Automated testing methods, storage media, and equipment for factory equipment based on big data.

Abstract

This invention provides an automated testing method, storage medium, and equipment for factory-produced equipment based on big data. The method includes: a big data terminal behavior analysis platform acquiring the current operating environment parameters of a target abnormal device and analyzing the current operating environment parameters to obtain analysis results; if the analysis results indicate data anomalies, an automated testing platform sends a test command to the target abnormal device; the target abnormal device downloads a task package from the automated testing platform according to the test command and executes all test cases within the task package to obtain the test results for each test case; the automated testing platform obtains the current defects of the target abnormal device based on the test results of each test case. This invention utilizes an automated testing platform to send test cases to a target abnormal device, and the target abnormal device runs the test cases, thereby identifying operational problems of the abnormal device through the testing platform.

Description

Technical Field

[0001] This invention relates to the field of terminal device testing technology, and in particular to automated testing methods, storage media, and equipment for factory-delivered equipment based on big data. Background Technology

[0002] Terminal devices have become an indispensable part of our lives, such as home gateways, home routers, outdoor LoRa gateways, home ZigBee devices, factory Bluetooth gateways, and edge devices. However, these devices are affected by environmental factors such as temperature, humidity, air pressure, signal complexity, and the degree of shielding from obstructions during operation, which can cause abnormal operation and unstable signals. These issues require repair by network engineers. Before repairing a terminal device, it is necessary to test the problematic device to identify the cause of the problem.

[0003] The existing common methods for testing terminal devices are usually for enterprises to test terminal devices through various means such as on-site debugging and analysis of the cause of problems, issuing commands and uploading operation logs. Using these methods is not only inefficient, but also requires uploading terminal device operation logs, and the uploaded data is very limited, making it difficult to locate the problem. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an automated testing method, storage medium, and equipment for factory equipment based on big data, which solves the problem of difficulty in locating abnormal equipment defects in existing technologies.

[0005] According to an embodiment of the present invention, an automated testing method for outgoing equipment based on big data includes:

[0006] The big data terminal behavior analysis platform acquires the current operating environment parameters of the target abnormal device, analyzes the current operating environment parameters, and obtains the analysis results. The environmental parameters include humidity, temperature, voltage, signal strength, and shielding degree of the shielding material.

[0007] If the analysis result indicates data anomaly, the automated testing platform sends a test command to the target abnormal device.

[0008] The target abnormal device downloads a task package from the automated testing platform according to the test command and executes all test cases within the task package to obtain the test results for each test case;

[0009] The automated testing platform obtains the current defects of the target abnormal device based on the test results of each test case.

[0010] As an optional embodiment of the present invention, the big data terminal behavior analysis platform may optionally acquire the current operating environment parameters of the target abnormal device, and analyze the current operating environment parameters to obtain analysis results, including:

[0011] The big data terminal behavior analysis platform obtains the corresponding normal operation model based on the device type of the target abnormal device.

[0012] The environmental parameters are input into the normal working model, which determines whether the environmental parameters are within the normal working range. If any value of the environmental parameters is outside the normal working range, the analysis result is considered abnormal data.

[0013] As an optional embodiment of the present invention, the target abnormal device downloading the task package from the automated testing platform according to the test command includes:

[0014] The target abnormal device sends a download task package request to the automated testing platform according to the test task in the test command, wherein the download task package request includes device information and test task;

[0015] The automated testing platform verifies the legality of the device information, and when the legality verification is successful, it obtains the corresponding task package according to the test task.

[0016] The task package is encrypted and then sent to the target abnormal device.

[0017] As an optional embodiment of the present invention, optionally, the task package is encrypted and then sent to the target abnormal device, including:

[0018] Generate a key and encrypt the task package using an encryption algorithm;

[0019] The encrypted task package is sent to the target abnormal device.

