CN117032008B - Remote monitoring method and system for ocean deepwater jacket - Google Patents
Remote monitoring method and system for ocean deepwater jacket Download PDFInfo
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Abstract
The application relates to the technical field of jackets, and provides a remote monitoring method and a remote monitoring system for a marine deep water jacket, wherein a plurality of sensor devices are arranged on the marine deep water jacket, each sensor device comprises a sensor body, a processing module and a communication module, and the method comprises the following steps: detecting whether sensor data to be sent to a management platform deployed with a digital twin model of the ocean deepwater jacket is real or not through a processing module; deleting in case of unreality; under the real condition, the data is sent to the management platform for data analysis, so that the authenticity of the monitoring data is ensured, the accuracy of the analysis result of the deepwater jacket digital twin model is ensured, the normal operation of the deepwater jacket digital twin model is ensured, and the production safety accidents caused by the unauthorism of the monitoring data and the incorrect analysis result are avoided.
Description
Technical Field
The application relates to the technical field of jackets, in particular to a remote monitoring method and a remote monitoring system for a marine deep water jacket.
Background
The deep-water jacket is a device capable of providing conditions for offshore construction of a platform, and is a space frame formed by welding a plurality of vertical upright posts (round steel pipes) and transverse and oblique connecting steel pipes. The main characteristics are high, big, heavy, complex structure and high manufacturing standard. In order to ensure the safety of offshore operations, the health state of the deepwater jacket needs to be monitored. The traditional method is that the health state of the deepwater jacket is judged in a manual detection mode, and when the deepwater jacket is detected to be in an unhealthy state, the deepwater jacket is maintained. However, the manual detection mode has the problem that the fault of the deep water jacket cannot be timely monitored, and misjudgment is easy to occur. Since the operation is performed at sea, there is a certain risk to the inspector. In order to solve the problems existing in manual detection, some students propose to construct a conduit architecture comprehensive monitoring digital twin system by utilizing a digital twin technology, online real-time continuous monitoring of a jacket structure can be realized through the digital twin system, structural safety early warning is realized through monitoring data, and safety evaluation and risk management and control based on field data are realized. However, if the monitoring data is not real, the integrated monitoring digital twin system for the conduit architecture and the analysis result are inaccurate, the failure problem of the conduit architecture may not be found in time, so that serious production accidents are caused, and therefore, how to ensure the authenticity of the monitoring data and how to ensure the accuracy of the analysis result output by the integrated monitoring digital twin system for the integrated conduit architecture is a technical problem to be solved.
Disclosure of Invention
Aiming at the technical problems, the purpose of the application is to provide a remote monitoring method and a remote monitoring system for a marine deepwater jacket, which aim at ensuring the authenticity of monitoring data and the accuracy of an analysis result output by a digital twin system for comprehensive monitoring of the jacket.
In a first aspect, an embodiment of the present application provides a method for remotely monitoring a deep sea jacket, where a plurality of sensor devices are disposed on the deep sea jacket, where the sensor devices include a sensor body, a processing module, and a communication module, and the method includes:
encrypting the data acquired by the sensor body through the processing module, and packaging the encrypted sensor data in a monitoring text; wherein the monitoring text is unencrypted;
detecting whether a device actively acquires the monitoring text or not through the processing module;
if detecting that a device actively acquires the monitoring text, deleting the monitoring text through the processing module and sending the invasion warning information of the sensor device by a hacker to a management platform deployed with an ocean deep water jacket digital twin model through the communication module;
If the fact that the monitoring text is actively acquired by the equipment is not detected, the monitoring text is sent to the management platform through the communication module, and the management platform executes the following steps:
receiving the monitoring text, and acquiring the encrypted sensor data from the monitoring text;
decrypting the encrypted sensor data, inputting the decrypted sensor data into the ocean deepwater jacket digital twin model for data analysis, and sending alarm information to a display module of the management platform when the analysis result does not meet the preset requirement.
Further, the step of encrypting the data collected by the sensor body includes:
acquiring the time of the last intrusion of the sensor device by a hacker as a first time;
subtracting the current time from the first time to obtain a duration;
determining an encryption algorithm corresponding to the time length and identification information of the type of the encryption algorithm according to the corresponding relation between the time length and the encryption algorithm; the shorter the duration is in the corresponding relation between the duration and the encryption algorithm, the higher the security of the corresponding encryption algorithm is;
the identification information of the encryption algorithm type is used for being bound with the monitoring text and transmitted to the management platform;
And encrypting the data acquired by the sensor body by using the encryption algorithm.
Further, the step of decrypting the encrypted sensor data includes:
acquiring identification information of the encryption algorithm type sent by the processing module;
matching the identification information of the encryption algorithm type with the identification information of each encryption algorithm type in a database; the database records the corresponding relation between the encryption algorithm type identification information and the encryption algorithm name;
and decrypting the encrypted sensor data by using a decryption method corresponding to the matched encryption algorithm.
