CN110488828B - Navigation light control method based on big data and artificial intelligence and robot system - Google Patents
Navigation light control method based on big data and artificial intelligence and robot system Download PDFInfo
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Abstract
Navigation light control method and robot system based on big data and artificial intelligence, including: the method comprises the steps of obtaining the type of an object to which a navigation light control device belongs, obtaining preset type data related to the object, obtaining a preset model, inputting the preset type data related to the object into the preset model, and using the output obtained through calculation of the preset model as a recommended navigation light control mode. The method and the system improve the intelligence and the high efficiency of the navigation light control through the navigation light control technology based on big data and artificial intelligence.
Description
Technical Field
The invention relates to the technical field of information, in particular to a navigation light control method and a robot system based on big data and artificial intelligence.
Background
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art: the navigation light control mode in the prior art is manually controlled, but errors often occur during manual control, so that various accidents are easily caused, and life and property losses are brought.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
Therefore, a navigation light control method and a robot system based on big data and artificial intelligence are needed to solve the defects of low automation degree and low intelligence of navigation light control in the prior art.
In a first aspect, an embodiment of the present invention provides a method for controlling a position light, where the method includes:
an object type acquiring step, which is used for acquiring the type of an object to which the navigation light control device belongs;
a data acquisition step for acquiring data of a preset type related to the object;
a preset model obtaining step for obtaining a preset model;
and a model calculation step, namely inputting the preset type of data related to the object into the preset model, and using the output obtained by calculation of the preset model as a recommended navigation light control mode.
Preferably, the method further comprises:
and a system control step for controlling the navigation light control device according to the recommended navigation light control mode.
Preferably, the first and second electrodes are formed of a metal,
the preset type is preset by a user or is acquired from a knowledge base;
the preset type of data comprises data which is relevant to the selection of the navigation light control mode;
the data related to the object comprises data of the object, environmental data of the object;
the preset type of data related to the object is the preset type of current data or recent data related to the object or data in the latest preset time period.
Preferably, the preset model obtaining step includes:
a history big data acquisition step, which is used for acquiring effective history big data of all objects of the type of the object; the historical big data comprises big data collected so far;
a corresponding data acquisition step, which is used for acquiring preset type data in the effective historical big data and a corresponding navigation light control mode;
a deep learning model initialization step for initializing a deep learning model;
an unsupervised training step, which is used for taking the preset type data in the effective historical big data as the input of the deep learning model and carrying out unsupervised training on the deep learning model;
a supervised training step, which is used for respectively taking the preset type of data in the historical big data and the corresponding navigation light control mode as the input and the output of the deep learning model, and carrying out supervised training on the deep learning model after unsupervised training;
and a preset model generating step, namely acquiring the deep learning model after supervised training as the preset model.
Preferably, the first and second liquid crystal display panels are,
the preset model obtaining step comprises:
a history big data acquisition step, which is used for acquiring effective history big data of all objects of the type of the object;
a corresponding data acquisition step, which is used for acquiring preset type data in the effective historical big data and a corresponding navigation light control mode;
a model data setting step, which is used for respectively taking the preset type data and the corresponding navigation light control mode in the effective historical big data as the data to be matched of the preset model and the corresponding data to be recommended;
the model calculation step includes:
a matching step, configured to perform fuzzy matching on the preset type of data related to the object and each of the data to be matched in the preset model;
a selecting step, configured to select the data to be matched in the preset model with the largest matching degree with the input preset type of data related to the object;
and a recommending step, namely acquiring data to be recommended corresponding to the selected data to be matched from the preset model as output obtained by calculation of the preset model, and taking the output as a recommended navigation light control mode.
Preferably, the history big data acquiring step comprises:
an owned control data acquisition step for acquiring historical data of all similar objects which are 'owned control' and 'have at least one common navigation light control mode with the object' as first big data;
an unmanned control data acquisition step for acquiring historical data of all similar objects which are unmanned and have a navigation light control mode common to the objects, wherein the selection effect of the navigation light control mode meets a preset condition, and the historical data serve as second big data;
a step of cleaning the manned control data, which is used for deleting the navigation light control mode which the object does not have and other corresponding data from the first big data to obtain third big data;
an unmanned control data cleaning step, which is used for deleting the navigation light control mode which the object does not have and other corresponding data from the second big data to obtain fourth big data;
and an effective history big data generating step, which is used for taking the third big data and the fourth big data as effective history big data.
