A shipborne AI intelligent terminal management system for ship management and dispatch

Abstract

The present invention relates to the field of ship obstacle positioning, and discloses a ship-borne AI intelligent terminal management system for ship management and scheduling. The system adopts a drone to take pictures of ships to generate visible light images, thermal imaging images and grayscale images, collects color values, temperature parameters and grayscale values ​​of each small map through images, and calculates corresponding color deviation coefficients, temperature difference coefficients and grayscale difference coefficients according to mathematical models, and then calculates the ship equipment failure degree coefficient according to these coefficients, and compares it with the ship equipment failure degree coefficient under normal working conditions, analyzes possible failures of the target ship according to the influence weights of the influencing factors on the comparison results, generates a report based on the analysis results and transmits it to maintenance personnel for maintenance and replacement through a visual interface, thereby realizing all-round and multi-angle inspection of obstacles, which is beneficial to improving the utilization rate of ships and extending their service life.

Description

Technical Field

[0001] The present invention relates to the field of ship obstacle positioning, and more specifically to a shipborne AI intelligent terminal management system for ship management and scheduling. Background Art

[0002] Ship management and dispatch are important links in ship operations, which are directly related to the safety, efficiency and economy of ships. With the continuous development of ship technology, ship equipment is becoming more and more complex, and the requirements for ship management and dispatch are also getting higher and higher. Ship obstacle positioning, as a key task in ship management and dispatch, aims to timely discover and deal with potential failures of ship equipment to ensure the safe operation of ships.

[0003] At present, ship obstacle location mainly relies on manual inspection and regular maintenance. This process usually includes the following steps: First, maintenance personnel conduct on-site inspections of various areas of the ship according to the established inspection routes and plans; second, through visual observation, touch and other methods, determine whether there are abnormalities or faults in the ship's equipment; finally, according to the inspection results, formulate corresponding maintenance plans and repair or replace the problems found.

[0004] However, traditional methods of locating ship obstacles have many shortcomings. First, manual inspections are limited by the observation ability and work scope of maintenance personnel, and it is difficult to cover all areas and details of the ship, resulting in a high rate of missed fault detection. Secondly, although regular maintenance can prevent faults to a certain extent, it cannot monitor the operating status of ship equipment in real time, making it difficult to detect problems in a timely manner, affecting the safety and efficiency of the ship. In addition, manual judgment of faults often relies on experience and subjective feelings, lacks objective and accurate data support, and leads to uncertainty in the judgment results. Finally, manual inspections and regular maintenance require a lot of manpower and time investment, which increases the cost of ship operation. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a shipborne AI intelligent terminal management system for ship management and scheduling to solve the problems existing in the above-mentioned background technology.

[0006] The present invention provides the following technical solutions: a shipborne AI intelligent terminal management system for ship management and dispatch, comprising a system operation database, a system central processing module and a user information terminal, and also comprising a map drawing module, an image acquisition module, a data acquisition module, a data processing module, a comprehensive analysis module, a fault degree assessment module and a human-computer interaction module;

[0007] The map drawing module is used to divide the small map into small maps, which are numbered 1, 2, 3, ..., n in sequence, and determine the target device;

[0008] The image acquisition module is used to acquire a first key image group;

[0009] The data acquisition module is used to acquire a first key data group according to the first key image group;

[0010] The data processing module is used to process the first key data group to obtain a second key data group;

[0011] The comprehensive analysis module obtains a comprehensive data output value based on the analysis of the second key data group;

[0012] The fault degree assessment module assesses the fault degree of the target ship according to the comprehensive data output value;

[0013] The human-computer interaction module outputs information to the user terminal based on the comprehensive text output value.

[0014] Preferably, the mapping module maps the target ship and divides the drawn ship map into n small maps according to the distribution of ship equipment, which are numbered 1, 2, 3, ..., n in sequence, to form an obstacle positioning map, and the ship equipment in each small map is numbered 1, 2, 3, ..., m in sequence.

[0015] Preferably, the image acquisition module places the drone 100 meters above the monitoring area, runs it at a constant speed along an "S"-shaped route starting from the first small map at a shooting angle of 45 degrees, takes pictures of each small map in turn, and generates the first key image group of the same location, including visible light images, thermal imaging images and grayscale images.

