Electromechanical equipment fault prediction, data acquisition management and storage system based on AI

Abstract

The invention relates to the technical field of data asset management, in particular to an electromechanical equipment fault prediction and data acquisition management and storage system based on AI, which comprises a data processing module, a data classification module, a data storage module, a data management module, a data transmission module, a data query module and a system management module. According to the system, images are re-recorded, texts are regenerated, received data are physically isolated in a camera shooting and image-text recognition mode, viruses are prevented from entering the system through the data, the re-recorded images and the regenerated texts are compared with original data in a deep learning mode, the consistency of the data is guaranteed, and the accuracy of the system is improved. And the accuracy of camera shooting and image-text recognition is guaranteed through a brightness adjustment mode, and errors are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data asset management, and in particular to an AI-based electromechanical equipment fault prediction, data acquisition management and storage system. Background Art

[0002] Electromechanical equipment is a general term for machinery, electrical appliances and electrical automation equipment, also known as electromechanical products. In order to ensure the normal operation of electromechanical equipment and reduce its failures, the fault prediction of electromechanical equipment is generally completed through an intelligent analysis system to facilitate the maintenance of electromechanical equipment. In order to ensure the analysis efficiency and accuracy of the intelligent analysis system, it is necessary to obtain the information of the electromechanical equipment of this model and a large amount of operating data and historical maintenance data of this model of electromechanical equipment, and organize the data so that the intelligent analysis system can accurately predict the failure of the electromechanical equipment based on the data and adjust the weights.

[0003] Therefore, data is managed through a data asset management system disclosed in the invention patent with publication number CN116992465A and a data asset management system disclosed in the invention patent with publication number CN115828335A.

[0004] However, during use, it was found that due to the different production management of each user, the security of the code and data uploaded to the electromechanical equipment is difficult to guarantee, and viruses are prone to exist. Data with viruses is directly uploaded to the system for management, which can easily paralyze the entire system or even cause all data to be lost. It is also inconvenient to convert different data into a unified format, resulting in poor practicality. Therefore, there is an urgent need for an AI-based electromechanical equipment fault prediction, data acquisition management and storage system. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides an AI-based electromechanical equipment fault prediction, data acquisition management and storage system which re-records images and regenerates texts through video and image-text recognition, and physically isolates the received data to prevent viruses from entering the system through the data, and compares the re-recorded images and regenerated texts with the original data through deep learning to ensure data consistency, and further ensures the accuracy of video and image-text recognition through light brightness adjustment to reduce the occurrence of errors.

[0006] The AI-based electromechanical equipment fault prediction, data acquisition management and storage system of the present invention includes:

[0007] Data processing module: Receives data and stores the received data separately. Then, through video and image-text recognition, the image is re-recorded and the text is re-generated to achieve physical isolation of the received data from the system. The re-recorded image and the re-generated text are then compared with the original data to ensure data consistency. The re-recorded image and the re-generated text are then transmitted to the data classification module.

[0008] Data classification module: classifies and labels data according to the type and model of electromechanical equipment, and compares the classified data with the stored data of the same type to determine whether there is the same data. If it is not, compresses the data and transmits the compressed data to the data storage module;

[0009] Data storage module: store data separately according to data labeling;

[0010] Data management module: limits the storage period of data according to the set threshold to ensure that the data storage module has enough space for use;

[0011] Data transmission module: retrieve and decompress the data required by the staff, and then transmit the decompressed data to the device specified by the staff;

[0012] Data query module: assists staff to interact with the system and enables staff to view stored data;

[0013] System management module: centrally manages the data processing module, data classification module, data storage module, data management module, data transmission module and data query module, verifies the identity information of the login personnel, and records the system operation process and the operation process of the login personnel.

[0014] Preferably, the data processing module includes:

[0015] Data receiving unit: receives the collected data;

[0016] Data temporary storage unit: stores the received data separately and automatically deletes the data after it exceeds the set storage period;

[0017] Data acquisition unit: photographs and performs image and text recognition on the received data, or directly performs image and text recognition on paper-based electromechanical equipment operation data;

[0018] Text extraction unit: extracts the recognized text data;

[0019] Text generation unit: generates text according to the extracted text data and saves it in a set format;

[0020] Deep learning data analysis unit: compares the recorded image with the original image data to ensure the consistency of the image, and compares the generated text with the original text data to ensure the consistency of the text content.

