A cloud-based voice service full-link stress testing method and related equipment

Abstract

This application discloses a cloud-based full-link stress testing method and related equipment for voice services, relating to the field of cloud testing. The method includes: obtaining historical voice service usage pattern statistics of actual vehicles associated with the target cloud, wherein the historical voice service usage pattern statistics include statistical time period information, number of vehicles calling the voice service information, and average number of times each vehicle calls the voice service information; controlling a simulated vehicle to establish a connection with the target cloud; controlling the simulated vehicle to perform full-link stress testing of the voice service on the target cloud based on the historical voice service usage pattern statistics, and obtaining the test results.

Description

Technical Field

[0001] This specification relates to the field of cloud testing; more specifically, this application relates to a cloud-based end-to-end stress testing method and related equipment for voice services. Background Technology

[0002] With the increasing popularity of smart cockpits, users are becoming more accustomed to accessing vehicle services through voice interaction. Currently, the voice services provided by smart cockpits include the final implementation of voice commands within the vehicle. However, there is a lack of load testing methods for the entire end-to-end testing process. Existing load tests for voice services often only support voice recognition and understanding, while end-to-end testing based on real vehicles is often lacking due to high costs.

[0003] Therefore, it is necessary to propose a cloud-based end-to-end stress testing method for voice services. Summary of the Invention

[0004] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] Firstly, this application proposes a cloud-based end-to-end stress testing method for voice services, including:

[0006] Obtain statistical information on the historical voice service usage patterns of the target cloud-connected vehicles. This statistical information includes the statistical time period, the number of vehicles calling the voice service, and the average number of times each vehicle calls the voice service.

[0007] Control the simulated vehicle to establish a connection with the aforementioned target cloud;

[0008] The simulated vehicle is controlled to perform a full-link stress test on the target cloud based on the statistical information of historical voice service usage patterns, and the test results are obtained.

[0009] In one feasible implementation, it further includes:

[0010] Compile all historical voice service usage information for the aforementioned target cloud platforms;

[0011] Based on the identity information of the simulated vehicle, exclude the simulated vehicle voice service usage information from all the above historical voice service usage information to obtain the actual vehicle historical voice service usage information.

[0012] Obtain the time periods of sporadic phenomena, which include periods of severe weather and periods of major events;

[0013] The voice service usage information corresponding to the time periods of the aforementioned occasional occurrences was removed from the aforementioned actual vehicle historical voice service usage information, and regularity statistics were performed to obtain the aforementioned actual vehicle historical voice service usage pattern statistics information.

[0014] In one feasible implementation, the aforementioned statistical time period information is determined based on traffic congestion time period information, and the time period lengths corresponding to the aforementioned statistical time period information are different.

[0015] In one feasible implementation, it also includes

[0016] Based on the above statistical information on the historical usage patterns of vehicle voice services, the operational pressure and time patterns of the target cloud are determined.

[0017] Based on the above operational pressure and time pattern information and the above historical voice service usage pattern statistics, a full-link stress test of the voice service was conducted on the above target cloud to reduce the impact of the above full-link stress test on the actual operation of the target cloud.

[0018] In one feasible implementation, the aforementioned historical voice service usage pattern statistics also include vehicle model call pattern information, network connection condition information, and abnormal behavior information. The network connection condition information includes 4G connection conditions, 5G connection conditions, and weak signal connection conditions. The aforementioned abnormal behavior information includes a large number of service request behaviors, network jitter behaviors, and disconnection and reconnection behaviors.

[0019] In one feasible implementation, it further includes:

[0020] A voice service request is generated based on the above vehicle model call pattern information;

[0021] Network conditions are randomly assigned based on the above network connectivity conditions;

[0022] During peak hours, a large number of service request behaviors are introduced, and network jitter and disconnection and reconnection behaviors are randomly inserted to conduct additional stress tests on the entire voice service chain and obtain test results under additional conditions.

[0023] In one feasible implementation, the above test results include one or more of QPS, TPS, response time, memory and CPU utilization.

[0024] Secondly, this application proposes a cloud-based end-to-end stress testing device for voice services, comprising:

[0025] The acquisition unit is used to acquire the historical voice service usage pattern statistics of the actual vehicles associated with the target cloud. The historical voice service usage pattern statistics include the statistical time period information, the number of vehicles calling the voice service information, and the average number of times each vehicle calls the voice service information.

[0026] The control unit is used to control the simulated vehicle to establish a connection with the aforementioned target cloud.

