CN115237040A - Ship equipment safety operation management method, system, device and medium - Google Patents
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Abstract
The invention provides a method, a system, a device and a medium for managing safe operation of ship equipment, which relate to the field of safety management and comprise the following steps: acquiring a plurality of pieces of equipment parameter information of target ship equipment, wherein each piece of equipment parameter information represents parameter information of one type of the target ship equipment; determining a parameter abnormality degree representing an abnormality degree of the equipment parameter information for each of the plurality of pieces of equipment parameter information; and when the parameter abnormality degree is greater than the abnormality degree threshold value, determining the equipment parameter information corresponding to the parameter abnormality degree as abnormal equipment parameter information, and sending an abnormal alarm corresponding to the abnormal equipment parameter information. The method can be realized by a ship equipment safety operation management system and a device. The method may also be performed after being read by computer instructions stored on a computer-readable storage medium. The method can be used for judging whether the ship equipment is abnormal or not, determining the abnormal degree of the ship equipment, timely reminding a user and improving navigation safety.
Description
Technical Field
The present disclosure relates to the field of safety management, and in particular, to a method, a system, a device, and a medium for managing safe operation of ship equipment.
Background
A ship is a primary vehicle that travels or moors to a body of water for transportation or operation. Compared with equipment on land, the ship has complex and changeable sea surface conditions due to limited marine materials when sailing, and brings difficulty to the safety management of the ship equipment. The operation and management of the ship equipment are generally carried out based on the experience of the crew, and the requirement on the crew is high.
Therefore, it is required to provide a method and a system for managing safe operation of ship equipment, which can realize intelligent safe management of the ship equipment.
Disclosure of Invention
One embodiment of the present specification provides a method for managing safe operation of ship equipment. The ship equipment safe operation management method comprises the following steps: acquiring a plurality of pieces of equipment parameter information of target ship equipment, wherein each piece of the equipment parameter information represents parameter information of one type of the target ship equipment; for each of the plurality of pieces of equipment parameter information, determining a parameter abnormality degree representing the abnormality degree of the equipment parameter information; and when the parameter abnormality degree is greater than an abnormality degree threshold value, determining the equipment parameter information corresponding to the parameter abnormality degree as abnormal equipment parameter information, and sending an abnormal alarm corresponding to the abnormal equipment parameter information.
One of the embodiments of the present specification provides a ship equipment safe operation management system. The ship equipment safe operation management system comprises: the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a plurality of pieces of equipment parameter information of target ship equipment, and each piece of the equipment parameter information represents parameter information of one type of the target ship equipment; a first determination module, configured to determine, for each of the plurality of pieces of device parameter information, a parameter abnormality degree that characterizes an abnormality degree of the device parameter information; and the alarm module is used for determining the equipment parameter information corresponding to the parameter abnormality degree as abnormal equipment parameter information and sending an abnormal alarm corresponding to the abnormal equipment parameter information when the parameter abnormality degree is greater than an abnormality degree threshold value.
One embodiment of the present specification provides a management apparatus for safe operation of ship equipment, including a processor and at least one memory; the at least one memory is for storing computer instructions; the at least one processor is configured to execute at least a portion of the computer instructions to implement the method for managing safe operation of marine equipment.
One of the embodiments of the present specification provides a computer-readable storage medium, where the storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes the management method for safe operation of the ship equipment.
Drawings
The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
FIG. 1 is a schematic diagram of an application scenario of a marine vessel equipment safety operation management system according to some embodiments herein;
FIG. 2 is an exemplary block diagram of a marine vessel equipment safe operation management system according to some embodiments herein;
FIG. 3 is an exemplary flow diagram of a method for managing safe operation of marine vessel equipment, according to some embodiments described herein;
FIG. 4 is an exemplary flow diagram illustrating determining a risk level of a target vessel device at a target stage according to some embodiments of the present description;
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 diagram of a method for managing safe operation of marine vessel equipment, according to further embodiments of the present description;
FIG. 7 is a schematic illustration of determining predicted plant operating parameter information according to some embodiments described herein.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system," "device," "unit," and/or "module" as used herein is a method for distinguishing between different components, elements, parts, portions, or assemblies of different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
The terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
Fig. 1 is a schematic diagram of an application scenario of a ship equipment safety operation management system according to some embodiments of the present specification.