[0021] Preferably, the deep learning data analysis unit adopts the PyTorch framework and convolutional neural network, compares the recorded image with the original image data through a qualified trained model to ensure the consistency of the image, and compares the generated text with the original text data.

[0022] Preferably, the data classification module includes:

[0023] Classification unit: receiving the data sent by the data processing module and classifying the data according to the type and model of the electromechanical equipment;

[0024] Annotation unit: annotate data according to the type and model of electromechanical equipment and the time of receipt;

[0025] Comparison unit: compares the marked data with the stored data of the same type to determine whether there is the same data;

[0026] Compression unit: compresses data.

[0027] Preferably, the data management module includes:

[0028] Storage period management unit: limits the storage period of data in the data storage module;

[0029] Data deletion unit: deletes data that exceeds the storage period to ensure that the data storage module has enough space for use;

[0030] Data recovery period management unit: recovers accidentally deleted data that has not exceeded the set recovery period.

[0031] Preferably, the data transmission module includes:

[0032] Data decompression unit: retrieves the data required by the staff according to their requirements and decompresses the data;

[0033] Data sending unit: sends the decompressed data to the device specified by the staff.

[0034] Preferably, the system management module includes:

[0035] Management unit: centrally control and manage the data processing module, data classification module, data storage module, data management module, data transmission module and data query module;

[0036] Identity verification unit: verify the identity of the person logging in;

[0037] Recording unit: records the system's operating procedures and the operation procedures of the logged-in personnel.

[0038] Preferably, the acquisition unit includes a brightness adjustment mechanism, a cooling mechanism, a first camera, a second camera and two groups of displays, the first camera, the second camera and the two groups of displays are all installed in the brightness adjustment mechanism, and the cooling mechanism is installed on the brightness adjustment mechanism; the brightness adjustment mechanism is used to provide appropriate brightness for the first camera and the second camera, the first camera is used to shoot the display displaying the original data, the other group of displays is used to display the recorded image or the regenerated text content, and the second camera is used to shoot the data displayed on the two groups of displays, so as to facilitate the deep learning data analysis module to determine whether the original data and the recorded image or the regenerated text content are the same, thereby improving the practicality of the acquisition unit.

[0039] Preferably, the brightness adjustment mechanism includes a sealed box, a fill light, a first polarizer, a second polarizer and an electric cylinder, the first camera, the second camera and two sets of displays are all installed inside the sealed box, the fill light is installed on the top of the sealed box, the first polarizer is fixedly installed on the top of the sealed box, the second polarizer is slidably installed on the top of the sealed box, and the second polarizer is located below the first polarizer, the electric cylinder is fixedly installed in the sealed box, and one end of the sealed box is installed on the second polarizer; the sealed box is used to block other surrounding light, the fill light is turned on to irradiate light into the sealed box, and the position of the second polarizer is adjusted by extending or contracting the electric cylinder, so that the first polarizer and the second polarizer cooperate to limit the light entering the sealed box, providing suitable brightness for the first camera and the second camera, thereby improving the practicality of the brightness adjustment mechanism.

[0040] Preferably, the cooling mechanism includes a cooling box, multiple groups of semiconductor refrigeration plates and a fan, the cooling box is installed on the sealed box, and the interior of the cooling box is communicated with the interior of the sealed box through a group of exhaust pipes, the multiple groups of semiconductor refrigeration plates are installed on the cooling box, and the cooling ends of the multiple groups of semiconductor refrigeration plates extend into the sealed box, the heat dissipation ends of the multiple groups of semiconductor refrigeration plates extend above the cooling box, and radiators are provided on the heat dissipation ends of the multiple groups of semiconductor refrigeration plates, the fan is installed on the cooling box, and the air intake of the fan is communicated with the interior of the cooling box, and the exhaust port of the fan is communicated with the interior of the sealed box through another group of exhaust pipes; turn on the fan, so that the air in the sealed box passes through a group of exhaust pipes, the cooling box, the fan and another group of exhaust pipes in turn, and then is discharged back into the sealed box, and the fan is continuously operated to circulate the air in the sealed box continuously, and during the circulation process, the multiple groups of semiconductor refrigeration plates cool the air, and then cool the first camera, the second camera and the two groups of displays, thereby improving the practicality of the cooling mechanism.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. Through video and image recognition, the image is re-recorded, the text is re-generated, and the received data is physically isolated to prevent viruses from entering the system through the data;

