A balance control method and system for deep - sea and far - sea platforms based on SCADA system

Abstract

The present invention relates to the technical field of control data processing, and provides a deep-sea and far-sea platform balance control method and system based on a SCADA system. The deep-sea and far-sea platform balance control method based on the SCADA system includes the following steps: S1, data distribution; S2, upper-level sorting; S3, upper-level processing; S4, combined sorting; S5, lower-level processing. Based on the method of calculating the gain direction by the upper computer and calculating the gain amount and executing control by the lower computer, the present invention can greatly reduce the calculation amount of cooperative control, thereby ensuring the smooth completion of the balance control of the system composed of multiple parts, and providing a solid and powerful overall framework for the balance control of the scalable deep-sea and far-sea platform system.

Description

Technical Field

[0001] The present invention relates to a balance control method and system for a deep - sea and far - sea platform based on a SCADA system, belonging to the technical field of control data processing. Background Art

[0002] The SCADA (Supervisory Control And Data Acquisition) system, namely the data acquisition and monitoring control system, is mainly applied to data acquisition, monitoring control, and process control in fields such as electric power, petroleum, chemical industry, and gas. Since the SCADA system is convenient for control, has flexible expansion, and is easy to integrate data control models for intelligent control, it is currently commonly used on deep - sea and far - sea platforms to control equipment.

[0003] With the increasing demand for deep - sea and far - sea exploration and development, deep - sea and far - sea platforms have become relatively more complex than before. For the convenience of functional expansion, the current mainstream architecture is to independently design multiple parts and then splice them to a central platform for unified coordination. For example, a central platform is spliced with a fishery platform, a exploration platform, and a mooring platform.

[0004] In the deep - sea and far - sea areas, since it is impossible to anchor to the ground or the seabed, balance control is an important part of overall control. When using the method of splicing multiple independently developed parts, since each part is independently developed and has different balance requirements (for example, the fishery platform has significantly lower balance requirements compared to the mooring platform) and balance structures, for balance control, it is necessary to consider both the independent balance control requirements of each part and the impact of each part on the balance control of the central platform. This makes it difficult to complete using the traditional central unified control method due to excessive computational complexity. Summary of the Invention

[0005] To solve the above - mentioned technical problems, the present invention provides a balance control method and system for a deep - sea and far - sea platform based on a SCADA system. Based on the method of calculating the gain direction by the upper computer and calculating the gain amount and executing control by the lower computer, the computational complexity of collaborative control can be greatly reduced, thereby ensuring the smooth completion of the balance control of a multi - part composition system and providing a solid overall framework for the balance control of an extensible deep - sea and far - sea platform system.

[0006] The present invention is achieved through the following technical solutions.

[0007] A balance control method for a deep - sea and far - sea platform based on a SCADA system provided by the present invention includes the following steps:

[0008] S1. Data distribution: Obtain sensing data from sensors, and merge the sensing data with the control data of the current time series as feedback data and send it to the upper-level on-site host;

[0009] S2. Upper-level sorting: After the on-site host receives the feedback data, separate the feedback data into sensing data and control data, normalize the sensing data and then update it into the sensing data FIFO cache queue, and directly update the control data into the control data FIFO cache queue;

[0010] S3. Upper-level processing: Respectively extract the sensing data and control data of the latest two time series from the sensing data FIFO cache queue and the control data FIFO cache queue, calculate the differences between the sensing data and control data of the two time series item by item, after fuzzy quantization of the differences, summarize the difference fuzzy quantities of the above sensing data and control data of multiple controllers and perform fuzzy inference to judge the gain direction of the difference fuzzy quantity of each control data relative to the overall situation of the difference fuzzy quantity of all sensing data, and return the gain direction data of the control data to the corresponding controller;

[0011] S4. Merging and sorting: While the controller normalizes the sensing data, waits for and receives the gain direction data, and obtains the control data of the current time series, and merges the normalized sensor, gain direction data and the control data of the current time series into the data to be processed;

[0012] S5. Lower-level processing: Use a preset control model to calculate the data to be processed to obtain the control data of the next time series, then switch to the next time series and control the actuator according to the control data; When calculating, use the control data of the current time series as the benchmark, use the value calculated from the normalized sensing data as the control increment, and use the gain direction data as the superposition symbol of the control increment.

[0013] The sensing data is displacement sensors, velocity sensors and acceleration sensors at multiple installation points.

[0014] The normalization processing of the sensing data includes fast Fourier transform, filtering, and scale transformation.

[0015] In step S3, the fuzzy quantization uses a membership function of multi-interval assignment.

