CN118072218A - Hard disk lighting detection method and device, electronic equipment and storage medium - Google Patents

Abstract

The disclosure provides a method, a device, an electronic device and a storage medium for detecting hard disk lighting, which relate to the technical field of computers and are applied to hard disk lighting detection equipment, wherein the method comprises the following steps: starting an image pick-up device and controlling equipment to be detected to start a hard disk lighting program; obtaining a hard disk lighting video and intercepting a picture to be detected; and detecting the picture to be detected by using the color detection model to obtain a detection result. Therefore, labor and time cost can be greatly saved, the probability of error occurrence in work is reduced, and the product quality and the test quality of the server are improved.

Description

Hard disk lighting detection method and device, electronic equipment and storage medium

Technical Field

The disclosure relates to the field of computer technology, and in particular, to a method and device for detecting hard disk lighting, an electronic device and a storage medium.

Background

The hard disk lighting test is an automatic test method for testing whether the lighting function of the hard disk backboard is normal. Such testing typically involves the use of a board card such as HBA (Host Bus Adapter) cards, RAID (Redundant Array of INDEPENDENT DISKS) cards, and the like, and a hard disk backplane. The hard disk lamp on the hard disk backboard is usually controlled by a main chip on the HBA card or the RAID card, with the upgrading of the board card technology, the types of the main chip are more and more, and buses of the hard disk lamp controlled by different HBA cards and RAID cards are also different. Therefore, a hard disk lighting test is required to ensure that the back panel lamp is operating properly.

In the hard disk lighting test, a tester needs to light the hard disk and the backboard by using a hard disk lighting instruction or a mode of changing related hard disk operation options in the BIOS, and then manually checks the correctness of the lighted lamp. For example, in the general server, the hard disk fault lamp is red long bright, the hard disk normal in-place state is green long bright, the hard disk positioning lamp is blue long bright, the hard disk reconstruction state is pink long bright, and the hard disk read-write lamp is green and blinks. The tester needs to use a manual judgment to identify the status lights.

However, a drawback of manually detecting the illuminated lamp is that, first, the prior art has the most basic physiological requirements (no red-green blindness) for the test person. Secondly, the hard disk and the backboard of the existing server need to be lighted up and observed by test staff in the whole course, when the backboard interfaces of the server to be tested are more, the test staff need to test the interfaces next to each other, labor and time are consumed, the phenomenon of missing test easily occurs in the manual test, and the product quality and the test quality of the server are relatively large potential threats.

Disclosure of Invention

The disclosure provides a method and a device for detecting hard disk lighting, an electronic device and a storage medium, so as to at least solve the above technical problems in the prior art.

According to a first aspect of the present disclosure, there is provided a method of detecting a hard disk lighting, applied to a hard disk lighting detection apparatus, the method comprising:

starting an image pick-up device and controlling equipment to be detected to start a hard disk lighting program;

Obtaining a hard disk lighting video and intercepting a picture to be detected;

and detecting the picture to be detected by using a color detection model to obtain a detection result.

In an embodiment, before the camera is turned on and the device to be detected is controlled to start the hard disk lighting program, the method further includes:

acquiring identity identification information of equipment to be detected and authenticating;

And when the equipment to be detected passes the authentication, sending a hard disk lighting program to the equipment to be detected.

In an embodiment, the controlling the device to be detected to start the hard disk lighting program includes:

Controlling the equipment to be detected to traverse slot information of all hard disks and acquiring a lighting sequence;

Sequentially executing lighting according to the lighting sequence;

After all the hard disks are lighted, turning off the lamps in turn according to the lighting sequence.

In an embodiment, detecting the picture to be detected by using a color detection model to obtain a detection result includes:

Extracting basic shapes and texture features in the picture to be detected to obtain a first feature map;

performing first pooling operation on the first feature map to obtain a first reduced feature map;

Extracting advanced information features in the first reduced feature map to obtain a second feature map;

performing a second pooling operation on the second feature map to obtain a second reduced feature map;

Converting the second reduced feature map into one-dimensional features, and outputting color category probabilities of the one-dimensional features by using a full connection layer;

and obtaining the detection result based on the color class probability and a preset color threshold.

In an embodiment, after obtaining the detection result, the method further includes:

and collecting and storing a test log, wherein the test log comprises a hard disk lighting video and a picture with normal hard disk lighting.

According to a second aspect of the present disclosure, there is provided a detection apparatus for hard disk lighting, applied to a hard disk lighting detection device, the apparatus comprising:

The starting module is used for starting the camera device and controlling the equipment to be detected to start a hard disk lighting program;

The acquisition module is used for acquiring the hard disk lighting video and intercepting the picture to be detected;

and the detection module is used for detecting the picture to be detected by using a color detection model to obtain a detection result.

