A service quality inspection method based on dynamic adaptation and a storage medium

Abstract

The application relates to a service quality inspection method based on dynamic adaptation and a storage medium, which forms a unified task pool according to quality inspection requirements of various services, samples a resource pool of a quality inspection area according to a resource type corresponding to different tasks, executes a processing engine corresponding to the service, automatically analyzes operation indexes of various services in combination with characteristics of dimensions such as a resource type, a data volume and a concurrency number, and publishes the operation indexes to a production environment; changes in a big data platform operation environment are monitored in real time, and operation indexes of various services are dynamically adjusted; scientific, orderly and safe management of various services is realized, stable and safe operation of the big data platform is ensured, and big data application requirements in various business scenarios are supported.

Description

Technical Field

[0001] The present application relates to the technical field of service quality inspection, and in particular to a service quality inspection method and storage medium based on dynamic adaptation. Background Art

[0002] In the digital economy era, big data platforms have gathered massive amounts of data, capabilities and other resources. Application ecosystem vendors have achieved sharing and use of big data resources by registering and applying for various services. Currently, the review of various services often focuses on business rules such as applicants and application reasons, as well as core indicators such as service performance and security.

[0003] Due to the large volume of data, large number of users, and complex business scenarios of big data platforms, it is difficult to build a test environment for service testing. Traditional service registration and review processes often only verify business rules such as applicants and application reasons. Core indicators such as service performance and security cannot be automatically tested. These technologies have the following shortcomings:

[0004] 1) Quality inspection is time-sensitive. In the era of big data, data volumes are enormous. Traditional methods, which rely solely on reviewing business rules, are unable to promptly detect services that fail to meet performance requirements. Once services are released to production, they often cause platform downtime, impacting the application of all business systems on the platform.

[0005] 2) Quality inspection has poor adaptability. The service operation indicators generated by traditional quality inspection methods are fixed and static. They cannot be automatically adjusted according to changes in the operating characteristics of the big data platform. They cannot ensure that various resources are used safely, reliably and in compliance with regulations, and there are certain security risks. Summary of the Invention

[0006] In response to the above technical problems, this application proposes a service quality inspection method and storage medium based on dynamic adaptation.

[0007] In the first aspect, the present application proposes a service quality inspection method based on dynamic adaptation, comprising the following steps:

[0008] S1: Acquire a service feature set and save the service feature set in a service feature table, acquire a detection solution set and save the detection solution set in a detection solution table;

[0009] S2: traverse the service feature set to obtain the running data volume and the detection scheme associated node, traverse the detection scheme set using the detection scheme associated node to obtain a sampling scheme, perform resource sampling using the running data volume and the sampling scheme to obtain sampling resources, and copy the sampling resources to the memory space of the quality inspection area;

[0010] S3: Using the detection scheme association node to traverse the detection scheme set, obtain the scheme type, detection interval, and processing engine, use the detection interval to divide the sampling resource into a plurality of sample data, call the processing engine to process each of the plurality of sample data, and obtain a detection result set;

[0011] S4: Analyze the detection result set using the SCA method and calculate the service operation index, and store the calculated operation index in the service feature table;

[0012] S5: Publishing the operating indicators to the big data platform operating environment, and automatically adjusting the operating indicators.

[0013] By adopting the above technical solution, this application will form a unified task pool for the quality inspection requirements of various services. According to the resource types corresponding to different tasks, a resource pool of the quality inspection area is sampled to execute the processing engine corresponding to the service. Combined with the characteristics of resource type, data volume, concurrency and other dimensions, the operating indicators of various services are automatically analyzed and published to the production environment; changes in the operating environment of the big data platform are monitored in real time, and the operating indicators of various services are dynamically adjusted; scientific, orderly and safe management of various services is achieved, ensuring the stable and safe operation of the big data platform and supporting big data application needs in various business scenarios.