[0016] In step S3, the fuzzy rules used in the fuzzy inference are set according to the equipment structure and the installation positions of the sensors and actuators.

[0017] In step S5, the control model is set according to the balance control design scheme of the equipment part where the controller is located.

[0018] The present invention also provides a deep-sea platform balance control system based on the SCADA system, including

[0019] A field host for executing step S2 and step S3 in the deep - sea platform balance control method based on the SCADA system as described above;

[0020] A controller for executing step S1, step S4 and step S5 in the deep - sea platform balance control method based on the SCADA system as described above;

[0021] The device consists of multiple parts, each part is controlled by a controller, each controller is connected to control multiple actuators and multiple sensors, and multiple controllers are connected and communicate with the field host through a control bus.

[0022] The field host is connected to a field database, a visualization module and a field server through a communication bus, where,

[0023] The field database is used to store historical data;

[0024] The visualization module is used to generate visualization charts based on historical data;

[0025] The field server is used to provide user interaction services, and the user interaction services include responding to user requests, sending historical data, and sending visualization charts.

[0026] A client interface and a wireless communication module are also connected to the communication bus.

[0027] The control bus adopts the Modbus protocol.

[0028] The beneficial effect of the present invention is that: based on the method of calculating the gain direction by the upper computer, calculating the gain amount by the lower computer and executing control, the calculation amount of collaborative control can be greatly reduced, so as to ensure that the balance control of the multi - part composition system can be successfully completed, providing a solid and powerful overall framework for the balance control of the scalable deep - sea platform system. Brief Description of the Drawings

[0029] Figure 1 is a connection schematic diagram of at least one embodiment of the present invention;

[0030] Figure 2 is Figure 1 the control flow schematic diagram of the field host in

[0031] Figure 3 is Figure 1 the control flow schematic diagram of the controller in

[0032] Figure 4 is the structural layout schematic diagram of an embodiment of the present invention. Detailed Embodiments

[0033] The technical solution of the present invention will be further described below, but the scope of protection is not limited thereto.

[0034] The first embodiment of the present invention relates to a deep - sea platform balance control method based on a SCADA system as shown in Figure 2 、 Figure 3 and includes the following steps:

[0035] S1. Data distribution: Obtain sensing data from sensors, and merge the sensing data with the control data of the current time sequence as feedback data and send it to the upper - level field host.

[0036] S2. Upper - level arrangement: After the field host receives the feedback data, separate the feedback data into sensing data and control data. Normalize the sensing data and then update it into the sensing data FIFO cache queue, and directly update the control data into the control data FIFO cache queue. The de - queued data of the sensing data FIFO cache queue and the control data FIFO cache queue are directly discarded, and only the first - in - first - out characteristic of the FIFO queue is used to ensure that the data obtained each time when retrieving data from the queue is strictly arranged in the latest time sequence.

[0037] S3. Upper - level processing: Take out the sensing data and control data of the latest two time sequences from the sensing data FIFO cache queue and the control data FIFO cache queue respectively, calculate the difference between the sensing data and control data of the two time sequences item by item, after fuzzy quantization of the difference, summarize the difference fuzzy quantities of the above - mentioned sensing data and control data of multiple controllers and perform fuzzy reasoning to judge the gain direction of the difference fuzzy quantity of each control data relative to the overall situation of the difference fuzzy quantity of all sensing data, and return the gain direction data of the control data to the corresponding controller.

[0038] S4. Merging and arrangement: While the controller normalizes the sensing data, waits for and receives the gain direction data, and obtains the control data of the current time sequence, and merges the normalized sensor, gain direction data, and control data of the current time sequence into data to be processed.

[0039] S5. Lower - level processing: Use a preset control model to calculate the data to be processed to obtain the control data of the next time sequence, then switch to the next time sequence and control the actuator according to the control data; when calculating, use the control data of the current time sequence as the benchmark, the value calculated for the normalized sensing data as the control increment, and the gain direction data as the superposition symbol of the control increment.

[0040] Thus, in essence, the present invention calculates the gain direction and gain amplitude separately. The on-site host calculates the gain direction, and its main purpose is to determine the increase or decrease effect of the action mechanism (each action mechanism corresponds to at least one control data) on the overall balance; the controller calculates the gain amplitude, and its main purpose is to determine the control amount. Due to the complexity of the deep-sea and far-sea platform, compared with the method of completely using the on-site host for overall control, this method is more convenient for independent development of each part.