In one embodiment, the apparatus further comprises; the authentication module is used for acquiring the identity identification information of the equipment to be detected and authenticating before the camera device is started and the equipment to be detected is controlled to start the hard disk lighting program; and when the equipment to be detected passes the authentication, sending a hard disk lighting program to the equipment to be detected.

In an embodiment, the starting module is specifically configured to:

Controlling the equipment to be detected to traverse slot information of all hard disks and acquiring a lighting sequence;

Sequentially executing lighting according to the lighting sequence;

After all the hard disks are lighted, turning off the lamps in turn according to the lighting sequence.

According to a third aspect of the present disclosure, there is provided an electronic device comprising:

at least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the methods described in the present disclosure.

According to a fourth aspect of the present disclosure, there is provided a non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of the present disclosure.

The method, the device, the electronic equipment and the storage medium for detecting the hard disk lighting are applied to the hard disk lighting detection equipment, and the method comprises the following steps: starting an image pick-up device and controlling equipment to be detected to start a hard disk lighting program; obtaining a hard disk lighting video and intercepting a picture to be detected; and detecting the picture to be detected by using the color detection model to obtain a detection result. Therefore, labor and time cost can be greatly saved, the probability of error occurrence in work is reduced, and the product quality and the test quality of the server are improved.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present disclosure will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. Several embodiments of the present disclosure are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings, in which:

In the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

Fig. 1 is a schematic diagram illustrating an implementation flow of a method for detecting a hard disk lighting in an embodiment of the disclosure;

fig. 2 illustrates a schematic flowchart of an implementation flow of identity authentication of a device to be detected according to an embodiment of the present disclosure;

Fig. 3 is a schematic diagram of an implementation flow for controlling a device to be detected to start a hard disk lighting program according to an embodiment of the present disclosure;

fig. 4 is a schematic diagram of an implementation flow of detecting a picture to be detected by using a color detection model according to an embodiment of the disclosure;

FIG. 5 is a schematic diagram of a detection device for hard disk lighting according to an embodiment of the present disclosure;

Fig. 6 shows a schematic diagram of a composition structure of an electronic device according to an embodiment of the present disclosure.

Detailed Description

In order to make the objects, features and advantages of the present disclosure more comprehensible, the technical solutions in the embodiments of the present disclosure will be clearly described in conjunction with the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, but not all embodiments. Based on the embodiments in this disclosure, all other embodiments that a person skilled in the art would obtain without making any inventive effort are within the scope of protection of this disclosure.

The present disclosure provides a method for detecting a hard disk lighting, which is applied to a hard disk lighting detection device, as shown in fig. 1, and includes:

Step 101: starting the image pick-up device and controlling the equipment to be detected to start the hard disk lighting program.

In this example, an imaging device for taking a picture or video in the course of performing hard disk lighting detection is provided on the hard disk lighting detection apparatus. And, the hard disk lighting detection device can send the hard disk lighting program to the device to be detected, and control the device to be detected to start the program.

Step 102: and acquiring a hard disk lighting video and intercepting a picture to be detected.

In this example, the device to be detected after starting the hard disk lighting program is photographed in real time by the image pickup device on the hard disk lighting detection device, and then, a key picture to be detected is taken from the photographed video to be detected.

Step 103: and detecting the picture to be detected by using the color detection model to obtain a detection result.

In this example, the color detection model is used to perform deep detection analysis on the picture to be detected, so as to obtain an accurate detection result. The color detection model is based on an image recognition algorithm, can recognize color change in the picture to be detected, and obtains a detection result by comparing the color change with a preset threshold value.

The present disclosure provides a method for detecting hard disk lighting, which is applied to a hard disk lighting detection device, and the method includes: starting an image pick-up device and controlling equipment to be detected to start a hard disk lighting program; obtaining a hard disk lighting video and intercepting a picture to be detected; and detecting the picture to be detected by using the color detection model to obtain a detection result. Therefore, labor and time cost can be greatly saved, the probability of error occurrence in work is reduced, and the product quality and the test quality of the server are improved.

In one example, before turning on the image capturing apparatus and controlling the device to be detected to start the hard disk lighting program, as shown in fig. 2, the method further includes:

step 201: and acquiring the identity identification information of the equipment to be detected and authenticating.