In some embodiments, the application scenario 100 of the ship equipment safety operation management system may include a server 110, a network 120, a database 130, a terminal device 140, and a target ship equipment 150.
In some embodiments, the application scenario 100 of the ship equipment safety operation management system may control the target road by implementing the methods and/or processes disclosed in this specification. For example, the processing device 112 may obtain a plurality of device parameter information for the target ship device; determining a parameter abnormality degree representing an abnormality degree of the equipment parameter information for each of the plurality of pieces of equipment parameter information; and when the parameter abnormality degree is greater than the abnormality degree threshold value, determining the equipment parameter information corresponding to the parameter abnormality degree as abnormal equipment parameter information, and sending an abnormal alarm corresponding to the abnormal equipment parameter information. For more on the device parameter information, parameter abnormality degree, abnormality alarm, see fig. 3 and its associated description.
The server 110 and the terminal device 140 may be connected via a network 120, and the server 110 and the database 130 may be connected via the network 120. The server 110 may include a processing device 112, and the processing device 112 may be configured to perform the ship equipment safety operation management method according to some embodiments of the present description. The network 120 may connect the components of the application scenario 100 of the ship equipment safety operation management system and/or connect the system with external resource components. The database 130 may be used to store data and/or instructions, for example, the database may store device parameter information, parameter abnormality degrees, and abnormality alerts. The database 130 may be directly connected to the server 110 or may be internal to the server 110. Terminal device 140 refers to one or more terminal devices or software. In some embodiments, the end device 140 may receive the exception alert sent by the processing device 112 and present it to the user. For example, when processing the abnormality alarm sent by the device 112, the terminal device 140 may send the abnormality alarm to the user in the form of voice, text, image, or the like. Illustratively, the end device 140 may include one or any combination of mobile devices 140-1, tablet computers 140-2, laptop computers 140-3, and other devices having input and/or output capabilities.
The target ship equipment 150 may be equipment that requires management of safe operation of the ship equipment. Illustratively, the target vessel equipment 150 may include, but is not limited to, a main engine 150-1, a boiler 150-2, a steering engine 150-3, a winch 150-4, an anchor machine, and the like. In some embodiments, the target vessel device 150 may have a plurality of device parameter information. For more on the target marine vessel plant 150 and the multiple plant parameter information, see fig. 3 and its associated description.
It should be noted that the application scenarios are provided for illustrative purposes only and are not intended to limit the scope of the present specification. It will be apparent to those skilled in the art that various modifications and variations can be made in light of the description herein. For example, the application scenario may also include a storage device. Also for example, application scenarios may be implemented on other devices to implement similar or different functionality. However, variations and modifications may be made without departing from the scope of the present description.
Fig. 2 is an exemplary block diagram of a ship equipment safe operation management system according to some embodiments of the present disclosure. As shown in fig. 2, the ship equipment safe 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 the target ship device, each of the plurality of device parameter information characterizing parameter information of one type of the target ship device. For specific details of the multiple device parameter information, see fig. 3 and its associated description.
In some embodiments, the first determination module may be configured to determine, for each of the plurality of device parameter information, a parameter abnormality degree characterizing a degree of abnormality of the device parameter information. For specific details of the parameter abnormality determination, see fig. 3 and its associated description.
In some embodiments, the alarm module may be configured to determine, when the parameter abnormality degree is greater than an abnormality degree threshold, device parameter information corresponding to the parameter abnormality degree as abnormal device parameter information, and issue an abnormality alarm corresponding to the abnormal device parameter information. For specific details of raising an exception alert, see FIG. 3 and its associated description.