[0027] The testing unit is used to control the simulated vehicle to perform full-link stress testing of the voice service on the target cloud based on the statistical information of historical voice service usage patterns, and to obtain the test results.

[0028] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the cloud-based voice service end-to-end stress testing method as described in any of the first aspects above.

[0029] Fourthly, this application also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the cloud-based full-link stress testing method for voice services according to any one of the first aspects.

[0030] In summary, the cloud-based full-link stress testing method for voice services in this application includes: obtaining historical voice service usage pattern statistics of actual vehicles associated with the target cloud, wherein the historical voice service usage pattern statistics include statistical time period information, number of vehicles calling the voice service information, and average number of voice service calls per vehicle information; controlling the simulated vehicle to establish a connection with the target cloud; controlling the simulated vehicle to perform a full-link stress test on the target cloud based on the historical voice service usage pattern statistics, and obtaining the test results. The cloud-based full-link stress testing method for voice services proposed in this application makes the test more closely resemble actual usage by simulating vehicle voice service requests based on historical usage patterns. This method covers usage in different time periods, simulates various network conditions and abnormal behaviors, and comprehensively evaluates the performance and stability of cloud services. Through automated simulation and real-time monitoring, test results are quickly obtained, and system bottlenecks and potential problems are identified. By monitoring key performance indicators such as QPS, TPS, response time, memory and CPU utilization, the performance and stability of cloud services are comprehensively evaluated.

[0031] The cloud-based full-link stress testing method for voice services proposed in this application, along with other advantages, objectives, and features of this application, will be partly apparent from the following description and partly understood by those skilled in the art through research and practice of this application. Attached Figure Description

[0032] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0033] Figure 1 A schematic diagram illustrating the process of a cloud-based end-to-end stress testing method for voice services, provided for an embodiment of this application;

[0034] Figure 2 A schematic diagram of a cloud-based voice service end-to-end stress testing device provided for embodiments of this application;

[0035] Figure 3 This is a schematic diagram of an electronic device structure for end-to-end stress testing of cloud-based voice services, provided as an embodiment of this application. Detailed Implementation

[0036] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will now be clearly and completely described in conjunction with the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0037] Please see Figure 1 This is a flowchart illustrating a cloud-based end-to-end stress testing method for voice services, provided in an embodiment of this application. Specifically, it may include:

[0038] S110. Obtain the historical voice service usage pattern statistics of the actual vehicles associated with the target cloud. The historical voice service usage pattern statistics include the statistical time period information, the number of vehicles calling the voice service information, and the average number of times each vehicle calls the voice service information.

[0039] For example, statistical analysis of actual vehicle usage of the voice service across different time periods is conducted to simulate real-world usage scenarios. Historical data is collected and analyzed to determine patterns in vehicle voice service usage across different time periods (e.g., hours, days, weeks, months). This includes, for example, the frequency of use and the number of requests within a specific time period. The number of vehicles that invoked the voice service within each time period is then tallied. Finally, the average number of voice service calls per vehicle within a given time period is calculated.

[0040] For example, if the statistical period is in hours, 500 vehicles use the voice service every day, and each vehicle calls the voice service an average of 20 times per day.

[0041] S120: Control the simulated vehicle to establish a connection with the aforementioned target cloud;

[0042] For example, simulate the connection between a real vehicle and a cloud service for comprehensive load testing. Create virtual vehicles by writing scripts or using simulation tools. These vehicles can simulate the behavior of real vehicles and establish connections with the target cloud. Set connection parameters based on historical statistics, such as connection frequency and concurrent connections.

[0043] For example, by accessing the cloud service providing the vehicle simulation environment via WebSocket, after the initial connection is established, the cloud service transmits the parameter information needed to create a new simulated vehicle. The cloud service then constructs an initial cloud-based simulated vehicle based on the transmitted information and returns a unique identifier for that vehicle to the local machine. At this point, a vehicle simulation has been created. Subsequently, audio files can be transmitted via the connection to simulate voice interaction scenarios such as in-vehicle wake-up and recognition.

[0044] S130. Control the simulated vehicle to perform a full-link stress test on the target cloud based on the statistical information of historical voice service usage patterns, and obtain the test results.

