Equipment performance detection method and device based on programming teaching program

Abstract

The invention provides an equipment performance detection method and device based on a programming teaching program, and the method comprises the steps: installing the programming teaching program on target terminal equipment, providing an editing page for the programming teaching program, and enabling the editing page to comprise at least one building block and at least one role which are configured in advance, the method comprises the following steps: when at least one role is controlled by at least one building block and runs for a preset time period, acquiring movement data corresponding to the at least one role; acquiring equipment parameters of the target terminal equipment and performance parameters of the target terminal equipment in the preset time period; and determining the performance level of the target terminal equipment according to the mobile data corresponding to the equipment parameters and the performance parameters.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method and apparatus for detecting device performance based on a programming teaching program. Background Art

[0002] At present, Scratch (a global graphical programming tool for children) is used as a programming teaching program in programming education scenarios. However, the running performance of the programming teaching program directly affects the learning experience and teaching effect, and thus it is necessary to perform performance testing on the installation equipment of the programming teaching program to identify whether the installation equipment can meet the normal operation requirements of the programming teaching program. However, traditional performance testing tools often require professional staff to operate and are not suitable for ordinary educational users; and corresponding performance testing tools need to be configured for different systems and different terminal types, which makes it difficult for performance testing tools to uniformly collect and analyze data; and it is difficult to adapt to different course content and teaching needs for targeted performance testing. As a result, it is difficult for educational institutions and individual users to choose equipment suitable for running programming teaching programs, and it is impossible to discover and solve performance problems in a timely manner, which affects the teaching quality and learning effects. Summary of the invention

[0003] In view of this, the purpose of the present application is to at least provide a device performance detection method and device based on a programming teaching program, by pre-configuring the building blocks used for detection on the editing page of the programming teaching program, so as to control at least one character on the editing page to run in the manner indicated by the building blocks, and detect the movement data of at least one character after running for a preset time period, and collect the device parameters of the target terminal device and the performance parameters of the target terminal device within the preset time period, and then determine the performance level of the target terminal device according to the device parameters, movement data and performance parameters corresponding to the target terminal device, thereby solving the technical problem in the prior art that it is difficult to timely discover the performance status of the target terminal device installed with the programming teaching program, and achieving the technical effect of improving the convenience of performance detection of the target terminal device and discovering the performance problems of the target terminal device in advance.

[0004] This application mainly includes the following aspects:

[0005] In a first aspect, an embodiment of the present application provides a device performance detection method based on a programming teaching program, wherein a programming teaching program is installed on a target terminal device and an editing page for the programming teaching program is provided, wherein the editing page includes at least one pre-configured building block and at least one role, and the method includes: when the at least one role is controlled by the at least one building block and runs for a preset time period, obtaining movement data corresponding to the at least one role respectively; obtaining device parameters of the target terminal device and performance parameters of the target terminal device within the preset time period; determining the performance level of the target terminal device according to the movement data and the performance parameters corresponding to the device parameters.

[0006] Optionally, determining the performance level of the target terminal device based on the mobile data and the performance parameters corresponding to the device parameters includes: inputting the device parameters, the mobile data and the performance parameters into a pre-trained machine learning model to obtain the performance level of the target terminal device predicted by the machine learning model; or, determining the device type to which the device parameters belong, and then determining a target mobile data interval corresponding to the device type into which the mobile data falls and a target performance data interval corresponding to the device type into which the performance parameters fall, so as to determine the performance level of the target terminal device through the target mobile data interval and the target performance data interval.

[0007] Optionally, the machine learning model is trained in the following manner: obtaining device parameters, mobile data and performance parameters corresponding to multiple terminal devices, and performing data preprocessing; marking the performance level of each terminal device after data preprocessing; using the device parameters, mobile data and performance parameters corresponding to each terminal device after data preprocessing as input data of the machine learning model, and using the performance level as a data label to train the machine learning model.

