the production date of the demand item to be put into production, the name of the demand item to be put into production, and the name of the application of the demand item to be put into production.

[0095] Figure 6 This is a schematic diagram of a production risk identification result provided by an embodiment of the present invention. Figure 6 As shown, the commissioning risk identification results include the following information: Task name: Centralized commissioning risk of X-year X-month version, Application abbreviation: F-XXXX, Commissioning date: 20240413, Identification time: 2024040809:00:00, Notified personnel: Personnel A, Personnel B, Personnel C, Number of preset risk identification scenarios: 5, Identification results: Result 1 and Result 2, Countermeasures: Measure 1 and Measure 2.

[0096] Optionally, when sending the production risk identification results to the user terminals corresponding to the users included in the risk identification result push list, the results may be sent via instant messaging software, email, or the like.

[0097] In step 406, by pushing the production risk identification results to users in the pre-configured push list, closed-loop control of production risk identification is achieved, making it easier for users to understand the production risk identification results and perform subsequent operations.

[0098] Furthermore, step 406 also provides for a production risk identification result specification, which is clear and convenient for users to understand the production risk identification result.

[0099] Optionally, the production risk identification method provided in this embodiment further includes the following step 400.

[0100] Step 400: Create risk identification scenario basic information and risk identification scenario content information.

[0101] The basic risk identification scenario information includes at least one of the following: scenario identifier, scenario name, and input person. The content information includes the risk identification results and response measures. The content information also includes at least one of the following: scenario identifier, scenario name, associated scenario names, step identifier, associated step identifier, and judgment logic.

[0102] Table 2 shows the basic information of risk identification scenarios. Table 3 shows the content information of risk identification scenarios.

[0103] Table 2 Basic information table of risk identification scenarios

[0104]

[0105]

[0106] Table 3 Risk identification scenario content information table

[0107]

[0108] The judgment logic in the risk identification scenario content information corresponds to the judgment method and judgment status in Table 3. It should be noted that there may be multiple risk identification results for each risk identification scenario in Table 3.

[0109] Optionally, step 400 can be performed by a risk identification standard comparison module. The risk identification standard comparison module standardizes the input of pre-set production risk identification scenarios. After the initial input of the production risk identification scenarios, they can be saved to the data storage module to facilitate reuse of subsequent tasks.

[0110] Optionally, before comparing the risk impact factor with a preset risk identification scenario to determine a target risk identification scenario that matches the risk impact factor, or before step 404 , the method further includes the following step 404 a .

[0111] Step 404a: Reuse and / or expand the risk identification scenario basic information and risk identification scenario content information to obtain a preset risk identification scenario.

[0112] Step 400 and step 404a improve the reuse capability of the basic information of risk identification scenarios by pre-creating the basic information of risk identification scenarios. By reusing or expanding the basic information of risk identification scenarios, a preset risk identification scenario can be obtained, thereby improving the efficiency of production risk identification.

[0113] The production risk identification method provided in this embodiment automatically collects the application list of required items to be put into production in each phase, version release status, test acceptance exception reports, test unclosed loop issues, unscheduled production verification case verification, unentered production window status, special production strategies, and uncompleted production drills, focusing on the overdue, unfinished or abnormal execution content of each stage to form a production risk influencing factor; through the task scheduling mechanism and production risk standard comparison trigger, dynamic monitoring of the entire production risk process and standardized production risk automatic generation are achieved, thereby improving the quality and efficiency of production risk identification. It has the following specific technical effects: 1. Improved production risk identification quality, automatically collected and generated production risk identification results, clear content, covering test conditions and various pre-production preparation work scenarios, ensuring the comprehensiveness and effectiveness of production risk; 2. Improved production risk identification efficiency, task initiation, preset scenario comparison, full process monitoring, result push, full process automation, effectively reducing manpower input; 3. Improved the reuse capability of preset risk identification scenarios, preset scenarios can be referenced, expanded, and reused, effectively improving the efficiency of production risk identification.

