Offshore work platform integrated management system based on cloud computing and big data

Abstract

The invention provides a comprehensive management system for an offshore operation platform based on cloud computing and big data, and the system employs the cloud computing and big data analysis technology for the data of an intelligent terminal of an equipment layer of the offshore operation platform. Comprising the steps of comprehensively displaying, analyzing and processing position information and state information of assets such as personnel and equipment of a maritime satellite launching and recycling work platform and actively pushing alarms, and analyzing data and decisions in real time through cloud big data collection, processing, mining and deduction. And the functions of deep man-machine fusion of the offshore platform and intelligent monitoring, operation and maintenance and management are realized. According to the invention, resources are flexibly allocated on an offshore operation platform through cloud computing, remote monitoring management, data storage and backup and intelligent decision support are realized, and managers are helped to make decisions quickly; data acquisition and integration, data analysis and mining, predictive maintenance and operation optimization are realized through big data, and the operation efficiency and the resource utilization rate are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of platform integrated management, and in particular relates to an integrated management system for offshore operating platforms based on cloud computing and big data. Background Art

[0002] With the rise of long-distance communication and big data collection and analysis technology, the advancement of integrated data management has gradually been recognized. The application of big data technology can transmit the navigation data recorded by the intelligent terminal during the operation of the platform to the system, which is convenient for the management system to grasp the operation status of the platform. Since there are many intelligent terminals on the offshore operation platform, different types of intelligent terminals have different data formats and data attributes when collecting data, and there are many types of interfaces, which brings considerable challenges to the intelligent monitoring, operation and management of the platform. Therefore, designing an effective terminal data intelligent management system has important application value.

[0003] The technical background of cloud computing includes elastic expansion of resources: the workload of offshore operating platforms may fluctuate greatly, and the elastic computing resources provided by cloud computing can quickly adapt to such changes to ensure sufficient computing resources; centralized data management: offshore operating platforms will generate a large amount of data, including equipment status, operation progress, environmental parameters, etc. Cloud computing provides powerful data storage and processing capabilities to facilitate centralized management and analysis of data; remote monitoring and control: through cloud computing technology, remote monitoring and control of offshore operating platforms can be achieved in shore-based centers to improve the safety and efficiency of operations; intelligent decision support: cloud computing platforms can integrate advanced analytical tools and artificial intelligence algorithms to help managers make faster and more accurate decisions.

[0004] The background of big data technology includes data collection and integration: sensors and other data acquisition devices on offshore operating platforms can generate a large amount of data, and big data technology can effectively collect and integrate this data; real-time monitoring and early warning: by analyzing data in real time, the equipment status can be monitored and early warnings can be issued in time to reduce the chance of failure; multi-source data fusion: offshore operating platforms involve various types of equipment and systems, and big data technology can fuse data from these different sources to form a complete information view; artificial intelligence and machine learning: using machine learning algorithms to train historical data can predict the health status of equipment and possible problems in the future; digital twin technology: by building a digital twin model of the offshore operating platform, actual operations can be simulated and the operating process can be optimized; cloud computing and edge computing: combining the powerful processing power of cloud computing and the low latency characteristics of edge computing, real-time data analysis and decision-making can be achieved.

[0005] The management model and data resource statistics method of traditional platforms can no longer meet actual needs. Most of the current platform statistics and management systems can record, query and print the basic information and operation status of the working platform users, but cannot conduct in-depth analysis of the existing platform information data. At the same time, the traditional platform system is easily affected by the current external interference factors during operation.

[0006] For the intelligent management of offshore operation platforms, in order to solve the problem of all-round, multi-granular, intelligent unmanned remote control of personnel, materials, electromechanical equipment and other assets in the offshore satellite launch and recovery operation platform under the conditions of limited resources and complex environment, it is necessary to design an intelligent unmanned comprehensive management system based on big data, monitor the operation of the platform through sensors and cameras, and determine whether there are any operating problems on the platform to complete the operation and maintenance and management work. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide an integrated management system for offshore operating platforms based on cloud computing and big data, which is used for deep integration of man and machine and intelligent monitoring, operation and maintenance and management of offshore platforms.

