Machine room environment management and control method and system

Abstract

The invention relates to a machine room environment management and control method and system, and belongs to the technical field of environment monitoring. The method comprises the steps that machine room environment data are acquired, an environment management and control instruction is generated through a preset environment threshold value, and the machine room environment data comprise the machine room temperature, the machine room humidity and the machine room smoke concentration; historical monitoring pictures of a machine room are obtained, and a standard monitoring picture of the machine room is obtained through preprocessing the pictures of the machine room, wherein the historical monitoring pictures of the machine room comprise a machine room water immersion picture and a machine room normal picture; a machine room monitoring data set is obtained by improving deep convolution generative adversarial network data expansion according to the machine room monitoring standard picture, and a machine room water immersion identification model is constructed through an improved HRNetV2 algorithm according to the machine room monitoring data set; a machine room monitoring picture is obtained, a machine room water immersion detection label is obtained through the machine room water immersion recognition model according to the machine room monitoring picture, a machine room water immersion alarm instruction is sent to a terminal, intelligent machine room environment supervision is achieved, and safety guarantee is provided for the machine room environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental monitoring, and in particular relates to a method and system for controlling a computer room environment. Background Art

[0002] The computer room is the core of enterprise information processing and storage. Its environmental conditions have a decisive impact on the stable operation of equipment, data security and system reliability. Preventive maintenance and rapid emergency response through computer room environmental control are crucial to maintaining the stability of the computer room environment.

[0003] However, there are still some problems with the existing computer room environment control methods: computer room management relies heavily on manual operations, including environmental monitoring, equipment inspection and troubleshooting, which not only consumes a lot of human resources, but also increases operating costs. The traditional video surveillance method to monitor the computer room environment is difficult to cover the computer room environment in all aspects, and this method is highly dependent on monitoring personnel. Monitoring personnel need to continuously concentrate on observing the video screen. Long-term manual monitoring can easily lead to fatigue, thereby reducing the overall efficiency of monitoring. In the case of relying on manual monitoring, the discovery and handling of faults are often not fast enough, and the negligence of monitoring personnel may lead to more serious losses. In addition, manual monitoring is difficult to respond to environmental changes in real time, which may affect the normal operation of equipment. Moreover, traditional monitoring methods usually lack the ability to deeply analyze monitoring data, and cannot predict potential problems through data analysis and perform preventive maintenance.

[0004] The existing computer room environment management lacks automated environmental adjustment and fault warning capabilities, and is unable to meet the needs of modern information technology development. Therefore, implementing real-time remote monitoring of the computer room environment and strengthening intelligent control have become urgent needs to ensure the stability of the network operating environment, equipment safety, and personnel safety. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention proposes a computer room environment control method and system.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method for controlling a computer room environment, comprising:

[0008] Acquire computer room environment data, and generate environment control instructions according to the computer room environment data through preset environment thresholds, wherein the computer room environment data includes computer room temperature, computer room humidity, and computer room smoke concentration;

[0009] Acquire historical monitoring pictures of the computer room, and obtain standard monitoring pictures of the computer room by preprocessing the pictures of the computer room according to the historical monitoring pictures of the computer room, wherein the historical monitoring pictures of the computer room include pictures of flooding in the computer room and pictures of normal computer room;

[0010] According to the standard monitoring image of the computer room, a computer room monitoring data set is obtained by improving the deep convolution generative adversarial network data expansion, and according to the computer room monitoring data set, a computer room water immersion recognition model is constructed by improving the HRNetV2 algorithm;

[0011] Obtain a computer room monitoring picture, obtain a computer room water immersion detection tag through the computer room water immersion recognition model according to the computer room monitoring picture, and send a computer room water immersion alarm instruction to the terminal according to the computer room water immersion tag.

[0012] Preferably, the acquiring of computer room environment data includes:

[0013] Install environmental monitoring equipment at key locations in the computer room, including temperature sensors, humidity sensors, and smoke sensors;

[0014] The computer room environment data is obtained through the environment monitoring device, and the environment monitoring device is operated uninterruptedly for 24 hours. The monitoring frequency of the computer room environment data is set to once every 5 minutes.