[0020] As an optional embodiment of the present invention, the generated key may optionally include:

[0021] Using the target abnormal device, randomly select two numbers m and n, and calculate mn = f;

[0022] The target abnormal device may be selected by any number c, where c satisfies: J(c / m) = J(c / n) = -1, where J is the Jacobian symbol;

[0023] The target anomalous device publishes (c,f) as the public key and (m,n) as the secret key.

[0024] As an optional embodiment of the present invention, the step of encrypting the task package using an encryption algorithm may include:

[0025] Let the task package be x, and represent x using seven bits, x = (x1, x2, x3, ..., x7). i ∈(0,1), 1≤i≤7;

[0026] The automated testing platform can be selected from any one of the following: i ∈(1, f-1);

[0027] If x i =1, then

[0028] If x i =0, then

[0029] The encrypted task package is (k1,k2,k3,...k r ) = D(x).

[0030] As an optional embodiment of the present invention, optionally, before the target abnormal device executes all test cases within the task package and obtains the test results of each test case, the method further includes: decrypting the encrypted task package, wherein the decryption includes:

[0031] Based on (k1, k2, k3, ... k r Given (x) and (m,n), solve for each k. r J(k) i / m) and J(k i / n);

[0032] If J(k) i / m)=J(k i If ( / n) = 1, then x i =0; if J(k) i / m)=J(k i If ( / n) = -1, then x i =1;

[0033] x i When = 0, then and

[0034] x i When = 1, then and

[0035] Solve for (k1,k2,k3,...k r ), and obtain the decrypted task package.

[0036] As an optional embodiment of the present invention, the method may further include improving the test case library based on the results of executing the test cases.

[0037] On the other hand, according to embodiments of the present invention, an electronic device is also provided, comprising:

[0038] A memory on which computer programs are stored;

[0039] A processor is used to execute the program in the memory to implement the automated testing method for factory equipment based on big data.

[0040] The technical principle of this invention is as follows: The environmental parameters of the target abnormal device are sent to a big data terminal behavior analysis platform. This platform analyzes the environmental parameters to obtain abnormal data and determine if the device has a problem. Then, an automated testing platform sends a test command to the target abnormal device. Upon receiving the command, the device requests to download a task package. The platform then downloads the task package and executes all test cases within it. The device sends the test results back to the platform, which identifies the device's defects, determining whether the problem is hardware or software. If it's hardware, a hardware engineer resolves it; if it's software, a software engineer resolves it. This method allows for accurate identification of the target abnormal device.

[0041] Compared to existing technologies, this invention has the following advantages: It utilizes a big data terminal behavior analysis platform to analyze the environmental parameters of abnormal devices, obtain device anomaly information, and send this information to the target abnormal device. Based on this information, test tasks are configured for the terminal. The terminal downloads and parses the task package through an automated testing platform, then runs all test cases within the task package and sends the results back to the automated testing platform. Testers can then locate device defects based on the results. If it's a software problem, it's resolved by software engineers; if it's a hardware problem, it's resolved by hardware engineers. This method accurately identifies the device's problems. Furthermore, the automated testing platform encrypts the task package during download to ensure data transmission security. Attached Figure Description

[0042] Figure 1 A flowchart of an automated testing method for factory equipment based on big data, provided as an embodiment of the present invention.

[0043] Figure 2The flowchart illustrates an automated testing method for factory equipment based on big data, as provided in another embodiment of the present invention. Detailed Implementation

[0044] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0045] Example 1

[0046] like Figure 1 As shown in the figure, this invention proposes an automated testing method for factory-shipped equipment based on big data, including:

[0047] Step S1: The big data terminal behavior analysis platform obtains the current operating environment parameters of the target abnormal device, analyzes the current operating environment parameters, and obtains the analysis results.

