Intelligent temperature control module for offshore platform

Abstract

The invention relates to the field of ocean platform instrument control, in particular to an intelligent temperature control module of an ocean platform. Wherein the temperature acquisition unit is used for detecting the temperature in a container and inputting the temperature into the temperature control unit through a cable; the temperature control unit is used for reading the temperature acquired in the temperature acquisition unit, and heating of the heater is achieved through a PI D control algorithm; the feedback adjusting unit adjusts a control strategy according to external conditions and optimizes the energy use efficiency; when the system is abnormal, the alarm notification unit notifies a user through sound, light or short messages / mails and the like. Continuous power is provided for the sensor through a wireless charging technology in seamless connection with the sensor, and the requirement for frequent maintenance is reduced; the design of the wireless charging module and the power supply module reduces the dependence on cables, and reduces the difficulty and cost of equipment maintenance.

Description

Technical Field

[0001] The present invention relates to the field of marine riser monitoring, and more specifically, to an intelligent temperature control module for an offshore platform. Background Art

[0002] Currently, FM200 carbon dioxide fire extinguishing devices need to be installed on each offshore oil production platform. The storage temperature of carbon dioxide in the FM200 fire extinguishing system is required to be between 0°C and 49°C. Due to the characteristics of the offshore oil production platforms in the Bohai Sea, the temperature in winter in the Bohai region is lower than 0°C, resulting in insufficient activity of the carbon dioxide cylinders in the FM200 container, which cannot play a fire extinguishing role and poses a safety hazard. The present invention designs a set of intelligent temperature control modules applicable to the FM200 containers of offshore platforms to realize the automatic adjustment and control of the temperature of the FM200 containers on offshore platforms. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent temperature control module for an offshore platform to solve the problems in the above-mentioned background art. Traditional riser monitoring mostly uses cable on-line or self-contained monitoring methods, but these methods have many unavoidable problems in complex marine environments. Cables are easily damaged, the power supply time of self-contained sensors is limited, and the difficulty of underwater recovery or maintenance is high. Usually, on-line sensors are difficult to deploy in deep waters, the difficulty of line maintenance is high, and the long-term working performance cannot be guaranteed. The working time of self-contained sensors underwater is generally only about 1-3 months. Maintaining or replacing sensors requires complex operations, increasing the maintenance cost and risk. Acoustic wave transmission in the underwater environment: Acoustic waves are easily interfered by other underwater acoustic signals or environmental noises, and have high power consumption and high cost.

[0004] To achieve the above purpose, the present invention aims to provide an intelligent temperature control module for an offshore platform, including:

[0005] A temperature acquisition unit, which is used to detect the temperature inside the container and input it into the temperature control unit through a cable;

[0006] A temperature control unit, which is used to read the temperature obtained from the temperature acquisition unit and control the heating and power-off of the heater through a PID control algorithm;

[0007] A feedback adjustment unit, which adjusts the control strategy according to external conditions to optimize the energy use efficiency;

[0008] An alarm notification unit, which notifies the user by sound, light, and text message when an abnormality occurs in the system.

[0009] As a further improvement of this solution, the temperature control unit includes a temperature control module and a fuse protection module;

[0010] The temperature control module reads the temperature obtained from the temperature acquisition unit and realizes the heating of the heater through the PID control algorithm. Considering the change of the external temperature, the recursive least squares algorithm is introduced to adjust the parameters and optimize the PID control algorithm, which is used to improve the performance of the system under different working conditions;

[0011] The fuse protection module monitors the temperature of the heater through intelligent monitoring and control algorithms to prevent equipment damage caused by excessive current.