Further, the method further comprises:
after the management platform receives and stores the monitoring text, the management platform detects whether a device sends a request for acquiring the monitoring text stored in the management platform;
if so, judging whether the IP address of the equipment is the appointed IP address;
if yes, judging that the management platform is not invaded by a hacker;
if not, information that unknown equipment exists to try to acquire sensor data is sent to a display module of the processing platform.
Further, the method further comprises:
Transmitting the monitoring text to a management platform deployed with a jacket structure stress change prediction model constructed based on a deep learning technology;
the management platform performs the following steps:
receiving the monitoring text, and acquiring the encrypted sensor data from the monitoring text;
decrypting the encrypted sensor data, inputting the decrypted sensor data into the jacket structure stress variation prediction model, carrying out regression prediction on the sensor data through the jacket structure stress variation prediction model, and deducing the jacket structure stress variation condition.
Further, before the step of sending the monitoring text to a management platform deployed with a jacket structure stress variation prediction model constructed based on a deep learning technology, the method further comprises:
deploying the jacket structure stress change prediction model on the management platform through deployment equipment;
the step of deploying the jacket structure stress variation prediction model to the management platform through deployment equipment comprises the following steps:
acquiring the data quantity of an original jacket structure stress change prediction model;
the method comprises the steps of obtaining a remaining storage space of the management platform as a first remaining storage space;
Subtracting the data volume from the first residual storage space to obtain a second residual storage space;
judging whether the second residual storage space is larger than a preset storage space or not;
if yes, deploying the original jacket structure stress change prediction model on the management platform;
if not, pruning is carried out on the original jacket structure stress change prediction model according to a preset first target, and a jacket structure stress change prediction model after pruning is obtained;
acquiring the data quantity of the jacket structure stress change prediction model after pruning;
subtracting the data quantity of the jacket structure stress change prediction model after pruning from the first residual storage space to obtain a third residual storage space;
judging whether the third residual storage space is larger than a preset storage space or not;
if yes, deploying the pruned jacket structure stress change prediction model on the management platform;
if not, continuing pruning the jacket structure stress change prediction model after pruning until the data quantity of the model subtracted from the storage space is larger than the preset storage space.
Further, the sensor device is mounted on the deepwater jacket in a cold welding mode.
Further, the sensor device comprises one or more of an acceleration sensor device, a stress meter sensor device, a displacement sensor device, a tilt angle sensor device and a gyroscope sensor device.
Further, the sensor device is a MEMS-based fiber optic sensor device.
In a second aspect, an embodiment of the present application provides a remote monitoring system for an ocean deepwater jacket, including a plurality of sensor devices and a management platform deployed with a digital twin model of the ocean deepwater jacket, where the sensor devices include a sensor body, a processing module and a communication module;
the processing module is configured to:
encrypting the data acquired by the sensor body, and packaging the encrypted sensor data in a monitoring text; wherein the monitoring text is unencrypted;
detecting whether a device actively acquires the monitoring text;
if detecting that a device actively acquires the monitoring text, controlling the communication module to send the hacking warning information of the sensor device to a management platform deployed with a digital twin model of the ocean deepwater jacket;
if the fact that the monitoring text is actively acquired by the equipment is not detected, the communication module is controlled to send the monitoring text to the management platform;
The management platform is configured to:
receiving the monitoring text, and acquiring the encrypted sensor data from the monitoring text;
decrypting the encrypted sensor data, inputting the decrypted sensor data into the ocean deepwater jacket digital twin model for data analysis, and sending alarm information to a display module of the management platform when the analysis result does not meet the preset requirement.
Further, the management platform is further configured to:
after the monitoring text is received and stored by the management platform, detecting whether a device sends a request for acquiring the monitoring text stored in the management platform or not;
if so, judging whether the IP address of the equipment is the appointed IP address;
if yes, judging that the management platform is not invaded by a hacker;
if not, information that unknown equipment exists to try to acquire sensor data is sent to a display module of the processing platform.
According to the embodiment of the application, the processing module of the sensor device is configured to encrypt the data collected by the sensor body, so that the safety of the sensor data can be ensured. The encrypted sensing data is packaged in the monitoring text and is not encrypted, and the monitoring text needs to be acquired firstly if the sensor data is stolen or modified, so that the unencrypted monitoring text is taken as a bait, when a device is detected to actively acquire the monitoring text, the sensor device can be considered to be invaded by a hacker, in the case, the sensor data invaded by the hacker is deleted, namely, the sensor data invaded by the hacker cannot be transmitted to the ocean deep water jacket digital twin model, the influence of the unreal data on the output result (analysis result) of the ocean deep water jacket digital twin model is avoided, the accuracy of the analysis result of the ocean deep water jacket digital twin model is ensured, the normal operation of the ocean deep water jacket digital twin model is ensured, and the production safety accidents caused by the unreality of the monitoring data and the incorrect analysis result are avoided. Meanwhile, the alarm information of the sensor device invaded by a hacker is sent to the management platform, so that a platform manager can make a coping strategy in time. And when the fact that the monitoring text is actively acquired by the equipment is not detected, the monitoring text is sent to the management platform through the communication module, so that the authenticity of the sensor data received by the management platform is ensured. Further, the encrypted sensor data is obtained from the monitoring text through the management platform, the encrypted sensor data is decrypted, the decrypted sensor data is input into the ocean deepwater jacket digital twin model for data analysis, and as the sensor data is real sensor data, the accuracy of an analysis result of the deepwater jacket digital twin model is guaranteed, the normal operation of the ocean deepwater jacket digital twin model is guaranteed, and production safety accidents caused by the unauthentic monitoring data and the incorrect analysis result are avoided.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a remote monitoring method of a marine deep water jacket according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a remote monitoring system for a marine deep water jacket according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, modules, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, modules, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any module and all combination of one or more of the associated listed items.
