Big data early warning system based on AI analysis model

Abstract

The invention relates to a big data early warning system based on an AI analysis model, and the system comprises a third collection device which is used for introducing the AI analysis model to intelligently analyze the actual distribution area corresponding to a ship according to the visual data of a reference image block; and the analysis early warning equipment is used for playing a tonnage overrun early warning signal to trigger a subsequent tonnage confirmation action when the estimated ship tonnage corresponding to the actual distribution area exceeds the maximum allowable entry tonnage corresponding to the target dock. The big data early warning system based on the AI analysis model is compact in structure and intelligent to control. The AI analysis model can be introduced to complete intelligent analysis of the actual distribution area corresponding to the ship according to multiple items of basic information screened in a targeted manner, and when the received estimated ship tonnage corresponding to the actual distribution area exceeds the maximum allowable entry tonnage corresponding to the target dock, the tonnage over-limit early warning signal is played. Therefore, the intelligent level of target dock management is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent management, and in particular to a big data early warning system based on an AI analysis model. Background Technology

[0002] A dock is a dock-like structure used for shipbuilding and repair. When flooded, it allows ships to enter and exit; when dewatered, it allows for shipbuilding and repair on a dry bottom. Docks can be divided into three categories: dry docks, flooded docks, and floating docks. Dry docks are the most commonly used, and the term "dock" generally refers to a dry dock. Docks evolved from the initial "ship pits." On tidal coasts, people used the rise and fall of water levels to raise and lower ships. At high tide, ships were led into a "ship pit" surrounded on three sides by earthen dikes. At low tide, the ships sat on pre-set supports, and the openings were sealed with embankments for repair work. When the ships were to leave the pit, the embankments were removed, and they were allowed to leave at high tide. Later, the earthen dikes were replaced with dock walls, and the embankments with dock gates. Water pumps were used to control the rise and fall of the water level within the dock, gradually evolving into dry docks.

[0003] Generally, each dock has a maximum allowable tonnage for ships to enter. However, in actual dock management, due to the complexity of the operating environment of each ship and the irregularity of the ship's three-dimensional shape, it is difficult to effectively analyze the accurate tonnage of each ship about to enter the dock, thus making it impossible to implement automatic access management of ship tonnage in the dock.

[0004] Among the existing technologies that can be referenced, invention CN116674709A discloses a ship draft measurement device and method. The draft measurement device includes a pressure signal processing and liquid level height recording and display device, a pressure detection device, and a T-shaped support. The pressure signal processing and liquid level height recording and display device is mounted on the crossbeam of the T-shaped support, and the pressure detection device is located at the bottom of the T-shaped support. When measuring the draft, the pressure detection device is submerged in the water and transmits the pressure signal to the pressure signal processing and liquid level height recording and display device in real time. The pressure signal processing and liquid level height recording and display device converts the received pressure signal into a liquid level height, displays it, and finally obtains the ship's draft data. Invention CN115675780A discloses a method, system, electronic device, and readable storage medium for predicting ship draft. The method includes: acquiring a historical ship lock passage dataset, which includes multiple qualitative parameters of the ship; dividing the historical ship lock passage dataset into multiple historical ship lock passage data subsets based on the qualitative parameters, each historical ship lock passage data subset including multiple quantitative parameters of the ship and the ship's draft; identifying multiple significant quantitative parameters that significantly affect the ship's draft among the multiple quantitative parameters; constructing an initial ship draft prediction model, and training the initial ship draft prediction model based on the multiple significant quantitative parameters and the ship's draft in each historical ship lock passage data subset to obtain a target ship draft prediction model; and determining the predicted draft of the ship to be predicted based on the target ship draft prediction model. Summary of the Invention

[0005] To overcome the technical problems in the existing technology, this invention proposes a big data early warning system based on an AI analysis model. The system can introduce an AI analysis model to intelligently analyze the actual distribution area of ​​ships based on multiple targeted basic information. When the estimated tonnage of a ship corresponding to the actual distribution area exceeds the maximum allowable tonnage of the target dock, an over-tonnage warning signal is played to trigger subsequent tonnage confirmation actions. Otherwise, the over-tonnage warning signal is temporarily suspended, thereby improving the intelligence level of target dock management.

