Offshore oil platform equipment monitoring method, device, equipment and medium

Abstract

The invention relates to an offshore oil platform equipment monitoring method and device, equipment and a medium, and is applied to the technical field of offshore equipment monitoring, and the method comprises the steps: obtaining historical monitoring information, and working data, monitoring data, image data and sound data of the equipment; the historical monitoring information is analyzed, fault parameter association is determined, and the fault parameter association is association between a parameter combination and a fault; comprehensively analyzing the monitoring data, the image data and the sound data based on the fault parameter association and the working data to obtain fault information; and determining an equipment maintenance strategy based on the fault information. The method has the effect of improving the monitoring efficiency and reliability of the offshore equipment.

Description

Technical Field

[0001] This application relates to the technical field of offshore equipment monitoring, and in particular to a method, apparatus, equipment and medium for monitoring equipment on offshore oil platforms. Background Technology

[0002] In the field of offshore oil exploration, offshore oil platform equipment is of paramount importance. As marine resource development deepens, offshore oil platforms are expanding in scale, with increasing numbers and more complex functions of equipment. The proper functioning of this equipment directly impacts oil extraction efficiency, production output, and operational safety. Equipment malfunctions can lead to production disruptions, causing significant economic losses, and even triggering safety accidents, while also negatively impacting the marine environment. Therefore, efficient and accurate monitoring of the status of offshore oil platform equipment is a crucial element in ensuring the smooth operation of offshore oil exploration.

[0003] In traditional offshore oil platform equipment monitoring, a common method is periodic manual inspection. Workers conduct on-site checks at predetermined intervals, inspecting the equipment for external damage, loose connections, and other issues, while also measuring key parameters using simple tools. Another approach involves installing single-type sensors, such as pressure or temperature sensors, to monitor specific equipment parameters in real time, issuing alarms when these parameters exceed preset ranges. However, both methods struggle to comprehensively analyze fault conditions, resulting in low efficiency and reliability in equipment monitoring. Summary of the Invention

[0004] To improve the efficiency and reliability of offshore equipment monitoring, this application provides a method, apparatus, equipment, and medium for monitoring offshore oil platform equipment.

[0005] Firstly, this application provides a method for monitoring equipment on offshore oil platforms, employing the following technical solution: A method for monitoring equipment on an offshore oil platform, comprising: Acquire historical monitoring information, equipment operating data, monitoring data, image data, and audio data; The historical monitoring information is analyzed to determine the correlation of fault parameters, which is the association between parameter combinations and faults; Based on the correlation of the fault parameters and the working data, the monitoring data, the image data, and the sound data are comprehensively analyzed to obtain fault information; Based on the fault information, a device maintenance strategy is determined.

[0006] By adopting the above technical solutions and collecting various types of data, the comprehensiveness of data collection is improved, providing a solid foundation for subsequent fault analysis. By analyzing historical monitoring information, the correlation of fault parameters is determined, thereby enabling rapid identification of fault information based on abnormal parameter combinations. By comprehensively analyzing various data through fault parameter correlation and working data, the reliability of fault information and the efficiency of fault information identification are improved, thus enhancing the efficiency and reliability of marine equipment monitoring.

[0007] Optionally, the step of analyzing the historical monitoring information to determine the correlation of fault parameters includes: The historical monitoring information is divided according to the combination of abnormal parameters to obtain multiple combinations of historical information; Count the first number of historical monitoring information entries with faults in each of the historical information combinations and the second number of all historical monitoring information entries in each of the historical information combinations; The failure ratio is calculated based on the first quantity and the second quantity; If the fault ratio is greater than the preset fault ratio, then the fault parameter association is determined based on the abnormal parameter combination.

[0008] By adopting the above technical solution, historical monitoring information is divided into multiple historical information combinations through abnormal parameter combinations. The fault ratio in each historical information combination is calculated to determine the fault parameter correlation, thereby improving the reliability of fault parameter correlation.

