Circuit board for unmanned cabin cleaning and unmanned cabin cleaning machine

Abstract

The invention discloses a circuit board for unmanned cabin cleaning and an unmanned cabin cleaning machine. The circuit board comprises a PCB, and a microprocessor, an Ethernet communication module, an analog quantity detection isolation unit, a power supply processing unit, a video signal fusion processing unit and a 5G communication module which are embedded in the PCB. The input end of the analog quantity detection and isolation unit is electrically connected with a plurality of external attitude sensing sensors, the output end of the analog quantity detection and isolation unit is electrically connected with the microprocessor, and the analog quantity detection and isolation unit is used for processing signals of the attitude sensing sensors; one side of the Ethernet communication module is electrically connected with the plurality of laser radars, and the other side of the Ethernet communication module is electrically connected with the microprocessor; one side of the power supply processing unit is electrically connected with the microprocessor, and the other side is electrically connected with external driving equipment; one side of the video signal fusion processing unit is electrically connected with the external image acquisition component, and the other side is electrically connected with the microprocessor. According to the invention, unmanned cabin cleaning operation or remote control operation cabin cleaning is realized, so that the cabin cleaning operation is more efficient and safer.

Description

Technical Field

[0001] This invention relates to the field of unmanned ship cleaning equipment technology, and particularly to a circuit board and unmanned cleaning machine for unmanned ship cleaning. Background Technology

[0002] Cleaning the hold is the final step in unloading bulk carriers, involving the collection of bulk materials that are inaccessible to unloading machines to the area below the hatch. Typically, excavators or loaders are used to remove bulk materials from the bulkheads, while loaders or pushers are used to collect the bulk materials from the bottom of the hold. These three types of machines are collectively referred to as cleaning machines. Meanwhile, the bulk materials in the corners, keel seams, and the last layer of coal ash particles at the bottom of the hold are collected by workers using shovels and brooms. This is the most labor-intensive unloading process.

[0003] Currently, cargo hold cleaning operations pose significant safety hazards and pose substantial health risks to workers. Furthermore, the low efficiency of cleaning operations can easily lead to cargo ship delays in port. The high incidence of injuries and fatalities during cargo hold cleaning is due to the semi-enclosed environment and the confined space requiring both human and machine operations. Safety risks primarily include: grab collisions, accidental grabbing, material leakage, falls, material collapse burying personnel, toxic gases, carbon dioxide deposition, and fires. The dirty, dusty, noisy, and high-temperature environment of the cleaning operations severely harms the health of workers. To ensure loading and unloading efficiency, cleaning positions typically operate on a 24-hour, three-shift system, requiring close cooperation between experienced supervisors, seasoned safety officers, skilled cleaning drivers, and cleaning workers. Many young people are unwilling to pursue this profession, and the training costs of experienced personnel mentoring newcomers are high. The cleaning and unloading operation is the least cost-effective unloading procedure. If not enough time is allocated for cleaning and unloading in advance, the terminal operator will have to choose between paying demurrage fees and the unsafe mixed cleaning and unloading operation. Generally, the demurrage fees for a 10,000-ton bulk carrier range from tens of thousands to hundreds of thousands of yuan per day, which is quite high. Summary of the Invention

[0004] This invention provides a circuit board and an unmanned cabin cleaning machine for unmanned cabin cleaning, in order to solve the technical problems mentioned in the background art.

[0005] To achieve the above objectives, the technical solution of the present invention is implemented as follows: The present invention provides a circuit board for unmanned cabin cleaning, including a PCB board and a microprocessor, an Ethernet communication module, an analog signal detection isolation unit, a power processing unit, a video signal fusion processing unit, and a 5G communication module respectively embedded on the PCB board; The input terminal of the analog signal detection isolation unit is electrically connected to multiple external attitude sensing sensors, and the output terminal is electrically connected to a microprocessor. The analog signal detection isolation unit is used to isolate and filter the signals from multiple attitude sensing sensors and transmit them to the microprocessor for signal processing. One side of the Ethernet communication module is electrically connected to multiple lidar sensors, and the other side is electrically connected to a microprocessor. One side of the power processing unit is electrically connected to the microprocessor, and the other side is electrically connected to the external driving device. One side of the video signal fusion processing unit is electrically connected to the external image acquisition component, and the other side is electrically connected to the microprocessor. The video signal fusion processing unit is used to perform video synthesis and splicing processing on the digital information output by the external image acquisition component to form a panoramic video signal, which is then transmitted to the monitoring screen of the external control center via the 5G communication module.

