CN114186699A - Quality inspection node optimization method and device, computer equipment and storage medium - Google Patents

Quality inspection node optimization method and device, computer equipment and storage medium Download PDF

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CN114186699A
CN114186699A CN202111288721.3A CN202111288721A CN114186699A CN 114186699 A CN114186699 A CN 114186699A CN 202111288721 A CN202111288721 A CN 202111288721A CN 114186699 A CN114186699 A CN 114186699A
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蔡汇聪
陈兆秦
方庆林
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Shenzhen Zhuiyi Technology Co Ltd
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Abstract

The application discloses a quality inspection node optimization method and device, computer equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: simulating a target number of request tasks, wherein the target number is determined by an application scene of triggering pressure test, and the request tasks are used for requesting quality inspection processing in customer service quality inspection; obtaining quality inspection log information of request processing corresponding to the execution target number of request tasks, wherein the quality inspection log information comprises a plurality of quality inspection processing nodes and response duration of each quality inspection processing node; acquiring quality inspection processing nodes with response time length exceeding a preset time length threshold from a plurality of quality inspection processing nodes as high time consumption nodes; and optimizing the performance parameters corresponding to the high time consumption nodes based on a preset time optimization strategy so as to reduce the response time of the high time consumption nodes. Therefore, the efficiency of locating time-consuming and high-delay nodes in the quality inspection service is improved, and the timeliness of optimizing the quality inspection processing nodes with problems is further improved.

Description

Quality inspection node optimization method and device, computer equipment and storage medium
Technical Field
The present application relates to the field of computer technologies, and in particular, to a quality inspection node optimization method and apparatus, a computer device, and a storage medium.
Background
In the related art, the presentation of log information as the system running state is a part of the vital importance of troubleshooting system problems. The current system prints and outputs the related log information of each quality inspection service through a tangent plane class, and provides key data information support for the function test of the system.
However, the system maintenance personnel checks the log information output by printing one by one, which is time-consuming and labor-consuming, and the efficiency of positioning the system problems is low.
Disclosure of Invention
In view of this, the present application provides a quality inspection node optimization method, apparatus, computer device, and storage medium.
In a first aspect, an embodiment of the present application provides a quality inspection node optimization method, where the method includes: simulating a target number of request tasks, wherein the target number is determined by an application scene of triggering pressure test, and the request tasks are used for requesting quality inspection processing in customer service quality inspection; obtaining quality inspection log information of the request processing corresponding to the execution of the target number of request tasks, wherein the quality inspection log information comprises a plurality of quality inspection processing nodes and the response duration of each quality inspection processing node; acquiring quality inspection processing nodes with response time length exceeding a preset time length threshold from the plurality of quality inspection processing nodes as high time consumption nodes; and optimizing the performance parameters corresponding to the high time consumption nodes based on a preset time optimization strategy so as to reduce the response time of the high time consumption nodes.
In a second aspect, an embodiment of the present application provides an apparatus for optimizing a quality inspection node, where the apparatus includes: the system comprises a task simulation module, a log acquisition module, a node determination module and a parameter optimization module. The task simulation module is used for simulating a target number of request tasks, the target number is determined by an application scene of triggering pressure test, and the request tasks are used for requesting quality inspection processing in customer service quality inspection; the log obtaining module is used for obtaining quality inspection log information which executes the request processing corresponding to the target number of request tasks, and the quality inspection log information comprises a plurality of quality inspection processing nodes and the response duration of each quality inspection processing node; the node determination module is used for acquiring quality inspection processing nodes with response time length exceeding a preset time length threshold from the plurality of quality inspection processing nodes as high time consumption nodes; and the parameter optimization module is used for optimizing the performance parameters corresponding to the high time consumption nodes based on a preset time optimization strategy so as to reduce the response time of the high time consumption nodes.
In a third aspect, an embodiment of the present application provides a computer device, including: one or more processors; a memory; one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the method of optimizing a quality check node provided by the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a program code is stored in the computer-readable storage medium, and the program code may be invoked by a processor to execute the optimization method of the quality inspection node provided in the first aspect.
In the scheme provided by the application, a target number of request tasks is simulated, the target number is determined by an application scene triggering pressure test, and the request tasks are used for requesting quality inspection processing in customer service quality inspection; obtaining quality inspection log information of the request processing corresponding to the execution of the target number of request tasks, wherein the quality inspection log information comprises a plurality of quality inspection processing nodes and the response duration of each quality inspection processing node; acquiring quality inspection processing nodes with response time length exceeding a preset time length threshold from a plurality of quality inspection processing nodes as high time consumption nodes; and optimizing the performance parameters corresponding to the high time consumption nodes based on a preset time optimization strategy so as to reduce the response time of the high time consumption nodes. Therefore, the problems of time and labor consumption caused by one-to-one inspection of each quality inspection processing node in log information by maintenance personnel are solved, the high time-consuming nodes are screened out based on the response time, performance parameters of the high time-consuming nodes are optimized according to a preset time optimization strategy, the efficiency of the high time-consuming and high delay nodes existing in the positioning quality inspection service is improved, and the timeliness of optimizing the quality inspection processing nodes with problems is further improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 shows a schematic diagram of an application scenario provided in an embodiment of the present application.
Fig. 2 is a schematic flowchart illustrating a method for optimizing a quality inspection node according to an embodiment of the present disclosure.
Fig. 3 is a flowchart illustrating a method for optimizing a quality inspection node according to another embodiment of the present application.
Fig. 4 shows a schematic content diagram of log information provided in an embodiment of the present application.
Fig. 5 shows a flow chart of the sub-steps of step S370 in fig. 4.
Fig. 6 is a flowchart illustrating an optimization method for a quality inspection node according to still another embodiment of the present application.
Fig. 7 shows a flow chart of the sub-steps of step S420 in fig. 6.
Fig. 8 is a flowchart illustrating a method for optimizing a quality inspection node according to another embodiment of the present application.
Fig. 9 is a block diagram of an optimization apparatus of a quality inspection node according to an embodiment of the present application.
Fig. 10 is a block diagram of a computer device for executing an optimization method of a quality inspection node according to an embodiment of the present application.
