CN117872328A - Intelligent fault detection and wind measurement system for 3D laser wind measurement radar - Google Patents
Intelligent fault detection and wind measurement system for 3D laser wind measurement radar Download PDFInfo
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Abstract
The application provides an intelligent fault detection and wind measurement system for a 3D laser wind measurement radar, which comprises an autonomous determination of fault information, an autonomous generation of a reconstruction strategy of a fault, an adaptive wind speed inversion algorithm and an autonomous wind measurement task planning. The system utilizes advanced trusted artificial intelligence and data analysis technology to realize fault self-diagnosis and reconstruction, accurate wind speed inversion and efficient wind measuring task planning so as to improve the performance and reliability of the cabin type wind measuring radar system.
Description
Technical Field
The application relates to the technical field of laser wind measurement, in particular to an intelligent fault detection and wind measurement system for a 3D laser wind measurement radar.
Background
The laser wind-finding radar is an atmosphere detecting instrument used in the fields of power and electric engineering and energy science and technology, and is started in the year of 2016, 6 and 7. The horizontal wind speed, the vertical wind speed, the temperature, the wind direction, the wind shear and the turbulence intensity are accurately measured by the laser wind-finding radar. However, the maintenance cost of the laser wind-finding radar is high, the problematic area is not easy to determine, and professional technicians are required to perform periodic maintenance.
Disclosure of Invention
The present application aims to solve, at least to some extent, one of the technical problems in the related art.
Therefore, the purpose of the application is to provide an intelligent fault detection and wind measurement system diagram for a 3D laser wind measurement radar, and aims to realize fault self-diagnosis and reconstruction, accurate wind speed inversion and efficient wind measurement task planning by utilizing advanced trusted artificial intelligence and data analysis technology so as to improve the performance and reliability of a cabin type wind measurement radar system.
To achieve the above objective, an embodiment of the present application provides an intelligent fault detection and wind measurement system for a 3D laser wind measurement radar, including:
the autonomous fault detection module is used for performing autonomous fault detection on the software part of the laser wind-finding radar;
the autonomous generation fault reconstruction strategy module is used for autonomously generating a reconstruction strategy based on an autonomous fault detection result of the autonomous fault detection module so as to ensure continuous operation of the laser wind-finding radar;
the self-adaptive wind speed and direction inversion module is used for inverting the wind speed and direction in the wind field where the laser wind measuring radar is located so that the laser wind measuring radar is timely adapted to the change of the ground wind field;
and the autonomous wind measuring task planning module is used for autonomously planning a wind measuring task according to a remotely issued wind measuring area and an instruction, and realizing wind field measurement, wind energy evaluation and wind field information acquisition around a fan at a fan station where the laser wind measuring radar is positioned.
Wherein, the autonomous fault detection module includes:
the real-time monitoring alarm unit is used for monitoring the working state and data output of the laser wind-finding radar in real time, and sending out an alarm and providing related fault information in time when an abnormal condition is found, so that operation and maintenance personnel can quickly take proper measures to maintain and remove faults;
the abnormality detection unit is used for automatically detecting and identifying the abnormality found by the real-time monitoring alarm unit so as to ensure the normal operation of the system;
the fault diagnosis, recording and analyzing unit is used for providing a fault diagnosis function according to the abnormal condition, recording and storing fault information, and simultaneously, rapidly and accurately identifying faults by analyzing the existing fault mode and historical data and combining a trusted artificial intelligence algorithm to assist operation and maintenance personnel in determining fault reasons and fault positions;
the long-term stability monitoring unit is used for monitoring the long-term stability of the laser wind-finding radar, detecting and analyzing factors possibly causing the performance degradation of the system so as to prevent or repair potential faults in advance;
the fault prediction and early warning unit is used for performing fault prediction and early warning according to historical data and fault mode analysis, discovering potential faults in advance, and reducing unpredictable halts or losses caused by the faults.
The fault diagnosis, recording and analyzing unit records and stores fault information at least including fault time, fault type and fault processing process; the long-term stability monitoring unit detects and analyzes factors that may lead to degradation in system performance including at least sensor aging and optical path drift.
The reconstruction strategy of the autonomous generation fault reconstruction strategy module comprises automatic parameter adjustment or scanning mode, a restarting module, an optical system recalibration, autonomous repair and optimization; wherein,
the reconstruction strategy is generated by a trusted artificial intelligence algorithm, so that accurate and reliable wind measurement data can be provided when a system fails.