In some embodiments, the ship equipment safety operation management system 200 may further include 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 in the target stage based on the parameter abnormality degree of each equipment parameter information 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 fault times corresponding to the at least one fault type based on the historical fault data; determining a 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 vessel 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 level determination, refer to fig. 3 and fig. 4 and their related descriptions, which are not described herein again.
In some embodiments, the marine vessel equipment safety operation management system 200 may further include a prediction module 250, which may be configured to: for each piece of equipment starting parameter information, determining predicted equipment operation parameter information of a type corresponding to the equipment starting parameter information based on the equipment starting parameter information, wherein the predicted equipment operation parameter information is predicted parameter information of the target ship equipment in the operation stage; and determining the risk degree of the target ship equipment in the operation stage based on the plurality of pieces of predicted equipment operation parameter information and the plurality of pieces of equipment operation parameter information. For specific details of the determination of the predicted plant 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 only and should not limit the present disclosure to the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. 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 into one module. For another example, the modules may share one storage device, and each module may have its own storage device. Such variations are within the scope of the present description.
Fig. 3 is an exemplary flow diagram of a method for managing safe operation of marine vessel equipment, according to some embodiments described herein. In some embodiments, flow 300 may be performed by a processing device. As shown in fig. 3, the process 300 may include the following steps:
in step 310, a plurality of device parameter information of the target ship device is obtained.
The equipment parameter information may be information of parameters related to the operation of the target vessel equipment. In some embodiments, each of the plurality of device parameter information characterizes one type of parameter information of the target vessel device. For example, the device parameter information may include types of temperature, water level, pressure, and the like, and the plurality of device parameter information may be temperature information, water level information, and pressure information, respectively. The equipment parameter information may also include personnel operation information related to the target vessel equipment. For example, when the target vessel equipment is a boiler, the plurality of equipment parameter information may further include ignition information, pre-scavenging information, cleaning information, and the like.
In some embodiments, the plurality of device parameter information may include a plurality of parameter information of the target marine device at the target stage. The target phase may be a certain period of time from when the target marine device is never turned on into operation. 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 stage corresponding device run parameter information, and 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 operation parameter information can be equipment parameter information related to the target ship equipment in an operation stage; the stop parameter information may be equipment parameter information that the target vessel equipment is involved in at a stop stage.
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 marine 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 in the target time period. For example, the device parameter information may be (62, 75,80,75,76, 77) for the temperature sequence of the target vessel device at the target stage.
In some embodiments, the device parameter information may be obtained by monitoring the target ship device through a corresponding variety of sensors. For example, temperature information is acquired 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, the device parameter information may also be obtained by manual input.
And step 320, determining a parameter abnormality degree representing the abnormality degree of the equipment parameter information for each of the plurality of pieces of equipment parameter information.
The parameter abnormality degree may be a parameter that characterizes the degree of abnormality of the device parameter information. When some equipment parameter information is not in the preset threshold range or is contrary to the preset state, the equipment parameter information can be judged to be abnormal, and different parameter abnormality degrees can be distributed to the equipment parameter information according to the abnormality 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 abnormality degree may be a value within 10 or 100, and a larger value indicates a higher abnormality degree of the corresponding equipment 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 apparatus parameter information. For example, for the equipment parameter information "boiler temperature", if the boiler temperature is 800 ℃, and the preset threshold range is 1300 ℃ to 1600 ℃, the parameter abnormality degree corresponding to the equipment parameter information may be 80; if the boiler temperature is 1000 ℃, the parameter abnormality degree corresponding to the equipment parameter information may be 20.
In some embodiments, the parameter abnormality degree corresponding to certain device parameter information may be calculated by formula (1):
wherein the content of the first and second substances,
the parameter abnormality degree;
is equipment parameter information;
and the standard equipment parameter information corresponding to the equipment parameter information. The standard equipment parameter information can be determined through factory presetting. When the standard equipment parameter information is a range value (such as a temperature range, a pressure range and the like), if the equipment parameter information is in the range, the parameter abnormality degree is 0; if the equipment parameter information is smaller than the range, the standard equipment parameter information can take the minimum value of the range; if the device parameter information is greater than the range, the standard device parameter information may take the maximum value of the range.