[0008] Optionally, the device parameters include device type, and the mobile data interval and performance data interval are determined in the following manner: for each device type, cluster analysis is performed on the mobile data and performance parameters corresponding to multiple terminal devices under the device type to obtain various classification clusters, and the performance levels corresponding to each classification cluster are determined by comparing the mobile data and performance parameters corresponding to the center points of each classification cluster; based on the mobile data and performance parameters corresponding to the terminal devices in each classification cluster, the mobile data interval and performance data interval corresponding to each performance level under the device type are determined.

[0009] Optionally, the device parameters include device type, and the mobile data interval and performance data interval are determined in the following manner: for each device type, the mobile data and performance parameters corresponding to multiple terminal devices under the device type are sorted, and based on the sorting results, the mobile data interval and performance data interval corresponding to each performance level under the device type are determined.

[0010] Optionally, the performance parameter includes: at least one basic performance parameter collected within the preset time period, and / or a composite performance parameter calculated by using a target performance parameter collected within the preset time period and the movement data.

[0011] Optionally, the method further includes: when the performance level of the target terminal device does not meet the running conditions of the programming teaching program, displaying corresponding prompt information on the editing page.

[0012] In the second aspect, an embodiment of the present application also provides a device performance detection device based on a programming teaching program, which installs a programming teaching program on a target terminal device and provides an editing page for the programming teaching program, wherein the editing page includes at least one pre-configured building block and at least one role, and the device includes: a first acquisition module, used to obtain movement data corresponding to the at least one role when the at least one role is controlled by the at least one building block and runs for a preset time period; a second acquisition module, used to obtain device parameters of the target terminal device and performance parameters of the target terminal device within the preset time period; a determination module, used to determine the performance level of the target terminal device based on the movement data and the performance parameters corresponding to the device parameters.

[0013] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the device performance detection method based on the programming teaching program described in the first aspect or any possible implementation manner of the first aspect.

[0014] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the device performance detection method based on a programming teaching program described in the above-mentioned first aspect or any possible implementation manner of the first aspect are executed.

[0015] The embodiment of the present application provides a device performance detection method and apparatus based on a programming teaching program, wherein the programming teaching program is installed on a target terminal device and an editing page for the programming teaching program is provided, wherein the editing page includes at least one pre-configured building block and at least one role, wherein the method includes: when the at least one role is controlled by the at least one building block and runs for a preset time period, obtaining the movement data corresponding to the at least one role respectively; obtaining the device parameters of the target terminal device and the performance parameters of the target terminal device within the preset time period; and determining the performance level of the target terminal device according to the movement data and the performance parameters corresponding to the device parameters. By pre-configuring the building blocks used for detection on the editing page of the programming teaching program, it is convenient to control at least one role on the editing page to run in the manner indicated by the building blocks, and to detect the movement data of at least one role after running for a preset time period, and to collect the device parameters of the target terminal device and the performance parameters of the target terminal device within the preset time period, and then to determine the performance level of the target terminal device according to the device parameters, movement data and performance parameters corresponding to the target terminal device, so as to solve the technical problem that it is difficult to timely discover the performance status of the target terminal device on which the programming teaching program is installed in the prior art, and to achieve the technical effect of improving the convenience of performance detection of the target terminal device and discovering the performance problems of the target terminal device in advance.

[0016] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a device performance detection method based on a programming teaching program provided in an embodiment of the present application is shown.

[0019] Figure 2 A functional module diagram of a device performance detection device based on a programming teaching program provided in an embodiment of the present application is shown.

[0020] Figure 3 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0021] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art, under the guidance of the content of the present application, can add one or more other operations to the flowchart, or remove one or more operations from the flowchart.

[0022] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0023] In the prior art, traditional performance testing tools often require professional knowledge and are not suitable for ordinary educational users. That is, it is difficult for parents or teachers to use performance testing tools to perform performance testing on terminal devices that install programming teaching programs. In addition, traditional performance testing tools cannot accurately reflect the actual operation of the installed Scratch program. Some terminal device systems may not be able to install performance testing tools, and lack a unified way to collect and analyze performance data from different devices. It is difficult to provide instant performance evaluation results. It is difficult to conduct targeted testing for different course content and teaching content, and it is impossible to adapt to different course content and teaching needs. As a result, educational institutions and individual users may find that the terminal device cannot run the Scratch program during class, and they cannot solve the performance problem in time, which affects the teaching quality and learning effect.