[0006] A big data early warning system based on an AI analysis model, provided by the present invention, includes:

[0007] The capture operation mechanism is set at the target dock and is used to perform a visual capture action on the water in front of the target dock once at a set time interval to obtain the corresponding real-time capture frame.

[0008] A step-by-step optimization mechanism, connected to the capture operation mechanism, includes a distortion processing device, a color level adjustment device, and a directional blurring device. The color level adjustment device is connected to both the distortion processing device and the directional blurring device. The distortion processing device performs distortion correction processing on the received real-time capture frame to obtain and output a corresponding distortion-processed image. The color level adjustment device performs multiple color index enhancement processes on the received distortion-processed image to obtain and output a corresponding color level adjusted image. The directional blurring device performs directional blurring processing on the received color level adjusted image to obtain and output a corresponding directional blurring image.

[0009] The first acquisition device, connected to the step-by-step optimization mechanism, is used to identify the image blocks of the ship in the directional blurred image according to the brightness value distribution range of the ship, and output them as reference image blocks. The brightness value distribution range of the ship is numerically limited by the upper limit brightness threshold and the lower limit brightness threshold of the ship.

[0010] The second acquisition device is connected to the first acquisition device and is used to acquire the number of pixels occupied by the reference image block, the number of pixel rows occupied by the reference image block, and the number of pixel columns occupied by the reference image block, and at the same time obtain the overall depth value of the reference image block.

[0011] The third acquisition device, connected to the second acquisition device, is used to introduce an AI analysis model to analyze the actual distribution area of ​​the ship based on the number of pixels occupied by the reference image block, the number of pixel rows occupied by the reference image block, the number of pixel columns occupied by the reference image block, and the overall depth value of the reference image block. The total number of models learned by the AI ​​analysis model is monotonically positively correlated with the interval length of the brightness value distribution interval corresponding to the ship.

[0012] The analysis and early warning device is connected to the third acquisition device and is used to play an over-tolerance early warning signal to trigger subsequent tolerance confirmation actions when the estimated ship tonnage corresponding to the actual distribution area exceeds the maximum allowable entry tonnage corresponding to the target dock.

[0013] Specifically, when the estimated ship tonnage corresponding to the actual distribution area exceeds the maximum allowable tonnage for entry into the target dock, the execution of the tonnage over-limit warning signal to trigger subsequent tonnage confirmation actions includes: using a numerical conversion function to represent the numerical mapping relationship between the received actual distribution area and its corresponding estimated ship tonnage.

[0014] The big data early warning system based on the AI ​​analysis model of this invention has a compact structure and intelligent operation. By introducing an AI analysis model to intelligently analyze the actual distribution area of ​​ships based on targeted selection of multiple basic information, and by playing an over-tolerance warning signal when the estimated tonnage of a ship corresponding to the actual distribution area exceeds the maximum allowable tonnage for entry into the target dock, the system improves the intelligence level of target dock management. Attached Figure Description

[0015] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0016] Figure 1 This is a block diagram illustrating the internal structure of a big data early warning system based on an AI analysis model according to embodiment 1 of the present invention.

[0017] Figure 2 This is a block diagram illustrating the internal structure of a big data early warning system based on an AI analysis model according to embodiment 2 of the present invention.

[0018] Figure 3 This is a block diagram illustrating the internal structure of a big data early warning system based on an AI analysis model according to embodiment 3 of the present invention. Detailed Implementation

[0019] The implementation scheme of the big data early warning system based on the AI ​​analysis model of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Implementation Plan 1

[0021] Figure 1 The diagram above shows the internal structure of a big data early warning system based on an AI analysis model according to embodiment 1 of the present invention. The system includes:

[0022] The capture operation mechanism is set at the target dock and is used to perform a visual capture action on the water in front of the target dock once at a set time interval to obtain the corresponding real-time capture frame.