[0009] Optionally, the step of comprehensively analyzing the monitoring data, image data, and sound data based on the fault parameter correlation and the working data to obtain fault information includes: The working intensity of the equipment and environmental data are determined based on the aforementioned working data; A data threshold is determined based on the workload and the environmental data; Based on the data threshold, the monitoring data, the image data, and the sound data are analyzed to determine the first abnormal information; Historical monitoring parameters are determined based on the aforementioned historical monitoring information; Based on the historical monitoring parameters, trend change analysis is performed on the monitoring data, the image data, and the sound data to determine the second anomaly information; Based on the first abnormal information and the second abnormal information, parameter abnormal information is determined; Fault information is determined based on the abnormal parameter information and the correlation of the fault parameters.

[0010] By adopting the above technical solution, when determining abnormal parameter information, not only is the abnormality judgment based on the data threshold, but also the trend change of each type of data is taken into account, which improves the reliability of abnormal parameter information and thus improves the reliability of fault information.

[0011] Optionally, determining the parameter anomaly information based on the first anomaly information and the second anomaly information includes: Parameters are found to be abnormally correlated; Based on the parameter anomaly correlation, error monitoring anomaly information is determined in the current anomaly information, and the current anomaly information includes the first anomaly information and the second anomaly information; The current abnormal information other than the error monitoring abnormal information is determined as the parameter abnormal information.

[0012] By adopting the above technical solution, and further judging the first and second abnormal information through parameter anomaly correlation, erroneous monitoring abnormal information caused by data acquisition errors can be screened out, thereby further improving the reliability of parameter anomaly information.

[0013] Optionally, determining the fault information based on the parameter anomaly information and the fault parameter association includes: The abnormal parameter information is associated with the fault parameter for matching to obtain at least one matching result; The fault level is determined based on the matching results and the preset level judgment rules. The fault information is determined based on the matching results and the fault level.

[0014] By adopting the above technical solution, when generating fault information, the fault level is also determined by matching results and preset level judgment rules, so that equipment maintenance can be better carried out according to the fault level.

[0015] Optionally, determining the equipment maintenance strategy based on the fault information includes: The fault level is determined based on the fault information; If the fault level is the first fault level, then the current time is determined as the equipment maintenance time; If the fault level is not the first fault level, then obtain the task level and task completion time of the current task; determine the equipment maintenance time based on the task level and task completion time; The equipment maintenance strategy is determined based on the fault information and the equipment maintenance time.

[0016] By adopting the above technical solution, the equipment maintenance strategy is determined by fully considering the fault level, the task level of the current task, and the task completion time, thereby improving the availability of the equipment maintenance strategy.

[0017] Optionally, the method further includes: Obtain historical fault information; The historical fault information is analyzed to determine the current fault frequency for each fault type; Obtain historical failure frequency, historical lifecycle, historical data acquisition frequency, and current lifecycle; A first correction coefficient is determined based on the historical failure frequency and the current failure frequency; The second correction coefficient is determined based on the historical life cycle and the current life cycle; The current data acquisition frequency is determined based on the first correction coefficient, the second correction coefficient, the fault parameter correlation, and the historical data acquisition frequency. The device is monitored based on the current data acquisition frequency.

[0018] By adopting the above technical solution, and by adjusting the data acquisition frequency of each type of data based on the analysis of failure frequency and equipment life cycle, the efficiency and reliability of data acquisition are improved, thereby improving the efficiency and reliability of marine equipment monitoring.

[0019] Secondly, this application provides a monitoring device for offshore oil platform equipment, which adopts the following technical solution: A monitoring device for offshore oil platform equipment, comprising: The data acquisition module is used to acquire historical monitoring information, equipment operating data, monitoring data, image data, and sound data; The correlation analysis module is used to analyze the historical monitoring information and determine the correlation of fault parameters, wherein the correlation of fault parameters is the relationship between parameter combinations and faults; The fault determination module is used to perform comprehensive analysis on the monitoring data, image data, and sound data based on the fault parameter correlation and the working data to obtain fault information; The equipment maintenance module is used to determine the equipment maintenance strategy based on the fault information.

[0020] By adopting the above technical solutions and collecting various types of data, the comprehensiveness of data collection is improved, providing a solid foundation for subsequent fault analysis. By analyzing historical monitoring information, the correlation of fault parameters is determined, thereby enabling rapid identification of fault information based on abnormal parameters. By comprehensively analyzing various data through fault parameter correlation and working data, the reliability of fault information and the efficiency of fault information identification are improved, thus enhancing the efficiency and reliability of marine equipment monitoring.