[0006] Furthermore, the circuit board also includes an output signal processing module electrically connected to the microprocessor. The output signal processing module is used to amplify and isolate the signal output by the microprocessor and input it to an external control device.

[0007] Furthermore, the circuit board also includes a communication processing module, which includes an RS485 communication processing unit and several debugging interfaces electrically connected to the RS485 communication processing unit. The RS485 communication processing unit is electrically connected to the microprocessor.

[0008] Furthermore, the circuit board also includes a switch input isolation unit electrically connected to the microprocessor. The switch input isolation unit serves as a reserved module for processing externally input switch signals.

[0009] Furthermore, the Ethernet communication module includes an Ethernet communication module and two Ethernet communication interfaces. The Ethernet communication module is electrically connected to the microprocessor, and the two Ethernet communication interfaces are built into the Ethernet communication module. One Ethernet communication interface is electrically connected to the first lidar, and the other Ethernet communication interface is electrically connected to the millimeter-wave lidar.

[0010] Furthermore, the 5G communication module includes a 5G communication unit and a 5G antenna. The 5G communication unit is electrically connected to the microprocessor, and the 5G antenna is electrically connected to the 5G communication unit.

[0011] Furthermore, the microprocessor has a built-in pose data fusion algorithm, which is used to fuse the pose data transmitted from multiple pose sensing sensors to generate a three-dimensional dynamic map, so as to realize the perception of the position and attitude of the cleaning machine itself.

[0012] Furthermore, the microprocessor has a built-in SLAM navigation algorithm, which is used to update the cabin map in real time to enable the unmanned cabin cleaning machine to navigate and perform its functions. The SLAM navigation algorithm updates the ship's cabin map in real time through the following steps: S11. Pre-build the dynamic model, prediction model, objective function for minimizing path error, and related constraints of the unmanned cabin cleaning aircraft; S12. Collect the current location and motion parameters of the unmanned cabin cleaning aircraft; S13. The initial prediction model, based on the information collected in S12 and combined with the dynamic model of the unmanned cabin cleaning machine, predicts and generates the motion trajectory of the unmanned cabin cleaning machine for several future steps. S14. Input the current point cloud state information and use the current point cloud state information to construct a reference trajectory, which is the current motion trajectory of the unmanned cabin cleaning machine. S15. Substitute the values ​​of the reference trajectory and the predicted motion trajectory into the objective function of minimizing the path error to generate trajectory correction data that meets the relevant constraints. S16. Optimize the current motion trajectory of the unmanned cabin cleaning machine using trajectory correction data, and then input the optimized motion trajectory into the dynamic model of the unmanned cabin cleaning machine to generate trajectory optimization input. S17. Input the trajectory optimization input into the drive module of the unmanned cabin cleaning machine to realize the trajectory control of the unmanned cabin cleaning machine; S18. During the operation of the unmanned cabin cleaning machine, cycle through S11 to S17 to achieve continuous optimization of the unmanned cabin cleaning machine's trajectory and closed-loop control of the unmanned cabin cleaning machine.