Fig. 11 is a storage unit for storing or carrying program codes for implementing the optimization method of the quality inspection node according to the embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
In the related art, the presentation of log information as the system running state is a part of the vital importance of troubleshooting system problems. The current system prints and outputs the related log information of each quality inspection service through a tangent plane class, and provides key data information support for the function test of the system.
However, the system maintenance personnel checks the log information output by printing one by one, which is time-consuming and labor-consuming, and the efficiency of positioning the system problems is low.
In view of the above problems, the inventors propose a quality inspection node optimization method, a quality inspection node optimization device, a computer device, and a storage medium, which can simulate a target number of request tasks, obtain a quality inspection processing node from the plurality of quality inspection processing nodes, where a response time length exceeds a preset time length threshold, and use the quality inspection processing node as a high time consumption node to optimize a performance parameter corresponding to the high time consumption node. This is described in detail below.
An application environment of the quality inspection node optimization method provided by the embodiment of the present application is described below.
Referring to fig. 1, fig. 1 is a schematic diagram illustrating an application environment provided for an embodiment of the present application, and the quality inspection node optimization method provided in the embodiment of the present application can be applied to the quality inspection node optimization system 10 shown in fig. 1. The optimization system 10 of the quality inspection node may include a pressure measurement tool 101 and a backend service 102, and the pressure measurement tool 101 and the backend service 102 may be connected through a wireless or wired network to implement data transmission between the pressure measurement tool 101 and the terminal service 102 based on the network connection, where the transmitted data includes, but is not limited to, a simulation request task, log information, and the like.
In this embodiment, the pressure measurement tool 101 and the backend service 102 may be deployed in the same electronic device or the same server; the pressure measurement tool 101 and the backend service 102 may also be deployed in different electronic devices or servers, which is not limited in this embodiment. When the pressure measurement tool 101 and the backend service 102 are deployed in different electronic devices or servers, the pressure measurement tool 101 and the backend service 102 need to be in the same network segment. Electronic devices include, but are not limited to, smart phones, tablets, laptop portable computers, desktop computers, and the like; the server may be an individual server, a server cluster, a local server, a cloud server, and the like, which is not limited in this embodiment.
In some embodiments, after the pressure measurement tool 101 and the backend service 102 complete network connection, the pressure measurement tool 101 may simulate a multi-channel quality inspection request task and send the task to the backend service 102, the backend service 102 returns quality inspection log information generated when the multi-channel quality inspection request task is executed to the pressure measurement tool 101, and the pressure measurement tool 101 may perform statistical analysis according to the quality inspection log information to determine a node having a problem in a quality inspection process, and perform targeted optimization on the node having the problem.
Referring to fig. 2, fig. 2 is a block diagram illustrating a method, an apparatus, a computer device, and a storage medium for optimizing quality inspection nodes according to an embodiment of the present disclosure. The method for optimizing the quality inspection node according to the embodiment of the present application will be described in detail with reference to fig. 2. The quality inspection node optimization method can comprise the following steps:
step S210: and simulating a target number of request tasks, wherein the target number is determined by an application scene triggering pressure test, and the request tasks are used for requesting quality inspection processing in customer service quality inspection.
In this embodiment, the pressure test is to run the test software for a long time or with an excessive load by simulating the software and hardware environment of the actual application and the system load of the user during the use process, so as to test the performance, reliability and stability of the system to be tested. Based on the method, each quality inspection processing node of the quality inspection service full flow can be tested to perform statistical analysis in a mode of performing pressure test on the quality inspection service full flow, so that the quality inspection processing node to be optimized is determined, and parameters or functional optimization is performed on the quality inspection processing node to be optimized in a targeted mode, so that the performance of the quality inspection service is improved.
First, a target number of request tasks can be simulated by the pressure testing tool, so as to test the performance of each quality inspection processing node when the back-end service executes the target number of request tasks simultaneously. The request task is used for requesting quality inspection processing in customer service quality inspection, and the target number of the simulated request task can be determined by an application scene triggering pressure testing; or a fixed numerical value preset by a pressure measuring person, which is not limited in this embodiment. The application scene of triggering the pressure test can comprise an instruction triggering scene and a timing triggering scene, wherein the instruction triggering scene can be understood as a pressure test which is performed by a pressure tester after inputting a starting instruction aiming at the pressure test before the quality inspection service is put into use, and the whole process of the quality inspection service is optimized based on a test result so as to improve the performance of the quality inspection service; the timing trigger scenario can be understood as that after the quality inspection service is put into use, the pressure test is periodically performed in combination with the historical use information, and the whole process of the quality inspection service is periodically optimized based on each test result, so that the quality inspection service can better meet the user requirements on the basis of improving the performance of the quality inspection service. The application scenarios of the trigger pressure test are different, the performance requirements for the quality inspection service may also be different, and the number of simulation request tasks adapted to the quality inspection service is also different, so that when the request service is simulated, the request services with different target numbers can be simulated according to the different application scenarios of the trigger pressure test.
Step S220: and obtaining quality inspection log information of the request processing corresponding to the execution of the target number of request tasks, wherein the quality inspection log information comprises a plurality of quality inspection processing nodes and the response duration of each quality inspection processing node.
In this embodiment, after the target number of request tasks are simulated, quality inspection processing may be further performed according to the simulated target number of request tasks. For example, 200 requests are simulated, and it can be understood that 200 quality inspection requests are processed simultaneously by simulating quality inspection service. The quality inspection service may be composed of a plurality of quality inspection processing nodes, and different functions performed by different quality inspection processing nodes are different, for example, the quality inspection service may include a quality inspection analysis node, a quality inspection scoring node, a result processing node, a quality inspection suggestion generation node, and the like, which is not limited in this embodiment. In order to better monitor and optimize the whole flow of the quality inspection service, therefore, quality inspection log information of request processing corresponding to the execution target number of request tasks can be obtained, wherein the quality inspection log information includes node Identification (ID) of a plurality of quality inspection processing nodes and response duration of each quality inspection processing node.
Step S230: and acquiring the quality inspection processing nodes with response time length exceeding a preset time length threshold from the plurality of quality inspection processing nodes as high time consumption nodes.