It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The application relates to jacket and intelligent monitoring technology. For jackets, it is classified according to the water depth, and generally, a jacket with a water depth of less than 60 meters is called a shallow jacket, a jacket with a water depth of more than 100 meters is called a deep jacket, and a jacket with a water depth of between them is called a shallow jacket. The jacket mainly includes major structure and auxiliary structure, and its major structure includes:
catheter leg: the vertical large-diameter circular tube stand column is a main stressed member for bearing and transmitting the load of the platform.
Lacing wire: the tubular coupling members between the conduit legs are also the primary stress members that bear and transfer the load of the platform.
Skirt sleeve: the main structure of the vertical connecting member between the pile and the jacket is the combination of a tubular object and a plate, and the vertical connecting member can transmit the load of the platform to the steel pile. Some steel piles of the jacket are directly driven into the guide pipe legs without skirt sleeves: some jackets have both leg stakes and skirts.
It should be noted that the jacket does not only include the catheter leg, the tendon, the skirt, but also includes other components, and the above components are listed only for easy understanding.
If there is a hacker to deliberately modify the sensor data, the analysis result output by the catheter architecture integrated monitoring digital twin system will be unrealistic and incorrect, if the analysis result output by the catheter architecture integrated monitoring digital twin system is unrealistic and incorrect, this will result in that the operation and maintenance personnel cannot find out a fault problem in time, which may cause serious safety production accidents, and from the viewpoint of avoiding these problems, the embodiment of the present application provides a remote monitoring method for an ocean deep water jacket, on which a plurality of sensor devices are arranged, the sensor devices including a sensor body, a processing module and a communication module, the method including steps S1-S4, as shown in fig. 1:
s1, encrypting the data acquired by the sensor body through the processing module, and packaging the encrypted sensor data in a monitoring text; wherein the monitoring text is unencrypted.
S2, detecting whether a device actively acquires the monitoring text or not through the processing module;
S3, if detecting that a device actively acquires the monitoring text, deleting the monitoring text through the processing module and sending the hacked invasion warning information of the sensor device to a management platform deployed with an ocean deep water jacket digital twin model through the communication module;
s4, if the fact that the equipment actively acquires the monitoring text is not detected, the monitoring text is sent to the management platform through the communication module, and the management platform executes the following steps:
receiving the monitoring text, and acquiring the encrypted sensor data from the monitoring text;
decrypting the encrypted sensor data, inputting the decrypted sensor data into the ocean deepwater jacket digital twin model for data analysis, and sending alarm information to a display module of the management platform when the analysis result does not meet the preset requirement.
In the embodiment of the application, the physical information of the jacket can be obtained by arranging the plurality of sensor devices on the marine deep water jacket, for example, stress strain conditions of the jacket structure can be monitored by arranging the plurality of stress meter sensing devices on the marine deep water jacket, so that stress characteristics of the conduit structure can be analyzed. It should be understood that stress refers to the internal forces that create interactions between parts within an object when the object is deformed by external forces (stress, humidity, temperature field changes, etc.). The internal force per unit area is called stress. For realizing monitoring to obtain a plurality of different types of data, different types of sensor devices can be arranged on the jacket, for example, acceleration data can be obtained by arranging acceleration sensor devices, and inclination angle data can be obtained by arranging inclinometers or gyroscopes. The kind and the layout point of the sensing device are selected according to the actual requirements. For the purpose of transmitting sensor data to the management platform, the communication module generally selects a satellite communication module, i.e. the sensor device is connected to the management platform via a satellite network, because of the few offshore base stations.
