Safety monitoring system suitable for ocean wind power platform

Abstract

The invention belongs to the technical field of wind power platform supervision, and particularly relates to a safety monitoring system suitable for an ocean wind power platform, which comprises a structure risk analysis module, an external risk judgment module, a generator set monitoring module, a generator set fault identification module and a background supervision end, according to the invention, the bearing risk of the bearing structure in the ocean wind power platform is analyzed through the structure risk analysis module, and corresponding processing measures are taken for the bearing structure when the structure high-risk signal is generated. When the structure low-risk signal is generated, the hidden danger condition of the environment where the ocean wind power platform is located and the fault condition of the generator set are analyzed through the external risk judgment module and the generator set fault recognition module, and when the external high-risk signal or the generator set fault early warning signal is generated, early warning is given out to remind background supervisors. Safe and stable operation of the ocean power generation platform is guaranteed, the supervision difficulty is reduced, and effective monitoring and control of the ocean wind power platform are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of wind power platform supervision, and in particular to a safety monitoring system suitable for an offshore wind power platform. Background Art

[0002] Offshore wind power platforms refer to facilities that utilize offshore wind energy resources to convert wind energy into electrical energy through wind turbines. They are important clean energy facilities. The electricity generated by wind turbines is transmitted to the onshore power grid through cables to meet people's electricity needs.

[0003] As an important part of renewable energy, the safety and stability of offshore wind power platforms are of vital importance, so they need to be monitored for operation. However, it is currently difficult to reasonably feedback the load-bearing risk of offshore wind power platforms, and when it is judged that the load-bearing risk is small, it is impossible to accurately analyze the external hidden dangers of offshore wind power platforms and abnormal conditions of generator sets, which is not conducive to ensuring the safe and stable operation of offshore power generation platforms and reducing the difficulty of their supervision, and it is impossible to achieve effective monitoring and control of offshore wind power platforms;

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a safety monitoring system suitable for offshore wind power platforms, which solves the problem that the prior art is difficult to reasonably feedback the load-bearing risk of offshore wind power platforms, and cannot accurately analyze the external hidden dangers of offshore wind power platforms and abnormal conditions of generator sets when the load-bearing risk is judged to be small, which is not conducive to ensuring the safe and stable operation of offshore power generation platforms and reducing the difficulty of their supervision.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A safety monitoring system suitable for an offshore wind power platform, comprising a structural risk analysis module, an external risk judgment module, a generator set monitoring module, a generator set fault identification module and a background monitoring terminal;

[0008] The structural risk analysis module is used to monitor the load-bearing structure of the offshore wind power platform, analyze the load-bearing risk of the load-bearing structure, generate a high-risk structure signal or a low-risk structure signal through analysis, and send the high-risk structure signal to the background supervision end, and send the low-risk structure signal to the external risk judgment module and the generator set monitoring module;

[0009] The external risk judgment module analyzes the hidden dangers of the environment where the offshore wind power platform is located after receiving the low-risk structural signal, generates an external high-risk signal or an external low-risk signal through analysis, and sends the external high-risk signal to the background supervision end;

[0010] When receiving the low-risk structure signal, the generator set monitoring module collects various operating data of the generator set in the offshore wind power platform, and transmits the various operating data of the generator set in the offshore wind power platform to the generator set fault identification module;

[0011] The generator set fault identification module analyzes the generator set abnormalities through the fault identification model and based on the various operating data of the generator set, and identifies the generator set fault accordingly. When a generator set fault is detected, a generator set fault warning signal is generated and sent to the background supervision end; the background supervision end issues a corresponding warning when it receives a structural high-risk signal, an external high-risk signal or a generator set fault warning signal.