[0012] As a further improvement of this solution, the temperature control module reads the temperature obtained from the temperature acquisition unit and realizes the heating of the heater through the PID control algorithm. Considering the change of the external temperature, the recursive least squares algorithm is introduced to adjust the parameters and optimize the PID control algorithm, which is used to improve the performance of the system under different working conditions. The specific implementation steps for the PID control algorithm to realize the heating of the heater are as follows:

[0013] S1.1. Set the target temperature range, where the lower limit temperature is 5°C and the upper limit temperature is 35°C;

[0014] S1.2. Set the initial PID parameters;

[0015] S1.3. Calculate the error according to the current temperature and the target temperature range;

[0016] S1.4. Calculate the change rate of the error and the cumulative value of the error; the mathematical expression of the change rate of the error is:

[0017]

[0018] In the formula, represents the change rate of the error e(t) with respect to time t; e(t) represents the error value at time point t, which is used to measure the difference between the current system state and the target state; Δt represents the time interval between two samplings;

[0019] The mathematical expression of the cumulative value of the error is:

[0020] Integral(t) = Integral(t - Δt) + e(t)·Δt;

[0021] In the formula, Integral(t) represents the cumulative value of the error at the current moment t;

[0022] S1.4. Calculate the output signal according to the PID control formula, and its mathematical expression of the output signal is:

[0023]

[0024] where \(u(t)\) is the output signal of the controller at the current time \(t\); \(K\) p represents the proportional term, which adjusts the output of the controller according to the current error \(e(t)\); \(K\) i represents the integral term, which adjusts the output of the controller according to the accumulated value of the error; \(K\) d represents the derivative term, which adjusts the output of the controller according to the rate of change of the error;

[0025] S1.5. Map the PID output \(u(t)\) to the control signal of the heater. When \(u(t)> \)start threshold, start the heater; when \(u(t)< \)stop threshold, stop the heater.

[0026] As a further improvement of this solution, in S1.3, when the current temperature is lower than the lower limit temperature, the error is:

[0027] \(e(t)=T\) low -T current ;

[0028] where \(e(t)\) represents the error value at time point \(t\), which is used to measure the difference between the current system state and the target state; \(T\) low represents the lowest temperature value in the set target temperature range; \(T\) current represents the actual temperature value currently measured by the system;

[0029] When the current temperature is higher than the upper limit temperature, the error is:

[0030] \(e(t)=T\) high -T current ;

[0031] where \(T\) high represents the highest temperature value in the set target temperature range;

[0032] When the current temperature is less than or equal to the upper limit temperature and greater than or equal to the lower limit temperature, the error is:

[0033] \(e(t)=0\).

[0034] As a further improvement of this solution, the temperature control module takes into account the change of the external temperature, introduces the recursive least squares algorithm to adjust the parameters, and the specific steps to optimize the PID control algorithm are:

[0035] S2.1. After the calculation of the error, the rate of change of the error, and the accumulated value of the error is completed, define the regression vector \(\varphi(t)\), then the mathematical expression of \(\varphi(t)\) is:

[0036] \(\varphi(t)=[y(t - 1),u(t - 1),\cdots,y(t - n),u(t - n)] T ;

[0037] Where, φ(t) represents the regression vector; y(t - 1) represents the output of the system at time t - 1; u(t - 1) represents the input of the system at time t - 1;

[0038] S2.2. Update the system parameter estimation using the recursive least squares algorithm;

[0039] S2.3. Adjust the PID parameters K 、K p 、K i and K d ;

[0040] S2.4. Apply the updated K p 、K i and K d to the output formula of the PID controller:

[0041] u′(t) = K p (t)e(t) + K i (t)Integral(t) + K d (t)Δe(t);

[0042] Where, u′(t) represents the output signal of the updated PID controller at time t;

[0043] S2.5. Repeat steps S2.1 to S2.4 until the system reaches a stable state.