[0048] It should be noted that environmental parameters include humidity, temperature, voltage, signal strength, and the shielding effectiveness of the shielding material. The big data terminal behavior analysis platform embeds several environmental models of normal device operation. This platform is existing technology and will not be elaborated upon here. First, the target malfunctioning device sends its operating environmental parameters and device information, including its model number, to the device management platform. The device management platform then sends these parameters to the big data terminal behavior analysis platform. The platform finds a normal operating model with the same model as the target malfunctioning device to test whether its operating environmental parameters exceed the normal operating range. If the temperature data exceeds the normal operating range, the platform outputs that the target malfunctioning device's temperature data is abnormal, indicating a defect in the device. Environmental parameters include humidity measured by a humidity sensor, temperature measured by a temperature sensor, voltage measured by a voltage sensor, the shielding effectiveness of the shielding material obtained through on-site environmental surveys, and signal strength measured by testing software.

[0049] Step S2: If the analysis result indicates data anomaly, the automated testing platform sends a test command to the target abnormal device.

[0050] In this embodiment, if the target abnormal device is found to have no problems with its operating environment after testing through the big data terminal behavior analysis platform, it is considered that the target abnormal device is not problematic. If the big data terminal behavior analysis platform detects a problem with the target abnormal device, the tester sends a test command to the device management platform, and then the device management platform sends a test command to the target abnormal device.

[0051] Step S3: The target abnormal device downloads the task package from the automated testing platform according to the test command and executes all test cases in the task package to obtain the test results of each test case;

[0052] In this embodiment, after receiving the test command, the target abnormal device sends a download task package request to the automated testing platform. The automated testing platform verifies the legitimacy of the target abnormal device. After the legitimacy verification is completed, the target abnormal device begins to download the task package. After the download is complete, the target abnormal device begins to execute all test cases in the task package and sends the test results to the automated testing platform. The test results include execution result statistics, execution log files, execution screenshot files, and execution network packet captures. Testers can view the current defects of the target abnormal device through the test results.

[0053] Step S4: The automated testing platform obtains the current defects of the target abnormal device based on the test results of each test case.

[0054] In summary, this embodiment sends the environmental parameters of the target malfunctioning device to the big data terminal behavior analysis platform through the device management platform. The big data terminal behavior analysis platform analyzes the environmental parameters of the target malfunctioning device to obtain abnormal data, including abnormal humidity, temperature, voltage, signal strength, and shielding effectiveness, thus confirming a problem with the target malfunctioning device. Then, an automated testing platform sends test commands to the device management platform, which in turn sends test commands to the target malfunctioning device. Upon receiving the test commands, the target malfunctioning device sends a request to download a task package to the automated testing platform. The automated testing platform verifies the legitimacy of the target malfunctioning device, downloads the task package, and executes all test cases within the task package. The target malfunctioning device sends the test results of all test cases to the automated testing platform. Testers use the automated testing platform to identify defects in the target malfunctioning device, determining whether the problem is hardware or software. If it is a hardware problem, it is resolved by hardware engineers; if it is a software problem, it is resolved by software engineers. This method accurately identifies the target malfunctioning device, and allows software or hardware engineers to address the problem specifically, improving the efficiency of resolving target malfunctioning device issues.

[0055] As an optional embodiment of the present invention, the big data terminal behavior analysis platform may optionally acquire the current operating environment parameters of the target abnormal device, and analyze the current operating environment parameters to obtain analysis results, including:

[0056] Step S11: The big data terminal behavior analysis platform obtains the corresponding normal operation model based on the device type of the target abnormal device.

[0057] In this embodiment, when the target abnormal device sends its current operating environment parameters to the big data terminal behavior analysis platform, it also sends the device information, including the device model, to the big data terminal behavior analysis platform. The big data terminal behavior analysis platform finds the normal operating model corresponding to the target abnormality based on the device information, and tests the environmental parameters of the target abnormal device through the normal operating model to determine whether the operating parameters of the target device are within the normal operating range.

[0058] Step S12: Input the environmental parameters into the normal working model. The normal working model determines whether the environmental parameters are within the normal working range. If any value of the environmental parameters is not within the normal working range, the analysis result is considered abnormal data.