[0013] Furthermore, the video signal fusion processing unit incorporates a video fusion and stitching algorithm, which performs video fusion and stitching on multiple video images using the following steps: S21. Collect ship parameters and position parameters of multiple 3D naked-eye cameras; use ship parameters to determine the fusion region and image fusion weighting parameters; then use ship parameters and position parameters of multiple 3D naked-eye cameras to correct the distortion of video images captured by multiple 3D naked-eye cameras. S22. Based on perspective transformation and with the help of the corrected video images, generate an image-to-final mapping table; stitch together video images captured by multiple 3D naked-eye cameras according to the image fusion weighting parameters and the image-to-final mapping table to generate a panoramic image. S23. Divide the video areas captured by the six 3D glasses-free cameras into two video areas, A and B. Video area A corresponds to the video areas captured by the 3D glasses-free cameras in the front, front left, and front right directions; video area B corresponds to the video areas captured by the 3D glasses-free cameras in the rear, rear left, and rear right directions. S24. Create Gaussian pyramids GaMaskA and GaMaskB for the fusion region in video regions A and B respectively. Create Gaussian pyramids GaA and GaB for the images to be fused in video regions A and B respectively. Then create Laplacian Gaussian pyramids LapA and LapB for the fusion region in video regions A and B respectively. S25. Output the images other than the top-level images in the Gaussian pyramids GaMaskA and GaMaskB of the fused regions to form the first image set (A+B)n-1; The top-level images An and Bn in the Gaussian pyramids GaA and GaB of the images to be fused are obtained through the first image set (A+B)n-1; then the top-level images An and Bn are linearly added and fused to form the second image set Ga(A+B)n-1. The images to be fused in each layer of the LapA and LapB Lapaus pyramids in the fusion region are linearly added together to form the first difference pyramid Lap(A+B). S26. Linearly add the second image set Ga(A+B)n-1 to the images outside the top layer of the first difference pyramid Lap(A+B) to obtain the third image set LapBlend(A+B)n-1; S27. Upsample the third image set LapBlend(A+B)n-1 to obtain the fourth image set (A+B)n-2. Linearly fuse the fourth image set (A+B)n-2 with the second difference pyramid Lap(A+B)n-2 formed by upsampling the first difference pyramid Lap(A+B) to obtain the fifth image set LapBlend(A+B)n-2. S28, loop S27, and finally obtain the final image at the bottom layer, LapBlend(A+B)0, thus completing the splicing and fusion of multiple video images.

[0014] Another aspect of the present invention provides an unmanned cabin cleaning machine, including a circuit board and an excavator with a bucket, multiple 3D naked-eye cameras, a first lidar, a millimeter-wave lidar, and multiple attitude sensing sensors; Multiple 3D naked-eye cameras are electrically connected to the video signal fusion processing unit. At least six of the multiple 3D naked-eye cameras are installed on the six sides of the excavator, namely the front, front left, front right, front rear, rear left, and rear right, to obtain video images from multiple directions. The first lidar is electrically connected to the Ethernet communication module to emit laser pulses and receive reflected signals in order to measure the distance and position of objects inside the cabin, thereby forming a point cloud map. The millimeter-wave lidar is electrically connected to the Ethernet communication module to provide the excavator with sensing capabilities. Multiple attitude sensing sensors are electrically connected to the analog quantity detection isolation unit. The attitude sensing sensors are used to sense the attitude of the excavator in order to obtain attitude data. The output signal processing module is electrically connected to the control equipment on the excavator to realize the automated control of the excavator.

[0015] The beneficial effects of this invention are: 1. This invention discloses a circuit board for unmanned cabin cleaning. The video signal fusion processing unit within the circuit board can perform video synthesis and splicing processing on the digital information output by the image acquisition unit to form a panoramic video signal, outputting a 360° panoramic digital video stream. This panoramic video stream can display every corner of the cabin from all angles, providing great convenience for remote monitoring centers. Monitoring personnel can view various situations inside the cabin in real time, ensuring the safe operation of the vessel and the safety of personnel. This invention not only improves monitoring efficiency but also provides strong support for vessel management and maintenance.

[0016] 2. This invention also discloses an unmanned cargo cleaning machine, which can perform unmanned or remotely controlled cargo cleaning operations, making the cleaning process more efficient and safer. Multiple attitude sensing sensors on the unmanned cleaning machine provide more comprehensive and accurate environmental information, enabling it to autonomously navigate and perform tasks in complex environments without human intervention. Simultaneously, the unmanned cleaning machine has a remote control function for cargo cleaning operations, allowing operators to monitor and control the cleaning operation from a safe distance, further improving the safety and flexibility of the operation. Furthermore, this remote control function significantly improves the efficiency of cargo cleaning operations, bringing tremendous convenience and benefits to the bulk cargo terminal port industry. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the circuit board structure in this invention; Figure 2 A schematic diagram showing the connection between the circuit board and other components inside the unmanned cabin cleaning aircraft; Figure 3 A schematic diagram illustrating the principle of real-time updating of the ship's cabin map for SLAM navigation algorithms; Figure 4 This is a flowchart of the video fusion and stitching algorithm. Detailed Implementation

[0018] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many other different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0019] It should be noted that when a component is referred to as "fixed" or "set" on another component, it can be directly on or indirectly on the other component. When a component is referred to as "connected" to another component, it can be directly connected to or indirectly connected to the other component.

[0020] It should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention.