Based on this, after the node ID and the response time of each quality inspection processing node are obtained, analysis and statistics can be further carried out, and high-time-consumption nodes are screened out. Specifically, it may be determined whether the response time length of each quality inspection processing node exceeds a preset time length threshold, and a quality inspection processing node whose response time length exceeds the preset time length threshold is obtained as a high time consuming node, which may be understood as a quality inspection processing node with a very low response speed to a quality inspection request, where the preset time length threshold may be preset, such as 5 milliseconds, and may also be adjusted according to different application scenarios. In the whole flow of the quality inspection service, if the response speed of a certain quality inspection processing node is slow, the efficiency of the quality inspection service may be affected, or if a plurality of quality inspection processing nodes exist at the same time, the response speed is slow, and the quality inspection may fail. Therefore, the high time-consuming nodes with longer response time and slow response speed can be screened out through the response time based on the quality inspection processing nodes.
Step S240: and optimizing the performance parameters corresponding to the high time consumption nodes based on a preset time optimization strategy so as to reduce the response time of the high time consumption nodes.
In this embodiment, after the high-time-consumption node is screened out, the response duration of the high-time-consumption node can be reduced by optimizing the performance parameter corresponding to the high-time-consumption node.
In some embodiments, the performance parameters corresponding to the high time consuming node may be optimized according to a preset duration optimization strategy, where the preset duration optimization strategy may be a code program that a developer may predict according to historical experience and may solve the high time consuming node. The number of the preset time duration optimization strategies can be various, when the performance parameters of the high-consumption nodes are optimized based on the time duration optimization strategies, each of the various time duration optimization strategies can be called respectively, the response time duration of the high-consumption nodes under the performance parameters optimized based on each time duration optimization strategy is obtained, and a plurality of response time durations to be selected are obtained; and acquiring a time length optimization strategy corresponding to the minimum response time length to be selected in the multiple response time lengths to be selected as an optimal time length optimization strategy, and optimizing the performance parameters corresponding to the high time consumption nodes for multiple times based on the optimal time length optimization strategy so as to reduce the response time length of the high time consumption nodes as much as possible and improve the efficiency of quality inspection services.
In other embodiments, after the high-time-consumption node is screened, a prompt message may be output, where the prompt message includes a node ID of the high-time-consumption node and a response duration, so as to prompt a developer to check a code program corresponding to the high-time-consumption node, and perform targeted optimization. Therefore, the response duration of the high time-consuming node is fed back to the developer, and the developer performs manual optimization, so that the performance of the high time-consuming node can be optimized more pertinently and timely, and the quality inspection efficiency of the quality inspection service is improved.
In this embodiment, a pressure test is performed on the quality inspection service in a multi-path request task simulation manner under a high concurrent environment, a high time consuming node with a response time length larger than a preset time length threshold is screened out from a plurality of quality inspection processing nodes in the whole quality inspection service flow, and then performance parameters corresponding to the high time consuming node are optimized according to a preset time length optimization strategy. Therefore, the problems of time and labor consumption caused by one-to-one inspection of each quality inspection processing node in the log information by maintenance personnel are solved, the efficiency of positioning the high-time-consuming and high-delay nodes in the quality inspection service is improved, and the timeliness of optimizing the quality inspection processing nodes with the problems is further improved; in addition, performance parameters corresponding to the high time consumption nodes can be optimized according to a preset duration optimization strategy, so that the optimization efficiency of the quality inspection processing nodes is improved, and further the efficiency of the quality inspection service full-process quality inspection is improved.
Referring to fig. 3, fig. 3 is a block diagram illustrating a method, an apparatus, a computer device, and a storage medium for optimizing a quality inspection node according to another embodiment of the present disclosure. The method for optimizing the quality inspection node according to the embodiment of the present application will be described in detail below with reference to fig. 3. The quality inspection node optimization method can comprise the following steps:
step S310: and simulating a target number of request tasks, wherein the target number is determined by an application scene triggering pressure test, and the request tasks are used for requesting quality inspection processing in customer service quality inspection.
Step S320: and obtaining quality inspection log information for executing the request processing corresponding to the target number of request tasks, wherein the quality inspection log information comprises a plurality of quality inspection processing nodes, the response duration of each quality inspection processing node and state information, and the state information comprises a success state, a failure state and a failure reason.
In this embodiment, the specific contents in step S310 to step S320 may refer to the contents in the foregoing embodiments, and are not described herein again.
Step S330: and obtaining the quality inspection processing nodes of which the response time length exceeds the preset time length threshold value in the plurality of quality inspection processing nodes as first candidate nodes.
In this embodiment, the reasons for the response time length of the quality inspection processing node exceeding the preset time length threshold may be different, and for different reasons, the corresponding performance parameter optimization manners are also different, so that the quality inspection processing node, of the plurality of quality inspection processing nodes, whose response time length exceeds the preset time length threshold may be obtained first and used as the first candidate node.
Step S340: and judging whether the state information of the first candidate node is the success state.
In this embodiment, as shown in fig. 4, the quality inspection log information includes a session ID, an execution time (i.e., a response time), state information, a failure reason, a phase log recording node information, and a message type corresponding to each request task, where the state information may include a success state/failure state, and the phase log recording node trust includes a node name (i.e., a node ID), state information, an execution time (i.e., a response time), a failure reason, a session index, and a child node of each quality inspection processing node.
Based on this, whether the first candidate node successfully executes the corresponding quality inspection processing in the quality inspection whole flow can be determined by determining whether the state information of the first candidate node acquired first is a successful state. For example, if the first candidate node is a quality inspection analysis node, it is determined whether the quality inspection analysis node is in a successful state, that is, it is determined whether the quality inspection analysis node successfully completes a quality inspection analysis process.
Step S350: and if so, acquiring the first candidate node as the high time-consuming node.
Optionally, if it is determined that the state information of the first candidate node is a successful state, it represents that the first candidate node successfully executes the quality inspection processing corresponding to the first candidate node, but the response time for executing the quality inspection processing corresponding to the first candidate node is longer, that is, the processing is successful but the speed is slower, so that the first candidate node can be acquired as a high-time-consumption node.