[0014] Preferably, the service feature set includes service ID, input parameters, output parameters, main resources, detection scheme associated nodes, status, notes, number of calls, operation indicators, operation data volume and adjustment plan, and the detection scheme set includes scheme ID, service ID, scheme type, scheme parameters, detection results, sampling scheme, processing engine, detection interval, last execution time, memory area and status.

[0015] Preferably, the method of sampling resources using the operating data volume and the sampling scheme to obtain sampled resources is as follows: multiplying the operating data volume and the sampling scheme to obtain the sampling quantity, and randomly extracting the sampling quantity of sampling resources from the main resources of the service.

[0016] Preferably, the step S3 specifically includes the following steps:

[0017] S31: using the detection scheme association node to traverse the associated detection scheme set, obtain the scheme type, detection interval, and processing engine, use the detection interval to divide the sampling resource into a plurality of sample data, call the processing engine for processing on each of the plurality of sample data, and obtain a detection result set;

[0018] S32: Checking the security of the service, wherein the checking the security of the service includes verifying whether the output parameters of the service are consistent with the actual returned results.

[0019] Preferably, the security detection of the service also includes preventing crawling of all resources, and the algorithm used to prevent crawling of all resources is that the single return quantity multiplied by the number of calls is greater than the current data volume of the resource.

[0020] Preferably, the use of the SCA method to analyze the detection result set and calculate the service operation index is specifically: operation index result = SCA (array [detection environment, detection result], resource data volume, deviation rate).

[0021] Preferably, the S4 specifically includes the following steps:

[0022] S41: Analyze the detection result set using the SCA method and calculate the service operation index, and store the calculated operation index in the service feature table;

[0023] S42: Cleaning the quality inspection area. Specifically, the cleaning of the quality inspection area includes regularly checking whether the interval between the last operation time of the retrieval solution and the current system time exceeds a set threshold. If the interval exceeds the set threshold, the cache area named memory area is automatically cleaned to release unused resources.

[0024] Preferably, the step S5 specifically includes: publishing the operating indicators to the big data platform operating environment, and monitoring the current data volume of the resources in real time, and automatically adjusting the operating indicators of the service if the change reaches a certain threshold.

[0025] Preferably, in step S5, the algorithm for automatically adjusting the operating index is: if the current data volume of the resource > the resource operating data volume * (1 + factor increase ratio), then the adjusted operating index = the current operating index * (1-indicator change ratio), wherein the algorithm for the factor increase ratio is the growth rate of the scale of two adjacent sampling data, and the algorithm for the indicator change ratio is: indicator change ratio = ((|current operating index|-minimum deviation rate)*(1-minimum deviation rate).

[0026] In a second aspect, the present application further proposes a computer-readable storage medium having a computer program stored thereon, which implements the calculation method described in the first aspect when the program is executed by a processor.

[0027] Compared with the prior art, the present invention has the following beneficial technical effects:

[0028] 1) The service quality inspection method is different. The method innovatively designs a service quality inspection area, integrates the quality inspection requirements of various services, forms a unified task pool, samples the resource pool of the quality inspection area according to the resource types corresponding to different tasks, executes the processing engine corresponding to the service, automatically analyzes the operation indexes of various services according to the characteristics of the resource types, data volume, concurrency and other dimensions, discovers the services with problems in time, and ensures the stable operation of the big data platform.

[0029] 2) The index adjustment mode is different. The application monitors the changes of the running environment of the big data platform in real time, dynamically adjusts the operation indexes of various services when the changes reach a certain threshold, and automatically starts the gradient detection process of the service to verify the accuracy of the operation indexes of the service; dynamically adapts to the complex and changeable running characteristics of the big data platform, realizes the scientific, orderly and safe management of various services, and supports the big data application requirements in various business scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0030] The accompanying drawings provide further understanding of the embodiments and are incorporated in and constitute a part of this specification. The drawings illustrate embodiments and serve to explain the principles of the present application. It will be readily appreciated that other embodiments and many of the intended advantages of the embodiments will become better understood because of the following detailed description, taken in conjunction with the accompanying drawings. The elements of the drawings are not necessarily to scale. Like reference numbers refer to corresponding similar parts.

