CN114205686A - Intelligent ship sensor configuration and monitoring method and system based on active sensing - Google Patents
Intelligent ship sensor configuration and monitoring method and system based on active sensing Download PDFInfo
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Abstract
The invention provides an intelligent ship sensor configuration and monitoring method and system based on active sensing. The intelligent ship sensor configuration method based on active sensing comprises the following steps: analyzing a causal path of a node where a sensor is located; based on the analysis result, obtaining a system fault characteristic matrix by adopting a causal path derivation method of a double causal bonding diagram; and acquiring a configuration scheme of the intelligent ship system equipment sensor according to the system fault characteristic matrix. The invention can optimize the configuration of the current ship sensor, realize the active sensing data of the intelligent ship, realize the dynamic evaluation and prediction of the running state of the ship, determine different event processing strategies and further carry out active defense on risk items in the system.
Description
Technical Field
The invention relates to intelligent ship monitoring equipment, in particular to an intelligent ship sensor configuration and monitoring method and system based on active sensing.
Background
With the rapid development of intelligent ship construction, the traditional sensor configuration scheme based on fault diagnosis and the system equipment state online monitoring method cannot adapt to the new requirements of intelligent ship operation and maintenance. Meanwhile, as the complexity of the intelligent ship system equipment is improved, various derived state parameter historical sensors and state monitoring real-time sensors have the characteristics of various sensor forms, large quantity, multiple dimensions, low value density and the like, and how to systematically dig the hidden value behind the sensor through active sensing is extremely important for evaluating and predicting the operation current situation of the intelligent ship and making processing strategies for different events.
Disclosure of Invention
In view of the defects of the prior art, the invention provides an intelligent ship sensor configuration and monitoring method and system based on active sensing, in order to adapt to the international maritime work convention rule of an intelligent ship, optimize the configuration of the current ship sensor, realize the active sensing sensor of the intelligent ship, realize the dynamic evaluation and prediction of the running state of the ship, determine different event processing strategies and further carry out active defense on risk items in the system.
The technical means adopted by the invention are as follows:
an intelligent ship sensor configuration method based on active sensing comprises the following steps:
acquiring existing sensor nodes of a ship, and further analyzing causal paths of the sensor nodes;
based on the acquired causal path of the sensor node, a system fault characteristic matrix is obtained by adopting a dual causal bond diagram causal path derivation method;
and acquiring the sensor which does not meet the fault isolatable requirement according to the system fault characteristic matrix, and optimizing the sensor which does not meet the fault isolatable requirement, thereby obtaining the configuration scheme of the intelligent ship system sensor. .
Further, the sensor types include a temperature sensor, a pressure sensor, a flow sensor, a liquid level sensor, a rotational speed sensor, a torque sensor, a gas-liquid discharge composition sensor, a voltage sensor, and a current sensor.
The invention also provides an intelligent ship monitoring method based on active sensing, which comprises the following steps:
configuring a sensor according to the intelligent ship sensor configuration method;
acquiring ship running state data based on each configured sensor;
sequentially preprocessing and extracting characteristics of the state data;
importing the data after the feature extraction into different prediction models according to system perception types, wherein the system perception types represent data forms correspondingly acquired by different subsystems of the ship;
fusing the prediction results of the prediction models to obtain a ship operation state evaluation prediction result;
and carrying out processing decision based on the ship running state evaluation prediction result.
Further, the status data includes: temperature data, pressure data, flow data, liquid level data, rotational speed data, torque data, gas-liquid discharge composition data, voltage data, and current data.
Further, the processing decision is carried out based on the prediction result of the ship running state evaluation, and the processing decision comprises the following steps:
carrying out acousto-optic early warning on abnormal conditions in advance according to the estimation and prediction result of the running state of the ship;
and feeding back early warning information to turbine operators on duty.