[0012] Furthermore, the specific analysis process of the structural risk analysis module includes:

[0013] The current settlement and inclination angle of the bearing structure in the offshore power generation platform are obtained and marked as the bearing settlement value and the bearing inclination value, respectively. The bearing settlement value and the bearing inclination value are numerically compared with the preset bearing settlement threshold and the preset bearing inclination threshold, respectively. If the bearing settlement value or the bearing inclination value exceeds the corresponding preset threshold, a high-risk signal of the structure is generated;

[0014] If both the bearing settlement value and the bearing inclination value do not exceed the corresponding preset threshold value, the bearing displacement is analyzed point by point to obtain the bearing risk value, and the structural risk coefficient is obtained by numerically calculating the bearing settlement value, the bearing inclination value and the bearing risk value. The structural risk coefficient is numerically compared with the preset structural risk coefficient threshold value. If the structural risk coefficient exceeds the preset structural risk coefficient threshold value, a high structural risk signal is generated; if the structural risk coefficient does not exceed the preset structural risk coefficient threshold value, a low structural risk signal is generated.

[0015] Furthermore, the specific analysis process of the bearing displacement point-by-point analysis is as follows:

[0016] A plurality of bearing points are set on the bearing structure of the marine power generation platform, and the real-time position coordinates of each bearing point are collected. Based on the real-time position coordinates of the corresponding two bearing points, the distance between the corresponding two bearing points is collected and marked as the actual distance measurement value; the difference between the actual distance measurement value and the initial distance value between the corresponding two bearing points is calculated and the absolute value is taken to obtain the distance variation value;

[0017] The distance variation value is numerically compared with the preset distance variation threshold. If the distance variation value exceeds the preset distance variation threshold, the corresponding distance variation value is marked as the distance anomaly value; the number of distance anomaly values ​​is obtained and the ratio is calculated with the number of distance variation values ​​to obtain the distance anomaly detection value, and the mean of all distance variation values ​​is calculated to obtain the distance analysis value, and the distance variation value with the largest value is marked as the distance variation amplitude value; the load-bearing shift risk value is obtained by numerically calculating the distance anomaly detection value, the distance analysis value and the distance variation amplitude value.

[0018] Furthermore, the specific analysis process of the external risk judgment module includes:

[0019] Through analysis, it is possible to determine in real time whether the offshore wind power platform is in an external risk warning state, obtain the total duration of the offshore wind power platform in the external warning state per unit time, and mark it as the external risk detection time value, and calculate the average of all external risk coefficients per unit time to obtain the external risk comprehensive evaluation value, and compare the external risk detection time value and the external risk comprehensive evaluation value with the preset external risk detection time threshold and the preset external risk comprehensive evaluation threshold. If the external risk detection time value or the external risk comprehensive evaluation value exceeds the corresponding preset threshold, an external high risk signal is generated; if neither the external risk detection time value nor the external risk comprehensive evaluation value exceeds the corresponding preset threshold, an external low risk signal is generated.

[0020] Furthermore, the specific analysis process of determining in real time whether the offshore wind power platform is in an external risk warning state is as follows:

[0021] The wind speed data of the environment where the offshore wind power platform is located is collected and marked as the platform wind inspection value, and the wave impact speed and wave height of the environment where the offshore wind power platform is located are collected and marked as the platform ocean impact value and the platform wave high inspection value respectively, and the external risk coefficient is obtained by numerically calculating the platform wind inspection value, the platform wave impact value and the platform wave high inspection value;

[0022] The external risk coefficient is numerically compared with a preset external risk coefficient threshold. If the external risk coefficient exceeds the preset external risk coefficient threshold, it is determined that the offshore wind power platform is in an external risk warning state.

[0023] Furthermore, the background supervision end is communicated with the maintenance management planning module, and the maintenance management planning module is used to set the monitoring period. If no high-risk structural signal is generated during the monitoring period, the risk status of the offshore wind power platform during the monitoring period will be comprehensively analyzed, and a strong platform management signal or a weak platform management signal will be generated through the analysis, and the strong platform management signal or the weak platform management signal will be sent to the background supervision end.

[0024] Furthermore, the specific analysis process of the maintenance management planning module is as follows:

[0025] The number of times external high-risk signals and generator set fault warning signals are generated during the monitoring period is collected and marked as external dangerous condition values ​​and fault potential values ​​respectively. The external dangerous condition values ​​and fault potential values ​​are numerically compared with the preset external dangerous condition thresholds and preset fault potential thresholds respectively. If the external dangerous condition value or the fault potential value exceeds the corresponding preset threshold, a strong management signal of the platform is generated.