[0023] For example, the capture mechanism incorporates an imaging lens, a timing unit, a filter, and a photoelectric sensor, with the filter disposed between the imaging lens and the photoelectric sensor;

[0024] In the capture operation mechanism, the timing unit is used to provide timing reference information for the visual capture action performed by the photoelectric sensor once every set time interval;

[0025] A step-by-step optimization mechanism, connected to the capture operation mechanism, includes a distortion processing device, a color level adjustment device, and a directional blurring device. The color level adjustment device is connected to both the distortion processing device and the directional blurring device. The distortion processing device performs distortion correction processing on the received real-time capture frame to obtain and output a corresponding distortion-processed image. The color level adjustment device performs multiple color index enhancement processes on the received distortion-processed image to obtain and output a corresponding color level adjusted image. The directional blurring device performs directional blurring processing on the received color level adjusted image to obtain and output a corresponding directional blurring image.

[0026] The first acquisition device, connected to the step-by-step optimization mechanism, is used to identify the image blocks of the ship in the directional blurred image according to the brightness value distribution range of the ship, and output them as reference image blocks. The brightness value distribution range of the ship is numerically limited by the upper limit brightness threshold and the lower limit brightness threshold of the ship.

[0027] The second acquisition device is connected to the first acquisition device and is used to acquire the number of pixels occupied by the reference image block, the number of pixel rows occupied by the reference image block, and the number of pixel columns occupied by the reference image block, and at the same time obtain the overall depth value of the reference image block.

[0028] The third acquisition device, connected to the second acquisition device, is used to introduce an AI analysis model to analyze the actual distribution area of ​​the ship based on the number of pixels occupied by the reference image block, the number of pixel rows occupied by the reference image block, the number of pixel columns occupied by the reference image block, and the overall depth value of the reference image block. The total number of models learned by the AI ​​analysis model is monotonically positively correlated with the interval length of the brightness value distribution interval corresponding to the ship.

[0029] The analysis and early warning device is connected to the third acquisition device and is used to play an over-tolerance early warning signal to trigger subsequent tolerance confirmation actions when the estimated ship tonnage corresponding to the actual distribution area exceeds the maximum allowable entry tonnage corresponding to the target dock.

[0030] Among them, when the estimated ship tonnage corresponding to the actual distribution area exceeds the maximum allowable tonnage for entry into the target dock, the execution of the tonnage over-limit warning signal to trigger subsequent tonnage confirmation actions includes: using a numerical conversion function to represent the numerical mapping relationship between the received actual distribution area and its corresponding estimated ship tonnage.

[0031] The analysis and early warning equipment is also used to temporarily suspend the playback of the tonnage over-limit early warning signal when the estimated ship tonnage corresponding to the actual distribution area is less than or equal to the maximum allowable tonnage to enter the target dock.

[0032] Implementation Plan 2

[0033] Figure 2 The internal structure block diagram of the big data early warning system based on the AI ​​analysis model shown in Embodiment 2 of the present invention includes:

[0034] The capture operation mechanism is set at the target dock and is used to perform a visual capture action on the water in front of the target dock once at a set time interval to obtain the corresponding real-time capture frame.

[0035] A step-by-step optimization mechanism, connected to the capture operation mechanism, includes a distortion processing device, a color level adjustment device, and a directional blurring device. The color level adjustment device is connected to both the distortion processing device and the directional blurring device. The distortion processing device performs distortion correction processing on the received real-time capture frame to obtain and output a corresponding distortion-processed image. The color level adjustment device performs multiple color index enhancement processes on the received distortion-processed image to obtain and output a corresponding color level adjusted image. The directional blurring device performs directional blurring processing on the received color level adjusted image to obtain and output a corresponding directional blurring image.

[0036] The first acquisition device, connected to the step-by-step optimization mechanism, is used to identify the image blocks of the ship in the directional blurred image according to the brightness value distribution range of the ship, and output them as reference image blocks. The brightness value distribution range of the ship is numerically limited by the upper limit brightness threshold and the lower limit brightness threshold of the ship.

[0037] The second acquisition device is connected to the first acquisition device and is used to acquire the number of pixels occupied by the reference image block, the number of pixel rows occupied by the reference image block, and the number of pixel columns occupied by the reference image block, and at the same time obtain the overall depth value of the reference image block.