The logic for generating the reconstruction strategy by the autonomous generation fault reconstruction strategy module is as follows:
automatic restart and recovery: when the laser wind-finding radar has temporary faults or abnormal conditions, restarting or recovering operations can be automatically carried out so as to recover the normal working state as soon as possible, and the downtime caused by faults is reduced;
autonomous repair and optimization: after the fault or abnormal condition of the laser wind-finding radar is detected, the software part automatically carries out simple repairing and optimizing measures so as to recover the performance of the laser wind-finding radar and improve the measurement accuracy.
The self-adaptive wind speed and direction inversion module comprises:
the data preprocessing unit is used for preprocessing the original data acquired by the laser wind-finding radar, and comprises data consistency checking, outlier rejection, background noise filtering and frequency spectrum accumulation so as to improve the data quality and accuracy;
the inversion model self-adaptive optimization unit is used for establishing an inversion model and reversely deducing an estimated value of a wind speed vector by analyzing laser echo signals under different azimuth angles and corresponding Doppler frequency shift information;
the inversion result evaluation unit is used for evaluating and verifying the inversion result and comparing the consistency and the relative error of the inversion wind speed and the reference data so as to evaluate the accuracy and the reliability of the algorithm.
The inversion model self-adaptive optimization unit performs self-adaptive optimization on the inversion model, and according to the difference between an inversion result and actual measurement data, updates inversion model and parameters in real time according to new laser echo signals, doppler frequency shift data, meteorological observation data and anemometer tower data, and dynamically adjusts model parameters, so that accuracy and stability of wind speed inversion are further improved.
Wherein, independently wind-testing task planning module includes:
the environment information sensing unit is used for sensing current environment information in real time through the historical data and the real-time data of the laser wind-finding radar and the meteorological data means;
the task demand analysis unit is used for analyzing specific requirements on the wind measuring task through input or preset task demands;
the task planning unit is used for automatically planning an optimal wind measuring task based on the environmental information data and task demand analysis;
the data analysis and wind energy evaluation unit is used for analyzing and evaluating wind energy according to the collected real-time wind measuring data, and evaluating the wind energy utilization condition of a fan station by utilizing the data of the actually measured wind energy information and the fan performance model to provide wind energy evaluation reports and suggestions.
The environmental information at least comprises meteorological parameters of wind speed, wind direction, temperature and humidity.
The input or preset task demands at least comprise a measuring mode, a measuring range and a measuring precision, measuring time and period required once, and observing arrangement of time periods; the optimization objective of automatically planning the optimal anemometry task is to maximize the measurement range, minimize the observation time or maximize the measurement accuracy.
Compared with the prior art, the intelligent fault detection and wind measurement system for the 3D laser wind measurement radar provided by the invention comprises an autonomous fault information determination, an autonomous fault generation reconstruction strategy, an adaptive wind speed inversion algorithm and an autonomous wind measurement task planning. The system utilizes advanced trusted artificial intelligence and data analysis technology to realize fault self-diagnosis and reconstruction, accurate wind speed inversion and efficient wind measuring task planning so as to improve the performance and reliability of the cabin type wind measuring radar system.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a schematic structural diagram of an intelligent fault detection and wind measurement system for a 3D laser wind measurement radar according to an embodiment of the present disclosure;
fig. 2 is a logic schematic diagram of an intelligent fault detection and wind measurement system for a 3D laser wind measurement radar according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
An intelligent fault detection and anemometry system for a 3D laser wind lidar according to an embodiment of the present application is described below with reference to the accompanying drawings.
Fig. 1 is a schematic structural diagram of an intelligent fault detection and wind measurement system for a 3D laser wind measurement radar according to an embodiment of the present application. Fig. 2 is a logic schematic diagram of an intelligent fault detection and wind measurement system for a 3D laser wind measurement radar according to an embodiment of the present application. The system comprises:
and the autonomous fault detection module 110 is configured to perform autonomous fault detection on the software portion of the laser wind-finding radar.
The autonomous fault detection module 110 includes:
the real-time monitoring alarm unit is used for monitoring the working state and data output of the laser wind-finding radar in real time, and can timely give an alarm and provide relevant fault information when abnormal conditions are found, so that operation and maintenance personnel can quickly take proper measures to maintain and remove faults.
The abnormality detection unit is used for automatically detecting and identifying the abnormality found by the real-time monitoring alarm unit so as to ensure the normal operation of the system.
The fault diagnosis, recording and analyzing unit is used for providing a fault diagnosis function according to the abnormal condition, recording and storing fault information, and at least comprises fault time, fault type and fault processing process; meanwhile, by analyzing the existing fault mode and historical data and combining a trusted artificial intelligence algorithm, the fault is rapidly and accurately identified, and operation and maintenance personnel are assisted in determining the cause and the position of the fault.
