CN115130855A - Big data base data management system for dredging construction of drag suction dredger - Google Patents

Big data base data management system for dredging construction of drag suction dredger Download PDF

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Publication number
CN115130855A
CN115130855A CN202210729891.9A CN202210729891A CN115130855A CN 115130855 A CN115130855 A CN 115130855A CN 202210729891 A CN202210729891 A CN 202210729891A CN 115130855 A CN115130855 A CN 115130855A
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data
signal source
process point
model
information
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韦纪军
张洪涛
周后亭
许向东
董恒瑞
张继潮
伍骏
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Cccc Guanghang Dredging Co ltd
Zhenjiang Yihua System Integration Co ltd
CCCC Guangzhou Dredging Co Ltd.
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Cccc Guanghang Dredging Co ltd
Zhenjiang Yihua System Integration Co ltd
CCCC Guangzhou Dredging Co Ltd.
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Priority to CN202210729891.9A priority Critical patent/CN115130855A/en
Publication of CN115130855A publication Critical patent/CN115130855A/en
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    • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
    • E02F—DREDGING; SOIL-SHIFTING
    • E02F1/00—General working methods with dredgers or soil-shifting machines
    • E—FIXED CONSTRUCTIONS
    • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
    • E02F—DREDGING; SOIL-SHIFTING
    • E02F3/00—Dredgers; Soil-shifting machines
    • E02F3/04—Dredgers; Soil-shifting machines mechanically-driven
    • E02F3/88—Dredgers; Soil-shifting machines mechanically-driven with arrangements acting by a sucking or forcing effect, e.g. suction dredgers
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    • E—FIXED CONSTRUCTIONS
    • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
    • E02F—DREDGING; SOIL-SHIFTING
    • E02F9/00—Component parts of dredgers or soil-shifting machines, not restricted to one of the kinds covered by groups E02F3/00 - E02F7/00
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    • E—FIXED CONSTRUCTIONS
    • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
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    • E02F9/00—Component parts of dredgers or soil-shifting machines, not restricted to one of the kinds covered by groups E02F3/00 - E02F7/00
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    • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
    • E02F—DREDGING; SOIL-SHIFTING
    • E02F9/00—Component parts of dredgers or soil-shifting machines, not restricted to one of the kinds covered by groups E02F3/00 - E02F7/00
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Abstract

The invention relates to a big data base data management system for dredging construction of a trailing suction hopper dredger, which comprises a signal source diagnosis model, a multi-dimensional process point model and a ship operation characteristic model. The signal source diagnosis model is used for managing signal source data of the trailing suction hopper dredger in dredging construction; the multi-dimensional process point model is used for managing process point information of the drag suction dredger in dredging construction; the ship operation characteristic model is used for managing the ship operation characteristics of the drag suction dredger in dredging construction. Based on the method, through the construction of each model, the management requirement of basic data in the dredging construction of the trailing suction hopper dredger is met, and the overall operation management efficiency and operation management experience are improved.

Description

Big data base data management system for dredging construction of drag suction dredger
Technical Field
The invention relates to the technical field of dredging engineering construction, in particular to a big data base data management system for dredging construction of a trailing suction hopper dredger.
Background
A drag suction dredger is a large self-propelled and bin-loading dredger provided with a drag head dredger and a hydraulic dredging device. When the dredger is used for dredging, the rake suction pipe is put down to the river bottom, mud is absorbed from the river bottom through the rake head and the mud suction pipe by utilizing the vacuum action of the mud pump and enters a mud bin of the dredger, and after the mud bin is full, the rake is lifted to sail to a mud throwing area to open a mud door for mud discharge, or the dug mud is directly discharged out of the dredger. The dredging engineering refers to an earth and stone engineering which adopts a dredger or other machines and manual work to excavate underwater and is carried out for widening and deepening water areas.