Step S360: if not, the first candidate node with the state information of the failure state is obtained and used as a second candidate node.
Alternatively, if it is determined that the state information of the first candidate node is not in the success state, the first candidate node whose state information is in the failure state may be acquired from the plurality of first candidate nodes as the second candidate node. The second candidate node may not fail due to network congestion or long quality inspection time, but may fail due to other reasons, such as an error in the usage of the interface, and therefore, the second candidate node may be further analyzed.
Step S370: and acquiring the high time-consuming node from the first candidate node based on the failure reason of the second candidate node.
In some embodiments, when the number of the first candidate nodes is multiple, referring to fig. 5, step S370 may include the following steps:
step S371: and judging whether the failure reason of the second candidate node is matched with a preset failure reason.
In this embodiment, as shown in fig. 4, the quality inspection log information includes failure reasons of each quality inspection processing node, where the failure reasons include but are not limited to network congestion, too long time consumption, and interface usage errors, and therefore, after a second candidate node is obtained, it may be further determined whether the failure reason of the second candidate node matches a preset failure reason, where the preset failure reason is preset, and the reason representing the failure of the quality inspection processing node is caused by network congestion, too long time consumption, and other reasons. For example, when the quality inspection processing node fails to process within the specified duration, it may be determined that the state information of the quality inspection processing node is in a failure state, and the failure state is added to the stage log of the node information corresponding to the quality inspection processing node, and at this time, too long time is consumed and added to the stage log of the node information.
Step S372: and if so, acquiring a plurality of first candidate nodes as the high-time-consumption nodes.
Optionally, if it is determined that the failure reason of the second candidate node matches the preset failure reason, it represents that the second candidate node screened from the plurality of first candidate nodes is determined to be in the failure state only because of the quality inspection time consumption, and therefore, in the subsequent optimization process, the corresponding performance parameter can be optimized through a preset time optimization strategy, and the response time of the second candidate node can be reduced. Based on this, the plurality of first candidate nodes can be directly used as the high-time-consumption nodes without screening the second candidate nodes from the plurality of first candidate nodes.
Step S373: and if not, acquiring other first candidate nodes except the second candidate node in the plurality of first candidate nodes as the high-time-consumption nodes, and acquiring the second candidate node as an abnormal node.
Optionally, if the failure reason of the second candidate node does not match the preset failure reason, it represents that the second candidate node screened from the plurality of first candidate nodes may be in a failure state caused by a reason similar to interface use misalignment. At this time, even if the performance parameters corresponding to the second candidate node are optimized through the preset duration optimization strategy, only some parameters affecting the processing duration are optimized, and the use mode of the interface is not changed into a correct use mode, so that the second candidate node is subjected to invalid optimization, and the waste of computing resources is also caused. Therefore, the second candidate node can be screened out from the plurality of first candidate nodes, that is, the second candidate node is obtained as an abnormal node, and meanwhile, other first candidate nodes except the second candidate node in the plurality of first candidate nodes are obtained as high-time-consumption nodes.
In some embodiments, after the second candidate node is obtained as an abnormal node, a parameter corresponding to the abnormal node may be optimized based on a preset function optimization policy, so as to reduce the failure probability of the quality inspection processing node in performing request processing. The preset function optimization strategy may be a strategy for adjusting other reasons except for network congestion or too long quality inspection time, and includes but is not limited to an interface optimization strategy, a parameter adjustment strategy, and the like. Therefore, the parameters of the abnormal nodes caused by other reasons except for the high-time-consumption nodes can be optimized in a targeted mode, and the probability that the quality inspection processing node fails to execute the request is reduced.
In other embodiments, after the second candidate node is obtained as an abnormal node, an optimization policy corresponding to a failure reason of the abnormal node may be determined as a target optimization policy according to the failure reason of the abnormal node; and optimizing parameters corresponding to the abnormal nodes based on the target optimization strategy so as to reduce the failure probability of the quality inspection processing nodes in executing request processing. In practical application, the failure reasons of the abnormal node may also include multiple kinds, so that after the abnormal node is obtained, an optimization strategy corresponding to the failure reason is further obtained according to the failure reason of the abnormal node and used as a target optimization strategy; and optimizing parameters corresponding to the abnormal nodes based on a target optimization strategy, so that the failure probability of executing quality inspection processing by the quality inspection processing nodes is reduced. Therefore, the corresponding optimization strategy is obtained according to the failure reason, the abnormal node can be optimized more pertinently and efficiently, and further the whole process of the quality inspection service can be optimized more efficiently and comprehensively.
Step S380: and optimizing the performance parameters corresponding to the high time consumption nodes based on a preset time optimization strategy so as to reduce the response time of the high time consumption nodes.
In this embodiment, the specific content in step S380 may refer to the content in the foregoing embodiments, and is not described herein again.
In this embodiment, when performing a high concurrent pressure test on a quality inspection service, first screening out a first candidate node according to a response duration, and determining a high time-consuming node and an abnormal node from the first candidate node according to a failure reason of the node; and optimizing the parameters of the high-time-consumption nodes by using a time length optimization strategy, so as to reduce the response time length of the high-time-consumption nodes, and simultaneously optimizing the parameters of the abnormal nodes by using a function optimization strategy, so as to reduce the failure probability of the quality inspection processing nodes in executing request processing. Therefore, comprehensive time-consuming detection is realized for each quality inspection processing node in the whole process of the quality inspection service, and the quality inspection processing node with problems is optimized by using a corresponding optimization strategy in a targeted manner, so that the efficiency and the comprehensiveness of the whole process optimization of the quality inspection service are improved.
Referring to fig. 6, fig. 6 is a flowchart illustrating a method, an apparatus, a computer device, and a storage medium for optimizing a quality inspection node according to still another embodiment of the present disclosure. The method for optimizing the quality inspection node according to the embodiment of the present application will be described in detail below with reference to fig. 6. The quality inspection node optimization method can comprise the following steps:
step S410: and if the application scene is an instruction triggering scene, simulating the target number of request tasks based on the application scene type carried in the opening instruction of the pressure test.