[0024] Based on this, the embodiment of the present application provides a device performance detection method and apparatus based on a programming teaching program, by pre-configuring building blocks for detection on the editing page of the programming teaching program, so as to control at least one role on the editing page to run in the manner indicated by the building blocks, and detect the movement data of at least one role after running for a preset time period, and collect the device parameters of the target terminal device and the performance parameters of the target terminal device within the preset time period, and then determine the performance level of the target terminal device according to the device parameters, movement data and performance parameters corresponding to the target terminal device, solve the technical problem that it is difficult to timely discover the performance status of the target terminal device installed with the programming teaching program in the prior art, and achieve the technical effect of improving the convenience of performance detection of the target terminal device and discovering the performance problems of the target terminal device in advance, which are specifically as follows:

[0025] See also Figure 1 , Figure 1 This is a flow chart of a device performance detection method based on a programming teaching program provided in an embodiment of the present application. Figure 1 As shown, the device performance detection method based on the programming teaching program provided in the embodiment of the present application includes the following steps:

[0026] S101: When the at least one character is controlled by the at least one building block and runs for a preset time period, movement data corresponding to the at least one character is acquired.

[0027] Among them, a programming teaching program is installed on the target terminal device and an editing page for the programming teaching program is provided, wherein the editing page includes at least one pre-configured building block and at least one role. The target terminal device refers to a terminal device on which the programming teaching program is pre-installed. For example, the terminal device may be a terminal device with a display function such as a smart phone, a desktop computer, a tablet computer, a smart watch, smart glasses, a smart TV, a laptop computer, an AR (augmented reality), a VR (virtual reality) device, etc. The editing page can be understood as an integrated development environment (IDE) for running the programming teaching program.

[0028] After the user logs in to the programming teaching program, the target terminal device displays the editing page of the programming teaching program, which includes an editing area, a role area, and a stage area. The role area is used to display multiple roles of the programming teaching program; the editing area is used to display pre-configured building blocks and at least one role, and the various parameters in the building blocks are also pre-configured; the stage area is used to control the role to run according to the building blocks in the editing area.

[0029] That is, at least one building block and at least one character controlled by it are pre-configured to reflect the operation status of the target terminal device through the movement data of at least one character, and the frame rate of the target terminal device is measured instead of the movement data. The pre-configured at least one building block and at least one character controlled by it can be specially built for the performance test of the target terminal device, or can be pre-selected from multiple building blocks used in the course.

[0030] Exemplarily, at least one building block is used to control a character to move at a constant speed, and the movement data refers to the distance moved by the character within a preset time period, and the movement distance can be determined by the straight-line distance of the pixel coordinates of the target position on the character before and after the preset time period. Since the Scratch program controls the movement of the character at a constant speed through program loops, if the performance of the target terminal device is poor, the execution time of each loop will increase, resulting in a reduction in the distance moved by the character within the same time, similar to the jerky animation caused by a drop in frame rate.

[0031] The preset time period can be represented by a building block, or it can be pre-configured to directly obtain the movement data after the preset time period. Regardless of the target terminal device, at least one building block is used to control the uniform motion of the character, so there is no need to develop performance testing tools according to the system type of the target terminal device, and there is no need to consider whether the target terminal device is a mobile terminal or an electric power terminal.

[0032] S102: Acquire device parameters of the target terminal device and performance parameters of the target terminal device within the preset time period.

[0033] Specifically, the device parameters of the target terminal device and its performance parameters within a preset time period are obtained by setting an API interface. The performance parameters include: at least one basic performance parameter collected within the preset time period, and / or a composite performance parameter calculated by the target performance parameter collected within the preset time period and the mobile data.

[0034] Among them, device parameters are used to reflect the configuration and system of the terminal device. Device parameters include device type, which includes operating system information (such as iOS system, Android system, Windows system, etc.) and terminal type (such as computer, notebook, tablet and mobile phone, etc.). Device parameters can also include device hardware information (such as CPU model, memory size, GPU graphics processor model, etc.), browser type and version and other parameters.