And 330, when the parameter abnormality degree is greater than the abnormality degree threshold value, determining the equipment parameter information corresponding to the parameter abnormality degree as abnormal equipment parameter information, and sending an abnormal alarm corresponding to the abnormal equipment parameter information.
The degree of abnormality threshold may be a threshold that determines whether a parameter degree of abnormality requires an alarm. The degree of abnormality threshold may be determined by manual setting.
The abnormal equipment parameter information may be equipment parameter information that may affect normal operation of the target marine equipment. And when the parameter abnormality degree is greater than the abnormality degree threshold value, determining the equipment parameter information corresponding to the parameter abnormality degree as abnormal equipment parameter information.
The abnormal alarm may be an alarm for reminding the user or warning the user. The abnormal alarm may include various forms of voice, vibration, text image, etc. In some embodiments, the processing device may determine a 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 device parameter information may correspond to different abnormal alarms, such as temperature information, pressure information, tightening speed, and the like, and when the boiler temperature is abnormally high, the corresponding abnormal alarm may be an image of the abnormal boiler temperature; when the tightening speed of the winch is too fast, a corresponding abnormal alarm may be a cable vibration. In some embodiments, the processing device may also determine an exception alert for the corresponding stage according to the type of the target stage. For example, the abnormal alarm corresponding to the starting stage may include an alarm for stopping starting; the abnormal alarm corresponding to the operation stage may include an alarm to stop the operation.
By the ship equipment safety operation management method in some embodiments of the present description, abnormal risk judgment of multiple 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 degree of identifying different abnormal alarms by a user can be improved, and the user can quickly react to different abnormal alarms.
FIG. 4 is an exemplary flow diagram illustrating determining a risk level of a target vessel device at a target stage according to some embodiments of the present description. In some embodiments, flow 400 may be performed by a processing device. As shown in fig. 4, the process 400 includes the following steps:
at step 410, a parameter weight for each of the plurality of device parameter information for the target phase is determined.
The parameter weight may be a proportion of the degree of parameter abnormality of each piece of equipment parameter information in the degree of risk. The parameter weight may reflect the degree of importance of the degree of abnormality of the parameter for each piece of equipment parameter information. The larger the parameter weight is, the larger the influence degree of the corresponding equipment parameter information on the safety risk of the target ship equipment is, and the larger the proportion of the parameter abnormality degree of the equipment parameter information in the risk degree is.
In some embodiments, the parametric 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 failure data for the target vessel device at the target stage.
The historical fault data may be data that is relevant when the target vessel equipment has historically failed. The historical fault data may include historical device parameter information, fault times, fault types, fault severity, device parameter information corresponding to the faults, and the like, where the fault types 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 failure data may be retrieved by calling from a storage device, a 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, determine 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 the weight of a parameter. The weight determination model may be a machine learning model, e.g., a neural network model, or the like. For a detailed description of the weight determination model, refer to fig. 5 and its related description.
In some embodiments, the processing device may further determine at least one fault type of the target ship 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 pieces of equipment parameter information of the target stage based on at least one fault type of the target ship equipment and the fault times 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 in a variety of ways based on the type and the number of faults. For example, the processing device may determine, through a preset rule, a parameter weight corresponding to the device parameter information based on the type and the number of failures.
Through the parameter weight described in some embodiments of the present specification, 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.
And step 420, determining the risk degree of the target ship equipment in the target stage based on the parameter abnormality degree of each equipment parameter information and the corresponding parameter weight.
The risk degree can be the risk degree that the target ship equipment has potential safety hazards. For example, the risk degree may be a value within 10 and 100, and a larger value indicates that the target ship equipment has a higher degree of safety hazard.
In some embodiments, the risk level may be determined by fitting, machine learning models, or the like.
In some embodiments, the risk degree may be determined based on a risk degree determination model, wherein the risk degree determination model may be a neural network model. For a detailed description of the risk determination model, refer to fig. 5 and its related description.