[0045] For example, the simulated vehicle sends voice service requests according to historical usage patterns. A wake-up audio is received to implement the wake-up scenario, and a specific voice command, such as "open the window," is received to implement the scenario where the user enters a voice command after being woken up. The number of requests and the time interval between each request for each vehicle during the test will be based on the average plus a small random offset. These requests should simulate real-world usage, including but not limited to request frequency, request content, and network conditions. During the load test, the response time, success rate, and resource usage (such as CPU, memory, and bandwidth) on the cloud are monitored and recorded in real time.

[0046] In summary, the cloud-based end-to-end stress testing method for voice services proposed in this application simulates vehicle voice service requests based on historical usage patterns, making the test more closely resemble real-world usage. This method covers usage scenarios across different time periods, simulates various network conditions and abnormal behaviors, and comprehensively evaluates the performance and stability of cloud services. Through automated simulation and real-time monitoring, test results are quickly obtained, identifying system bottlenecks and potential problems. By monitoring key performance indicators such as QPS, TPS, response time, memory and CPU utilization, the performance and stability of cloud services are comprehensively evaluated.

[0047] In some examples, it also includes:

[0048] Compile all historical voice service usage information for the aforementioned target cloud platforms;

[0049] Based on the identity information of the simulated vehicle, exclude the simulated vehicle voice service usage information from all the above historical voice service usage information to obtain the actual vehicle historical voice service usage information.

[0050] Obtain the time periods of sporadic phenomena, which include periods of severe weather and periods of major events;

[0051] The voice service usage information corresponding to the time periods of the aforementioned occasional occurrences was removed from the aforementioned actual vehicle historical voice service usage information, and regularity statistics were performed to obtain the aforementioned actual vehicle historical voice service usage pattern statistics information.

[0052] For example, extract all historical voice service usage records from cloud logs and databases. These records should include detailed information for each request, such as the timestamp of the request, the vehicle ID that issued the request, the type of request, and other relevant information. Ensure that all possible usage data is obtained to provide a comprehensive data foundation for subsequent analysis and simulation.

[0053] After obtaining all historical voice service usage information, it is necessary to identify and exclude usage records of simulated vehicles based on vehicle IDs. This ensures that the dataset only contains usage information from actual vehicles, thus avoiding interference from simulated vehicle data in the statistical results. By filtering out records from simulated vehicles, we can obtain a more accurate historical usage dataset of actual vehicles.

[0054] Use the weather service API to query weather records from the past year to identify periods of severe weather. Simultaneously, record periods of significant events using news data or specific event logs. These periods will be marked as anomalies that may significantly impact the use of voice services.

[0055] The abnormal time periods have been identified in the steps described above. Voice service usage records within these time periods are then removed from the dataset by comparing timestamps. For example, if a request's timestamp falls within a identified period of severe weather, that record will be deleted.

[0056] After cleaning the data, statistical analysis is performed to identify patterns. For example, the average number of requests per hour and the average number of requests per vehicle can be calculated. These statistics can then be used to generate a model reflecting normal usage patterns. For instance, the analysis might show peak usage periods (e.g., 8 AM to 10 AM) and off-peak usage periods (e.g., late at night).

[0057] In some examples, the above statistical time period information is determined based on traffic congestion time period information, and the time period lengths corresponding to the above statistical time period information are different.

[0058] For example, the time period is not fixed; the time period information can be determined based on traffic congestion periods, with shorter time periods occurring during periods of greater congestion. Instead, it is set based on the duration of peak and trough usage. For instance, if vehicle usage is the same from 11 PM to 5 AM, that's one time period; if there's a peak usage period from 7 AM to 9 AM, that's also one time period. This leads to a test scenario similar to the following:

[0059]

[0060]

[0061] In some examples, it also includes:

[0062] Based on the above statistical information on the historical usage patterns of vehicle voice services, the operational pressure and time patterns of the target cloud are determined.

[0063] Based on the above operational pressure and time pattern information and the above historical voice service usage pattern statistics, a full-link stress test of the voice service was conducted on the above target cloud to reduce the impact of the above full-link stress test on the actual operation of the target cloud.

[0064] For example, the number of voice service requests for each time period (e.g., hourly) is extracted from historical data. The average number of requests R(t) and the peak number of requests Rmax(t) for each time period are calculated.

[0065] Establish a time-varying model P(t) to describe the operational pressure of the target cloud at various time periods. This model can be used to predict the operational pressure within any given time period.

[0066] For example: P(t) = α*R(t) + β*Rmax(t)

[0067] Here, α and β are weighting parameters used to adjust the impact of average and peak requests.

