Automatic observation sunshine duration quality detection method, device and equipment and storage medium

Abstract

The application provides an automatic observation sunshine duration quality detection method, device and equipment and a storage medium, and relates to the technical field of data quality detection. The method comprises the following steps: acquiring observation station basic features, environmental features and sunshine picture shooting features of a target observation station; performing sunshine duration prediction and scene inspection according to the observation station basic features, the environmental features and the sunshine picture shooting features, and obtaining predicted sunshine duration and sunshine duration observation scenes of the target observation station; performing first quality detection on the automatic observation sunshine duration to be detected according to the predicted sunshine duration, and determining the confidence of the first quality detection result according to the sunshine duration observation scenes; determining suspicious data in the first quality detection result according to the confidence, and performing spatial consistency inspection and sunshine picture consistency inspection on the suspicious data, and updating the first quality detection result and the confidence according to the inspection result. The application can improve the accuracy and reliability of the automatic observation sunshine duration data.

Description

Technical Field

[0001] This invention relates to the field of data quality detection technology, and in particular to an automatic observation method, apparatus, equipment, and storage medium for detecting the quality of sunshine duration. Background Technology

[0002] In contemporary meteorological operations, obtaining accurate sunshine duration data has become an indispensable foundation for key tasks such as weather forecasting, in-depth climate analysis, and the provision of high-quality meteorological services. Taking weather forecasting as an example, sunshine duration is a crucial factor influencing changes in key meteorological elements such as surface temperature and air humidity, and changes in these elements further affect the dynamic development and evolution of weather systems. In the field of climate analysis, long-term trends in sunshine duration are extremely important for revealing the patterns and laws of climate change. In meteorological services, accurate sunshine duration data can provide scientific planting and irrigation recommendations for agricultural production, and also provide accurate power forecasts for the solar photovoltaic power generation industry, helping the industry to plan and manage energy output more effectively. With the continuous advancement of meteorological operations and the increasing demands for precision, more stringent standards have been set for the quality control of automatically observed sunshine duration data. This urgently requires us to adopt advanced quality control technologies to ensure the accuracy and reliability of the data to meet the growing operational needs.

[0003] In the current field of quality control for automatically observed sunshine hours, several mainstream techniques are commonly used, including but not limited to threshold-based verification techniques and time series analysis techniques. Threshold-based verification techniques rely on setting a reasonable threshold range for sunshine hours. This method is simple and easy to implement, but it is highly sensitive to the threshold setting; even a small error can lead to the incorrect processing of a large amount of normal data. Furthermore, this method lacks effective identification capabilities for anomalies within the threshold range, failing to guarantee data accuracy. Time series analysis techniques, on the other hand, focus on utilizing the temporal correlation of sunshine hours data itself for anomaly detection and data repair. However, when there is significant noise interference in the observed data, or when affected by sudden environmental factors such as cloud cover or instrument malfunction, the accuracy of time series analysis is significantly impacted, leading to incorrect judgments and processing.

[0004] Besides the two methods mentioned above, other quality control techniques exist, but they often have limitations. For example, they may not fully consider the influence of various complex factors on sunshine duration data, such as geographical location, seasonal variations, and atmospheric transparency. Therefore, these methods often cannot comprehensively and effectively solve data quality problems when processing sunshine duration data, and may even introduce new errors.

[0005] In view of the above, there is an urgent need to conduct more in-depth research and improvement on existing sunshine duration quality control technologies, so as to comprehensively consider the various influencing factors of sunshine duration data and be able to adapt to various complex observation environments, thereby ensuring the accuracy and reliability of automatically observed sunshine duration data and providing solid data support for meteorological research and decision-making in related fields. Summary of the Invention

[0006] This invention provides an automatic observation method, apparatus, device, and storage medium for detecting the quality of sunshine duration, in order to solve the problem that traditional methods are unable to ensure the accuracy and reliability of automatically observed sunshine duration data.

[0007] In a first aspect, embodiments of the present invention provide an automatic method for detecting the quality of sunshine duration, comprising: Acquire the basic characteristics, environmental characteristics, and sunshine image capture characteristics of the target observation station; the target observation station is the observation station where the sunshine duration to be automatically observed is located. Based on the basic characteristics of the observation station, the environmental characteristics, and the characteristics of the sunshine images, sunshine duration is predicted and scene verification is performed to obtain the predicted sunshine duration and sunshine duration observation scene of the target observation station. The first quality detection is performed on the automatically observed sunshine duration to be detected based on the predicted sunshine duration, and the confidence level of the first quality detection result is determined based on the sunshine duration observation scenario; Based on the confidence level, suspicious data in the first quality inspection result is identified, and spatial consistency test and solar image consistency test are performed on the suspicious data. The first quality inspection result and the confidence level are updated based on the test results.