[0031] Figure 1 is a flowchart of a service quality inspection method based on dynamic adaptation in an embodiment of the application.

[0032] Figure 2 is a schematic diagram of the overall analysis process of a service quality inspection method based on dynamic adaptation.

[0033] Figure 3 is a structural schematic diagram of a computer system of an electronic device suitable for implementing the embodiments of the application. DETAILED DESCRIPTION

[0034] The application will be described in further detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related application, and not to limit the application. In addition, it should be noted that, for the convenience of description, only the parts related to the application are shown in the drawings.

[0035] It should be noted that the embodiments and features in the embodiments in the application can be combined with each other without conflict. The application will be described in detail below with reference to the drawings and embodiments.

[0036] As Figure 1As shown, the embodiment of the present application discloses a service quality inspection method based on dynamic adaptation, including the following steps:

[0037] S1: Obtain a service feature set and save it in a service feature table, obtain a detection solution set and save it in a detection solution table;

[0038] In a specific embodiment, the service feature set Yn includes the service ID, input parameters, output parameters, main resources, detection solution-related nodes, status, notes, number of calls, operating indicators, operating data volume, adjustment plan, and deviation rate. This allows the characteristics of different types of services and matching engine information to be obtained.

[0039] The specific service characteristics are shown in Table 1:

[0040]

[0041]

[0042] Table 1

[0043] The detection scheme set Zn includes scheme ID, service ID, scheme type, scheme parameters, detection results, sampling scheme, processing engine, detection interval, last execution time, memory area and status, so as to obtain the operation information of various detection schemes.

[0044] The specific detection scheme is shown in Table 2:

[0045] Property Name Property Description Remark FAId Project ID LWY Service ID FALS Solution Type 1-concurrency, 2-response speed, 3-security... FACX Solution Parameters Such as: data scale, number of connections ZCZG Test results CYFA Sampling plan array[magnitude, ratio] clyc Processing Engine ZGar Detection range LastTime Last execution time CZI Memory Area zt state 0-unavailable, 1-available

[0046] Table 2

[0047] S2: Traverse the service feature set to obtain the running data volume and detection scheme associated nodes. Use the detection scheme associated nodes to traverse the detection scheme set to obtain a sampling scheme. Use the running data volume and sampling scheme to sample resources to obtain sampling resources, and copy the sampling resources to the memory space of the quality inspection area.

[0048] The quality inspection area is initialized by step S2. In a specific embodiment, S2 specifically includes: traversing the service feature set Yn, and performing resource sampling based on the service's main resource Yn.ZSJY and the associated node Yn.GLZD. The sampling scheme is: obtaining the current data volume Yn.JYSL of the service's main resource Yn.ZSJY by statistics, which can be obtained by calling the query interface; traversing the detection scheme set Zn using the detection scheme associated node GLZD to obtain the sampling scheme Zn.CYFA of each detection scheme. The association condition is Yn.LWId=Zn.LWId; use the running data volume Yn.JYSL and the sampling plan Zn.CYFA to sample resources to obtain sampling resources, and obtain the sampling quantity Yn.CYSL of each resource. Specifically, Yn.CYSL (sampling quantity)=Yn.JYSL (running data volume)*Zn.CYFA (sampling plan); then randomly extract the sampling quantity bar of sampling resources from the main resource Yn.ZSJY of the service, and copy the sampling resources to the memory space of the quality inspection area.

[0049] In the specific example, the sampling plan is shown in Table 3:

[0050]

[0051] Table 3

[0052] For example, if the running data volume of the main resource of ID1 (service 1) is 1.5 million, 300,000 records will be randomly selected to enter the memory space of the quality inspection area.

[0053] In a specific embodiment, a memory space CZId of N megabytes is first allocated to the quality inspection zone, and then the sampling resources are stored in the memory space CZId. The quality inspection zone only stores data of the sampling resources.