[0059] In this embodiment, if the environmental parameters of the target abnormal device are within the normal operating range, it indicates that the target abnormal device has no problem, and the big data terminal behavior analysis platform outputs that the target abnormal device is a normal device and has no operating problems; if the environmental parameters of the target abnormal device are not within the normal operating range, it indicates that the target abnormal device has a problem, and the big data terminal behavior analysis platform issues abnormal data corresponding to the problem; if the voltage operation data of the target abnormal device has a problem, the big data terminal behavior analysis platform issues voltage abnormal data.

[0060] In this embodiment, the target abnormal device sends its environmental parameters and device data to the big data terminal behavior analysis platform through the device management platform. The big data terminal behavior analysis platform uses the device information of the target device to find its normal working model corresponding to the target abnormality. The environmental parameters of the target abnormal device are tested through the normal working model to determine whether the operating parameters of the target device are within the normal working range. If the environmental parameters of the target abnormal device are not within its corresponding operating range, the big data terminal behavior analysis platform sends the abnormal working data of the target abnormal device to the device management platform to determine that there is a problem with the target abnormal device.

[0061] As an optional embodiment of the present invention, the target abnormal device downloading the task package from the automated testing platform according to the test command includes:

[0062] Step S31: The target abnormal device sends a download task package request to the automated testing platform according to the test task in the test command, wherein the download task package request includes device information and test task.

[0063] In this embodiment, after the target abnormal device receives the test command, it sends a download task package request and the device information of the target abnormal device to the automated test platform through the device management platform. The automated test platform verifies the legitimacy of the target abnormal device through the SDK based on the device information of the target abnormal device. After the verification is successful, the target abnormal device downloads the task package.

[0064] Step S32: The automated testing platform verifies the legality of the device information. When the legality verification is successful, it obtains the corresponding task package according to the test task.

[0065] In this embodiment, the task package contains multiple test cases, and each test case contains at least one test script.

[0066] Step S33: Encrypt the task package and send it to the target abnormal device.

[0067] In summary, in this embodiment, the device information (such as MAC address, device parameters, device model, etc.) of the target abnormal device is verified by an automated testing platform. After the verification is successful, the automated testing platform encrypts the task package and sends the encrypted task package to the device management platform. The device management platform decrypts the encrypted task package to obtain all the original test cases. The device management platform runs all the test cases on the target abnormal device and obtains the test results for each test case. The device management platform then sends all the test results to the automated testing platform. Testers can then identify the defects of the target abnormal device based on the results from the automated testing platform.

[0068] As an optional embodiment of the present invention, optionally, the task package is encrypted and then sent to the target abnormal device, including:

[0069] Step S331: Generate a key.

[0070] Step S332: Encrypt the task package using an encryption algorithm.

[0071] In this embodiment, the key is generated through a device management platform.

[0072] Step S333: Send the encrypted task package to the target abnormal device.

[0073] In this embodiment, the automated testing platform encrypts the task package using a key generated by the device management platform. In summary, in this embodiment, the device management platform first generates a key, then sends the key to the automated testing platform. The automated testing platform encrypts the task package using this key, and then sends the encrypted task package to the device management platform via the SDK. The device management platform decrypts the encrypted task package using the key to obtain all test cases. The target faulty device then executes all test cases to obtain the test results.

[0074] As an optional embodiment of the present invention, the generated key may optionally include:

[0075] Step S3311: Using the target abnormal device, randomly select two numbers m and n, and calculate mn = f.

[0076] In this embodiment, m and n are prime numbers, which can be automatically generated by the device management platform.

[0077] Step S3312: The target abnormal device may be selected by any number c, which satisfies: J(c / m)=J(c / n)=-1, where J is the Jacobian symbol.

[0078] In this embodiment, c is a natural number.

[0079] The target anomalous device publishes (c,f) as the public key and (m,n) as the secret key.

[0080] In summary, in this embodiment, the key includes a public key (c,f) and a secret key (m,n). The device management platform sends the generated public key (c,f) to the automated testing platform. The automated testing platform uses the public key (c,f) to encrypt the task package and sends the encrypted task package back to the device management platform. The device management platform uses the secret key (m,n) to decrypt the task package encrypted with the public key (c,f) to obtain the original test cases. This method effectively ensures the security of data transmission.