[0026] Furthermore, if the external risk value and the fault potential value do not exceed the corresponding preset threshold value, a number of detection periods are set during the monitoring period. If an external high-risk signal or a generator set fault warning signal is generated during the corresponding detection period, the corresponding detection period is marked as an unstable power generation period; if no external high-risk signal and generator set fault warning signal are generated during the corresponding detection period, the corresponding detection period is marked as a stable power generation period;

[0027] The number of unstable power generation periods during the monitoring period is obtained and marked as unstable power generation time value, and the number of unstable power generation periods between two adjacent stable power generation periods is marked as unstable power generation duration value, the unstable power generation duration value is numerically compared with the preset unstable power generation duration threshold, and the number of unstable power generation duration values ​​exceeding the preset unstable power generation duration threshold during the monitoring period is marked as unstable power generation abnormality value;

[0028] The total duration of the generator set that cannot generate electricity normally due to faults during the monitoring period is collected and marked as the power generation downtime value, and the maintenance management planning coefficient is obtained by numerically calculating the external dangerous condition value, the fault hidden danger value, the unstable power generation condition value, the unstable power generation value and the power generation downtime value; the maintenance management planning coefficient is numerically compared with the preset maintenance management planning coefficient threshold. If the maintenance management planning coefficient exceeds the preset maintenance management planning coefficient threshold, a strong management signal of the platform is generated; if the maintenance management planning coefficient does not exceed the preset maintenance management planning coefficient threshold, a weak management signal of the platform is generated.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. In the present invention, the load-bearing risk of the load-bearing structure in the offshore wind power platform is analyzed through the structural risk analysis module, and corresponding treatment measures are taken for the load-bearing structure when a high-risk structural signal is generated. When a low-risk structural signal is generated, the hidden dangers of the environment in which the offshore wind power platform is located and the fault conditions of the generator set are analyzed and timely warned through the external risk judgment module and the generator set fault identification module, which is conducive to ensuring the safe and stable operation of the offshore power generation platform and reducing the difficulty of its supervision, and realizing effective monitoring and control of the offshore wind power platform;

[0031] 2. In the present invention, if no high-risk structural signal is generated during the monitoring period, the risk status of the offshore wind power platform during the monitoring period will be comprehensively analyzed through the maintenance management planning module, and a strong management signal for the platform or a weak management signal for the platform will be generated through analysis, which is conducive to the back-end supervisory personnel to scientifically formulate subsequent management plans for the offshore wind power platform, realize reasonable supervision planning for the offshore wind power platform, further reduce the work difficulty of the back-end supervisory personnel, and have a high degree of intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0033] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0034] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Embodiment 1: Figure 1 As shown, the present invention proposes a safety monitoring system suitable for an offshore wind power platform, comprising a structural risk analysis module, an external risk judgment module, a generator set monitoring module, a generator set fault identification module and a background monitoring terminal;

[0037] The structural risk analysis module monitors the load-bearing structure of the offshore wind power platform, analyzes the load-bearing risk of the load-bearing structure, generates a high-risk structure signal or a low-risk structure signal through analysis, and sends the high-risk structure signal to the background supervision end, and sends the low-risk structure signal to the external risk judgment module and the generator set monitoring module;

[0038] When the back-end supervisor receives a high-risk signal of the structure, it will issue a corresponding warning, which can reasonably analyze and accurately determine the load-bearing safety of the load-bearing structure in the offshore wind power platform, and promptly remind the back-end supervisor to take corresponding treatment measures on the load-bearing structure, thereby ensuring the subsequent operation safety of the offshore wind power platform; the specific analysis process of the structural risk analysis module is as follows:

[0039] The current settlement amount and inclination angle of the bearing structure in the marine power generation platform are obtained and marked as the bearing settlement value and the bearing inclination value, respectively. The bearing settlement value and the bearing inclination value are numerically compared with the preset bearing settlement threshold and the preset bearing inclination threshold, respectively. If the bearing settlement value or the bearing inclination value exceeds the corresponding preset threshold, it indicates that the bearing risk of the bearing structure of the marine power generation platform is relatively large, and a high-risk signal of the structure is generated;