[0016] Preferably, the color value and acquisition method are as follows: according to the generated visible light image, the red channel value, the green channel value and the blue channel value of each pixel of the target ship are collected to obtain the color value , where S is the color value, g, r, b are the green channel value, red channel value, and blue channel value respectively, and β is a constant.

[0017] Preferably, the temperature parameter is obtained by uniformly taking the temperature values ​​of a number of points on the surface of the target ship according to the generated thermal imaging image, and recording them as t1, t2, ..., t n , calculate the average temperature of the target ship , where t i Represents the temperature of the i-th small map.

[0018] Preferably, the grayscale value is obtained by: according to the generated grayscale image, it is sequentially recorded as b1, b2, ..., b n, where each area is regarded as a small map of the target ship, the grayscale value of each pixel in the small map of the target ship is collected, and the average grayscale value of the small map of the target ship is calculated , where p i is the grayscale value of the i-th small map.

[0019] Preferably, the data processing module includes a color value processing unit, a temperature value processing unit and a grayscale value processing unit, which are used to transmit the first key data group to the corresponding processing unit to obtain the second key data group, including a color deviation coefficient, a temperature difference coefficient and a grayscale difference coefficient.

[0020] Preferably, the calculation formula of the color deviation coefficient is: , L is the color deviation coefficient of the target ship, S i.j is the color value of the jth ship equipment in the i-th small map of the target ship, S0 is the color value of the ship in normal working condition, m is the total number of ship equipment in the target ship, and n is the total number of small maps.

[0021] Preferably, the calculation formula of the temperature difference coefficient is: , where W is the temperature difference coefficient, t max is the maximum temperature of the target ship, t min is the minimum temperature of the target ship, T i is the average temperature of the target ship, m is the total number of ship equipment in the target ship, α is the light intensity, if the value of W exceeds It is considered a serious abnormality. There may be some equipment failure, resulting in only partial normal operation of the target ship, or all equipment failure, resulting in excessive temperature and inability to operate. If the value of W is lower than This indicates that there may be a line disconnection problem that causes the entire ship equipment to fail to operate normally. If the value of W is The range indicates that only a few ships may have obstructions, resulting in a decrease in the surface utilization of the target ship.

[0022] Preferably, the calculation formula of the grayscale difference coefficient is: , where H is the grayscale difference coefficient, P i is the average gray value of the i-th target ship small map, P i+2 is the average gray value of the i+2th target ship small map, P i+1 is the average grayscale value of the i+1th target ship small map, Q is the grayscale value of the target ship in normal working state, and the closer the value of H is to 0, the smaller the grayscale difference between the target ship and the ship in normal working state is.

[0023] Preferably, the comprehensive analysis module calculates the comprehensive data output value, specifically the ship equipment failure degree coefficient, based on the second key data group obtained in the data processing module and transmitted to the ship equipment utilization function model.

[0024] Preferably, the calculation formula of the ship equipment failure degree coefficient is: , where X is the ship equipment failure degree coefficient, , is a constant, e is a constant, L is the color deviation coefficient of the target ship, W is the temperature difference coefficient, and P is the average gray value of the small map of the target ship.

[0025] Preferably, the specific implementation process of the fault degree assessment module is:

[0026] The obtained ship equipment failure degree coefficient is compared and analyzed with the ship equipment failure degree coefficient under normal working conditions, and an appropriate repair plan is formulated according to the influence weight of the influencing factor comparison result. If the grayscale difference coefficient comparison result has the greatest impact, it means that there is dust accumulation on the surface of the ship equipment. If the color deviation coefficient comparison result has the greatest impact, it means that there are dents in the ship equipment. If the temperature difference coefficient comparison result has the greatest impact, it means that there may be obstructions on the surface of the ship equipment.

[0027] Technical effects and advantages of the present invention:

[0028] The present invention forms an obstacle positioning map by drawing a ship map and dividing it into multiple small maps, which is conducive to comprehensive analysis of obstacles affecting the operation of the ship. The ship equipment area of ​​the ship is photographed by an unmanned aerial vehicle to generate visible light images, thermal imaging images and grayscale images of the same position. The color value, temperature parameter and grayscale value of the target ship small map are collected through the generated image. The color deviation coefficient, temperature difference coefficient and grayscale difference coefficient are obtained by pre-processing the collected color value, temperature parameter and grayscale value. The obtained color deviation coefficient, temperature difference coefficient and grayscale difference coefficient are then re-processed through a function model to obtain a ship equipment failure degree coefficient. By comparing the ship equipment failure degree coefficient with the ship equipment failure degree coefficient under normal working conditions, the possible obstacles in the obstacle area are analyzed and a maintenance plan is specified. Finally, a report generated by the maintenance plan is passed to the maintenance personnel to perform human-computer interaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the system flow of the present invention.