[0038] The third acquisition device, connected to the second acquisition device, is used to introduce an AI analysis model to analyze the actual distribution area of ​​the ship based on the number of pixels occupied by the reference image block, the number of pixel rows occupied by the reference image block, the number of pixel columns occupied by the reference image block, and the overall depth value of the reference image block. The total number of models learned by the AI ​​analysis model is monotonically positively correlated with the interval length of the brightness value distribution interval corresponding to the ship.

[0039] An analysis and early warning device, connected to the third acquisition device, is used to play an over-tolerance early warning signal to trigger subsequent tolerance confirmation actions when the estimated ship tonnage corresponding to the actual distribution area exceeds the maximum allowable tonnage for entry into the target dock. A decibel measuring mechanism, connected to the distortion processing device, the color gradation adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device, is used to measure the nearby noise decibels of each of the following devices: distortion processing device, color gradation adjustment device, directional blurring device, first acquisition device, second acquisition device, and third acquisition device.

[0040] The decibel measuring mechanism is connected to the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device, respectively, and is used to measure the nearby noise decibels of each of the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device. The decibel measuring mechanism includes multiple decibel measuring units, which are connected to the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device, respectively, to complete the separate measurement of the nearby noise decibels of each of the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device.

[0041] The decibel measurement mechanism includes multiple decibel measurement units, which are respectively connected to the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device to separately measure the nearby noise decibels of each of the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device. The multiple decibel measurement units are multiple decibel sensing circuits, respectively connected to the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device to separately measure the nearby noise decibels of each of the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device.

[0042] The plurality of decibel measurement units are plurality of decibel sensing circuits, which are respectively connected to the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device to complete the separate measurement of the nearby noise decibels of each of the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device. The plurality of decibel sensing circuits have the same structure.

[0043] The plurality of decibel measurement units are plurality of decibel sensing circuits, which are respectively connected to the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device to complete the separate measurement of the nearby noise decibels of each of the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device. The plurality of decibel sensing circuits have the same upper limit value and lower limit value for decibel measurement.

[0044] Implementation Plan 3

[0045] Figure 3 The internal structure block diagram of the big data early warning system based on the AI ​​analysis model shown in Embodiment 3 of the present invention includes:

[0046] The capture operation mechanism is set at the target dock and is used to perform a visual capture action on the water in front of the target dock once at a set time interval to obtain the corresponding real-time capture frame.

[0047] A step-by-step optimization mechanism, connected to the capture operation mechanism, includes a distortion processing device, a color level adjustment device, and a directional blurring device. The color level adjustment device is connected to both the distortion processing device and the directional blurring device. The distortion processing device performs distortion correction processing on the received real-time capture frame to obtain and output a corresponding distortion-processed image. The color level adjustment device performs multiple color index enhancement processes on the received distortion-processed image to obtain and output a corresponding color level adjusted image. The directional blurring device performs directional blurring processing on the received color level adjusted image to obtain and output a corresponding directional blurring image.

[0048] The first acquisition device, connected to the step-by-step optimization mechanism, is used to identify the image blocks of the ship in the directional blurred image according to the brightness value distribution range of the ship, and output them as reference image blocks. The brightness value distribution range of the ship is numerically limited by the upper limit brightness threshold and the lower limit brightness threshold of the ship.

[0049] The second acquisition device is connected to the first acquisition device and is used to acquire the number of pixels occupied by the reference image block, the number of pixel rows occupied by the reference image block, and the number of pixel columns occupied by the reference image block, and at the same time obtain the overall depth value of the reference image block.

[0050] The third acquisition device, connected to the second acquisition device, is used to introduce an AI analysis model to analyze the actual distribution area of ​​the ship based on the number of pixels occupied by the reference image block, the number of pixel rows occupied by the reference image block, the number of pixel columns occupied by the reference image block, and the overall depth value of the reference image block. The total number of models learned by the AI ​​analysis model is monotonically positively correlated with the interval length of the brightness value distribution interval corresponding to the ship.