Therefore, a trailing suction hopper dredger plays a crucial role in dredging. Meanwhile, as the complexity of the dredging engineering and the equipment complexity of the trailing suction hopper dredger are both high, a method for effectively managing the dredging construction of the trailing suction hopper dredger is lacked in the dredging engineering operation, and the overall operation management efficiency and operation management experience are influenced.
Disclosure of Invention
Therefore, the basic data management system for the dredging construction big data of the trailing suction hopper dredger is needed to overcome the defects that the whole operation management efficiency and the operation management experience are influenced due to the lack of a mode for effectively managing the dredging construction of the trailing suction hopper dredger in the operation of dredging engineering.
A big data base data management system for dredging construction of a trailing suction hopper dredger comprises:
the signal source diagnosis model is used for managing signal source data of the trailing suction hopper dredger in dredging construction;
the multi-dimensional process point model is used for managing process point information of the drag suction dredger in dredging construction;
the ship operation characteristic model is used for managing the ship operation characteristics of the drag suction dredger in dredging construction.
The big data base data management system for the dredging construction of the trailing suction hopper dredger comprises a signal source diagnosis model, a multi-dimensional process point model and a ship operation characteristic model. The signal source diagnosis model is used for managing signal source data of the trailing suction hopper dredger in dredging construction; the multidimensional process point model is used for managing process point information of the drag suction dredger in dredging construction; the ship operation characteristic model is used for managing the ship operation characteristics of the drag suction dredger in dredging construction. Based on the method, through the construction of each model, the management requirement of basic data in the dredging construction of the trailing suction hopper dredger is met, and the overall operation management efficiency and operation management experience are improved.
In one embodiment, the signal source diagnostic model comprises:
the signal diagnosis module is used for performing signal diagnosis on the signal source data to obtain diagnosis data;
the diagnosis synchronization module is used for executing synchronization processing on the signal source data;
the signal source classifying and configuring module is used for carrying out configuration modification on the signal source data and the diagnosis data;
and the data pushing module is used for pushing the signal source data.
In one embodiment, the signal diagnosis module comprises:
the disconnection judging unit is used for diagnosing whether a signal source corresponding to the signal source data is disconnected;
the water inlet judging unit is used for diagnosing whether the signal source corresponding to the signal source data enters water or not;
the boundary crossing judging unit is used for diagnosing whether the signal source corresponding to the signal source data crosses the boundary or not;
and the fault judging unit is used for diagnosing whether the signal source corresponding to the signal source data has a fault.
In one embodiment, the data pushing module comprises:
the diagnosis query unit is used for querying abnormal information in the signal source data;
and the diagnosis pushing unit is used for pushing the signal source data to a set target.
In one embodiment, the multi-dimensional process point model comprises:
the process point signal source calculating unit is used for calculating the process point information;
the process point information classification configuration unit is used for performing classification configuration on the process point information;
a process point content modification configuration unit for performing content configuration on the process point information
The process point generating unit is used for generating process points corresponding to the process point information;
the process point storage unit is used for storing the process point information;
the process point query and push unit is used for querying the process point information;
and the soil property information setting unit is used for setting soil property information related to the process point information.
In one embodiment, the process point signal source calculating unit is configured to calculate an instantaneous value, an average value, and an accumulated value of the process point information.
In one embodiment, the process point information classification configuration unit is configured to classify the process point information into vessel position data, operation characteristic data, dredging production data, dredging process data, power consumption data and/or oil consumption data.
In one embodiment, the process point storage unit is used for storing the process point information in a data classification model; the data classification model comprises a position information basic data model, an operation information basic data model, a yield information basic data model, a process information basic data model, a power consumption information basic data model, a fuel consumption information basic data model and/or an equipment operation basic data model.
In one embodiment, the vessel operation characteristics include berthing, loading, flooding, unloading, idling, full-load, blowing off, and/or bow blowing.
In one embodiment, the method further comprises the following steps:
and the historical data import model is used for importing the historical data.