Specifically, if the application scene is an instruction triggering scene, responding to a starting instruction of a triggering pressure test, and acquiring a preset number corresponding to an application scene type of a request task carried in the starting instruction as a target number; a target number of requested tasks is simulated. It is understood that when a start command is input by a pressure measurement person, the computer device responds to the start command and simulates a target number of requested tasks, accordingly; optionally, the pressure measurement personnel may select the current application scene type at the same time when inputting the start instruction, and correspondingly, the computer device may obtain the preset number corresponding to the current application scene type as the target number. The computer device may be pre-stored with a plurality of application scene types and a preset number corresponding to each application scene type, where the application scene types include, but are not limited to, a user high peak usage scene and a user low peak usage scene. Therefore, different concurrent number of request tasks can be simulated for different application scenes of the quality inspection service in a more targeted manner, so that the parameters of the quality inspection processing nodes in the quality inspection service can be optimized under the condition of being more suitable for the actual scene.
Step S420: and if the application scene is a timing trigger scene, simulating the target number of request tasks based on the historical quality inspection log information.
In some embodiments, referring to fig. 7, step S420 may include the following steps:
step S421: and if the application scene is a timing trigger scene, acquiring historical quality inspection log information of the request processing corresponding to the execution of the historical actual request task at intervals of preset time, wherein the actual request task is a request task actually input by a user.
In this embodiment, considering that the user quantity of the quality inspection service actually used after the quality inspection service is put into actual use is related to the efficiency of the quality inspection service, when the processing efficiency of the whole flow of the quality inspection service is high, a larger user quantity may be brought, and even the maximum user concurrency amount simulated when the pressure test is performed before the quality inspection service is put into use may be exceeded, so that the quality inspection efficiency of the quality inspection service may be caused. Based on the method, after the quality inspection service is put into practical use, the pressure test can be periodically carried out on the whole flow of the quality inspection service at regular time, so that the quality inspection processing nodes in the whole flow of the quality inspection service can be optimized in time, and the quality inspection efficiency of the quality inspection service is improved.
Specifically, if the application scenario is a timing trigger scenario, that is, a preset timing trigger program triggers a pressure test of the pressure testing tool, and historical quality inspection log information for executing the request processing corresponding to the historical actual request task is acquired every other preset time, where the actual request task may be understood as a request task actually input by the user. For example, the historical quality inspection log information may be all the historical quality inspection log information after the quality inspection service is put into practical use, or may be the historical quality inspection log information within a month, which is not limited in this embodiment.
Step S422: and acquiring the user concurrency amount corresponding to each time period in the plurality of time periods in the historical quality control log information to obtain the user concurrency amounts.
The plurality of time periods may be preset, or may be randomly determined by a computer device, which is not limited in this embodiment. For example, the historical quality control log information may be historical quality control log information for 30 days, and the plurality of time periods may be historical quality control log information for 3 weeks for 30 days, or historical quality control log information for 1 week, 3 weeks, and 5 weeks for 30 days.
Step S423: determining the target number based on the plurality of user concurrency amounts.
Based on this, after the user concurrency amount of each time period in the multiple time periods is obtained, the target number can be determined according to the multiple user concurrency amounts.
In some embodiments, an average of the concurrency of the plurality of users may be obtained as the target number. Therefore, the target number of the simulated request tasks can be more fitted with the concurrency of the historical users, the simulated high-concurrency pressure test scene is fitted with the high-concurrency use scene in the actual scene, and the performance parameters of the subsequently adjusted quality inspection processing nodes are more suitable for the actual use scene.
In other embodiments, a plurality of specified user concurrency amounts may be further obtained from the plurality of user concurrency amounts, where the specified user concurrency amount is a user concurrency amount greater than a preset concurrency amount in the plurality of user concurrency amounts, and then an average value of the plurality of specified user concurrency amounts is obtained as the target amount. Therefore, the target number of the simulated request tasks can be more fit with the highest concurrency of the historical users, the simulated high-concurrency pressure test scene is fit with the high-concurrency use scene in the actual scene, and the performance stability of the quality inspection service under the high concurrency is improved.
Step S424: simulating the target number of requested tasks.
In this embodiment, the specific content in step S424 may refer to the content in the foregoing embodiments, and is not described herein again.
Step S430: and obtaining quality inspection log information of the request processing corresponding to the execution of the target number of request tasks, wherein the quality inspection log information comprises a plurality of quality inspection processing nodes and the response duration of each quality inspection processing node.
Step S440: and acquiring the quality inspection processing nodes with response time length exceeding a preset time length threshold from the plurality of quality inspection processing nodes as high time consumption nodes.
Step S450: and optimizing the performance parameters corresponding to the high time consumption nodes based on a preset time optimization strategy so as to reduce the response time of the high time consumption nodes.
In this embodiment, the specific contents in step S430 to step S450 may refer to the contents in the foregoing embodiments, and are not described herein again.
In the embodiment, different target numbers of request tasks are simulated according to different application scenes, so that the quality inspection service is subjected to pressure testing under the condition that the request tasks are more suitable for the actual application scenes, and the parameters of the quality inspection processing nodes in the quality inspection service are optimized based on the result of the pressure testing. Therefore, the performance parameters of the quality inspection processing nodes which are adjusted subsequently are more suitable for actual use scenes, and the performance stability of the quality inspection service under high concurrency is improved more pertinently.
Referring to fig. 8, fig. 8 is a block diagram illustrating a method, an apparatus, a computer device, and a storage medium for optimizing quality inspection nodes according to another embodiment of the present disclosure. The method for optimizing the quality inspection node according to the embodiment of the present application will be described in detail below with reference to fig. 7. The quality inspection node optimization method can comprise the following steps:
step S510: and simulating a target number of request tasks, wherein the target number is determined by an application scene triggering pressure test, and the request tasks are used for requesting quality inspection processing in customer service quality inspection.
Step S520: and obtaining quality inspection log information of the request processing corresponding to the execution of the target number of request tasks, wherein the quality inspection log information comprises a plurality of quality inspection processing nodes and the response duration of each quality inspection processing node.