[0044] As a further improvement of this solution, in the above S2.2, when using the recursive least squares algorithm to update the system parameter estimation, the mathematical expression of the prediction error is:

[0045]

[0046] Where, y(t) represents the actual output of the system at time t; φ T (t) represents the transpose of the regression vector φ T (t); represents the estimation of the system parameters at time t - 1;

[0047] The mathematical expression of the gain matrix is:

[0048] K(t) = P(t - 1)φ(t)[λ + φ T (t)P(t - 1)φ(t)] -1 ;

[0049] Where, K(t) represents the gain matrix; P(t - 1) represents the covariance matrix at time t - 1; λ represents the forgetting factor, which controls the forgetting speed of old data;

[0050] The mathematical expression of parameter estimation is:

[0051]

[0052] Where, represents the estimated value of the system parameters at time t;

[0053] The mathematical expression of the covariance matrix is:

[0054]

[0055] Where P(t) represents the covariance matrix at time t.

[0056] As a further improvement of this solution, in S2.3, the system parameters estimated by the recursive least squares algorithm are Adjust PID parameter K p , K i and K d , then after adjustment K p The mathematical expression of (t) is:

[0057]

[0058] In the formula, α determines the estimated value of the system parameters Proportional gain K p (t) the extent of the impact; The first element represents the estimated value of the system parameter at time t; β is used to provide additional flexibility to adjust the base value of the proportional gain;

[0059] Adjusted K i The mathematical expression of (t) is:

[0060]

[0061] In the formula, γ determines the estimated value of the system parameters For integral gain K i (t) the extent of the impact; The second element represents the estimated value of the system parameters at time t; δ is used to provide additional flexibility to adjust the base value of the integral gain;

[0062] Adjusted K d The mathematical expression of (t) is:

[0063]

[0064] Where ∈ determines the estimated value of the system parameters For differential gain K d (t) the extent of the impact; Represents the third element of the estimated value of the system parameters at time t; ζ is used to provide additional flexibility to adjust the base value of the differential gain.

[0065] As a further improvement of this solution, the fuse protection module monitors the temperature of the heater through intelligent monitoring and control algorithms to prevent damage to the device caused by excessive current. The specific implementation steps for the intelligent monitoring and control algorithms to monitor the heater temperature are as follows:

[0066] S3.1. Set the alarm temperature for early warning.

[0067] S3.2. Filter the collected temperature and current data to reduce noise interference.

[0068] S3.3. Calculate the temperature change rate of the heater to monitor the rising speed of the temperature.

[0069] S3.4. Determine whether there is an abnormality based on the current temperature, current current, and temperature change rate.

[0070] As a further improvement of this solution, in S3.3, to calculate the temperature change rate of the heater to monitor the rising speed of the temperature, the mathematical expression of the temperature change rate is;

[0071]

[0072] In the formula, Represents the change speed of temperature over time; T current (t) represents the actual temperature at the current moment t; T current (t - Δt) represents the temperature value of the system at the previous sampling moment, used to calculate the change amount of temperature; Δt represents the sampling time interval.

[0073] As a further improvement of this solution, in S3.4, the method for determining whether there is an abnormality based on the current temperature, current current, and temperature change rate is that when T current (t) > T alarm Or I current (t) > I max When, trigger the anomaly detection mechanism; the temperature change rate Exceeds the set safety threshold, then it is considered that the heater may have an overheating risk.

[0074] Compared with the prior art, the beneficial effects of the present invention:

[0075] The wireless charging technology that is seamlessly connected to the sensor provides continuous power for the sensor, reducing the need for frequent maintenance; the design of the wireless charging module and the power supply module reduces the dependence on cables and lowers the difficulty and cost of equipment maintenance; the waterproof and sealed design and corrosion-resistant materials are adopted to ensure the long-term stable operation of the device in harsh marine environments; the arranged indicator lights are used to indicate states such as charging, charging completion, and faults through different colors and blinking frequencies. Description of the Drawings

[0076] [[ID=I5]] Figure 1 This is the overall flowchart of the present invention.