[0015] Preferably, generating an environmental control instruction according to the computer room environment data by using a preset environmental threshold comprises:

[0016] Preset environmental thresholds, the preset environmental thresholds including preset computer room temperature thresholds, preset computer room humidity thresholds, and preset computer room smoke concentration thresholds;

[0017] The environmental control instructions include cooling equipment start-up instructions, cooling equipment shutdown instructions, ventilation and dehumidification equipment start-up instructions, ventilation and dehumidification equipment shutdown instructions, and computer room smoke warning instructions;

[0018] When the temperature of the equipment room is higher than the preset equipment room temperature threshold, a cooling device startup instruction is generated; when the temperature of the equipment room drops to within the preset equipment room temperature threshold, a cooling device shutdown instruction is generated;

[0019] When the humidity in the equipment room is higher than the preset equipment room humidity threshold, a ventilation and dehumidification equipment startup instruction is generated; when the humidity in the equipment room drops below the preset equipment room humidity threshold, a ventilation and dehumidification equipment shutdown instruction is generated;

[0020] When the smoke concentration in the computer room is higher than the preset smoke concentration threshold in the computer room, a computer room smoke warning instruction is sent to the terminal, and an audible and visual alarm is activated.

[0021] Preferably, obtaining a standard computer room monitoring picture by computer room picture preprocessing according to the computer room historical monitoring picture comprises:

[0022] De-noising the historical monitoring picture of the computer room by using an adaptive median filtering algorithm to obtain a historical monitoring denoised picture of the computer room;

[0023] The denoised pictures of the historical monitoring of the computer room are enhanced by a histogram equalization algorithm to obtain a standard picture of the computer room monitoring.

[0024] Preferably, the improved deep convolutional generative adversarial network includes a generator and a discriminator, and a residual block is introduced after the transposed convolution layer of the generator. A coordinate attention mechanism and an AdaLIN layer are added to each residual block to enhance the details and authenticity of the generated image.

[0025] Preferably, the improved HRNetV2 algorithm includes a backbone network, a feature integration block and a prediction head. The backbone network is used to extract computer room water immersion feature maps with different resolutions. A deep routing attention module is introduced into the feature integration block. The importance weights of computer room water immersion feature maps with different resolutions are calculated by the deep routing attention module and weighted fusion is performed. The weighted fused computer room water immersion feature maps are then recalibrated through a gating mechanism to enhance useful features and suppress noise. The prediction head converts the computer room water immersion feature map output by the feature integration block into a computer room water immersion recognition result.

[0026] Preferably, the acquiring of a computer room monitoring picture, obtaining a computer room water immersion detection tag through the computer room water immersion recognition model according to the computer room monitoring picture, and sending a computer room water immersion alarm instruction to the terminal according to the computer room water immersion tag comprises:

[0027] Obtain monitoring images of the computer room through an intelligent inspection robot, which is equipped with a high-definition monitoring camera and inspects according to a preset inspection route;

[0028] The computer room monitoring picture is obtained by preprocessing the computer room picture to obtain a computer room monitoring standard picture;

[0029] Inputting the computer room monitoring standard picture into the computer room water immersion recognition model to obtain a computer room water immersion detection label, wherein the computer room water immersion detection label includes a computer room water immersion label and a computer room normal label;

[0030] When the machine room water immersion detection tag is a machine room water immersion tag, a machine room water immersion alarm instruction is sent to the terminal, and an audible and visual alarm is activated.

[0031] A computer room environment control system, applied to the above-mentioned computer room environment control method, includes a computer room environment control module, a computer room monitoring data set construction module, and a computer room water immersion identification module;

[0032] The computer room environment control module is used to obtain computer room environment data and generate environment control instructions according to the computer room environment data through a preset environment threshold, wherein the computer room environment data includes computer room temperature, computer room humidity, and computer room smoke concentration;

[0033] The computer room monitoring data set construction module is used to obtain the historical monitoring pictures of the computer room and obtain the computer room monitoring standard pictures through computer room picture preprocessing, and obtain the computer room monitoring data set through the improved deep convolution generative adversarial network data expansion according to the computer room monitoring standard pictures;

[0034] The computer room water immersion recognition module is used to build a computer room water immersion recognition model based on the computer room monitoring data set by improving the HRNetV2 algorithm, obtain computer room monitoring pictures and obtain computer room water immersion detection labels through the computer room water immersion recognition model, and send a computer room water immersion alarm instruction to the terminal according to the computer room water immersion label.