[0035] Among them, the basic performance parameters include the memory occupancy rate, CPU (Central Processing Unit) usage rate and network delay collected at each moment in a preset time period. The composite performance parameters include at least one of the following: the average memory occupancy rate obtained by averaging the memory occupancy rates at each moment of collection, the average CPU usage rate obtained by averaging the CPU usage rates at each moment of collection, the highest memory occupancy rate determined among the memory occupancy rates at each moment of collection, the highest CPU usage rate determined among the CPU usage rates at each moment of collection, and the CPU efficiency calculated according to the ratio of mobile data to the average CPU usage rate.

[0036] S103: Determine the performance level of the target terminal device according to the mobile data and the performance parameters corresponding to the device parameters.

[0037] Determining the performance level of the target terminal device based on the mobile data and the performance parameters corresponding to the device parameters includes: inputting the device parameters, the mobile data and the performance parameters into a pre-trained machine learning model to obtain the performance level of the target terminal device predicted by the machine learning model; or, determining the device type to which the device parameters belong, and then determining a target mobile data interval corresponding to the device type into which the mobile data falls and a target performance data interval corresponding to the device type into which the performance parameters fall, so as to determine the performance level of the target terminal device through the target mobile data interval and the target performance data interval.

[0038] That is to say, the performance level of the target terminal device corresponding to the device parameters, the mobile data and the performance parameters is predicted through a pre-trained machine learning model, or the target data interval to which the mobile data and the performance parameters belong is determined through the data intervals of each performance level corresponding to the device type to which the device parameters belong, so as to determine the performance level of the target terminal device through the target data interval.

[0039] The machine learning model is trained in the following manner: obtaining device parameters, mobile data and performance parameters corresponding to multiple terminal devices, and performing data preprocessing; marking the performance level of each terminal device after data preprocessing; using the device parameters, mobile data and performance parameters corresponding to each terminal device after data preprocessing as input data of the machine learning model, and using the performance level as a data label to train the machine learning model.

[0040] That is to say, it is necessary to obtain the device parameters, mobile data and performance parameters corresponding to multiple terminal devices respectively. When any of the device parameters, mobile data and performance parameters is abnormal, the device parameters, mobile data and performance parameters corresponding to the terminal device are deleted, and the data is standardized (such as Z-score standard score standardization), or one type of data is unified into the same unit to achieve data preprocessing. Generally, missing data, data out of range, etc. are regarded as abnormal data.

[0041] Furthermore, a model is established through a machine learning algorithm (such as random forest or gradient boosting tree), and the device parameters, mobile data and performance parameters of each terminal device obtained after data preprocessing are used as a training set. The performance level of each terminal device is determined by manual averaging, and the performance level is used as a data label to train the machine learning model, so that the machine learning model can predict the performance level of the terminal device through the device parameters, mobile data and performance parameters of the terminal device.

[0042] Among them, the input device parameters are for the model to consider the device type and make corresponding judgments. Since the running fluency of computers is generally higher than that of mobile phones, it is necessary to prevent a certain type of device from having a low-end performance level, which will cause users of this device type to need to change their devices, thereby causing a large number of users to have a reduced user experience due to increased costs. Therefore, it is necessary to use the device parameters as input data for the model. When manually marking the labels corresponding to the training set of the model, it is necessary to consider the device type to reduce the gap between different device types.

[0043] Alternatively, if the device parameters are not input, the mobile data and performance parameters of different device types can be pre-processed by weighted scoring to reduce the data gap between different systems and different terminal types. Exemplarily, the gap between different device types is reduced by multiplying the mobile data and performance parameters of computer types by a coefficient less than one and / or multiplying the mobile data and performance parameters of mobile phones and tablet types by a coefficient greater than one.