Corresponding to the sensor configuration method, the invention also provides an intelligent ship sensor configuration system based on active sensing, which comprises the following steps:
the analysis module is used for acquiring the existing sensor nodes of the ship and further analyzing the causal path of each sensor node;
the matrix construction module is used for obtaining a system fault characteristic matrix by adopting a dual cause and effect bonding diagram cause and effect path derivation method on the acquired cause and effect paths of the sensor nodes;
and the configuration module is used for acquiring the sensors which do not meet the fault isolatable requirement according to the system fault characteristic matrix and optimizing the sensors which do not meet the fault isolatable requirement so as to obtain the intelligent ship system equipment sensor configuration scheme.
Further, the sensor types include a temperature sensor, a pressure sensor, a flow sensor, a liquid level sensor, a rotational speed sensor, a torque sensor, a gas-liquid discharge composition sensor, a voltage sensor, and a current sensor.
Corresponding to the ship monitoring method, the invention also provides an intelligent ship monitoring system based on active sensing, which comprises the following steps:
a sensor configuration module for configuring sensors according to the intelligent ship sensor configuration method;
the data acquisition module is used for acquiring the running state data of the ship based on each configured sensor;
the preprocessing module is used for preprocessing the state data based on a streaming computing mode;
the prediction module is used for importing the preprocessed data into different prediction models according to the perception type;
the fusion module is used for fusing the prediction results of the prediction models so as to obtain the ship operation state evaluation prediction result;
and carrying out processing decision based on the ship running state evaluation prediction result.
Further, the status data includes: temperature data, pressure data, flow data, liquid level data, rotational speed data, torque data, gas-liquid discharge composition data, voltage data, and current data.
Further, the processing decision is carried out based on the prediction result of the ship running state evaluation, and the processing decision comprises the following steps:
carrying out acousto-optic early warning on abnormal conditions in advance according to the estimation and prediction result of the running state of the ship;
and feeding back early warning information to turbine operators on duty.
Compared with the prior art, the invention has the following advantages:
the intelligent ship active sensing system can adapt to the international maritime work convention rule of the intelligent ship, optimizes the configuration of the current ship sensor, realizes the active sensing data of the intelligent ship, realizes the dynamic evaluation and prediction of the running state of the ship, determines different event processing strategies and further actively defends risk items in the system.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flowchart of an intelligent ship sensor configuration method based on active sensing according to the present invention.
Fig. 2 is a flowchart of an intelligent ship monitoring method based on active sensing according to the present invention.
FIG. 3 is a layout view of a host fuel unit sensor before optimization in an embodiment of the present invention.
FIG. 4 is a schematic diagram of a sensor configuration based on dual causal path derivation, taking a host fuel unit as an example, according to an embodiment of the present invention.
Fig. 5 is a layout diagram of the optimized host fuel unit sensors in an embodiment of the present invention.
Fig. 6 is a schematic structural diagram of an active sensing system according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the sensors so used may be interchanged under appropriate circumstances such that embodiments of the invention described herein may be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in fig. 1, a smart ship sensor configuration method based on active sensing includes the steps of:
s101, existing sensor nodes of the ship are obtained, and causal paths of the sensor nodes are further analyzed.
Specifically, the invention aims to improve the sensor layout rule of the old ship monitoring system, and the layout rule of the old ship sensor nodes comprises the following steps: 1. comprehensive and reliable monitoring data can be acquired in a complex cabin environment; 2. the number of the sensors is as small as possible, so that the whole monitoring system is ensured to be within a reasonable cost range; 3. the obtained monitoring data can be used for monitoring important parts of the structure 4. the monitoring data is sensitive to the change of the related performance of the structure. Generally speaking, it only requires the layout of sensors to satisfy fault monitoring, but lacks the function of actively sensing data to achieve fault isolation and diagnosis. In order to improve and optimize the existing shipbuilding specifications, the causal path of each sensor node can be automatically calculated based on the old sensor nodes according to prior knowledge or a constraint condition and an optimization algorithm.
