CN116027724B - Ship equipment risk monitoring method and system - Google Patents
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Abstract
The embodiment of the specification provides a ship equipment risk monitoring method and system, wherein the method comprises the following steps: acquiring a plurality of pieces of equipment parameter information of target ship equipment, wherein the plurality of pieces of equipment parameter information comprise the plurality of pieces of equipment parameter information of the target ship equipment in a target stage, and the target stage comprises one of a starting stage, an operating stage and a stopping stage; determining, for each of the plurality of device parameter information, a parameter anomaly degree that characterizes an anomaly degree of the device parameter information; determining a parameter weight for each of the plurality of device parameter information for the target phase; and processing the parameter anomaly degree and the corresponding parameter weight of each device parameter information based on a risk degree determination model, and determining the risk degree of the target ship device in the target stage, wherein the risk degree determination model is a machine learning model.
Description
Description of the division
The application is a divisional application filed in China with the application date of 2022, 9, 23, the application number of 202211164198.8 and the name of 'a method, a system, a device and a medium for managing the safe operation of ship equipment'.
Technical Field
The present disclosure relates to the field of security management, and in particular, to a method, a system, a device, and a medium for monitoring risk of a ship device.
Background
Ships are the main vehicles that navigate or berth in water for transportation or operation. Compared with land equipment, the marine ship has the advantages that marine materials are limited during navigation, sea conditions are complex and changeable, and safety management of the marine ship equipment is difficult. The ship equipment is usually operated and managed based on the experience of the crew, and the requirements on the crew are high.
Therefore, there is a need to provide a risk monitoring method and system for ship equipment, which can realize intelligent safety management of the ship equipment.
Disclosure of Invention
One of the embodiments of the present specification provides a risk monitoring method for ship equipment. The method comprises the following steps: acquiring a plurality of pieces of equipment parameter information of target ship equipment, wherein the plurality of pieces of equipment parameter information comprise the plurality of pieces of equipment parameter information of the target ship equipment in a target stage, and the target stage comprises one of a starting stage, an operating stage and a stopping stage; determining, for each of the plurality of device parameter information, a parameter anomaly degree that characterizes an anomaly degree of the device parameter information; determining a parameter weight for each of the plurality of device parameter information for the target phase; and processing the parameter anomaly degree and the corresponding parameter weight of each device parameter information based on a risk degree determination model, and determining the risk degree of the target ship device in the target stage, wherein the risk degree determination model is a machine learning model.
One of the embodiments of the present specification provides a risk monitoring system for ship equipment. The system comprises: the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a plurality of pieces of equipment parameter information of target ship equipment, the plurality of pieces of equipment parameter information comprise a plurality of pieces of equipment parameter information of the target ship equipment in a target stage, and the target stage comprises one of a starting stage, an operating stage and a stopping stage; a first determining module configured to determine, for each of the plurality of device parameter information, a parameter anomaly degree that characterizes an anomaly degree of the device parameter information; and a second determining module configured to determine a parameter weight of each of the plurality of device parameter information of the target phase; and processing the parameter anomaly degree and the corresponding parameter weight of each device parameter information based on a risk degree determination model, and determining the risk degree of the target ship device in the target stage, wherein the risk degree determination model is a machine learning model.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a schematic illustration of an application scenario of a marine vessel facility safety operation management system according to some embodiments of the present description;
FIG. 2 is an exemplary block diagram of a marine vessel facility safety operation management system according to some embodiments of the present disclosure;
FIG. 3 is an exemplary flow chart of a marine vessel facility safety operation management method according to some embodiments of the present disclosure;
FIG. 4 is an exemplary flow chart for determining a risk of a target marine facility at a target stage according to some embodiments of the present disclosure;
FIG. 5 is an exemplary block diagram of a weight determination model and a risk determination model according to some embodiments of the present description;
FIG. 6 is an exemplary flow chart of a marine vessel facility safety operation management method according to further embodiments of the present disclosure;
FIG. 7 is a schematic diagram illustrating determining predicted device operating parameter information according to some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
The terms "a," "an," "the," and/or "the" are not specific to the singular, but may include the plural, unless the context clearly indicates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
Fig. 1 is a schematic view of an application scenario of a marine vessel facility safety operation management system according to some embodiments of the present specification.
In some embodiments, the application scenario 100 of the ship device safe operation management system may include a server 110, a network 120, a database 130, a terminal device 140, and a target ship device 150.
