A performance detection method and related device for artificial intelligence model

Abstract

This application discloses a performance testing method and related device for an artificial intelligence model, relating to the field of artificial intelligence technology. Performance testing information is determined through a displayed task creation interface; a first link address, a second link address, a target sample data tag, and a target communication address are obtained based on the performance testing information; a test task is generated based on the first link address, the second link address, and the target sample data tag; and a server can obtain target evaluation performance indicators based on the test task. This allows the display of a capability management interface, which is used to display target evaluation performance indicators and evaluation performance indicators corresponding to the target version number. This achieves the purpose of reducing the difficulty in determining whether the performance of the artificial intelligence model has been significantly improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a performance detection method and related device for an artificial intelligence model. Background Art

[0002] As AI technology advances, AI models need to be continuously updated. Whether an updated AI model needs to be released depends on whether its performance has significantly improved. If the performance of the AI ​​model has significantly improved, it makes sense to re-release the AI ​​model so that users can benefit from these improvements.

[0003] Managing sample data and the performance of various AI model versions is a complex and fragmented task. This fragmentation makes it difficult to determine whether the performance of an AI model has been significantly improved. Summary of the Invention

[0004] In view of the above problems, this application provides a performance testing method and related device for an artificial intelligence model to reduce the complexity of determining whether the performance of an artificial intelligence model has been significantly improved. The specific solution is as follows:

[0005] The first aspect of the present application provides a performance testing method for an artificial intelligence model, comprising:

[0006] In response to the test task creation operation, controlling the display of a task creation interface;

[0007] Determine performance testing information through the create task interface, where the performance testing information includes a target artificial intelligence model identifier, a target sample data identifier, a target test scenario, a target version number of the artificial intelligence model to be compared, and a target server identifier;

[0008] In response to determining to create a test task operation, searching for a first link address of the artificial intelligence model corresponding to the target artificial intelligence model identifier from a preset correspondence between artificial intelligence model identifiers and link addresses of artificial intelligence models;

[0009] Searching for a second link address of the sample data corresponding to the target sample data identifier from a preset correspondence between the sample data identifier and the link address of the sample data;

[0010] Searching for a target sample data label corresponding to the target test scenario from a preset correspondence between the test scenario and the sample data label;

[0011] Searching for the target communication address corresponding to the target server identifier from the preset correspondence between the server identifier and the server's communication address;

[0012] A test task is generated based on the first link address, the second link address, and the target sample data label, wherein the test task is used to instruct the server with the target communication address to run the artificial intelligence model at the first link address, where the input of the artificial intelligence model at the first link address is the sample data located at the second link address; and is used to instruct the server to obtain a target evaluation performance indicator based on the target sample data label and a prediction result; the prediction result is the output of the artificial intelligence model at the first link address;

[0013] Sending the test task to a server having the target communication address;

[0014] Obtaining the target evaluation performance indicator fed back by the server;

[0015] Searching for the performance evaluation index corresponding to the target version number from the preset correspondence between the version number and the performance evaluation index;

[0016] Control and display a capability management interface, wherein the capability management interface is used to display the target evaluation performance indicator and the evaluation performance indicator corresponding to the target version number.

[0017] In one possible implementation, the performance detection information further includes a target evaluation script identifier, a target startup script template identifier, and script parameters, and the performance detection method of the artificial intelligence model further includes:

[0018] Based on the target evaluation script identifier, the target startup script template identifier and the script parameters, obtaining an instruction for generating an evaluation script;

[0019] The generate evaluation script instruction is used to instruct the server to fill the script parameters into the target startup script template to obtain a target evaluation script; the target startup script template is a template corresponding to the target evaluation script identifier and the target startup script template identifier found from the correspondence between the preset evaluation script identifier, the startup script template identifier and the startup script template;

[0020] The target evaluation script is used to obtain target evaluation performance indicators based on the target sample data labels and prediction results.

[0021] In one possible implementation, the performance detection information further includes a target test environment identifier, and the performance detection method of the artificial intelligence model further includes:

[0022] The target interface number corresponding to the target test environment identifier is searched from the preset correspondence between the test environment identifier and the interface number.

[0023] In a possible implementation, sending the test task to the server having the target communication address includes:

[0024] Sending the test task to the service corresponding to the target interface number contained in the server having the target communication address;

[0025] The server is used to run the artificial intelligence model at the first link address in the test environment corresponding to the target interface number.

[0026] In a possible implementation, the performance detection information further includes a target test type, wherein:

[0027] If the target test type is model perception, the test task is further used to instruct the server to obtain the target evaluation performance indicator of the artificial intelligence model at the first link address;

[0028] If the target test type is a capability test, the test task is further used to instruct the server to obtain the target evaluation performance index of the artificial intelligence model at the first link address and the performance index of the judgment business logic script;

[0029] Among them, the output of the artificial intelligence model at the first link address is the input of the judgment business logic script, the judgment business logic script is used to assess whether there is a risk, and the judgment business logic script is stored in the server.

[0030] A second aspect of the present application provides a performance detection device for an artificial intelligence model, comprising:

[0031] A first control and display module is used for controlling and displaying a task creation interface in response to a test task creation operation;

[0032] A first determination module is configured to determine performance test information through the task creation interface, wherein the performance test information includes a target artificial intelligence model identifier, a target sample data identifier, a target test scenario, a target version number of the artificial intelligence model to be compared, and a target server identifier;

[0033] A first search module is configured to, in response to determining to create a test task operation, search for a first link address of the artificial intelligence model corresponding to the target artificial intelligence model identifier from a preset correspondence between artificial intelligence model identifiers and link addresses of artificial intelligence models;

[0034] A second search module is configured to search for a second link address of the sample data corresponding to the target sample data identifier from a preset correspondence between the sample data identifier and the link address of the sample data;

[0035] A third search module is used to search for a target sample data label corresponding to the target test scenario from a preset correspondence between the test scenario and the sample data label;

[0036] a fourth search module, configured to search for a target communication address corresponding to the target server identifier from a preset correspondence between server identifiers and server communication addresses;

[0037] A first generation module is configured to generate a test task based on the first link address, the second link address, and the target sample data label, wherein the test task is configured to instruct a server having the target communication address to run the artificial intelligence model at the first link address, wherein the input of the artificial intelligence model at the first link address is the sample data located at the second link address; and to instruct the server to obtain a target evaluation performance indicator based on the target sample data label and a prediction result; wherein the prediction result is the output of the artificial intelligence model at the first link address;

[0038] A first sending module, configured to send the test task to a server having the target communication address;

[0039] A first acquisition module is used to obtain the target evaluation performance indicator fed back by the server;

[0040] A fifth search module, configured to search for the evaluation performance indicator corresponding to the target version number from a preset correspondence between version numbers and evaluation performance indicators;

[0041] The second control display module is used to control the display of a capability management interface, where the capability management interface is used to display the target evaluation performance indicator and the evaluation performance indicator corresponding to the target version number.