[0044] Among them, the performance level can be divided into high-end, mid-end, and low-end to describe the operation of the terminal device. Exemplarily, for a terminal device with a high performance level, when the terminal device runs a programming teaching program, the frame rate is high, that is, the mobile data is high, the memory occupancy rate is small, the CPU usage rate is small, and the network delay is small; for a terminal device with a mid-end performance level, when the terminal device runs a programming teaching program, the frame rate is between the high-end and the low-end, the memory occupancy rate is between the high-end and the low-end, the CPU usage rate is between the high-end and the low-end, and the network delay is between the high-end and the low-end; for a terminal device with a low performance level, when the terminal device runs a programming teaching program, the frame rate is low, that is, the mobile data is small, the memory occupancy rate is large, the CPU usage rate is large, and the network delay is large.

[0045] For example, after determining that the performance level of the target terminal device is a low-end terminal device, a prompt message can be displayed on the editing page of the target terminal device to remind students to clear the background programs of the target terminal device and only retain the programming teaching program to prevent the low-end terminal device from being unable to carry too many devices and affecting the teaching effect. The corresponding version of the course can also be issued according to the performance of the target terminal.

[0046] The mobile data interval and the performance data interval are determined in the following manner: for each device type, the mobile data and performance parameters corresponding to the multiple terminal devices under the device type are clustered and analyzed to obtain various classification clusters; the performance levels corresponding to the various classification clusters are determined by comparing the mobile data and performance parameters corresponding to the center points of the various classification clusters; based on the mobile data and performance parameters corresponding to the terminal devices in each classification cluster, the mobile data interval and performance data interval corresponding to each performance level under the device type are determined.

[0047] Among them, various device types include Windows system computers, Android system mobile phones, Android system tablets, iOS system mobile phones, iOS system computers, etc., so as to distinguish different device types according to each operating system and its corresponding terminal type.

[0048] That is, the mobile data and performance parameters corresponding to the multiple terminal devices are also preprocessed to remove abnormal data, and the remaining data after removing the abnormal data is standardized to ensure that the data is on the same scale; K-means clustering is performed on the data after data preprocessing, and the sum of squares of deviations within the whole (WCSS) corresponding to the division into 1 to 10 clusters is gradually calculated by the elbow rule, so as to select the cluster where the WCSS decrease speed begins to slow down as the optimal number of clusters. For example, the optimal number of clusters is generally three.

[0049] Based on this, Python's scikit-learn library is used to perform K-means clustering on each device type according to the optimal number of clusters to obtain the center point of each cluster corresponding to each device type. The performance level corresponding to each cluster is determined by comparing the mobile data and performance parameters corresponding to the center point of each cluster. The cluster with the largest mobile data, the smallest memory usage, the smallest CPU usage, and the smallest network delay is regarded as the cluster with the high-end performance level, the cluster with the smallest mobile data, the largest memory usage, the largest CPU usage, and the largest network delay is regarded as the cluster with the low-end performance level, and the remaining cluster is regarded as the cluster with the mid-end performance level. Furthermore, for a cluster with a high-end performance level, the minimum value of the mobile data in the cluster is used as the lower limit value of the mobile data interval corresponding to the high-end performance level, the maximum value of the mobile data in the cluster is used as the upper limit value of the mobile data interval corresponding to the high-end performance level, the minimum value of the memory occupancy rate in the cluster is used as the lower limit value of the memory occupancy rate interval corresponding to the high-end performance level, the maximum value of the memory occupancy rate in the cluster is used as the upper limit value of the memory occupancy rate interval corresponding to the high-end performance level, the minimum value of the CPU usage rate in the cluster is used as the lower limit value of the CPU usage rate interval corresponding to the high-end performance level, the maximum value of the CPU usage rate in the cluster is used as the upper limit value of the CPU usage rate interval corresponding to the high-end performance level, the minimum value of the network delay in the cluster is used as the lower limit value of the network delay interval corresponding to the high-end performance level, and the maximum value of the network delay in the cluster is used as the upper limit value of the network delay interval corresponding to the high-end performance level, thereby obtaining the mobile data interval and performance data interval corresponding to the high-end performance level under the device type. By analogy, the mobile data interval and performance data interval corresponding to different performance levels under different device types are obtained.

[0050] Alternatively, the mobile data interval and performance data interval are determined in the following manner: for each device type, the mobile data and performance parameters corresponding to multiple terminal devices under the device type are sorted, and based on the sorting results, the mobile data interval and performance data interval corresponding to each performance level under the device type are determined.