[0008] In one possible implementation, the sunshine duration observation scenarios include geographical and climatic scenarios, time-period illumination scenarios, weather interference scenarios, and equipment operating condition scenarios; Based on the basic characteristics of the observation station, the environmental characteristics, and the characteristics of the sunshine duration images, a scene verification is performed to obtain the sunshine duration observation scene of the target observation station, including: Based on the basic characteristics of the observation station, scene verification is performed to obtain the geographical and climatic scenes of the target observation station; Scene verification is performed based on the basic characteristics of the observation station and the characteristics of the sunshine images to obtain the time-based illumination scene and equipment condition scene of the target observation station. Based on the environmental characteristics, scene verification is performed to obtain weather interference scenarios for the target observation station.

[0009] In one possible implementation, determining the confidence level of the first quality detection result based on the sunshine duration observation scenario includes: The main influencing scenarios of the first quality inspection result are determined based on the geographical and climatic scenarios, the time period and illumination scenarios, the weather interference scenarios, and the equipment operating condition scenarios. The confidence level of the first quality detection result is determined based on the distance between the main influencing scenario and the corresponding standard scenario. The standard scenario is one of the following corresponding to the main influencing scenario: standard geographical and climatic scenario, standard time period illumination scenario, standard weather interference scenario, and standard equipment operating condition scenario.

[0010] In one possible implementation, the main influencing scenarios of the first quality inspection result are determined based on the geographical and climatic scenarios, the time-of-day illumination scenarios, the weather interference scenarios, and the equipment operating condition scenarios, including: Calculate the distance between the geographic climate scenario and the standard geographic climate scenario, the distance between the time period illumination scenario and the standard time period illumination scenario, the distance between the weather interference scenario and the standard weather interference scenario, and the distance between the equipment operating condition scenario and the standard equipment operating condition scenario. The scenario corresponding to the maximum value of the distance is determined as the main influencing scenario of the first quality detection result.

[0011] In one possible implementation, calculating the distance between the geographic climate type scene and the standard geographic climate type scene includes: Calculate the similarity between the basic features of the observation station and the basic features of the standard observation station corresponding to the standard geographical and climatic scenario, and use the similarity as the distance between the geographical and climatic scenario and the standard geographical and climatic scenario.

[0012] In one possible implementation, the sunshine duration observation scenario includes a geographic and climatic scenario; Spatial consistency checks are performed on the suspicious data, including: Based on the aforementioned geographical and climatic scenarios, determine the similar spatial regions of the target observation station; The automatic observation sunshine duration of each observation station within the similar spatial area is obtained and recorded as the reference automatic observation sunshine duration; Spatial consistency checks are performed on the suspicious data based on the automatically observed sunshine duration data of each reference.

[0013] In one possible implementation, the suspicious data undergoes a solar radiation image consistency check, including: Obtain the sunshine images corresponding to the suspicious data; The presence or absence of sunlight is determined from the aforementioned sunshine images; The results of the sunshine presence / absence judgment are compared with the suspicious data to obtain the sunshine image consistency test results for the suspicious data.

[0014] Secondly, embodiments of the present invention provide an automatic observation and quality detection device for sunshine duration, comprising: The acquisition module is used to acquire the basic characteristics, environmental characteristics, and sunshine image capture characteristics of the target observation station; the target observation station is the observation station where the sunshine duration to be automatically observed is located. The processing module is used to predict sunshine duration and verify the scene based on the basic characteristics of the observation station, the environmental characteristics and the characteristics of the sunshine image, so as to obtain the predicted sunshine duration and sunshine duration observation scene of the target observation station. The quality detection module is used to perform a first quality detection on the automatically observed sunshine duration to be detected based on the predicted sunshine duration, and to determine the confidence level of the first quality detection result based on the sunshine duration observation scenario; The quality inspection module is used to determine suspicious data in the first quality inspection result based on the confidence level, and to perform spatial consistency inspection and solar image consistency inspection on the suspicious data, and update the first quality inspection result and the confidence level based on the inspection results.