[0068] Based on the time-varying model P(t), the load for stress testing in each time period is determined. For example, during high-load periods (such as peak hours), the load for stress testing is reduced to avoid interference with actual services; during low-load periods, the load for stress testing can be appropriately increased.

[0069] Distribute the simulated vehicle's requests evenly across multiple time periods to avoid sending a large number of requests concentrated in one period. For example, if the model shows that 8 pm to 10 pm is a high-load period, the load test requests during this period can be evenly distributed across the preceding and following time periods.

[0070] Extract the number of requests per hour from historical data. Assume that the average number of requests is 1000 during a certain period (e.g., from 8 am to 9 am every day), and the peak number of requests is 1500.

[0071] Establish a time-varying model: P(t) = 0.7 * R(t) + 0.3 * Rmax(t); For 8:00 AM to 9:00 AM: P(8-9) = 0.7 * 1000 + 0.3 * 1500 = 1150

[0072] Perform end-to-end load testing:

[0073] During high-load periods (such as from 8 am to 9 am), reduce the load test, for example, reduce it to 70% of the total load, that is, the load test is: Ltest(8-9)=0.7*1150=805.

[0074] During low-load periods (e.g., 2 PM to 4 PM), increase the load test, for example, to 130% of the total load, i.e., the load test is: Ltest(14-16) = 1.3 * Paverage

[0075] Among them, Paverage's average operating pressure throughout the day.

[0076] In some examples, the aforementioned historical voice service usage statistics also include vehicle model call patterns, network connection conditions, and abnormal behavior information. The network connection conditions include 4G connection conditions, 5G connection conditions, and weak signal connection conditions. The abnormal behavior information includes a large number of service request behaviors, network jitter behaviors, and disconnection and reconnection behaviors.

[0077] For example, statistics were compiled on the frequency of voice service calls for different car models at different times. For instance, statistics show that car model A has an average of 20 calls per day from 8:00 to 9:00 AM, while car model B has an average of 15 calls per day from 6:00 to 7:00 PM.

[0078] Record the network connection conditions for voice service requests, including 4G, 5G, and weak signal connection conditions. Statistically analyze the request success rate and latency under different network conditions. For example, the success rate for a 4G network is 95%, with a latency of 50ms; the success rate for a 5G network is 99%, with a latency of 10ms; and the success rate under weak signal conditions is 70%, with a latency of 200ms.

[0079] Record the occurrence of abnormal behaviors, including a large number of service requests, network jitter, and disconnection / reconnection. Statistically analyze the frequency and impact of these abnormal behaviors. For example, five random network jitter events occur daily, each lasting 30 seconds; a large number of service requests occurs weekly, lasting 10 minutes. Based on the statistical results, establish an abnormal behavior model.

[0080] By comprehensively analyzing vehicle dispatch patterns, network connectivity conditions, and abnormal behavior information, a model of operational pressure and time patterns is established. The model can be represented as:

[0081]

[0082] in:

[0083] R i (t) represents the average number of times vehicle model i is called during time period t.

[0084] N j (t) is the influencing factor of network condition j in time period t (such as request success rate, latency).

[0085] E k (t) represents the occurrence of abnormal behavior k within time period t.

[0086] α i β j and γ k These are the corresponding weight parameters.

[0087] In some examples, it also includes:

[0088] A voice service request is generated based on the above vehicle model call pattern information;

[0089] Network conditions are randomly assigned based on the above network connectivity conditions;

[0090] During peak hours, a large number of service request behaviors are introduced, and network jitter and disconnection and reconnection behaviors are randomly inserted to conduct additional stress tests on the entire voice service chain and obtain test results under additional conditions.

[0091] For example, all voice service usage records can be extracted from the database, and a comprehensive statistical analysis can be performed to obtain vehicle model call patterns, network connection conditions, and abnormal behavior information.

[0092] Based on a vehicle model call pattern model, voice service requests consistent with real-world usage are generated hourly. Network conditions are randomly assigned to each request based on a network connectivity model. Additional load testing is conducted by introducing abnormal behavior, such as generating a large number of service requests per minute during peak hours (8:00 AM to 9:00 AM) to test the cloud service's processing capacity. Network jitter and disconnection / reconnection events are inserted at random time intervals to test the stability of the cloud service. The response time, success rate, and resource usage of the cloud service are recorded during the load testing. The test results are analyzed to evaluate the performance and stability of the cloud service.