As in the above steps S1-S3, the processing module of the sensor device is configured to encrypt the data collected by the sensor body, so that the safety of the sensor data can be ensured. Generally, data collected by the sensor device is directly transmitted to the management platform, without the management platform actively sending a request for obtaining sensor data, by encapsulating the encrypted sensor data in the monitoring text and not encrypting the monitoring text, since the monitoring text needs to be obtained first if the sensor data is stolen or modified, the monitoring text is used as a bait, and when a device is detected to actively obtain the monitoring text, the sensor data can be considered to be invaded by a hacker. It should be noted that, there are various ways to determine whether a device actively acquires the monitoring text, for example, if a reading instruction of the monitoring text (read from the storage module of the sensor device) is sent by the processing module of the sensor device, then it is not considered that there is a device actively acquiring the monitoring text, but the sensor device actively reads out and transmits the sensor data to the management platform. If the reading instruction of the monitoring text is not sent by the processing module of the sensing device but sent by other equipment, it can be judged that one equipment actively acquires the monitoring text. Since the sensor data may be modified by a hacker after the hacking, the authenticity of the sensor data cannot be guaranteed under the condition, in order to ensure that the analysis result of the deep water jacket digital twin model is correct, the normal operation of the marine deep water jacket digital twin model is guaranteed, the sensor data under the condition needs to be filtered out/deleted, and the hacking intrusion warning information of the sensor device is sent to a management platform deployed with the marine deep water jacket digital twin model, so that a platform manager can make a coping strategy in time. It should be noted that, because the sensing device will send the collected data to the management platform in real time, deleting the sensor data at a certain moment only can make the model unable to display the corresponding analysis result at the moment, and because the time interval between two sensor data packets is very short, the normal operation of the model will not be affected. The management platform may be a cluster of large computer devices. Digital twinning technology is an important means for achieving a high degree of fusion of physical and numerical models. Digital twinning is a numerical model of a real physical structure that not only reflects the actual state of the physical structure, but also records the digital lines of the physical structure over time. The digital twin model of the ocean deepwater jacket can be obtained from a third party and also can be built independently. Under the condition of autonomous construction, simulation software can be utilized to realize digital twin of the jacket according to marine deepwater jacket design drawings, environmental monitoring data, jacket structure monitoring data and related performance (such as mechanical performance) algorithms, such as jacket lever strength calculation methods, rigidity calculation methods and fatigue damage calculation methods. The jacket digital twin model can be formed by utilizing the limited measuring point data to uninterruptedly iterate and optimize the model; inverting the state data of all the components: and (3) carrying out multi-working condition structure calculation through a digital twin model, and realizing the inversion of the intensity and the vibration mode of all the components.
And step S4, when the fact that the equipment actively acquires the monitoring text is not detected, the monitoring text is sent to the management platform through the communication module, and therefore the authenticity of the sensor data received by the management platform is guaranteed. Further, the encrypted sensor data is obtained from the monitoring text through the management platform, the encrypted sensor data is decrypted, the decrypted sensor data is input into the ocean deep water jacket digital twin model for data analysis, and the accuracy of the analysis result of the deep water jacket digital twin model is guaranteed because the sensor data is real sensor data, and the normal operation of the ocean deep water jacket digital twin model is guaranteed. The analysis result of the deepwater jacket digital twin model can be jacket lever strength, fatigue analysis condition, rigidity and the like, and the deepwater jacket digital twin model is designed according to actual requirements.
According to the embodiment of the application, the processing module of the sensor device is configured to encrypt the data collected by the sensor body, so that the safety of the sensor data can be ensured. The encrypted sensing data is packaged in the monitoring text and is not encrypted, and the monitoring text needs to be acquired firstly if the sensor data is stolen or modified, so that the unencrypted monitoring text is taken as a bait, when a device is detected to actively acquire the monitoring text, the sensor device can be considered to be invaded by a hacker, in the case, the sensor data invaded by the hacker is deleted, namely, the sensor data invaded by the hacker cannot be transmitted to the ocean deep water jacket digital twin model, the influence of the unreal data on the output result (analysis result) of the ocean deep water jacket digital twin model is avoided, the accuracy of the analysis result of the ocean deep water jacket digital twin model is ensured, the normal operation of the ocean deep water jacket digital twin model is ensured, and the production safety accidents caused by the unreality of the monitoring data and the incorrect analysis result are avoided. Meanwhile, the alarm information of the sensor device invaded by a hacker is sent to the management platform, so that a platform manager can make a coping strategy in time. And when the fact that the monitoring text is actively acquired by the equipment is not detected, the monitoring text is sent to the management platform through the communication module, so that the authenticity of the sensor data received by the management platform is ensured. Further, the encrypted sensor data is obtained from the monitoring text through the management platform, the encrypted sensor data is decrypted, the decrypted sensor data is input into the ocean deepwater jacket digital twin model for data analysis, and as the sensor data is real sensor data, the accuracy of an analysis result of the deepwater jacket digital twin model is guaranteed, the normal operation of the ocean deepwater jacket digital twin model is guaranteed, and production safety accidents caused by the unauthentic monitoring data and the incorrect analysis result are avoided.
In some embodiments, the step of encrypting the data collected by the sensor body includes:
acquiring the time of the last intrusion of the sensor device by a hacker as a first time;
subtracting the current time from the first time to obtain a duration;
determining an encryption algorithm corresponding to the time length and identification information of the type of the encryption algorithm according to the corresponding relation between the time length and the encryption algorithm; the shorter the duration is in the corresponding relation between the duration and the encryption algorithm, the higher the security of the corresponding encryption algorithm is; the identification information of the encryption algorithm type is used for being bound with the monitoring text and transmitted to the management platform;
and encrypting the data acquired by the sensor body by using the encryption algorithm.