In some embodiments, the processing device may further perform calculation according to formula (2) based on the parameter weight and the parameter abnormality degree of each of the plurality of pieces of device parameter information, to obtain the risk degree of the target ship device at the target stage:
wherein the content of the first and second substances,
at a target stage for a target vessel plant
The risk level of (c);
a parameter weight for each of the plurality of device parameter information;
a parameter abnormality degree for each of the plurality of pieces of equipment parameter information; and n is the number of parameters.
In some embodiments, when the risk degree of the target ship device is greater than the risk degree threshold value, indicating that the target ship device may be at a safety risk, the processing device may issue an exception alarm to the user to enable the user to determine whether to perform further operations. Where further operations may include, but are not limited to, shutdown operations, the risk threshold may be determined by manual settings.
It should be noted that the above description related to the flow 400 is only for illustration and description, and does not limit the applicable scope of the present specification. Various modifications and changes to flow 400 will be apparent to those skilled in the art in light of this description. However, such modifications and variations are still within the scope of the present specification. For example, the process 400 may also include a pre-processing step.
Fig. 5 is an exemplary structural diagram of a weight determination model and a risk determination model according to the present specification.
As shown in FIG. 5, inputs to weight determination model 512 may include historical fault data 510 and device parameter information type 511, and outputs may include parameter weights 513 for each of a plurality of device parameter information (i.e., parameter weight 1 (513-1), parameter weight 2 (513-2), \ 8230; \8230; parameter weight n (513-n)).
In some embodiments, the model may be determined based on a large number of training samples with identifications training weights. Specifically, a training sample with a label is input into an initial weight determination model, and the weight determination model parameters are updated through training. 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 way 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, the training may be based on a gradient descent method. In some embodiments, when the preset condition is met, the training is finished, and a trained weight determination model is obtained. Wherein the preset condition may be a loss function convergence.
As shown in fig. 5, the input of the risk degree determination model 515 may include a parameter weight 513 and a parameter abnormality degree 514 for each of the plurality of device parameter information, and the output may include a risk degree 516.
In some embodiments, the risk determination model may be trained based on a large number of training samples with identifications. Specifically, a training sample with a mark is input into an initial risk degree determination model, and the risk degree determination model parameters are updated through training. In some embodiments, the training samples may be historical parameter weights and parameter degrees of outliers. In some embodiments, the identification may be a historical risk level. In some embodiments, the way training samples and identifications are obtained may be manual annotation acquisition. In some embodiments, training may be performed by various methods based on the training samples. For example, the training may be based on a gradient descent method. In some embodiments, when the preset condition is met, the training is finished, and a trained risk degree determination model is obtained. Wherein the preset condition may be a loss function convergence.
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 the sample phase, the type of equipment parameter information, and the degree of parameter abnormality, identified as the degree of risk for the preceding sample phase. Inputting historical fault data into a weight determination model to obtain the parameter weight of each piece of output equipment parameter information; and taking the parameter weight of each piece of equipment parameter information as training sample data, and inputting the parameter abnormal degree into a risk degree determining model to obtain the output risk degree. And constructing a loss function based on the identifier and the output of the risk degree determination model, and synchronously updating the 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.
Through the risk degree determination model in some embodiments of the specification, the 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 identification parameter weight is prevented from being obtained when the weight determination model is trained independently, and the model process is simplified.
Fig. 6 is an exemplary flow chart of a method for managing safe operation of a marine vessel installation according to further embodiments of the present disclosure. In some embodiments, flow 600 may be performed by a processing device. As shown in fig. 6, the process 600 includes the following steps:
step 610, acquiring equipment starting parameter information and equipment operating parameter information of the target ship equipment. The equipment starting parameter information comprises a plurality of pieces of parameter information of the target ship equipment in a starting stage, and the equipment operation parameter information comprises a plurality of pieces of parameter information of the target ship equipment in an operation stage. For a detailed description of step 610, refer to fig. 3 and its associated description.