[0043] 2. Compare the re-recorded images and re-generated texts with the original data through deep learning to ensure data consistency;

[0044] 3. Ensure the accuracy of video and image recognition by adjusting the brightness and reduce the occurrence of errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a structural schematic diagram of the AI-based electromechanical equipment fault prediction, data acquisition management and storage system of the present invention;

[0046] Figure 2 It is a structural schematic diagram of a data processing module of the present invention;

[0047] Figure 3 It is a structural schematic diagram of the data classification module of the present invention;

[0048] Figure 4 It is a structural diagram of the data management module of the present invention;

[0049] Figure 5 It is a structural schematic diagram of a data transmission module of the present invention;

[0050] Figure 6 It is a structural diagram of the system management module of the present invention;

[0051] Figure 7 It is a schematic diagram of the axonometric structure of the data acquisition unit of the present invention;

[0052] Figure 8 It is a front cross-sectional structural schematic diagram of the data acquisition unit of the present invention;

[0053] Fig. 9 It is a right side cross-sectional structural schematic diagram of the data acquisition unit of the present invention;

[0054] Fig.10 It is a schematic diagram of the axonometric cross-section structure of the data acquisition unit of the present invention.

[0055] Markings in the attached drawings: 1. first camera; 2. second camera; 3. display; 4. sealing box; 5. fill light; 6. first polarizing plate; 7. second polarizing plate; 8. electric cylinder; 9. cooling box; 10. semiconductor refrigeration plate; 11. fan. DETAILED DESCRIPTION

[0056] In order to facilitate understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0057] Example 1

[0058] like Figures 1 to 10 As shown, the AI-based electromechanical equipment fault prediction, data acquisition management and storage system includes:

[0059] Data processing module: Receives data and stores the received data separately. Then, through video and image-text recognition, the image is re-recorded and the text is re-generated to achieve physical isolation of the received data from the system. The re-recorded image and the re-generated text are then compared with the original data to ensure data consistency. The re-recorded image and the re-generated text are then transmitted to the data classification module.

[0060] Data classification module: classifies and labels data according to the type and model of electromechanical equipment, and compares the classified data with the stored data of the same type to determine whether there is the same data. If it is not, compresses the data and transmits the compressed data to the data storage module;

[0061] Data storage module: store data separately according to data labeling;

[0062] Data management module: limits the storage period of data according to the set threshold to ensure that the data storage module has enough space for use;

[0063] Data transmission module: retrieve and decompress the data required by the staff, and then transmit the decompressed data to the device specified by the staff;

[0064] Data query module: assists staff to interact with the system and enables staff to view stored data;

[0065] System management module: centrally manage the data processing module, data classification module, data storage module, data management module, data transmission module and data query module, and verify the identity information of the login personnel, and record the system operation process and the operation process of the login personnel;

[0066] The data processing module comprises:

[0067] Data receiving unit: receives the collected data;

[0068] Data temporary storage unit: stores the received data separately and automatically deletes the data after it exceeds the set storage period;

[0069] Data acquisition unit: photographs and performs image and text recognition on the received data, or directly performs image and text recognition on paper-based electromechanical equipment operation data;

[0070] Text extraction unit: extracts the recognized text data;

[0071] Text generation unit: generates text according to the extracted text data and saves it in a set format;

[0072] Deep learning data analysis unit: compares the recorded image with the original image data to ensure the consistency of the image, and compares the generated text with the original text data to ensure the consistency of the text content;

[0073] The deep learning data analysis unit uses the PyTorch framework and convolutional neural network to compare the recorded image with the original image data through a trained model to ensure the consistency of the image, and compares the generated text with the original text data;

[0074] The data classification module comprises:

[0075] Classification unit: receiving the data sent by the data processing module and classifying the data according to the type and model of the electromechanical equipment;

[0076] Annotation unit: annotate data according to the type and model of electromechanical equipment and the time of receipt;

[0077] Comparison unit: compares the marked data with the stored data of the same type to determine whether there is the same data;

[0078] Compression unit: compresses data;

[0079] The data management module comprises:

[0080] Storage period management unit: limits the storage period of data in the data storage module;

[0081] Data deletion unit: deletes data that exceeds the storage period to ensure that the data storage module has enough space for use;