[0054] S3: Use the detection scheme association node to traverse the detection scheme set, obtain the scheme type, detection interval, and processing engine, use the detection interval to divide the sampled resources into several pieces of sampled data, call the processing engine to process each piece of sampled data, and obtain a set of detection results; S3 is used to implement tiered detection of the service. In a specific embodiment, S3 specifically includes:

[0055] S31: Using the detection scheme association node, traverse the associated detection scheme set to obtain the scheme type, detection interval, and processing engine. Using the detection interval, divide the sampling resource into several pieces of sample data. Invoke the processing engine for processing each piece of sample data to obtain a set of detection results.

[0056] In a specific embodiment, S31 specifically includes: traversing Yn, traversing the associated detection solution set Zn using the detection solution association node GLZD, obtaining the detection solution Zn.FALS, the detection interval Zn.ZCCZ, and the processing engine Zn.clyc, with the association condition Yn.LWId = Zn.LWId; dividing the sampling resource into several pieces of sampling data according to the detection interval Zn.ZCCZ, calling the processing engine Zn.Clyc to process the several pieces of sampling data in the quality inspection area, and obtaining the detection result set Rn;

[0057] Among them, for sampling data within 10 million, the initial value of the detection interval is 3; for sampling data within 100 million, the initial value of the detection interval is 5; for sampling data above 1 billion, the initial value of the detection interval is 20.

[0058] In a specific example, for example, 300,000 pieces of sampled data and a detection interval of 3, the detection schemes are shown in Table 4:

[0059] Service ID Solution Type Processing Engine Testing environment ID1 Response speed Engine 1 100,000 ID1 Response speed Engine 1 200,000 ID1 Response speed Engine 1 300,000

[0060] Table 4

[0061] S32: Check the security of the service. This includes verifying whether the service's output parameters are consistent with the actual return results. Specifically, verify whether Yn.SCCS matches the attributes of Rn. If not, the service request is terminated and an exception code is returned. Rn is the actual return result of the service execution. The attributes vary depending on the service. For example, a call record query service returns call record requirements, while an accommodation query service returns accommodation records.

[0062] In a specific embodiment, checking the security of the service also includes preventing crawling of all resources. The algorithm used to prevent crawling of all resources is that the number of single returns multiplied by the number of calls is greater than the current data volume of the resource, that is, Yn.DWCS*Rn.count>Yn.JYSL.

[0063] S4: Analyze the detection result set using the SCA method and calculate the service operation index, and store the calculated operation index in the service feature table;

[0064] In a specific embodiment, S4 specifically includes the following steps:

[0065] S41: Analyze the detection result set using the SCA method and calculate the service operation index, and store the calculated operation index in the service feature table;

[0066] The operating indicators defined by traditional quality inspection methods are fixed and static, and cannot adapt to changes in different business scenarios. This application will obtain the test results of different detection intervals based on the ladder detection algorithm. This application uses the SCA method to analyze the relationship between the change pattern of the detection interval and the change pattern of the detection result, and combines the result deviation rate caused by the interference item to calculate the operating indicator result Yn.WSJB of each service, and stores the calculated operating indicator in the corresponding record of the service feature library; the specific algorithm is: Yn.WSJB = SCA (array [detection environment, detection result], resource data volume, deviation rate), as shown in Table 5:

[0067]

[0068]

[0069] Table 5

[0070] The calculated operating indicator result is Yn.WSJB = [maximum concurrency, -28.5%]. 28.5% is the average of the decrease in the maximum concurrency. The specific calculation is (concurrency decrease 1 + concurrency decrease 2 + ... + concurrency decrease n) / n. For example, if the first sampling result is a maximum concurrency of 100, and the second sampling result is a maximum concurrency of 80, then concurrency decrease 1 = 20%. If the third sampling result is a maximum concurrency of 50, then concurrency decrease 2 = 37.5%. The average of 20% and 37.5% is 28.5%.