In some embodiments, the application scenario 100 of the marine vessel installation safety operation management system may control the target road by implementing the methods and/or processes disclosed in the present specification. For example, the processing device 112 may obtain a plurality of device parameter information for the target marine device; determining, for each of a plurality of device parameter information, a parameter anomaly degree that characterizes an anomaly degree of the device parameter information; when the parameter anomaly degree is larger than the anomaly degree threshold value, determining the equipment parameter information corresponding to the parameter anomaly degree as abnormal equipment parameter information, and sending out an abnormal alarm corresponding to the abnormal equipment parameter information. For more on device parameter information, parameter anomalies, anomaly alerts see FIG. 3 and its associated description.
Server 110 and terminal device 140 may be connected via network 120, and server 110 may be connected to database 130 via network 120. The server 110 may include a processing device 112, and the processing device 112 may be used to perform the ship device safe operation management method described in some embodiments of the present specification. The network 120 may connect components of the application scenario 100 of the marine plant safety operation management system and/or connect the system with external resource components. Database 130 may be used to store data and/or instructions, for example, the database may store device parameter information, parameter anomalies, and anomaly alerts. Database 130 may be directly connected to server 110 or internal to server 110. Terminal device 140 refers to one or more terminal devices or software. In some embodiments, terminal device 140 may receive the anomaly alert sent by processing device 112 and present it to the user. For example, upon processing the abnormality alert sent by the device 112, the terminal device 140 may send the abnormality alert to the user in the form of voice, text, images, or the like. By way of example, the terminal device 140 may include one or any combination of mobile device 140-1, tablet computer 140-2, laptop computer 140-3, and the like, as well as other input and/or output enabled devices.
The target ship apparatus 150 may be an apparatus that needs to perform a ship apparatus safety operation management. By way of example, the target marine apparatus 150 may include, but is not limited to, a host 150-1, a boiler 150-2, a steering engine 150-3, a hoist 150-4, an anchor, and the like. In some embodiments, the target marine device 150 may have a plurality of device parameter information. For more on the target vessel device 150 and a plurality of device parameter information see fig. 3 and its associated description.
It should be noted that the application scenario is provided for illustrative purposes only and is not intended to limit the scope of the present description. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the present description. For example, the application scenario may also include a storage device. As another example, application scenarios may be implemented on other devices to implement similar or different functionality. However, variations and modifications do not depart from the scope of the present description.
Fig. 2 is an exemplary block diagram of a marine vessel facility safety operation management system according to some embodiments of the present description. As shown in fig. 2, the ship apparatus safety operation management system 200 may include an acquisition module 210, a first determination module 220, and an alarm module 230.
In some embodiments, the obtaining module may be configured to obtain a plurality of device parameter information of a target vessel device, each of the plurality of device parameter information characterizing one type of parameter information of the target vessel device. For specific details regarding the plurality of device parameter information, see fig. 3 and its associated description.
In some embodiments, the first determining module may be configured to determine, for each of the plurality of device parameter information, a parameter anomaly indicative of a degree of anomaly of the device parameter information. For specific details regarding parameter anomaly determination, see FIG. 3 and its associated description.
In some embodiments, the alarm module may be configured to determine, when the parameter anomaly degree is greater than an anomaly degree threshold, device parameter information corresponding to the parameter anomaly degree as abnormal device parameter information, and issue an anomaly alarm corresponding to the abnormal device parameter information. For specific details regarding the issuing of an anomaly alert, see FIG. 3 and its associated description.
In some embodiments, the marine vessel equipment safety operation management system 200 may further comprise a second determination module 240, which may be configured to: determining a parameter weight for each of the plurality of device parameter information for the target phase; and determining the risk degree of the target ship equipment at the target stage based on the parameter anomaly degree of the parameter information of each equipment and the corresponding parameter weight.
In some embodiments, the second determination module may be further to: acquiring historical fault data of the target ship equipment in the target stage; determining at least one fault type of the target ship equipment and the number of faults corresponding to the at least one fault type based on the historical fault data; and determining the parameter weight of each of the plurality of equipment parameter information of the target stage based on the at least one fault type of the target ship equipment and the number of faults corresponding to the at least one fault type. For specific details of the parameter weight determination and the risk determination, refer to fig. 3, fig. 4 and their related descriptions, and are not described herein.
In some embodiments, the marine vessel installation safety operation management system 200 may further comprise a prediction module 250, which may be configured to: determining, for each of the plurality of device start-up parameter information, predicted device operation parameter information of a type corresponding to the device start-up parameter information based on the device start-up parameter information, wherein the predicted device operation parameter information is parameter information of the predicted target ship device in the operation stage; and determining the risk degree of the target ship equipment in the operation stage based on a plurality of pieces of predicted equipment operation parameter information and the plurality of pieces of equipment operation parameter information. For specific details regarding the determination of predictive device operating parameter information, see FIG. 5 and its associated description.