[0042] In a possible implementation, the performance detection information further includes a target evaluation script identifier, a target startup script template identifier, and script parameters, and also includes:

[0043] A second acquisition module is used to acquire an evaluation script generation instruction based on the target evaluation script identifier, the target startup script template identifier and the script parameters;

[0044] The generate evaluation script instruction is used to instruct the server to fill the script parameters into the target startup script template to obtain a target evaluation script; the target startup script template is a template corresponding to the target evaluation script identifier and the target startup script template identifier found from the correspondence between the preset evaluation script identifier, the startup script template identifier and the startup script template;

[0045] The target evaluation script is used to obtain target evaluation performance indicators based on the target sample data labels and prediction results.

[0046] The third aspect of the present application provides a computer program product, including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements the performance detection method of the artificial intelligence model of the above-mentioned first aspect or any implementation method of the first aspect.

[0047] A fourth aspect of the present application provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0048] The memory is used to store computer programs;

[0049] The processor is used to execute the computer program so that the electronic device can implement the performance detection method of the artificial intelligence model of the above-mentioned first aspect or any implementation method of the first aspect.

[0050] In a fifth aspect, the present application provides a computer storage medium, which carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement the performance detection method of the artificial intelligence model of the above-mentioned first aspect or any implementation method of the first aspect.

[0051] By means of the above technical solution, the present application provides a performance testing method for an artificial intelligence model, which determines performance testing information through a displayed task creation interface; searches for a first link address of the artificial intelligence model corresponding to the target artificial intelligence model identifier from the correspondence between the preset artificial intelligence model identifier and the link address of the artificial intelligence model; searches for a second link address of the sample data corresponding to the target sample data identifier from the correspondence between the preset sample data identifier and the link address of the sample data; searches for a target sample data tag corresponding to the target test scenario from the correspondence between the preset test scenario and the sample data tag; searches for a target communication address corresponding to the target server identifier from the correspondence between the preset server identifier and the server's communication address; generates a test task based on the first link address, the second link address, and the target sample data tag; and the server can obtain a target evaluation performance indicator based on the test task. This allows the display of a capability management interface, which is used to display the target evaluation performance indicator and the evaluation performance indicator corresponding to the target version number. This reduces the complexity of determining whether the performance of the artificial intelligence model has been significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The above and other features, advantages, and aspects of the various embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that the originals and elements are not necessarily drawn to scale.

[0053] Figure 1 A schematic diagram of the system architecture provided for this application;

[0054] Figure 2 This is a schematic diagram of an optional hardware structure of a terminal 100 provided in this application;

[0055] Figure 3 A schematic diagram of the structure of a server 200 provided in this application;

[0056] Figure 4 A flowchart of a performance testing method for an artificial intelligence model provided in this application;

[0057] Figure 5 A schematic diagram of an implementation method of the task creation interface provided by this application;

[0058] Figures 6a to 6c A schematic diagram of an interface for setting a correspondence between a sample data identifier and a link address of the sample data provided in an embodiment of the present application;

[0059] Figure 7 A schematic diagram of an implementation of the capability management interface provided in this application;

[0060] Figure 8 A schematic diagram of an implementation of a window for displaying historical records provided by this application;

[0061] Figure 9 A schematic diagram of the structure of a performance detection device for an artificial intelligence model provided in this application;

[0062] Figure 10 A schematic diagram of the structure of the electronic device provided for this application; DETAILED DESCRIPTION

[0063] The following describes the embodiments of the present application in conjunction with the accompanying drawings. The terms used in the implementation methods of the present application are only used to explain the specific embodiments of the present application and are not intended to limit the present application.

[0064] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0065] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, and this is merely a way of distinguishing the objects of the same attributes when describing them in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0066] In the field of artificial intelligence, managing sample data and evaluating performance metrics for each updated version of an AI model is a complex and fragmented task. This fragmentation makes it difficult to determine whether an AI model's performance has significantly improved. The following is an expanded description of this issue:

[0067] As the amount of sample data increases and the types of sample data diversify, effective sample data management becomes particularly important. Sample data needs to be cleaned, labeled, and maintained, tasks that often require a significant amount of manpower and time.

[0068] AI models frequently need to be updated to adapt to new requirements. Each version of an AI model needs to be tracked and compared to determine whether the newer version is truly superior to the older version. The lack of an effective version control mechanism makes it difficult to track the evolution and performance changes of AI models.

[0069] For example, when evaluating the performance of an artificial intelligence model (i.e., evaluating performance indicators), it is necessary to consider multiple evaluation indicators, such as accuracy, precision, recall, F1 (F1 Score), ROC (Receiver Operating Characteristic) curve, and AUC (Area Under the Curve) value.

[0070] Based on this, the embodiments of this application establish a centralized management system for unified storage and management of the following information: sample data, AI models of different versions, and performance metrics for each AI model version. This centralized management system can record detailed information about each AI model update, including changes in performance metrics, thereby simplifying the performance comparison process. This is explained in detail below.

[0071] See also Figure 1 , Figure 1A schematic diagram of a system architecture is shown. The system may include a terminal 100, a server 200 and a database 300. The server 200 may include one or more servers ( Figure 1 In the example, a server is included, and the server 200 can provide the method provided in the embodiment of the present application for one or more terminals.