[0093] This application's embodiments cover various factors such as vehicle model, network, and abnormal behavior, ensuring the comprehensiveness of the simulation and testing. Through detailed data analysis and modeling, the simulation process is made as close as possible to real-world usage, improving testing accuracy. By rationally scheduling load and time for stress testing, the testing process is ensured not to interfere with actual service, improving testing effectiveness.

[0094] In some examples, the test results mentioned above include one or more of QPS, TPS, response time, memory and CPU utilization.

[0095] For example, during load testing, the response time, success rate, and resource usage (such as CPU, memory, and bandwidth) of the cloud service are recorded in real time. Based on the analysis of the test results, the performance of the cloud service under different conditions is evaluated. QPS (Queries Per Second) measures the system's processing capacity under high load. TPS (Transactions Per Second) measures the number of transactions processed per second, evaluating the system's performance when handling complex transactions. Response time is the time required from request to response, reflecting the system's responsiveness and user experience. Memory usage is the proportion of memory resources used by the system during operation, evaluating the system's resource consumption and memory management efficiency. CPU usage is the proportion of CPU resources used by the system during operation, evaluating the system's processing capacity and CPU resource utilization.

[0096] Understandably, the final output results can be displayed based on the time period and overall time of the test plan.

[0097] like Figure 2 As shown, this application proposes a cloud-based end-to-end stress testing device for voice services, comprising:

[0098] The acquisition unit 21 is used to acquire the historical voice service usage pattern statistics of the actual vehicles associated with the target cloud. The historical voice service usage pattern statistics include the statistical time period information, the number of vehicles calling the voice service information, and the average number of times each vehicle calls the voice service information.

[0099] Control unit 22 is used to control the simulated vehicle to establish a connection with the aforementioned target cloud;

[0100] Test unit 23 is used to control the simulated vehicle to perform full-link stress test on the target cloud based on the statistical information of historical voice service usage patterns, and to obtain the test results.

[0101] A cloud-based end-to-end stress testing device for voice services can also perform the following methods:

[0102] Compile all historical voice service usage information for the aforementioned target cloud platforms;

[0103] Based on the identity information of the simulated vehicle, exclude the simulated vehicle voice service usage information from all the above historical voice service usage information to obtain the actual vehicle historical voice service usage information.

[0104] Obtain the time periods of sporadic phenomena, which include periods of severe weather and periods of major events;

[0105] The voice service usage information corresponding to the time periods of the aforementioned occasional occurrences was removed from the aforementioned actual vehicle historical voice service usage information, and regularity statistics were performed to obtain the aforementioned actual vehicle historical voice service usage pattern statistics information.

[0106] In some examples, the above statistical time period information is determined based on traffic congestion time period information, and the time period lengths corresponding to the above statistical time period information are different.

[0107] In some examples, it also includes

[0108] Based on the above statistical information on the historical usage patterns of vehicle voice services, the operational pressure and time patterns of the target cloud are determined.

[0109] Based on the above operational pressure and time pattern information and the above historical voice service usage pattern statistics, a full-link stress test of the voice service was conducted on the above target cloud to reduce the impact of the above full-link stress test on the actual operation of the target cloud.

[0110] In some examples, the aforementioned historical voice service usage statistics also include vehicle model call patterns, network connection conditions, and abnormal behavior information. The network connection conditions include 4G connection conditions, 5G connection conditions, and weak signal connection conditions. The abnormal behavior information includes a large number of service request behaviors, network jitter behaviors, and disconnection and reconnection behaviors.

[0111] In some examples, it also includes:

[0112] A voice service request is generated based on the above vehicle model call pattern information;

[0113] Network conditions are randomly assigned based on the above network connectivity conditions;

[0114] During peak hours, a large number of service request behaviors are introduced, and network jitter and disconnection and reconnection behaviors are randomly inserted to conduct additional stress tests on the entire voice service chain and obtain test results under additional conditions.

[0115] In some examples, the test results mentioned above include one or more of QPS, TPS, response time, memory and CPU utilization.

[0116] like Figure 3 As shown, this application embodiment also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of any of the above-mentioned methods for end-to-end stress testing of cloud-based voice services.

[0117] Since the electronic device described in this embodiment is the device used to implement a cloud-based voice service end-to-end stress testing device in this application embodiment, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in this application embodiment. Therefore, how the electronic device implements the method in this application embodiment will not be described in detail here. Any device used by those skilled in the art to implement the method in this application embodiment is within the scope of protection of this application.