Step S530: and acquiring the quality inspection processing nodes with response time length exceeding a preset time length threshold from the plurality of quality inspection processing nodes as high time consumption nodes.
Step S540: and optimizing the performance parameters corresponding to the high time consumption nodes based on a preset time optimization strategy so as to reduce the response time of the high time consumption nodes.
In this embodiment, the specific contents in step S510 to step S540 may refer to the contents in the foregoing embodiments, and are not described herein again.
Step S550: and judging whether the current response time of the high time-consuming node is less than or equal to the preset time threshold.
Step S560: and if the current response time of the high time consumption node is less than or equal to the preset time threshold, acquiring a performance parameter obtained by optimizing the parameter corresponding to the high time consumption node based on a preset time optimization strategy, and using the performance parameter as a standard performance parameter.
In this embodiment, after optimizing the performance parameter corresponding to the high time consuming node based on the preset time optimization strategy, the response time for the current high time consuming node to execute the request processing corresponding to the request task may be further determined, and if the current response time is less than or equal to the preset time threshold, the parameter optimization of the high time consuming node is represented to reach the preset standard, that is, the response time of the high time consuming node is successfully reduced. At this time, the optimized performance parameters of the high time-consuming nodes can be obtained and used as the labeled performance parameters, quality inspection processing can be performed based on the high time-consuming nodes and the standard performance parameters in the subsequent practical application scene, and the quality inspection processing efficiency of the high time-consuming nodes is greatly optimized.
Step S570: and if the current response time of the high time consumption nodes is not greater than the preset time threshold, executing the request tasks of the simulation target number to the step of optimizing the parameters corresponding to the high time consumption nodes based on a preset time optimization strategy until the response time of each quality inspection processing node in the plurality of quality inspection processing nodes does not exceed the preset time threshold.
Optionally, if the current response time of the high time consuming node is greater than a preset time threshold, executing a simulation target number of request tasks to the step of optimizing the parameters corresponding to the high time consuming node based on a preset time optimization strategy until the response time of each quality inspection processing node in the plurality of quality inspection processing nodes does not exceed the preset time threshold, which means that the response time of the high time consuming node does not reach the preset time threshold completely after one-time parameter optimization, that is, the optimization is not successful, and at this time, performing multiple pressure tests and optimizations on the high time consuming node to adjust the parameters corresponding to the high time consuming node to the performance parameters meeting the preset standard.
In this embodiment, after the high time-consuming node is optimized, the response time of the optimized high time-consuming node may be detected again, and when the response time of the high time-consuming node does not satisfy the preset time threshold, the parameters of the high time-consuming node are optimized for multiple times, so that the response time of the high time-consuming node meets the preset standard. Therefore, the accuracy of optimization of the quality inspection processing nodes in the quality inspection service is ensured, and the efficiency of the quality inspection service in quality inspection operation is improved.
Referring to fig. 9, a block diagram of an optimization apparatus 600 for quality inspection nodes according to an embodiment of the present disclosure is shown. The apparatus 600 may include: task simulation module 610, log acquisition module 620, node determination module 630, and parameter optimization module 640.
The task simulation module 610 is configured to simulate a target number of request tasks, where the target number is determined by an application scenario triggering a pressure test, and the request tasks are used for requesting quality inspection processing in customer service quality inspection.
The log obtaining module 620 is configured to obtain quality inspection log information for executing the request processing corresponding to the target number of request tasks, where the quality inspection log information includes a plurality of quality inspection processing nodes and a response duration of each quality inspection processing node.
The node determining module 630 is configured to obtain, from the multiple quality inspection processing nodes, a quality inspection processing node whose response time exceeds a preset time threshold as a high time consuming node.
The parameter optimization module 640 is configured to optimize the performance parameters corresponding to the high time consuming nodes based on a preset time optimization strategy, so as to reduce the response time of the high time consuming nodes.
In some embodiments, the quality inspection log information further includes status information of each quality inspection processing node, where the status information includes a success status, a failure status, and a failure reason, and the node determining module 630 may include: the device comprises a first node acquisition unit, a judgment unit and a high-time-consumption node acquisition unit. The first node obtaining unit may be configured to obtain, as a first candidate node, a quality inspection processing node of the multiple quality inspection processing nodes whose response time length exceeds the preset time length threshold. The judging unit may be configured to judge whether the status information of the first candidate node is the success status. The high-time-consuming node obtaining unit may specifically be configured to: if the state information of the first candidate node is the success state, acquiring the first candidate node as the high time-consuming node; and if the state information of the first candidate node is uneven and is in the success state, acquiring the first candidate node with the state information in the failure state as a second candidate node, and acquiring the high-time-consumption node from the first candidate node based on the failure reason of the second candidate node.
In this manner, the high time-consuming node acquiring unit may include: the reason matches the subunit. The reason matching subunit may be specifically configured to determine whether the failure reason of the second candidate node matches a preset failure reason; if so, acquiring a plurality of first candidate nodes as the high-time-consumption nodes; and if not, acquiring other first candidate nodes except the second candidate node in the plurality of first candidate nodes as the high-time-consumption nodes, and acquiring the second candidate node as an abnormal node.
In some embodiments, the optimization apparatus 600 of the quality inspection node may further include: and an abnormal parameter optimization module. After the second candidate node is obtained as the abnormal node, the abnormal parameter optimization module may be configured to optimize a parameter corresponding to the abnormal node based on a preset function optimization policy, so as to reduce a failure probability of the quality inspection processing node in executing request processing.
In other embodiments, the quality inspection node optimization apparatus 600 may further include: and an abnormal parameter optimization module. The abnormal parameter optimization module may be configured to determine, according to a failure cause of the abnormal node, an optimization policy corresponding to the failure cause as a target optimization policy; and optimizing parameters corresponding to the abnormal nodes based on the target optimization strategy so as to reduce the failure probability of the quality inspection processing nodes in executing request processing.
In some embodiments, the task simulation module 610 may include: an instruction trigger simulation unit and a timing trigger simulation unit. The instruction triggering simulation unit may be configured to simulate the target number of request tasks based on the application scene type carried in the opening instruction of the pressure test if the application scene is an instruction triggering scene. The timing trigger simulation unit may be configured to simulate a target number of requested tasks based on the historical quality inspection log information if the application scenario is the timing trigger scenario.