[0072] Among them, an application or a web page can be installed on the terminal 100, and the above application and web page can provide an interface. The terminal 100 can receive relevant parameters entered by the user on the interface, such as performance detection information, and send the above parameters to the server 200. The server 200 can interact with the database 300 based on the received parameters to obtain processing results, and return the processing results to the terminal 100.

[0073] Exemplarily, terminal 100 is a terminal for R&D personnel; database 300 may store the correspondence between AI model identifiers and AI model link addresses, the correspondence between sample data identifiers and sample data link addresses, the correspondence between test scenarios and sample data labels, and the correspondence between server identifiers and server communication addresses. Exemplarily, database 300 may also store various AI models, various sample data, and various sample data labels.

[0074] For example, the number of databases 300 may be one or more.

[0075] It should be understood that in some optional implementations, the terminal 100 can also complete the action of interacting with the database 300 based on the received parameters to obtain the processing results by itself without the need for the cooperation of the server, and the embodiments of the present application are not limited thereto.

[0076] Next describe Figure 1 The product form of the mid-terminal 100;

[0077] The terminal 100 in the embodiment of the present application can be a mobile phone, a tablet computer, a wearable device, an in-vehicle device, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), etc., and the embodiment of the present application does not impose any restrictions on this.

[0078] Figure 2 A schematic diagram of an optional hardware structure of the terminal 100 is shown.

[0079] refer to Figure 2 As shown, the terminal 100 may include a radio frequency unit 110, a memory 120, an input unit 130, a display unit 140, a camera 150 (optional), an audio circuit 160 (optional), a speaker 161 (optional), a microphone 162 (optional), an earphone jack 163 (optional), a processor 170, an external interface 180, a power supply 190 and other components. Those skilled in the art will understand that Figure 2 This is merely an example of a terminal and does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown in the figure, or a combination of certain components, or different components.

[0080] The input unit 130 can be used to receive input digital or character information and generate key signal input related to user settings and function control of the terminal. Specifically, the input unit 130 may include a touch screen 131 (optional) and / or other input devices 132. The touch screen 131 can collect user touch operations on or near it (for example, operations performed on or near the touch screen using a finger, joint, stylus, or any other suitable object) and drive corresponding connected devices according to pre-set programs. The touch screen can detect user touch actions on the touch screen, convert the touch actions into touch signals and transmit them to the processor 170, and can receive and execute commands sent by the processor 170; the touch signals include at least touch point coordinate information. The touch screen 131 can provide an input interface and an output interface between the terminal 100 and the user. In addition, touch screens can be implemented using various types, such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch screen 131, the input unit 130 may also include other input devices. Specifically, the other input devices 132 may include, but are not limited to, one or more of a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, a joystick, and the like.

[0081] Among them, the input device 132 can receive input data and the like.

[0082] The display unit 140 can be used to display information input by the user or information provided to the user, various menus of the terminal 100, interactive interfaces, file display and / or playback of any multimedia file. In the embodiment of the present application, the display unit 140 can be used to display interfaces, processing results, etc.

[0083] Memory 120 can be used to store instructions and data. It primarily includes an instruction storage area and a data storage area. The data storage area can store various data, such as multimedia files and text. The instruction storage area can store software units such as the operating system, applications, and instructions required for at least one function, or subsets or extensions thereof. It may also include non-volatile random access memory (RAM). It provides processor 170 with management functions for the hardware, software, and data resources within the computing and processing device, supporting control software and applications. It is also used to store multimedia files and running programs and applications.

[0084] The processor 170 is the control center of the terminal 100. It connects all components of the terminal 100 using various interfaces and circuits. By executing instructions stored in the memory 120 and accessing data stored therein, it executes various functions of the terminal 100 and processes data, thereby providing overall control of the terminal device. Optionally, the processor 170 may include one or more processing units. Preferably, the processor 170 may integrate an application processor and a modem processor, with the application processor primarily processing the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into the processor 170. In some embodiments, the processor and memory may be implemented on a single chip; in other embodiments, they may be implemented on separate chips. The processor 170 may also generate corresponding operational control signals and send them to the corresponding components of the computing and processing device. It may also read and process data in the software, particularly the data and programs in the memory 120, to enable the various functional modules therein to perform their corresponding functions, thereby controlling the corresponding components to operate as instructed.

[0085] Among them, the memory 120 can be used to store software codes related to the performance detection method of the artificial intelligence model, the processor 170 can execute the steps of the performance detection method of the artificial intelligence model, and can also schedule other units (such as the above-mentioned input unit 130 and display unit 140) to implement corresponding functions.

[0086] The RF unit 110 (optional) can be used to send and receive information or receive and send signals during a call. For example, after receiving downlink information from the base station, it is passed to the processor 170 for processing; in addition, the designed uplink data is sent to the base station. Generally, the RF circuit includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (LNA), a duplexer, etc. In addition, the RF unit 110 can also communicate with network devices and other devices via wireless communication. This wireless communication can use any communication standard or protocol, including but not limited to Global System of Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), etc.

[0087] In this embodiment of the present application, the radio frequency unit 110 can send data to the server 200 and receive processing results sent by the server 200.

[0088] It should be understood that the radio frequency unit 110 is optional and can be replaced by other communication interfaces, such as a network port.

[0089] The terminal 100 also includes a power supply 190 (such as a battery) for supplying power to various components. Preferably, the power supply can be logically connected to the processor 170 through a power management system, thereby managing functions such as charging, discharging, and power consumption through the power management system.

[0090] The terminal 100 further includes an external interface 180 , which may be a standard Micro USB interface or a multi-pin connector, and may be used to connect the terminal 100 to other devices for communication, or to connect a charger to charge the terminal 100 .

[0091] Although not shown, the terminal 100 may also include a flashlight, a wireless fidelity (WiFi) module, a Bluetooth module, sensors with different functions, etc., which will not be described in detail here. Some or all of the methods described below can be applied to Figure 2 In the terminal 100 shown.