[0030] Figure 2 A schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0031] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are merely illustrative. The shipborne AI intelligent terminal management system for ship management and scheduling involved in the present invention is not limited to the various structures recorded in the following embodiments. All other implementations obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0032] refer to Figure 1-2 The present invention provides a shipborne AI intelligent terminal management system for ship management and scheduling, including a system operation database, a system central processing module and a user information terminal, and also includes a map drawing module, an image acquisition module, a data acquisition module, a data processing module, a comprehensive analysis module, a fault degree assessment module and a human-computer interaction module.

[0033] The map drawing module is used to divide the small map into small maps, which are numbered 1, 2, 3, ..., n in sequence, and determine the target device;

[0034] The image acquisition module is used to acquire a first key image group;

[0035] The data acquisition module is used to acquire a first key data group according to the first key image group;

[0036] The data processing module is used to process the first key data group to obtain a second key data group;

[0037] The comprehensive analysis module obtains a comprehensive data output value based on the analysis of the second key data group;

[0038] The fault degree assessment module assesses the fault degree of the target ship according to the comprehensive data output value;

[0039] The human-computer interaction module outputs information to the user terminal based on the comprehensive text output value.

[0040] The mapping module maps the target ship and divides the drawn ship map into n small maps according to the distribution of ship equipment, which are numbered 1, 2, 3, ..., n in sequence to form an obstacle positioning map, and the ship equipment in each small map is numbered 1, 2, 3, ..., m in sequence.

[0041] The image acquisition module places the drone 100 meters above the monitoring area, and runs at a constant speed starting from the first small map according to an "S"-shaped route at a shooting angle of 45 degrees, and takes pictures of each small map in turn to generate the first key image group of the same position, including a visible light image, a thermal imaging image, and a grayscale image;

[0042] In this embodiment, it should be specifically explained that the visible light image is an unprocessed image visible to the human eye, and is used to extract the color value of the target ship. The thermal imaging image detects the temperature distribution on the surface of an object through infrared radiation, and is used to collect the temperature parameters of the target ship. The grayscale image is an image that only contains black, white and middle gray brightness levels, and is used to extract the grayscale value of the target ship. The grayscale value can reflect the depressions on the surface of the ship, and the grayscale value of the depressed part is larger than that of the flat part.

[0043] In this embodiment, it should be specifically explained that the color value and the acquisition method are to collect the red channel value, green channel value and blue channel value of each pixel of the target ship according to the generated visible light image to obtain the color value , where S is the color value, g, r, b are the green channel value, red channel value and blue channel value respectively, and β is a constant;

[0044] The temperature parameter is obtained by uniformly taking the temperature values ​​of several points on the surface of the target ship according to the generated thermal imaging image, and recording them as t1, t2, ..., t n , calculate the average temperature of the target ship , where t i Represents the temperature of the i-th small map;

[0045] The grayscale value is obtained by: according to the generated grayscale image, it is recorded as b1, b2, ..., b n , where each area is regarded as a small map of the target ship, the grayscale value of each pixel in the small map of the target ship is collected, and the average grayscale value of the small map of the target ship is calculated , where p i is the grayscale value of the i-th small map.

[0046] The data processing module includes a color value processing unit, a temperature value processing unit and a gray value processing unit, which are used to transmit the first key data group to the corresponding processing unit to obtain the second key data group, including the color deviation coefficient, the temperature difference coefficient and the gray difference coefficient;

[0047] The calculation formula of the color deviation coefficient is: , L is the color deviation coefficient of the target ship, S i.j is the color value of the jth ship equipment in the i-th small map of the target ship, S0 is the color value of the ship in normal working condition, m is the total number of ship equipment in the target ship, n is the total number of small maps, and the closer the value of L is to 1, the smaller the degree of color deviation is, and the more stable the coating state is;

