CN113052377A - Embedded computing method for ship real-time monitoring system - Google Patents

Embedded computing method for ship real-time monitoring system Download PDF

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CN113052377A
CN113052377A CN202110306962.XA CN202110306962A CN113052377A CN 113052377 A CN113052377 A CN 113052377A CN 202110306962 A CN202110306962 A CN 202110306962A CN 113052377 A CN113052377 A CN 113052377A
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陈萌
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Abstract

The invention discloses an embedded computing method for a ship real-time monitoring system, which comprises the steps of S1, parallelly acquiring real-time running state data of a plurality of ships, wherein the real-time running state data of the ships comprise ship monitoring information and software and hardware diagnosis information; s2, packing and transmitting the real-time running state data of a plurality of ships at the same time to an embedded processing module; s3, fusing the real-time running state data of a plurality of time periods by the embedded processing module, and predicting the running state of the ship by combining the ship running big data; s4, the step of issuing the ship operation status to the local server and the remote server for reference evaluation and decision making includes the following steps. The invention can monitor the running state of each monitoring module, and simultaneously monitor software and hardware, and carry out automatic failure prediction on various systems in the ship.

Description

Embedded computing method for ship real-time monitoring system
Technical Field
The invention relates to the technical field of ship monitoring, in particular to an embedded computing device and method for a ship real-time monitoring system.
Background
The embedded system has more and more complex functions when applied to ships, such as a ship power system, an anchor and mooring device, a steering and steering device, a lifesaving and fire-fighting device, a communication and navigation device, a lighting and signal device, a ventilation and air conditioning and refrigerating device, a ballast water system, a bilge water drainage system, a liquid tank depth measurement and ventilation system, a seawater and domestic fresh water system, ship electrical equipment and the like, the more software programs are involved in the operation process, and the more defects are generated; meanwhile, the hardware equipment involved in the embedded system also needs to monitor the failure of the hardware equipment.
For example, patent document CN106125657A discloses a system for remote monitoring and real-time maintenance and management of ship working conditions, which includes: the system comprises an image real-time acquisition unit, a fuel monitoring unit, a primary analysis unit, a plurality of wireless transceiving modules, a signal processing unit, a data storage module and a danger information calculation grading module; a danger information broadcasting module; the management system can monitor important ship equipment in real time, discover and collect the working condition information of the ship equipment in time, classify and arrange the collected working condition information of the equipment, and the monitoring center on the shore base can analyze the transmitted working condition information.
The technical scheme disclosed by the patent can only monitor the running state and fault information of the ship, namely, the information is analyzed and fed back only when the monitored object reaches a certain degree. In the prior art, a fault prediction technology also exists, because components related to faults of a cruising ability monitoring system, a ship working condition monitoring system and the like and running system software are complex, automatic failure prediction cannot be accurately performed on various systems in a ship, a technician is usually required to perform complicated troubleshooting and recording to finally determine failure or fault sources, manpower is wasted, and the difficulty of troubleshooting is high.
Disclosure of Invention
The invention provides an embedded computing method for a ship real-time monitoring system, which aims to solve the problems in the background technology and adopts a technical scheme that:
an embedded computing method for a ship real-time monitoring system comprises the following steps:
s1, parallelly collecting real-time running state data of a plurality of ships, wherein the real-time running state data of the ships comprise ship monitoring information and software and hardware diagnosis information;
s2, packing and transmitting the real-time running state data of a plurality of ships at the same time to an embedded processing module;
s3, fusing the real-time running state data of a plurality of time periods by the embedded processing module, and predicting the running state of the ship by combining the ship running big data;
and S4, distributing the running state of the ship to the local server and the remote server for reference evaluation and decision.
The software and hardware diagnostic information obtaining method in the step S1 comprises the following steps: by adding the module function detection code when the monitoring information is output, the function detection code is uniquely determined, and if the function detection code changes when being transmitted to the embedded processing module, the module is judged to have a fault or a failure.
Receiving the real-time running state data of the plurality of ships in the S2 by adopting a parallel interface module at the same time, and storing corresponding time labels when packaging the data;
the method for fusing the real-time operation state data of a plurality of time periods in S3 specifically includes: unpacking the packed data and then classifying the real-time running state data of the ship according to a time sequence, namely combining the data of each monitoring module in the same time period together for analysis; the combination of the ship operation big data is specifically to compare the fused data with ship historical operation data, if various data are abnormal in different degrees or one data is abnormal, tracking and searching are carried out aiming at the abnormality, and corresponding data are called out for further analysis so as to predict the ship operation state.