[0041] An implementation of the FIFO buffer queue in the control system is to use an array of pointers or a linked list of pointers, and an independent addition method is adopted. For each new variable added, the last variable is deleted and the remaining variables are shifted backward. Taking the array of pointers as an example, in this solution, the sensing data FIFO buffer queue is an array of pointers [*x 1 ,*x 2 ,……,*x n , the control data FIFO buffer queue is an array of pointers [*y 1 ,*y 2 ,……,*y n . When a new sensing data *x is added to the sensing data FIFO buffer queue, first make *x n =*x n-1 item by item according to n-- until n - 1 equals 0, and then make *x 1 =*x. The operation of adding a new control data *y to the control data FIFO buffer queue is the same. First make *y n =*y n-1 item by item according to n-- until n - 1 equals 0, and then make *y 1 =*y. This ensures that *x 1 and *x 2 in the sensing data FIFO buffer queue are the sensing data of the latest two time sequences, and *y 1 and *y 2 in the control data FIFO buffer queue are the control data of the latest two time sequences.

[0042] It is easy to understand that the principle of the above calculation of the gain direction is to take out the sensing data *x 1 and *x 2 of the latest two time sequences in the sensing data FIFO buffer queue, and the control data *y 1 and *y 2 of the latest two time sequences in the control data FIFO buffer queue. After that, calculate the difference *Δx between the sensing data *x 1 and *x 2 of the latest two time sequences, and the control data *y 1 and *y 2The differences *Δy, *Δx, and *Δy are all pointers pointing to an array or structure storing multiple numerical values. Then, a relatively mature fuzzy control method in the prior art (including fuzzy quantization, fuzzy reasoning, and solving a specific control value in a fuzzy calculation manner) can be used to calculate the gain direction Δz. Essentially, it is to calculate a specific numerical value based on multiple numerical values using a fuzzy algorithm. The fuzzy control method can refer to, for example, a design method of a fuzzy controller disclosed in a Chinese patent with the application number CN202010792987.0, a method for determining noise reduction parameters and its device, an active noise reduction method and its device disclosed in a Chinese patent with the application number CN202110873136.3, a fuzzy control method for eccentric vibration of a generator rotor based on a magnetorheological damper disclosed in a Chinese patent with the application number CN202211086550.0, etc. The specific fuzzy algorithm formula used belongs to the code level rather than the architecture level involved in this application and is not in the same technical category as this application, so it is not within the scope of discussion in this application.

[0043] The preset control model in step S5 is preset in the subsystem according to actual requirements. For example, for a typical application, such as Figure 4As shown in the figure, the central platform is a wind power foundation platform, on which a wind turbine is installed for power generation. A fishing platform, a living platform, a mooring platform, and a photovoltaic platform are respectively docked and assembled on the side of the central platform. The fishing platform, the living platform, the mooring platform, and the photovoltaic platform are all called subsystems. The one that plays an actual control role in each subsystem is called a controller. Each subsystem is integrally developed by other independent departments (including the development of the overall structure, circuit connection, and control software), or directly uses third-party products for docking structure transformation. Due to different development teams, the controllers of each subsystem have significant differences in the actuators and control logics for balance control, that is, the controllers of the subsystems have their own independently preset control models. Therefore, the process of step S5 is essentially a calculation process of the controller, but the output of the controller is truncated and the output items are processed for symbols (that is, the gain direction data is used as the superposition symbol of the control increment), and then returned to the execution control module connected to the controller for control (this operation requires the subsystem development department to provide an API interface). As for the balance control of the fishing platform, a typical prior art solution is a combined deep-sea fishery cultivation system disclosed in a Chinese patent with the application number CN202210882705.5, which realizes balance control based on a wave compensator. As for the balance control of the photovoltaic platform, another typical prior art solution is a floating photovoltaic platform system for improving ocean adaptability disclosed in a Chinese patent with the application number CN202410365496.6, which realizes balance control based on adjusting the buoyancy distribution. It can be seen that the prior art has different controls for different functional platforms. This application mainly focuses on data scheduling and control processing at the architecture level, so the control details of multiple subsystems will not be elaborated one by one.

[0044] The second embodiment of the present invention is substantially the same as the first embodiment. The main difference is that the sensing data is displacement sensors, velocity sensors, and acceleration sensors at multiple installation points.

[0045] Further, the normalization processing of the sensing data includes fast Fourier transform, filtering, and scale transformation.

[0046] Further, in step S3, the fuzzy quantization adopts a membership function of multi-interval assignment.

[0047] Further, the fuzzy rules used in the fuzzy inference in step S3 are set according to the device structure and the installation positions of the sensors and actuators.

[0048] Further, in step S5, the control model is set according to the balance control design scheme of the device part where the controller is located.