[0021] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0023] It should also be noted that in the embodiments of this application, the same reference numerals are used to represent the same component or part. For the same part in the embodiments of this application, the reference numerals may only be used to mark one part or component as an example. It should be understood that the reference numerals are also applicable to other identical parts or components.

[0024] Reference Figure 1 This application provides a circuit board for unmanned cabin cleaning, including a PCB board and a microprocessor, an Ethernet communication module, an analog signal detection isolation unit, a power processing unit, a video signal fusion processing unit, and a 5G communication module respectively embedded on the PCB board; The input terminal of the analog signal detection isolation unit is electrically connected to multiple external attitude sensing sensors, and the output terminal is electrically connected to a microprocessor. The analog signal detection isolation unit is used to isolate and filter the signals from multiple attitude sensing sensors and transmit them to the microprocessor for signal processing. One side of the Ethernet communication module is electrically connected to multiple lidar sensors, and the other side is electrically connected to a microprocessor. The power processing unit is electrically connected to the microprocessor on one side and to external drive devices (such as drive motors and hydraulic rods on an excavator) on the other side. One side of the video signal fusion processing unit is electrically connected to the external image acquisition component, and the other side is electrically connected to the microprocessor. The video signal fusion processing unit is used to perform video synthesis and splicing processing on the digital information output by the external image acquisition component to form a panoramic video signal, which is then transmitted to the monitoring screen of the external control center via the 5G communication module.

[0025] The video signal fusion processing unit within the circuit board can perform video synthesis and splicing processing on the digital information output from the image acquisition unit to form a panoramic video signal, outputting a 360° panoramic digital video stream. This panoramic video stream can display every corner of the cabin from all angles, providing great convenience for the remote monitoring center. Monitoring personnel can view various situations inside the cabin in real time, ensuring the safe operation of the vessel and the safety of personnel. This invention not only improves monitoring efficiency but also provides strong support for the management and maintenance of vessels.

[0026] In some embodiments, the circuit board further includes an output signal processing module electrically connected to the microprocessor. The output signal processing module is used to amplify and isolate the signal output by the microprocessor and input it to an external control device (such as a control trolley valve, relay, etc.).

[0027] In some embodiments, the circuit board further includes a communication processing module, which includes an RS485 communication processing unit and a plurality of debugging interfaces electrically connected to the RS485 communication processing unit. The RS485 communication processing unit is electrically connected to a microprocessor.

[0028] In some embodiments, the circuit board further includes a switch input isolation unit electrically connected to the microprocessor. This switch input isolation unit serves as a reserved module for processing externally input switch signals (e.g., switch signals output from bucket limit switches, sweeping mechanism limit switches, and emergency stop switches). For example, the switch input isolation unit can determine whether the drive motor has reached its operating position based on its high or low potential, thereby adjusting the switching state of the drive motor.

[0029] In some embodiments, the Ethernet communication module includes an Ethernet communication module and two Ethernet communication interfaces. The Ethernet communication module is electrically connected to a microprocessor, and the two Ethernet communication interfaces are built into the Ethernet communication module. One Ethernet communication interface is electrically connected to a first lidar, and the other Ethernet communication interface is electrically connected to a millimeter-wave lidar.

[0030] In some embodiments, the 5G communication module includes a 5G communication unit and a 5G antenna, wherein the 5G communication unit is electrically connected to the microprocessor and the 5G antenna is electrically connected to the 5G communication unit.

[0031] In some embodiments, the microprocessor has a built-in pose data fusion algorithm, which is used to fuse pose data transmitted from multiple pose sensing sensors to generate a three-dimensional dynamic map, so as to realize the perception of the position and attitude of the cleaning machine itself.

[0032] In some embodiments, the microprocessor has a built-in SLAM navigation algorithm, which is used to update the cabin map in real time to enable the unmanned cabin cleaning machine to navigate and perform its functions. Reference Figure 3 The SLAM navigation algorithm updates the ship's cabin map in real time, including the following steps: S11. Pre-build the dynamic model, prediction model, objective function for minimizing path error, and related constraints of the unmanned cabin cleaning aircraft; S12. Collect the current location and motion parameters of the unmanned cabin cleaning aircraft; S13. The initial prediction model, based on the information collected in S12 and combined with the dynamic model of the unmanned cabin cleaning machine, predicts and generates the motion trajectory of the unmanned cabin cleaning machine for several future steps. S14. Input the current point cloud state information and use the current point cloud state information to construct a reference trajectory, which is the current motion trajectory of the unmanned cabin cleaning machine. S15. Substitute the values ​​of the reference trajectory and the predicted motion trajectory into the objective function of minimizing the path error to generate trajectory correction data that meets the relevant constraints. S16. Optimize the current motion trajectory of the unmanned cabin cleaning machine using trajectory correction data, and then input the optimized motion trajectory into the dynamic model of the unmanned cabin cleaning machine to generate trajectory optimization input. S17. Input the trajectory optimization input into the drive module of the unmanned cabin cleaning machine to realize the trajectory control of the unmanned cabin cleaning machine; S18. During the operation of the unmanned cabin cleaning machine, cycle through S11 to S17 to achieve continuous optimization of the unmanned cabin cleaning machine's trajectory and closed-loop control of the unmanned cabin cleaning machine.