[0015] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0016] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0017] In this embodiment of the invention, by acquiring the basic characteristics, environmental characteristics, and sunshine image capture characteristics of the target observation station, in addition to predicting the sunshine duration of the target observation station, scene verification is also performed. Based on the sunshine duration observation scene obtained from the verification, the confidence level of the first quality detection result of the automatic observation sunshine duration to be detected based on the predicted sunshine duration is more accurately assessed. Then, based on the confidence level, suspicious data in the first quality detection result is accurately identified. Furthermore, through spatial consistency verification of suspicious data and sunshine image consistency verification, the accuracy and reliability of automatic observation sunshine duration data are improved by integrating multi-source data, so as to provide solid data support for meteorological research and decision-making in related fields. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the implementation of the automatic observation and quality detection method for sunshine duration provided in this embodiment of the invention. Figure 2This is a schematic diagram of the automatic sunshine duration quality detection device provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0020] See Figure 1 The flowchart illustrating the implementation of the automatic observation method for detecting sunshine duration quality provided in this embodiment of the invention is shown below: Step 101: Obtain the basic characteristics, environmental characteristics, and sunshine image capture characteristics of the target observation station; the target observation station is the observation station where the sunshine duration to be automatically observed is located.

[0021] Among them, the basic characteristics of the observation station are those that distinguish it from other observation stations, such as its latitude and longitude, altitude, observation season, and time of day. Environmental characteristics refer to the synchronous environmental features of the sunshine duration to be detected, such as atmospheric transparency, cloud cover, visibility, and wind speed. Sunshine image capture characteristics refer to the features of the sunshine images captured corresponding to the sunshine duration to be detected, such as the image capture time, shooting angle, grayscale statistics of the sky area, and light spot characteristics.

[0022] This implementation can inspect the scene by acquiring the basic characteristics, environmental characteristics, and sunshine image capture characteristics of the observation station. When conducting quality testing on the automatic sunshine duration to be tested, it can distinguish different scenes, thereby more accurately assessing the quality of the automatic sunshine duration to be tested.

[0023] Step 102: Based on the basic characteristics, environmental characteristics, and characteristics of the sunshine images taken at the observation station, sunshine duration is predicted and the scene is verified to obtain the predicted sunshine duration and sunshine duration observation scene of the target observation station.

[0024] In this embodiment, sunshine duration is predicted based on the basic characteristics, environmental characteristics, and sunshine image capture characteristics of the observation station. For example, historical basic characteristics, historical environmental characteristics, and historical sunshine image capture characteristics of the target observation station can be obtained, along with the corresponding historical sunshine duration, forming a training set. This training set is then used to train a sunshine duration prediction model, which is then used to predict sunshine duration based on the model and the observation station's basic characteristics, environmental characteristics, and sunshine image capture characteristics. On the other hand, scene verification is performed based on the observation station's basic characteristics, environmental characteristics, and sunshine image capture characteristics.

[0025] In one embodiment, the sunshine duration observation scenarios include geographical and climatic scenarios, time-of-day illumination scenarios, weather interference scenarios, and equipment operating condition scenarios.

[0026] Based on this, step 102 includes: Scene verification is performed based on the basic characteristics of the observation station to obtain the geographical and climatic scenes of the target observation station.

[0027] Scene verification was conducted based on the basic characteristics of the observation station and the characteristics of the sunshine images to obtain the time-related illumination scenes and equipment operating condition scenes of the target observation station.

[0028] Scene verification is conducted based on environmental characteristics to obtain weather interference scenarios for the target observation station.

[0029] In this embodiment, in order to accurately assess the impact of different scenarios on the quality detection of automatic observation sunshine duration, four scenario classification dimensions are determined by combining the basic characteristics of the observation station, environmental characteristics, and sunshine image shooting characteristics: geographical climate, time period illumination, weather interference, and equipment operating conditions, so as to cover the variable observation scenarios.

[0030] For example, geographical and climatic scenarios can include high-latitude cold regions, mid-latitude temperate zones, low-latitude tropical zones, plateaus, plains, and coastal areas, which can be determined based on the basic characteristics of the observation station. Time-of-day illumination scenarios can include dawn, noon, dusk, strong sunlight on sunny days, soft sunlight on cloudy days, and weak sunlight on overcast days, which can be determined by combining the basic characteristics of the observation station and the characteristics of the sunlight image capture. Equipment operating condition scenarios can include normal shooting, backlighting, and low-temperature, low-power operating conditions, which can also be determined by combining the basic characteristics of the observation station and the characteristics of the sunlight image capture. Weather interference scenarios can include no interference, cloud cover, dust storms, light rain, moderate rain, and heavy rain, which can be determined based on environmental characteristics.

[0031] For example, for the verification of each type of scenario, a corresponding scenario verification model can be obtained by training a neural network model with the corresponding labeled data, and the relevant features can be input into the scenario verification model to obtain the corresponding scenario.