[0021] Thirdly, this application provides an electronic device that adopts the following technical solution: An electronic device includes a processor coupled to a memory; The memory stores a computer program that can be loaded by a processor and executed as described in any of the first aspects of the offshore oil platform equipment monitoring method.

[0022] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium storing a computer program capable of being loaded by a processor and executing the offshore oil platform equipment monitoring method according to any one of the first aspects. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a method for monitoring equipment on an offshore oil platform provided in an embodiment of this application.

[0024] Figure 2 This is a structural block diagram of an offshore oil platform equipment monitoring device provided in an embodiment of this application.

[0025] Figure 3 This is a structural block diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0026] The present application will be further described in detail below with reference to the accompanying drawings.

[0027] This application provides a method for monitoring equipment on an offshore oil platform. This method can be executed by an electronic device, which can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, desktop computer, etc., but is not limited to these.

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0030] like Figure 1 As shown, a method for monitoring equipment on an offshore oil platform is described in the following steps (S101-S104): Step S101: Obtain historical monitoring information, equipment operating data, monitoring data, image data, and sound data.

[0031] Historical monitoring information is retrieved from the database. This information includes whether a fault exists, the type of fault when it exists, abnormal parameter combinations, and normal parameter combinations. Abnormal parameter combinations are combinations of parameters that are abnormal in the historical monitoring information, while normal parameter combinations are combinations of parameters that are not abnormal in the historical monitoring information. The parameters include monitoring data, image data, and sound data.

[0032] Retrieve device operating data from the database. This operating data includes, but is not limited to, the device's operating intensity and environmental data.

[0033] Various sensors, high-definition cameras, and sound acquisition devices are installed in multiple locations on the device. The sensors include temperature sensors, pressure sensors, vibration sensors, acceleration sensors, humidity sensors, and flow sensors. Monitoring data is obtained from various sensors, image data is obtained from high-definition cameras, and sound data is obtained from sound acquisition devices.

[0034] Step S102: Analyze historical monitoring information to determine the correlation of fault parameters.

[0035] Among them, fault parameter association is the association between the abnormal occurrence of each parameter in the parameter combination (at least one parameter) and the corresponding fault type. For example, the simultaneous occurrence of abnormality of parameter A, parameter B and parameter C indicates that a fault of type x has occurred.

[0036] Specifically, the historical monitoring information is analyzed to determine the correlation of fault parameters, including: dividing the historical monitoring information according to the combination of abnormal parameters to obtain multiple historical information combinations; counting the first number of historical monitoring information with faults in each historical information combination and the second number of all historical monitoring information in the historical information combination; calculating the fault ratio based on the first number and the second number; if the fault ratio is greater than the preset fault ratio, then the correlation of fault parameters is determined based on the combination of abnormal parameters.

[0037] In this embodiment, historical monitoring information corresponding to the same abnormal parameter combination is divided into a group to obtain multiple historical information combinations. For each historical information combination, the first number of historical monitoring information with faults in the historical information combination and the second number of all historical monitoring information in the historical information combination are counted. The fault ratio = the first number / the second number. If the fault ratio of a historical information combination is greater than the preset fault ratio (preset, not specifically limited here), it means that the abnormal parameter combination corresponding to the historical information combination can be used to determine that the device has a fault. The fault type corresponding to the abnormal parameter combination is the fault type with the most occurrences in the historical information combination. The fault parameter association includes all abnormal parameter combinations corresponding to the first historical information combination and the fault types corresponding to the abnormal parameter combinations. The first historical information combination is the historical information combination with a fault ratio greater than the preset fault ratio.

[0038] Step S103: Based on the correlation of fault parameters and working data, a comprehensive analysis of monitoring data, image data and sound data is performed to obtain fault information.

[0039] Specifically, based on fault parameter correlation and operational data, a comprehensive analysis of monitoring data, image data, and sound data is performed to obtain fault information, including: determining the equipment's operating intensity and environmental data based on operational data; determining data thresholds based on operating intensity and environmental data; analyzing monitoring data, image data, and sound data based on data thresholds to determine the first anomaly; determining historical monitoring parameters based on historical monitoring information; performing trend change analysis on monitoring data, image data, and sound data based on historical monitoring parameters to determine the second anomaly; determining parameter anomaly information based on the first and second anomaly information; and determining fault information based on parameter anomaly information and fault parameter correlation.