[0035] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned computer room environment control method when executing the computer program.

[0036] A storage medium containing computer executable instructions, wherein the computer executable instructions are used to execute the above-mentioned computer room environment control method when executed by a computer processor.

[0037] The beneficial effects of the present invention are:

[0038] (1) By acquiring the computer room environment data in real time, quickly detecting environmental anomalies, and generating environmental control instructions based on preset environmental thresholds, the computer room environment is ensured to be always in the best condition. Preventive measures are taken before environmental parameters reach critical values ​​to avoid equipment damage or operation interruption.

[0039] (2) By acquiring the historical monitoring pictures of the computer room and preprocessing the pictures, the standard monitoring pictures of the computer room are obtained. The adaptive median filtering algorithm can effectively remove the noise in the historical monitoring pictures of the computer room without affecting the edges and details of the image, thereby improving the clarity of the image. The histogram equalization algorithm is used to enhance the contrast of the image and reduce the impact of light conditions on the quality of the monitoring image in an uneven or dim computer room environment, thereby improving the accuracy and efficiency of subsequent image analysis and recognition.

[0040] (3) Based on the standard images of the computer room monitoring, the computer room monitoring dataset is obtained by improving the deep convolutional generative adversarial network data expansion, which can generate more diverse training samples and help improve the generalization ability of the model. By introducing a residual block after the transposed convolution layer of the generator, and adding a coordinate attention mechanism and an AdaLIN layer to each residual block, the details and authenticity of the generated image can be enhanced;

[0041] (4) By improving the HRNetV2 algorithm, a computer room flooding recognition model is constructed. By introducing a deep routing attention module in the feature integration block, useful features can be enhanced and noise can be suppressed, thereby improving the model's recognition accuracy and speed of computer room flooding. By analyzing real-time monitoring images through the computer room flooding recognition model, the computer room flooding situation can be discovered in time and a computer room flooding label can be generated. When flooding occurs, the computer room flooding alarm command can be quickly sent to the terminal, so that the operation and maintenance personnel can take timely measures to reduce the damage to the computer room equipment caused by flooding. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0043] Figure 1 The present invention is a flowchart of a method for controlling a computer room environment. DETAILED DESCRIPTION

[0044] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0045] See also Figure 1 , a computer room environment control method, comprising:

[0046] S1: Acquire computer room environment data, and generate environment control instructions according to the computer room environment data through preset environment thresholds, wherein the computer room environment data includes computer room temperature, computer room humidity, and computer room smoke concentration;

[0047] S2: Obtain historical monitoring pictures of the computer room, and obtain standard monitoring pictures of the computer room by preprocessing the pictures of the computer room according to the historical monitoring pictures of the computer room, wherein the historical monitoring pictures of the computer room include pictures of flooding in the computer room and pictures of normal computer room;

[0048] S3: According to the standard monitoring image of the computer room, a computer room monitoring data set is obtained by improving the deep convolution generative adversarial network data expansion, and according to the computer room monitoring data set, a computer room water immersion recognition model is constructed by improving the HRNetV2 algorithm;

[0049] S4: Obtain a monitoring picture of the computer room, obtain a water immersion detection tag of the computer room through the water immersion recognition model of the computer room according to the monitoring picture of the computer room, and send a water immersion alarm instruction of the computer room to the terminal according to the water immersion tag of the computer room.

[0050] In this embodiment, the acquisition of the computer room environment data and the generation of the environment control instruction according to the computer room environment data by using the preset environment threshold are specifically implemented by the following steps:

[0051] S101: Installing environmental monitoring equipment at key locations in the computer room, wherein the environmental monitoring equipment includes a temperature sensor, a humidity sensor, and a smoke sensor;

[0052] S102: Acquire the computer room environment data through the environment monitoring device, operate the environment monitoring device uninterruptedly for 24 hours, and set the monitoring frequency of the computer room environment data to once every 5 minutes;

[0053] S103: Preset environmental thresholds, where the preset environmental thresholds include a preset computer room temperature threshold, a preset computer room humidity threshold, and a preset computer room smoke concentration threshold;