[0071] The deviation rate Yn.PQL is initialized according to a specific algorithm based on the resource volume and sampling ratio. For example, if the volume is in the millions, Yn.PQL = [-15%, 15%]; if the volume is in the hundreds of millions and the sampling ratio exceeds 50%, Yn.PQL = 0; if the sampling ratio does not exceed 50%, Yn.PQL = [-5%, 5%]; if the volume is in the tens of billions and the sampling ratio exceeds 80%, Yn.PQL = [-5%, 5%]; if 10% < sampling ratio < 80%, Yn.PQL - [-10%, 10%]; if the sampling ratio is 10% lower, YnPQL = [-15%, 15%];).

[0072] S42: Cleaning the quality inspection area, specifically, regularly checking whether the interval between the last operation time of the retrieval solution and the current system time exceeds the set threshold. If it exceeds the set threshold, the cache area named memory area is automatically cleaned to release unused resources.

[0073] In a specific embodiment, it is regularly checked whether the interval between the last operation time of the retrieval solution and the current system time exceeds a set threshold, such as Day(sysDate-Yn.LastTime)>1, then the cache area named Yn.CZId is automatically cleared to release unused resources.

[0074] S5: Publish the operating indicators to the big data platform operating environment and automatically adjust the operating indicators.

[0075] The service operation indicators generated by traditional quality inspection methods are fixed and static. They cannot be automatically adjusted according to changes in the operating characteristics of the big data platform. They cannot ensure that various resources are used safely, reliably and in compliance with regulations, and there are certain security risks. This algorithm monitors changes in the operating environment of the big data platform in real time. When the changes reach a certain threshold, it automatically adjusts the service operation indicators. At the same time, it automatically starts the service tiered detection process regularly to verify the accuracy of the service operation indicators and dynamically adapt to the complex and changeable operating characteristics of the big data platform.

[0076] In a specific embodiment, step S5 specifically includes: publishing the operating indicators to the big data platform operating environment, and monitoring the current data volume of the resources in real time. If the change reaches a certain threshold, the operating indicators of the service are automatically adjusted.

[0077] In a specific embodiment, if the current data volume of a resource > the resource's operating data volume + the increase in the adjustment plan, the service's operating index is dynamically adjusted. More specifically, if the current data volume of a resource > the resource's operating data volume * (1 + the factor increase ratio), then the adjusted operating index is set to = the current operating index * (1 - the index change ratio). That is, if (current data volume) > Yn.JYSL (operating data volume) * (1 + Yn.TZFA. factor increase ratio) {Yn.WSJB. value = Yn.WSJB. value * (1 - Yn.TZFA. index change ratio);}.

[0078] The factor growth ratio is calculated by dividing the growth rate of the data size between two consecutive samplings. For example, in Table 5, the data size of the first sampling is 100,000, and the data size of the second sampling is 200,000. Therefore, the factor growth ratio is +100%. The indicator change ratio is calculated as follows: indicator change ratio = (|current operating indicator| + minimum deviation rate) * (1 - maximum deviation rate). For example, in Table 5, since the current operating indicator Yn.WSJB = [maximum number of concurrent users, -28.5%], the indicator change ratio = (28.5% - 15%) * (1 - 15%) = 12%. Therefore, Yn.TZFA (adjustment plan) = [data size, 100%, maximum number of concurrent users, -12%].

[0079] In one embodiment, see Figure 2 , Figure 2 The overall analysis process of the service quality inspection method based on dynamic adaptation of the embodiment of the application is shown. Figure 2 As shown in the figure, the method is designed for the scene of service quality detection under massive data, a service quality inspection area is designed, through two algorithms of service gradient detection and index dynamic adjustment, the quality inspection requirements of various services are formed into a unified task pool, according to the resource types required by different tasks, a resource pool of the quality inspection area is sampled, a processing engine corresponding to the service is executed, combined with the characteristics of resource types, data volume, concurrency and other dimensions, the running indexes of various services published to the production environment are output; according to the actual running situation of the big data platform, the running indexes of various services are dynamically adjusted; scientific, orderly and safe management of various services is realized, the stable and safe operation of the big data platform is ensured, and the big data application requirements in various business scenarios are supported.