[0077] The meanings of the reference numerals in the figure are as follows:

[0078] 1. Temperature acquisition unit; 2. Temperature control unit; 3. Feedback regulation unit; 4. Alarm notification unit;

[0079] 21. Temperature control module; 22. Fuse protection module. Detailed Embodiments

[0080] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0081] Embodiment: Please refer to Figure 1 As shown, a smart temperature control module for an offshore platform is provided, including:

[0082] A temperature acquisition unit 1, which is used to detect the temperature inside the container and input it into the temperature control unit 2 through a cable;

[0083] A temperature control unit 2, which is used to read the temperature obtained in the temperature acquisition unit 1 and control the heating and power-off of the heater through a PID control algorithm;

[0084] A feedback regulation unit 3, which adjusts the control strategy according to external conditions to optimize the energy usage efficiency;

[0085] An alarm notification unit 4, which notifies the user by means of sound, light, and text message when an abnormality occurs in the system.

[0086] In this embodiment, the temperature control unit 2 includes a temperature control module 21 and a fuse protection module 22;

[0087] The temperature control module 21 reads the temperature obtained from the temperature acquisition unit 1 and realizes the heating of the heater through the PID control algorithm. Considering the change of the external temperature, the recursive least squares algorithm is introduced to adjust the parameters and optimize the PID control algorithm, which is used to improve the performance of the system under different working conditions;

[0088] The fuse protection module 22 monitors the temperature of the heater through intelligent monitoring and control algorithms to prevent equipment damage caused by excessive current.

[0089] In this embodiment, the temperature control module 21 reads the temperature obtained from the temperature acquisition unit 1 and realizes the heating of the heater through the PID control algorithm. Considering the change of the external temperature, the recursive least squares algorithm is introduced to adjust the parameters and optimize the PID control algorithm, which is used to improve the performance of the system under different working conditions. The specific implementation steps for the PID control algorithm to realize the heating of the heater are as follows:

[0090] S1.1. Set the target temperature range, where the lower limit temperature is 5°C and the upper limit temperature is 35°C;

[0091] S1.2. Set the initial PID parameters;

[0092] S1.3. Calculate the error according to the current temperature and the target temperature range;

[0093] S1.4. Calculate the change rate of the error and the cumulative value of the error; the mathematical expression of the change rate of the error is:

[0094]

[0095] In the formula, represents the change rate of the error e(t) with respect to time t; e(t) represents the error value at time point t, which is used to measure the difference between the current system state and the target state; Δt represents the time interval between two samplings;

[0096] The mathematical expression of the cumulative value of the error is:

[0097] Integral(t) = Integral(t - Δt) + e(t)·Δt;

[0098] In the formula, Integral(t) represents the cumulative value of the error at the current moment t;

[0099] S1.4. Calculate the output signal according to the PID control formula, and its mathematical expression of the output signal is:

[0100]

[0101] In the formula, u(t) is the controller output signal at the current moment t; K pRepresents the proportional term, which adjusts the output of the controller according to the current error e(t); K i Represents the integral term, which adjusts the output of the controller according to the accumulated value of the error; K d Represents the derivative term, which adjusts the output of the controller according to the rate of change of the error;

[0102] S1.5. Map the PID output u(t) to the control signal of the heater. When u(t) > the start threshold, start the heater; when u(t) < the stop threshold, stop the heater.

[0103] In step S1.3 of this embodiment, when the current temperature is lower than the lower limit temperature, the error is:

[0104] e(t) = T low - T current ;

[0105] In the formula, e(t) represents the error value at time point t, which is used to measure the difference between the current system state and the target state; T low Represents the lowest temperature value in the set target temperature range; T current Represents the actual temperature value currently measured by the system;

[0106] When the current temperature is higher than the upper limit temperature, the error is:

[0107] e(t) = T high - T current ;

[0108] In the formula, T high Represents the highest temperature value in the set target temperature range;

[0109] When the current temperature is less than or equal to the upper limit temperature and greater than or equal to the lower limit temperature, the error is:

[0110] e(t) = 0.