In this example, after the imaging device of the hard disk lighting device is turned on and the shooting angle is adjusted, it is determined that the hard disk lighting detection device and the device to be detected are in the same network environment and stable communication connection is established, so that information transmission and authentication between the devices are ensured by the connection. The hard disk lighting detection equipment performs identity authentication on the equipment to be detected by acquiring the equipment identification information of the equipment to be detected, so that the accuracy of data is improved, and the follow-up detection work provides safety guarantee.

Step 201: and when the authentication of the equipment to be detected is passed, sending a hard disk lighting program to the equipment to be detected.

In this example, after confirming that the identity of the device to be detected passes, the hard disk lighting device further transmits a hard disk lighting program thereto. And responding to the operation instruction of program starting, and controlling the equipment to be detected to start the program by the hard disk lighting equipment.

In one example, controlling the device to be detected to initiate a hard disk lighting program, as shown in fig. 3, includes:

Step 301: and controlling the equipment to be detected to traverse slot information of all the hard disks and acquiring the lighting sequence.

In this example, the hard disk lighting device controls the device to be detected to traverse all the hard disks in turn, and obtains slot information of each hard disk, thereby determining the lighting sequence of the hard disk.

Step 302: lighting is sequentially performed according to the lighting sequence.

In this example, according to the determined lighting sequence, the hard disks on the device to be detected are controlled to sequentially perform lighting so that the image pickup device picks up the picture to be detected after each hard disk completes lighting.

Step 303: after all the hard disks are lighted, the lamps are turned off in sequence according to the lighting sequence.

In this example, after all the hard disks finish lighting, the hard disks on the device to be detected are controlled to perform turning-off sequentially according to the lighting sequence, so that the image pickup device picks up the picture to be detected after each hard disk finishes turning-off.

It should be noted that the image capturing device may collect a plurality of pictures that are turned on or off for each hard disk, and then select a picture with higher definition and quality from the pictures as a picture to be detected for subsequent detection operation.

In one example, the detecting the picture to be detected by using the color detection model, to obtain a detection result, as shown in fig. 4, includes:

step 401: and extracting basic shapes and texture features in the picture to be detected to obtain a first feature map.

In this example, an HSV (Hue, saturation, value) model will be used to perform detection processing on a picture to be detected. HSV is a color space according to the visual characteristics of colors, also called a hexagonal pyramid model (Hexcone Model), in which the color parameters of the acquired picture are hue (H), saturation (S) and brightness (V), respectively, and the HSV model focuses on color representation in terms of color, darkness, etc. compared to the ordinary RGB (Red, greed, blue) model.

Therefore, after the picture to be detected is acquired, the RGB picture is first converted into the HSV picture by the cv2.cvttcolor (img, cv2.color_bgr2hsv) function of the OpenCV library in Python. Then, the converted picture to be detected is cut to a preset picture size, for example, 100×100 pixels.

And then, inputting the adjusted picture to be detected into a prediction model for prediction. The predictive model is a convolutional neural network (Convolutional Neural Network, CNN) that includes two convolutional layers (Conv 2D) and a pooling layer (MaxPooling D), and one fully-connected layer (Dense). Wherein the convolution layer extracts image features by performing a convolution operation on the input image using a filter (convolution kernel).

The first convolution layer comprises a plurality of convolution kernels and a first activation function, and the convolution layer is utilized to learn and extract basic shapes and texture features in the picture to be detected, so that a first feature map is obtained. Preferably, the first convolution layer contains 32 convolution kernels, each convolution kernel has a size of 3×3, and the first activation function is a ReLU (RECTIFIED LINEAR Unit) activation function, and the activation function introduces nonlinearity, so as to increase the expression capability of the model.

Step 402: and carrying out first pooling operation on the first feature map to obtain a first reduced feature map.

In this example, the first feature map is input into a first pooling layer for performing a first pooling operation, where the pooling layer is configured to reduce a spatial dimension of the feature map, and reduce a requirement for computing resources. The first pooling operation is to maximally pool the first feature map for reducing the size of the feature map, a maximum value in a pooling window is reserved, and the rest values are ignored, thereby forming a downsampled first scaled feature map. Preferably, the first pooling operation has a pooling rate of 2.

Step 403: and extracting high-level information features in the first reduced feature map to obtain a second feature map.

In this example, the first scaled-down feature map is input into a second convolution layer, which includes a number of convolution kernels and a second activation function, and features of greater detail in the first scaled-down feature map, i.e., advanced information features, are further extracted using this convolution layer learning, thereby obtaining a second feature map. Preferably, the second convolution layer includes 64 convolution kernels, each convolution kernel having a size of 3×3 pixels, and the second activation function is a ReLU activation function.