[0008] The technical solution adopted by the present invention to solve the above-mentioned technical problems is: a comprehensive management system for offshore operating platforms based on cloud computing and big data, including a perception monitoring module, an information processing module and an application service module; the perception monitoring module is used to collect intelligent terminal data of the offshore operating platform; the information processing module is used to encrypt, parse and store the data collected by the perception monitoring module; the application service module receives data through a server and a communication network, and provides users with data collection-driven data display, service management and data management functions.

[0009] According to the above scheme, the perception monitoring module, information processing module and application service module cooperate to form a system architecture including a perception interaction layer, a control processing layer and an application layer. Data is transmitted between layers through the network to monitor the operation process of the offshore operation platform, analyze and judge the operation status, and evaluate and diagnose abnormal status alarms; the perception interaction layer includes sensor positioning devices, video front ends, smart sensors, QR codes and RFID tags, which are used to collect, perceive, monitor and control changes in building environment and status information in real time and uninterruptedly, and summarize the collected data into the database system through the network to form a continuous and traceable dynamic monitoring record, providing a real-time data source for the static data model; the control processing layer is a comprehensive management platform integrating big data, cloud computing and artificial intelligence technologies, which is used to perform real-time analysis, calculation and decision support on the data information transmitted by the perception interaction layer, and send control instructions to the application layer; the application layer includes personnel and digital smart devices equipped with smart wearable devices, which are used to perform tasks according to the instructions sent by the control processing layer, and feed back the real-time status to the perception interaction layer to complete the closed loop of the data flow.

[0010] According to the above scheme, the data collected by the perception monitoring module includes associated data, spatiotemporal data, text data and image data; the perception monitoring module includes a comprehensive display unit, a task management unit, an equipment monitoring unit, a security monitoring unit and a personnel positioning unit; the comprehensive display unit is used to automatically switch the comprehensive display and control page according to different task conditions, and display the important parameters and status under the current task conditions; the task management unit is used to customize the design of monitoring scenarios through the task process arrangement tool, and automatically trigger the designed monitoring interface according to the task process; the equipment monitoring unit is used to monitor the status of electromechanical equipment on the entire platform, the hardware status of IT information equipment, the operating system, the software status, the interface status and the real-time operating status information of the main system equipment; the security monitoring unit is used to obtain the time when the personnel enters the security area according to the data collected by the perception monitoring module, and judge whether the time of personnel entry is within the preset normal time period; when the identity of the personnel or the time of entry does not meet the preset requirements, the alarm information including regional warning, face recognition and intrusion alarm is pushed in real time; the personnel positioning unit is used to display the current personnel position information of each unit in real time on the three-dimensional map of the operation platform, and provide personnel information warnings in relevant areas, including warnings of personnel involvement in dangerous areas and warnings of personnel who have not evacuated safely.

[0011] According to the above scheme, the perception monitoring module is divided into different blocks according to the layout area. The nodes of each block include full nodes and light nodes; full nodes are used to collect and store monitoring data and generate data summaries; light nodes are distributed data units used to store the latest block data, including summary values ​​generated by full nodes.

[0012] According to the above scheme, the information processing module function supports the system to call the data interface, which is used to provide data support for the platform operation and maintenance management; the information processing module eliminates redundant information of the offshore operation platform through data cleaning, including clearing duplicate fields, clearing fields with the same meaning and deleting redundant data; the information processing module is equipped with operators for multi-party range query, neighbor query, status evaluation and diagnosis, multi-party safety summation, data set merging, random sample selection and status comparison.

[0013] Furthermore, the specific steps of the information processing module to securely sum the data to merge similar data from multiple parties during operation are as follows:

[0014] First, each block randomly generates an n-1 degree polynomial and sets the locally stored data as the constant term of the polynomial;

[0015] Each block then substitutes n specific values ​​into the polynomial for calculation;

[0016] Finally, the Lagrange interpolation formula is used to sum the polynomial results calculated by each block; the original data of each block is saved locally during the summation process to ensure data security;

[0017] By comparing the data summation results of multiple blocks with a given value, the specific steps are:

[0018] Assume that the data stored by each party is a i , the given comparison value is b;

[0019] Divide b into n equal parts, let Judgment∑ i c i Is it a positive number?