[0092] Next describe Figure 1 The product form of the server 200;

[0093] Figure 3 A structural diagram of a server 200 is provided, such as Figure 3 As shown, the server 200 includes a bus 201, a processor 202, a communication interface 203, and a memory 204. The processor 202, the memory 204, and the communication interface 203 communicate with each other via the bus 201.

[0094] The bus 201 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0095] The processor 202 may be any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0096] The memory 204 may include volatile memory, such as random access memory (RAM). The memory 204 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0097] Among them, the memory 204 can be used to store software codes related to the performance detection method of the artificial intelligence model, the processor 202 can execute the steps of the performance detection method of the artificial intelligence model of the chip, and can also schedule other units to implement corresponding functions.

[0098] It should be understood that the above-mentioned terminal 100 and server 200 can be centralized or distributed devices, and the processors in the above-mentioned terminal 100 and server 200 (such as processor 170 and processor 202) can be hardware circuits (such as application specific integrated circuit (ASIC), field-programmable gate array (FPGA), general-purpose processor, digital signal processor (DSP), microprocessor or microcontroller, etc.), or a combination of these hardware circuits. For example, the processor can be a hardware system with an instruction execution function, such as a CPU, DSP, etc., or a hardware system without an instruction execution function, such as an ASIC, FPGA, etc., or a combination of the above-mentioned hardware systems without an instruction execution function and hardware systems with an instruction execution function.

[0099] Reference Figure 4 , Figure 4 A flow chart of a performance testing method for an artificial intelligence model provided in an embodiment of the present application is shown as follows: Figure 4 As shown, a performance detection method for an artificial intelligence model provided in an embodiment of the present application may include steps S401 to S411, and these steps are described in detail below.

[0100] Step S401: In response to the operation of creating a test task, controlling the display of a task creation interface.

[0101] Exemplarily, the operation of creating a test task may be an operation of touching a corresponding button, or a voice operation, or a preset gesture operation.

[0102] Step S402: Determine performance test information through the task creation interface, where the performance test information includes a target artificial intelligence model identifier, a target sample data identifier, a target test scenario, a target version number of the artificial intelligence model to be compared, and a target server identifier.

[0103] In order for those skilled in the art to better understand the task creation interface provided by the embodiment of the present application, the following examples are given to illustrate. Figure 5 , which is a schematic diagram of an implementation method of creating a task interface provided in an embodiment of the present application.

[0104] like Figure 5As shown, you can select the target AI model identifier using the "Task Type" selection box. Exemplarily, the AI ​​model identifier is represented by the name of the AI ​​model. Exemplarily, the pre-set AI model identifiers are: Computer Vision Intelligent Reasoning (CV), Natural Language Processing (NLP) large model reasoning, multimodal large model reasoning, and Retrieval-Augmented Generation Assessment (RAGAS).

[0105] CV intelligent reasoning refers to the process of analyzing, understanding, and reasoning about image or video data using artificial intelligence technologies such as deep learning in the field of computer vision. This includes tasks such as image recognition, object detection, and semantic segmentation, as well as more complex scene understanding such as behavior recognition and event prediction.

[0106] Inference on large NLP models refers to the process of analyzing and processing text data using large, pre-trained models in the field of natural language processing. These models are typically pre-trained on large amounts of text data to learn general representations of language, which can then be fine-tuned for specific tasks. Tasks involved in inference on large NLP models include text classification, sentiment analysis, question-answering systems, and machine translation. During inference, the model can understand the semantics of the text and generate responses or perform other NLP tasks.

[0107] Multimodal large-scale model reasoning refers to the process of reasoning with large models that integrate information from multiple modalities (such as vision, text, and audio). This type of reasoning can process and understand relationships between cross-modal data, for example, combining image content with related text descriptions or matching speech signals with the speaker's facial expressions. Multimodal reasoning is particularly important in tasks such as visual question answering (VQA) and visual commonsense reasoning (VCR), which require models to possess cross-modal semantic understanding and commonsense reasoning capabilities.

[0108] The RAGAS capability assessment is an evaluation framework for Retrieval-Augmented Generation (RAG) systems. RAG systems combine retrieval and generation, first retrieving relevant information from large amounts of data and then generating answers or content based on that information. The RAGAS evaluation framework provides a set of metrics, such as context precision, context recall, faithfulness, and answer relevancy, for comprehensively evaluating the performance of RAG systems. This evaluation approach focuses not only on the quality of the resulting generated content but also on how the system leverages the retrieved information to improve its generation results.

[0109] In the embodiment of this application, the selected "artificial intelligence model identifier" is referred to as the "target artificial intelligence model identifier".

[0110] For example, it can be achieved by Figure 5 In the "Server ID" field, select the target server ID. Exemplarily, the server ID can be represented by the server name. Exemplarily, different server IDs correspond to different server types. Exemplarily, different server IDs correspond to servers running different test environments. Exemplarily, different test environments can run on the same server.

[0111] For example, it can be achieved by Figure 5 Select the sample data identifier in "Data Selection". Exemplarily, the sample data identifier can be represented by the name of the sample data; Exemplarily, the type of the sample data can be one or more of: voice, video, text, or image.

[0112] For example, it can be achieved by Figure 5 Select the target test scenario in "Test Scenario." It's understandable that a sample data set may have multiple sample data labels, and the sample data labels for the same sample data in different test scenarios may not be exactly the same. The following example illustrates this.

[0113] For example, the sample data is a sample image, and the sample image includes a person, an escalator, and a gas tank. The sample data labels of the sample image include the person's location area, the escalator's location area, and the gas tank's location area. If the test scene is an unmanned escalator, the sample data labels corresponding to the test scene are the person's location area and the escalator's location area; if the test scene is a safety hazard, the sample data labels corresponding to the test scene are the person's location area and the gas tank's location area.

[0114] In summary, the test scenario is used to indicate the type of sample data label. After determining the sample data, the corresponding type of sample data label can be selected from the sample data labels corresponding to the sample data based on the determined test scenario. For example, the types of sample data labels indicated by the test scenario "unmanned escalator" include: people and escalators. Therefore, "people's location area" and "escalator's location area" can be selected from the sample data labels corresponding to the sample data: "people's location area", "escalator's location area", and "gas tank's location area".