In this embodiment of the present application, the correspondence between the duration and the encryption algorithm may be: the first time length corresponds to the first level of encryption algorithm, the second time length corresponds to the second level of encryption algorithm, and the nth time length corresponds to the nth level of encryption algorithm. The first time period is smaller than the second time period, and the second time period is smaller than the nth time period. Wherein the security of the first level of encryption algorithm is higher than the security of the second level of encryption algorithm, which is higher than the security of the nth level of encryption algorithm. Generally, the higher the security of an encryption algorithm, the greater the resource consumption and the slower the operation speed. The sensor device is more frequently invaded by a hacker, so that the security of an encryption algorithm is higher, and the decryption difficulty is higher, so that the data security can be ensured, and the real-time performance of the deepwater jacket digital twin model for receiving the sensor data can be ensured.
In some embodiments, the step of decrypting the encrypted sensor data comprises:
acquiring identification information of the encryption algorithm type sent by the processing module;
matching the identification information of the encryption algorithm type with the identification information of each encryption algorithm type in a database; the database records the corresponding relation between the encryption algorithm type identification information and the encryption algorithm name;
and decrypting the encrypted sensor data by using a decryption method corresponding to the matched encryption algorithm.
In the embodiment of the application, the processing module binds and sends the identification information of the encryption algorithm type and the monitoring text to the management platform, and the corresponding relation between the identification information of the encryption algorithm type and the encryption algorithm name is recorded in the database of the management platform, so that after the management platform receives the identification information of the encryption algorithm type sent by the processing module, the encryption algorithm matched with the identification information of the encryption algorithm type can be found from the database of the management platform, and the encrypted sensor data is decrypted by using a decryption algorithm corresponding to the encryption algorithm.
In some embodiments, the method of remotely monitoring a marine deep water jacket further comprises:
After the management platform receives and stores the monitoring text, the management platform detects whether a device sends a request for acquiring the monitoring text stored in the management platform;
if so, judging whether the IP address of the equipment is the appointed IP address;
if yes, judging that the management platform is not invaded by a hacker;
if not, information that unknown equipment exists to try to acquire sensor data is sent to a display module of the management platform.
According to the method and the device for acquiring the monitoring text, when the fact that the device sends a request for acquiring the monitoring text stored in the management platform and the IP address of the device is not the appointed IP address is detected, information that the device is unknown and tries to acquire sensor data is sent to the display module of the management platform, and therefore platform managers can make coping strategies timely.
In some embodiments, the method of remotely monitoring a marine deep water jacket further comprises:
transmitting the monitoring text to a management platform deployed with a jacket structure stress change prediction model constructed based on a deep learning technology;
the management platform performs the following steps:
receiving the monitoring text, and acquiring the encrypted sensor data from the monitoring text;
Decrypting the encrypted sensor data, inputting the decrypted sensor data into the jacket structure stress variation prediction model, carrying out regression prediction on the sensor data through the jacket structure stress variation prediction model, and deducing the jacket structure stress variation condition.
In this embodiment of the present application, the model for predicting the stress variation of the jacket structure constructed based on the deep learning technology may be a sub-model in the deep-water jacket digital twin model, that is, the deep-water jacket digital twin model is constructed by using a digital twin technology and a machine learning technology (specifically, the deep-water jacket digital twin model is constructed by using a digital twin technology and a machine learning technology), and the deep-water jacket digital twin model may be obtained from a third party or may be constructed independently. The network structure of the model to be trained may be ResNet, shuffleNet, mobileNet, etc., and the loss function may be a cross entropy loss function, a square loss function, a mean square error loss function, etc. The training stop condition may be that the number of training times satisfies a preset number of times, or that the accuracy of the predicted result is greater than a target value.
In some embodiments, before the step of sending the monitoring text to a management platform deployed with a jacket structure stress variation prediction model constructed based on a deep learning technique, the method further comprises:
deploying the jacket structure stress change prediction model on the management platform through deployment equipment;
the step of deploying the jacket structure stress variation prediction model to the management platform through deployment equipment comprises the following steps:
acquiring the data quantity of an original jacket structure stress change prediction model;
the method comprises the steps of obtaining a remaining storage space of the management platform as a first remaining storage space;
subtracting the data volume from the first residual storage space to obtain a second residual storage space;
judging whether the second residual storage space is larger than a preset storage space or not;
if yes, deploying the original jacket structure stress change prediction model on the management platform;
if not, pruning is carried out on the original jacket structure stress change prediction model according to a preset first target, and a jacket structure stress change prediction model after pruning is obtained;
acquiring the data quantity of the jacket structure stress change prediction model after pruning;
Subtracting the data quantity of the jacket structure stress change prediction model after pruning from the first residual storage space to obtain a third residual storage space;
judging whether the third residual storage space is larger than a preset storage space or not;
if yes, deploying the pruned jacket structure stress change prediction model on the management platform;
if not, continuing pruning the jacket structure stress change prediction model after pruning until the data quantity of the model subtracted from the storage space is larger than the preset storage space.