In this manner, the instruction trigger simulation unit may be specifically configured to: if the application scene is the instruction triggering scene, responding to a starting instruction for triggering the pressure test, and acquiring a preset number corresponding to the application scene type of the request task carried in the starting instruction as the target number; simulating the target number of requested tasks.
In this manner, the timing trigger analog unit may include: the system comprises a history log obtaining subunit, a concurrency quantity obtaining subunit, a target quantity determining subunit and a task simulating subunit. The history log obtaining subunit may be configured to, if the application scenario is a timing trigger scenario, obtain, every preset time interval, history quality inspection log information for performing processing request corresponding to a history actual request task, where the actual request task is a request task actually input by a user. The concurrency amount obtaining subunit may be configured to obtain a user concurrency amount corresponding to each of multiple time periods in the historical quality inspection log information, so as to obtain multiple user concurrency amounts. The target number determination subunit may be configured to determine the target number based on the plurality of user concurrency amounts. The task simulation subunit may be configured to simulate the target number of requested tasks.
In some embodiments, the optimization apparatus 600 of the quality inspection node may further include: and a time length judging module. Wherein, the duration judging module can be specifically configured to: after optimizing the performance parameters corresponding to the high time consumption nodes based on the preset time optimization strategy, judging whether the current response time of the high time consumption nodes is less than or equal to the preset time threshold; if the current response time of the high time consumption node is less than or equal to the preset time threshold, acquiring a performance parameter obtained by optimizing a parameter corresponding to the high time consumption node based on a preset time optimization strategy, and taking the performance parameter as a standard performance parameter; and if the current response time of the high time consumption nodes is not greater than the preset time threshold, executing the request tasks of the simulation target number to the step of optimizing the parameters corresponding to the high time consumption nodes based on a preset time optimization strategy until the response time of each quality inspection processing node in the plurality of quality inspection processing nodes does not exceed the preset time threshold.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and modules may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, the coupling between the modules may be electrical, mechanical or other type of coupling.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
In summary, in the scheme provided by the embodiment of the present application, a target number of request tasks is simulated, the target number is determined by an application scenario triggering a pressure test, and the request tasks are used for requesting quality inspection processing in customer service quality inspection; obtaining quality inspection log information of the request processing corresponding to the execution of the target number of request tasks, wherein the quality inspection log information comprises a plurality of quality inspection processing nodes and the response duration of each quality inspection processing node; acquiring quality inspection processing nodes with response time length exceeding a preset time length threshold from a plurality of quality inspection processing nodes as high time consumption nodes; and optimizing the performance parameters corresponding to the high time consumption nodes based on a preset time optimization strategy so as to reduce the response time of the high time consumption nodes. Therefore, the problems of time and labor consumption caused by one-to-one inspection of each quality inspection processing node in log information by maintenance personnel are solved, the high time-consuming nodes are screened out based on the response time, performance parameters of the high time-consuming nodes are optimized according to a preset time optimization strategy, the efficiency of the high time-consuming and high delay nodes existing in the positioning quality inspection service is improved, and the timeliness of optimizing the quality inspection processing nodes with problems is further improved.
A computer device provided by the present application will be described with reference to fig. 10.
Referring to fig. 10, fig. 10 is a block diagram illustrating a computer device 700 according to an embodiment of the present application, where the method for optimizing a quality inspection node according to the embodiment of the present application can be executed by the computer device 700. Computer device 700 may be, among other things, a device that is capable of running applications.
The computer device 700 in the embodiments of the present application may include one or more of the following components: a processor 701, a memory 702, and one or more applications, wherein the one or more applications may be stored in the memory 702 and configured to be executed by the one or more processors 701, the one or more programs configured to perform a method as described in the aforementioned method embodiments.
Processor 701 may include one or more processing cores. The processor 701 interfaces with various components throughout the computer device 700 using various interfaces and lines to perform various functions of the computer device 700 and process data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 702 and invoking data stored in the memory 702. Alternatively, the processor 701 may be implemented in hardware using at least one of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor 701 may integrate one or a combination of a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a modem, and the like. Wherein, the CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing display content; the modem is used to handle wireless communications. It is understood that the modem may be integrated into the processor 701 and implemented by a single communication chip.
The Memory 702 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). The memory 702 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 702 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing various method embodiments described below, and the like. The storage data area may also store data created by the computer device 700 during use (such as the various correspondences described above), and so on.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and modules may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, the coupling or direct coupling or communication connection between the modules shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or modules may be in an electrical, mechanical or other form.
In addition, functional modules in the embodiments of the present application may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
Referring to fig. 11, a block diagram of a computer-readable storage medium according to an embodiment of the present application is shown. The computer-readable medium 800 has stored therein a program code that can be called by a processor to execute the method described in the above-described method embodiments.
The computer-readable storage medium 800 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. Alternatively, the computer-readable storage medium 800 includes a non-transitory computer-readable storage medium. The computer readable storage medium 800 has storage space for program code 810 to perform any of the method steps of the method described above. The program code can be read from or written to one or more computer program products. The program code 810 may be compressed, for example, in a suitable form.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not necessarily depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (12)

1. A method for optimizing quality inspection nodes, the method comprising:
simulating a target number of request tasks, wherein the target number is determined by an application scene of triggering pressure test, and the request tasks are used for requesting quality inspection processing in customer service quality inspection;
obtaining quality inspection log information of the request processing corresponding to the execution of the target number of request tasks, wherein the quality inspection log information comprises a plurality of quality inspection processing nodes and the response duration of each quality inspection processing node;
acquiring quality inspection processing nodes with response time length exceeding a preset time length threshold from the plurality of quality inspection processing nodes as high time consumption nodes;
and optimizing the performance parameters corresponding to the high time consumption nodes based on a preset time optimization strategy so as to reduce the response time of the high time consumption nodes.