[0111] The PID controller can accurately adjust the working state of the heating or cooling device according to the difference (error) between the current temperature and the target temperature, ensuring that the system can maintain a constant temperature in a dynamic environment; the temperature in the marine environment fluctuates greatly, and PID control can quickly respond to and compensate for these external disturbances through the coordinated work of its proportional, integral, and derivative terms, thereby reducing the temperature deviation; by appropriately adjusting the PID parameters, the oscillation phenomenon in the system can be effectively suppressed, avoiding large fluctuations or overshoots of the temperature, which is crucial for protecting sensitive equipment; the marine platform may experience different working loads and environmental conditions, and the PID control has a certain adaptive ability and can adapt to these changes by adjusting the gain to ensure the stable operation of the system.

[0112] In this embodiment, considering the change of the external temperature, the temperature control module 21 introduces the recursive least squares algorithm to adjust the parameters. The specific steps for optimizing the PID control algorithm are as follows:

[0113] S2.1. After calculating the error, the rate of change of the error, and the cumulative value of the error, define the regression vector φ(t). The mathematical expression of φ(t) is:

[0114] φ(t) = [y(t - 1), u(t - 1),..., y(t - n), u(t - n)]T;

[0115] In the formula, φ(t) represents the regression vector; y(t - 1) represents the output of the system at time t - 1; u(t - 1) represents the input of the system at time t - 1;

[0116] S2.2. Update the system parameter estimation using the recursive least squares algorithm;

[0117] S2.3. Adjust the PID parameters K 、K p 、K i and K d according to the system parameters estimated by the recursive least squares algorithm;

[0118] S2.4. Apply the updated K p 、K i and K d to the output formula of the PID controller:

[0119] u′(t) = K p (t)e(t) + K i (t)Integral(t) + K d (t)Δe(t);

[0120] In the formula, u′(t) represents the output signal of the updated PID controller at time t; it can directly control the working states of devices such as heaters and cooling devices; through error compensation and dynamic adjustment, ensure that the system can reach the target temperature quickly and stably; optimize the response speed, eliminate the steady-state error, and find a balance between stability and robustness; combine the dynamically adjusted parameters to enhance the adaptability and intelligent level of the system; by analyzing the change of u′(t), the system state can be evaluated and potential problems can be identified.

[0121] S2.5. Repeat steps S2.1 to S2.4 until the system reaches a stable state.

[0122] The main purpose of introducing the recursive least squares (RLS) algorithm into the temperature control module is to improve the adaptability and accuracy of the PID controller, enabling it to maintain an efficient and stable working state in a changing external environment. The RLS algorithm helps the PID controller better understand and respond to the actual behavior of the system by updating the system model parameters in real time, thus achieving more precise and reliable temperature control.

[0123] In this embodiment S2.2, when using the recursive least squares algorithm to update the system parameter estimation, the mathematical expression of the prediction error is:

[0124]

[0125] where y(t) represents the actual output of the system at time t; φ T (t) represents the transpose of the regression vector φ T (t); represents the estimation of the system parameters at time t - 1;

[0126] The mathematical expression of the gain matrix is:

[0127] K(t) = P(t - 1)φ(t)[λ + φ T (t)P(t - 1)φ(t)] -1 ;

[0128] where K(t) represents the gain matrix. A larger K(t) makes the system more sensitive to errors and thus responds faster; a smaller K(t) weakens the response speed; P(t - 1) represents the covariance matrix at time t - 1; λ represents the forgetting factor, which controls the forgetting speed of old data;

[0129] The mathematical expression of the parameter estimation is:

[0130]

[0131] where represents the estimated value of the system parameters at time t;

[0132] The mathematical expression of the covariance matrix is:

[0133]

[0134] where P(t) represents the covariance matrix at time t.