[0040] In this embodiment, the working intensity and environmental data of the device are retrieved from the working data. Different working intensities and different environmental data correspond to different anomaly judgment thresholds. Based on the working intensity and environmental data, the data thresholds under the current working intensity and current environmental data are retrieved from the database. There are multiple threshold ranges for the data thresholds, and each threshold range corresponds to an anomaly level or no anomaly. The monitoring data and sound data are compared with the corresponding data thresholds respectively. If the monitoring data or sound data is within the abnormal threshold range, a first anomaly information is generated. The first anomaly information includes abnormal monitoring data and / or abnormal sound data and the corresponding anomaly level. The image data is identified by a preset image recognition model (e.g., a deep learning model), and the recognition result is compared with the threshold range. If the recognition result is within the abnormal threshold range, the first anomaly information also includes abnormal image data and the corresponding anomaly level.

[0041] Historical monitoring parameters are determined from historical monitoring information. These parameters include data from abnormal parameter combinations and normal parameter combinations, specifically historical monitoring data, historical image data, and historical audio data. Data analysis tools (e.g., Python, Excel) are used to analyze the trend changes of the historical monitoring parameters (historical monitoring data and historical audio data) and current monitoring parameters (monitoring data and audio data) for each type of data. A preset image recognition model is used to analyze the trend changes of historical image data and current image data. If a trend change satisfies a preset abnormal trend change, a second abnormality information is generated. This second abnormality information includes the current monitoring parameter (at least one of monitoring data, image data, and audio data) that satisfies the preset abnormal trend change, along with its corresponding abnormality level (the preset abnormal trend change includes the abnormality level). The parameter abnormality information is determined based on the first and second abnormality information. Finally, fault information is determined by associating the parameter abnormality information with fault parameters.

[0042] More specifically, determining parameter anomaly information based on the first anomaly information and the second anomaly information includes: obtaining parameter anomaly associations; determining error monitoring anomaly information in the current anomaly information based on the parameter anomaly associations, wherein the current anomaly information includes the first anomaly information and the second anomaly information; and determining the current anomaly information other than the error monitoring anomaly information as parameter anomaly information.

[0043] In this embodiment, parameter anomaly associations are retrieved from the database. For example, if parameter A is abnormal, parameter B will also be abnormal, meaning that parameter A and parameter B are associated. For each abnormal parameter in the current anomaly information (including the first and second anomaly information), the associated parameter is searched from the parameter anomaly associations. If there is an associated parameter corresponding to an abnormal parameter in the current anomaly information, then there is no monitoring error (data acquisition error) for that abnormal parameter. If there is no associated parameter corresponding to an abnormal parameter in the current anomaly information, then there is a monitoring error (data acquisition error) for that abnormal parameter. The current anomaly information corresponding to that abnormal parameter is determined as error monitoring anomaly information. Current anomaly information other than error monitoring anomaly information is determined as parameter anomaly information.

[0044] Furthermore, determining fault information based on parameter anomaly information and fault parameter association includes: matching parameter anomaly information with fault parameter association to obtain at least one matching result; determining the fault level based on the matching result and a preset level judgment rule; and determining fault information based on the matching result and the fault level.

[0045] In this embodiment, the abnormal parameters in the parameter anomaly information are matched with the parameter combinations in the fault parameter association to obtain at least one matching result. The matching result includes the abnormal parameters in the parameter anomaly information, the anomaly level of each abnormal parameter, and the fault type in the fault parameter association. That is, the matching fault type is searched from the fault parameter association based on the parameter combination (at least one abnormal parameter) in the parameter anomaly information. The preset level judgment rule includes that if a fault type (pre-set, not specifically limited here) exists in all the matching results of a device, then the fault level is the first fault level; otherwise, the highest anomaly level among all matching results is determined as the fault level. The matching result and the fault level are jointly determined as the fault information.

[0046] Step S104: Determine equipment maintenance strategy based on fault information.