[0115] In summary, the results required to be predicted by the artificial intelligence model may be different in different test scenarios.

[0116] For example, it can be achieved by Figure 5In the "Compare Calibration Version" section, select the target version number of the AI ​​model to be compared. For example, you can select one or more target version numbers. The following example illustrates the version number.

[0117] For the same artificial intelligence model AA, if the artificial intelligence model AA is trained on January 1, 2024, and its version number is determined to be 1.0; if the artificial intelligence model AA is updated on February 1, 2024, and the updated version number is determined to be 2.0; if the artificial intelligence model AA is updated again on March 1, 2024, and the updated version number is determined to be 3.0; if the updated artificial intelligence model AA needs to be evaluated again at the current time, the target version number of the artificial intelligence model to be compared can be selected from one or more of 1.0, 2.0 and 3.0.

[0118] Step S403: In response to determining to create a test task operation, the first link address of the artificial intelligence model corresponding to the target artificial intelligence model identifier is searched from the correspondence between the preset artificial intelligence model identifier and the link address of the artificial intelligence model.

[0119] Exemplarily, the operation of determining to create a test task may be an operation of touching a corresponding button, or a voice operation, or a preset gesture operation.

[0120] Exemplarily, the link address may be a storage address where the artificial intelligence model is stored in a database.

[0121] Step S404: searching for a second link address of the sample data corresponding to the target sample data identifier from the preset correspondence between the sample data identifier and the link address of the sample data.

[0122] Step S405: searching for a target sample data label corresponding to the target test scenario from the preset correspondence between the test scenarios and the sample data labels.

[0123] The following describes the process of setting the correspondence between the sample data identifier and the link address of the sample data.

[0124] like Figures 6a to 6c , which is a schematic diagram of an interface for setting the correspondence between a sample data identifier and a link address of the sample data provided in an embodiment of the present application.

[0125] For example, in response to the operation of creating a data set, the display may be controlled Figure 6a It is understandable that multiple sample data are needed to evaluate the artificial intelligence model. Then, multiple sample data used to evaluate the same artificial intelligence model can be stored in the same data set, that is, one data set includes one or more sample data; for example, Figure 5In "Data Selection", you can select the corresponding dataset identifier (which can be represented by the dataset name); the sample data in the dataset with this dataset identifier are used to evaluate the artificial intelligence model.

[0126] pass Figure 6a You can set the dataset name of the created dataset. Assume that the dataset name is aas. After creating the dataset, you can enter the user interface corresponding to the dataset, such as Figure 6b shown.

[0127] pass Figure 6b You can enter the "Image Import" Figure 6c The user interface shown. Figure 6c The user interface shown can upload local images to the database 300. In this way, a corresponding relationship between sample data and the link address of the sample data, as well as a corresponding relationship between the sample data and the data set is established.

[0128] For example, "Text Import" is used to import text, and the imported text is used as sample text; "Video Import" is used to import video, and the imported video is used as sample video. "Text Import" and "Video Import" are not listed in the Figure 6b Shown in.

[0129] For example, through Figure 6b The user interface shown can select a sample image or sample text, and then enter the sample data label of the sample data by touching the "Add Label" displayed user interface. In this way, the corresponding relationship between the sample data and the sample data label is established and stored in the database 300.

[0130] It is understandable that different data sets correspond to different test scenarios, and different test scenarios correspond to different sample data labels.

[0131] Step S406: searching for the target communication address corresponding to the target server identifier from the preset correspondence between the server identifier and the server's communication address.

[0132] Illustratively, the communication address includes but is not limited to an IP (Internet Protocol Address) address.

[0133] Step S407: Generate a test task based on the first link address, the second link address and the target sample data label.

[0134] The test task is used to instruct the server with the target communication address to run the artificial intelligence model at the first link address, where the input of the artificial intelligence model at the first link address is the sample data located at the second link address; and is used to instruct the server to obtain the target evaluation performance indicator based on the target sample data label and the prediction result; the prediction result is the output of the artificial intelligence model at the first link address.

[0135] Exemplarily, the artificial intelligence model at the first link address is an artificial intelligence model that has been trained or updated.

[0136] It is understandable that after the performance test information is determined through the task creation interface, if the operation of determining to create a test task is performed, a test task can be generated.

[0137] Exemplarily, target evaluation performance indicators include, but are not limited to, accuracy, precision, recall, F1 (F1 Score), ROC (Receiver Operating Characteristic) curve, and AUC (Area Under the Curve) value.

[0138] Assuming that the sample data is a sample image, the sample data label of the sample image includes the location area of ​​the person and the location area of ​​the escalator. After the sample data is input into the artificial intelligence model, the output prediction result of the artificial intelligence model includes the predicted location area of ​​the person and the predicted location area of ​​the escalator.

[0139] Step S408: Send the test task to the server having the target communication address.

[0140] Step S409: Obtain the target evaluation performance indicator fed back by the server.

[0141] Step S410: searching for the evaluation performance indicator corresponding to the target version number from the preset correspondence between version numbers and evaluation performance indicators.

[0142] Exemplarily, the “correspondence between version numbers and evaluation performance indicators” may be stored in the database 300 .

[0143] Step S411: controlling the display of a capability management interface, wherein the capability management interface is used to display the target evaluation performance indicator and the evaluation performance indicator corresponding to the target version number.

[0144] like Figure 7 , which is a schematic diagram of an implementation method of the capability management interface provided in an embodiment of the present application.

[0145] like Figure 7As shown, the capability management interface can display the following content: the test scenario selected through the task creation interface, the version number of the currently tested artificial intelligence model, and the test time.

[0146] Figure 7 The following example illustrates how to obtain target evaluation performance indicators including recall rate and precision rate.

[0147] For example, if you want to view the evaluation performance indicators of the target version of the artificial intelligence model, you can trigger "history" to display Figure 8 The window shown.