[0135] In this embodiment S2.3, according to the system parameters estimated by the recursive least squares algorithm adjust the PID parameters K p , K i and K d , then after adjustment, Kp (t) has the following mathematical expression:

[0136]

[0137] Where α determines the estimated value of the system parameters Regarding the proportional gain K p (t), if α is larger, then the change of will have a greater impact on K p (t); if α is smaller, then the impact will be weakened; represents the first element of the estimated value of the system parameters at time t; β is used to provide additional flexibility to adjust the base value of the proportional gain, even if is zero, β still ensures that K p (t) has a non-zero base value;

[0138] The adjusted K i (t) has the following mathematical expression:

[0139]

[0140] Where γ determines the estimated value of the system parameters Regarding the integral gain K i (t); represents the second element of the estimated value of the system parameters at time t; δ is used to provide additional flexibility to adjust the base value of the integral gain;

[0141] The adjusted K d (t) has the following mathematical expression:

[0142]

[0143] Where ∈ determines the estimated value of the system parameters Regarding the derivative gain K d (t); represents the third element of the estimated value of the system parameters at time t; ζ is used to provide additional flexibility to adjust the base value of the derivative gain.

[0144] In this embodiment, the fuse protection module 22 monitors the temperature of the heater through an intelligent monitoring and control algorithm to prevent damage to the device due to excessive current. The specific implementation steps for the intelligent monitoring and control algorithm to monitor the temperature of the heater are as follows:

[0145] S3.1. Set the alarm temperature for early warning;

[0146] S3.2. Filter the collected temperature and current data to reduce noise interference; if there is noise in the measurement data of the temperature sensor, directly using the temperature change rate formula may cause unstable calculation results.

[0147] S3.3. Calculate the temperature change rate of the heater to monitor the rising speed of the temperature.

[0148] S3.4. Determine whether there is an abnormality based on the current temperature, current current, and temperature change rate.

[0149] In this embodiment, in addition to performing fuse protection by judging the current temperature, current current, and temperature change rate, there is also a manual circuit breaker, which is used to cut off the circuit when the current exceeds the safe value to prevent damage caused by overload or short circuit. This protection mechanism helps prevent electrical fires and equipment damage; different from the automatic circuit breaker, the manual circuit breaker requires manual intervention to turn on or off the circuit. This makes it very suitable for situations where power needs to be cut off after human judgment, such as cutting off the power during maintenance or in an emergency; the manual circuit breaker can serve as an isolation point for the circuit to ensure that the circuit is completely powered off during maintenance or inspection, protecting the safety of the staff; once the circuit breaker trips due to overload or short circuit, the manual circuit breaker can restore power supply through a simple manual reset operation without the need to replace any components, which is convenient and fast; due to its relatively simple design, the manual circuit breaker is usually very reliable and has low maintenance requirements, suitable for various different environmental conditions.

[0150] In this embodiment S3.3, calculate the temperature change rate of the heater to monitor the rising speed of the temperature, and its mathematical expression of the temperature change rate is;

[0151]

[0152] In the formula, represents the change speed of temperature with time. During the heating or cooling process, can be used to judge whether the heater is working effectively or whether the system is approaching a steady state; T current (t) represents the actual temperature at the current moment t; T current (t - Δt) represents the temperature value of the system at the previous sampling moment, which is used to calculate the temperature change; Δt represents the sampling time interval, and the size of Δt will affect the accuracy and stability of the numerical differentiation. A smaller Δt improves the accuracy but may introduce noise; a larger Δt will reduce the accuracy.

[0153] In this embodiment S3.4, the method for judging whether there is an abnormality based on the current temperature, current current, and temperature change rate is that when T current (t) > T alarm or I current(t) > I max When triggered, an anomaly detection mechanism is activated; when the temperature change rate exceeds the set safety threshold, it is considered that there may be a risk of overheating of the heater.