Step 404: and carrying out second pooling operation on the second feature map to obtain a second reduced feature map.

In this example, the second feature map is input into the second pooling layer for a second pooling operation, thereby forming a downsampled second scaled-down feature map. Preferably, the second pooling operation has a pooling rate of 2.

Step 405: and converting the second reduced feature map into one-dimensional features, and outputting the color category probability of the one-dimensional features by using the full connection layer.

In this example, the full-connection layer connects all neurons of the previous layer with each neuron of the current layer, flattens the feature map of the second reduced feature map output by the pooling layer to obtain one-dimensional features, classifies the features by the neurons of the full-connection layer, and finally outputs the classification result to a conversion probability value by using a third activation function, and judges whether the picture to be detected is a lighting picture according to the probability value. Preferably, the fully connected layer has 1 neuron, and the third activation function is a Sigmoid activation function, which can output a probability between 0 and 1 of the classification result of the fully connected layer, for indicating the possibility that the image belongs to the color class.

Step 406: and obtaining a detection result based on the color class probability and a preset color threshold.

In the prediction stage, the prediction model calculates the input test image through forward propagation to obtain a color class probability, and judges according to a preset color threshold value, so as to determine whether the image belongs to a specified color class.

If the color class probability is greater than or equal to the preset color threshold, the picture is considered to be the designated color, namely, the lighting of all hard disks in the picture to be detected is successful, otherwise, the lighting of part of hard disks in the picture to be detected is considered to be failed, and the detection result is recorded for subsequent feedback.

In one example, the training process of the predictive model is as follows:

step 1: and acquiring a data set to be trained. And reading a plurality of hard disk lighting pictures and a plurality of hard disk unlit pictures of the pictures from an OpenCV library in Python, and adjusting the sizes of the pictures to be the preset sizes. Then, the hard disk lighting picture (the hard disk lighting color is yellow) is marked as 1, and the picture which is not lighted on the hard disk is marked as 0.

Step 2: and constructing a CNN model. The CNN model includes two convolutional layers, a pooling layer, and a fully-connected layer.

Step 3: and inputting the data set to be trained into the CNN model for multi-round and multi-batch training. In the training phase, training of the model uses Adam optimizers to minimize the binary cross entropy loss function and evaluate the performance of the model in terms of accuracy. The model minimizes the loss function and improves the accuracy by back-propagating and optimizing the data to be trained, thereby obtaining a trained prediction model.

In one example, assuming that there are 20 hard disks to be hard disk lighting detection, the prediction process of the prediction model is as follows:

Step 1: the image pickup device collects 20 images which are lighted by 20 hard disks, and 20 images which are not lighted by 20 hard disks.

Step 2: and inputting the picture to be detected into a prediction model to obtain a prediction result. Wherein, the lamps all light the picture to output the color class probability mode. Cnn (HSV) =1, and the lamps all do not light to output the color class probability mode. Cnn (HSV) =0.

If 1 or 2 or 9 pictures such as the picture with the disk not being lighted (namely, the partial lamp is not lighted) appear, the color category probability obtained by leading the pictures to be detected into the prediction model is 0.ltoreq.mode.CNN (HSV) <1.

Step 3: and comparing the color class probability with a preset color threshold value to obtain a detection result. In the actual training model, a certain small error exists (for example, the quantity of training data is not large enough), so the predicted color threshold value can be set to be slightly smaller than 1, for example, 0.99, and if the color class probability of the picture to be detected is greater than or equal to 0.99, the picture to be detected is positioned to accord with the specified color, namely, the lighting of all 20 hard disks is bright.

In one example, after obtaining the detection result, the method further comprises: and collecting and storing a test log, wherein the test log comprises a hard disk lighting video and a picture with normal hard disk lighting.

In this example, after the detection of the hard disk lighting is completed, a detailed test log needs to be collected and stored, and the test log not only includes video records of the hard disk lighting, but also covers pictures when the hard disk lighting is normal, so that a plurality of inconveniences caused by manually collecting the test log are greatly simplified.

The present disclosure also provides a detection device for hard disk lighting, as shown in fig. 5, applied to a hard disk lighting detection device, the device comprising:

The starting module 501 is used for starting the camera device and controlling the equipment to be detected to start a hard disk lighting program;

The obtaining module 502 is configured to obtain a hard disk lighting video and intercept a picture to be detected;

and the detection module 503 is configured to detect the picture to be detected by using the color detection model, so as to obtain a detection result.