And the long-term stability monitoring unit is used for monitoring the long-term stability of the laser wind-finding radar, detecting and analyzing factors possibly causing the performance degradation of the system, including at least sensor aging and optical path drift, so as to prevent or repair potential faults in advance.
The fault prediction and early warning unit is used for performing fault prediction and early warning according to historical data and fault mode analysis, discovering potential faults in advance, and reducing unpredictable halts or losses caused by the faults.
The fault detection types and detection methods involved in the specific embodiment are as follows:
TABLE 1 fault types and detection methods
And the autonomous generation fault reconstruction policy module 120 is configured to autonomously generate a reconstruction policy based on an autonomous fault detection result of the autonomous fault detection module, so as to ensure continuous operation of the laser wind lidar.
And after the fault occurs, generating a proper reconstruction strategy according to the type and degree of the fault, so as to ensure the continuous operation of the radar system.
The reconstruction strategies of the autonomously generated fault reconstruction strategy module 120 include automatically adjusting parameters or scan patterns, restarting the module, recalibrating the optical system, autonomous repair and optimization; wherein,
the reconstruction strategy is generated by a trusted artificial intelligence algorithm, so that accurate and reliable wind measurement data can be provided when a system fails.
Wherein, the logic of generating the reconstruction policy by the autonomous generation failure reconstruction policy module 120 is:
automatic restart and recovery: when the laser wind-finding radar has temporary faults or abnormal conditions, restarting or recovering operations can be automatically carried out so as to recover the normal working state as soon as possible, and the downtime caused by faults is reduced;
autonomous repair and optimization: after the fault or abnormal condition of the laser wind-finding radar is detected, the software part automatically carries out simple repairing and optimizing measures so as to recover the performance of the laser wind-finding radar and improve the measurement accuracy.
The adaptive wind speed and direction inversion module 130 is configured to invert the wind speed and direction in the wind field where the laser wind measuring radar is located, so that the laser wind measuring radar is adapted to the change of the ground wind field in time.
The existing self-adaptive wind speed and direction inversion method has the following problems:
1. instability: inversion algorithms for laser anemometry radar wind speed and direction are sensitive to changes in the measurement environment or conditions. For example, factors such as poor visibility, atmospheric turbulence, etc., can have an impact on the quality of the laser wind measurement, resulting in instability of the inversion results.
2. Measurement error: since there are various error sources in the measurement process, such as noise, instrument drift, etc., these errors can have an impact on the inversion result. Current inversion algorithms require further improvements and optimizations in handling measurement errors.
3. Complicated atmospheric environment: the laser wind-finding radar can be influenced by the complexity of the atmospheric environment in practical application, such as turbulence, turbulence pulsation, meteorological abnormality and the like. These factors can present certain difficulties in inverting both wind speed and direction.
According to the self-adaptive wind speed inversion algorithm of the laser wind-finding radar, through links such as data preprocessing, self-adaptive optimization of an inversion model, inversion result evaluation and the like, adjustment and optimization are needed to be carried out by combining specific practical application scenes and data characteristics, so that accurate and stable wind speed inversion is realized, and the change of a ground wind field can be adapted in time.
Specifically, the adaptive wind speed and direction inversion module 130 includes:
the data preprocessing unit is used for preprocessing the original data acquired by the laser wind-finding radar, and comprises data consistency checking, outlier rejection, background noise filtering and frequency spectrum accumulation so as to improve the data quality and accuracy;
the inversion model self-adaptive optimization unit is used for establishing an inversion model and reversely deducing an estimated value of a wind speed vector by analyzing laser echo signals under different azimuth angles and corresponding Doppler frequency shift information;
the inversion model self-adaptive optimization unit performs self-adaptive optimization on the inversion model, and updates inversion model and parameters in real time according to new laser echo signals, doppler frequency shift data, meteorological observation data and anemometer tower data and the difference between inversion results and actual measurement data, so that model parameters are dynamically adjusted, and the accuracy and stability of wind speed inversion are further improved.
The inversion result evaluation unit is used for evaluating and verifying the inversion result and comparing the consistency and the relative error of the inversion wind speed and the reference data so as to evaluate the accuracy and the reliability of the algorithm.
And the autonomous wind measuring task planning module 140 is used for autonomously planning a wind measuring task according to a remotely issued wind measuring area and an instruction, and realizing wind field measurement, wind energy evaluation and wind field information acquisition around a fan at which the laser wind measuring radar is positioned.