[0154] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent temperature control module for an offshore platform, characterized in that, including, a temperature acquisition unit (1) for detecting the temperature inside the container and inputting it into the temperature control unit (2) through a cable; a temperature control unit (2) for reading the temperature obtained by the temperature acquisition unit (1) and controlling the heating and power-off of the heater through a PID control algorithm; a feedback adjustment unit (3) for adjusting the control strategy according to external conditions to optimize the energy usage efficiency; an alarm notification unit (4) for notifying the user by means of sound, light, and text message when an abnormality occurs in the system.

2. The intelligent temperature control module for an offshore platform according to claim 1, characterized in that The temperature control unit (2) includes a temperature control module (21) and a fuse protection module (22); The temperature control module (21) reads the temperature obtained by the temperature acquisition unit (1) and realizes the heating of the heater through a PID control algorithm. Considering the change of the external temperature, a recursive least squares algorithm is introduced to adjust the parameters and optimize the PID control algorithm for improving the performance of the system under different working conditions; The fuse protection module (22) monitors the temperature of the heater through an intelligent monitoring and control algorithm to prevent equipment damage caused by excessive current.

3. The intelligent temperature control module for an offshore platform according to claim 2, wherein The temperature control module (21) reads the temperature obtained by the temperature acquisition unit (1) and realizes the heating of the heater through a PID control algorithm. Considering the change of the external temperature, a recursive least squares algorithm is introduced to adjust the parameters and optimize the PID control algorithm for improving the performance of the system under different working conditions. The specific implementation steps for the PID control algorithm to realize the heating of the heater are as follows: S1.

1. Set the target temperature range, where the lower limit temperature is 5°C and the upper limit temperature is 35°C; S1.

2. Set the initial PID parameters; S1.

3. Calculate the error according to the current temperature and the target temperature range; S1.

4. Calculate the change rate of the error and the cumulative value of the error. The mathematical expression for the change rate of the error is: In the formula, represents the rate of change of the error e(t) with respect to time t; e(t) represents the error value at time point t, which is used to measure the difference between the current system state and the target state; Δt represents the time interval between two samplings; The mathematical expression for the cumulative value of the error is: Integral(t) = Integral(t - Δt) + e(t)·Δt; In the formula, Integral(t) represents the cumulative value of the error at the current moment t; S1.

4. Calculate the output signal according to the PID control formula, and its mathematical expression for the output signal is: where, u(t) is the output signal of the controller at the current moment t; K p represents the proportional term, which adjusts the output of the controller according to the current error e(t); K i represents the integral term, which adjusts the output of the controller according to the cumulative value of the error; K d represents the derivative term, which adjusts the output of the controller according to the rate of change of the error; S1.

5. Map the PID output u(t) to the control signal of the heater. When u(t) > the start threshold, start the heater; when u(t) < the stop threshold, stop the heater.

4. The intelligent temperature control module for an offshore platform according to claim 3, wherein In S1.3, when the current temperature is less than the lower limit temperature, the error is: e(t) = T low -T current ; Where e(t) represents the error value at time point t, which is used to measure the difference between the current system state and the target state; T low represents the lowest temperature value in the set target temperature range; T curtent Indicates the actual temperature value currently measured by the system; When the current temperature is greater than the upper limit temperature, the error is: e(t) = T high -T current ; Where T high represents the highest temperature value in the set target temperature range; When the current temperature is less than or equal to the upper limit temperature and greater than or equal to the lower limit temperature, the error is: e(t) = 0.

5. The intelligent temperature control module for an offshore platform according to claim 2, wherein The specific steps for the temperature control module (21) to introduce a recursive least squares algorithm to adjust the parameters and optimize the PID control algorithm considering the change of the external temperature are as follows: S2.

1. After calculating the error, the change rate of the error, and the cumulative value of the error, define the regression vector φ(t). Then the mathematical expression for φ(t) is: φ(t) = [y(t - 1), u(t - 1),..., y(t - n), u(t - n)] T ; Where, φ(t) represents the regression vector; y(t - 1) represents the output of the system at time t - 1; u(t - 1) represents the input of the system at time t - 1; S2.