[0082] Data recovery period management unit: recovers accidentally deleted data that has not exceeded the set recovery period;

[0083] The data transmission module comprises:

[0084] Data decompression unit: retrieves the data required by the staff according to their requirements and decompresses the data;

[0085] Data sending unit: sends the decompressed data to the device specified by the staff;

[0086] The system management module includes:

[0087] Management unit: centrally control and manage the data processing module, data classification module, data storage module, data management module, data transmission module and data query module;

[0088] Identity verification unit: verify the identity of the person logging in;

[0089] Recording unit: records the system operation process and the operation process of the login personnel;

[0090] The acquisition unit includes a brightness adjustment mechanism, a cooling mechanism, a first camera 1, a second camera 2 and two sets of displays 3. The first camera 1, the second camera 2 and the two sets of displays 3 are all installed in the brightness adjustment mechanism, and the cooling mechanism is installed on the brightness adjustment mechanism.

[0091] The brightness adjustment mechanism includes a sealed box 4, a fill light 5, a first polarizing plate 6, a second polarizing plate 7 and an electric cylinder 8, the first camera 1, the second camera 2 and the two sets of displays 3 are all installed inside the sealed box 4, the fill light 5 is installed on the top of the sealed box 4, the first polarizing plate 6 is fixedly installed on the top of the sealed box 4, the second polarizing plate 7 is slidably installed on the top of the sealed box 4, and the second polarizing plate 7 is located below the first polarizing plate 6, the electric cylinder 8 is fixedly installed in the sealed box 4, and one end of the sealed box 4 is installed on the second polarizing plate 7;

[0092] The system management module centrally manages the data processing module, the data classification module, the data storage module, the data management module, the data transmission module and the data query module, and verifies the identity information of the login personnel, records the system operation process and the login personnel's operation process, receives the data through the data processing module, and stores the received data separately, and then provides appropriate brightness for the first camera 1 and the second camera 2 through the brightness adjustment mechanism, shoots the display 3 displaying the original data through the first camera 1, re-records the image, and regenerates the text to achieve physical isolation of the received data from the system, and then displays the recorded image or regenerated text content through another group of displays 3, and shoots the data displayed on the two groups of displays 3 through the second camera 2, so that the deep learning data analysis module can determine whether the original data and the recorded image or regenerated text content are the same, and compares the re-recorded image and the regenerated text with the original data. To ensure the consistency of the data, the re-recorded image and the re-generated text are then transmitted to the data classification module, and then the data classification module is used to classify and label the data according to the type and model of the electromechanical equipment, and the classified data is compared with the stored data of the same type to determine whether there is the same data. If not, the data is compressed and the compressed data is transmitted to the data storage module, and then the data storage module is used to store the data separately according to the data labeling. At the same time, the data management module is used to limit the storage period of the data according to the set threshold to ensure that the data storage module has enough space to use, and then the data required by the staff is called out and decompressed through the data transmission module, and then the decompressed data is transmitted to the equipment specified by the staff, and the data query module is used to assist the staff to interact with the system, so that the staff can view the stored data, thereby improving the practicality of AI-based electromechanical equipment fault prediction, data acquisition management and storage systems.

[0093] Example 2

[0094] like Figures 1 to 10 As shown, the AI-based electromechanical equipment fault prediction, data acquisition management and storage system includes:

[0095] Data processing module: Receives data and stores the received data separately. Then, through video and image-text recognition, the image is re-recorded and the text is re-generated to achieve physical isolation of the received data from the system. The re-recorded image and the re-generated text are then compared with the original data to ensure data consistency. The re-recorded image and the re-generated text are then transmitted to the data classification module.

[0096] Data classification module: classifies and labels data according to the type and model of electromechanical equipment, and compares the classified data with the stored data of the same type to determine whether there is the same data. If it is not, compresses the data and transmits the compressed data to the data storage module;

[0097] Data storage module: store data separately according to data labeling;

[0098] Data management module: limits the storage period of data according to the set threshold to ensure that the data storage module has enough space for use;

[0099] Data transmission module: retrieve and decompress the data required by the staff, and then transmit the decompressed data to the device specified by the staff;

[0100] Data query module: assists staff to interact with the system and enables staff to view stored data;