[0047] Specifically, determining the equipment maintenance strategy based on fault information includes: determining the fault level based on the fault information; if the fault level is the first fault level, then determining the current time as the equipment maintenance time; if the fault level is not the first fault level, then obtaining the task level and task completion time of the current task; determining the equipment maintenance time based on the task level and task completion time; and determining the equipment maintenance strategy based on the fault information and equipment maintenance time.

[0048] In this embodiment, the fault level is determined from the fault information; if the fault level is the first fault level, it indicates that the equipment fault is relatively serious and requires immediate shutdown for maintenance, and the current time is determined as the equipment maintenance time; if the fault level is not the first fault level, the task level and task completion time of the current task are obtained from the database or from the staff; if the task level is higher than the preset task level (preset, not specifically limited here), it indicates that the task is relatively important and not suitable for immediate shutdown for maintenance, then the interval between the task completion time and the current time is calculated, and if the interval does not exceed the maintenance grace time corresponding to the fault level (different fault levels...), the maintenance level is determined as follows: The maintenance grace period varies depending on the fault level (which can be obtained from the database; the higher the fault level, the shorter the maintenance grace period). Maintenance is performed at the task completion time, which is then designated as the equipment maintenance time. If the interval exceeds the maintenance grace period corresponding to the fault level, the earliest mid-wait time for the current task is obtained from the database or from staff, and the earliest mid-wait time is designated as the equipment maintenance time. If the task level is not higher than the preset task level, the current time is designated as the equipment maintenance time. The equipment maintenance strategy involves sending fault information to maintenance personnel, enabling them to perform equipment maintenance at the designated maintenance time.

[0049] Specifically, the method also includes: acquiring historical fault information; analyzing the historical fault information to determine the current fault frequency for each fault type; acquiring historical fault frequency, historical lifecycle, historical data acquisition frequency, and current lifecycle; determining a first correction coefficient based on the historical fault frequency and the current fault frequency; determining a second correction coefficient based on the historical lifecycle and the current lifecycle; determining the current data acquisition frequency based on the first correction coefficient, the second correction coefficient, fault parameter correlation, and historical data acquisition frequency; and monitoring the equipment based on the current data acquisition frequency.

[0050] In this embodiment, historical fault information is obtained from a database, including fault time and fault type. Statistical analysis of the historical fault information is performed using data analysis tools (e.g., Python) to determine the current fault frequency for each fault type, for example, once per month. The database is also used to obtain the historical fault frequency for each fault type, the historical lifecycle of the device when the data acquisition frequency was last modified, the historical data acquisition frequency for each type of parameter, and the current lifecycle of the device. The database stores the correspondence between historical fault frequency, current fault frequency, and a first correction coefficient, as well as the correspondence between historical lifecycle, current lifecycle, and a second correction coefficient. Based on the historical fault frequency and the current fault frequency... The system retrieves the first correction coefficient from the database and the second correction coefficient from the database based on the historical lifecycle and the current lifecycle. If parameter C corresponds to fault type m in the fault parameter association, then the current data acquisition frequency of parameter C = historical data acquisition frequency of parameter C × first correction coefficient corresponding to fault type m × second correction coefficient. If parameter C corresponds to both fault type m and fault type n in the fault parameter association, then the current data acquisition frequency of parameter C = historical data acquisition frequency of parameter C × maximum first correction coefficient (the maximum value among the first correction coefficients corresponding to fault type m and fault type n) × second correction coefficient. The system monitors the equipment according to the current data acquisition frequency, that is, it acquires various types of data according to the current data acquisition frequency.

[0051] Figure 2 This is a structural block diagram of an offshore oil platform equipment monitoring device 200 provided in an embodiment of this application.

[0052] like Figure 2 As shown, the offshore oil platform equipment monitoring device 200 mainly includes: The data acquisition module 201 is used to acquire historical monitoring information, equipment operating data, monitoring data, image data, and sound data; The correlation analysis module 202 is used to analyze historical monitoring information and determine the correlation of fault parameters. The correlation of fault parameters is the relationship between parameter combinations and faults. The fault determination module 203 is used to perform comprehensive analysis of monitoring data, image data and sound data based on fault parameter correlation and working data to obtain fault information; Equipment maintenance module 204 is used to determine equipment maintenance strategies based on fault information.