[0020] Each block generates a random positive number d i , calculated through the secret sharing protocol (∑ i c i )(∑ i d i ), judge the operation status of each block according to the calculation results, if ∑ i c i is a positive number and (∑ i c i )(∑ i d i ) is greater than 3, then each block is considered to be operating normally.

[0021] Furthermore, the specific steps of the information processing module to obtain the union of the data sets of multiple blocks and output the elements in the union are:

[0022] First, each block adds noise data to the local data set through the differential privacy mechanism and sends the noise-added data to other blocks;

[0023] Then each block performs a union operation on the collected data set;

[0024] Finally, each block obtains the final data union by removing the locally added noise data.

[0025] Furthermore, the specific steps of the information processing module using the K-Means clustering algorithm to randomly select samples are as follows:

[0026] First, k samples are randomly selected from the data set as the initial clustering centers;

[0027] Then calculate the distance from each category data set to each cluster center, and assign the sample to the cluster center closest to it;

[0028] Finally, the cluster center of each category is recalculated according to the new sample classification, and the above sample allocation steps are repeated until the classification of all samples is stable and no longer changes.

[0029] Furthermore, the specific steps of the data encryption method adopted by the information processing module are:

[0030] Let U be the data encryption model of ship intelligent terminal; is the offshore platform data in space; μ is the data security index; n and λ are the encrypted data volume and data reflection degree parameters; p is the extracted platform initial terminal data; q is the deep learning space ratio function; the offshore platform data is aggregated into the data space using the deep learning algorithm, and the offshore platform data encryption model is constructed based on the cluster analysis results:

[0031]

[0032] According to the above scheme, the application service module is used to provide services such as comprehensive display, model management and monitoring data management, interact with the display of available equipment and facilities, and together constitute the application layer; the application service module manages the access and call of various facilities, equipment and system application functions on the platform by formulating access control strategies, including generating access control strategies through relevant attributes in the comprehensive management system, and using efficient algorithms to achieve rapid retrieval of strategies; the application service module mines commonly used access control strategies by applying frequent pattern item algorithms, so that each block unit reaches a consensus on attribute induction and strategy formulation; an attribute prefix tree is constructed based on the mined high-frequency strategies to achieve rapid judgment of attribute-based access strategies.

[0033] The beneficial effects of the present invention are:

[0034] 1. The present invention is a cloud computing and big data-based integrated management system for offshore operating platforms. By introducing cloud computing and big data analysis technologies, the intelligent terminal data of the equipment layer of the offshore operating platform, including the location information and status information of the personnel, equipment and other assets of the offshore satellite launch and recovery operating platform, is comprehensively displayed, analyzed and processed, and alarms are actively pushed. Through the collection, processing, mining and deduction of big data in the cloud, real-time data analysis and decision-making are carried out, thus realizing the deep integration of man and machine and the intelligent monitoring, operation and maintenance and management functions of the offshore platform.

[0035] 2. The application of cloud computing adopted by the present invention on offshore operating platforms includes:

[0036] (1) Flexible resource allocation: Utilize the elastic computing power of cloud computing to dynamically allocate computing resources according to the actual needs of offshore operations, ensuring that there is sufficient computing power to support operational tasks during peak hours;

[0037] (2) Remote monitoring and management: The cloud platform can be used to remotely monitor the offshore operation platform, including equipment status, environmental parameters, operation progress, etc. Managers can understand the on-site situation without having to be on site in person;

[0038] (3) Data storage and backup: Cloud computing provides massive data storage capabilities, which can store various types of data generated by offshore operating platforms and provide secure data backup and disaster recovery services;

[0039] (4) Intelligent decision support: Combined with big data analysis and machine learning algorithms, cloud computing can provide intelligent decision support to help managers make decisions quickly, such as job optimization and fault prediction.