[0081] As an optional embodiment of the present invention, the step of encrypting the task package using an encryption algorithm may include:

[0082] Step S3321: Let the task package be x, and represent x using seven-bit binary, x = (x1, x2, x3, ... x7). i ∈(0,1),1≤i≤7.

[0083] Step S3322: Select one of the automated testing platforms s i ∈(1, f-1).

[0084] Step S3323, if x i =1, then If x i =0, then

[0085] The encrypted task package is (k1,k2,k3,...k r ) = D(x).

[0086] As an optional embodiment of the present invention, optionally, before the target abnormal device executes all test cases within the task package and obtains the test results of each test case, the method further includes: decrypting the encrypted task package, wherein the decryption includes:

[0087] Step S51: Based on (k1,k2,k3,...k r Given (x) and (m,n), solve for each k. r J(k) i / m) and J(k i / n);

[0088] Step S52, if J(k) i / m)=J(k i If ( / n) = 1, then x i =0; if J(k) i / m)=J(k i If ( / n) = -1, then x i =1;

[0089] Step S53, x i When = 0, then and x i When = 1, then and

[0090] Step S54: Solve for (k1,k2,k3,...k r ), and obtain the decrypted task package.

[0091] As an optional embodiment of the present invention, the method may further include improving the test case library based on the results of executing the test cases.

[0092] Testers improve test cases by analyzing problems found in target abnormal equipment using big data-based automated testing methods for outgoing equipment. This includes adding test cases or adjusting parameters within test cases, making the test cases on the automated testing platform more complete.

[0093] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned automated testing method for factory equipment based on big data.

[0094] Computer-readable storage media are used to store various types of data to support the operation of the electronic device. This data may include, for example, instructions for any application or method used to operate on the electronic device, as well as application-related data. The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0095] An electronic device, comprising:

[0096] A memory on which computer programs are stored;

[0097] A processor is used to execute the program in the memory to implement the automated testing method for factory equipment based on big data.

[0098] As a preferred embodiment of this invention, the electronic device may include a processor, a memory, and may also include one or more of a multimedia component, an input / output (I / O) interface, and a communication component.

[0099] The processor controls the overall operation of the electronic device to complete all or part of the steps in the above-mentioned automated testing method for factory equipment based on big data.

[0100] Memory is used to store various types of data to support the operation of the electronic device. This data may include, for example, instructions for any application or method used to operate on the electronic device, as well as application-related data. Memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0101] The multimedia component may include a screen and an audio component, wherein the screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals; for example, the audio component may include a microphone for receiving external audio signals, the received audio signals may be further stored in memory or transmitted via a communication component; the audio component may also include at least one speaker for outputting audio signals.

[0102] I / O interfaces provide interfaces between the processor and other interface modules, such as keyboards, mice, buttons, etc.; these buttons can be virtual buttons or physical buttons.

[0103] The communication component is used for wired or wireless communication between the electronic device and other devices; wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G or 5G, or one or more combinations thereof, and the corresponding communication component may include: Wi-Fi module, Bluetooth module, NFC module, mobile communication module.

[0104] As a preferred embodiment, the electronic device may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described automated testing method for factory equipment based on big data.

[0105] In addition, the computer-readable storage medium provided in this embodiment can be the memory including program instructions, which can be executed by the processor of an electronic device to complete the above-mentioned automated testing method for factory equipment based on big data.

[0106] Example 2

[0107] like Figure 2 As shown in the figure, this embodiment provides an automated testing method for factory-delivered equipment based on big data. This method utilizes IoT terminals of various product models to report data to corresponding device management platforms. For example, product one router data is reported to the router management platform, product two gateway data is reported to the gateway device management platform, and product three Bluetooth tag data is reported to the indoor positioning device management platform. The reported data includes various dimensions that can affect the device's operating status, such as temperature, humidity, voltage, signal complexity, and shielding. The device management platform then forwards the reported data to a big data terminal behavior analysis platform. The big data terminal behavior analysis platform then determines whether each set of data is working normally and returns the determination result to the device management platform. The determination result includes whether the data is normal or abnormal. If the result returned to the device management platform is abnormal, the tester configures test commands and test tasks for the terminal. The test task contains a task package, which includes multiple test cases.