It should be noted that the above description of the system and its modules is for convenience of description only and is not intended to limit the present description to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. For example, the acquisition module 210, the first determination module 220, the alert module 230, the second determination module 240, and the prediction module 250 may be integrated in one module. For another example, each module may share one storage device, or each module may have a respective storage device. Such variations are within the scope of the present description.
Fig. 3 is an exemplary flowchart of a ship apparatus safety operation management method according to some embodiments of the present specification. In some embodiments, the process 300 may be performed by a processing device. As shown in fig. 3, the process 300 may include the steps of:
step 310, obtaining a plurality of device parameter information of the target ship device.
The equipment parameter information may be information of parameters related to the operation of the target ship equipment. In some embodiments, each of the plurality of equipment parameter information characterizes one type of parameter information of the target marine equipment. For example, the device parameter information may include a type of temperature, water level, pressure, etc., and the plurality of device parameter information may be temperature information, water level information, pressure information, respectively. The equipment parameter information may also include personnel operation information related to the target vessel equipment. For example, when the target marine device is a boiler, the plurality of device parameter information may further include ignition information, sweep gas information, cleaning information, and the like.
In some embodiments, the plurality of equipment parameter information may include a plurality of parameter information of the target vessel equipment at the target stage. The target phase may be a certain period of time during which the target vessel equipment is never turned on to run. The target phase may include one of a start phase, a run phase, and a stop phase. The target stage may have parameter information of a corresponding stage, for example, start-up stage corresponding device start-up parameter information, run-up stage corresponding device run parameter information, stop stage corresponding device stop parameter information, where the device start-up parameter information may be device parameter information related to the target ship device at the start-up stage; the operating parameter information may be device parameter information that the target ship device involves at the operating stage; the stopping parameter information may be equipment parameter information that the target ship equipment involves at the stopping phase.
The device parameter information may be a device parameter or a sequence of device parameters. For example, the plant parameter information may be that the temperature of the target vessel plant at the target stage is 75 ℃. When the device parameter information is a device parameter sequence, the device parameter information may include device parameters at a plurality of time points within the target time period. For example, the plant parameter information may be a temperature sequence of the target vessel plant at the target stage (62,75,80,75,76,77).
In some embodiments, the device parameter information may be obtained by monitoring the target marine device via a corresponding variety of sensors. For example, temperature information is obtained by a temperature sensor; acquiring pressure information through a pressure sensor; water level information and the like are acquired by a water level sensor. In some embodiments, device parameter information may also be obtained by manual input.
Step 320, for each of the plurality of device parameter information, determines a parameter anomaly degree that characterizes an anomaly degree of the device parameter information.
The parameter anomaly may be a parameter that characterizes the degree of anomaly of the device parameter information. When certain equipment parameter information is not in the preset threshold range or is against the preset state, the equipment parameter information can be judged to be abnormal, and different parameter abnormal degrees can be allocated to the equipment parameter information according to the abnormal degree of the equipment parameter information so as to represent the abnormal state of the equipment parameter information. The preset threshold range and the preset state can be determined based on manual setting. The parameter anomaly degree may be a value within 10 or 100, and a larger value indicates a higher degree of anomaly of the corresponding device parameter information.
In some embodiments, for each device parameter information, the magnitude of the parameter anomaly may be related to the degree of deviation of the device parameter information from a preset threshold range. The smaller the degree of deviation, the smaller the degree of parameter abnormality of the device parameter information. For example, for the device parameter information "boiler temperature", if the boiler temperature is 800 ℃, and the preset threshold range is 1300 ℃ to 1600 ℃, the parameter anomaly corresponding to the device parameter information may be 80; if the boiler temperature is 1000 ℃, the parameter anomaly corresponding to the equipment parameter information can be 20.
In some embodiments, the parameter anomaly corresponding to a certain device parameter information may be calculated by the formula (1):
wherein Y is the degree of parameter anomaly; c is equipment parameter information; c (C) 0 And the standard equipment parameter information corresponding to the equipment parameter information. The standard equipment parameter information can be determined through factory preset. When the standard equipment parameter information is a range value (such as a temperature interval, a pressure interval and the like), if the equipment parameter information is in the range, the parameter anomaly degree is 0; if the device parameter information is smaller than the range, the standard device parameter information can take the minimum value of the range; if the device parameter information is greater than the above range, the standard device parameter information may take the maximum value of the above range.
And 330, when the parameter anomaly degree is greater than the anomaly degree threshold value, determining the device parameter information corresponding to the parameter anomaly degree as abnormal device parameter information, and sending out an abnormal alarm corresponding to the abnormal device parameter information.
The abnormality threshold may be a threshold that determines whether parameter abnormality requires an alarm. The outlier threshold may be determined by manual setting.