[0148] like Figure 8 As shown in the figure, the evaluation performance indicators corresponding to the target version number selected in the task creation interface can be displayed. This allows you to check whether the performance of the AI ​​model has been significantly improved.

[0149] The embodiment of the present application provides a performance testing method for an artificial intelligence model, which determines performance testing information through a displayed task creation interface; searches for a first link address of the artificial intelligence model corresponding to the target artificial intelligence model identifier from the correspondence between the preset artificial intelligence model identifier and the link address of the artificial intelligence model; searches for a second link address of the sample data corresponding to the target sample data identifier from the correspondence between the preset sample data identifier and the link address of the sample data; searches for a target sample data tag corresponding to the target test scenario from the correspondence between the preset test scenario and the sample data tag; searches for a target communication address corresponding to the target server identifier from the correspondence between the preset server identifier and the communication address of the server; generates a test task based on the first link address, the second link address, and the target sample data tag; and the server can obtain a target evaluation performance indicator based on the test task. This allows the display of a capability management interface, which is used to display the target evaluation performance indicator and the evaluation performance indicator corresponding to the target version number. This reduces the complexity of determining whether the performance of the artificial intelligence model has been significantly improved.

[0150] In an optional implementation, the performance detection information also includes a target evaluation script identifier, a target startup script template identifier, and script parameters. The performance detection method of the artificial intelligence model also includes the following steps: based on the target evaluation script identifier, the target startup script template identifier, and the script parameters, obtaining an instruction to generate an evaluation script.

[0151] Among them, the generate evaluation script instruction is used to instruct the server to fill the script parameters into the target startup script template to obtain the target evaluation script; the target startup script template is a template corresponding to the target evaluation script identifier and the target startup script template identifier found from the correspondence between the preset evaluation script identifier, startup script template identifier and startup script template.

[0152] The target evaluation script is used to obtain target evaluation performance indicators based on the target sample data labels and prediction results.

[0153] The following combination Figure 5 The above content will be explained.

[0154] pass Figure 5 The "Job" in the job selects a target evaluation script ID from multiple evaluation script IDs. One evaluation script ID can include multiple startup script template IDs.

[0155] pass Figure 5 In the Startup Script dialog box, select the target startup script template ID from multiple startup script template IDs. Figure 5 Enter the script parameters in "Please enter the startup script parameters".

[0156] Exemplarily, the “correspondence between the evaluation script identifier, the startup script template identifier, and the startup script template” may be stored in the server.

[0157] In an optional implementation, the performance detection information also includes a target test environment identifier, and the performance detection method of the artificial intelligence model also includes: searching for the target interface number corresponding to the target test environment identifier from the correspondence between the preset test environment identifier and the interface number.

[0158] The server includes multiple interface numbers. Different interface numbers correspond to different services, and different test environments run in different services.

[0159] Exemplary test environments include, but are not limited to, DEV (Development Environment), FAT (Functional Acceptance Testing Environment), UAT (User Acceptance Testing Environment), and PROD (Production Environment).

[0160] Correspondingly, sending the test task to the server having the target communication address includes: sending the test task to the service corresponding to the target interface number included in the server having the target communication address.

[0161] The server is used to run the artificial intelligence model at the first link address in the test environment corresponding to the target interface number.

[0162] In an optional implementation, the performance detection information further includes a target test type. If the target test type is model perception, the test task is used to instruct the server to obtain the target evaluation performance indicator of the artificial intelligence model at the first link address. If the target test type is capability testing, the test task is used to instruct the server to obtain the target evaluation performance indicator of the artificial intelligence model at the first link address and the performance indicator of the business logic script.

[0163] Among them, the output of the artificial intelligence model at the first link address is the input of the judgment business logic script, the judgment business logic script is used to assess whether there is a risk, and the judgment business logic script is stored in the server.

[0164] Exemplarily, the business logic script may be stored in a server, a database, or a terminal.

[0165] Combine Figure 5 , can be achieved through Figure 5 The displayed "Test Type" selects a test type from among model awareness, rule recognition, and ability test.

[0166] For example, if the test type is model perception, it means that the artificial intelligence model has been updated, but the judgment business logic script has not been updated. At this time, the server needs to feedback the target evaluation performance indicators of the artificial intelligence model; if the test type is rule recognition, it means that the artificial intelligence model has not been updated, but the judgment business logic script has been updated. At this time, the server needs to feedback the performance indicators of the changed judgment business logic script. The performance indicators of the judgment business logic script may include accuracy. If the test type is capability test, it means that the artificial intelligence model has been updated and the judgment business logic script has been updated. At this time, the server needs to feedback the performance indicators of the changed judgment business logic script and the target evaluation performance indicators of the artificial intelligence model.

[0167] The following examples illustrate the artificial intelligence model and the judgment business logic script.

[0168] Assume the sample data is a sample image, and the sample data labels for the sample image include the location of the person and the location of the escalator. After inputting the sample data into the AI ​​model, the AI ​​model can output a prediction result. The discriminant business logic script can use the prediction result to determine whether a person is at risk while riding the escalator. It is understandable that with technological development, the discriminant business logic script is also changing. For example, the safety rule established in January 2024 requires that a person's hands must be on the escalator handle. The judgment logic of the discriminant business logic script is to determine whether the location of the person's hand area overlaps with the location of the escalator handle. If overlap occurs, there is no risk; if not, there is a risk. The safety rule changed in May 2024 is to require that a person's hands must be on the escalator handle or that the person's eyes are directed at the escalator. After the safety rule change, the judgment logic of the discriminant business logic script is to determine whether the location of the person's hand area overlaps with the location of the escalator handle, or whether the person's eyes are directed at the escalator. If both of these conditions are false, there is a risk; if one of these conditions is true, there is no risk.

[0169] The above introduces a performance detection method of an artificial intelligence model provided by an embodiment of the present application. The following will introduce a device for executing the performance detection method of the above artificial intelligence model.