[0040] If the bearing settlement value and the bearing inclination value do not exceed the corresponding preset threshold value, the bearing displacement is analyzed point by point to obtain the bearing displacement risk value, specifically: a number of bearing points are set on the bearing structure of the offshore power generation platform, and the real-time position coordinates of each bearing point are collected. Based on the real-time position coordinates of the corresponding two bearing points, the distance between the corresponding two bearing points is collected and marked as the actual distance measurement value; the actual distance measurement value and the initial distance value between the corresponding two bearing points are calculated and the absolute value is taken to obtain the distance change value;

[0041] Compare the distance variation value with the preset distance variation threshold, if the distance variation value exceeds the preset distance variation threshold, mark the corresponding distance variation value as the distance anomaly value; obtain the number of distance anomaly values ​​and calculate the ratio of the number of distance variation values ​​to obtain the distance anomaly detection value, calculate the mean of all distance variation values ​​to obtain the distance analysis value, and mark the distance variation value with the largest value as the distance variation amplitude value;

[0042] The load displacement value F is obtained by numerically calculating the position difference detection value W, the position distance analysis value R and the position distance variation value S through the formula F=a1*W+(a2*R+a3*S) / 2; wherein a1, a2 and a3 are preset proportional coefficients, and a1>a2>a3>0; and the larger the value of the load displacement value F is, the more serious the deformation and displacement condition of the bearing structure of the offshore wind power platform is;

[0043] By formula The structural risk coefficient Q is obtained by numerically calculating the bearing settlement value E, the bearing inclination value T and the bearing displacement value F; wherein hy1, hy2 and hy3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the structural risk coefficient Q is, the greater the overall bearing risk of the bearing structure of the offshore power generation platform is;

[0044] The structural risk coefficient Q is numerically compared with the preset structural risk coefficient threshold. If the structural risk coefficient Q exceeds the preset structural risk coefficient threshold, it indicates that the bearing risk of the load-bearing structure of the offshore power generation platform is generally large, and a high structural risk signal is generated; if the structural risk coefficient Q does not exceed the preset structural risk coefficient threshold, it indicates that the bearing risk of the load-bearing structure of the offshore power generation platform is generally small, and a low structural risk signal is generated.

[0045] The external risk judgment module analyzes the hidden dangers of the environment in which the offshore wind power platform is located after receiving the low-risk structural signal, generates an external high-risk signal or an external low-risk signal through analysis, and sends the external high-risk signal to the background supervision end. When the background supervision end receives the external high-risk signal, it issues a corresponding warning, which can accurately feedback the degree of adverse effects of the marine environment on the offshore wind power platform, and promptly remind the background supervision personnel to continue to pay attention to the operation status of the offshore wind power platform, so as to achieve effective supervision of the offshore wind power platform and significantly reduce the difficulty of supervision of the offshore wind power platform. The specific analysis process of the external risk judgment module is as follows:

[0046] The wind speed data of the environment in which the offshore wind power platform is located is collected and marked as the platform wind inspection value, and the wave impact speed and wave height of the environment in which the offshore wind power platform is located are collected and marked as the platform ocean impact value and the platform wave height inspection value respectively;

[0047] The external risk coefficient LP is obtained by numerically calculating the platform wind detection value LK, the platform wave impact value LM and the platform wave high detection value LG through the formula LP=(np1*LK+np2*LM+np3*LG) / 3; wherein np1, np2 and np3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the external risk coefficient LP is, the worse the marine environment condition of the marine power generation platform is;

[0048] The external risk coefficient LP is numerically compared with the preset external risk coefficient threshold. If the external risk coefficient LP exceeds the preset external risk coefficient threshold, it indicates that the current marine environment of the offshore power generation platform is in a bad state. The offshore wind power platform is judged to be in an external risk warning state. The total time that the offshore wind power platform is in the external warning state per unit time is obtained and marked as the external risk detection time value. The external risk comprehensive evaluation value is obtained by averaging all external risk coefficients per unit time.

[0049] The external risk detection time value and the external risk comprehensive evaluation value are numerically compared with the preset external risk detection time threshold and the preset external risk comprehensive evaluation threshold. If the external risk detection time value or the external risk comprehensive evaluation value exceeds the corresponding preset threshold, it indicates that the current marine environment has a large impact on the marine power generation platform, which is not conducive to the safe and stable operation of the marine power generation platform, and an external high-risk signal is generated; if the external risk detection time value and the external risk comprehensive evaluation value do not exceed the corresponding preset threshold, it indicates that the current marine environment has a small impact on the marine power generation platform, which is conducive to the safe and stable operation of the marine power generation platform, and an external low-risk signal is generated.