Preferably, the object is a vehicle; the vehicle comprises an unmanned vehicle; the preset type of data related to the object comprises one or more of road condition data of a road section where the vehicle is located currently, visibility data of the road section where the vehicle is located currently, vehicle lamp brightness limitation data of the road section where the vehicle is located currently, a speed limit range of the road section where the vehicle is located currently, a vehicle type of the vehicle and current preset data of the vehicle.
Preferably, the object is a ship; the vessel comprises an unmanned vessel; the preset type of data related to the object comprises one or more of sea condition data of a current navigation section of the ship, visibility data of the current navigation section of the ship, navigation light brightness control index data of the current navigation section of the ship, meteorological data of the current navigation section of the ship, wind power data of the current navigation section of the ship, current demand data of the ship, the ship type of the ship and current preset data of the ship.
Preferably, the object is an aircraft; the aircraft comprises an unmanned aerial vehicle; the preset type of data related to the object comprises one or more of weather data of a current flight segment of the airplane, visibility data of the current flight segment of the airplane, wind data of the current flight segment of the airplane, the airplane type and current preset data of the airplane.
In a second aspect, an embodiment of the present invention provides a system, where the system performs the steps in the position light control method according to any one of the first aspect; the system includes a robotic system.
The embodiment of the invention has the advantages and beneficial effects that:
according to the embodiment of the invention, the preset model is obtained through learning from the historical big data, the navigation light control mode which is required to be adopted at present is obtained through calculation of the preset model and the current data, and the historical data and the current data comprise the data of the object to which the power navigation light control system belongs and the environmental data, so that the obtained preset model and the recommended navigation light control mode are more in line with the requirements of the object and the environment and are more efficient, and therefore, the embodiment of the invention can enable the switching of the navigation light control mode to be more intelligent and efficient. For example, for the switching of the navigation light control of the unmanned ship, the data to be considered includes one or more of sea condition data of the current navigation section of the ship, exhaust pollution control index data of the current navigation section, noise control index data of the current navigation section, meteorological data of the current navigation section, wind power data of the current navigation section, other current demand data, ship type, other preset data, and the like. Wherein, the navigation segment can be replaced by sea area. Other preset data currently include control index data of noise, speed and the like during combat. However, in the prior art, only manual switching or automatic switching according to speed is available, the manual switching has high requirements on users and is not intelligent, and automatic switching according to speed only considers speed factors and is not suitable for wider application scenes, and in many application scenes, not only the speed but also more environmental factors and factors of objects to which the power navigation light control system belongs are considered.
The embodiment of the invention provides a navigation light control method and a robot system based on big data and artificial intelligence, which comprises the following steps: the method comprises the steps of obtaining the type of an object to which a navigation light control device belongs, obtaining preset type data related to the object, obtaining a preset model, inputting the preset type data related to the object into the preset model, and using output obtained through calculation of the preset model as a recommended navigation light control mode. The method and the system improve the intelligence and the high efficiency of the navigation light control through the navigation light control technology based on big data and artificial intelligence.
Drawings
Fig. 1 is a flowchart of a position light control method according to embodiment 2 of the present invention;
FIG. 2 is a flowchart of the default model obtaining step provided in embodiment 4 of the present invention;
fig. 3 is a flowchart of a preset model obtaining step according to embodiment 5 of the present invention;
FIG. 4 is a flowchart of the model calculation steps provided in embodiment 5 of the present invention;
fig. 5 is a schematic block diagram of a position light control system provided in embodiment 11 of the present invention;
FIG. 6 is a schematic block diagram of a preset model module according to embodiment 13 of the present invention;
FIG. 7 is a schematic block diagram of a default model module provided in embodiment 14 of the present invention;
fig. 8 is a schematic block diagram of a model calculation module provided in embodiment 14 of the present invention.