And S102, obtaining a system fault characteristic matrix by adopting a dual cause and effect bonding diagram cause and effect path derivation method based on the acquired cause and effect paths of the sensor nodes.
Specifically, the layout of the sensors before optimization is shown in fig. 3, which comprises a light oil daily cabinet 1, a heavy oil daily cabinet 2, a differential pressure liquid level meter 3, a three-way change-over valve 4, an inlet coarse filter 5, a fuel supply pump 6, a fuel differential pressure meter 7, a fuel flow meter 8, a fuel self-cleaning filter 9, a fuel circulating pump 10, a heater 11 and a temperature detector 12. The sensor configuration relationships derived based on dual causal paths, for example, for the host fuel unit obtained according to fig. 3 are shown in fig. 4. The dual causal bond graph defines a well-defined node element for each (analytical redundancy relation) ARR, so all components on the ARR path can be derived directly through the derivation of causal relations without the need to derive specific ARR mathematical expressions. The node elements of ARR1-ARR8 selected as shown in FIG. 4 are C2 (heavy oil day tank), C1 (light oil day tank), RF1 (filter), RP1 (resistance loss of screw pump), RV4 (valve), RF2 (self-cleaning filter before circulating pump), RP2 (resistance loss of circulating pump), and RH (resistance element connected to heat source), respectively. It should be noted that no matter which passive element is selected as the node element, the fault diagnosis characteristics of the components are the same despite the different FSMs generated by the passive elements, and the performance index requirements of the sensor layout for realizing all-around state sensing and fault isolation can be met. The causal path is derived as follows, using ARR1 in fig. 4 as an example:
ARR1:
1、SS:PC2→e4→e3→e2→e1
2、RV1→f6→f5→f1
3、RV2→e13→e12→e6→RV1→f6→f5→f1
all system components corresponding to ARR1 can be obtained by derivation of the above path:
ARR2＝ARR2(RV1、RV2、C1)ARR3＝ARR3(RV1、RV2、RV3、
ARR4＝ARR4(QP1、RV3、RP1)RAR)R5＝ARR5(RV4)
ARR6＝ARR6(QP1、RV3、RP1)ARR7＝ARR7(RP2、RV5、QP2)
ARR8＝ARR8(RV6、RH)
by deriving the ARRs as described above, the fault signature matrix FSM of the oil supply unit can be obtained ignoring the faults of the sensors, as shown in table 1.
TABLE 1 Fault signature matrix FSM
Mb and Ib on the right in the FSM represent fault perceptible and fault isolatable, respectively. As can be seen from Table 1, all components meet the sensible performance index, but the fault feature vectors of RV1 and RV2 are [11100000], and the fault feature vectors of RH and RV6 are [00000001], and the fault feature vectors are linearly related, so that the fault isolatable performance index is not met when one component fails. Note that although the fault feature vectors of QP1 and RP1 and QP2 and RP2 are the same, QP1 and RP1 correspond to the same component, and QP2 and RP2 correspond to the same component, so that they still satisfy the performance index for fault isolation.
S103, acquiring the sensor which does not meet the fault isolation requirement according to the system fault characteristic matrix, and optimizing the sensor which does not meet the fault isolation requirement, so as to obtain the configuration scheme of the intelligent ship system sensor. As shown in the table, the sensors correspond to 8 analytic redundancy relations of ARR1-ARR8, Mb represents fault diagnosable, and Ib represents fault isolable, the method is based on the premise that the optimization of the sensor layout is realized, namely, the fault isolable, when the bonding diagram is used for deduction, not all faults identified by the sensors can be isolated, so that the sensor with the Ib of 0 needs to be optimized, the optimization mode is that the sensors are added until a fault feature matrix acquired according to a new layout diagram of the newly added sensors is 1 in two columns of Mb diagnosable and Ib isolable, and as shown in Table 2. The new layout of the sensor addition is shown in fig. 5.
TABLE 2 optimized Fault signature matrix FSM
Neither more nor less than this is possible, which has the advantage that optimization is achieved at minimal cost.