[0101] System management module: centrally manage the data processing module, data classification module, data storage module, data management module, data transmission module and data query module, and verify the identity information of the login personnel, and record the system operation process and the operation process of the login personnel;

[0102] The data processing module comprises:

[0103] Data receiving unit: receives the collected data;

[0104] Data temporary storage unit: stores the received data separately and automatically deletes the data after it exceeds the set storage period;

[0105] Data acquisition unit: photographs and performs image and text recognition on the received data, or directly performs image and text recognition on paper-based electromechanical equipment operation data;

[0106] Text extraction unit: extracts the recognized text data;

[0107] Text generation unit: generates text according to the extracted text data and saves it in a set format;

[0108] Deep learning data analysis unit: compares the recorded image with the original image data to ensure the consistency of the image, and compares the generated text with the original text data to ensure the consistency of the text content;

[0109] The deep learning data analysis unit uses the PyTorch framework and convolutional neural network to compare the recorded image with the original image data through a trained model to ensure the consistency of the image, and compares the generated text with the original text data;

[0110] The data classification module comprises:

[0111] Classification unit: receiving the data sent by the data processing module and classifying the data according to the type and model of the electromechanical equipment;

[0112] Annotation unit: annotate data according to the type and model of electromechanical equipment and the time of receipt;

[0113] Comparison unit: compares the marked data with the stored data of the same type to determine whether there is the same data;

[0114] Compression unit: compresses data;

[0115] The data management module comprises:

[0116] Storage period management unit: limits the storage period of data in the data storage module;

[0117] Data deletion unit: deletes data that exceeds the storage period to ensure that the data storage module has enough space for use;

[0118] Data recovery period management unit: recovers accidentally deleted data that has not exceeded the set recovery period;

[0119] The data transmission module comprises:

[0120] Data decompression unit: retrieves the data required by the staff according to their requirements and decompresses the data;

[0121] Data sending unit: sends the decompressed data to the device specified by the staff;

[0122] The system management module includes:

[0123] Management unit: centrally control and manage the data processing module, data classification module, data storage module, data management module, data transmission module and data query module;

[0124] Identity verification unit: verify the identity of the person logging in;

[0125] Recording unit: records the system operation process and the operation process of the login personnel;

[0126] The acquisition unit includes a brightness adjustment mechanism, a cooling mechanism, a first camera 1, a second camera 2 and two sets of displays 3. The first camera 1, the second camera 2 and the two sets of displays 3 are all installed in the brightness adjustment mechanism, and the cooling mechanism is installed on the brightness adjustment mechanism.

[0127] The brightness adjustment mechanism includes a sealed box 4, a fill light 5, a first polarizing plate 6, a second polarizing plate 7 and an electric cylinder 8, the first camera 1, the second camera 2 and the two sets of displays 3 are all installed inside the sealed box 4, the fill light 5 is installed on the top of the sealed box 4, the first polarizing plate 6 is fixedly installed on the top of the sealed box 4, the second polarizing plate 7 is slidably installed on the top of the sealed box 4, and the second polarizing plate 7 is located below the first polarizing plate 6, the electric cylinder 8 is fixedly installed in the sealed box 4, and one end of the sealed box 4 is installed on the second polarizing plate 7;

[0128] The cooling mechanism includes a cooling box 9, a plurality of semiconductor refrigeration plates 10 and a fan 11. The cooling box 9 is installed on the sealed box 4, and the interior of the cooling box 9 is communicated with the interior of the sealed box 4 through a group of exhaust pipes. The plurality of semiconductor refrigeration plates 10 are installed on the cooling box 9, and the refrigeration ends of the plurality of semiconductor refrigeration plates 10 are extended into the sealed box 4, and the heat dissipation ends of the plurality of semiconductor refrigeration plates 10 are extended to the top of the cooling box 9. Radiators are provided on the heat dissipation ends of the plurality of semiconductor refrigeration plates 10. The fan 11 is installed on the cooling box 9, and the air intake of the fan 11 is communicated with the interior of the cooling box 9, and the exhaust port of the fan 11 is communicated with the interior of the sealed box 4 through another group of exhaust pipes.