In this embodiment of the present application, the preset storage space may ensure that the management platform may operate quickly. According to the embodiment of the application, whether the space of the management platform is enough for deployment and the management platform can be kept to run quickly is judged before the jacket structure stress variation prediction model is deployed, if not, pruning is carried out on the jacket structure stress variation prediction model according to the target until the data size of the model subtracted from the residual storage space of the management platform is larger than the preset storage space, so that the jacket structure stress variation prediction model can be deployed on the management platform, and the quick running of the management platform is guaranteed. It should be noted that, when the accuracy of the prediction result of the jacket structure stress variation prediction model after pruning cannot reach the target, then the jacket structure stress variation prediction model meeting the target performance may be deployed on the management platform by adding the storage space to the management platform, but the cost is relatively higher due to the addition of hardware in this way.
In some embodiments, the sensor device is mounted on the deep water jacket by means of cold welding.
In this application embodiment, cold welding installs the sensor, and low temperature is little to the parent metal injury, non-deformable, not undercut, and the arc point energy is accurate, and the heat affected zone is very little, and the welding seam is pleasing to the eye smooth and can reduce post treatment.
In some embodiments, the sensor device comprises one or more of an acceleration sensor device, a stress gauge sensor device, a displacement sensor device, a tilt angle sensor device, a gyro sensor device.
In the embodiment of the application, the acceleration data of the jacket can be monitored through the acceleration sensor device, the stress data of the jacket can be monitored through the stress meter sensor device, the displacement data of the jacket can be monitored through the displacement sensor device, and the inclination angle data of the jacket can be monitored through the inclination angle sensor device or the gyroscope sensor device.
In some embodiments, the sensor device is a MEMS-based fiber optic sensor device.
The optical fiber sensor device based on the MEMS (Micro-Electro-Mechanical System) is safe, free from electromagnetic interference, corrosion-resistant, pollution-resistant and lightning-resistant, can be applied under severe conditions such as strong electromagnetic interference, high lightning stroke, inflammability and explosiveness, and the like, and is a chip-level sensor, shock-resistant and vibration-resistant, and has the characteristics of high precision, high stability and high reliability, and is suitable for long-term monitoring, so that the accuracy of data can be ensured and monitoring data can be stably obtained through the optical fiber sensor device based on the MEMS.
As shown in fig. 2, an embodiment of the present application further provides a remote monitoring system for an ocean deepwater jacket, which includes a plurality of sensor devices and a management platform deployed with a digital twin model of the ocean deepwater jacket, wherein the sensor devices include a sensor body, a processing module and a communication module;
the processing module is configured to:
encrypting the data acquired by the sensor body, and packaging the encrypted sensor data in a monitoring text; wherein the monitoring text is unencrypted;
detecting whether a device actively acquires the monitoring text;
if detecting that a device actively acquires the monitoring text, controlling the communication module to send the hacking warning information of the sensor device to a management platform deployed with a digital twin model of the ocean deepwater jacket;
if the fact that the monitoring text is actively acquired by the equipment is not detected, the communication module is controlled to send the monitoring text to the management platform;
the management platform is configured to:
receiving the monitoring text, and acquiring the encrypted sensor data from the monitoring text;
decrypting the encrypted sensor data, inputting the decrypted sensor data into the ocean deepwater jacket digital twin model for data analysis, and sending alarm information to a display module of the management platform when the analysis result does not meet the preset requirement.
In some embodiments, encrypting the data collected by the sensor body includes:
acquiring the time of the last intrusion of the sensor device by a hacker as a first time;
subtracting the current time from the first time to obtain a duration;
determining an encryption algorithm corresponding to the time length and identification information of the type of the encryption algorithm according to the corresponding relation between the time length and the encryption algorithm; the shorter the duration is in the corresponding relation between the duration and the encryption algorithm, the higher the security of the corresponding encryption algorithm is;
the identification information of the encryption algorithm type is used for being bound with the monitoring text and transmitted to the management platform;
and encrypting the data acquired by the sensor body by using the encryption algorithm.
In some embodiments, the decrypting the encrypted sensor data includes:
acquiring identification information of the encryption algorithm type sent by the processing module;
matching the identification information of the encryption algorithm type with the identification information of each encryption algorithm type in a database; the database records the corresponding relation between the encryption algorithm type identification information and the encryption algorithm name;
And decrypting the encrypted sensor data by using a decryption method corresponding to the matched encryption algorithm.
In some embodiments, the management platform is further configured to:
after the management platform receives and stores the monitoring text, the management platform detects whether a device sends a request for acquiring the monitoring text stored in the management platform;
if so, judging whether the IP address of the equipment is the appointed IP address;
if yes, judging that the management platform is not invaded by a hacker;
if not, information that unknown equipment exists to try to acquire sensor data is sent to a display module of the processing platform.
In some embodiments, the processing module is further configured to: transmitting the monitoring text to a management platform deployed with a jacket structure stress change prediction model constructed based on a deep learning technology;
the management platform is further configured to:
receiving the monitoring text, and acquiring the encrypted sensor data from the monitoring text;
decrypting the encrypted sensor data, inputting the decrypted sensor data into the jacket structure stress variation prediction model, carrying out regression prediction on the sensor data through the jacket structure stress variation prediction model, and deducing the jacket structure stress variation condition.