Detailed Description
The technical solutions in the examples of the present invention are described in detail below with reference to the embodiments of the present invention.
The methods in various embodiments of the present invention include various combinations of the following steps:
example 1:
a navigation light control method comprises an object type obtaining step S100, a data obtaining step S200, a preset model obtaining step S300, a model calculating step S400 and a system control step S500.
An object type acquiring step S100 for acquiring a type of an object to which the position light control device belongs. The navigation light control device is a navigation light control device of the object. The navigation light control device is mounted on the object so that the object is the object to which the navigation light control device belongs. The object comprises a vehicle, a ship, an airplane and the like or other vehicles or systems or equipment needing to be provided with navigation light control devices. Thereby, the navigation light control mode of the navigation light control device of the object of the type is determined according to the object type, and the preset type of data related to the object related to the navigation light control mode is determined.
A data obtaining step S200, configured to obtain preset types of data related to the object. And calculating to obtain a recommended navigation light control mode according to the preset type of data related to the object.
A preset model obtaining step S300, configured to obtain a preset model. And establishing a corresponding relation between the preset type of data related to the object and the navigation light control mode through a preset model. The input format of the preset model is a preset type of data format related to the object, and the output format is a data format of a navigation light control mode; the data format of the navigation light control modes can use a digital format, and each navigation light control mode is coded into a number; the navigation light of the vehicle comprises front and rear lamps; the navigation lights of the ship comprise a starboard light, a front mast light, a rear mast light and a stern light; the navigation lights of the airplane comprise red anti-collision lights, wing lights, headlight lights, runway disengagement lights, high-brightness white strobe lights and the like. The navigation light control modes include a control mode of on/off of each light in the navigation light, brightness, frequency of flashing light, and the like (for example, a left front lamp of a vehicle is turned on), and a navigation light control mode in which one or more navigation light control modes are mixed;
a model calculation step S400, configured to input the preset type of data related to the object into the preset model, and use an output obtained through calculation by the preset model as a recommended navigation light control mode. Thereby providing a recommended navigation light control mode for controlling the power navigation light control system. Wherein the output is an output of the preset model;
example 2:
the method according to embodiment 1, further comprising a system control step S500, as shown in fig. 1.
A system control step S500 for controlling the navigation light control device according to the recommended navigation light control mode; therefore, the power navigation light control system can operate in a more optimal mode, and the intelligence and the efficiency of the power navigation light control system of the object are improved. The method is specifically configured to determine whether the recommended navigation light control mode is consistent with the current navigation light control mode: and if not, sending a control instruction for switching the current navigation light control mode to the recommended navigation light control mode to the navigation light control device.
Example 3:
the method of embodiment 1, wherein,
the preset type is preset by a user or is obtained from a knowledge base;
the preset type of data comprises data which is relevant to the selection of the navigation light control mode;
the data related to the object comprises data of the object, environmental data of the object; the data of the object includes attribute data, monitoring data, and the like of the object.
The preset type of data related to the object is the preset type of current data or recent data related to the object or data in the latest preset time period.
Example 4:
according to the method as described in example 1,
the preset model acquiring step S300 includes a history big data acquiring step S311, a corresponding data acquiring step S312, a deep learning model initializing step S313, an unsupervised training step S314, a supervised training step S315, and a preset model generating step S316, as shown in fig. 2.
A history big data obtaining step S311, configured to obtain valid history big data of all objects of the type to which the object belongs;
a corresponding data obtaining step S312, configured to obtain preset type of data related to the object in the valid historical big data and a corresponding navigation light control manner, where the preset type of data related to the object is preset type of historical data;
a deep learning model initialization step S313 for initializing a deep learning model;
an unsupervised training step S314, configured to perform unsupervised training on the deep learning model by using the preset type of data related to the object in the valid historical big data as an input of the deep learning model;
a supervised training step S315, configured to respectively use the preset type of data related to the object in the historical big data and the navigation light control manner corresponding to the preset type of data as input and output of the deep learning model, and perform supervised training on the deep learning model after unsupervised training;
a preset model generating step S316, configured to acquire the deep learning model after the supervised training as the preset model.