[0129] The data processing module, data classification module, data storage module, data management module, data transmission module and data query module are centrally managed through the system management module, and the identity information of the login personnel is checked, the system operation process and the operation process of the login personnel are recorded, the data is received through the data processing module, and the received data is stored separately, and then the brightness adjustment mechanism provides appropriate brightness for the first camera 1 and the second camera 2, and the first camera 1 is used to shoot the display 3 displaying the original data, the image is re-recorded, and the text is regenerated to achieve physical isolation of the received data from the system, and then the recorded image or the regenerated text content is displayed through another group of displays 3, and the data displayed on the two groups of displays 3 are shot by the second camera 2, so that the deep learning data analysis module can determine whether the original data and the recorded image or the regenerated text content are the same, and the re-recorded image and the regenerated text are compared with the original data to ensure data consistency, and at the same time, the fan 11 is turned on to make the air in the sealed box 4 pass through a group of exhaust pipes, a cooling box 9, the fan 11 and another group of exhaust pipes in turn, and then discharged back to the sealed box 4, and The air in the sealed box 4 is continuously circulated by the continuous operation of the fan 11. During the circulation process, the multiple groups of semiconductor refrigeration plates 10 cool the air, and then cool the first camera 1, the second camera 2 and the two groups of displays 3. Then, the re-recorded image and the re-generated text are transmitted to the data classification module, and then the data classification module is used to classify and annotate the data according to the type and model of the electromechanical equipment, and the classified data is compared with the stored data of the same type to determine whether there is the same data. If it is not, the data is compressed and the compressed data is transmitted to the data storage module, and then the data storage module is used to store the data separately according to the data annotation. At the same time, the data management module is used to limit the storage period of the data according to the set threshold to ensure that the data storage module has enough space for use. Then, the data required by the staff is called out and decompressed through the data transmission module, and then the decompressed data is transmitted to the equipment specified by the staff. The data query module is used to assist the staff to interact with the system so that the staff can view the stored data, thereby improving the practicality of the AI-based electromechanical equipment fault prediction, data acquisition management and storage system.

[0130] The main functions achieved by the present invention are: improving system security, improving data accuracy, and improving recognition accuracy;

[0131] 1. Improve system security: Through video and image recognition, re-record images, re-generate texts, and physically isolate received data to prevent viruses from entering the system through data;

[0132] 2. Improve data accuracy: Use deep learning to compare re-recorded images and re-generated text with the original data to ensure data consistency;

[0133] 3. Improve recognition accuracy: Ensure the accuracy of video and image recognition by adjusting the brightness of the light, and reduce the occurrence of errors.

[0134] The installation method, connection method or setting method of the AI-based electromechanical equipment fault prediction, data acquisition management and storage system of the present invention are all common mechanical methods, and can be implemented as long as they can achieve their beneficial effects; the first camera 1, the second camera 2, the display 3, the fill light 5, the electric cylinder 8, the semiconductor refrigeration plate 10 and the fan 11 of the AI-based electromechanical equipment fault prediction, data acquisition management and storage system of the present invention are purchased on the market, and technical personnel in the industry only need to install and operate them according to the accompanying instruction manual, without the need for technical personnel in this field to pay creative labor.

[0135] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. AI-based electromechanical equipment fault prediction, data acquisition management and storage system, characterized by: include: Data processing module: Receives data and stores it separately. Then, through video and image-text recognition, it re-records the image and re-generates the text to achieve physical isolation of the received data from the system. Then, it compares the re-recorded image and re-generated text with the original data to ensure data consistency. Then, it transmits the re-recorded image and re-generated text to the data classification module. Data classification module: classifies and labels data according to the type and model of electromechanical equipment, and compares the classified data with the stored data of the same type to determine whether there is the same data. If it is not, compresses the data and transmits the compressed data to the data storage module; Data storage module: store data separately according to data labeling; Data management module: limits the storage period of data according to the set threshold to ensure that the data storage module has enough space for use; Data transmission module: retrieve and decompress the data required by the staff, and then transmit the decompressed data to the device specified by the staff; Data query module: assists staff to interact with the system and enables staff to view stored data; System management module: centrally manages the data processing module, data classification module, data storage module, data management module, data transmission module and data query module, verifies the identity information of the login personnel, and records the system operation process and the operation process of the login personnel.