In some embodiments, the remote monitoring system of the marine deep water jacket further comprises a deployment device configured to:
acquiring the data quantity of an original jacket structure stress change prediction model;
the method comprises the steps of obtaining a remaining storage space of the management platform as a first remaining storage space;
subtracting the data volume from the first residual storage space to obtain a second residual storage space;
judging whether the second residual storage space is larger than a preset storage space or not;
if yes, deploying the original jacket structure stress change prediction model on the management platform;
if not, pruning is carried out on the original jacket structure stress change prediction model according to a preset first target, and a jacket structure stress change prediction model after pruning is obtained;
acquiring the data quantity of the jacket structure stress change prediction model after pruning;
subtracting the data quantity of the jacket structure stress change prediction model after pruning from the first residual storage space to obtain a third residual storage space;
judging whether the third residual storage space is larger than a preset storage space or not;
if yes, deploying the pruned jacket structure stress change prediction model on the management platform;
If not, continuing pruning the jacket structure stress change prediction model after pruning until the data quantity of the model subtracted from the storage space is larger than the preset storage space.
In some embodiments, the sensor device is mounted on the deep water jacket by means of cold welding.
In some embodiments, the sensor device comprises one or more of an acceleration sensor device, a stress gauge sensor device, a displacement sensor device, a tilt angle sensor device, a gyro sensor device.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.
Claims (10)
1. The utility model provides a remote monitoring method of ocean deep water jacket, its characterized in that has laid a plurality of sensor devices on the ocean deep water jacket, sensor device includes sensor body, processing module and communication module, the method includes:
encrypting the data acquired by the sensor body through the processing module, and packaging the encrypted sensor data in a monitoring text; wherein the monitoring text is unencrypted;
detecting whether a device actively acquires the monitoring text or not through the processing module;
if detecting that a device actively acquires the monitoring text, deleting the monitoring text through the processing module and sending the invasion warning information of the sensor device by a hacker to a management platform deployed with an ocean deep water jacket digital twin model through the communication module;
If the fact that the monitoring text is actively acquired by the equipment is not detected, the monitoring text is sent to the management platform through the communication module, and the management platform executes the following steps:
receiving the monitoring text, and acquiring the encrypted sensor data from the monitoring text;
decrypting the encrypted sensor data, inputting the decrypted sensor data into the ocean deepwater jacket digital twin model for data analysis, and sending alarm information to a display module of the management platform when the analysis result does not meet the preset requirement.
2. The method for remotely monitoring a marine deep water jacket according to claim 1, wherein the step of encrypting the data collected by the sensor body comprises:
acquiring the time of the last intrusion of the sensor device by a hacker as a first time;
subtracting the current time from the first time to obtain a duration;
determining an encryption algorithm corresponding to the time length and identification information of the type of the encryption algorithm according to the corresponding relation between the time length and the encryption algorithm; the shorter the duration is in the corresponding relation between the duration and the encryption algorithm, the higher the security of the corresponding encryption algorithm is; the identification information of the encryption algorithm type is used for being bound with the monitoring text and transmitted to the management platform;
And encrypting the data acquired by the sensor body by using the encryption algorithm.
3. The method of remotely monitoring a marine deep water jacket of claim 2, wherein the step of decrypting the encrypted sensor data comprises:
acquiring identification information of the encryption algorithm type sent by the processing module;
matching the identification information of the encryption algorithm type with the identification information of each encryption algorithm type in a database; the database records the corresponding relation between the encryption algorithm type identification information and the encryption algorithm name;
and decrypting the encrypted sensor data by using a decryption method corresponding to the matched encryption algorithm.
4. The method of remotely monitoring a marine deep water jacket of claim 1, further comprising:
after the management platform receives and stores the monitoring text, the management platform detects whether a device sends a request for acquiring the monitoring text stored in the management platform;
if so, judging whether the IP address of the equipment is a designated IP address;
if yes, judging that the management platform is not invaded by a hacker;
If not, information that unknown equipment exists to try to acquire sensor data is sent to a display module of the processing platform.
5. The method of remotely monitoring a marine deep water jacket of claim 1, further comprising:
transmitting the monitoring text to a management platform deployed with a jacket structure stress change prediction model constructed based on a deep learning technology;
the management platform performs the following steps:
receiving the monitoring text, and acquiring the encrypted sensor data from the monitoring text;
decrypting the encrypted sensor data, inputting the decrypted sensor data into the jacket structure stress variation prediction model, carrying out regression prediction on the sensor data through the jacket structure stress variation prediction model, and deducing the jacket structure stress variation condition.