And step 620, determining a parameter abnormality degree representing the abnormality degree of the equipment parameter information for each of the plurality of pieces of equipment parameter information. For a detailed description of step 620, refer to fig. 3 and its associated description.
And step 640, determining predicted device operation parameter information of a type corresponding to the device start parameter information based on the device start parameter information for each of the device start parameter information.
The predicted equipment operation parameter information may refer to predicted parameter information of the target ship equipment in an operation stage. For example, the plurality of pieces of equipment start-up parameter information may include temperature information of the target ship equipment in a start-up phase, and the processing equipment may predict the temperature information of the target ship equipment in an operation phase based on the temperature information of the target ship equipment in the start-up phase. In some embodiments, the processing device may determine the predicted device operating parameter information by way of a machine learning model, mathematical fitting, or the like.
In some embodiments, the processing device may further process the startup parameter vector based on an operating parameter prediction 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 startup parameter vectors and the outputs may include a plurality of predicted plant operating parameter information.
In some embodiments, the operating parameter prediction model may be trained based on a number of training samples with identifications. Specifically, a training sample with an identifier is input into an initial operation parameter prediction model, and parameters of the operation parameter prediction model are updated through training. In some embodiments, the training samples may be historical startup parameter vectors. In some embodiments, the identification may be device operational parameter information corresponding to the historical startup parameter vector. In some embodiments, the way training samples and identifications are obtained may be manual annotation obtaining. In some embodiments, training may be performed by various methods based on the training samples. For example, the training may be based on a gradient descent method. In some embodiments, when the preset condition is met, the training is finished, and a trained operation parameter prediction model is obtained. Wherein the preset condition may be a loss function convergence.
In some embodiments, the processing device may also determine the predicted device operating parameter information by vector similarity. For a specific description of determining the information of the operation parameters of the prediction device through the vector similarity, refer to fig. 7 and the related description thereof.
And 650, determining the risk degree of the target ship equipment in the operation stage based on the predicted equipment operation parameter information and the equipment operation parameter information.
In some embodiments, the processing device may determine a risk level of the target marine vessel device during the operational phase 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 pieces of predicted device operation parameter information and a plurality of pieces of device operation parameter information, and determine a risk degree of the target ship device in the operation stage based on the deviation degree, where the deviation degree may be a parameter representing a difference between the predicted device operation parameter and the device operation parameter in an ideal state, and the greater the deviation degree, the greater the difference between the predicted device operation parameter and the device operation parameter in the ideal state, and the greater the corresponding risk degree. For example, when the plurality of pieces of predicted equipment operation parameter information and the plurality of pieces of equipment operation parameter information deviate by 80%, the risk degree of the target ship equipment at the operation stage may be 80. In some embodiments, the plurality of predicted plant operating parameter information and the degree of deviation between the plurality of plant operating parameter information may be determined by mathematical fitting or the like.
In some embodiments, the processing device may determine, through a preset relationship table, an acquisition frequency of the multiple pieces of device parameter information of the current stage based on the risk degree of the target ship device at the previous stage. For example, the processing device may determine, through a preset relationship table, a frequency of acquiring the parameter information of the multiple devices in the operation phase based on the risk level of the target ship device in the start-up phase. In some embodiments, the higher the risk degree of the previous stage is, the higher the possibility that the target ship equipment is characterized to have a fault and have an operational failure is, the higher the risk degree is, so that the frequency of acquiring the multiple pieces of equipment parameter information of the current stage can be increased to further acquire the change condition of the equipment parameter information. Through the process, multi-frequency monitoring for high-risk target ship equipment can be realized.
FIG. 7 is an exemplary flow diagram illustrating the determination of predicted plant operating parameter information according to some embodiments of the present description. In some embodiments, flow 700 may be performed by a processing device. As shown in fig. 7, the process 700 may include the following steps:
step 710, a startup parameter vector is constructed based on the device startup parameter information.