[0054] S104: The environmental control instructions include a cooling device start instruction, a cooling device shutdown instruction, a ventilation and dehumidification device start instruction, a ventilation and dehumidification device shutdown instruction, and a machine room smoke warning instruction;

[0055] When the temperature of the equipment room is higher than the preset equipment room temperature threshold, a cooling device startup instruction is generated; when the temperature of the equipment room drops to within the preset equipment room temperature threshold, a cooling device shutdown instruction is generated;

[0056] When the humidity in the equipment room is higher than the preset equipment room humidity threshold, a ventilation and dehumidification equipment startup instruction is generated; when the humidity in the equipment room drops below the preset equipment room humidity threshold, a ventilation and dehumidification equipment shutdown instruction is generated;

[0057] When the smoke concentration in the computer room is higher than the preset smoke concentration threshold in the computer room, a computer room smoke warning instruction is sent to the terminal, and an audible and visual alarm is activated.

[0058] In this embodiment, the process of obtaining the standard monitoring picture of the computer room by preprocessing the computer room picture according to the historical monitoring picture of the computer room is specifically implemented by the following steps:

[0059] S201: De-noising the historical monitoring picture of the computer room by using an adaptive median filtering algorithm to obtain a historical monitoring denoised picture of the computer room;

[0060] The adaptive median filtering algorithm automatically adjusts the size of the filtering window according to the local characteristics of the historical monitoring picture of the computer room, and obtains the historical monitoring denoised picture of the computer room by removing the salt and pepper noise by processing the historical monitoring picture of the computer room pixel by pixel;

[0061] S202: A standard computer room monitoring picture is obtained by enhancing the computer room historical monitoring denoised picture through a histogram equalization algorithm.

[0062] In this embodiment, the computer room monitoring data set is obtained by improving the deep convolution generative adversarial network data expansion according to the computer room monitoring standard picture, and the computer room water immersion recognition model is constructed according to the computer room monitoring data set by improving the HRNetV2 algorithm, which is specifically implemented by the following steps:

[0063] S301: Inputting the standard image of the computer room monitoring into the improved deep convolutional generative adversarial network for adversarial training;

[0064] S302: The improved deep convolutional generative adversarial network includes a generator and a discriminator. The generator gradually converts the input low-dimensional noise vector into high-dimensional image data through a series of transposed convolutional layers. A residual block is introduced after the transposed convolutional layer. A coordinate attention mechanism and an AdaLIN layer are added to each residual block to enhance the details and authenticity of the generated image. The goal of the generator is to generate a fake computer room monitoring standard picture to deceive the discriminator.

[0065] The discriminator uses a convolutional neural network to extract features from the input image and outputs a probability value of the authenticity of the computer room monitoring standard picture. The goal of the discriminator is to distinguish between the real computer room monitoring standard picture and the fake computer room monitoring standard picture generated by the generator;

[0066] The generator and the discriminator are jointly trained through an adversarial process. The generator and the discriminator compete with each other. The generator tries to generate more and more realistic pictures, while the discriminator tries to better identify true and false pictures. The performance of the discriminator is measured using a binary cross entropy loss function. The gap between the fake computer room monitoring standard pictures and the real computer room monitoring standard pictures is narrowed by adjusting the parameters of the generator to generate pictures that are closer and closer to the real computer room monitoring standards.

[0067] It should be noted that the AdaLIN layer is a layer used to normalize input data in deep learning. It combines the characteristics of layer normalization and instance normalization, and introduces adaptive parameters to dynamically adjust the impact of these two normalization methods. In addition, the low-dimensional noise vector of the input generator is generated by a random number generator according to the preset noise vector dimension. The generator has a special input layer for receiving the low-dimensional noise vector. During the training process, a new low-dimensional noise vector is randomly generated at each iteration and input into the input layer of the generator.

[0068] S303: The improved HRNetV2 algorithm includes a backbone network, a feature integration block and a prediction head. The backbone network is used to extract computer room water immersion feature maps with different resolutions. A deep routing attention module is introduced into the feature integration block. The importance weights of computer room water immersion feature maps with different resolutions are calculated by the deep routing attention module and weighted fusion is performed. The weighted fusion computer room water immersion feature maps are recalibrated through a gating mechanism to enhance useful features and suppress noise. The prediction head converts the computer room water immersion feature map output by the feature integration block into a computer room water immersion recognition result.