[0040] 3. The application of big data of the present invention in offshore operation platforms includes:

[0041] (1) Data collection and integration: Big data technology can collect data from various sensors, equipment and external data sources on offshore platforms and integrate them on a unified platform;

[0042] (2) Data analysis and mining: Through big data analysis technology, valuable information can be extracted from massive data, such as equipment health status, operating efficiency, safety risks, etc.

[0043] (3) Predictive maintenance: Using historical data and machine learning models, we can predict possible problems with equipment, perform maintenance in advance, and reduce unplanned downtime;

[0044] (4) Operation optimization: Analyzing operation data can identify bottlenecks in the operation process, optimize operation plans, and improve operation efficiency and resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a principle framework diagram of an embodiment of the present invention.

[0046] Figure 2 It is a block division composition diagram of the perception monitoring module according to an embodiment of the present invention.

[0047] Figure 3 It is a flow chart of analysis, evaluation and diagnosis of the control processing module of an embodiment of the present invention.

[0048] Figure 4 It is an access control strategy diagram of the application service module of the embodiment of the present invention. DETAILED DESCRIPTION

[0049] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0050] See also Figure 1 The embodiment of the present invention includes a perception monitoring module, an information processing module and an application service module, wherein the perception monitoring module is used to collect data from various intelligent terminals of the offshore operation platform. After the information processing module encrypts, parses, stores and processes the operation data of various devices of the offshore operation platform, it is transmitted to the system application service module through the server and the communication network, providing users with data collection-driven data display, service management and data management functions.

[0051] The integrated management system continues to monitor the status and collect data of various operating equipment on the platform through sensors and cameras in various areas of the offshore operation platform. The perception monitoring module is designed as a perception interaction layer in the system, facing various participating equipment, facilities and personnel on the platform. Multiple interactive nodes collect and store various types of multi-source data, including associated data, spatiotemporal data, text data and image data, such as Figure 1 The perception monitoring module shown includes a comprehensive display unit, a task management unit, an equipment monitoring unit, a security monitoring unit and a personnel positioning unit.

[0052] The comprehensive display unit automatically switches the comprehensive display and control page according to different task conditions, and displays the important parameters and status under the current task conditions; the task management unit customizes the design of monitoring scenarios through the task process arrangement tool, and automatically triggers the designed monitoring interface according to the task process; the equipment monitoring unit monitors the status of electromechanical equipment on the entire platform, the hardware status of IT information equipment, the operating system, the software status, the interface status and the real-time operating status information of the main system equipment; the security monitoring unit obtains the entry time of personnel into the security area based on camera monitoring, and determines whether the entry time of personnel is within the preset normal time period. When the personnel or entry time does not meet the preset requirements, the alarm information is pushed in real time, including regional warning, face recognition, intrusion alarm and other functions; the personnel positioning unit displays the current personnel location information of each unit in real time on the three-dimensional map of the operation platform, and provides personnel information warnings in related areas, such as warnings for personnel entering dangerous areas and warnings for personnel who have not evacuated safely.

[0053] like Figure 2 As shown in the figure, the perception monitoring module is divided into different blocks according to components such as satellite launch towers, launch pads, recovery equipment and control systems. The nodes of each block are divided into full nodes and light nodes. The full node is responsible for collecting and storing monitoring data in the entire management system and generating data summaries; the light node, as a distributed data unit in the management system, only stores the latest block data, which includes the summary value generated by the full node.

[0054] like Figure 3 As shown in the figure, based on the data collection of the perception and monitoring module, the information processing module analyzes the operating status according to the operating data of each device. In order to meet the security and analysis and processing requirements of each block data, a variety of operators are designed in the module, including multi-party range query, neighbor query, status evaluation and diagnosis, as well as multi-party security summation, data set merging and status comparison, etc., to evaluate the health status of the platform equipment and perform intelligent diagnosis, thereby realizing intelligent operation and maintenance management.