Further, the types of sensors referred to in the present application include temperature sensors, pressure sensors, flow sensors, liquid level sensors, rotational speed sensors, torque sensors, gas-liquid discharge component sensors, voltage sensors, current sensors, and the like.
The invention also provides an intelligent ship monitoring method based on active sensing, which comprises the following steps:
s201, configuring the sensor according to the intelligent ship sensor configuration method.
S202, acquiring ship running state data based on the configured sensors.
S203, preprocessing and feature extraction are carried out on the state data in sequence. The preprocessing aims to improve data quality and ensure accuracy and reliability of subsequent state analysis and intelligent operation and maintenance decision making, and common methods in the field of ships include denoising, filtering, enhancing, normalizing and the like. And feature extraction, namely, performing time domain, frequency domain or time-frequency domain feature extraction on the preprocessed data to obtain feature data.
And S204, importing the preprocessed data into different prediction models according to the perception type.
Specifically, the sensing type is a data form of different systems, and each subsystem of the ship corresponds to one sensing type ex: fuel oil systems, lubricating oil systems, cooling water systems, exhaust gas discharge systems, boiler systems, and the like. And (3) aiming at the characteristic difference of the perception types of each subsystem of the ship, predicting the data by adopting different models respectively. The existing models can be mainly classified into four types based on an empirical model, a reliability model, a physical model and a data driving model according to the mechanism of the method, such as a method based on literature [1], and the comprehensive diagnosis and trend prediction analysis of the running state of the marine power generation diesel engine are realized by establishing the data driving model by utilizing instantaneous rotating speed signals and monitored thermal parameters. . Based on the method of the document [2], in order to accurately position fault elements, a mathematical model suitable for ship power system fault diagnosis under the condition of considering protection or circuit breaker failure is established, a quantum genetic algorithm is utilized to solve the fault diagnosis mathematical model, a typical ship power system fault example is utilized to verify the method,
s205, fusing the prediction results of the prediction models to obtain a ship running state evaluation prediction result;
and S206, processing and deciding based on the ship running state evaluation prediction result.
The scheme of the intelligent ship sensor configuration method is further explained by specific application examples.
As shown in fig. 3, taking a host fuel unit as an example, the fuel unit is modeled by a double-causal bond graph, and seven potential sensors and two flow sensors (the potential sensors are Pc2, Pc1, Pf1, P1, Pf2, P2, T; the flow sensors are Qv2, Qv 6. the potential sensors are connected with AE keys, and the flow sensors are connected with AF keys) on nodes are connected by double-causal bonds and are strong keys, that is, the calculation directions of two power variables are consistent with the node direction. The causal relationship continues along the causal path until passed to the node element. The bonding graph nodes represent the basic energy processes, i.e. the transfer or storage of energy, consumption, etc. For these basic energy processes, a specific type name of the node is used. For example, electrical energy storage in a capacitor or potential energy storage in a mechanical spring is represented by a node of type C, and a passive element receiving power variable information is represented by a node of type I, R. The node element is a component connected by two or more energy bonds.