The abnormal equipment parameter information may be equipment parameter information that may affect the normal operation of the target ship equipment. And when the parameter anomaly degree is larger than the anomaly degree threshold value, determining the equipment parameter information corresponding to the parameter anomaly degree as abnormal equipment parameter information.
The abnormal alarm may be an alarm for reminding the user, alerting the user. The anomaly alert may include various forms of speech, vibration, text image, etc. In some embodiments, the processing device may determine the corresponding abnormal alarm according to the type of the abnormal device parameter information and according to a preset correspondence table. For example, the types of the abnormal equipment parameter information are temperature information, pressure information, tightening speed and the like, different abnormal alarms can be corresponding, and when the temperature of the boiler is abnormally high, the corresponding abnormal alarms can be images of the temperature abnormality of the boiler; when the tightening speed of the winch is too high, the corresponding abnormal alarm may be cable vibration. In some embodiments, the processing device may also determine an exception alert for the corresponding stage based on the type of target stage. For example, the abnormal alarm corresponding to the start-up phase may include an alarm to stop the start-up; the abnormal alarms corresponding to the run phase may include an alarm to stop running.
By the ship equipment safety operation management method disclosed by some embodiments of the specification, abnormal risk judgment of a plurality of equipment parameters can be realized, and abnormal equipment parameters with potential safety hazards can be found out; in addition, corresponding abnormal alarms are sent out according to different types of abnormal equipment parameters, so that the identification degree of the user on different abnormal alarms can be improved, and the user can quickly respond to different abnormal alarms.
Fig. 4 is an exemplary flow chart for determining a risk of a target marine device at a target stage according to some embodiments of the present description. In some embodiments, the process 400 may be performed by a processing device. As shown in fig. 4, the process 400 includes the steps of:
step 410 determines a parameter weight for each of a plurality of device parameter information for the target phase.
The parameter weight may be a specific gravity of the parameter anomaly of each device parameter information in the risk degree. The parameter weight may reflect an importance degree of the parameter abnormality of each device parameter information. The greater the parameter weight is, the greater the degree of influence of the corresponding equipment parameter information on the safety risk of the target ship equipment is, and the greater the ratio of the parameter anomaly of the equipment parameter information in the risk degree is.
In some embodiments, the parameter weights may be determined by fitting, machine learning models, or the like. In some embodiments, the parameter weights may be determined based on a weight determination model.
In some embodiments, the processing device may obtain historical fault data for the target vessel device at the target stage.
The historical fault data may be data relating to a historical failure of the target marine facility. The historical fault data may include information such as historical device parameter information, number of faults, fault type, severity of the fault, device parameter information corresponding to the fault, and the like, wherein the fault type may include over-high temperature, over-low temperature, over-high pressure, over-low pressure, over-high speed, over-low speed, and the like. In some embodiments, the historical fault data may be retrieved by calling from a storage device, database.
In some embodiments, the processing device may process the device parameter information type of the device parameter information of the target phase and the historical fault data based on a weight determination model, determining a parameter weight for each of the plurality of device parameter information, wherein the weight determination model is a machine learning model.
The weight determination model may be a model for determining parameter weights. The weight determination model may be a machine learning model, e.g., a neural network model, etc. For a detailed description of the weight determination model, see fig. 5 and its associated description.
In some embodiments, the processing device may further determine at least one fault type of the target vessel device and a number of faults corresponding to the at least one fault type based on the historical fault data; and determining the parameter weight of each of the plurality of equipment parameter information of the target stage based on at least one fault type of the target ship equipment and the number of faults corresponding to the at least one fault type.
In some embodiments, the processing device may determine the parameter weight corresponding to the device parameter information based on the fault type and the number of faults in various ways. For example, the processing device may determine, according to a preset rule, a parameter weight corresponding to the device parameter information based on the fault type and the fault number.
Through the parameter weights in some embodiments of the present disclosure, the influence degree of each piece of equipment parameter information on the risk degree can be added in the process of determining the risk degree, so as to improve the matching degree of the risk degree and the actual situation; in addition, the parameter weight is determined based on the weight determination model, so that the intelligent degree of the determination process can be improved, and subjective influence caused by manual determination is avoided.
Step 420, determining the risk degree of the target ship equipment in the target stage based on the parameter anomaly degree of each equipment parameter information and the corresponding parameter weight.
The risk level may be a risk level of potential safety hazards of the target marine equipment. By way of example, the risk level may be a value within 10, 100, with a larger value indicating a higher level of potential safety hazard to the target marine equipment.
In some embodiments, the risk level may be determined by fitting, machine learning models, and the like.
In some embodiments, the risk level may be determined based on a risk level determination model, wherein the risk level determination model may be a neural network model. For a detailed description of the risk determination model, see fig. 5 and its associated description.