[0114] Figure 7 This is a schematic diagram of the structure of a production risk identification device provided by an embodiment of the present invention. The device is set in an electronic device. Figure 7 As shown, the production risk identification device provided by this embodiment includes the following modules: a first acquisition module 71 , a second acquisition module 72 , a first determination module 73 and a second determination module 74 .

[0115] The first acquisition module 71 is used to acquire a risk identification object.

[0116] The risk identification object includes a set of applications of demand items to be put into production.

[0117] The second acquisition module 72 is configured to acquire the risk impact factor of the risk identification object in the corresponding first time period and second time period.

[0118] Among them, the first time stage is the full life stage of the demand corresponding to the demand item to be put into production, and the second time stage is the production preparation stage of the demand item to be put into production.

[0119] The first determination module 73 is configured to compare the risk impact factor with a preset risk identification scenario to determine a target risk identification scenario that matches the risk impact factor.

[0120] The risk identification scenario is used to indicate the risk identification results and response measures.

[0121] The second determining module 74 is configured to determine the target identification result and target response measures corresponding to the target risk identification scenario as the production risk identification result.

[0122] In one embodiment, the risk influencing factors include at least one of the following: version release status of each application in the application set of the demand item to be put into production, test acceptance exception report, test unclosed loop problem, unscheduled production verification case verification, unentered production window, special production strategy, and unfinished production drill.

[0123] In one embodiment, the apparatus further includes: a first creation module configured to create risk identification task information, wherein the risk identification task information indicates a monitoring start time, a monitoring end time, a production date of the required item to be put into production, a production date range, and a list of production risk identification results to be pushed.

[0124] The first acquisition module 71 is specifically configured to: start the risk identification task at the monitoring start time according to the risk identification task information, and acquire an application set of the to-be-commissioned demand items that meet the commissioning date range as the risk identification object.

[0125] In one embodiment, the second acquisition module 72 is specifically used to: between the monitoring start time and the monitoring end time, through the risk identification task, regularly execute the step of obtaining the risk impact factor of the risk identification object in the corresponding first time period and the second time period.

[0126] In one embodiment, the first determination module 73 is specifically used to: between the monitoring start time and the monitoring end time, through the risk identification task, regularly execute the step of comparing the risk impact factor with the preset risk identification scenario to determine the target risk identification scenario that matches the risk impact factor.

[0127] In one embodiment, the apparatus includes a sending module configured to send the commissioning risk identification result to a user terminal corresponding to a user included in a risk identification result push list. The commissioning risk identification result further includes at least one of the following: the number of pre-set risk identification scenarios, the users included in the risk identification result push list, the time of identification of the commissioning risk identification result, the commissioning date of the item to be commissioned, the name of the item to be commissioned, and the name of the application of the item to be commissioned.

[0128] In one embodiment, the apparatus further includes a second creation module configured to create risk identification scenario basic information and risk identification scenario content information. The risk identification scenario basic information includes at least one of the following: a scenario identifier, a scenario name, and an input person. The risk identification scenario content information includes risk identification results and response measures. The risk identification scenario content information further includes at least one of the following: a scenario identifier, a scenario name, associated scenario names, a step identifier, associated step identifiers, and judgment logic.

[0129] In one embodiment, the first determination module 73 is specifically used to: for each risk identification scenario, judge the risk impact factor according to the judgment logic indicated by the risk identification scenario; if there is a risk impact factor that meets the risk identification result of the risk identification scenario, then determine the risk identification scenario as the target risk identification scenario.

[0130] The production risk identification device provided in the embodiment of the present invention can execute the production risk identification method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0131] Figure 8 1 is a schematic diagram of the structure of an electronic device that implements the production risk identification method according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided for example only and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0132] like Figure 8As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0133] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0134] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processors, controllers, microcontrollers, etc. Processor 11 executes the various methods and processes described above, such as the production risk identification method.

[0135] In some embodiments, the commissioning risk identification method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the commissioning risk identification method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to execute the commissioning risk identification method in any other appropriate manner (e.g., by means of firmware).