The invention also discloses an embedded computing device for the ship real-time monitoring system, which comprises a plurality of monitoring systems, a parallel receiving module, an embedded processing module and a local server;
the monitoring systems are used for monitoring the real-time running state of the ship, and the monitoring information comprises ship monitoring information and software and hardware diagnosis information.
The parallel receiving module is connected with the plurality of monitoring systems, receives monitoring information sent by the plurality of monitoring systems simultaneously, packs the monitoring information and transmits the packed monitoring information to the embedded processing module in a time-sharing manner.
The embedded processing module is used for fusing information of the monitoring systems at different times and comprehensively pre-judging the running state of the ship according to the big data of the running state of the ship in the local server.
And the local server is used for transmitting the ship running state big data to the embedded processing module.
The embedded computing device also includes a remote server in communication with the local server.
The monitoring systems comprise a ship energy storage module, a navigational speed instruction signal module, a power battery electric quantity monitoring module, a liquefied natural gas electric quantity monitoring module and an electromechanical equipment monitoring module. The ship energy storage module, the navigational speed instruction signal module, the power battery electric quantity monitoring module, the liquefied natural gas electric quantity monitoring module and the electromechanical equipment monitoring module are all added with function detection when monitoring information is output, the function detection comprises software self-checking, the function detection code of the module can be added when the monitoring information is output through the software self-checking, the function detection code is uniquely determined, and if the function detection code is transmitted to the embedded processing module, the monitoring module relative to the function detection code is determined to have a fault or lose efficacy.
Preferably, the ship energy storage module comprises a battery management system, a storage battery, a super capacitor and a bidirectional DC/DC converter, the ship energy storage module is connected with the solar power generation module and the wind power generation module, and the solar power generation module and the wind power generation module store the converted electric energy in the ship energy storage module.
Preferably, the power battery electric quantity detection module and the liquefied natural gas electric quantity monitoring module both use collected data as source data to calculate real-time endurance; and monitoring data in real time according to the calculated real-time endurance, and controlling the monitored equipment.
Preferably, the calculation of the real-time endurance is the sum of the calculation of the real-time power battery power supply endurance, the calculation of the real-time liquefied natural gas power supply endurance and the calculation of the propagation energy storage module power supply endurance.
Preferably, the speed command signal module controls the working modes or the start and stop of the power battery electric quantity, the liquefied natural gas electric quantity, the power utilization module and the ship energy storage module according to the energy requirement by calculating the energy requirement of the power grid of the ship, or controls the energy generated by each power generation module to be merged into the ship power grid or enter the energy storage module.
The monitoring object of the electromechanical equipment monitoring module comprises generator equipment, a speed regulating device, a synchronizing device, a power regulating device, an unloading device, power equipment, hydraulic equipment and steering engine equipment; the sensors included in each monitoring module include a gas leakage sensor, a fire sensor, a current sensor, a vibration sensor, a temperature sensor, a voltage sensor, and the like.
Preferably, the type of the electromechanical device and the sensor is set to have module IDs, each module ID passes login verification of the embedded processing module and is connected to the embedded processing module after passing the login verification, the embedded processing module classifies collected information type codes according to the module, and then the monitored information is converted into output information; when the embedded processing module monitors abnormal information, the abnormal information is added into the output information, and then the abnormal output information is divided into a plurality of grades, such as high abnormality, medium abnormality and low abnormality, wherein the high abnormality represents that the output information is most important, the medium abnormality represents that the output information is secondary importance, and the low abnormality represents that the output information is unimportant; after the grade is judged, the output abnormal information of the priority is carried out, the high abnormal information is classified and output preferentially, and the alarm module gives an alarm.
The parallel receiving module comprises a multi-bus interface, the multi-bus interface comprises a CAN bus and an RS485 bus interface so as to meet the requirement of different monitoring systems for transmitting data, the communication between the CAN bus and the RS485 bus CAN be realized by a programmable logic module, and the communication is preferably realized by connecting a main device FPGA with a CAN bus controller and an RS485 bus controller.
The embedded processing module comprises an embedded processor, an alarm module, a display control module, a storage module and a communication module; the embedded processor is connected with the alarm module, the display control module, the storage module and the communication module. The embedded processor unpacks the packed data after receiving the packed data, and classifies the real-time running state data of the ship according to a time sequence, namely the data of each monitoring module in the same time period are combined together for analysis; and comparing the classified real-time running state data of the ship with historical running data of the ship, if various data are abnormal in different degrees or one data is abnormal, tracking and searching the abnormality, and calling out corresponding data for further analysis to predict the running state of the ship.