[0053] As an optional implementation of this embodiment, the correlation analysis module 202 is specifically used to analyze historical monitoring information and determine fault parameter correlations, including: dividing the historical monitoring information according to abnormal parameter combinations to obtain multiple historical information combinations; counting the first number of historical monitoring information with faults in each historical information combination and the second number of all historical monitoring information in the historical information combination; calculating the fault ratio based on the first number and the second number; if the fault ratio is greater than a preset fault ratio, then determining the fault parameter correlation based on the abnormal parameter combinations.

[0054] As an optional implementation of this embodiment, the fault determination module 203 is specifically used to perform comprehensive analysis of monitoring data, image data, and sound data based on fault parameter correlation and working data to obtain fault information, including: determining the working intensity of the equipment and environmental data based on working data; determining data thresholds based on working intensity and environmental data; analyzing monitoring data, image data, and sound data based on data thresholds to determine first abnormal information; determining historical monitoring parameters based on historical monitoring information; performing trend change analysis on monitoring data, image data, and sound data based on historical monitoring parameters to determine second abnormal information; determining parameter abnormal information based on the first abnormal information and the second abnormal information; and determining fault information based on parameter abnormal information and fault parameter correlation.

[0055] As an optional implementation of this embodiment, the fault determination module 203 is specifically used to determine parameter abnormal information based on the first abnormal information and the second abnormal information, including: obtaining parameter abnormal association; determining error monitoring abnormal information in the current abnormal information based on the parameter abnormal association, wherein the current abnormal information includes the first abnormal information and the second abnormal information; and determining the current abnormal information other than the error monitoring abnormal information as parameter abnormal information.

[0056] As an optional implementation of this embodiment, the fault determination module 203 is specifically used to determine fault information based on parameter anomaly information and fault parameter association, including: matching parameter anomaly information with fault parameter association to obtain at least one matching result; determining the fault level based on the matching result and preset level judgment rules; and determining fault information based on the matching result and fault level.

[0057] As an optional implementation of this embodiment, the equipment maintenance module 204 is specifically used to determine the equipment maintenance strategy based on fault information, including: determining the fault level based on the fault information; if the fault level is the first fault level, then determining the current time as the equipment maintenance time; if the fault level is not the first fault level, then obtaining the task level and task completion time of the current task; determining the equipment maintenance time based on the task level and task completion time; and determining the equipment maintenance strategy based on the fault information and equipment maintenance time.

[0058] As an optional implementation of this embodiment, the offshore oil platform equipment monitoring device 200 is further specifically used for: acquiring historical fault information; analyzing the historical fault information to determine the current fault frequency of each fault type; acquiring historical fault frequency, historical lifecycle, historical data acquisition frequency, and current lifecycle; determining a first correction coefficient based on the historical fault frequency and the current fault frequency; determining a second correction coefficient based on the historical lifecycle and the current lifecycle; determining the current data acquisition frequency based on the first correction coefficient, the second correction coefficient, fault parameter correlation, and historical data acquisition frequency; and monitoring the equipment based on the current data acquisition frequency.

[0059] In one example, the module in any of the above devices may be one or more integrated circuits configured to implement the above methods, such as one or more application-specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0060] For example, when modules in a device can be implemented via a processing element scheduler, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor capable of calling programs. Alternatively, these modules can be integrated together as a system-on-a-chip (SOC).

[0061] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the above-described device and module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0062] Figure 3 This is a structural block diagram of an electronic device 300 provided in an embodiment of this application.

[0063] like Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302, and may further include one or more of an information input / output (I / O) interface 303, a communication component 304, and a communication bus 305.

[0064] The processor 301 controls the overall operation of the electronic device 300 to complete all or part of the steps of the aforementioned offshore oil platform equipment monitoring method. The memory 302 stores various types of data to support the operation of the electronic device 300. This data may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as one or more of Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0065] I / O interface 303 provides an interface between processor 301 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 304 is used for wired or wireless communication between electronic device 300 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 304 may include a Wi-Fi component, a Bluetooth component, and an NFC component.

[0066] The electronic device 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the offshore oil platform equipment monitoring method given in the above embodiments.

[0067] The communication bus 305 may include a path for transmitting information between the aforementioned components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0068] Electronic device 300 may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers, and may also be servers.