[0050] When receiving the low-risk structural signal, the generator set monitoring module collects various operating data of the generator set in the offshore wind power platform, and transmits various operating data of the generator set in the offshore wind power platform (including power output, temperature of internal components, vibration amplitude, etc.) to the generator set fault identification module;

[0051] The generator set fault identification module uses a fault identification model (the model can be a classifier based on machine learning, such as a support vector machine, a neural network or a random forest, or a model based on deep learning, such as a convolutional neural network (CNN) or a recurrent neural network (RNN), and the model can learn the mapping relationship between different fault types and features of the generator set through training) and analyzes the generator set abnormality based on various operating data of the generator set, thereby identifying the generator set fault;

[0052] When a generator set failure is detected, a generator set failure warning signal is generated and sent to the background supervision end. When the background supervision end receives the generator set failure warning signal, a corresponding warning is issued to remind the background supervision personnel to make reasonable control and disposal measures for the generator set, further ensuring the safe and stable operation of the offshore power generation platform and reducing its supervision difficulty. It has a high degree of intelligence.

[0053] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the background supervision end is communicated with the maintenance management planning module, and the maintenance management planning module is used to set the monitoring period. Preferably, the monitoring period is thirty days. If no high-risk structural signal is generated during the monitoring period, the risk status of the offshore wind power platform during the monitoring period is comprehensively analyzed, and a strong management signal or a weak management signal of the platform is generated through the analysis.

[0054] And send the platform strong management signal or platform weak management signal to the background supervision end. When the background supervision end receives the platform strong management signal, it strengthens the subsequent operation supervision of the offshore wind power platform, which is conducive to the background supervision personnel to scientifically formulate the subsequent management plan for the offshore wind power platform, realize the reasonable supervision planning of the offshore wind power platform, and has a high degree of intelligence, which further reduces the work difficulty of the background supervision personnel. The specific analysis process of the maintenance management planning module is as follows:

[0055] The number of times external high-risk signals and generator set fault warning signals are generated during the monitoring period is collected and marked as external dangerous condition values ​​and fault potential values, respectively. The external dangerous condition values ​​and fault potential values ​​are numerically compared with the preset external dangerous condition thresholds and preset fault potential thresholds, respectively. If the external dangerous condition value or the fault potential value exceeds the corresponding preset threshold, indicating that the operation of the marine power generation platform is not smooth during the monitoring period, and the subsequent supervision of the marine power generation platform needs to be strengthened, then a strong management signal for the platform is generated.

[0056] Furthermore, if the external risk value and the fault potential value do not exceed the corresponding preset threshold value, a number of detection periods are set during the monitoring period. If an external high-risk signal or a generator set fault warning signal is generated during the corresponding detection period, the corresponding detection period is marked as an unstable power generation period; if no external high-risk signal and generator set fault warning signal are generated during the corresponding detection period, the corresponding detection period is marked as a stable power generation period;

[0057] The number of unstable power generation periods during the monitoring period is obtained and marked as unstable power generation time value, and the number of unstable power generation periods between two adjacent stable power generation periods is marked as unstable power generation duration value, the unstable power generation duration value is numerically compared with the preset unstable power generation duration threshold, and the number of unstable power generation duration values ​​exceeding the preset unstable power generation duration threshold during the monitoring period is marked as unstable power generation abnormality value;

[0058] And the total time during which the generator set cannot generate electricity normally due to faults during the monitoring period is collected and marked as the power generation stop time value;

[0059] By formula The external dangerous condition value Yeg, the fault potential value Ykp, the unstable power generation condition value Ysw, the unstable power generation abnormal value Yng and the power generation shutdown value Ywq are numerically calculated to obtain the maintenance management planning coefficient YM; wherein sy1, sy2, sy3, sy4, sy5 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the maintenance management planning coefficient YM, the more unsmooth the operation of the marine power generation platform during the monitoring period;

[0060] The maintenance management planning coefficient YM is numerically compared with the preset maintenance management planning coefficient threshold. If the maintenance management planning coefficient YM exceeds the preset maintenance management planning coefficient threshold, it indicates that the operation of the marine power generation platform during the monitoring period is not smooth, and the subsequent supervision of the marine power generation platform needs to be strengthened, then a strong management signal of the platform is generated; if the maintenance management planning coefficient YM does not exceed the preset maintenance management planning coefficient threshold, it indicates that the operation of the marine power generation platform during the monitoring period is relatively smooth, then a weak management signal of the platform is generated.