[0051] The analysis and early warning device is connected to the third acquisition device and is used to play an over-tolerance early warning signal to trigger subsequent tolerance confirmation actions when the estimated ship tonnage corresponding to the actual distribution area exceeds the maximum allowable entry tonnage corresponding to the target dock.

[0052] A voice control processing device is disposed near the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device, and is used to provide the voice control services required by the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device respectively.

[0053] The voice control processing device is located near the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device, and is used to provide the voice control services required by the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device respectively. The voice control processing device includes a sound acquisition unit, a value conversion unit, and a signal transmission unit.

[0054] The voice control processing device, located near the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device, is used to provide the voice control services required by the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device respectively. It also includes: the numerical conversion unit is connected to both the sound acquisition unit and the signal transmission unit.

[0055] The system includes a voice control processing device located near the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device. This device provides the required voice control services to each of the three devices: the voice acquisition unit collects sound signals from the vicinity of the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device, and determines whether each collected sound signal is a voice control signal, sending only the voice control signals to the numerical conversion unit for control signal parsing.

[0056] In addition, in the big data early warning system based on the AI ​​analysis model, the numerical mapping relationship between the received actual distribution area and the corresponding estimated ship tonnage is represented by a numerical conversion function, which uses the received actual distribution area as its input parameter and the estimated ship tonnage corresponding to the received actual distribution area as its output parameter.

[0057] Therefore, the outstanding substantive features of this invention are reflected in:

[0058] First: Based on the brightness value distribution range corresponding to the ship, the image blocks of the ship in the directional blurred image are identified as reference image blocks for output. The brightness value distribution range corresponding to the ship is numerically limited by the upper limit brightness threshold and the lower limit brightness threshold corresponding to the ship.

[0059] Secondly, the number of pixels occupied by the reference image block, the number of pixel rows occupied by the reference image block, and the number of pixel columns occupied by the reference image block are collected. At the same time, the overall depth value of the reference image block is obtained, so as to provide reliable and effective basic information for the intelligent analysis of the subsequent AI analysis model.

[0060] Furthermore, an AI analysis model is introduced to intelligently analyze the actual distribution area of ​​ships based on multiple targeted selections of basic information. The total number of models learned by the AI ​​analysis model is monotonically positively correlated with the interval length of the brightness value distribution interval corresponding to the ships.

[0061] Finally: When the estimated tonnage of the vessel corresponding to the actual distribution area exceeds the maximum allowable tonnage of the target dock, an over-tonnage warning signal is played to trigger subsequent tonnage confirmation actions; otherwise, the over-tonnage warning signal is temporarily suspended, thereby improving the intelligence level of target dock management.

[0062] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / ROM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0063] Various features of the invention have been described in detail with reference to embodiments. However, it should be understood that these specific descriptions are merely illustrative, and the invention can be best understood within the scope of the appended claims.

Claims

1. A big data early warning system based on an AI analysis model, characterized in that, The system includes: The capture operation mechanism is set at the target dock and is used to perform a visual capture action on the water in front of the target dock once at a set time interval to obtain the corresponding real-time capture frame. A step-by-step optimization mechanism, connected to the capture operation mechanism, includes a distortion processing device, a color level adjustment device, and a directional blurring device. The color level adjustment device is connected to both the distortion processing device and the directional blurring device. The distortion processing device performs distortion correction processing on the received real-time capture frame to obtain and output a corresponding distortion-processed image. The color level adjustment device performs multiple color index enhancement processes on the received distortion-processed image to obtain and output a corresponding color level adjusted image. The directional blurring device performs directional blurring processing on the received color level adjusted image to obtain and output a corresponding directional blurring image. The first acquisition device, connected to the step-by-step optimization mechanism, is used to identify the image blocks of the ship in the directional blurred image according to the brightness value distribution range of the ship, and output them as reference image blocks. The brightness value distribution range of the ship is numerically limited by the upper limit brightness threshold and the lower limit brightness threshold of the ship. The second acquisition device is connected to the first acquisition device and is used to acquire the number of pixels occupied by the reference image block, the number of pixel rows occupied by the reference image block, and the number of pixel columns occupied by the reference image block, and at the same time obtain the overall depth value of the reference image block. The third acquisition device, connected to the second acquisition device, is used to introduce an AI analysis model to analyze the actual distribution area of ​​the ship based on the number of pixels occupied by the reference image block, the number of pixel rows occupied by the reference image block, the number of pixel columns occupied by the reference image block, and the overall depth value of the reference image block. The total number of models learned by the AI ​​analysis model is monotonically positively correlated with the interval length of the brightness value distribution interval corresponding to the ship. The analysis and early warning device is connected to the third acquisition device and is used to play an over-tolerance early warning signal to trigger subsequent tolerance confirmation actions when the estimated ship tonnage corresponding to the actual distribution area exceeds the maximum allowable entry tonnage corresponding to the target dock. Specifically, when the estimated ship tonnage corresponding to the actual distribution area exceeds the maximum allowable tonnage for entry into the target dock, the execution of the tonnage over-limit warning signal to trigger subsequent tonnage confirmation actions includes: using a numerical conversion function to represent the numerical mapping relationship between the received actual distribution area and its corresponding estimated ship tonnage.