[0033] In some embodiments, refer to Figure 4 The video signal fusion processing unit has a built-in video fusion and stitching algorithm, which performs video fusion and stitching on multiple video images using the following steps: S21. Collect ship parameters and position parameters of multiple 3D naked-eye cameras; use ship parameters to determine the fusion region and image fusion weighting parameters; then use ship parameters and position parameters of multiple 3D naked-eye cameras to correct the distortion of video images captured by multiple 3D naked-eye cameras. S22. Based on perspective transformation and with the help of the corrected video images, generate an image-to-final mapping table; stitch together video images captured by multiple 3D naked-eye cameras according to the image fusion weighting parameters and the image-to-final mapping table to generate a panoramic image. S23. Divide the video areas captured by the six 3D glasses-free cameras into two video areas, A and B. Video area A corresponds to the video areas captured by the 3D glasses-free cameras in the front, front left, and front right directions; video area B corresponds to the video areas captured by the 3D glasses-free cameras in the rear, rear left, and rear right directions. S24. Create Gaussian pyramids GaMaskA and GaMaskB for the fusion region in video regions A and B respectively. Create Gaussian pyramids GaA and GaB for the images to be fused in video regions A and B respectively. Then create Laplacian Gaussian pyramids LapA and LapB for the fusion region in video regions A and B respectively. S25. Output the images other than the top-level images in the Gaussian pyramids GaMaskA and GaMaskB of the fused regions to form the first image set (A+B)n-1; The top-level images An and Bn in the Gaussian pyramids GaA and GaB of the images to be fused are obtained through the first image set (A+B)n-1; then the top-level images An and Bn are linearly added and fused to form the second image set Ga(A+B)n-1. The images to be fused in each layer of the LapA and LapB Lapaus pyramids in the fusion region are linearly added together to form the first difference pyramid Lap(A+B). S26. Linearly add the second image set Ga(A+B)n-1 to the images outside the top layer of the first difference pyramid Lap(A+B) to obtain the third image set LapBlend(A+B)n-1; S27. Upsample the third image set LapBlend(A+B)n-1 to obtain the fourth image set (A+B)n-2. Linearly fuse the fourth image set (A+B)n-2 with the second difference pyramid Lap(A+B)n-2 formed by upsampling the first difference pyramid Lap(A+B) to obtain the fifth image set LapBlend(A+B)n-2. S28, loop S27, and finally obtain the final image at the bottom layer, LapBlend(A+B)0, thus completing the splicing and fusion of multiple video images.

[0034] Reference Figure 2 In another aspect, the present invention also provides an unmanned cabin cleaning machine, including a circuit board and an excavator with a bucket, multiple 3D naked-eye cameras, a first lidar, a millimeter-wave lidar, and multiple attitude sensing sensors. Multiple 3D naked-eye cameras are electrically connected to the video signal fusion processing unit. At least six of the multiple 3D naked-eye cameras are installed on the six sides of the excavator, namely the front, front left, front right, front rear, rear left, and rear right, to obtain video images from multiple directions. The first lidar is electrically connected to the Ethernet communication module to emit laser pulses and receive reflected signals in order to measure the distance and position of objects inside the cabin, thereby forming a point cloud map. The millimeter-wave lidar is electrically connected to the Ethernet communication module to provide the excavator with sensing capabilities. Multiple attitude sensing sensors are electrically connected to the analog quantity detection isolation unit. The attitude sensing sensors are used to sense the attitude of the excavator in order to obtain attitude data. The output signal processing module is electrically connected to the control equipment on the excavator (such as control solenoid valves, relays, etc.) to realize the automated control of the excavator.