In some embodiments, the processing device may further calculate, based on the parameter weight and the parameter anomaly of each of the plurality of device parameter information, by formula (2), a risk of the target ship device at the target stage:
F i ＝k 1 Y 1 +k 2 Y 2 +k 3 Y 3 +...+k n Y n (2)
Wherein F is i The risk degree of the target ship equipment in the target stage i is set; k (k) 1 、k 2 、k 3 、...k n A parameter weight for each of the plurality of device parameter information; y is Y 1 、Y 2 、Y 3 、...Y n Parameter anomaly degree for each of a plurality of device parameter information; n is the number of parameters.
In some embodiments, when the risk of the target marine device is greater than the risk threshold, indicating that the target marine device may be at a safety risk, the processing device may alert the user to an anomaly to allow the user to determine whether to perform further operations. Wherein the further operations may include, but are not limited to, a shutdown operation, and the risk threshold may be determined by manual setting.
It should be noted that the above description of the process 400 is for purposes of illustration and description only, and is not intended to limit the scope of applicability of the present disclosure. Various modifications and changes to flow 400 will be apparent to those skilled in the art in light of the present description. However, such modifications and variations are still within the scope of the present description. For example, the process 400 may also include a preprocessing step.
Fig. 5 is an exemplary structural diagram of the weight determination model and the risk degree determination model according to the present specification.
As shown in fig. 5, the input of the weight determination model 512 may include historical fault data 510 and device parameter information types 511, and the output may include a parameter weight 513 (i.e., parameter weight 1 (513-1), parameter weight 2 (513-2), … … parameter weight n (513-n)) for each of the plurality of device parameter information.
In some embodiments, the model may be determined based on a number of training samples with identifications. Specifically, the training sample with the identification is input into an initial weight determining model, and parameters of the model are determined through training and updating the weight. In some embodiments, the training samples may be historical failure data. In some embodiments, the identification may be a parameter weight. In some embodiments, the manner in which the training samples and identifications are obtained may be manual annotation acquisition. In some embodiments, training may be performed by various methods based on training samples. For example, training may be based on a gradient descent method. In some embodiments, when the preset condition is met, training is ended, and a trained weight determination model is obtained. The preset condition may be that the loss function converges.
As shown in fig. 5, the input of the risk level determination model 515 may include a parameter weight 513 and a parameter anomaly 514 for each of the plurality of device parameter information, and the output may include a risk level 516.
In some embodiments, the risk level determination model may be trained based on a number of training samples with identifications. Specifically, a training sample with a mark is input into an initial risk degree determining model, and parameters of the risk degree determining model are updated through training. In some embodiments, the training samples may be historical parameter weights as well as parameter anomalies. In some embodiments, the identification may be a historical risk level. In some embodiments, the manner in which the training samples and identifications are obtained may be manual annotation acquisition. In some embodiments, training may be performed by various methods based on training samples. For example, training may be based on a gradient descent method. In some embodiments, when the preset condition is satisfied, training is ended, and a trained risk degree determination model is obtained. The preset condition may be that the loss function converges.
In some embodiments, the risk determination model may be trained in conjunction with the weight determination model. In some embodiments, the training samples of the joint training may include historical fault data for sample phases, types of device parameter information, and parameter anomalies, identified as risk for the aforementioned sample phases. Inputting the historical fault data into a weight determining model to obtain the parameter weight of each of the plurality of output equipment parameter information; and taking the parameter weight of each of the plurality of pieces of equipment parameter information as training sample data, and inputting the parameter anomaly degree into a risk degree determining model to obtain an output risk degree. And constructing a loss function based on the identification and the output of the risk degree determination model, and synchronously updating parameters of the risk degree determination model and the weight determination model. And obtaining a trained risk degree determination model and a trained weight determination model through parameter updating.
According to the risk degree determination model disclosed by some embodiments of the specification, intelligent determination of the risk degree of the target ship equipment can be realized, and subjective influence caused by manual determination is avoided; in addition, the risk degree determination model and the weight determination model can be obtained through combined training, the acquisition of the identification parameter weight during the independent training of the weight determination model is avoided, and the model flow is simplified.
Fig. 6 is an exemplary flowchart of a ship apparatus safety operation management method according to other embodiments of the present specification. In some embodiments, the process 600 may be performed by a processing device. As shown in fig. 6, the process 600 includes the steps of:
step 610, acquiring a plurality of device start parameter information and a plurality of device operation parameter information of the target ship device. The device starting parameter information comprises a plurality of parameter information of the target ship device in a starting stage, and the device starting parameter information comprises a plurality of parameter information of the target ship device in an operating stage. See fig. 3 and its associated description for a detailed description of step 610.