Drawings
FIG. 1 is a block diagram of a basic data management system for dredging construction big data of a drag suction dredger according to an embodiment;
FIG. 2 is a block diagram of a basic data management system for dredging construction big data of a drag suction dredger according to an embodiment;
FIG. 3 is a block diagram of a basic data management system of dredging construction big data of a drag suction dredger according to an embodiment;
fig. 4 is a schematic view of an operational characteristic relationship of a ship according to an embodiment.
Detailed Description
For better understanding of the objects, technical solutions and effects of the present invention, the present invention will be further explained with reference to the accompanying drawings and examples. Meanwhile, the following described examples are only for explaining the present invention, and are not intended to limit the present invention.
The embodiment of the invention provides a big data base data management system for dredging construction of a trailing suction hopper dredger.
Fig. 1 is a block diagram of a basic data management system for a dredging construction big data of a trailing suction hopper dredger according to an embodiment, and as shown in fig. 1, the basic data management system for the dredging construction big data of the trailing suction hopper dredger according to an embodiment includes:
the signal source diagnosis model 100 is used for managing signal source data of the drag suction dredger in dredging construction;
the multidimensional process point model 101 is used for managing process point information of the drag suction dredger in dredging construction;
the ship operation characteristic model 102 is used for managing the ship operation characteristics of the drag suction dredger in dredging construction.
Among them, a drag suction dredger has a plurality of signal sources such as a sensor, a sonar, a positioning device, and the like in dredging construction. The signal source generates corresponding signal source data in the dredging construction process.
Meanwhile, in the dredging construction process, a plurality of process points exist for correspondingly recording the operation state according to the pre-configuration and the manual configuration of the trailing suction hopper dredger.
In one embodiment, fig. 2 is a block diagram of a big data base management system for dredging construction of a trailing suction hopper dredger according to another embodiment, and as shown in fig. 2, a signal source diagnostic model 100 includes:
a signal diagnosis module 200, configured to perform signal diagnosis on signal source data to obtain diagnosis data;
a diagnosis synchronization module 201, configured to perform synchronization processing on the signal source data;
a signal source classifying and configuring module 202, configured to modify the signal source data and the diagnostic data;
and the data pushing module 203 is used for pushing the signal source data.
The signal diagnosis module 200 is used for modifying and upgrading the existing ship-side communication software, increasing signal source data points at the ship side of the trailing suction hopper dredger, and transmitting data to the platform-side server for recording diagnosis data. And recording the diagnosis data in a database of the platform end server in a field recording mode of the signal source diagnosis table.
In one embodiment, fig. 3 is a block diagram of a basic data management system for dredging construction big data of a trailing suction hopper dredger according to still another embodiment, and as shown in fig. 3, a signal diagnosis module 200 includes:
a disconnection determining unit 300 configured to diagnose whether a signal source corresponding to the signal source data is disconnected;
a water inlet determination unit 301, configured to diagnose whether a signal source corresponding to the signal source data enters water;
an out-of-range determining unit 302, configured to diagnose whether the signal source corresponding to the signal source data is out of range;
a failure determination unit 303, configured to diagnose whether a signal source corresponding to the signal source data has a failure.
In one embodiment, the disconnection determining unit 300 determines based on raw data of the sensor, and when the sampled value data of the sensor is in the vicinity of a disconnection value, it is determined that the sensor signal is disconnected. The water inflow determination unit 301 determines that the sensor is in water inflow when the sampling value of the sensor is in a normal state for a while, is in the vicinity of the water inflow value for a while, and continuously jumps for more than 3 times, according to the original dredging determination of the sensor. The boundary crossing determining unit 302 determines whether the actual value of the sensor is in the boundary crossing range, and if the actual value of the sensor is smaller than the boundary crossing minimum value or larger than the boundary crossing maximum value, the sensor is considered to alarm. The fault determination unit 303 establishes a fault determination model for the redundant sensors from other reference signal sources of the same class. The sensor is used as a signal source and comprises: draft sensor, liquid level radar sensor.
In one embodiment, the diagnostic synchronization module 201 expands the transmitted data based on the communication interface software to ensure the synchronization of the signal source data used in diagnostic dredging.