The local server also runs an Automatic Identification System (AIS) of a ship and is in communication connection with the remote server.
Preferably, the display control module is used for displaying the health parameters and information of the whole ship, and controlling the starting and stopping of the remote operation electromechanical equipment and emergency shutdown.
Preferably, the communication module communicates with operators of other electromechanical devices.
Preferably, the storage module adopts a high-capacity Flash and an SDRAM, the SDRAM is an operation space of a program for starting an operating system, and the high-capacity Flash is used for storing real-time monitoring data of the embedded processing module, so that the operation health condition and health trend of each module can be conveniently analyzed, and early warning can be conveniently performed.
The invention has the beneficial effects that: monitoring information in the ship energy storage module, the navigational speed instruction signal module, the power battery electric quantity monitoring module, the liquefied natural gas electric quantity monitoring module and the electromechanical equipment monitoring module is fused, the running states of the monitoring modules are monitored simultaneously, software and hardware are monitored simultaneously, various systems in the ship are subjected to automatic failure prediction, full-automatic remote failure or failure source determination is achieved, and troubleshooting and periodic maintenance are facilitated.
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Fig. 1 is a diagram of an embedded computing device for a real-time ship monitoring system.
Fig. 2 is a diagram of an embedded computing method for a real-time ship monitoring system.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1, an embedded computing device for a real-time ship monitoring system includes a plurality of monitoring systems 1, a parallel receiving module 2, an embedded processing module 3, and a local server 4;
the monitoring systems 1 are used for monitoring the real-time running state of the ship, and the monitoring information comprises ship monitoring information and software and hardware diagnosis information;
the parallel receiving module 2 is connected with the plurality of monitoring systems 1, receives monitoring information sent by the plurality of monitoring systems 1 at the same time, packs the monitoring information and transmits the monitoring information to the embedded processing module 3 in a time-sharing manner;
the embedded processing module 3 is used for fusing information of the plurality of monitoring systems 1 at different times and comprehensively pre-judging the ship running state according to the ship running state big data in the local server 4.
And the local server 4 is used for transmitting the ship running state big data to the embedded processing module 3.
The embedded computing device also includes a remote server 5 in communication with the local server 4.
The multiple monitoring systems 1 comprise a ship energy storage module, a navigational speed instruction signal module, a power battery electric quantity monitoring module, a liquefied natural gas electric quantity monitoring module and an electromechanical equipment monitoring module. The ship energy storage module, the navigational speed instruction signal module, the power battery electric quantity monitoring module, the liquefied natural gas electric quantity monitoring module and the electromechanical equipment monitoring module are all added with function detection when monitoring information is output, the function detection comprises software self-checking, the function detection code of the module can be added when the monitoring information is output through the software self-checking, the function detection code is uniquely determined, and if the function detection code is transmitted to the embedded processing module, the monitoring module relative to the function detection code is determined to have a fault or lose efficacy.
In a preferred embodiment, the ship energy storage module comprises a battery management system, a storage battery, a super capacitor and a bidirectional DC/DC converter, the ship energy storage module is connected with a solar power generation module and a wind power generation module, and the solar power generation module and the wind power generation module store the converted electric energy in the ship energy storage module.
As a preferred embodiment, the power battery electric quantity detection module and the liquefied natural gas electric quantity monitoring module both use collected data as source data to calculate real-time endurance; and monitoring data in real time according to the calculated real-time endurance, and controlling the monitored equipment.
In a preferred embodiment, the calculating the real-time endurance is the sum of calculating the real-time power battery powered endurance, the real-time liquefied natural gas powered endurance and the propagation energy storage module powered endurance.
As a preferred embodiment, the speed command signal module controls the working modes or the start and stop of the power battery, the lng power, the power utilization module and the ship energy storage module according to the energy demand by calculating the energy demand of the power grid of the ship, or controls whether the energy generated by each power generation module is merged into the ship power grid or enters the energy storage module.
As a preferred embodiment, the monitoring object of the electromechanical device monitoring module comprises a generator device, a speed regulating device, a synchronizing device, a power regulating device, an unloading device, a power device, a hydraulic device and a steering engine device; the sensors included in each monitoring module include a gas leakage sensor, a fire sensor, a current sensor, a vibration sensor, a temperature sensor, a voltage sensor, and the like.