[0069] S304: Perform model training using the improved HRNetV2 algorithm according to the computer room monitoring data set, adopt a cross entropy loss function and an Adam optimizer, and iterate the training to a preset number of iterations to obtain a computer room water immersion recognition model.

[0070] In this embodiment, the acquisition of the computer room monitoring picture, obtaining the computer room water flooding detection tag through the computer room water flooding recognition model according to the computer room monitoring picture, and sending the computer room water flooding alarm instruction to the terminal according to the computer room water flooding tag are specifically implemented by the following steps:

[0071] S401: Obtaining monitoring images of the computer room through an intelligent inspection robot, wherein the intelligent inspection robot is equipped with a high-definition monitoring camera and inspects according to a preset inspection route;

[0072] S402: The computer room monitoring picture is obtained by preprocessing the computer room picture to obtain a computer room monitoring standard picture;

[0073] S403: Inputting the computer room monitoring standard image into the computer room water immersion recognition model to obtain a computer room water immersion detection label, wherein the computer room water immersion detection label includes a computer room water immersion label and a computer room normal label;

[0074] S404: When the computer room water flooding detection tag is a computer room water flooding tag, a computer room water flooding alarm instruction is sent to the terminal, and an audible and visual alarm is activated.

[0075] A computer room environment control system includes a computer room environment control module, a computer room monitoring data set construction module, and a computer room water immersion identification module;

[0076] The computer room environment control module is used to obtain computer room environment data and generate environment control instructions according to the computer room environment data through a preset environment threshold, wherein the computer room environment data includes computer room temperature, computer room humidity, and computer room smoke concentration;

[0077] The computer room monitoring data set construction module is used to obtain the historical monitoring pictures of the computer room and obtain the computer room monitoring standard pictures through computer room picture preprocessing, and obtain the computer room monitoring data set through the improved deep convolution generative adversarial network data expansion according to the computer room monitoring standard pictures;

[0078] The computer room water immersion recognition module is used to build a computer room water immersion recognition model based on the computer room monitoring data set by improving the HRNetV2 algorithm, obtain computer room monitoring pictures and obtain computer room water immersion detection labels through the computer room water immersion recognition model, and send a computer room water immersion alarm instruction to the terminal according to the computer room water immersion label.

[0079] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device.

[0080] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0081] The program code included on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0082] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for controlling a computer room environment, characterized in that: include: Acquire computer room environment data, and generate environment control instructions according to the computer room environment data through preset environment thresholds, wherein the computer room environment data includes computer room temperature, computer room humidity, and computer room smoke concentration; Acquire historical monitoring pictures of the computer room, and obtain standard monitoring pictures of the computer room by preprocessing the pictures of the computer room according to the historical monitoring pictures of the computer room, wherein the historical monitoring pictures of the computer room include pictures of flooding in the computer room and pictures of normal computer room; According to the standard monitoring picture of the computer room, a computer room monitoring data set is obtained by improving the deep convolution generative adversarial network data expansion, and according to the computer room monitoring data set, a computer room water immersion recognition model is constructed by improving the HRNetV2 algorithm; Acquire a computer room monitoring picture, obtain a computer room water immersion detection tag through the computer room water immersion recognition model according to the computer room monitoring picture, and send a computer room water immersion alarm instruction to the terminal according to the computer room water immersion tag.

2. The computer room environment control method according to claim 1, characterized in that: The obtaining of computer room environment data includes: Install environmental monitoring equipment at key locations in the computer room, including temperature sensors, humidity sensors, and smoke sensors; The computer room environment data is obtained through the environment monitoring device, and the environment monitoring device is operated uninterruptedly for 24 hours. The monitoring frequency of the computer room environment data is set to once every 5 minutes.