The historical big data can be acquired online through a network or from a historical big database. The effective historical big data comprises preset type data related to the object and a navigation light control mode corresponding to the preset type data; the valid historical big data is collected over a long period of time in the past. The preset type of data related to the object and the corresponding navigation light control mode thereof are acquired by each acquired object at each acquired moment, and both the preset type of data related to the object at each acquired moment of each acquired object and the navigation light control mode of each acquired object at each acquired moment are acquired, wherein each acquired object belongs to an object set of the type to which the object belongs.
Wherein the type to which the object belongs comprises an object (including a vehicle) having at least one common navigation light control mode with the object.
Example 5:
according to the method as described in example 1,
the preset model acquiring step S300 includes a history big data acquiring step S321, a corresponding data acquiring step S322, and a model data setting step S323, as shown in fig. 3.
A history big data obtaining step S321, configured to obtain valid history big data of all objects of the type to which the object belongs;
a corresponding data obtaining step S322, configured to obtain preset type data related to the object in the valid historical big data and a corresponding navigation light control mode; the preset type of data related to the object is preset type historical data;
a model data setting step S323, configured to use the preset type of data related to the object in the valid historical big data and the navigation light control manner corresponding to the preset type of data as data to be matched with the preset model and data to be recommended corresponding to the preset model, respectively.
The model calculating step S400 includes a matching step S421, a selecting step S422, and a recommending step S423, as shown in fig. 4.
A matching step S421, configured to perform fuzzy matching on the preset type of data related to the object and each of the to-be-matched data in the preset model;
a selecting step S422, configured to select the data to be matched in the preset model with the largest matching degree with the input data of the preset type and related to the object;
and a recommending step S423, configured to obtain data to be recommended corresponding to the selected data to be matched from the preset model, as an output calculated by the preset model, and use the output as a recommended navigation light control mode.
Example 4 uses big data, deep learning techniques, and example 5 uses big data and its recommendation techniques.
Example 6:
according to the method as described in embodiment 4 or 5,
the history big data acquisition step S321 includes:
manned control data acquisition step S321-1: the method comprises the steps of acquiring historical data of all similar objects (such as vehicles) which are controlled by people (such as driving by people) and have at least one common navigation light control mode with the objects as first big data; the same-class object is an object of the type to which the object belongs; the homogeneous object and the object both belong to the type to which the object belongs; the historical data comprises the preset type data and a corresponding navigation light control mode; because a person is intelligent, the way of controlling the position of the navigation lights selected by an object when the person is controlling (e.g., driving), is more reliable, and the historical big data of an unmanned (e.g., unmanned) object (e.g., vehicle) such as an unmanned aerial vehicle, unmanned ship, etc. is not necessarily reliable. The manned control comprises the selection of a navigation light control mode by a person;
unmanned control data acquisition step S321-2: acquiring historical data of all similar objects (such as vehicles) which are in unmanned control (such as unmanned driving), have a selection effect of a navigation light control mode meeting a preset condition and have at least one common navigation light control mode with the objects as second big data; the historical data comprises the preset type data and a corresponding navigation light control mode; the selection effect of the navigation light control mode meets the preset condition, and the user score of the selection effect of the navigation light control mode is larger than the preset threshold value. The unmanned control comprises the selection of a navigation light control mode by an unmanned person;
manned control data washing step S321-3: deleting the navigation light control mode which the object does not have and other data corresponding to the navigation light control mode from the first big data, wherein the navigation light control mode which the object does not have and the data corresponding to the preset type of the navigation light control mode are deleted from the first big data, and third big data are obtained;
unmanned control data washing step S321-4: deleting the navigation light control mode which the object does not have and other data corresponding to the navigation light control mode from the second big data, wherein the navigation light control mode which the object does not have and the data corresponding to the preset type of the navigation light control mode are deleted from the second big data, and fourth big data are obtained;
valid history big data generation step S321-5: and taking the third big data and the fourth big data as valid history big data.
Example 7:
according to the method as described in example 1,
wherein the object is a vehicle; the vehicle comprises an unmanned vehicle;
the preset type of data related to the object comprises one or more of road condition data of a current road section of the vehicle, exhaust pollution control index data of the current road section, noise control index data of the current road section, speed limit range of the current road section, vehicle type, other preset data and the like. Wherein the track segments may be replaced with zones. Other preset data currently include control index data of noise, speed and the like during combat.