[0051] Exemplarily, for each device type, a statistical analysis is performed on the mobile data and performance parameters corresponding to each terminal device under the device type, and the mobile data under the device type is sorted in descending order. The 25% quantile after sorting is used as the lower limit value of the mobile data corresponding to the high-end performance level, and the 50% quantile after sorting is used as the lower limit value of the mobile data corresponding to the mid-end performance level. That is, the quantile greater than the 25% quantile of the mobile data is used as the mobile data interval corresponding to the high-end performance level of the mobile data, the quantile greater than the 50% quantile of the mobile data and less than or equal to the 25% quantile of the mobile data is used as the mobile data interval corresponding to the mid-end performance level of the mobile data; and the quantile less than or equal to the 50% quantile of the mobile data is used as the mobile data interval corresponding to the low-end performance level of the mobile data. And so on, the mobile data intervals and performance data intervals corresponding to different device types at each performance level are obtained.

[0052] Exemplarily, for a device type, mobile data greater than 5000 pixels is used as the mobile data interval corresponding to the high-end performance level, mobile data greater than 4000 pixels and less than or equal to 5000 pixels is used as the mobile data interval corresponding to the mid-end performance level, and mobile data less than or equal to 4000 pixels is used as the mobile data interval corresponding to the low-end performance level. An average CPU usage rate less than 30% is used as the average CPU usage rate interval corresponding to the high-end performance level, an average CPU usage rate greater than or equal to 30% and less than 45% is used as the average CPU usage rate interval corresponding to the mid-end performance level, and an average CPU usage rate greater than or equal to 45% is used as the average CPU usage rate interval corresponding to the low-end performance level.

[0053] Therefore, by distinguishing the mobile data intervals and performance data intervals corresponding to different device types at each performance level, the judgment gap between different device types can be reduced. Since computers generally run more smoothly than mobile phones, this prevents the same type of device from having a low-end performance level, which causes users to need to replace their devices, thereby avoiding a reduction in user experience due to increased costs.

[0054] That is to say, first, determine the target device type corresponding to the device parameters of the target terminal device; then determine the target mobile data interval under the target device type into which the mobile data falls, and determine the target performance data interval under the target device type into which the performance parameters fall; and then, determine the performance level of the target terminal device through the target mobile data interval and the target performance data interval.

[0055] If the target mobile data interval and the target performance data interval both belong to the interval corresponding to the same performance level under the target device type, the performance level corresponding to the target mobile data interval and the target performance data interval shall be used as the performance level of the target terminal device; if the target mobile data interval and the target performance data interval belong to the interval corresponding to different performance levels under the target device type, then according to the preset priority, the performance level corresponding to the target data interval with a higher priority shall be selected as the performance level of the target terminal device.

[0056] Exemplarily, if the target device type corresponding to the device parameters of the target terminal device is a Windows system computer, and it is determined that the mobile data of the target terminal device falls into the mobile data interval corresponding to the high-end performance level corresponding to the Windows system computer, and the performance parameters fall into the performance data interval corresponding to the mid-end performance level corresponding to the Windows system computer, and the priority of the mobile data is higher than the priority of the performance parameters, then it is determined that the performance level of the target terminal device is the high-end performance level.

[0057] The method further includes: when the performance level of the target terminal device does not meet the running conditions of the programming teaching program, displaying corresponding prompt information on the editing page.

[0058] For example, when the performance level of the target terminal device is lower than the preset performance level, it is considered that the performance level of the target terminal device does not meet the operating conditions of the programming teaching program, that is, the programming teaching program may be stuck when running on the target terminal device. Furthermore, it is necessary to display corresponding prompt information on the editing page, which is used to prompt the user to change the terminal device, or to suggest the user to frequently clean up the system, uninstall unnecessary software, clear other programs running in the background when running the programming teaching program, and reduce the clarity when playing the video.