The startup parameter vector may be a feature vector that reflects device startup parameter information characteristics. Each element in the startup parameter vector may correspondingly characterize a device startup parameter. For example, when the target ship equipment is a boiler, the elements in the startup parameter vector may correspond to equipment startup parameters such as temperature information, pressure information, etc. that characterize the boiler in the startup phase.
And 720, acquiring historical equipment starting parameter information of the target ship equipment in the starting stage.
The historical equipment starting parameter information may include multiple sets of historical equipment starting parameter information, and each set of historical equipment starting parameter information includes multiple historical equipment starting parameters of the target ship equipment in one starting stage. For example, when the target ship equipment is a boiler, the historical equipment start parameter information may include historical equipment start parameters such as temperature information and pressure information of a plurality of historical start stages, that is, may include historical equipment start parameters such as temperature information and pressure information of each start stage in the present month.
At step 730, a plurality of historical startup parameter vectors are determined based on historical device startup parameter information.
The historical startup parameter vector may be a feature vector that reflects the characteristics of the historical startup parameter. Each historical startup parameter vector may include a set of historical device parameter information. For example, when the target ship equipment is a boiler, the elements of the historical start-up parameter vector may include historical start-up parameters such as temperature information and pressure information of a certain historical start-up stage, that is, the historical start-up parameters such as temperature information and pressure information of each start-up stage in the month may constitute a plurality of historical start-up parameter vectors.
At step 740, a similarity between the startup parameter vector and each of the plurality of historical startup parameter vectors is determined.
The similarity may be a degree of similarity between vectors. The higher the similarity is, the more similar the actual operating conditions of the target ship equipment at the starting stage respectively corresponding to the characteristic starting parameter vector and the historical starting parameter vector are. In some embodiments, the similarity may be determined by calculating a cosine value, an Euclidean distance, etc., between the startup parameter vector and each of the plurality of historical startup parameter vectors.
And step 750, determining the historical starting parameter vector with the maximum similarity as a target historical starting parameter vector.
The target historical activation parameter vector may be a feature vector corresponding to a most similar one of the plurality of historical device activation parameter information to the device activation parameter information. In some embodiments, the similarity magnitudes corresponding to each historical startup parameter vector may be compared, and the one with the largest similarity may be used as the target historical startup parameter vector.
Step 760, determining a plurality of historical device operation parameter information of the target historical starting parameter vector in the operation stage as a plurality of predicted device operation parameter information of the target ship device in the operation stage.
Through the process of determining the predicted equipment operation parameter information in some embodiments of the description, the operation parameters can be predicted based on the starting parameters, the risk of the operation stage can be predicted in the equipment starting stage, further risk avoidance can be performed based on the predicted risk, and the operation safety of the equipment 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, and when the computer reads the computer instructions from the storage medium, the computer executes the method.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered as illustrative only and not limiting, of the present invention. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the specification. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments described herein. Other variations are also possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.
Claims (10)
1. A ship equipment safe operation management method, characterized by comprising:
acquiring a plurality of pieces of equipment parameter information of target ship equipment, wherein each piece of equipment parameter information represents parameter information of one type of the target ship equipment;
determining, for each of the plurality of pieces of equipment parameter information, a parameter abnormality degree that characterizes an abnormality degree of the equipment parameter information;
and when the parameter abnormality degree is greater than the abnormality degree threshold value, determining the equipment parameter information corresponding to the parameter abnormality degree as abnormal equipment parameter information, and sending an abnormal alarm corresponding to the abnormal equipment parameter information.
2. The marine vessel installation safe-operation management method of claim 1, wherein the plurality of installation parameter information includes a plurality of parameter information of the target marine vessel installation at a target phase, the target phase including one of a start-up phase, a run phase, and a stop phase, the method further comprising:
determining a parameter weight for each of the plurality of device parameter information for the target phase; and
and determining the risk degree of the target ship equipment in the target stage based on the parameter abnormality degree of each equipment parameter information and the corresponding parameter weight.