6. The method of remotely monitoring a marine deep water jacket according to claim 5, further comprising, prior to the step of transmitting the monitoring text to a management platform deployed with a jacket structure stress variation prediction model constructed based on a deep learning technique:
Deploying the jacket structure stress change prediction model on the management platform through deployment equipment;
the step of deploying the jacket structure stress variation prediction model to the management platform through deployment equipment comprises the following steps:
acquiring the data quantity of an original jacket structure stress change prediction model;
the method comprises the steps of obtaining a remaining storage space of the management platform as a first remaining storage space;
subtracting the data volume from the first residual storage space to obtain a second residual storage space;
judging whether the second residual storage space is larger than a preset storage space or not;
if yes, deploying the original jacket structure stress change prediction model on the management platform;
if not, pruning is carried out on the original jacket structure stress change prediction model according to a preset first target, and a jacket structure stress change prediction model after pruning is obtained;
acquiring the data quantity of the jacket structure stress change prediction model after pruning;
subtracting the data quantity of the jacket structure stress change prediction model after pruning from the first residual storage space to obtain a third residual storage space;
judging whether the third residual storage space is larger than a preset storage space or not;
If yes, deploying the pruned jacket structure stress change prediction model on the management platform;
if not, continuing pruning the jacket structure stress change prediction model after pruning until the data quantity of the model subtracted from the storage space is larger than the preset storage space.
7. The method of remotely monitoring a marine deep water jacket according to claim 1, wherein the sensor device is mounted to the deep water jacket by cold welding.
8. The method of remotely monitoring a marine deep water jacket of claim 1, wherein the sensor device comprises one or more of an acceleration sensor device, a stress gauge sensor device, a displacement sensor device, a tilt angle sensor device, a gyroscopic sensor device.
9. The remote monitoring system for the ocean deepwater jacket is characterized by comprising a plurality of sensor devices and a management platform with an ocean deepwater jacket digital twin model deployed, wherein the sensor devices comprise a sensor body, a processing module and a communication module;
the processing module is configured to:
encrypting the data acquired by the sensor body, and packaging the encrypted sensor data in a monitoring text; wherein the monitoring text is unencrypted;
Detecting whether a device actively acquires the monitoring text;
if detecting that a device actively acquires the monitoring text, controlling the communication module to send the hacking warning information of the sensor device to a management platform deployed with a digital twin model of the ocean deepwater jacket;
if the fact that the monitoring text is actively acquired by the equipment is not detected, the communication module is controlled to send the monitoring text to the management platform;
the management platform is configured to:
receiving the monitoring text, and acquiring the encrypted sensor data from the monitoring text;
decrypting the encrypted sensor data, inputting the decrypted sensor data into the ocean deepwater jacket digital twin model for data analysis, and sending alarm information to a display module of the management platform when the analysis result does not meet the preset requirement.
10. The marine deep water jacket remote monitoring system of claim 9, wherein the management platform is further configured to:
after the monitoring text is received and stored by the management platform, detecting whether a device sends a request for acquiring the monitoring text stored in the management platform or not;
If so, judging whether the IP address of the equipment is a designated IP address;
if yes, judging that the management platform is not invaded by a hacker;
if not, information that unknown equipment exists to try to acquire sensor data is sent to a display module of the processing platform.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107196910A (en) * | 2017-04-18 | 2017-09-22 | 国网山东省电力公司电力科学研究院 | Threat early warning monitoring system, method and the deployment framework analyzed based on big data |
| CN108833425A (en) * | 2018-06-26 | 2018-11-16 | 九江职业技术学院 | A kind of network safety system and method based on big data |
| CN113422779A (en) * | 2021-07-02 | 2021-09-21 | 南京联成科技发展股份有限公司 | Active security defense system based on centralized management and control |
| CN114024740A (en) * | 2021-11-03 | 2022-02-08 | 长春嘉诚信息技术股份有限公司 | Threat trapping method based on secret tag bait |
| CN114564763A (en) * | 2022-02-25 | 2022-05-31 | 上海睿恳信息科技有限公司 | Server protection system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10594716B2 (en) * | 2018-01-26 | 2020-03-17 | Connecticut Information Security LLC | System and method for detecting computer network intrusions |
-
2023
- 2023-07-06 CN CN202310826933.5A patent/CN117032008B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107196910A (en) * | 2017-04-18 | 2017-09-22 | 国网山东省电力公司电力科学研究院 | Threat early warning monitoring system, method and the deployment framework analyzed based on big data |
| CN108833425A (en) * | 2018-06-26 | 2018-11-16 | 九江职业技术学院 | A kind of network safety system and method based on big data |
| CN113422779A (en) * | 2021-07-02 | 2021-09-21 | 南京联成科技发展股份有限公司 | Active security defense system based on centralized management and control |
| CN114024740A (en) * | 2021-11-03 | 2022-02-08 | 长春嘉诚信息技术股份有限公司 | Threat trapping method based on secret tag bait |
| CN114564763A (en) * | 2022-02-25 | 2022-05-31 | 上海睿恳信息科技有限公司 | Server protection system |
Non-Patent Citations (1)
| Title |
|---|
| 网络诱捕式入侵防御模型的设计;陈凌, 黄皓;计算机应用;20050928(09);全文 * |
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