[0055] The above-mentioned information processing module functions support the system to call the data interface, thereby providing data support for platform operation and maintenance management.

[0056] Among them, the information processing module can eliminate redundant information of the offshore operation platform through data clarification. First, it clears duplicate fields, such as the same equipment operation data or operation time, and then cleans fields with the same meaning, such as equipment failure or equipment abnormality data, and finally deletes redundant data, such as notes and aliases, which are not suitable for big data analysis.

[0057] During operation, the information processing module will securely sum the data to merge similar data from multiple parties. First, each block randomly generates an n-1-degree polynomial and sets the locally stored data as the constant term of the polynomial. Then, each block substitutes n specific values ​​into the polynomial for calculation. Finally, based on the polynomial results calculated by each block, the Lagrange interpolation formula is used to obtain the sum. During the summation process, the original data of each block is always kept locally to ensure data security.

[0058] After the data is merged and summed, the information processing module uses state comparison to compare the data summation results of multiple blocks by giving a value. Assuming that the data stored by each party is a i , given a comparison value b, first divide b into n equal parts, and then let Just determine ∑ i c i Is it a positive number? In addition, each block generates a random positive number d i , calculated through the secret sharing protocol (∑ i c i )(∑ i d i ), the operating status of each block can be determined.

[0059] Before the information processing module performs evaluation and diagnosis, it is necessary to find the union of the data sets of multiple blocks and output the elements in the union. First, each block adds some noise data to the local data set through the differential privacy mechanism, and sends the noisy data to other blocks. Then each block performs a union operation on the collected data sets. Finally, each block obtains the final data union by removing the noise data added locally.

[0060] Finally, the information processing module uses the K-Means clustering algorithm to divide the data set into k categories. The algorithm first randomly selects k samples from the data set as the initial cluster centers, then calculates the distance from each category of the data set to these cluster centers, and assigns the samples to the cluster centers closest to them. Finally, the cluster centers of each category are recalculated according to the new sample classification, and the sample assignment steps are repeated until the classification of all samples is stable and no longer changes. By using the above-mentioned basic operators, the analysis, evaluation and diagnosis of the platform are completed, providing data support for platform operation and maintenance management.

[0061] In the process of information processing, the present invention adopts a data encryption method to ensure that the data of each block will not be leaked during the calculation process. The offshore operation platform data is aggregated into the data space using a deep learning algorithm, and an offshore operation platform data encryption model is constructed based on the cluster analysis results:

[0062]

[0063] Where U is the data encryption model of the ship intelligent terminal; is the offshore operating platform data in space; μ is the data security index; n and λ are the parameters of encrypted data volume and data reflection degree; q is the deep learning space scale function.

[0064] The integrated management system is also designed with an application layer to form an application service module. The application service module provides services such as integrated display, model management and monitoring data management, and interacts with display available equipment and facilities to form an application layer. Figure 4 As shown in Figure 1, the application service module manages the access and call of each facility, device and system application function on the platform by formulating access control strategies. Specifically, it generates access control strategies through relevant attributes in the integrated management system and uses efficient algorithms to quickly retrieve these strategies. For example, when a block unit U (such as a transmitter) requests to access a data D i When the system summarizes it into four-dimensional attributes for processing A(U)=attr i ,attr d ,attr a ,attr e ,The access control is based on four attributes: the starting block attribute, the data access attribute (including the block to which the data belongs and the data description, etc.), the operation attribute and the environment attribute. Finally, the ,integrated management system uses these attribute information A(U) to determine whether access to the block where the data is ,allowed.

[0065] In the application service module, the present invention mines common access control strategies by applying frequent pattern item algorithms such as the Apriori algorithm in the management system, so that each block unit reaches a consensus on attribute induction and strategy formulation. Based on the mined high-frequency strategies, the system constructs an attribute prefix tree, thereby realizing rapid judgment of access strategies based on attributes.