2. The method according to claim 1, wherein the quality inspection log information further includes status information of each quality inspection processing node, the status information includes a success status, a failure status, and a failure reason, and the acquiring, from the plurality of quality inspection processing nodes, a quality inspection processing node whose response duration exceeds a preset duration threshold as a high-time-consuming node includes:
obtaining a quality inspection processing node of which the response time length exceeds the preset time length threshold value in the plurality of quality inspection processing nodes as a first candidate node;
judging whether the state information of the first candidate node is the success state;
if so, acquiring the first candidate node as the high time-consuming node;
if not, acquiring a first candidate node with the state information of the failure state as a second candidate node;
and acquiring the high time-consuming node from the first candidate node based on the failure reason of the second candidate node.
3. The method according to claim 2, wherein when the number of the first candidate nodes is multiple, the obtaining the high time-consuming node from the first candidate nodes based on the failure reason of the second candidate node comprises:
judging whether the failure reason of the second candidate node is matched with a preset failure reason;
if so, acquiring a plurality of first candidate nodes as the high-time-consumption nodes;
and if not, acquiring other first candidate nodes except the second candidate node in the plurality of first candidate nodes as the high-time-consumption nodes, and acquiring the second candidate node as an abnormal node.
4. The method of claim 3, wherein after said obtaining the second candidate node as an abnormal node, the method further comprises:
and optimizing parameters corresponding to the abnormal nodes based on a preset function optimization strategy so as to reduce the failure probability of the quality inspection processing node in executing request processing.
5. The method of claim 3, wherein after said obtaining the second candidate node as an abnormal node, the method further comprises:
determining an optimization strategy corresponding to the failure reason according to the failure reason of the abnormal node, and using the optimization strategy as a target optimization strategy;
and optimizing parameters corresponding to the abnormal nodes based on the target optimization strategy so as to reduce the failure probability of the quality inspection processing nodes in executing request processing.
6. The method of claim 1, wherein simulating a target number of requested tasks comprises:
if the application scene is an instruction triggering scene, simulating a target number of request tasks based on the application scene type carried in the opening instruction of the pressure test;
and if the application scene is a timing trigger scene, simulating the target number of request tasks based on the historical quality inspection log information.
7. The method according to claim 6, wherein if the application scenario is an instruction triggering scenario, simulating a target number of request tasks based on an application scenario type carried in an opening instruction of the stress test comprises:
if the application scene is the instruction triggering scene, responding to a starting instruction for triggering the pressure test, and acquiring a preset number corresponding to the application scene type of the request task carried in the starting instruction as the target number;
simulating the target number of requested tasks.
8. The method of claim 6, wherein if the application scenario is a timing trigger scenario, simulating a target number of requested tasks based on historical quality inspection log information comprises:
if the application scene is a timing trigger scene, acquiring historical quality inspection log information of request processing corresponding to an execution historical actual request task at intervals of preset time, wherein the actual request task is a request task actually input by a user;
acquiring user concurrency corresponding to each time period in a plurality of time periods in the historical quality inspection log information to obtain a plurality of user concurrency;
determining the target number based on the plurality of user concurrency amounts;
simulating the target number of requested tasks.
9. The method according to any one of claims 1 to 8, wherein after the optimizing the performance parameter corresponding to the time-consuming node based on the preset duration optimization strategy, the method further comprises:
judging whether the current response time length of the high time-consuming node is less than or equal to the preset time length threshold value or not;
if the current response time of the high time consumption node is less than or equal to the preset time threshold, acquiring a performance parameter obtained by optimizing a parameter corresponding to the high time consumption node based on a preset time optimization strategy, and taking the performance parameter as a standard performance parameter;
and if the current response time of the high time consumption nodes is not greater than the preset time threshold, executing the request tasks of the simulation target number to the step of optimizing the parameters corresponding to the high time consumption nodes based on a preset time optimization strategy until the response time of each quality inspection processing node in the plurality of quality inspection processing nodes does not exceed the preset time threshold.
10. An apparatus for optimizing a quality inspection node, the apparatus comprising:
the task simulation module is used for simulating a target number of request tasks, the target number is determined by an application scene of triggering pressure test, and the request tasks are used for requesting quality inspection processing in customer service quality inspection;
the log obtaining module is used for obtaining quality inspection log information which executes the request processing corresponding to the target number of request tasks, and the quality inspection log information comprises a plurality of quality inspection processing nodes and the response duration of each quality inspection processing node;
the node determination module is used for acquiring quality inspection processing nodes with response time length exceeding a preset time length threshold from the plurality of quality inspection processing nodes as high time consumption nodes;
and the parameter optimization module is used for optimizing the performance parameters corresponding to the high time consumption nodes based on a preset time optimization strategy so as to reduce the response time of the high time consumption nodes.
11. A computer device, comprising:
one or more processors;
a memory;
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs configured to perform the method of any of claims 1-9.
12. A computer-readable storage medium, characterized in that a program code is stored in the computer-readable storage medium, which program code can be called by a processor to perform the method according to any of claims 1-9.
CN202111288721.3A 2021-11-02 2021-11-02 Quality inspection node optimization method and device, computer equipment and storage medium Pending CN114186699A (en)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114971079A (en) * 2022-06-29 2022-08-30 中国工商银行股份有限公司 Second killing type transaction processing optimization method and device
CN116502865A (en) * 2023-06-19 2023-07-28 江西财经大学 Intelligent management method and system for industrial enterprise production
WO2024041018A1 (en) * 2022-08-23 2024-02-29 京东科技控股股份有限公司 Method and apparatus for adjusting response duration of stress test system, and device and medium
CN114971079B (en) * 2022-06-29 2024-05-28 中国工商银行股份有限公司 Second killing type transaction processing optimization method and device

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114971079A (en) * 2022-06-29 2022-08-30 中国工商银行股份有限公司 Second killing type transaction processing optimization method and device
CN114971079B (en) * 2022-06-29 2024-05-28 中国工商银行股份有限公司 Second killing type transaction processing optimization method and device
WO2024041018A1 (en) * 2022-08-23 2024-02-29 京东科技控股股份有限公司 Method and apparatus for adjusting response duration of stress test system, and device and medium
CN116502865A (en) * 2023-06-19 2023-07-28 江西财经大学 Intelligent management method and system for industrial enterprise production

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