[0048] The calculation formula of the temperature difference coefficient is: , where W is the temperature difference coefficient, t max is the maximum temperature of the target ship, t min is the minimum temperature of the target ship, T i is the average temperature of the target ship, m is the total number of target ships, α is the light intensity, if the value of W exceeds It is considered a serious abnormality. There may be some equipment failure, resulting in only partial normal operation of the target ship, or all equipment failure, resulting in excessive temperature and inability to operate. If the value of W is lower than This indicates that there may be a line disconnection problem that causes the entire ship to be unable to operate normally. If the value of W is The range of this indicates that only a few ships may have obstructions, which may reduce the surface utilization of the target ship;

[0049] The calculation formula of the grayscale difference coefficient is: , where H is the grayscale difference coefficient, P i is the average gray value of the i-th target ship small map, P i+2 is the average gray value of the i+2th target ship small map, P i+1 is the average grayscale value of the i+1th target ship small map, Q is the grayscale value of the target ship in normal working state, and the closer the value of H is to 0, the smaller the grayscale difference between the target ship and the ship in normal working state is, and the smoother the surface is without depression.

[0050] The comprehensive analysis module calculates the comprehensive data output value, specifically the ship equipment failure degree coefficient, according to the second key data group obtained in the data processing module and transmitted to the ship equipment utilization degree function model;

[0051] In this embodiment, it should be specifically explained that the calculation formula of the ship equipment failure degree coefficient is: , where X is the ship equipment failure degree coefficient, , is a constant, e is a constant, L is the color deviation coefficient of the target ship, W is the temperature difference coefficient, and P is the average gray value of the small map of the target ship.

[0052] The specific implementation process of the fault degree assessment module is as follows:

[0053] The obtained ship equipment failure degree coefficient is compared and analyzed with the ship equipment failure degree coefficient under normal working conditions, and an appropriate repair plan is formulated according to the influence weight of the influencing factor comparison result. If the grayscale difference coefficient comparison result has the greatest impact, it means that there is dust accumulation on the surface of the ship equipment. If the color deviation coefficient comparison result has the greatest impact, it means that there are dents in the ship equipment. If the temperature difference coefficient comparison result has the greatest impact, it means that there may be obstructions on the surface of the ship equipment.

[0054] The human-computer interaction module is used to generate a report according to the analysis result of the fault degree assessment module and transmit the report to the maintenance personnel through a visual interface to perform human-computer interaction.

[0055] In this embodiment, it should be specifically explained that the report presents a specific obstacle location plan. According to the comparative analysis results of the areas divided by the map drawing module and the fault degree assessment module, the areas where obstacles exist are located, and maintenance personnel maintain and replace the areas that need maintenance.

[0056] In the present embodiment, it should be specifically explained that the difference between the present embodiment and the prior art lies mainly in that the present embodiment forms an obstacle positioning map by drawing a ship map and dividing it into multiple small maps, which is conducive to a comprehensive analysis of obstacles affecting the operation of the ship, and photographs the ship equipment area of ​​the ship by a drone to generate a visible light image, a thermal imaging image and a grayscale image of the same position, and collects the color value, temperature parameter and grayscale value of the target ship small map through the generated image, and obtains the color deviation coefficient, temperature difference coefficient and grayscale difference coefficient by data preprocessing the collected color value, temperature parameter and grayscale value, and then obtains the ship equipment failure degree coefficient by data reprocessing through a function model, and compares the ship equipment failure degree coefficient with the ship equipment failure degree coefficient under normal working condition, analyzes the possible obstacles in the obstacle area and specifies the maintenance plan, and finally transmits the maintenance plan generation report to the maintenance personnel for human-computer interaction.