Step 620, for each of the plurality of device parameter information, determines a parameter anomaly degree that characterizes an anomaly degree of the device parameter information. See fig. 3 and its associated description for a detailed description of step 620.
Step 630, when the parameter anomaly degree is greater than the anomaly degree threshold value, determining the device parameter information corresponding to the parameter anomaly degree as the anomaly device parameter information, and sending out an anomaly alarm corresponding to the anomaly device parameter information. See fig. 3 and its associated description for a detailed description of step 630.
In step 640, for each of the plurality of device start-up parameter information, the processing device may determine predicted device operational parameter information of a type corresponding to the device start-up parameter information based on the device start-up parameter information.
The predicted equipment operation parameter information may refer to parameter information of the predicted target ship equipment at an operation stage. For example, the plurality of device start-up parameter information may include temperature information of the target ship device at a start-up stage, and the processing device may predict the temperature information of the target ship device at an operation stage based on the aforementioned temperature information of the target ship device at the start-up stage. In some embodiments, the processing device may determine the predictive device operating parameter information by way of a machine learning model, mathematical fit, or the like.
In some embodiments, the processing device may also process the startup parameter vector based on the operating parameter predictive model to determine a plurality of predicted device operating parameter information. Wherein the operating parameter prediction model may be a neural network model.
The inputs to the operating parameter prediction model may include a start-up parameter vector and the outputs may include a plurality of predicted device operating parameter information.
In some embodiments, the operating parameter prediction model may be trained based on a number of identified training samples. Specifically, the training sample with the identification is input into an initial operation parameter prediction model, and the parameters of the operation parameter prediction model are updated through training. In some embodiments, the training samples may be historical starting parameter vectors. In some embodiments, the identification may be device operating parameter information corresponding to the historical boot parameter vector. In some embodiments, the manner in which the training samples and identifications are obtained may be manual annotation acquisition. In some embodiments, training may be performed by various methods based on training samples. For example, training may be based on a gradient descent method. In some embodiments, when the preset condition is met, training is ended, and a trained running parameter prediction model is obtained. The preset condition may be that the loss function converges.
In some embodiments, the processing device may also determine predictive device operating parameter information by vector similarity. For a detailed description of the determination of predicted device operating parameter information by vector similarity, see fig. 7 and its associated description.
Step 650, determining a risk level of the target ship device at the operation stage based on the plurality of predicted device operation parameter information and the plurality of device operation parameter information.
In some embodiments, the processing device may determine a risk level of the target marine device at the operational stage based on the plurality of predicted device operational parameter information and the plurality of device operational parameter information. In some embodiments, the processing device may determine a plurality of predicted device operating parameter information and a degree of deviation between the plurality of device operating parameter information, and determine a risk degree of the target ship device in the operating phase based on the degree of deviation, where the degree of deviation may be a parameter that characterizes a difference between the predicted device operating parameter and the device operating parameter in the ideal state, the greater the degree of deviation, the greater the difference between the predicted device operating parameter and the device operating parameter in the ideal state, and the greater the corresponding risk degree. For example, when the degree of deviation between the plurality of predicted equipment operation parameter information and the plurality of equipment operation parameter information is 80%, the risk of the target ship equipment at the operation stage may be 80. In some embodiments, the plurality of predicted device operating parameter information and the degree of deviation between the plurality of device operating parameter information may be determined by mathematical fitting or the like.
In some embodiments, the processing device may determine, based on the risk degree of the target ship device at the previous stage, the acquisition frequency of the plurality of device parameter information at the current stage through a preset relationship table. For example, the processing device may determine, based on the risk level of the target ship device in the start-up phase, the frequency of acquiring the plurality of device parameter information in the operation phase through the preset relationship table. In some embodiments, the higher the risk degree of the previous stage, the greater the possibility that the fault and the misoperation exist in the target ship equipment are represented, and the higher the risk degree is, so that the acquisition frequency of a plurality of pieces of equipment parameter information in the current stage can be improved to further acquire the change condition of the equipment parameter information. By the process, multi-frequency monitoring of the high-risk target ship equipment can be realized.
FIG. 7 is an exemplary flow chart for determining predicted device operating parameter information according to some embodiments of the present description. In some embodiments, the process 700 may be performed by a processing device. As shown in fig. 7, the flow 700 may include the steps of:
step 710, constructing a boot parameter vector based on the device boot parameter information.
The boot parameter vector may be a feature vector that reflects the characteristics of the device boot parameter information. Each element in the boot parameter vector may correspond to a device boot parameter. For example, when the target ship device is a boiler, the elements in the start-up parameter vector may correspond to device start-up parameters, such as temperature information, pressure information, etc., that characterize the aforementioned boiler at the start-up stage.