As a preferred embodiment, the type of the electromechanical device and the sensor is set to have module IDs, each module ID passes login verification of the embedded processing module and is connected to the embedded processing module after passing, the embedded processing module classifies collected information type codes according to the module, and then converts the monitored information into output information; when the embedded processing module monitors abnormal information, the abnormal information is added into the output information, and then the abnormal output information is divided into a plurality of grades, such as high abnormality, medium abnormality and low abnormality, wherein the high abnormality represents that the output information is most important, the medium abnormality represents that the output information is secondary importance, and the low abnormality represents that the output information is unimportant; after the grade is judged, the output abnormal information of the priority is carried out, the high abnormal information is classified and output preferentially, and the alarm module gives an alarm.
As a preferred embodiment, the parallel receiving module includes a multi-bus interface, the multi-bus interface includes a CAN bus and an RS485 bus interface to meet the requirement of different monitoring systems for transmitting data, the communication between the CAN bus and the RS485 bus CAN be realized by a programmable logic module, and is preferably realized by connecting a main device FPGA with a CAN bus controller and an RS485 bus controller.
As a preferred embodiment, the embedded processing module comprises an embedded processor, an alarm module, a display control module, a storage module and a communication module; the embedded processor is connected with the alarm module, the display control module, the storage module and the communication module. The embedded processor unpacks the packed data after receiving the packed data, and classifies the real-time running state data of the ship according to a time sequence, namely the data of each monitoring module in the same time period are combined together for analysis; and comparing the classified real-time running state data of the ship with historical running data of the ship, if various data are abnormal in different degrees or one data is abnormal, tracking and searching the abnormality, and calling out corresponding data for further analysis to predict the running state of the ship.
In a preferred embodiment, the local server further operates an Automatic Identification System (AIS) for a vessel, communicatively coupled to the remote server.
As a preferred embodiment, the display and control module is used for displaying health parameters and information of the whole ship, and controlling the starting, stopping and emergency shutdown of the remote operation electromechanical equipment.
In a preferred embodiment, the communication module communicates with operators of other electromechanical devices.
As a preferred implementation mode, the storage module adopts a high-capacity Flash and an SDRAM, the SDRAM is the running space of a program for starting an operating system, and the high-capacity Flash is used for storing real-time monitoring data of the embedded processing module, so that the running health condition and the health trend of each module can be conveniently analyzed, and early warning can be conveniently carried out.
Example 2
As shown in fig. 2, an embedded computing method for a real-time ship monitoring system includes the following steps:
s1, parallelly collecting real-time running state data of a plurality of ships, wherein the real-time running state data of the ships comprise ship monitoring information and software and hardware diagnosis information;
s2, packing and transmitting the real-time running state data of a plurality of ships at the same time to an embedded processing module;
s3, fusing the real-time running state data of a plurality of time periods by the embedded processing module, and predicting the running state of the ship by combining the ship running big data;
and S4, distributing the running state of the ship to the local server and the remote server for reference evaluation and decision.
As a preferred embodiment, the software and hardware diagnostic information obtaining method in S1 is: by adding the module function detection code when the monitoring information is output, the function detection code is uniquely determined, and if the function detection code changes when being transmitted to the embedded processing module, the module is judged to have a fault or a failure.
As a preferred embodiment, in S2, the ship real-time operation state data are received simultaneously by using a parallel interface module, and corresponding time tags are saved when data are packaged.
As a preferred embodiment, the method for fusing the real-time operation state data of multiple time periods in S3 specifically includes: and unpacking the packed data and then classifying the real-time running state data of the ship according to a time sequence, namely combining the data of each monitoring module in the same time period together for analysis. The combination of the ship operation big data is specifically to compare the fused data with ship historical operation data, if various data are abnormal in different degrees or one data is abnormal, tracking and searching are carried out aiming at the abnormality, and corresponding data are called out for further analysis so as to predict the ship operation state.
Monitoring information in the ship energy storage module, the navigational speed instruction signal module, the power battery electric quantity monitoring module, the liquefied natural gas electric quantity monitoring module and the electromechanical equipment monitoring module is fused, the running states of the monitoring modules are monitored simultaneously, software and hardware are monitored simultaneously, various systems in the ship are subjected to automatic failure prediction, full-automatic remote failure or failure source determination is achieved, and troubleshooting and periodic maintenance are facilitated.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (5)