[0059] If the performance level of the target terminal device is higher than or equal to the preset performance level, it is considered that the performance level of the target terminal device meets the operating conditions of the programming teaching program, that is, the probability of abnormal conditions such as freezing when the programming teaching program runs on the target terminal device is low. At this time, the prompt information may not be displayed on the editing page, or the user may be prompted on the editing page to try more complex building block projects, such as multi-role interaction or advanced graphic effects, or explore some advanced functions such as custom blocks or complex data visualization.

[0060] For example, user feedback can also be counted to evaluate whether the performance level evaluation of the target terminal device is accurate, so as to adjust the machine learning module and data interval accordingly in combination with user feedback to improve the prediction accuracy of the performance level of the target terminal device. The actual use experience of the programming teaching program running on the device can be obtained through user feedback, and a user-friendly performance report can be generated, including strengths, weaknesses and improvement suggestions, to further improve the user experience.

[0061] Therefore, as long as the building blocks are configured, the application can disguise the detection process as classroom content, and obtain the mobile data after the preset time period by embedding a special performance detection node in the course. Combined with the modified operating environment, the performance parameters of the target terminal device when running the programming teaching program are automatically collected, and the performance level of the target terminal device is evaluated by analyzing the collected data. This method is simple and easy to use, suitable for non-professional users, and provides accurate performance evaluation for the running process of the programming teaching program, as well as being able to collect and analyze data from different devices and provide real-time performance feedback. It has good scalability and can be integrated into various courses, significantly improving the quality and effectiveness of programming education.

[0062] By designing more diverse building blocks, for example, you can design multiple different characters to run to increase the difficulty of the test, you can evaluate the operation under different difficulties, and improve the accuracy and stability of the test by optimizing the character's movement algorithm; and collect the performance parameters of the device in a preset time period to reflect the performance changes in real time, and optimize the data collection frequency to balance data accuracy and system burden; by collecting mobile data, ensure the consistency of performance detection solutions on different operating systems and hardware platforms, and modify it for IDE, suitable for various installation environments of programming teaching programs.

[0063] Based on the same application concept, the embodiments of the present application also provide a device corresponding to the device performance detection method based on the programming teaching program provided in the above embodiments. Since the principle of solving the problem by the device in the embodiments of the present application is similar to the device performance detection method based on the programming teaching program in the above embodiments of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0064] like Figure 2 As shown, Figure 2A functional module diagram of a device performance detection device based on a programming teaching program provided in an embodiment of the present application, wherein a programming teaching program is installed on a target terminal device and an editing page for the programming teaching program is provided, wherein the editing page includes at least one pre-configured building block and at least one role, and the device performance detection device 10 based on the programming teaching program includes: a first acquisition module 101, for acquiring movement data corresponding to the at least one role when the at least one role is controlled by the at least one building block and runs for a preset time period; a second acquisition module 102, for acquiring device parameters of the target terminal device and performance parameters of the target terminal device within the preset time period; a determination module 103, for determining the performance level of the target terminal device according to the movement data and the performance parameters corresponding to the device parameters.

[0065] Based on the same application concept, see Figure 3 As shown, it is a structural schematic diagram of an electronic device provided in an embodiment of the present application, the electronic device 20 includes: a processor 201, a memory 202 and a bus 203, the memory 202 stores machine-readable instructions executable by the processor 201, when the electronic device 20 is running, the processor 201 and the memory 202 communicate through the bus 203, and the machine-readable instructions are executed by the processor 201 when it is running, as described in any of the steps of the device performance detection method based on the programming teaching program in the above embodiments.

[0066] Specifically, when the machine-readable instructions are executed by the processor 201, the following processing can be performed: when the at least one character is controlled by the at least one building block and runs for a preset time period, obtaining movement data corresponding to the at least one character respectively; obtaining device parameters of the target terminal device and performance parameters of the target terminal device within the preset time period; determining the performance level of the target terminal device based on the movement data and the performance parameters corresponding to the device parameters.

[0067] Based on the same application concept, an embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the device performance detection method based on the programming teaching program provided in the above embodiment are executed.