[0080] In further embodiments, a timing task can be set to perform gradient detection on the service and dynamic adjustment of the running index at regular intervals to ensure the accuracy of the running index of the service.

[0081] Compared with the prior art, the application has the following beneficial technical effects:

[0082] 1) The service quality inspection method is different, the algorithm innovatively designs a service quality inspection area, integrates the quality inspection requirements of various services, forms a unified task pool, samples a resource pool of the quality inspection area according to the resource types corresponding to different tasks, executes a processing engine corresponding to the service, and automatically analyzes the running indexes of various services in combination with the characteristics of resource types, data volume, concurrency and other dimensions, discovers services with problems in time, and ensures the stable operation of the big data platform.

[0083] 2) The index adjustment method is different, the application monitors the changes of the running environment of the big data platform in real time, dynamically adjusts the running indexes of various services when the changes reach a certain threshold, and automatically starts the gradient detection process of the service to verify the accuracy of the running index of the service; dynamically adapts the complex and changeable running characteristics of the big data platform, realizes scientific, orderly and safe management of various services, and supports big data application requirements in various business scenarios.

[0084] 3) Practicality: through optimization of different engine characteristics, combination with the service feature library, automatic analysis of the characteristics of the quality inspection tasks of different types of services by the computer program, matching of the corresponding processing engine, automatic analysis and adjustment of the service running index, and solving of the problem that service quality inspection cannot adapt to complex and changeable running scenes for a long time in the big data era.

[0085] 4) Innovation: Two algorithms, service-level quality inspection and dynamic indicator adjustment, are proposed. The quality inspection requirements of various services are integrated into a unified task pool. Based on the resource types corresponding to different tasks, a resource pool of quality inspection areas is sampled and formed. The processing engine corresponding to the execution service automatically analyzes the operating indicators of various services based on the characteristics of resource type, data volume, concurrency, and other dimensions, and publishes them to the production environment. The system monitors changes in the operating environment of the big data platform in real time and dynamically adjusts the operating indicators of various services. This system implements scientific, orderly, and secure management of various services, ensures the stable and secure operation of the big data platform, and supports the big data application needs in various business scenarios.

[0086] Reference below Figure 3 , which shows a structural diagram of a computer system 100 suitable for implementing an electronic device of an embodiment of the present application. Figure 3 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0087] like Figure 3 As shown, the computer system 100 includes a central processing unit (CPU) 101, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 102 or a program loaded from a storage unit 108 into a random access memory (RAM) 103. Various programs and data required for the operation of the system 100 are also stored in the RAM 103. The CPU 101, ROM 102, and RAM 103 are connected to each other via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0088] The following components are connected to the I / O interface 105: an input section 106 including a keyboard, a mouse, and the like; an output section 107 including a liquid crystal display (LCD) and speakers; a storage section 108 including a hard disk; and a communication section 109 including a network interface card such as a LAN card or a modem. The communication section 109 performs communication processing via a network such as the Internet. A drive 110 is also connected to the I / O interface 105 as needed. Removable media 111, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 110 as needed, so that computer programs read therefrom can be installed into the storage section 108 as needed.

[0089] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 109, and / or installed from a removable medium 111. When the computer program is executed by the central processing unit (CPU) 101, the above-mentioned functions defined in the method of the present application are performed.

[0090] As another aspect, the present application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed by the electronic device, the electronic device implements the following when executing. Figure 1 The method shown in .

[0091] It should be noted that the computer-readable storage medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable storage medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code contained on a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical cable, RF, or any suitable combination thereof.