Example 8:
according to the method as described in example 1,
wherein the object is a ship; the vessel comprises an unmanned vessel;
the preset type of data related to the object comprises one or more of sea condition data of a current navigation section of the ship, exhaust pollution control index data of the current navigation section, noise control index data of the current navigation section, meteorological data of the current navigation section, wind power data of the current navigation section, current other demand data, ship type, current other preset data and the like. Wherein, the navigation segment can be replaced by sea area. Other preset data currently include control index data for noise, speed, etc. during combat.
Example 9:
according to the method as described in example 1,
wherein the object is an aircraft; the aircraft comprises an unmanned aerial vehicle;
the preset type of data related to the object comprises one or more of weather data of a current flight segment of the airplane, noise pollution control index data of the current flight segment, wind data of the current flight segment, a model, other preset data and the like. Wherein, the navigation section can be replaced by an airspace. Other preset data currently include control index data for noise, speed, etc. during combat.
Example 10:
a navigation light control system comprises an object type acquisition module 100, a data acquisition module 200, a preset model acquisition module 300 and a model calculation module 400.
The object type acquiring module 100 is configured to acquire a type of an object to which the position light control device belongs.
A data obtaining module 200, configured to obtain data of a preset type related to the object.
A preset model obtaining module 300, configured to obtain a preset model.
And the model calculation module 400 is configured to input the preset type of data related to the object into the preset model, and output obtained through calculation by the preset model is used as a recommended position light control mode.
Example 11:
the system of embodiment 10, further comprising a system control module 500, as shown in fig. 5.
And the system control module 500 is used for controlling the navigation light control device according to the recommended navigation light control mode. Specifically, whether the recommended navigation light control mode is consistent with the current navigation light control mode is judged: and if not, sending a control instruction for switching the current navigation light control mode to the recommended navigation light control mode to the navigation light control device.
Example 12:
the system of embodiment 10, wherein,
the preset type is preset by a user or is acquired from a knowledge base;
the preset type of data comprises data which is relevant to the selection of the navigation light control mode;
the data related to the object comprises data of the object, environmental data of the object;
the preset type of data related to the object is the preset type of current data or recent data related to the object or data in the latest preset time period.
Example 13:
according to the system as set forth in embodiment 10,
the preset model obtaining module 300 includes a historical big data obtaining module 311, a corresponding data obtaining module 312, a deep learning model initializing module 313, an unsupervised training module 314, a supervised training module 315, and a preset model generating module 316, as shown in fig. 6.
A history big data obtaining module 311, configured to obtain valid history big data of all objects of the type to which the object belongs;
a corresponding data obtaining module 312, configured to obtain preset types of data related to the object in the valid historical big data and a corresponding navigation light control manner;
a deep learning model initialization module 313 configured to initialize a deep learning model;
an unsupervised training module 314, configured to perform unsupervised training on the deep learning model by using the preset type of data related to the object in the valid historical big data as an input of the deep learning model;
the supervised training module 315 is configured to respectively use the preset type of data related to the object in the historical big data and the corresponding navigation light control mode as input and output of the deep learning model, and perform supervised training on the deep learning model after unsupervised training;
a preset model generating module 316, configured to obtain the deep learning model after the supervised training as the preset model.
Example 14:
according to the system as set forth in embodiment 10,
the preset model obtaining module 300 includes a history big data obtaining module 321, a corresponding data obtaining module 322, and a model data setting module 323, as shown in fig. 7.
A history big data obtaining module 321, configured to obtain valid history big data of all objects of the type to which the object belongs;
a corresponding data obtaining module 322, configured to obtain preset type of data related to the object in the valid historical big data and a corresponding navigation light control mode;
and the model data setting module 323 is configured to respectively use the preset type of data related to the object in the valid historical big data and the corresponding navigation light control mode as the data to be matched of the preset model and the corresponding data to be recommended.