2. Update the system parameter estimation using the recursive least squares algorithm; S2.

3. System parameters estimated according to the recursive least squares algorithm Adjust the PID parameters K p , K i and K d ; S2.

4. Apply the updated K p , K i and K d to the output formula of the PID controller: u′(t) = K p (t)e(t) + K i (t)Integral(t) + K d (t)Δe(t); Where, u′(t) represents the output signal of the updated PID controller at time t; S2.

5. Repeat steps S2.1 to S2.4 until the system reaches a stable state.

6. The intelligent temperature control module for an offshore platform according to claim 5, characterized in that In the above S2.2, when using the recursive least squares algorithm to update the system parameter estimation, the mathematical expression of the prediction error is: where, y(t) represents the actual output of the system at time t; φ T (t) represents the transpose of the regression vector φ T (t); represents the estimation of the system parameters at time t - 1; The mathematical expression of the gain matrix is: K(t) = P(t - 1)φ(t)[λ + φ T (t)P(t - 1)φ(t)] -1 ; Where, K(t) represents the gain matrix; P(t - 1) represents the covariance matrix at time t - 1; λ represents the forgetting factor, which controls the forgetting rate of old data; The mathematical expression of the parameter estimation is: In the formula, represents the estimated value of the system parameters at time t; The mathematical expression of the covariance matrix is: Where, P(t) represents the covariance matrix at time t.

7. The offshore platform intelligent temperature control module according to claim 6, characterized in that: In S2.3, the system parameters estimated by the recursive least squares algorithm are Adjust PID parameter K p , K i and K d , then after adjustment K p The mathematical expression of (t) is: where α determines the estimated value of the system parameters for the proportional gain K p (t) the degree of influence; represents the first element of the estimated value of the system parameters at time t; β is used to provide additional flexibility to adjust the base value of the proportional gain; Adjusted K i (t)'s mathematical expression is: where γ determines the estimated value of the system parameters for the integral gain K i (t) the degree of influence; represents the second element of the estimated value of the system parameters at time t; δ is used to provide additional flexibility to adjust the base value of the integral gain; Adjusted K d The mathematical expression of (t) is: where ∈ determines the estimated value of the system parameters For the differential gain K d (t) the degree of influence; represents the third element of the estimated value of the system parameters at time t; ζ is used to provide additional flexibility to adjust the base value of the differential gain.

8. The intelligent temperature control module for an offshore platform according to claim 2, characterized in that, The fuse protection module (22) monitors the temperature of the heater through an intelligent monitoring and control algorithm to prevent damage to the device due to excessive current. The specific implementation steps of the intelligent monitoring and control algorithm for monitoring the temperature of the heater are as follows: S3.

1. Set the alarm temperature for early warning; S3.

2. Filter the collected temperature and current data to reduce noise interference; S3.

3. Calculate the temperature change rate of the heater to monitor the rising speed of the temperature; S3.

4. Judge whether there is an abnormality according to the current temperature, current current and temperature change rate.

9. The intelligent temperature control module for an offshore platform according to claim 8, characterized in that, In the above S3.3, when calculating the temperature change rate of the heater to monitor the rising speed of the temperature, the mathematical expression of the temperature change rate is; In the formula, represents the rate of change of temperature with time; T current (t) represents the actual temperature at the current moment t; T current (t - Δt) represents the temperature value of the system at the previous sampling moment, which is used to calculate the change in temperature; Δt represents the sampling time interval.

10. The intelligent temperature control module for an offshore platform according to claim 8, characterized in that, In S3.4, the method for determining whether there is an abnormality based on the current temperature, the current current, and the temperature change rate is that when T current (t)>T alarm or I current (t)>I max , the abnormality detection mechanism is triggered; if the temperature change rate exceeds the set safety threshold, it is considered that there may be a risk of overheating of the heater.

Images