[0057] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0058] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A shipborne AI intelligent terminal management system for ship management and dispatch, including a system operation database, a system central processing module and a user information terminal, characterized in that: It also includes a map drawing module, an image acquisition module, a data acquisition module, a data processing module, a comprehensive analysis module, a fault degree assessment module and a human-computer interaction module; The map drawing module is used to divide the small map into small maps, which are numbered 1, 2, 3, ..., n in sequence, and determine the target device; The image acquisition module is used to acquire a first key image group; The image acquisition module places the drone 100 meters above the monitoring area, and runs it at a constant speed starting from the first small map according to an "S"-shaped route at a shooting angle of 45 degrees, and takes pictures of each small map in turn to generate the first key image group of the same position, including a visible light image, a thermal imaging image, and a grayscale image; The data acquisition module is used to acquire a first key data group according to the first key image group; The data acquisition module sequentially acquires the first key data group of the small map of the target ship in the image according to the first key image group generated by the image acquisition module, including color value, temperature parameter and gray value; The color value and acquisition method are as follows: according to the generated visible light image, the red channel value, the green channel value and the blue channel value of each pixel point of the target ship are collected to obtain the color value , where S is the color value, g, r, b are the green channel value, red channel value and blue channel value respectively, and β is a constant; The temperature parameter is obtained by uniformly taking the temperature values ​​of several points on the surface of the target ship according to the generated thermal imaging image, and recording them as t1, t2, ..., t n , calculate the average temperature of the target ship , where t i Represents the temperature of the i-th small map; The grayscale value is obtained by: according to the generated grayscale image, it is recorded as b1, b2, ..., b n , where each area is regarded as a small map of the target ship, the grayscale value of each pixel in the small map of the target ship is collected, and the average grayscale value of the small map of the target ship is calculated , where p i is the grayscale value of the i-th small map; The data processing module is used to process the first key data group to obtain a second key data group; The data processing module includes a color value processing unit, a temperature value processing unit and a gray value processing unit, which are used to transmit the first key data group to the corresponding processing unit to obtain the second key data group, including the color deviation coefficient, the temperature difference coefficient and the gray difference coefficient; The calculation formula of the color deviation coefficient is: , L is the color deviation coefficient of the target ship, S i.j is the color value of the jth ship equipment in the i-th small map of the target ship, S0 is the color value of the ship in normal working state, m is the total number of ship equipment in the target ship, and n is the total number of small maps; The calculation formula of the temperature difference coefficient is: , where W is the temperature difference coefficient, t max is the maximum temperature of the target ship, t min is the minimum temperature of the target ship, T i is the average temperature of the target ship, m is the total number of ship equipment in the target ship, α is the light intensity, if the value of W exceeds It is considered a serious abnormality. There may be some equipment failure, resulting in only partial normal operation of the target ship, or all equipment failure, resulting in excessive temperature and inability to operate. If the value of W is lower than This indicates that there may be a line disconnection problem that causes the entire ship equipment to fail to operate normally. If the value of W is The range of this indicates that only a few ships may have obstructions, which may reduce the surface utilization of the target ship; The calculation formula of the grayscale difference coefficient is: , where H is the grayscale difference coefficient, P i is the average gray value of the i-th target ship small map, P i+2 is the average gray value of the i+2th target ship small map, P i+1 is the average grayscale value of the i+1th target ship small map, Q is the grayscale value of the target ship in normal working state, and the closer the value of H is to 0, the smaller the difference in grayscale between the target ship and the ship in normal working state is; The comprehensive analysis module obtains a comprehensive data output value based on the analysis of the second key data group; The fault degree assessment module assesses the fault degree of the target ship according to the comprehensive data output value; The specific implementation process of the fault degree assessment module is as follows: Compare and analyze the obtained ship equipment failure degree coefficient with the ship equipment failure degree coefficient under normal working conditions, and formulate an appropriate repair plan according to the influence weight of the influencing factor comparison result. If the grayscale difference coefficient comparison result has the greatest impact, it means that there is dust accumulation on the surface of the ship equipment. If the color deviation coefficient comparison result has the greatest impact, it means that there is a dent in the ship equipment. If the temperature difference coefficient comparison result has the greatest impact, it means that there may be an obstruction on the surface of the ship equipment. The human-computer interaction module outputs information to the user terminal based on the comprehensive text output value.

2. According to claim 1, a shipborne AI intelligent terminal management system for ship management and dispatching is characterized by: The mapping module maps the target ship and divides the drawn ship map into n small maps according to the distribution of ship equipment, which are numbered 1, 2, 3, ..., n in sequence to form an obstacle positioning map, and the ship equipment in each small map is numbered 1, 2, 3, ..., m in sequence.

3. According to claim 1, a shipborne AI intelligent terminal management system for ship management and dispatching is characterized by: The comprehensive analysis module calculates the comprehensive data output value, specifically the ship equipment failure degree coefficient, according to the second key data group obtained in the data processing module and transmitted to the ship equipment utilization degree function model; The calculation formula of the ship equipment failure degree coefficient is: , where X is the ship equipment failure degree coefficient, , is a constant, e is a constant, L is the color deviation coefficient of the target ship, W is the temperature difference coefficient, and P is the average gray value of the small map of the target ship.

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