[0032] Step 103: Perform a first quality check on the automatically observed sunshine duration to be detected based on the predicted sunshine duration, and determine the confidence level of the first quality check result based on the sunshine duration observation scenario.

[0033] For example, the predicted sunshine duration can be compared with the automatically observed sunshine duration to be detected, and a first quality detection result can be obtained through comparison. In this embodiment, considering that the predicted sunshine duration may not be accurate, the confidence level of the first quality detection result is also determined based on the sunshine duration observation scenario.

[0034] In one embodiment, determining the confidence level of the first quality detection result based on the sunshine duration observation scenario includes: The main influencing scenarios for the first quality inspection result are determined based on geographical and climatic scenarios, time of day and lighting scenarios, weather interference scenarios, and equipment operating condition scenarios.

[0035] The confidence level of the first quality test result is determined based on the distance between the main influencing scenario and the corresponding standard scenario. The standard scenario is one of the following: standard geographical and climatic scenario, standard time period illumination scenario, standard weather interference scenario, and standard equipment operating condition scenario, which corresponds to the main influencing scenario.

[0036] In one embodiment, the main influencing scenarios of the first quality inspection result are determined based on geographical and climatic scenarios, time-of-day illumination scenarios, weather interference scenarios, and equipment operating condition scenarios, including: Calculate the distance between geographic climate scenarios and standard geographic climate scenarios, the distance between time-period illumination scenarios and standard time-period illumination scenarios, the distance between weather interference scenarios and standard weather interference scenarios, and the distance between equipment operating condition scenarios and standard equipment operating condition scenarios.

[0037] The scenario corresponding to the maximum value in the distance is determined as the main influencing scenario of the first quality detection result.

[0038] For example, calculating the distance between a geographic climate scenario and a standard geographic climate scenario includes: Calculate the similarity between the basic features of the observation station and the basic features of the standard observation station corresponding to the standard geographic and climatic scenario, and use the similarity as the distance between the geographic and climatic scenario and the standard geographic and climatic scenario.

[0039] The calculation process for the distance between other scenarios and the corresponding standard scenarios is similar to the calculation process for the distance between geographical and climatic scenarios and standard geographical and climatic scenarios, and will not be repeated here.

[0040] In this embodiment, by calculating the distance between the geographic climate scenario and the standard geographic climate scenario, the distance between the time period illumination scenario and the standard time period illumination scenario, the distance between the weather interference scenario and the standard weather interference scenario, and the distance between the equipment condition scenario and the standard equipment condition scenario, the scenario with the greatest deviation (that is, the scenario corresponding to the maximum value among the distances) can be determined. The greater the deviation of the scenario, the lower the accuracy or reliability of the predicted sunshine hours, and the greater the impact on the confidence of the first quality detection result.

[0041] For example, after performing a first quality check on the automatically observed sunshine hours to be detected based on the predicted sunshine hours, a basic confidence level can be determined for the first quality check result of each automatically observed sunshine hour to be detected based on the confidence level of each predicted sunshine hours. Then, an attenuation factor is determined based on the distance between the main influencing scene and the corresponding standard scene. The basic confidence level is attenuated according to the attenuation factor, and the attenuated confidence level is used as the confidence level of the first quality check result.

[0042] Step 104: Determine the suspicious data in the first quality inspection result based on the confidence level, and perform spatial consistency test and solar image consistency test on the suspicious data. Update the first quality inspection result and confidence level based on the test results.

[0043] In this embodiment, to further improve the reliability of the automatic sunshine duration quality detection, suspicious data, i.e., data with low confidence in the first quality detection result, are further examined. For example, if after obtaining the predicted sunshine duration and the automatically observed sunshine duration for a certain observation station for a day and performing quality detection, it is determined that the difference between the predicted sunshine duration and the automatically observed sunshine duration from 17:00 to 18:00 is large, meaning the first quality detection result for the automatically observed sunshine duration from 17:00 to 18:00 is abnormal, but the corresponding first quality detection result has low confidence, then the automatically observed sunshine duration from 17:00 to 18:00 is further examined.

[0044] For example, sunshine duration observation scenarios include geographical and climatic scenarios. Based on this, spatial consistency checks are performed on suspicious data, including: Identify similar spatial regions for the target observation station based on geographical and climatic scenarios.

[0045] The automatic sunshine duration of each observation station within a similar spatial region is obtained and recorded as the reference automatic sunshine duration.

[0046] Spatial consistency checks were performed on the suspicious data based on the automatically observed sunshine duration data from each reference.

[0047] For example, performing a consistency check on sunshine images for suspicious data includes: Obtain the sunshine images corresponding to the suspicious data.

[0048] Determine whether there is sunlight in the photos.