[0108] After receiving the test command, the terminal sends a request to the automated testing platform to download the automated testing SDK.

[0109] The automated testing platform returns the SDK address for the terminal to download and complete the initialization process, including setting the device ID (e.g., MAC address) and key.

[0110] The terminal initiates a device legitimacy verification request to the automated testing platform through the SDK.

[0111] The automated testing platform verifies the legitimacy of the device. Once the verification is successful, it returns a request to access the token via a subsequent interface. All subsequent data is transmitted encrypted via the token to ensure the security of the entire data. The terminal requests an automated testing task from the automated testing platform, with key parameters including the device number and product model.

[0112] The automated testing platform queries the corresponding test task based on the terminal's device number and product model and returns it to the terminal. The entire data return is in JSON format.

[0113] The terminal parses the corresponding JSON data and uses it to execute all the issued test cases. Each test case corresponds to a running script. The terminal initiates a script download request via HTTP, and the entire request data is in JSON format.

[0114] The automated testing platform returns a script to the terminal, and then the terminal executes the downloaded script.

[0115] The terminal reports the results of the executed scripts to the automated testing platform from the perspective of test cases. The main results include execution result statistics, execution log files, execution screenshot files, execution network packet captures, and other information, and the execution results are statistically analyzed.

[0116] The statistical execution results are submitted to the automated testing platform via an HTTP FORM form through the device management platform. Testers use the statistical execution results to determine the problem of the device. The file types can be various types of data such as logs, screenshots, and network packet captures.

[0117] Testers analyze and statistically analyze execution results to improve the test case library; equipment R&D engineers analyze the results of automated test cases to improve hardware or software quality, making the automated testing platform more complete.

[0118] The device management platform is used for device information access. Devices can connect to the device management platform through various protocols, mainly but not limited to HTTP, UDP, TCP, MQTT, COAP, etc. The device management platform is also used for device information management, including basic information such as device SN, MAC, name, model, etc., as well as other information such as power level and signal strength. The device management platform is also used for device data reporting, completing the reporting of the device's own working data through various protocols. The device management platform is also used for device command issuance, completing the issuance of device commands through a web interface, thereby controlling the device, such as upgrading and executing test tasks.

[0119] The automated testing platform can manage product and model information in a directory tree structure via a web interface; manage all test cases in a directory tree structure via a web interface; manage test tasks via a web interface; import IoT terminals via a web interface and set the number and key for each terminal to prepare for device verification; terminals download test tasks from the automated testing platform, execute test cases, and report the test case results; testers improve test cases based on the execution results; and R&D engineers improve the hardware or software of the device based on the execution results.

[0120] The Terminal Automated Testing Task SDK is used by terminals to verify device legitimacy with the automated testing platform using device ID and key. It also allows terminals to request test tasks via HTTP, with the automated testing platform returning the tasks to be executed. Each task contains numerous test cases, which the terminal executes sequentially. Furthermore, it enables terminals to execute test cases, download and execute the test scripts contained within them, and report test results after each test case is completed. The reported results include detailed data such as test case execution statistics, test case execution logs, screenshots of test case execution, and packet captures.

[0121] Based on the big data terminal behavior analysis platform, the system analyzes whether the device is in a working state. If not, the device management platform issues a command to the terminal to execute automated testing tasks. Through automated testing tasks, the system obtains data on the device's working status under various environments. This allows R&D engineers to analyze the execution results and make adjustments to the device's software or hardware to improve product quality. Testers can also analyze the execution results to improve the test case library within the automated testing platform, making its product testing coverage more comprehensive and further improving product quality.