[0136] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0137] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0138] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0139] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0140] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0141] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0142] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the production risk identification method provided by any embodiment of the present invention.

[0143] The computer program product may be implemented in a computer program code for performing the operations of the present invention written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​and conventional procedural programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone 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 a remote computer, the remote computer may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0144] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0145] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for identifying production risks, characterized in that: The method comprises: Obtaining a risk identification object; wherein the risk identification object includes an application set of demand items to be put into production; Obtaining the risk impact factor of the risk identification object in the corresponding first time stage and second time stage; wherein the first time stage is the full life stage of the demand corresponding to the demand item to be put into production, and the second time stage is the production preparation stage of the demand item to be put into production; Comparing the risk impact factor with a preset risk identification scenario to determine a target risk identification scenario that matches the risk impact factor; wherein the risk identification scenario is used to indicate the risk identification result and response measures; The target identification results and target response measures corresponding to the target risk identification scenario are determined as the production risk identification results.

2. The method according to claim 1, characterized in that The risk influencing factors include at least one of the following: version release status of each application in the application set of the demand item to be put into production, test acceptance exception report, test unclosed loop problem, unscheduled production verification case verification, unentered production window, special production strategy, and unfinished production drill.

3. The method according to claim 2, characterized in that The method further comprises: Create risk identification task information; wherein the risk identification task information is used to indicate the monitoring start time, monitoring end time, the production date of the required item to be put into production, the production date range, and the production risk identification result push list; The obtaining of the risk identification object includes: According to the risk identification task information, the risk identification task is started at the monitoring start time, and a set of applications of the to-be-put-into-production demand items that meet the production date range is obtained as the risk identification object.

4. The method according to claim 3, characterized in that The obtaining of the risk impact factor of the risk identification object in the corresponding first time period and second time period includes: Between the monitoring start time and the monitoring end time, regularly executing the step of obtaining the risk impact factor of the risk identification object in the corresponding first time period and second time period through the risk identification task; The comparing the risk impact factor with a preset risk identification scenario to determine a target risk identification scenario that matches the risk impact factor includes: Between the monitoring start time and the monitoring end time, the risk identification task is used to regularly compare the risk impact factor with a preset risk identification scenario to determine a target risk identification scenario that matches the risk impact factor.

5. The method according to claim 3, characterized in that The method further comprises: The production risk identification result is sent to the user terminal corresponding to the user included in the risk identification result push list; wherein, the production risk identification result also includes at least one of the following: the number of the preset risk identification scenarios, the users included in the risk identification result push list, the identification time of the production risk identification result, the production date of the demand item to be put into production, the name of the demand item to be put into production, and the name of the application of the demand item to be put into production.

6. The method according to any one of claims 1 to 5, characterized in that The method further comprises: Creating risk identification scenario basic information and risk identification scenario content information; wherein the risk identification scenario basic information includes at least one of the following: scenario identification, scenario name, and input person; the risk identification scenario content information includes: risk identification results and response measures; the risk identification scenario content information also includes at least one of the following: scenario identification, scenario name, associated scenario names, step identification, associated step identification, and judgment logic; Before comparing the risk impact factor with a preset risk identification scenario to determine a target risk identification scenario that matches the risk impact factor, the method further includes: The risk identification scenario basic information and the risk identification scenario content information are reused and / or expanded to obtain the preset risk identification scenario.

7. The method according to any one of claims 1 to 5, characterized in that The comparing the risk impact factor with a preset risk identification scenario to determine a target risk identification scenario that matches the risk impact factor includes: For each risk identification scenario, the risk impact factor is judged according to the judgment logic indicated by the risk identification scenario; If there is a risk influencing factor that satisfies the risk identification result of the risk identification scenario, the risk identification scenario is determined as the target risk identification scenario.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the production risk identification method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is used to enable a processor to implement the production risk identification method according to any one of claims 1 to 7 when executed.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the method for identifying commissioning risks according to any one of claims 1 to 7.

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