2. The big data early warning system based on AI analysis model as described in claim 1, characterized in that: The analysis and early warning equipment is also used to temporarily suspend the playback of the tonnage over-limit early warning signal when the estimated ship tonnage corresponding to the actual distribution area is less than or equal to the maximum allowable entry tonnage corresponding to the target dock.

3. The big data early warning system based on an AI analysis model as described in claim 2, characterized in that, The system also includes: The decibel measuring mechanism is connected to the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device, respectively, and is used to measure the nearby noise decibels of each of the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device; The decibel measuring mechanism is connected to the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device, respectively, and is used to measure the nearby noise decibels of each of the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device. The decibel measuring mechanism includes multiple decibel measuring units, which are connected to the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device, respectively, to perform separate measurements of the nearby noise decibels of each of the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device.

4. The big data early warning system based on AI analysis model as described in claim 3, characterized in that: The decibel measurement mechanism includes multiple decibel measurement units, which are respectively connected to the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device to separately measure the nearby noise decibels of each of the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device. The multiple decibel measurement units are multiple decibel sensing circuits, respectively connected to the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device to separately measure the nearby noise decibels of each of the distortion processing device, the color level adjustment device, the directional bokeh device, the first acquisition device, the second acquisition device, and the third acquisition device.

5. The big data early warning system based on an AI analysis model as described in claim 4, characterized in that: The plurality of decibel measurement units are plurality of decibel sensing circuits, which are respectively connected to the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device to complete the separate measurement of the nearby noise decibels of each of the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device. The plurality of decibel sensing circuits have the same structure.

6. The big data early warning system based on AI analysis model as described in claim 5, characterized in that: The plurality of decibel measurement units are plurality of decibel sensing circuits, which are respectively connected to the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device to complete the separate measurement of the nearby noise decibels of each of the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device. The plurality of decibel sensing circuits have the same upper limit value and lower limit value for decibel measurement.

7. The big data early warning system based on an AI analysis model as described in any one of claims 3-6, characterized in that, The system also includes: A voice control processing device is disposed near the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device, and is used to provide the voice control services required by each of the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device respectively.

8. The big data early warning system based on AI analysis model as described in claim 7, characterized in that: A voice-controlled processing device is disposed near the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device, and is used to provide the voice control services required by the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device respectively. The voice-controlled processing device includes a sound acquisition unit, a value conversion unit, and a signal transmission unit.

9. The big data early warning system based on AI analysis model as described in claim 8, characterized in that: A voice control processing device, disposed near the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device, for providing the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device with their respective required voice control services, further includes: the numerical conversion unit is connected to the sound acquisition unit and the signal transmission unit respectively; The voice control processing device, located near the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device, is used to provide the voice control services required by each of the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device. It further includes: the sound acquisition unit is used to acquire sound signals near the distortion processing device, the color level adjustment device, the directional blurring device, the first acquisition device, the second acquisition device, and the third acquisition device, and to determine whether each acquired sound signal is a sound control signal, so that only the sound control signal is sent to the numerical conversion unit for control signal parsing.

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