The model calculation module 400 includes a matching module 421, a selecting module 422, and a recommending module 423, as shown in fig. 8.
A matching module 421, configured to perform fuzzy matching on the preset type of data related to the object and each of the to-be-matched data in the preset model;
a selecting module 422, configured to select the data to be matched in the preset model with the largest matching degree with the input preset type of data related to the object;
and the recommending module 423 is configured to obtain data to be recommended corresponding to the selected data to be matched from the preset model, use the data to be recommended as an output calculated by the preset model, and use the output as a recommended navigation light control mode.
Example 15:
according to the system as set forth in embodiment 13 or 14,
the history big data obtaining module 321 includes:
the manned control data acquisition module 321-1: the method comprises the steps of acquiring historical data of all similar objects (such as vehicles) which are controlled by people (such as driving by people) and have at least one common navigation light control mode with the objects as first big data; the homogeneous objects are objects of the types of the objects; the homogeneous object and the object both belong to the type to which the object belongs; the historical data comprises the preset type data and a corresponding navigation light control mode;
the unmanned control data acquisition module 321-2: acquiring historical data of all similar objects (such as vehicles) which are under the conditions of unmanned control (such as unmanned driving) and navigation light control mode selection effect meeting preset conditions and have at least one common navigation light control mode with the objects as second big data; the historical data comprises the preset type data and a corresponding navigation light control mode;
the manned control data washing module 321-3: deleting the navigation light control mode which the object does not have and other data corresponding to the navigation light control mode from the first big data, wherein the navigation light control mode which the object does not have and the data of the preset type corresponding to the navigation light control mode are deleted from the first big data, and third big data are obtained;
the unmanned control data cleaning module 321-4: deleting the navigation light control mode which the object does not have and other data corresponding to the navigation light control mode from the second big data, wherein the navigation light control mode which the object does not have and the data corresponding to the preset type of the navigation light control mode are deleted from the second big data, and fourth big data are obtained;
the valid history big data generation module 321-5: and taking the third big data and the fourth big data as valid history big data.
Example 16:
the system of embodiment 10, comprising:
wherein the object is a vehicle; the vehicle comprises an unmanned vehicle;
the preset type of data related to the object comprises one or more of road condition data of a current road section of the vehicle, exhaust pollution control index data of the current road section, noise control index data of the current road section, a speed limit range of the current road section, a vehicle type, other preset data and the like. Wherein the track segments may be replaced with zones. Other preset data currently include control index data of noise, speed and the like during combat.
Example 17:
according to the system as set forth in embodiment 10,
wherein the object is a ship; the vessel comprises an unmanned vessel;
the preset type of data related to the object comprises one or more of sea state data of a current located section of the ship, exhaust pollution control index data of the current located section, noise control index data of the current located section, meteorological data of the current located section, wind power data of the current located section, current other demand data, ship type, current other preset data and the like. Wherein, the navigation section can be replaced by sea area. Other preset data currently include control index data of noise, speed and the like during combat.
Example 18:
according to the system as set forth in embodiment 10,
wherein the object is an aircraft; the aircraft comprises an unmanned aerial vehicle;
the preset type of data related to the object comprises one or more of weather data of the current flight segment of the airplane, noise pollution control index data of the current flight segment, wind data of the current flight segment, airplane models, other preset data and the like. Wherein, the navigation segment can be replaced by an airspace. Other preset data currently include control index data for noise, speed, etc. during combat.
Example 19:
there is provided a robot system in which the systems as described in embodiments 10 to 18 are respectively arranged.
The methods and systems of the various embodiments described above may be performed and deployed on computers, servers, cloud servers, supercomputers, robots, embedded devices, electronic devices, and the like.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of position light control, the method comprising:
an object type acquiring step, which is used for acquiring the type of an object to which the navigation light control device belongs;
a data acquisition step for acquiring data of a preset type related to the object;
a preset model obtaining step for obtaining a preset model;
and a model calculation step, namely inputting the preset type of data related to the object into the preset model, and using the output obtained by calculation of the preset model as a recommended navigation light control mode.
2. The position light control method of claim 1, further comprising:
and a system control step for controlling the navigation light control device according to the recommended navigation light control mode.