[0049] The third embodiment of the present invention relates to a balance control system for a deep-sea platform based on a SCADA system as shown in Figure 1 the figure, including

[0050] A field host for performing step S2 and step S3 in the first embodiment or the second embodiment;

[0051] A controller for performing step S1, step S4 and step S5 in the first embodiment or the second embodiment;

[0052] The device consists of multiple parts, each part is controlled by a controller, each controller is connected to control multiple actuators and multiple sensors, and multiple controllers are connected and communicate with the field host through a control bus.

[0053] The fourth embodiment of the present invention is substantially the same as the third embodiment. The main difference is that the field host is connected with a field database, a visualization module and a field server through a communication bus. Among them,

[0054] The field database is used to store historical data;

[0055] The visualization module is used to generate visualization charts according to historical data;

[0056] The field server is used to provide user interaction services. The user interaction services include responding to user requests, sending historical data, and sending visualization charts.

[0057] Furthermore, a client interface and a wireless communication module are also connected to the communication bus.

[0058] Furthermore, the control bus adopts the Modbus protocol.

Claims

1. A deep sea platform balance control method based on SCADA system, characterized in that: The following steps are involved: S1, data distribution: obtain sensor data from the sensor, and combine the sensor data with the current timing control data as feedback data and send it to the upper field host; S2, upper level sorting: after receiving the feedback data, the field host separates the feedback data into sensor data and control data, normalizes the sensor data and places it into the sensor data FIFO cache queue for update, and directly places the control data into the control data FIFO cache queue for update; S3, upper processing: taking out the latest two time series of sensor data and control data from the sensor data FIFO cache queue and the control data FIFO cache queue respectively, comparing and calculating the difference of the sensor data and control data of the two time series item by item, fuzzy quantizing the difference, summarizing the difference fuzzy quantities of the sensor data and control data of multiple controllers and performing fuzzy reasoning to determine the gain direction of the difference fuzzy quantity of each control data relative to the overall situation of the difference fuzzy quantities of all sensor data, and returning the gain direction data of the control data to the corresponding controller; S4, merging and sorting: the controller normalizes the sensor data while waiting for and receiving the gain direction data, and obtains the control data of the current time sequence, and formats the normalized sensor, the gain direction data and the control data of the current time sequence into the data to be processed; S5, lower-level processing: using a preset control model to calculate the data to be processed, obtaining the control data of the next time sequence, then switching to the next time sequence and controlling the actuator according to the control data; During calculation, the control data of the current time series is used as a reference, the value calculated for the normalized sensor data is used as a control increment, and the gain direction data is used as a superposition symbol of the control increment.

2. The deep sea platform balance control method based on SCADA system as claimed in claim 1, characterized in that: The sensing data are displacement sensors, velocity sensors and acceleration sensors at multiple installation points.

3. The deep sea platform balance control method based on SCADA system as claimed in claim 1, characterized in that: The normalization processing of the sensor data includes fast Fourier transform, filtering, and scale transformation.

4. The deep sea platform balance control method based on SCADA system as claimed in claim 1, characterized in that: In step S3, the fuzzy quantization adopts a membership function assigned by multiple intervals.

5. The deep sea platform balance control method based on SCADA system as claimed in claim 1, characterized in that: The fuzzy rules used in the fuzzy reasoning in step S3 are set according to the equipment structure and the installation positions of the sensors and actuators.

6. The deep sea platform balance control method based on SCADA system as claimed in claim 1, characterized in that: In step S5, the control model is set according to the balance control design scheme of the equipment part where the controller is located.

7. A deep sea platform balance control system based on SCADA system, characterized in that: include, A field host, used to execute step S2 and step S3 in the deep-sea platform balance control method based on the SCADA system as described in any one of claims 1 to 6; A controller, used to execute step S1, step S4 and step S5 in the deep-sea platform balance control method based on the SCADA system according to any one of claims 1 to 6; The device is composed of multiple parts, each part is controlled by a controller, each controller is connected to control multiple actuators and multiple sensors, and multiple controllers are connected and communicated with the on-site host through a control bus.

8. The deep sea platform balance control system based on SCADA system as claimed in claim 7, characterized in that: The field host is connected to a field database, a visualization module and a field server via a communication bus, wherein: The on-site database is used to store historical data; The visualization module is used to generate visualization charts based on historical data; The on-site server is used to provide user interaction services, including responding to user requests, sending historical data, and sending visual charts.

9. The deep sea platform balance control system based on SCADA system as claimed in claim 8, characterized in that: The communication bus is also connected with a client interface and a wireless communication module.

10. The deep sea platform balance control system based on SCADA system as claimed in claim 7, characterized in that: The control bus adopts Modbus protocol.

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