Step 720, obtaining historical equipment starting parameter information of the target ship equipment in a starting stage.
The historical equipment starting parameter information can comprise a plurality of sets of historical equipment starting parameter information, and each set of historical equipment starting parameter information comprises a plurality of historical equipment starting parameters of the target ship equipment in a starting stage. For example, when the target ship device is a boiler, the historical device starting parameter information may include historical device starting parameters such as temperature information and pressure information of a plurality of historical starting stages, that is, may include historical device starting parameters such as temperature information and pressure information of each starting stage in the present month.
Step 730, determining a plurality of historical boot parameter vectors based on the historical device boot parameter information.
The historical starting parameter vector may be a feature vector reflecting the features of the historical starting parameter. Each historical boot parameter vector may include a set of historical device parameter information. For example, when the target ship device is a boiler, the elements of the historical starting parameter vector may include the historical device starting parameters such as temperature information and pressure information of a certain historical starting stage, that is, the historical device starting parameters such as temperature information and pressure information of each starting stage in the month may form a plurality of historical starting parameter vectors.
Step 740 determines a similarity between the start-up parameter vector and each of the plurality of historical start-up parameter vectors.
The similarity may be a degree of similarity between vectors. The higher the similarity is, the more similar the actual running conditions of the target ship equipment in the starting stage are, wherein the actual running conditions correspond to the starting parameter vector and the historical starting parameter vector respectively. In some embodiments, the similarity may be determined by calculating a cosine value, euclidean distance, etc. between the start parameter vector and each of the plurality of historical start parameter vectors.
Step 750, determining the historical starting parameter vector with the greatest similarity as the target historical starting parameter vector.
The target historical boot parameter vector may be a feature vector of the plurality of historical device boot parameter information that corresponds to a most similar one of the device boot parameter information. In some embodiments, the magnitudes of the similarities corresponding to each of the historical starting parameter vectors may be compared, and the one with the greatest similarity may be used as the target historical starting parameter vector.
Step 760, determining the plurality of historical equipment operation parameter information of the target historical starting parameter vector in the operation stage as a plurality of predicted equipment operation parameter information of the target ship equipment in the operation stage.
According to the flow for determining the predicted equipment operation parameter information, which is described in some embodiments of the present disclosure, the operation parameter is predicted based on the starting parameter, the risk of the operation stage can be predicted in the equipment starting stage, further risk avoidance is performed based on the predicted risk, and the safety of equipment operation is improved.
One of the embodiments of the present specification provides a ship equipment safety operation management device, including a processor and at least one memory; at least one memory for storing computer instructions; at least one processor is configured to execute at least some of the computer instructions to implement the method.
One of the embodiments of the present specification provides a computer-readable storage medium storing computer instructions that when read by a computer in the storage medium, the computer performs the method.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (6)
1. A method of risk monitoring of marine equipment, the method comprising:
Acquiring a plurality of pieces of equipment parameter information of target ship equipment, wherein the plurality of pieces of equipment parameter information comprise the plurality of pieces of equipment parameter information of the target ship equipment in a target stage, and the target stage comprises one of a starting stage, an operating stage and a stopping stage;
determining, for each of the plurality of device parameter information, a parameter anomaly degree that characterizes an anomaly degree of the device parameter information;
determining a parameter weight for each of said plurality of equipment parameter information for said target stage, said parameter weight characterizing a degree of security risk impact of each of said equipment parameter information on said target vessel equipment, and a degree of importance reflecting said parameter anomaly for each of said equipment parameter information, including,
acquiring historical fault data of the target ship equipment in the target stage;
processing the device parameter information type and the historical fault data of the plurality of device parameter information of the target stage based on a weight determination model, determining a parameter weight for each of the plurality of device parameter information, the weight determination model being a machine learning model, wherein,
the input of the weight determination model comprises historical fault data and equipment parameter information types, and the output comprises parameter weights of each of the plurality of pieces of equipment parameter information;
Processing the parameter anomaly degree and the corresponding parameter weight of each device parameter information based on a risk degree determining model, determining the risk degree of the target ship device in the target stage, wherein the risk degree determining model is a machine learning model, and the risk degree represents the risk degree of potential safety hazards of the target ship device;
the risk degree determining model and the weight determining model are obtained through joint training, a training sample of the joint training comprises historical fault data of a sample stage, equipment parameter information types and parameter anomaly degrees, and the risk degree of the sample stage is identified; the joint training includes:
inputting the historical fault data into the weight determination model to obtain the parameter weight of each of the plurality of output device parameter information;
inputting the parameter weight of each of the plurality of device parameter information as training sample data and the parameter anomaly degree into the risk degree determination model, and outputting the risk degree;
constructing a loss function based on the identification and the output of the risk degree determining model, synchronously updating parameters of the risk degree determining model and the weight determining model, and obtaining the trained risk degree determining model and the trained weight determining model through parameter updating;
Determining the risk level of the target marine device at the operational stage comprises:
acquiring equipment starting parameter information of the target ship equipment in the starting stage and equipment operation parameter information of the target ship equipment in the operation stage;
constructing a starting parameter vector based on the equipment starting parameter information;
processing the starting parameter vector based on an operation parameter prediction model to determine operation parameter information of a plurality of prediction devices, wherein the operation parameter prediction model is a neural network model;
the risk of the target marine device at the operational stage is determined based on the plurality of predicted device operational parameter information and the device operational parameter information.