[0035] Unmanned cargo handling vehicles (UAVs) enable unmanned or remotely controlled cargo handling operations, making these processes more efficient and safer. Multiple attitude sensing sensors on the UAV provide comprehensive and accurate environmental information, allowing it to navigate and perform tasks autonomously in complex environments without human intervention. Furthermore, the UAV's remote control capability allows operators to monitor and control the cleaning process from a safe distance, further enhancing safety and flexibility. This remote control function also significantly improves the efficiency of cargo handling operations, bringing substantial convenience and benefits to the bulk cargo terminal port industry.

[0036] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A circuit board for unmanned de-canning, characterized by, The PCB board, and the microprocessor, the Ethernet communication module, the analog quantity detection isolation unit, the power supply processing unit, the video signal fusion processing unit and the 5G communication module embedded on the PCB board respectively; The input end of the analog quantity detection isolation unit is electrically connected with a plurality of external attitude perception sensors, and the output end is electrically connected with the microprocessor, and the analog quantity detection isolation unit is used for isolating and filtering the signals of the plurality of attitude perception sensors and transmitting the signals to the microprocessor for signal processing; One side of the Ethernet communication module is electrically connected with a plurality of laser radars respectively, and the other side is electrically connected with the microprocessor; One side of the power supply processing unit is electrically connected with the microprocessor, and the other side is electrically connected with external driving equipment respectively; One side of the video signal fusion processing unit is electrically connected with an external image acquisition component, and the other side is electrically connected with the microprocessor, and the video signal fusion processing unit is used for video synthesis splicing processing of digital information output by the external image acquisition component, forming a panoramic video signal, and transmitting the panoramic video signal to a monitoring screen of an external control center through the 5G communication module.

2. A circuit board for unmanned de-canning according to claim 1, wherein, Further comprising an output signal processing module electrically connected with the microprocessor, the output signal processing module is used for amplifying and isolating the signals output by the microprocessor and inputting the signals to external control equipment.

3. A circuit board for unmanned de-canning according to claim 2, wherein, Further comprising a communication processing module, the communication processing module comprises an RS485 communication processing unit and a plurality of debugging interfaces electrically connected with the RS485 communication processing unit, and the RS485 communication processing unit is electrically connected with the microprocessor.

4. A circuit board for unmanned de-canning according to claim 2, wherein, Further comprising a switching value input isolation unit electrically connected with the microprocessor, the switching value input isolation unit is used for processing external input switching values as a reserved module.

5. A circuit board for unmanned de-canning according to claim 2, wherein, The Ethernet communication module comprises two Ethernet communication interfaces, the Ethernet communication module is electrically connected with the microprocessor, and the two Ethernet communication interfaces are built in the Ethernet communication module, one of the two Ethernet communication interfaces is electrically connected with the first laser radar, and the other Ethernet communication interface is electrically connected with the millimeter wave laser radar.

6. A circuit board for unmanned de-canning according to claim 2, wherein, The 5G communication module comprises a 5G communication unit and a 5G antenna, the 5G communication unit is electrically connected with the microprocessor, and the 5G antenna is electrically connected with the 5G communication unit.

7. A circuit board for unmanned de-canning according to claim 2, wherein, The microprocessor is built in a pose data fusion algorithm, the pose data fusion algorithm is used for fusing the attitude data transmitted by the plurality of attitude perception sensors, generating a three-dimensional space dynamic map, so as to realize the perception of the position and attitude of the unmanned cleaning machine.

8. A circuit board for unmanned de-canning according to claim 2, wherein, The microprocessor is built in a SLAM navigation algorithm, the SLAM navigation algorithm is used for updating the ship cabin map in real time, so as to realize the navigation and action of the unmanned cleaning machine; The SLAM navigation algorithm for updating the ship cabin map in real time comprises the following steps: S11, a dynamic model, a prediction model, a target function of path error minimization and related constraints of the unmanned cleaning machine are constructed in advance; S12, the current position of the unmanned cleaning machine and the current motion parameters are collected; S13, the initial prediction model generates the motion trajectory of the unmanned cleaning machine in the future according to the information collected in S12 and in combination with the dynamic model of the unmanned cleaning machine; S14, input the current point cloud state information, and construct a reference trajectory using the current point cloud state information, the reference trajectory being a current motion trajectory of the unmanned cleaning machine; S15, substituting the reference trajectory and the predicted motion trajectory into a target function of path error minimization, to generate trajectory correction data meeting relevant constraint conditions; S16, using the trajectory correction data to optimize the current motion trajectory of the unmanned cleaning machine, and then inputting the optimized motion trajectory into a dynamics model of the unmanned cleaning machine to generate trajectory optimization input; S17, inputting the trajectory optimization input into a driving module of the unmanned cleaning machine to realize trajectory control of the unmanned cleaning machine; S18, in the process of operation of the unmanned cleaning machine, repeating S11 to S17 to realize continuous optimization of the trajectory of the unmanned cleaning machine and closed-loop control of the unmanned cleaning machine.