[0122] "Terminal" here refers to IoT terminals in general; "product" refers to IoT device categories, such as optical modems, routers, Bluetooth gateways, LoRa gateways, sensor devices, etc.; "model" refers to the model number under each category, such as Bluetooth gateway-NB9384, router-AX3000Z.

[0123] The big data terminal behavior analysis platform obtains normal operating models of terminals through big data analysis, such as temperature -10°C to 0°C, distance 10 meters, and SSID signal value -60 to -90.

[0124] Device management platform: Different types of devices and systems use different communication methods, such as HTTP, UDP, TCP, MQTT, COAP, etc. The definition of an IoT device management platform is an IoT link system that supports various communication methods of devices, mainly including device data reporting and command issuance.

[0125] A test case refers to the basic unit in which a terminal executes a test, and the reported results are also based on test cases.

[0126] A test task refers to a test task that contains multiple test cases; what the terminal receives is a single test task.

[0127] A script refers to a test case that contains one or more test scripts. A test case is a description of the content to be executed, and the specific process to be executed is the script contained in the test case.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An automated testing method for factory-shipped equipment based on big data, characterized in that, include: The big data terminal behavior analysis platform acquires the current operating environment parameters of the target abnormal device, analyzes the current operating environment parameters, and obtains the analysis results. The environmental parameters include humidity, temperature, voltage, signal strength, and shielding degree of the shielding material. If the analysis result indicates data anomaly, the automated testing platform sends a test command to the target abnormal device. The target abnormal device downloads a task package from the automated testing platform according to the test command and executes all test cases within the task package to obtain the test results for each test case; The automated testing platform obtains the current defects of the target abnormal device based on the test results of each test case; The target abnormal device downloads a task package from the automated testing platform according to the test command, including: generating a key and encrypting the task package using an encryption algorithm; The generated key includes: Use the target anomalous device to select any two prime numbers and and order ; The target abnormal device can be any natural number. , satisfy: ,in, The symbol for Jacobi; The target abnormal device was announced. For public keys, The secret key; The encryption of the task package using an encryption algorithm includes: Let the task package be ,Will Represented using seven binary bits. , ; Choose one of the automated testing platforms. ; Let the encrypted task package be , , ; like ,but ; like ,but .

2. The automated testing method for factory equipment based on big data as described in claim 1, characterized in that: The big data terminal behavior analysis platform acquires the current operating environment parameters of the target abnormal device, analyzes the current operating environment parameters, and obtains the analysis results, including: The big data terminal behavior analysis platform obtains the corresponding normal operation model based on the device type of the target abnormal device. The environmental parameters are input into the normal working model, which determines whether the environmental parameters are within the normal working range. If any value of the environmental parameters is outside the normal working range, the analysis result is abnormal data.

3. The automated testing method for factory equipment based on big data as described in claim 1, characterized in that: The target abnormal device downloads a task package from the automated testing platform according to the test command, including: The target abnormal device sends a download task package request to the automated testing platform according to the test task in the test command, wherein the download task package request includes device information and test task; The automated testing platform verifies the legality of the device information, and when the legality verification is successful, it obtains the corresponding task package according to the test task. Generate a key and encrypt the task package using an encryption algorithm; The encrypted task package is sent to the target abnormal device.

4. The automated testing method for factory equipment based on big data as described in claim 1, characterized in that, Before the target abnormal device executes all test cases within the task package and obtains the test results for each test case, the method further includes: decrypting the encrypted task package, wherein the decryption includes: based on and Solve each of and ; like 1, then ;like ,but ; At that time, ,and ; At that time, ,and ; Solve Obtain the decrypted task package.

5. The automated testing method for factory equipment based on big data as described in claim 1, characterized in that, The method also includes improving the test case library based on the results of executing test cases.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the automated testing method for factory equipment based on big data as described in any one of claims 1-5.

7. An electronic device, characterized in that, include: A memory on which computer programs are stored; A processor for executing the program in the memory to implement the automated testing method for factory equipment based on big data as described in any one of claims 1-5.

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