1. An embedded computing method for a ship real-time monitoring system comprises the following steps:
s1, parallelly collecting real-time running state data of a plurality of ships, wherein the real-time running state data of the ships comprise ship monitoring information and software and hardware diagnosis information;
s2, packing and transmitting the real-time running state data of a plurality of ships at the same time to an embedded processing module;
s3, fusing the real-time running state data of a plurality of time periods by the embedded processing module, and predicting the running state of the ship by combining the ship running big data;
and S4, distributing the running state of the ship to the local server and the remote server for reference evaluation and decision.
2. The embedded computing method for the ship real-time monitoring system according to claim 1, wherein: the software and hardware diagnostic information obtaining method in step S1 includes: by adding the module function detection code when outputting the monitoring information, the function detection code is uniquely determined.
3. An embedded computing method for a real-time ship monitoring system according to claims 1 and 2, characterized in that: and in the step S2, the real-time operation state data of the plurality of ships are received simultaneously by using a parallel interface module, and corresponding time tags are stored when data are packed.
4. The embedded computing method for the ship real-time monitoring system according to claim 1, wherein: the method for fusing the real-time operation state data of a plurality of time periods in step S3 specifically includes: unpacking the packed data and then classifying the real-time running state data of the ship according to a time sequence, namely combining the data of each monitoring module in the same time period together for analysis; the combining of the ship operation big data is specifically to compare the fused data with ship historical operation data.
5. The embedded computing method for the ship real-time monitoring system according to claim 4, wherein: after the fused data are compared with historical ship operation data, if various data are abnormal in different degrees or one data is abnormal, tracking and searching are carried out aiming at the abnormality, and corresponding data are called out for further analysis so as to predict the ship operation state.
CN202110306962.XA 2021-03-23 2021-03-23 Embedded computing method for ship real-time monitoring system Withdrawn CN113052377A (en)

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CN113847950A (en) * 2021-09-23 2021-12-28 大连海事大学 Intelligent ship equipment state monitoring system based on cloud computing and information interaction method

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113847950A (en) * 2021-09-23 2021-12-28 大连海事大学 Intelligent ship equipment state monitoring system based on cloud computing and information interaction method

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