[0069] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for monitoring equipment on offshore oil platforms.

[0070] The computer-readable storage medium may include various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0071] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0072] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing application concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions claimed in this application.

Claims

1. A method of monitoring equipment on an offshore oil platform, characterized by, The method comprises: acquiring historical monitoring information, working data of equipment, monitoring data, image data and sound data; analyzing the historical monitoring information to determine a fault parameter association, which is an association between a parameter combination and a fault; comprehensively analyzing the monitoring data, the image data and the sound data based on the fault parameter association and the working data to obtain fault information; determining an equipment maintenance strategy based on the fault information.

2. The method of claim 1, wherein, The analyzing the historical monitoring information to determine a fault parameter association comprises: dividing the historical monitoring information according to abnormal parameter combinations to obtain a plurality of historical information combinations; counting a first number of the historical monitoring information that exists in a fault in each of the historical information combinations and a second number of all the historical monitoring information in the historical information combination; calculating a fault ratio based on the first number and the second number; if the fault ratio is greater than a preset fault ratio, determining a fault parameter association based on the abnormal parameter combination.

3. The method of claim 1, wherein, The comprehensively analyzing the monitoring data, the image data and the sound data based on the fault parameter association and the working data to obtain fault information comprises: determining working intensity and environmental data of the equipment based on the working data; determining a data threshold based on the working intensity and the environmental data; analyzing the monitoring data, the image data and the sound data based on the data threshold to determine first abnormal information; determining historical monitoring parameters based on the historical monitoring information; performing trend change analysis on the monitoring data, the image data and the sound data based on the historical monitoring parameters to determine second abnormal information; determining parameter abnormal information based on the first abnormal information and the second abnormal information; determining the fault information based on the parameter abnormal information and the fault parameter association.

4. The method of claim 3, wherein, The determining parameter abnormal information based on the first abnormal information and the second abnormal information comprises: acquiring a parameter abnormal association; determining error monitoring abnormal information in current abnormal information based on the parameter abnormal association, the current abnormal information including the first abnormal information and the second abnormal information; determining the parameter abnormal information as the current abnormal information excluding the error monitoring abnormal information.

5. The method of claim 3, wherein, The determining the fault information based on the parameter abnormal information and the fault parameter association comprises: matching the parameter abnormal information with the fault parameter association to obtain at least one matching result; determining a fault level based on the matching result and a preset level judgment rule; determining the fault information based on the matching result and the fault level.

6. The method of claim 1, wherein, The determining an equipment maintenance strategy based on the fault information comprises: determining a fault level based on the fault information; if the fault level is a first fault level, determining a current time as an equipment maintenance time; if the fault level is not the first fault level, acquiring a task level of a current task and a task completion time; determining an equipment maintenance time based on the task level and the task completion time. Determine the equipment maintenance strategy based on the fault information and the equipment maintenance time.

7. The method of claim 1, wherein, The method further comprises: Obtain historical fault information; Analyze the historical fault information to determine the current fault frequency of each fault type; Obtain historical fault frequency, historical life cycle, historical data acquisition frequency, and current life cycle; Determine a first correction coefficient based on the historical fault frequency and the current fault frequency; Determine a second correction coefficient based on the historical life cycle and the current life cycle; Determine a current data acquisition frequency based on the first correction coefficient, the second correction coefficient, the fault parameter correlation, and the historical data acquisition frequency; Monitor the equipment based on the current data acquisition frequency.

8. An offshore oil platform equipment monitoring device, characterized by Comprise: A data acquisition module for obtaining historical monitoring information, working data of equipment, monitoring data, image data, and sound data; A correlation analysis module for analyzing the historical monitoring information to determine a fault parameter correlation, which is the correlation between a parameter combination and a fault; A fault determination module for comprehensively analyzing the monitoring data, the image data, and the sound data based on the fault parameter correlation and the working data to obtain fault information; An equipment maintenance module for determining an equipment maintenance strategy based on the fault information.

9. An electronic device, comprising: Comprise a processor coupled with a memory; The processor is configured to execute a computer program stored in the memory to enable the electronic device to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Comprise computer programs or instructions that, when executed on a computer, enable the computer to perform the method of any one of claims 1 to 7.

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