3. The ship equipment safe-operation management method according to claim 2, wherein the determining of the parameter weight of each of the plurality of pieces of equipment parameter information of the target phase includes:
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 fault times corresponding to the at least one fault type based on the historical fault data;
determining a 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 vessel equipment and the number of faults corresponding to the at least one fault type.
4. The marine vessel equipment safety operation management method according to claim 2, wherein the plurality of pieces of equipment parameter information include equipment start-up parameter information and equipment operation parameter information, and the acquiring the plurality of pieces of equipment parameter information of the target marine vessel equipment includes:
acquiring the equipment starting parameter information and the equipment operating parameter information of the target ship equipment, wherein the equipment starting parameter information comprises a plurality of parameter information of the target ship equipment in the starting stage, and the equipment operating parameter information comprises a plurality of parameter information of the target ship equipment in the operating stage;
the method further comprises the following steps:
for each piece of equipment starting parameter information, determining predicted equipment operation parameter information of a type corresponding to the equipment starting parameter information based on the equipment starting parameter information, wherein the predicted equipment operation parameter information is predicted parameter information of the target ship equipment in the operation stage;
and determining the risk degree of the target ship equipment in the operation stage based on the predicted equipment operation parameter information and the equipment operation parameter information.
5. A ship equipment safe operation management system, characterized by comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a plurality of pieces of equipment parameter information of target ship equipment, and each piece of the equipment parameter information represents parameter information of one type of the target ship equipment;
a first determination module configured to determine, for each of the plurality of pieces of device parameter information, a parameter abnormality degree that characterizes an abnormality degree of the device parameter information; and
and the alarm module is used for determining the equipment parameter information corresponding to the parameter abnormality degree as abnormal equipment parameter information and sending an abnormal alarm corresponding to the abnormal equipment parameter information when the parameter abnormality degree is greater than the abnormality degree threshold value.
6. The marine vessel equipment safe-operation management system of claim 5, wherein the plurality of equipment parameter information includes a plurality of parameter information of the target marine vessel equipment at a target phase, the target phase including one of a start-up phase, a run-up phase, and a stop phase, the system further comprising a second determination module to:
determining a parameter weight for each of the plurality of device parameter information for the target phase; and
and determining the risk degree of the target ship equipment in the target stage based on the parameter abnormality degree of each equipment parameter information and the corresponding parameter weight.
7. The marine vessel equipment safe-operation management system of claim 6, 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 fault times corresponding to the at least one fault type based on the historical fault data;
determining a 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 vessel equipment and the number of faults corresponding to the at least one fault type.
8. The marine vessel equipment safety operation management system of claim 6, wherein the plurality of equipment parameter information includes a plurality of equipment start-up parameter information and a plurality of equipment operation parameter information, and the acquisition module is further configured to:
acquiring the equipment starting parameter information and the equipment operating parameter information of the target ship equipment, wherein the equipment starting parameter information comprises a plurality of parameter information of the target ship equipment in the starting stage, and the equipment operating parameter information comprises a plurality of parameter information of the target ship equipment in the operating stage;
the system further comprises a prediction module to:
for each piece of equipment starting parameter information, determining predicted equipment operation parameter information of a type corresponding to the equipment starting parameter information based on the equipment starting parameter information, wherein the predicted equipment operation parameter information is predicted parameter information of the target ship equipment in the operation stage;
and determining the risk degree of the target ship equipment in the operation stage based on the predicted equipment operation parameter information and the equipment operation parameter information.
9. A marine vessel equipment safety operation management apparatus, comprising at least one processor and at least one memory;
the at least one memory is for storing computer instructions;
the at least one processor is configured to execute at least a portion of the computer instructions to implement the method of managing safe operation of marine vessel equipment as claimed in any one of claims 1 to 4.
10. A computer-readable storage medium, wherein the storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer executes the management method for safe operation of marine vessel equipment according to any one of claims 1 to 4.
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| CN202310037844.2A CN116027724B (en) | 2022-09-23 | 2022-09-23 | Ship equipment risk monitoring method and system |
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