2. The method of claim 1, wherein the determining the parameter weight for each of the plurality of device parameter information for the target phase comprises:
acquiring historical fault data of the target ship equipment in the target stage;
determining at least one fault type of the target ship equipment and the number of faults corresponding to the at least one fault type based on the historical fault data;
and determining the parameter weight of each of the plurality of equipment parameter information of the target stage based on the at least one fault type of the target ship equipment and the number of faults corresponding to the at least one fault type.
3. The method of claim 2, wherein the historical fault data includes historical device parameter information, number of faults, fault type, fault severity, device parameter information corresponding to the fault.
4. A marine vessel installation risk monitoring system, the system comprising:
an acquisition module for acquiring a plurality of device parameter information of a target vessel device, the plurality of device parameter information including a plurality of device parameter information of the target vessel device at a target stage, the target stage including one of a start stage, an run stage, and a stop stage, and further configured to:
acquiring equipment starting parameter information of the target ship equipment in the starting stage and equipment operation parameter information of the target ship equipment in the operation stage;
a first determining module configured to determine, for each of the plurality of device parameter information, a parameter anomaly degree that characterizes an anomaly degree of the device parameter information;
a second determination module for determining a parameter weight for each of the plurality of equipment parameter information of the target stage, the parameter weight characterizing a degree of security risk impact of each of the equipment parameter information on the target vessel equipment, and a degree of importance reflecting the parameter anomaly of each of the equipment parameter information, and further for,
Acquiring historical fault data of the target ship equipment in the target stage;
processing the device parameter information type and the historical fault data of the plurality of device parameter information of the target stage based on a weight determination model, determining a parameter weight for each of the plurality of device parameter information, the weight determination model being a machine learning model, wherein,
the input of the weight determination model comprises historical fault data and equipment parameter information types, and the output comprises parameter weights of each of the plurality of pieces of equipment parameter information; and
processing the parameter anomaly degree and the corresponding parameter weight of each device parameter information based on a risk degree determining model, determining the risk degree of the target ship device in the target stage, wherein the risk degree determining model is a machine learning model, and the risk degree represents the risk degree of potential safety hazards of the target ship device;
the risk degree determining model and the weight determining model are obtained through joint training, a training sample of the joint training comprises historical fault data of a sample stage, equipment parameter information types and parameter anomaly degrees, the risk degree is identified as the risk degree of the sample stage, and the second determining module is further used for:
Inputting the historical fault data into the weight determination model to obtain the parameter weight of each of the plurality of output device parameter information;
inputting the parameter weight of each of the plurality of device parameter information as training sample data and the parameter anomaly degree into the risk degree determination model, and outputting the risk degree;
constructing a loss function based on the identification and the output of the risk degree determining model, synchronously updating parameters of the risk degree determining model and the weight determining model, and obtaining the trained risk degree determining model and the trained weight determining model through parameter updating;
the system further includes a prediction module for:
constructing a starting parameter vector based on the equipment starting parameter information;
processing the starting parameter vector based on an operation parameter prediction model to determine operation parameter information of a plurality of prediction devices, wherein the operation parameter prediction model is a neural network model;
the risk of the target marine device at the operational stage is determined based on the plurality of predicted device operational parameter information and the device operational parameter information.
5. The system of claim 4, wherein the second determination module is further to:
acquiring historical fault data of the target ship equipment in the target stage;
determining at least one fault type of the target ship equipment and the number of faults corresponding to the at least one fault type based on the historical fault data;
and determining the parameter weight of each of the plurality of equipment parameter information of the target stage based on the at least one fault type of the target ship equipment and the number of faults corresponding to the at least one fault type.
6. The system of claim 5, wherein the historical fault data includes historical device parameter information, number of faults, fault type, fault severity, device parameter information corresponding to the fault.
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| CN115839848B (en) * | 2023-02-22 | 2023-05-05 | 中国船舶集团有限公司第七一九研究所 | Data acquisition and management method for ship diagnosis |
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