3. The position light control method according to claim 1,
the preset type is preset by a user or is obtained from a knowledge base;
the preset type of data comprises data which is relevant to the selection of the navigation light control mode;
the data related to the object comprises data of the object, environmental data of the object;
the preset type of data related to the object is the preset type of current data or recent data related to the object or data in the latest preset time period.
4. The position light control method according to claim 1, wherein the preset model obtaining step includes:
a history big data acquisition step, which is used for acquiring effective history big data of all objects of the type of the object;
a corresponding data acquisition step, which is used for acquiring preset type data in the effective historical big data and a corresponding navigation light control mode;
a deep learning model initialization step for initializing a deep learning model;
an unsupervised training step, which is used for taking the preset type data in the effective historical big data as the input of the deep learning model and carrying out unsupervised training on the deep learning model;
a supervised training step, which is used for respectively taking the preset type of data in the historical big data and the navigation light control mode corresponding to the preset type of data as the input and the output of the deep learning model, and carrying out supervised training on the deep learning model after unsupervised training;
and a preset model generation step, which is used for acquiring the deep learning model after the supervised training as the preset model.
5. The position light control method according to claim 1,
the preset model obtaining step comprises:
a history big data acquisition step, which is used for acquiring effective history big data of all objects of the type of the object;
a corresponding data acquisition step, which is used for acquiring preset type data in the effective historical big data and a corresponding navigation light control mode;
a model data setting step, which is used for respectively using the preset type data and the corresponding navigation light control mode in the effective historical big data as the data to be matched of the preset model and the corresponding data to be recommended;
the model calculation step includes:
a matching step, configured to perform fuzzy matching on the preset type of data related to the object and each of the data to be matched in the preset model;
a selecting step, configured to select the data to be matched in the preset model with the largest matching degree with the input preset type of data related to the object;
and a recommending step, namely acquiring data to be recommended corresponding to the selected data to be matched from the preset model as output obtained by calculation of the preset model, and taking the output as a recommended navigation light control mode.
6. The position light control method according to claim 4 or 5, wherein the historical big data acquisition step comprises:
an owned control data acquisition step for acquiring historical data of all similar objects which are 'owned control' and 'have at least one common navigation light control mode with the object' as first big data;
an unmanned control data acquisition step for acquiring historical data of all similar objects which are 'unmanned control' and have 'at least one common navigation light control mode with the object' and 'the selection effect of the navigation light control mode meets a preset condition' as second big data;
a step of cleaning the manned control data, which is used for deleting the navigation light control mode which the object does not have and other corresponding data from the first big data to obtain third big data;
an unmanned control data cleaning step, which is used for deleting the navigation light control mode which the object does not have and other corresponding data from the second big data to obtain fourth big data;
and an effective history big data generating step, which is used for taking the third big data and the fourth big data as effective history big data.
7. The position light control method according to claim 1, wherein the object is a vehicle; the vehicle comprises an unmanned vehicle; the preset type of data related to the object comprises one or more of road condition data of a road section where the vehicle is located currently, visibility data of the road section where the vehicle is located currently, vehicle lamp brightness limitation data of the road section where the vehicle is located currently, a speed limit range of the road section where the vehicle is located currently, a vehicle type of the vehicle and current preset data of the vehicle.
8. The position light control method according to claim 1, wherein the object is a ship; the vessel comprises an unmanned vessel; the preset type of data related to the object comprises one or more of sea condition data of a current navigation section of the ship, visibility data of the current navigation section of the ship, navigation light brightness control index data of the current navigation section of the ship, meteorological data of the current navigation section of the ship, wind power data of the current navigation section of the ship, current demand data of the ship, the ship type of the ship and current preset data of the ship.
9. The position light control method according to claim 1, wherein the object is an airplane; the aircraft comprises an unmanned aerial vehicle; the preset type of data related to the object comprises one or more of weather data of a current flight segment of the airplane, visibility data of the current flight segment of the airplane, wind data of the current flight segment of the airplane, airplane models and current preset data of the airplane.
10. A system characterized in that it performs the steps in the position light control method according to any one of claims 1-9; the system includes a robotic system.
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