[0061] The working principle of the present invention is as follows: when in use, the load-bearing structure of the offshore wind power platform is monitored through the structural risk analysis module, the load-bearing risk of the load-bearing structure is analyzed, and corresponding treatment measures are taken on the load-bearing structure when a high-risk structural signal is generated to ensure the subsequent safe operation of the offshore wind power platform. When a low-risk structural signal is generated, the hidden danger conditions of the environment in which the offshore wind power platform is located and the fault conditions of the generator set are analyzed through the external risk judgment module and the generator set fault identification module. When an external high-risk signal or a generator set fault warning signal is generated, a warning is issued to remind the background supervisory personnel, thereby further ensuring the safe and stable operation of the offshore power generation platform and reducing the difficulty of its supervision, thereby realizing effective monitoring and control of the offshore wind power platform with a high degree of intelligence.

[0062] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A safety monitoring system suitable for an offshore wind power platform, characterized in that: It includes structural risk analysis module, external risk judgment module, generator set monitoring module, generator set fault identification module and background supervision terminal; The structural risk analysis module is used to monitor the load-bearing structure of the offshore wind power platform, analyze the load-bearing risk of the load-bearing structure, generate a high-risk structure signal or a low-risk structure signal through analysis, and send the high-risk structure signal to the background supervision end, and send the low-risk structure signal to the external risk judgment module and the generator set monitoring module; The external risk judgment module analyzes the hidden dangers of the environment where the offshore wind power platform is located after receiving the low-risk structural signal, generates an external high-risk signal or an external low-risk signal through analysis, and sends the external high-risk signal to the background supervision end; When receiving the low-risk structure signal, the generator set monitoring module collects various operating data of the generator set in the offshore wind power platform, and transmits the various operating data of the generator set in the offshore wind power platform to the generator set fault identification module; The generator set fault identification module analyzes the generator set abnormality based on the fault identification model and various operating data of the generator set, and identifies the generator set fault accordingly. When a generator set fault is detected, a generator set fault warning signal is generated and sent to the background supervision end; The background monitoring end issues a corresponding warning when it receives a structural high-risk signal, an external high-risk signal or a generator set failure warning signal.

2. A safety monitoring system suitable for an offshore wind power platform according to claim 1, characterized in that: The specific analysis process of the structural risk analysis module includes: The current settlement and inclination angle of the bearing structure in the offshore power generation platform are obtained and marked as the bearing settlement value and the bearing inclination value respectively. If the bearing settlement value or the bearing inclination value exceeds the corresponding preset threshold, a high-risk signal of the structure is generated; If the bearing settlement value and the bearing inclination value do not exceed the corresponding preset threshold value, the bearing displacement is analyzed point by point to obtain the bearing risk value, and the structural risk coefficient is obtained by numerically calculating the bearing settlement value, the bearing inclination value and the bearing risk value. If the structural risk coefficient exceeds the preset structural risk coefficient threshold, a high structural risk signal is generated; if the structural risk coefficient does not exceed the preset structural risk coefficient threshold, a low structural risk signal is generated.

3. A safety monitoring system suitable for an offshore wind power platform according to claim 2, characterized in that: The specific analysis process of the point-by-point analysis of load displacement is as follows: A plurality of bearing points are set on the bearing structure of the marine power generation platform, and the real-time position coordinates of each bearing point are collected. Based on the real-time position coordinates of the corresponding two bearing points, the distance between the corresponding two bearing points is collected and marked as the actual distance measurement value; the difference between the actual distance measurement value and the initial distance value between the corresponding two bearing points is calculated and the absolute value is taken to obtain the distance variation value; If the distance variation value exceeds the preset distance variation threshold, the corresponding distance variation value is marked as the distance variation value; the number of distance variation values ​​is obtained and the ratio is calculated with the number of distance variation values ​​to obtain the distance variation detection value, and the average of all distance variation values ​​is calculated to obtain the distance analysis value, and the distance variation value with the largest value is marked as the distance variation amplitude value; the load-bearing shift risk value is obtained by numerically calculating the distance variation detection value, the distance analysis value and the distance variation amplitude value.