In one example, the apparatus further comprises; the authentication module 504 is configured to obtain identity information of the device to be detected and perform authentication before starting the camera and controlling the device to be detected to start a hard disk lighting program; and when the authentication of the equipment to be detected is passed, sending a hard disk lighting program to the equipment to be detected.

In one example, the initiation module 501 is specifically configured to:

controlling equipment to be detected to traverse slot information of all hard disks and acquiring a lighting sequence; sequentially performing lighting according to the lighting sequence; after all the hard disks are lighted, the lamps are turned off in sequence according to the lighting sequence.

In one example, the detection module 503 is specifically configured to:

Extracting basic shapes and texture features in a picture to be detected to obtain a first feature map;

performing first pooling operation on the first feature map to obtain a first reduced feature map;

extracting advanced information features in the first reduced feature map to obtain a second feature map;

performing second pooling operation on the second feature map to obtain a second reduced feature map;

Converting the second reduced feature map into one-dimensional features, and outputting color category probabilities of the one-dimensional features by using the full-connection layer;

And obtaining a detection result based on the color class probability and a preset color threshold.

In one example, the apparatus further includes a collection module 505 to:

And collecting and storing a test log, wherein the test log comprises a hard disk lighting video and a picture with normal hard disk lighting.

According to embodiments of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

Fig. 6 illustrates a schematic block diagram of an example electronic device 600 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 6, the electronic device 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

Various components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, mouse, etc.; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 601 performs the respective methods and processes described above, for example, a detection method of hard disk lighting. For example, in some embodiments, the method of detecting hard disk lighting may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the above-described detection method of hard disk lighting may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the detection method of the hard disk lighting by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.

The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel or sequentially or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present disclosure, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

The foregoing is merely specific embodiments of the disclosure, but the protection scope of the disclosure is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the disclosure, and it is intended to cover the scope of the disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.

Claims (10)

1. A method for detecting lighting of a hard disk, which is applied to a hard disk lighting detection device, the method comprising:

starting an image pick-up device and controlling equipment to be detected to start a hard disk lighting program;

Obtaining a hard disk lighting video and intercepting a picture to be detected;

and detecting the picture to be detected by using a color detection model to obtain a detection result.

2. The method of claim 1, wherein before the turning on of the image capturing apparatus and controlling the device to be detected to start the hard disk lighting program, the method further comprises:

acquiring identity identification information of equipment to be detected and authenticating;

And when the equipment to be detected passes the authentication, sending a hard disk lighting program to the equipment to be detected.

3. The method of claim 1, wherein controlling the device to be detected to initiate a hard disk lighting program comprises:

Controlling the equipment to be detected to traverse slot information of all hard disks and acquiring a lighting sequence;

Sequentially executing lighting according to the lighting sequence;

After all the hard disks are lighted, turning off the lamps in turn according to the lighting sequence.

4. The method according to claim 1, wherein detecting the picture to be detected by using a color detection model to obtain a detection result comprises:

Extracting basic shapes and texture features in the picture to be detected to obtain a first feature map;

performing first pooling operation on the first feature map to obtain a first reduced feature map;

Extracting advanced information features in the first reduced feature map to obtain a second feature map;

performing a second pooling operation on the second feature map to obtain a second reduced feature map;

Converting the second reduced feature map into one-dimensional features, and outputting color category probabilities of the one-dimensional features by using a full connection layer;

and obtaining the detection result based on the color class probability and a preset color threshold.

5. The method of claim 1, wherein after obtaining the detection result, the method further comprises:

and collecting and storing a test log, wherein the test log comprises a hard disk lighting video and a picture with normal hard disk lighting.

6. A hard disk lighting detection apparatus, characterized by being applied to a hard disk lighting detection device, comprising:

The starting module is used for starting the camera device and controlling the equipment to be detected to start a hard disk lighting program;

The acquisition module is used for acquiring the hard disk lighting video and intercepting the picture to be detected;

and the detection module is used for detecting the picture to be detected by using a color detection model to obtain a detection result.

7. The method of claim 6, wherein the apparatus further comprises; the authentication module is used for acquiring the identity identification information of the equipment to be detected and authenticating before the camera device is started and the equipment to be detected is controlled to start the hard disk lighting program; and when the equipment to be detected passes the authentication, sending a hard disk lighting program to the equipment to be detected.

8. The method according to claim 1, wherein the starting module is specifically configured to:

Controlling the equipment to be detected to traverse slot information of all hard disks and acquiring a lighting sequence;

Sequentially executing lighting according to the lighting sequence;

After all the hard disks are lighted, turning off the lamps in turn according to the lighting sequence.

9. An electronic device, comprising:

at least one processor; and

A memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-5.