In the ARRs (Analytic Redundancy Relations) in the FIG. 3, virtual sensor simulation measuring points are arranged in a bonding diagram model (the positions of the measuring points are old sensor layouts, and can refer to Chenghiping, Zhengping, Main propulsion power device [ M ]. Dalian university of maritime publishers, 2012.513-515), a group of Analytic Redundancy Relations, namely system residuals, are deduced by using structural information and causal relation constraints of a bonding diagram, and a sensor layout method based on the requirements of fault detectability and isolation is provided by analyzing the Relations of the residuals, faults and sensor configurations, so that the sensor configuration scheme with the least quantity is selected on the premise of meeting the system diagnostic performance to the maximum extent. The method is characterized in that the old sensor only meets the condition that the fault can be diagnosed but not isolated, in order to improve the reliability of state monitoring, the layout of the sensor is optimized again by adopting the scheme, the fault diagnosis and isolation are realized with the least sensors and the lowest cost, and the specific method is to deduce the old layout again, remove unnecessary sensor layout and add necessary sensor layout. The derivation of complex constraint equations is avoided through the double causal bonding graph, and the related component information of the current node can be directly judged through the retrieval of the causal path. Specifically, in the present embodiment, the node elements of the selected ARR1-ARR8 are C2 (heavy oil day tank), C1 (light oil day tank), RF1 (filter), RP1 (resistance loss of screw pump), RV4 (valve), RF2 (self-cleaning filter before circulating pump), RP2 (resistance loss of circulating pump), and RH (resistance element connected to heat source), respectively. It should be noted that no matter which passive element is selected as the node element, the fault diagnosis characteristics of the components are the same despite the different FSMs generated by the passive elements, and the performance index requirements of the sensor layout for realizing all-around state sensing and fault isolation can be met.
The scheme of the intelligent ship monitoring system based on active sensing of the invention is further explained by a specific application example.
As shown in fig. 4, modeling a dual causal bond diagram using a host as an example; the system comprises a data acquisition module, a state evaluation module and an operation module.
The data acquisition module comprises a sensor and is used for acquiring state data of the nodes after the optimized configuration; the state data includes, but is not limited to, temperature data, pressure data, flow data, liquid level data, rotational speed data, torque data, gas-liquid discharge composition data, voltage data, current data; and after the data acquisition module collects the original state data, the original state data is uploaded to the state evaluation module in real time through a 5G network.
The state evaluation module adopts a streaming computing mode, firstly carries out data preprocessing on received original state data, then leads the preprocessed data into different prediction models according to perception types, and the data can evaluate and predict the running state in a short time in the future according to correction and iteration of related algorithms in the prediction models.
The operation module carries out acousto-optic early warning on abnormal conditions in advance according to the evaluation prediction result of the state evaluation module, and meanwhile, the communication unit in the operation module and turbine operators on duty feed back early warning information in time to assist the turbine operators on duty to process and make decisions on different prediction events, so that the economic loss caused by unknown fault risks can be reduced to a great extent, the intelligence of operation and maintenance of the intelligent ship is improved, and the reliability of operation of the intelligent ship is improved.
Corresponding to the sensor configuration method, the invention also provides an intelligent ship sensor configuration system based on active sensing, which comprises the following steps:
the analysis module is used for acquiring the existing sensor nodes of the ship and further analyzing the causal path of each sensor node;
the matrix construction module is used for obtaining a system fault characteristic matrix by adopting a dual cause and effect bonding diagram cause and effect path derivation method on the acquired cause and effect paths of the sensor nodes;
and the configuration module is used for acquiring a configuration scheme of the intelligent ship system equipment sensor according to the system fault characteristic matrix.
For the embodiments of the present invention, the description is simple because it corresponds to the above embodiments, and for the related similarities, please refer to the description in the above embodiments, and the detailed description is omitted here.
Corresponding to the ship monitoring method, the invention also provides an intelligent ship monitoring system based on active sensing, which comprises the following steps:
a sensor configuration module for configuring sensors according to the intelligent ship sensor configuration method;
the data acquisition module is used for acquiring ship running state data based on each configured sensor, and uploading the state data in real time through a 5G network after acquiring original state data;
the preprocessing module is used for preprocessing the state data based on a streaming computing mode;
the prediction module is used for importing the preprocessed data into different prediction models according to the perception type;
the fusion module is used for fusing the prediction results of the prediction models so as to obtain the ship operation state evaluation prediction result;
and carrying out processing decision based on the ship running state evaluation prediction result.
Reference documents:
[1]YU F P.An oneline monitoring and fault diagnosing system of marine diesel generator[J].Navigation of China，2003(2):76-78.