[0092] Computer program code for performing the operations of the present application can be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can 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 can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0093] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0094] The above describes specific embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0095] In the description of this application, it should be understood that the terms "upper", "lower", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limiting this application. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The simple fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used for improvement. Any reference signs in the claims should not be interpreted as limiting the scope.

Claims

1. A service quality inspection method based on dynamic adaptation, characterized in that: The following steps are involved: S1: Acquire a service feature set and save the service feature set in a service feature table, acquire a detection solution set and save the detection solution set in a detection solution table; S2: traverse the service feature set to obtain the running data volume and the detection scheme associated node, traverse the detection scheme set using the detection scheme associated node to obtain a sampling scheme, perform resource sampling using the running data volume and the sampling scheme to obtain sampling resources, and copy the sampling resources to the memory space of the quality inspection area; S3: Using the detection scheme association node to traverse the detection scheme set, obtain the scheme type, detection interval, and processing engine, use the detection interval to divide the sampling resource into a plurality of sample data, call the processing engine to process each of the plurality of sample data, and obtain a detection result set; S4: Analyze the detection result set using the SCA method and calculate the service operation index, and store the calculated operation index in the service feature table; S5: Publish the operating indicator to the big data platform operating environment, and monitor the current data volume of the resource in real time. If the change reaches a certain threshold, the operating indicator is automatically adjusted; wherein, the algorithm for automatically adjusting the operating indicator is: if the current data volume of the resource > the resource operating data volume * (1 + factor increase ratio), then let the adjusted operating indicator = current operating indicator * (1-indicator change ratio), wherein the algorithm for the factor increase ratio is the growth rate of the scale of two adjacent sampled data, and the algorithm for the indicator change ratio is: indicator change ratio = (|current operating indicator| + minimum deviation rate) * (1-maximum deviation rate).

2. A service quality inspection method based on dynamic adaptation according to claim 1, characterized in that: The service feature set includes service ID, input parameters, output parameters, main resources, detection scheme associated nodes, status, notes, number of calls, operation indicators, operation data volume and adjustment plan; the detection scheme set includes scheme ID, service ID, scheme type, scheme parameters, detection results, sampling scheme, processing engine, detection interval, last execution time, memory area and status.

3. A service quality inspection method based on dynamic adaptation according to claim 1, characterized in that: The method of sampling resources using the operating data volume and the sampling scheme to obtain sampled resources is as follows: multiplying the operating data volume and the sampling scheme to obtain a sampling quantity, and randomly extracting a sampling quantity of sampling resources from the main resources of the service.

4. The service quality inspection method based on dynamic adaptation according to claim 1, characterized in that: The S3 specifically includes the following steps: S31: using the detection scheme association node to traverse the associated detection scheme set, obtain the scheme type, detection interval, and processing engine, use the detection interval to divide the sampling resource into a plurality of sample data, call the processing engine for processing on each of the plurality of sample data, and obtain a detection result set; S32: Checking the security of the service, wherein the checking the security of the service includes verifying whether the output parameters of the service are consistent with the actual returned results.

5. The service quality inspection method based on dynamic adaptation according to claim 4, characterized in that: The security detection of the service also includes preventing crawling of all resources. The algorithm used to prevent crawling of all resources is that the single return quantity multiplied by the number of calls is greater than the current data volume of the resource.

6. A service quality inspection method based on dynamic adaptation according to claim 1, characterized in that: The algorithm for analyzing the detection result set using the SCA method and calculating the service operation index is: operation index result = SCA (array [detection environment, detection result], resource data volume, deviation rate).

7. The service quality inspection method based on dynamic adaptation according to claim 1, characterized in that: The S4 specifically includes the following steps: S41: Analyze the detection result set using the SCA method and calculate the service operation index, and store the calculated operation index in the service feature table; S42: Cleaning the quality inspection area. Specifically, the cleaning of the quality inspection area includes regularly checking whether the interval between the last operation time of the retrieval solution and the current system time exceeds a set threshold. If the interval exceeds the set threshold, the cache area named memory area is automatically cleaned to release unused resources.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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