2. The AI-based electromechanical equipment fault prediction, data acquisition management and storage system according to claim 1, characterized in that: The data processing module comprises: Data receiving unit: receives the collected data; Data temporary storage unit: stores the received data separately and automatically deletes the data after it exceeds the set storage period; Data acquisition unit: photographs and performs image and text recognition on the received data, or directly performs image and text recognition on paper-based electromechanical equipment operation data; Text extraction unit: extracts the recognized text data; Text generation unit: generates text according to the extracted text data and saves it in a set format; Deep learning data analysis unit: compares the recorded image with the original image data to ensure the consistency of the image, and compares the generated text with the original text data to ensure the consistency of the text content.

3. The AI-based electromechanical equipment fault prediction, data acquisition management and storage system according to claim 2, characterized in that: The deep learning data analysis unit adopts the PyTorch framework and convolutional neural network, compares the recorded image with the original image data through a qualified trained model to ensure the consistency of the image, and compares the generated text with the original text data.

4. The AI-based electromechanical equipment fault prediction, data acquisition management and storage system according to claim 1, characterized in that: The data classification module comprises: Classification unit: receiving the data sent by the data processing module and classifying the data according to the type and model of the electromechanical equipment; Annotation unit: annotate data according to the type and model of electromechanical equipment and the time of receipt; Comparison unit: compares the marked data with the stored data of the same type to determine whether there is the same data; Compression unit: compresses data.

5. The AI-based electromechanical equipment fault prediction, data acquisition management and storage system according to claim 1, characterized in that: The data management module comprises: Storage period management unit: limits the storage period of data in the data storage module; Data deletion unit: deletes data that exceeds the storage period to ensure that the data storage module has enough space for use; Data recovery period management unit: recovers accidentally deleted data that has not exceeded the set recovery period.

6. The AI-based electromechanical equipment fault prediction, data acquisition management and storage system according to claim 1, characterized in that: The data transmission module comprises: Data decompression unit: retrieves the data required by the staff according to their requirements and decompresses the data; Data sending unit: sends the decompressed data to the device specified by the staff.

7. The AI-based electromechanical equipment fault prediction, data acquisition management and storage system according to claim 1, characterized in that: The system management module includes: Management unit: centrally control and manage the data processing module, data classification module, data storage module, data management module, data transmission module and data query module; Identity verification unit: verify the identity of the person logging in; Recording unit: records the system's operating procedures and the operation procedures of the logged-in personnel.

8. The AI-based electromechanical equipment fault prediction, data acquisition management and storage system according to claim 2, characterized in that: The acquisition unit comprises a brightness adjustment mechanism, a cooling mechanism, a first camera (1), a second camera (2) and two groups of displays (3); the first camera (1), the second camera (2) and the two groups of displays (3) are all installed in the brightness adjustment mechanism, and the cooling mechanism is installed on the brightness adjustment mechanism.

9. The AI-based electromechanical equipment fault prediction, data acquisition management and storage system according to claim 8, characterized in that: The brightness adjustment mechanism comprises a sealed box (4), a fill light (5), a first polarizing plate (6), a second polarizing plate (7) and an electric cylinder (8); the first camera (1), the second camera (2) and two sets of displays (3) are all installed inside the sealed box (4); the fill light (5) is installed on the top of the sealed box (4); the first polarizing plate (6) is fixedly installed on the top of the sealed box (4); the second polarizing plate (7) is slidably installed on the top of the sealed box (4); and the second polarizing plate (7) is located below the first polarizing plate (6); the electric cylinder (8) is fixedly installed in the sealed box (4); and one end of the sealed box (4) is installed on the second polarizing plate (7).

10. The AI-based electromechanical equipment fault prediction, data acquisition management and storage system according to claim 9, characterized in that: The cooling mechanism comprises a cooling box (9), a plurality of semiconductor refrigeration plates (10) and a fan (11); the cooling box (9) is mounted on the sealed box (4), and the interior of the cooling box (9) is communicated with the interior of the sealed box (4) through a group of exhaust pipes; the plurality of semiconductor refrigeration plates (10) are mounted on the cooling box (9), and the cooling ends of the plurality of semiconductor refrigeration plates (10) are extended into the sealed box (4); the heat dissipation ends of the plurality of semiconductor refrigeration plates (10) are extended above the cooling box (9); radiators are arranged on the heat dissipation ends of the plurality of semiconductor refrigeration plates (10); the fan (11) is mounted on the cooling box (9), and the air intake of the fan (11) is communicated with the interior of the cooling box (9); and the exhaust of the fan (11) is communicated with the interior of the sealed box (4) through another group of exhaust pipes.

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