[2]HOU X G，WANG J L。Fault diagnosis method of the shipboard power system[J].Ship and Electronics Technology，2013，33(12):1-4)
the above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. The intelligent ship sensor configuration method based on active sensing is characterized by comprising the following steps:
acquiring existing sensor nodes of a ship, and further analyzing causal paths of the sensor nodes;
based on the acquired causal path of the sensor node, a system fault characteristic matrix is obtained by adopting a dual causal bond diagram causal path derivation method;
and acquiring the sensor which does not meet the fault isolatable requirement according to the system fault characteristic matrix, and optimizing the sensor which does not meet the fault isolatable requirement, thereby obtaining the configuration scheme of the intelligent ship system sensor.
2. The smart vessel active perception based sensor configuration method as claimed in claim 1, wherein the sensor types include a temperature sensor, a pressure sensor, a flow sensor, a level sensor, a rotation speed sensor, a torque sensor, a gas-liquid discharge component sensor, a voltage sensor, and a current sensor.
3. An intelligent ship monitoring method based on active sensing is characterized by comprising the following steps:
configuring a sensor according to the smart vessel sensor configuration method of claim 1;
acquiring ship running state data based on each configured sensor;
sequentially preprocessing and extracting characteristics of the state data;
importing the data after the feature extraction into different prediction models according to system perception types, wherein the system perception types represent data forms correspondingly acquired by different subsystems of the ship;
fusing the prediction results of the prediction models to obtain a ship operation state evaluation prediction result;
and carrying out processing decision based on the ship running state evaluation prediction result.
4. The intelligent vessel monitoring method based on active perception according to claim 3, wherein the status data includes: temperature data, pressure data, flow data, liquid level data, rotational speed data, torque data, gas-liquid discharge composition data, voltage data, and current data.
5. The intelligent vessel monitoring method based on active perception according to claim 3, wherein the processing decision based on the vessel operation state evaluation prediction result comprises:
carrying out acousto-optic early warning on abnormal conditions in advance according to the estimation and prediction result of the running state of the ship;
and feeding back early warning information to turbine operators on duty.
6. An intelligent ship sensor configuration system based on active sensing, comprising:
the analysis module is used for acquiring the existing sensor nodes of the ship and further analyzing the causal path of each sensor node;
the matrix construction module is used for obtaining a system fault characteristic matrix by adopting a dual cause and effect bonding diagram cause and effect path derivation method on the acquired cause and effect paths of the sensor nodes;
and the configuration module is used for acquiring the sensors which do not meet the fault isolatable requirement according to the system fault characteristic matrix and optimizing the sensors which do not meet the fault isolatable requirement so as to obtain the intelligent ship system equipment sensor configuration scheme.
7. The smart vessel active perception based sensor configuration system of claim 6 wherein the sensor types include temperature sensors, pressure sensors, flow sensors, level sensors, speed sensors, torque sensors, gas liquid discharge composition sensors, voltage sensors, and current sensors.
8. An intelligent ship monitoring system based on active sensing, comprising:
a sensor configuration module for configuring sensors according to the smart vessel sensor configuration method of claim 1;
the data acquisition module is used for acquiring the running state data of the ship based on each configured sensor;
the preprocessing module is used for preprocessing the state data based on a streaming computing mode;
the prediction module is used for importing the preprocessed data into different prediction models according to the perception type;
the fusion module is used for fusing the prediction results of the prediction models so as to obtain the ship operation state evaluation prediction result;
and carrying out processing decision based on the ship running state evaluation prediction result.
9. The smart vessel monitoring system based on active perception according to claim 8, wherein the status data includes: temperature data, pressure data, flow data, liquid level data, rotational speed data, torque data, gas-liquid discharge composition data, voltage data, and current data.
10. The smart vessel monitoring system based on active perception according to claim 8, wherein processing decisions based on the vessel operation state evaluation prediction results include:
carrying out acousto-optic early warning on abnormal conditions in advance according to the estimation and prediction result of the running state of the ship;
and feeding back early warning information to turbine operators on duty.
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