4. A safety monitoring system suitable for an offshore wind power platform according to claim 1, characterized in that: The specific analysis process of the external risk judgment module includes: Through analysis, it is possible to determine in real time whether the offshore wind power platform is in an external risk warning state, obtain the total duration of the offshore wind power platform in the external warning state per unit time and mark it as the external risk detection time value, and calculate the average of all external risk coefficients per unit time to obtain the external risk comprehensive evaluation value. If the external risk detection time value or the external risk comprehensive evaluation value exceeds the corresponding preset threshold, an external high risk signal is generated; if neither the external risk detection time value nor the external risk comprehensive evaluation value exceeds the corresponding preset threshold, an external low risk signal is generated.

5. A safety monitoring system suitable for an offshore wind power platform according to claim 4, characterized in that: The specific analysis process of judging in real time whether an offshore wind power platform is in an external risk warning state is as follows: The wind speed data of the environment in which the offshore wind power platform is located is collected and marked as the platform wind inspection value, and the wave impact speed and wave height of the environment in which the offshore wind power platform is located are collected and marked as the platform ocean impact value and the platform wave high inspection value respectively. The external risk coefficient is obtained by numerically calculating the platform wind inspection value, the platform wave impact value and the platform wave high inspection value; if the external risk coefficient exceeds the preset external risk coefficient threshold, it is judged that the offshore wind power platform is in an external risk warning state.

6. A safety monitoring system suitable for an offshore wind power platform according to claim 1, characterized in that: The backend supervision end is communicated with the maintenance management planning module, which is used to set the monitoring period. If no high-risk structural signal is generated during the monitoring period, a comprehensive analysis of the risk status of the offshore wind power platform during the monitoring period is conducted, and a strong platform management signal or a weak platform management signal is generated through the analysis, and the strong platform management signal or the weak platform management signal is sent to the backend supervision end.

7. A safety monitoring system suitable for an offshore wind power platform according to claim 6, characterized in that: The specific analysis process of the maintenance management planning module is as follows: The number of times external high-risk signals and generator set fault warning signals are generated during the monitoring period are collected and marked as external dangerous condition values ​​and fault potential values ​​respectively. If the external dangerous condition value or the fault potential value exceeds the corresponding preset threshold, a strong management signal of the platform is generated.

8. A safety monitoring system suitable for an offshore wind power platform according to claim 7, characterized in that: If the external risk value and the fault potential value do not exceed the corresponding preset threshold value, several detection periods are set during the monitoring period. If an external high-risk signal or a generator set fault warning signal is generated during the corresponding detection period, the corresponding detection period is marked as an unstable power generation period; If no external high-risk signal or generator set failure warning signal is generated during the corresponding detection period, the corresponding detection period is marked as a stable power generation period; The number of unstable power generation periods during the monitoring period is obtained and marked as unstable power generation time value, and the number of unstable power generation periods between two adjacent stable power generation periods is marked as unstable power generation duration value, the unstable power generation duration value is numerically compared with the preset unstable power generation duration threshold, and the number of unstable power generation duration values ​​exceeding the preset unstable power generation duration threshold during the monitoring period is marked as unstable power generation abnormality value; The total duration of the generator set being unable to generate electricity normally due to faults during the monitoring period is collected and marked as the power generation downtime value, and the maintenance management planning coefficient is obtained by numerically calculating the external dangerous condition value, the fault hidden danger value, the power generation unstable condition value, the power generation unstable abnormal value and the power generation downtime value; if the maintenance management planning coefficient exceeds the preset maintenance management planning coefficient threshold, a strong management signal of the platform is generated; If the maintenance management planning coefficient does not exceed the preset maintenance management planning coefficient threshold, a platform weak management signal is generated.

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