[0170] See also Figure 9 , Figure 9 This is a schematic diagram of the structure of a performance detection device for an artificial intelligence model provided in an embodiment of the present application. Figure 9 As shown, the performance detection device of the artificial intelligence model includes:

[0171] A first control and display module 901 is used to control and display a task creation interface in response to a test task creation operation;

[0172] A first determination module 902 is configured to determine performance test information through the task creation interface, wherein the performance test information includes a target artificial intelligence model identifier, a target sample data identifier, a target test scenario, a target version number of the artificial intelligence model to be compared, and a target server identifier;

[0173] A first search module 903 is configured to, in response to determining to create a test task operation, search for a first link address of an artificial intelligence model corresponding to the target artificial intelligence model identifier from a preset correspondence between artificial intelligence model identifiers and link addresses of artificial intelligence models;

[0174] A second search module 904 is configured to search for a second link address of the sample data corresponding to the target sample data identifier from a preset correspondence between the sample data identifier and the link address of the sample data;

[0175] The third search module 905 is used to search for the target sample data label corresponding to the target test scenario from the preset correspondence between the test scenario and the sample data label;

[0176] The fourth search module 906 is configured to search for a target communication address corresponding to the target server identifier from a preset correspondence between server identifiers and server communication addresses;

[0177] A first generation module 907 is configured to generate a test task based on the first link address, the second link address, and the target sample data label, wherein the test task is configured to instruct the server having the target communication address to run the artificial intelligence model at the first link address, wherein the input of the artificial intelligence model at the first link address is the sample data at the second link address; and to instruct the server to obtain a target evaluation performance indicator based on the target sample data label and a prediction result; wherein the prediction result is the output of the artificial intelligence model at the first link address;

[0178] A first sending module 908 is configured to send the test task to a server having the target communication address;

[0179] A first acquisition module 909 is configured to acquire the target evaluation performance indicator fed back by the server;

[0180] A fifth search module 910 is configured to search for the evaluation performance indicator corresponding to the target version number from a preset correspondence between version numbers and evaluation performance indicators;

[0181] The second control and display module 911 is used to control and display a capability management interface, where the capability management interface is used to display the target evaluation performance indicator and the evaluation performance indicator corresponding to the target version number.

[0182] In an optional implementation, the performance detection information further includes a target evaluation script identifier, a target startup script template identifier, and script parameters, and further includes:

[0183] A second acquisition module is used to acquire an evaluation script generation instruction based on the target evaluation script identifier, the target startup script template identifier and the script parameters;

[0184] The generate evaluation script instruction is used to instruct the server to fill the script parameters into the target startup script template to obtain a target evaluation script; the target startup script template is a template corresponding to the target evaluation script identifier and the target startup script template identifier found from the correspondence between the preset evaluation script identifier, the startup script template identifier and the startup script template;

[0185] The target evaluation script is used to obtain target evaluation performance indicators based on the target sample data labels and prediction results.

[0186] In an optional implementation, the performance detection information further includes a target test environment identifier and:

[0187] The sixth search module is configured to search for a target interface number corresponding to the target test environment identifier from a preset correspondence between the test environment identifier and the interface number.

[0188] In an optional implementation, the first sending module includes:

[0189] A sending unit, configured to send the test task to a service corresponding to the target interface number contained in a server having the target communication address;

[0190] The server is used to run the artificial intelligence model at the first link address in the test environment corresponding to the target interface number.

[0191] In an optional implementation, the performance test information further includes a target test type, wherein:

[0192] If the target test type is model perception, the test task is further used to instruct the server to obtain the target evaluation performance indicator of the artificial intelligence model at the first link address;

[0193] If the target test type is a capability test, the test task is further used to instruct the server to obtain the target evaluation performance index of the artificial intelligence model at the first link address and the performance index of the judgment business logic script;

[0194] Among them, the output of the artificial intelligence model at the first link address is the input of the judgment business logic script, the judgment business logic script is used to assess whether there is a risk, and the judgment business logic script is stored in the server.

[0195] An electronic device is also provided in an embodiment of the present application. Figure 10 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic device in the embodiments of the present application may include, but is not limited to, fixed terminals such as mobile phones, laptops, PDAs (personal digital assistants), PADs (tablet computers), desktop computers, etc. Figure 10 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0196] like Figure 10As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 1001, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1008 into a random access memory (RAM) 1003. When the electronic device is powered on, the RAM 1003 also stores various programs and data required for the operation of the electronic device. The processing device 1001, ROM 1002, and RAM 1003 are interconnected via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0197] Typically, the following devices may be connected to the I / O interface 1005: an input device 1006 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 1007 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1008 including, for example, a memory card, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Figure 10 The electronic device is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0198] An embodiment of the present application also provides a computer program product including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements a performance detection method of any artificial intelligence model provided in the embodiment of the present application.

[0199] A computer-readable storage medium is also provided in an embodiment of the present application. The storage medium carries one or more computer programs. When one or more computer programs are executed by an electronic device, the electronic device can implement a performance detection method for any artificial intelligence model provided in an embodiment of the present application.

[0200] It should also be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided in this application, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines.

[0201] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course can also be implemented by special hardware including application-specific integrated circuits, special CPUs, special memories, special components, etc. In general, all functions performed by computer programs can be easily implemented with corresponding hardware, and the specific hardware structures used to implement the same function can also be diverse, such as analog circuits, digital circuits or special circuits, etc. However, for the present application, software program implementation is a better implementation method in most cases. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer's floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk or optical disk, etc., and includes a number of instructions to enable a computer device (which can be a personal computer, training equipment, or network equipment, etc.) to execute the methods described in each embodiment of the present application.