[0068] Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, etc. When the computer program on the storage medium is run, the above-mentioned device performance detection method based on the programming teaching program can be executed, by pre-configuring the building blocks used for detection on the editing page of the programming teaching program, so as to control at least one role on the editing page to run in the manner indicated by the building blocks, and detect the movement data of at least one role after running for a preset time period, and collect the device parameters of the target terminal device and the performance parameters of the target terminal device within the preset time period, and then, determine the performance level of the target terminal device according to the device parameters, movement data and performance parameters corresponding to the target terminal device, thereby solving the technical problem in the prior art that it is difficult to timely discover the performance status of the target terminal device on which the programming teaching program is installed, and achieving the technical effect of improving the convenience of performance detection of the target terminal device and discovering performance problems of the target terminal device in advance.

[0069] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here. In the several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0070] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0071] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0072] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0073] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A device performance detection method based on a programming teaching program, characterized in that: A programming teaching program is installed on a target terminal device and an editing page for the programming teaching program is provided, wherein the editing page includes at least one pre-configured building block and at least one role. The method includes: When the at least one character is controlled by the at least one building block and runs for a preset time period, obtaining movement data corresponding to the at least one character respectively; Acquire device parameters of the target terminal device and performance parameters of the target terminal device within the preset time period; The performance level of the target terminal device is determined according to the mobile data and the performance parameters corresponding to the device parameters.

2. The method according to claim 1, characterized in that The determining the performance level of the target terminal device according to the mobile data and the performance parameter corresponding to the device parameter includes: Inputting the device parameters, the movement data and the performance parameters into a pre-trained machine learning model to obtain a performance level of the target terminal device predicted by the machine learning model; or, Determine the device type to which the device parameters belong, and then determine the target mobile data interval corresponding to the device type into which the mobile data falls and the target performance data interval corresponding to the device type into which the performance parameters fall, so as to determine the performance level of the target terminal device through the target mobile data interval and the target performance data interval.

3. The method according to claim 2, characterized in that The machine learning model is trained in the following way: Obtain device parameters, mobile data and performance parameters corresponding to multiple terminal devices, and perform data preprocessing; Mark the performance level of each terminal device after data preprocessing; The device parameters, mobile data and performance parameters corresponding to each terminal device after data preprocessing are used as input data of the machine learning model, and the performance level is used as a data label to train the machine learning model.

4. The method according to claim 2, characterized in that: The device parameters include the device type, and the mobile data interval and the performance data interval are determined in the following manner: For each device type, cluster analysis is performed on the mobile data and performance parameters corresponding to multiple terminal devices under this device type to obtain various classification clusters. By comparing the mobile data and performance parameters corresponding to the center points of each classification cluster, the performance level corresponding to each classification cluster is determined. According to the mobile data and performance parameters corresponding to the terminal devices in each classification cluster, the mobile data interval and performance data interval corresponding to each performance level under the device type are determined.

5. The method according to claim 2, characterized in that: The device parameters include the device type, and the mobile data interval and the performance data interval are determined in the following manner: For each device type, the mobile data and performance parameters corresponding to multiple terminal devices under the device type are sorted. Based on the sorting result, the mobile data interval and the performance data interval corresponding to each performance level under the device type are determined.

6. The method according to any one of claims 1 to 5, characterized in that: The performance parameter includes: at least one basic performance parameter collected within the preset time period, and / or a composite performance parameter calculated by using a target performance parameter collected within the preset time period and the movement data.

7. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: When the performance level of the target terminal device does not meet the running conditions of the programming teaching program, corresponding prompt information is displayed on the editing page.

8. A device performance detection device based on a programming teaching program, characterized in that: Installing a programming teaching program on a target terminal device and providing an editing page for the programming teaching program, wherein the editing page includes at least one pre-configured building block and at least one role, the apparatus includes: A first acquisition module, configured to acquire movement data corresponding to the at least one character when the at least one character is controlled by the at least one building block and runs for a preset time period; A second acquisition module, used to acquire device parameters of the target terminal device and performance parameters of the target terminal device within the preset time period; A determination module is used to determine the performance level of the target terminal device according to the mobile data and the performance parameters corresponding to the device parameters.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the device performance detection method based on the programming teaching program as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the device performance detection method based on a programming teaching program as described in any one of claims 1 to 7 are executed.

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