[0066] Specifically, the integrated management system for offshore operating platforms of the present invention forms a three-layer architecture of the system through the coordinated cooperation of three modules, which consists of a perception interaction layer, a control processing layer, and an application layer. The layers use the network to transmit data information, and jointly realize the monitoring of the operation process of the offshore operating platform, analyze and judge the operation status, and evaluate and diagnose abnormal status alarms.

[0067] The perception interaction layer consists of information collection devices such as sensor positioning devices, video front-ends, smart sensors, QR codes and RFID tags. After these devices are installed on-site at the platform facilities, they can collect, perceive, monitor and control changes in building environment and status information in real time and continuously. These collected data are aggregated into the database system through the network to form a continuous and traceable dynamic monitoring record, providing a real-time data source for the static data model.

[0068] The control and processing layer is composed of a comprehensive management platform that integrates big data, cloud computing and artificial intelligence technologies. The platform performs real-time analysis, calculation and decision support on the data information transmitted by the perception layer, and sends control instructions to the devices in the application layer.

[0069] The application layer includes personnel equipped with smart wearable devices and digital smart devices, which perform tasks according to the instructions sent by the control processing layer, and feed back the implementation status to the perception layer in real time to complete the closed loop of the data flow.

[0070] The above embodiments are only used to illustrate the design ideas and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, any equivalent changes or modifications made based on the principles and design ideas disclosed by the present invention are within the protection scope of the present invention.

Claims

1. An integrated management system for offshore operating platforms based on cloud computing and big data, characterized by: It includes perception monitoring module, information processing module and application service module; The perception monitoring module is used to collect intelligent terminal data from offshore operating platforms; The information processing module is used to encrypt, parse and store the data collected by the perception monitoring module; The application service module receives data through the server and communication network, and provides users with data collection-driven data display, service management and data management functions.

2. According to claim 1, a cloud computing and big data based integrated management system for offshore operating platforms, characterized in that: The perception monitoring module, information processing module and application service module cooperate to form a system architecture including the perception interaction layer, control processing layer and application layer. Data is transmitted between layers through the network to monitor the operation process of the offshore operation platform, analyze and judge the operation status, and evaluate and diagnose abnormal status alarms; The perception interaction layer includes sensor positioning devices, video front-ends, smart sensors, QR codes and RFID tags, which are used to collect, perceive, monitor and control changes in building environment and status information in real time and continuously, and aggregate the collected data into the database system through the network to form a continuous and traceable dynamic monitoring record, providing a real-time data source for the static data model; The control processing layer is a comprehensive management platform that integrates big data, cloud computing and artificial intelligence technologies. It is used to perform real-time analysis, calculation and decision support on the data information transmitted by the perception interaction layer, and send control instructions to the application layer. The application layer includes personnel equipped with smart wearable devices and digital smart devices, which are used to perform tasks according to the instructions sent by the control processing layer and feed back the real-time status to the perception interaction layer to complete the closed loop of the data flow.

3. According to claim 1, a cloud computing and big data based integrated management system for offshore operating platforms, characterized in that: The data collected by the perception monitoring module includes correlation data, spatiotemporal data, text data, and image data; The perception monitoring module includes a comprehensive display unit, a task management unit, an equipment monitoring unit, a security monitoring unit, and a personnel positioning unit; The integrated display unit is used to automatically switch the integrated display and control page according to different task conditions, and display the important parameters and status under the current task conditions; The task management unit is used to customize the design of monitoring scenarios through the task process arrangement tool, and automatically trigger the designed monitoring interface according to the task process; The equipment monitoring unit is used to monitor the status of all-platform electromechanical equipment, IT information equipment hardware status, operating system, software status, interface status and real-time operating status information of major system equipment; The security monitoring unit is used to obtain the time when a person enters the security area based on the data collected by the perception monitoring module, and determine whether the time of entry is within the preset normal time period; when the identity of the person or the time of entry does not meet the preset requirements, it pushes alarm information including area alert, face recognition and intrusion alarm in real time; The personnel positioning unit is used to display the current personnel location information of each unit in real time on the three-dimensional map of the work platform, and provide personnel information warnings in related areas, including warnings for personnel entering dangerous areas and warnings for personnel who have not evacuated safely.