[0202] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0203] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, a computer, a training device or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website, a computer, a training device or a data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

Claims

1. A performance testing method for an artificial intelligence model, characterized in that: include: In response to the test task creation operation, controlling the display of a task creation interface; Determine performance testing information through the create task interface, where the performance testing information includes a target artificial intelligence model identifier, a target sample data identifier, a target test scenario, a target version number of the artificial intelligence model to be compared, and a target server identifier; In response to determining to create a test task operation, searching for a first link address of the artificial intelligence model corresponding to the target artificial intelligence model identifier from a preset correspondence between artificial intelligence model identifiers and link addresses of artificial intelligence models; Searching for a second link address of the sample data corresponding to the target sample data identifier from a preset correspondence between the sample data identifier and the link address of the sample data; Searching for a target sample data label corresponding to the target test scenario from a preset correspondence between the test scenario and the sample data label; Searching for the target communication address corresponding to the target server identifier from the preset correspondence between the server identifier and the server's communication address; Generate a test task based on the first link address, the second link address, and the target sample data label, wherein the test task is used to instruct the server with the target communication address to run the artificial intelligence model at the first link address, where the input of the artificial intelligence model at the first link address is the sample data located at the second link address; and for instructing the server to obtain a target evaluation performance indicator based on the target sample data label and the prediction result; the prediction result is the output of the artificial intelligence model at the first link address; Sending the test task to a server having the target communication address; Obtaining the target evaluation performance indicator fed back by the server; Searching for the performance evaluation index corresponding to the target version number from the preset correspondence between the version number and the performance evaluation index; Control and display a capability management interface, wherein the capability management interface is used to display the target evaluation performance indicator and the evaluation performance indicator corresponding to the target version number.

2. The performance testing method of the artificial intelligence model according to claim 1, characterized in that: The performance detection information also includes a target evaluation script identifier, a target startup script template identifier, and script parameters. The performance detection method of the artificial intelligence model also includes: Based on the target evaluation script identifier, the target startup script template identifier and the script parameters, obtaining an instruction for generating an evaluation script; The generate evaluation script instruction is used to instruct the server to fill the script parameters into the target startup script template to obtain a target evaluation script; the target startup script template is a template corresponding to the target evaluation script identifier and the target startup script template identifier found from the correspondence between the preset evaluation script identifier, the startup script template identifier and the startup script template; The target evaluation script is used to obtain target evaluation performance indicators based on the target sample data labels and prediction results.

3. The performance testing method of the artificial intelligence model according to claim 1, characterized in that: The performance detection information also includes a target test environment identifier, and the performance detection method of the artificial intelligence model also includes: The target interface number corresponding to the target test environment identifier is searched from the preset correspondence between the test environment identifier and the interface number.

4. The performance testing method of the artificial intelligence model according to claim 3 is characterized in that: The sending of the test task to the server having the target communication address includes: Sending the test task to the service corresponding to the target interface number contained in the server having the target communication address; The server is used to run the artificial intelligence model at the first link address in the test environment corresponding to the target interface number.

5. The performance testing method of the artificial intelligence model according to claim 1, characterized in that: The performance test information also includes the target test type, wherein: If the target test type is model perception, the test task is further used to instruct the server to obtain the target evaluation performance indicator of the artificial intelligence model at the first link address; If the target test type is a capability test, the test task is further used to instruct the server to obtain the target evaluation performance index of the artificial intelligence model at the first link address and the performance index of the judgment business logic script; Among them, the output of the artificial intelligence model at the first link address is the input of the judgment business logic script, the judgment business logic script is used to assess whether there is a risk, and the judgment business logic script is stored in the server.

6. A performance testing device for an artificial intelligence model, characterized in that: include: A first control and display module is used for controlling and displaying a task creation interface in response to a test task creation operation; A first determination module is configured to determine performance test information through the task creation interface, wherein the performance test information includes a target artificial intelligence model identifier, a target sample data identifier, a target test scenario, a target version number of the artificial intelligence model to be compared, and a target server identifier; A first search module is configured to, in response to determining to create a test task operation, search for a first link address of the artificial intelligence model corresponding to the target artificial intelligence model identifier from a preset correspondence between artificial intelligence model identifiers and link addresses of artificial intelligence models; A second search module is configured to search for a second link address of the sample data corresponding to the target sample data identifier from a preset correspondence between the sample data identifier and the link address of the sample data; A third search module is used to search for a target sample data label corresponding to the target test scenario from a preset correspondence between the test scenario and the sample data label; a fourth search module, configured to search for a target communication address corresponding to the target server identifier from a preset correspondence between server identifiers and server communication addresses; a first generation module, configured to generate a test task based on the first link address, the second link address, and the target sample data label, wherein the test task is configured to instruct a server having the target communication address to run the artificial intelligence model at the first link address, wherein the input of the artificial intelligence model at the first link address is the sample data located at the second link address; and for instructing the server to obtain a target evaluation performance indicator based on the target sample data label and the prediction result; the prediction result is the output of the artificial intelligence model at the first link address; A first sending module, configured to send the test task to a server having the target communication address; A first acquisition module is used to obtain the target evaluation performance indicator fed back by the server; A fifth search module, configured to search for the evaluation performance indicator corresponding to the target version number from a preset correspondence between version numbers and evaluation performance indicators; The second control display module is used to control the display of a capability management interface, where the capability management interface is used to display the target evaluation performance indicator and the evaluation performance indicator corresponding to the target version number.

7. The performance detection device of the artificial intelligence model according to claim 6, characterized in that: The performance detection information also includes a target evaluation script identifier, a target startup script template identifier, and script parameters, and also includes: A second acquisition module is used to acquire an evaluation script generation instruction based on the target evaluation script identifier, the target startup script template identifier and the script parameters; The generate evaluation script instruction is used to instruct the server to fill the script parameters into the target startup script template to obtain a target evaluation script; the target startup script template is a template corresponding to the target evaluation script identifier and the target startup script template identifier found from the correspondence between the preset evaluation script identifier, the startup script template identifier and the startup script template; The target evaluation script is used to obtain target evaluation performance indicators based on the target sample data labels and prediction results.

8. A computer program product, characterized in that The method comprises computer-readable instructions, which, when executed on an electronic device, enable the electronic device to implement the performance detection method of the artificial intelligence model as claimed in any one of claims 1 to 5.

9. An electronic device, characterized in that: comprising at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the electronic device can implement the performance detection method of the artificial intelligence model as described in any one of claims 1 to 5.

10. A computer storage medium, characterized in that The storage medium carries one or more computer programs, which, when executed by an electronic device, enable the electronic device to implement the performance detection method of the artificial intelligence model as described in any one of claims 1 to 5.

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