[0118] In practical implementation, when the computer program 311 is executed by the processor, it can achieve the following: Figure 1 Any of the corresponding implementation methods in the embodiments.

[0119] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0120] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0122] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0124] This application also provides a computer program product, which includes computer software instructions. When the computer software instructions are run on a processing device, the processing device executes the cloud-based voice service end-to-end stress test process in the corresponding embodiment.

[0125] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0126] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0127] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0128] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0129] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0130] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0131] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A cloud-based end-to-end stress testing method for voice services, characterized in that, include: Obtain statistical information on the historical voice service usage patterns of the target cloud-connected vehicles. The historical voice service usage pattern statistical information includes statistical time period information, number of vehicles calling the voice service information, average number of times each vehicle calls the voice service information, vehicle model calling pattern information, network connection condition information, and abnormal behavior information. The abnormal behavior information includes a large number of service request behaviors, network jitter behaviors, and disconnection and reconnection behaviors. Control the simulated vehicle to establish a connection with the target cloud; The simulated vehicle is controlled to perform a full-link stress test on the target cloud based on the historical voice service usage pattern statistics, and the test results are obtained. A voice service request is generated based on the vehicle model call pattern information; Network conditions are randomly assigned based on the aforementioned network connectivity conditions; During peak hours, a large number of service request behaviors are introduced, and network jitter behaviors and disconnection and reconnection behaviors are randomly inserted to conduct additional stress tests on the entire voice service link and obtain test results under additional conditions.

2. The cloud-based end-to-end stress testing method for voice services according to claim 1, characterized in that, Also includes: Collect all historical voice service usage information of the target cloud; Based on the identity information of the simulated vehicle, the simulated vehicle voice service usage information in all historical voice service usage information is excluded to obtain the actual vehicle historical voice service usage information. The time periods of sporadic phenomena are obtained, including periods of severe weather and periods of major events; The voice service usage information corresponding to the time period of the occasional phenomenon is removed from the actual vehicle historical voice service usage information, and regularity statistics are performed to obtain the statistical information on the usage patterns of the actual vehicle historical voice service.

3. The cloud-based end-to-end stress testing method for voice services according to claim 1, characterized in that, The statistical time period information is determined based on the traffic congestion time period information, and the time period lengths corresponding to the statistical time period information are different.

4. The cloud-based end-to-end stress testing method for voice services according to claim 1, characterized in that, Also includes: Based on the statistical information on the historical voice service usage patterns of the vehicle, the operational pressure and time patterns of the target cloud are determined. Based on the operational pressure and time pattern information and the historical voice service usage pattern statistics, a full-link stress test of the voice service is performed on the target cloud to reduce the impact of the full-link stress test on the actual operation of the target cloud.

5. The cloud-based end-to-end stress testing method for voice services according to claim 1, characterized in that, The network connection condition information includes 4G connection conditions, 5G connection conditions, and weak signal connection conditions.

6. The cloud-based end-to-end stress testing method for voice services according to any one of claims 1 to 5, characterized in that, The test results include one or more of QPS, TPS, response time, memory and CPU utilization.

7. A cloud-based end-to-end stress testing device for voice services, characterized in that, include: The acquisition unit is used to acquire the historical voice service usage pattern statistics of the actual vehicles associated with the target cloud. The historical voice service usage pattern statistics include statistical time period information, number of vehicles calling the voice service information, average number of times each vehicle calls the voice service information, vehicle model calling pattern information, network connection condition information, and abnormal behavior information. The abnormal behavior information includes a large number of service request behaviors, network jitter behaviors, and disconnection and reconnection behaviors. The control unit is used to control the simulated vehicle to establish a connection with the target cloud. The testing unit is used to control the simulated vehicle to perform a full-link stress test on the target cloud based on the historical voice service usage pattern statistics, and to obtain the test results; The testing unit is also used to generate voice service requests based on the vehicle model calling pattern information; randomly allocate network conditions based on the network connection conditions; introduce the large number of service request behaviors during peak periods, and randomly insert the network jitter behavior and the disconnection and reconnection behavior to perform additional stress testing on the entire voice service link, and obtain test results under additional conditions.

8. An electronic device, comprising: The memory and processor are characterized in that the processor is used to implement the steps of the cloud-based voice service end-to-end stress testing method as described in any one of claims 1-6 when executing a computer program stored in the memory.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the cloud-based voice service end-to-end stress testing method as described in any one of claims 1-6.

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