4. The integrated management system for offshore operation platforms based on cloud computing and big data according to claim 1 is characterized by: The perception monitoring module is divided into different blocks according to the layout area. The nodes of each block include full nodes and light nodes. The full nodes are used to collect and store monitoring data and generate data summaries. Light nodes are distributed data units that store the latest block data, including summary values ​​generated by full nodes.

5. The integrated management system for offshore operation platforms based on cloud computing and big data according to claim 1 is characterized by: The information processing module function supports the system to call the data interface, which is used to provide data support for platform operation and maintenance management; the information processing module eliminates redundant information of the offshore operation platform through data cleaning, including clearing duplicate fields, cleaning fields with the same meaning, and deleting redundant data; The information processing module is equipped with operators for multi-party range query, nearest neighbor query, state evaluation and diagnosis, multi-party secure summation, data set merging, random sample selection and state comparison.

6. The integrated management system for offshore operating platforms based on cloud computing and big data according to claim 5 is characterized by: The specific steps of the information processing module to securely sum the data to merge similar data from multiple parties during operation are as follows: First, each block randomly generates an n-1 degree polynomial and sets the locally stored data as the constant term of the polynomial; Each block then substitutes n specific values ​​into the polynomial for calculation; Finally, the Lagrange interpolation formula is used to sum the polynomial results calculated by each block; the original data of each block is saved locally during the summation process to ensure data security; By comparing the data summation results of multiple blocks with a given value, the specific steps are: Assume that the data stored by each party is a i , the given comparison value is b; Divide b into n equal parts, let Judgment∑ i c i Is it a positive number? Each block generates a random positive number d i , calculated through the secret sharing protocol (∑ i c i )(∑ i d i ), judge the operation status of each block according to the calculation results, if Σ i c i is a positive number and (Σ i c i )(∑ i d i ) is greater than 3, then each block is considered to be operating normally.

7. The integrated management system for offshore operation platforms based on cloud computing and big data according to claim 5 is characterized by: The specific steps of the information processing module to find the union of multiple block data sets and output the elements in the union are: First, each block adds noise data to the local data set through the differential privacy mechanism and sends the noise-added data to other blocks; Then each block performs a union operation on the collected data set; Finally, each block obtains the final data union by removing the locally added noise data.

8. The integrated management system for offshore operation platforms based on cloud computing and big data according to claim 5 is characterized by: The specific steps of randomly selecting samples using the K-Means clustering algorithm in the information processing module are as follows: First, k samples are randomly selected from the data set as the initial clustering centers; Then calculate the distance from each category data set to each cluster center, and assign the sample to the cluster center closest to it; Finally, the cluster center of each category is recalculated according to the new sample classification, and the above sample allocation steps are repeated until the classification of all samples is stable and no longer changes.

9. The integrated management system for offshore operation platforms based on cloud computing and big data according to claim 8, characterized in that: The specific steps of the data encryption method adopted by the information processing module are: Let U be the data encryption model of ship intelligent terminal; is the offshore platform data in space; μ is the data security index; n and λ are the encrypted data volume and data reflection degree parameters; p is the extracted platform initial terminal data; q is the deep learning space ratio function; the offshore platform data is aggregated into the data space using the deep learning algorithm, and the offshore platform data encryption model is constructed based on the cluster analysis results:

10. The integrated management system for offshore operation platforms based on cloud computing and big data according to claim 1, characterized in that: The application service module is used to provide comprehensive display, model management, monitoring data management and other services, interact with display available equipment and facilities, and together constitute the application layer; The application service module manages the access and invocation of various facilities, equipment and system application functions on the platform by formulating access control strategies, including generating access control strategies through relevant attributes in the integrated management system and using efficient algorithms to achieve rapid retrieval of strategies; The application service module mines common access control strategies by applying the frequent pattern item algorithm, so that each block unit can reach a consensus on attribute induction and strategy formulation; it builds an attribute prefix tree based on the mined high-frequency strategies to achieve rapid judgment of attribute-based access strategies.

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