In one embodiment, the signal source classification and configuration module 202 is based on a platform server, and for a diagnosed signal source, the type of its diagnostic data is: and (4) modifying information background configurations such as disconnection, water inlet, boundary crossing, faults and the like.
In one embodiment, as shown in fig. 3, the data pushing module 203 includes:
a diagnosis query unit 400, configured to query abnormal information in the signal source data;
a diagnosis pushing unit 401, configured to push the signal source data to a set target.
The diagnosis query unit 400 queries currently diagnosed abnormal sensor information for the interactive terminal, and has a one-key query function to query abnormal information in the current sensor. The interactive end comprises a Web end.
The diagnosis pushing unit 401 realizes intelligent pushing of diagnosis information for the client. And automatic diagnosis is carried out at a background regularly, and automatic pushing is carried out to different users according to different authorities and pushing strategies. The client comprises a computer end or a mobile end.
In one embodiment, as shown in FIG. 2, the multi-dimensional process point model 101 includes:
a process point signal source calculating unit 500, configured to calculate the process point information;
a process point information classifying and configuring unit 501, configured to perform classifying and configuring on the process point information;
a process point content modification configuration unit 502 for performing content configuration on the process point information
A process point generating unit 503, configured to generate a process point corresponding to the process point information;
a process point storage unit 504, configured to store the process point information;
a process point query and push unit 505, configured to query the process point information;
and a soil property information setting unit 506, configured to set soil property information related to the process point information.
The process point signal source calculating unit 500 generates a process point signal periodically according to a "process point list" based on the platform. The process point is generated once every minute, and the mode of calculating the process point is as follows: "instantaneous value": taking an actual value of a signal at the last moment; "average value": the signal points were averaged over one minute. "cumulative value": and taking the data accumulation value of the signal point within one minute.
The process point information classifying and configuring unit 501 may classify and configure information included in the process points through background configuration software. The user can add, delete and modify the classification information. The main categories of process point contained information include: vessel position data, operational characteristic data, dredging yield data, dredging process data, power consumption data and/or oil consumption data.
The process point content modification configuration unit 502 is at the platform end, and information contained in the process point can be added and modified through background configuration software. For the modification of the process point, the user with the given authority of the system must modify the process point.
The process point generating unit 503 has manual generation and automatic generation functions:
manually generating: and a manual generation button is arranged in the platform, and the time required to be generated is selected for manual generation.
And (3) automatic generation: and automatically generating the process point data of the previous day by the platform from the next day to the next day.
In one embodiment, the process point storage unit 504 performs tag classification storage on each data included in the process point by using a data classification model, directly generates the process point at the ship end, and transmits the process point to the platform-end data center. The data classification model comprises a position information basic data model, an operation information basic data model, a yield information basic data model, a process information basic data model, a power consumption information basic data model, a fuel consumption information basic data model and/or an equipment operation basic data model.
The process point query and push unit 505 can provide data services such as fast query, index, and output when other business models request data from the underlying data layer.
The soil property information setting unit 506 may be used to modify the soil property information of the ship during a period of ship construction. Soil property information is added, and the classification and corresponding ID of soil property are as follows: organic soil, peat, silt, mucky soil, cohesive soil, silt, sand, gravel and/or rock.
In one embodiment, FIG. 4 is a schematic diagram of a ship operating characteristic relationship of an embodiment, as shown in FIG. 4, the ship operating characteristic includes berthing, loading, overflowing, dumping, empty sailing, full sailing, blowing to the shore, and/or bow jet.
In one embodiment, as shown in fig. 1, the system for managing big data base of dredging construction of a trailing suction hopper dredger according to an embodiment further includes:
and a historical data import model 103 for importing historical data.
The historical data import model 103 operates on the platform side, and can import the historical data recorded by the ship side into the platform database. The data files of the history file (the NST file) of the SCADA system are copied to the platform center. And importing the data into a database through data import software, and automatically generating process points.