In the embodiment of the invention, the autonomous wind-measuring task planning function of the laser wind-measuring radar is an intelligent decision system, and the autonomous planning of the measuring area, the time range, the target precision and the like of the wind-measuring task is realized according to the remotely issued wind-measuring area and the instruction, so as to realize wind field measurement, wind energy evaluation of a fan station and wind field information acquisition around the fan.
Specifically, the autonomous wind mission planning module 140 includes:
the environment information sensing unit is used for sensing current environment information in real time through the historical data and the real-time data of the laser wind-finding radar and the meteorological data means; the environmental information at least comprises meteorological parameters of wind speed, wind direction, temperature and humidity.
The task demand analysis unit is used for analyzing specific requirements on the wind measuring task through input or preset task demands; the input or preset task demands at least comprise a measuring mode, a measuring range and a measuring precision, measuring time and period required once, and observing arrangement of time periods; the optimization objective of automatically planning the optimal anemometry task is to maximize the measurement range, minimize the observation time or maximize the measurement accuracy.
The task planning unit is used for automatically planning an optimal wind measuring task based on the environmental information data and task demand analysis;
the data analysis and wind energy evaluation unit is used for analyzing and evaluating wind energy according to the collected real-time wind measuring data, and evaluating the wind energy utilization condition of a fan station by utilizing the data of the actually measured wind energy information and the fan performance model to provide wind energy evaluation reports and suggestions.
In summary, the intelligent fan cabin type laser wind-finding radar autonomous wind-finding task planning function should be capable of autonomously planning and optimizing the wind-finding task according to real-time environmental information and task requirements, and has the characteristics of data analysis and wind energy evaluation. Therefore, the efficiency, accuracy and reliability of wind measuring tasks can be improved, and better support is provided for wind energy management and optimization.
In order to achieve the above embodiments, the present application further proposes an electronic device including: a processor, and a memory communicatively coupled to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the methods provided by the previous embodiments.
In order to implement the above-mentioned embodiments, the present application also proposes a computer-readable storage medium in which computer-executable instructions are stored, which when executed by a processor are adapted to implement the methods provided by the foregoing embodiments.
In order to implement the above embodiments, the present application also proposes a computer program product comprising a computer program which, when executed by a processor, implements the method provided by the above embodiments.
The processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the user related in the application all accord with the regulations of related laws and regulations, and do not violate the popular public order.
It should be noted that personal information from users should be collected for legitimate and reasonable uses and not shared or sold outside of these legitimate uses. In addition, such collection/sharing should be performed after receiving user informed consent, including but not limited to informing the user to read user agreements/user notifications and signing agreements/authorizations including authorization-related user information before the user uses the functionality. In addition, any necessary steps are taken to safeguard and ensure access to such personal information data and to ensure that other persons having access to the personal information data adhere to their privacy policies and procedures.
The present application contemplates embodiments that may provide a user with selective prevention of use or access to personal information data. That is, the present disclosure contemplates that hardware and/or software may be provided to prevent or block access to such personal information data. Once personal information data is no longer needed, risk can be minimized by limiting data collection and deleting data. In addition, personal identification is removed from such personal information, as applicable, to protect the privacy of the user.
In the foregoing descriptions of embodiments, descriptions of the terms "one embodiment," "some embodiments," "example," "particular example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. An intelligent fault detection and wind measurement system for a 3D laser wind measurement radar, comprising:
the autonomous fault detection module is used for performing autonomous fault detection on the software part of the laser wind-finding radar;
the autonomous generation fault reconstruction strategy module is used for autonomously generating a reconstruction strategy based on an autonomous fault detection result of the autonomous fault detection module so as to ensure continuous operation of the laser wind-finding radar;
the self-adaptive wind speed and direction inversion module is used for inverting the wind speed and direction in the wind field where the laser wind measuring radar is located so that the laser wind measuring radar is timely adapted to the change of the ground wind field;
and the autonomous wind measuring task planning module is used for autonomously planning a wind measuring task according to a remotely issued wind measuring area and an instruction, and realizing wind field measurement, wind energy evaluation and wind field information acquisition around a fan at a fan station where the laser wind measuring radar is positioned.