3. The computer room environment control method according to claim 1, characterized in that: Generating an environment control instruction according to the computer room environment data by using a preset environment threshold comprises: Preset environmental thresholds, the preset environmental thresholds including preset computer room temperature thresholds, preset computer room humidity thresholds, and preset computer room smoke concentration thresholds; The environmental control instructions include cooling equipment start-up instructions, cooling equipment shutdown instructions, ventilation and dehumidification equipment start-up instructions, ventilation and dehumidification equipment shutdown instructions, and computer room smoke warning instructions; When the temperature of the equipment room is higher than the preset equipment room temperature threshold, a cooling device startup instruction is generated; when the temperature of the equipment room drops to within the preset equipment room temperature threshold, a cooling device shutdown instruction is generated; When the humidity in the equipment room is higher than the preset equipment room humidity threshold, a ventilation and dehumidification equipment startup instruction is generated; when the humidity in the equipment room drops below the preset equipment room humidity threshold, a ventilation and dehumidification equipment shutdown instruction is generated; When the smoke concentration in the computer room is higher than the preset smoke concentration threshold in the computer room, a computer room smoke warning instruction is sent to the terminal, and an audible and visual alarm is activated.

4. The computer room environment control method according to claim 1, characterized in that: The obtaining of a standard monitoring picture of the computer room by preprocessing the computer room picture according to the historical monitoring picture of the computer room comprises: De-noising the historical monitoring picture of the computer room by using an adaptive median filtering algorithm to obtain a historical monitoring denoised picture of the computer room; The denoised pictures of the historical monitoring of the computer room are enhanced by a histogram equalization algorithm to obtain a standard picture of the computer room monitoring.

5. The computer room environment control method according to claim 1, characterized in that: The improved deep convolutional generative adversarial network includes a generator and a discriminator. A residual block is introduced after the transposed convolution layer of the generator. A coordinate attention mechanism and an AdaLIN layer are added to each residual block to enhance the details and authenticity of the generated image.

6. The computer room environment control method according to claim 1, characterized in that: The improved HRNetV2 algorithm includes a backbone network, a feature integration block and a prediction head. The backbone network is used to extract computer room water immersion feature maps with different resolutions. A deep routing attention module is introduced into the feature integration block. The importance weights of computer room water immersion feature maps with different resolutions are calculated by the deep routing attention module and weighted fusion is performed. The weighted fused computer room water immersion feature maps are recalibrated through a gating mechanism to enhance useful features and suppress noise. The prediction head converts the computer room water immersion feature map output by the feature integration block into a computer room water immersion recognition result.

7. The computer room environment control method according to claim 1, characterized in that: The acquiring of the monitoring picture of the computer room, obtaining the water immersion detection tag of the computer room through the water immersion recognition model of the computer room according to the monitoring picture of the computer room, and sending the water immersion alarm instruction of the computer room to the terminal according to the water immersion tag of the computer room include: Obtain monitoring images of the computer room through an intelligent inspection robot, which is equipped with a high-definition monitoring camera and inspects according to a preset inspection route; The computer room monitoring picture is obtained by preprocessing the computer room picture to obtain a computer room monitoring standard picture; Inputting the computer room monitoring standard picture into the computer room water immersion recognition model to obtain a computer room water immersion detection label, wherein the computer room water immersion detection label includes a computer room water immersion label and a computer room normal label; When the machine room water immersion detection tag is a machine room water immersion tag, a machine room water immersion alarm instruction is sent to the terminal, and an audible and visual alarm is activated.

8. A computer room environment control system, applied to the computer room environment control method according to any one of claims 1 to 7, characterized in that: It includes computer room environment control module, computer room monitoring data set construction module, and computer room water flooding identification module; The computer room environment control module is used to obtain computer room environment data and generate environment control instructions according to the computer room environment data through a preset environment threshold, wherein the computer room environment data includes computer room temperature, computer room humidity, and computer room smoke concentration; The computer room monitoring data set construction module is used to obtain the historical monitoring pictures of the computer room and obtain the computer room monitoring standard pictures through computer room picture preprocessing, and obtain the computer room monitoring data set through the improved deep convolution generative adversarial network data expansion according to the computer room monitoring standard pictures; The computer room water immersion recognition module is used to build a computer room water immersion recognition model based on the computer room monitoring data set by improving the HRNetV2 algorithm, obtain computer room monitoring pictures and obtain computer room water immersion detection labels through the computer room water immersion recognition model, and send a computer room water immersion alarm instruction to the terminal according to the computer room water immersion label.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the computer room environment control method as described in any one of claims 1-7 is implemented.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions, when executed by a computer processor, are used to execute the computer room environment control method as described in any one of claims 1-7.

Images