The big data base data management system for the dredging construction of the drag suction dredger comprises a signal source diagnosis model 100, a multi-dimensional process point model 101 and a ship operation characteristic model 102. The signal source diagnosis model 100 is used for managing signal source data of the trailing suction hopper dredger in dredging construction; the multidimensional process point model 101 is used for managing process point information of the drag suction dredger in dredging construction; the ship operation characteristic model 102 is used for managing the ship operation characteristics of the drag suction dredger in dredging construction. Based on the method, through the construction of each model, the management requirement of basic data in the dredging construction of the trailing suction hopper dredger is met, and the overall operation management efficiency and operation management experience are improved.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the 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. The utility model provides a big data basis data management system of trailing suction hopper dredger dredging construction which characterized in that includes:
the signal source diagnosis model is used for managing signal source data of the trailing suction hopper dredger in dredging construction;
the multi-dimensional process point model is used for managing process point information of the drag suction dredger in dredging construction;
the ship operation characteristic model is used for managing the ship operation characteristics of the drag suction dredger in dredging construction.
2. The drag suction dredger dredging construction big data base data management system according to claim 1, wherein the signal source diagnostic model comprises:
the signal diagnosis module is used for performing signal diagnosis on the signal source data to obtain diagnosis data;
the diagnosis synchronization module is used for executing synchronization processing on the signal source data;
the signal source classifying and configuring module is used for carrying out configuration modification on the signal source data and the diagnosis data;
and the data pushing module is used for pushing the signal source data.
3. The drag suction dredger dredging construction big data base data management system according to claim 2, wherein the signal diagnosis module comprises:
the disconnection judging unit is used for diagnosing whether a signal source corresponding to the signal source data is disconnected;
the water inlet judging unit is used for diagnosing whether the signal source corresponding to the signal source data enters water or not;
the boundary crossing judging unit is used for diagnosing whether the signal source corresponding to the signal source data crosses the boundary or not;
and the fault judging unit is used for diagnosing whether the signal source corresponding to the signal source data has a fault or not.
4. The drag suction dredger dredging construction big data base data management system according to claim 2, wherein the data pushing module comprises:
the diagnosis query unit is used for querying abnormal information in the signal source data;
and the diagnosis pushing unit is used for pushing the signal source data to a set target.
5. The drag suction dredge dredging construction big data base data management system of claim 1, wherein the multi-dimensional process point model comprises:
the process point signal source calculating unit is used for calculating the process point information;
the process point information classification configuration unit is used for performing classification configuration on the process point information;
a process point content modification configuration unit for performing content configuration on the process point information
The process point generating unit is used for generating process points corresponding to the process point information;
the process point storage unit is used for storing the process point information;
the process point query and push unit is used for querying the process point information;
and the soil property information setting unit is used for setting soil property information related to the process point information.
6. The drag suction dredger dredging construction big data base data management system of claim 5, wherein the process point signal source calculating unit is configured to calculate an instantaneous value, an average value and an accumulated value of the process point information.
7. The drag suction dredger dredging construction big data base data management system according to claim 5, wherein the process point information classification configuration unit is configured to classify the process point information into ship position data, operation characteristic data, dredging yield data, dredging process data, power consumption data and/or oil consumption data.
8. The drag suction dredger dredging construction big data base data management system according to claim 5, wherein the process point storage unit is configured to store the process point information in a data classification model; the data classification model comprises a position information basic data model, an operation information basic data model, a yield information basic data model, a process information basic data model, a power consumption information basic data model, a fuel consumption information basic data model and/or an equipment operation basic data model.
9. The trailing suction hopper dredger dredging construction big data base data management system of claim 1, wherein the vessel operation characteristics include berthing, loading, overflow, mud discharge, empty sailing, full sailing, blowing on the shore, and/or bow jet.
10. The drag suction dredger dredging construction big data base data management system according to claim 1, further comprising:
and the historical data import model is used for importing the historical data.
CN202210729891.9A 2022-06-24 2022-06-24 Big data base data management system for dredging construction of drag suction dredger Pending CN115130855A (en)

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