2. The intelligent fault detection and anemometry system for 3D lidar of claim 1, wherein the autonomous fault detection module comprises:
the real-time monitoring alarm unit is used for monitoring the working state and data output of the laser wind-finding radar in real time, and sending out an alarm and providing related fault information in time when an abnormal condition is found, so that operation and maintenance personnel can quickly take proper measures to maintain and remove faults;
the abnormality detection unit is used for automatically detecting and identifying the abnormality found by the real-time monitoring alarm unit so as to ensure the normal operation of the system;
the fault diagnosis, recording and analyzing unit is used for providing a fault diagnosis function according to the abnormal condition, recording and storing fault information, and simultaneously, rapidly and accurately identifying faults by analyzing the existing fault mode and historical data and combining a trusted artificial intelligence algorithm to assist operation and maintenance personnel in determining fault reasons and fault positions;
the long-term stability monitoring unit is used for monitoring the long-term stability of the laser wind-finding radar, detecting and analyzing factors possibly causing the performance degradation of the system so as to prevent or repair potential faults in advance;
the fault prediction and early warning unit is used for performing fault prediction and early warning according to historical data and fault mode analysis, discovering potential faults in advance, and reducing unpredictable halts or losses caused by the faults.
3. The intelligent fault detection and anemometry system for 3D lidar of claim 2, wherein the fault diagnosis, recording and analysis unit records and saves fault information including at least fault time, fault type, fault handling process; the long-term stability monitoring unit detects and analyzes factors that may lead to degradation in system performance including at least sensor aging and optical path drift.
4. The intelligent fault detection and anemometry system for 3D lidar of claim 1 wherein the reconstruction strategy of the autonomously generated fault reconstruction strategy module comprises automatically adjusting parameters or scan patterns, restarting modules, recalibrating optical systems, autonomous repair and optimization; wherein,
the reconstruction strategy is generated by a trusted artificial intelligence algorithm, so that accurate and reliable wind measurement data can be provided when a system fails.
5. The intelligent fault detection and anemometry system for 3D lidar of claim 4 wherein the logic of the autonomous generation fault reconstruction policy module to generate the reconstruction policy is:
automatic restart and recovery: when the laser wind-finding radar has temporary faults or abnormal conditions, restarting or recovering operations can be automatically carried out so as to recover the normal working state as soon as possible, and the downtime caused by faults is reduced;
autonomous repair and optimization: after the fault or abnormal condition of the laser wind-finding radar is detected, the software part automatically carries out simple repairing and optimizing measures so as to recover the performance of the laser wind-finding radar and improve the measurement accuracy.
6. The intelligent fault detection and anemometry system for 3D lidar of claim 1, wherein the adaptive wind speed and direction inversion module comprises:
the data preprocessing unit is used for preprocessing the original data acquired by the laser wind-finding radar, and comprises data consistency checking, outlier rejection, background noise filtering and frequency spectrum accumulation so as to improve the data quality and accuracy;
the inversion model self-adaptive optimization unit is used for establishing an inversion model and reversely deducing an estimated value of a wind speed vector by analyzing laser echo signals under different azimuth angles and corresponding Doppler frequency shift information;
the inversion result evaluation unit is used for evaluating and verifying the inversion result and comparing the consistency and the relative error of the inversion wind speed and the reference data so as to evaluate the accuracy and the reliability of the algorithm.
7. The intelligent fault detection and wind measurement system for the 3D laser wind measurement radar according to claim 6, wherein the inversion model self-adaptive optimization unit performs self-adaptive optimization on an inversion model, updates inversion model and parameters in real time according to new laser echo signals, doppler frequency shift data, meteorological observation data and wind measurement tower data according to the difference between an inversion result and actual measurement data, and dynamically adjusts model parameters to further improve accuracy and stability of wind speed inversion.
8. The intelligent fault detection and anemometry system for 3D lidar of claim 1, wherein the autonomous anemometry mission planning module comprises:
the environment information sensing unit is used for sensing current environment information in real time through the historical data and the real-time data of the laser wind-finding radar and the meteorological data means;
the task demand analysis unit is used for analyzing specific requirements on the wind measuring task through input or preset task demands;
the task planning unit is used for automatically planning an optimal wind measuring task based on the environmental information data and task demand analysis;
the data analysis and wind energy evaluation unit is used for analyzing and evaluating wind energy according to the collected real-time wind measuring data, and evaluating the wind energy utilization condition of a fan station by utilizing the data of the actually measured wind energy information and the fan performance model to provide wind energy evaluation reports and suggestions.
9. The intelligent fault detection and anemometry system for 3D lidar of claim 8 wherein the environmental information includes at least meteorological parameters of wind speed, wind direction, temperature, humidity.
10. The intelligent fault detection and anemometry system for 3D lidar of claim 8 wherein the input or preset task requirements include at least manner, range and accuracy of measurement, time and period required for one measurement, schedule of observation time period; the optimization objective of automatically planning the optimal anemometry task is to maximize the measurement range, minimize the observation time or maximize the measurement accuracy.
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