9. A circuit board for unmanned de-canning according to claim 2, wherein, The video signal fusion processing unit is internally provided with a video fusion and splicing algorithm, which adopts the following steps to perform video fusion and splicing on multiple video images: S21, collecting ship parameters and position parameters of multiple 3D naked-eye cameras; determining a fusion region and image fusion weighting parameters by using the ship parameters; and then performing distortion correction on video images captured by the multiple 3D naked-eye cameras by using the ship parameters and the position parameters of the multiple 3D naked-eye cameras; S22, generating a mapping table of images and final corresponding positions based on perspective transformation and by means of the corrected video images; and splicing the video images captured by the multiple 3D naked-eye cameras according to the image fusion weighting parameters and the mapping table of images and final corresponding positions to generate a panoramic image; S23, dividing video regions captured by the six 3D naked-eye cameras into two video regions A and B, wherein the A video region corresponds to video regions captured by the 3D naked-eye cameras in the front, left front and right front directions, and the B video region corresponds to video regions captured by the 3D naked-eye cameras in the back, left back and right back directions; S24, creating a fusion region Gaussian pyramid GaMaskA and GaMaskB on the A and B video regions respectively, creating a to-be-fused image Gaussian pyramid GaA and GaB on the A and B video regions respectively, and then creating a fusion region Laplacian Gaussian pyramid LapA and LapB on the A and B video regions respectively; S25, outputting images other than top-level images in the fusion region Gaussian pyramids GaMaskA and GaMaskB to form a first image set (A+B)n-1; obtaining top-level to-be-fused images An and Bn in the to-be-fused image Gaussian pyramids GaA and GaB through the first image set (A+B)n-1; then linearly adding the top-level to-be-fused images An and Bn to form a second image set Ga(A+B)n-1; linearly adding to-be-fused images in each layer of the fusion region Laplacian Gaussian pyramids LapA and LapB to form a first difference pyramid Lap(A+B); S26, linearly adding the second image set Ga(A+B)n-1 and images other than top-level images in the first difference pyramid Lap(A+B) to obtain a third image set LapBlend(A+B)n-1. S27, the third image set LapBlend(A+B)n-1 is up-sampled to obtain a fourth image set (A+B)n-2, and the fourth image set (A+B)n-2 is linearly fused with the second difference pyramid Lap(A+B)n-2 formed by up-sampling the first difference pyramid Lap(A+B) to obtain a fifth image set LapBlend(A+B)n-2; S28, cycle S27, finally obtain the final image LapBlend(A+B)0 of the bottom layer, thus completing the splicing and fusion of multiple video images.

10. An unmanned cargo hold, characterized in that, The excavator with a bucket, the image acquisition component, the first laser radar, the millimeter wave laser radar, and the plurality of attitude perception sensors include the circuit board according to any one of claims 2 to 9. The image acquisition component includes a plurality of 3D naked-eye cameras, and the plurality of 3D naked-eye cameras are electrically connected with the video signal fusion processing unit. At least six 3D naked-eye cameras in the plurality of 3D naked-eye cameras are respectively installed on the front, left front, right front, back, left back, and right back of the excavator to obtain video images in multiple directions. The first laser radar is electrically connected with the Ethernet communication module, is used for emitting laser pulses and receiving reflection signals to measure the distance and position of objects in the cabin, and forms a point cloud map. The millimeter wave laser radar is electrically connected with the Ethernet communication module, and is used for providing perception capability for the excavator. The plurality of attitude perception sensors are electrically connected with the analog quantity detection isolation unit, and the attitude perception sensors are used for perceiving the attitude of the excavator to obtain attitude data. The output signal processing module is electrically connected with the control device on the excavator to realize automatic control of the excavator.

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