[0049] Compare the results of the sunshine presence / absence judgment with the suspicious data to obtain the test results of sunshine image consistency test on the suspicious data.

[0050] In this embodiment, by performing spatial consistency checks and sunshine image consistency checks on suspicious data, the first quality detection result of the suspicious data can be corrected and its confidence level updated. For example, assuming that the first quality detection result of a suspicious data point is abnormal and has a low confidence level, after performing spatial consistency checks and sunshine image consistency checks, it is determined that the suspicious data is inconsistent with or mostly inconsistent with the sunshine durations observed by various reference automatic observation systems, and the sunshine presence / absence judgment result is also inconsistent with the suspicious data. In this case, the first quality detection result can be determined to be abnormal and its confidence level increased. If the first quality detection result of a suspicious data point is abnormal and has a low confidence level, after performing spatial consistency checks and sunshine image consistency checks, it is determined that the suspicious data is consistent with or mostly consistent with the sunshine durations observed by various reference automatic observation systems, and the sunshine presence / absence judgment result is also consistent with the suspicious data. In this case, the first quality detection result can be determined to be corrected to normal and its confidence level increased.

[0051] This invention, through acquiring the basic characteristics, environmental features, and sunshine image capture features of a target observation station, not only predicts sunshine duration for the target observation station but also performs scene verification. Based on the sunshine duration observation scene obtained from the verification, it more accurately assesses the confidence level of the first quality detection result of the automatically observed sunshine duration obtained from the predicted sunshine duration. Then, based on the confidence level, it accurately identifies suspicious data in the first quality detection result. Furthermore, through spatial consistency verification of the suspicious data and sunshine image consistency verification, it integrates multi-source data to improve the accuracy and reliability of automatically observed sunshine duration data, providing solid data support for meteorological research and decision-making in related fields.

[0052] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0053] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0054] Figure 2 A schematic diagram of the automatic sunshine duration quality detection device provided in an embodiment of the present invention is shown. For ease of explanation, only the parts related to the embodiment of the present invention are shown, and are described in detail below: like Figure 2 As shown, the automatic sunshine duration quality detection device includes: The acquisition module 21 is used to acquire the basic characteristics, environmental characteristics, and sunshine image capture characteristics of the target observation station; the target observation station is the observation station where the sunshine duration to be automatically observed is located.

[0055] The processing module 22 is used to predict sunshine duration and verify the scene based on the basic characteristics, environmental characteristics and sunshine image shooting characteristics of the observation station, so as to obtain the predicted sunshine duration and sunshine duration observation scene of the target observation station.

[0056] The quality detection module 23 is used to perform a first quality detection on the automatically observed sunshine duration to be detected based on the predicted sunshine duration, and to determine the confidence level of the first quality detection result based on the sunshine duration observation scenario.

[0057] The quality inspection module 24 is used to determine suspicious data in the first quality inspection result based on the confidence level, and to perform spatial consistency inspection and solar image consistency inspection on the suspicious data, and update the first quality inspection result and confidence level based on the inspection results.

[0058] In one possible implementation, the sunshine duration observation scenarios include geographical and climatic scenarios, time-of-day illumination scenarios, weather interference scenarios, and equipment operating condition scenarios; processing module 22 is specifically used for: Scene verification is performed based on the basic characteristics of the observation station to obtain the geographical and climatic scenes of the target observation station.

[0059] Scene verification was conducted based on the basic characteristics of the observation station and the characteristics of the sunshine images to obtain the time-related illumination scenes and equipment operating condition scenes of the target observation station.

[0060] Scene verification is conducted based on environmental characteristics to obtain weather interference scenarios for the target observation station.

[0061] In one possible implementation, the quality inspection module 23 is specifically used for: The main influencing scenarios for the first quality inspection result are determined based on geographical and climatic scenarios, time of day and lighting scenarios, weather interference scenarios, and equipment operating condition scenarios.

[0062] The confidence level of the first quality test result is determined based on the distance between the main influencing scenario and the corresponding standard scenario. The standard scenario is one of the following: standard geographical and climatic scenario, standard time period illumination scenario, standard weather interference scenario, and standard equipment operating condition scenario, which corresponds to the main influencing scenario.

[0063] In one possible implementation, the quality inspection module 23 is specifically used for: Calculate the distance between geographic climate scenarios and standard geographic climate scenarios, the distance between time-period illumination scenarios and standard time-period illumination scenarios, the distance between weather interference scenarios and standard weather interference scenarios, and the distance between equipment operating condition scenarios and standard equipment operating condition scenarios.

[0064] The scenario corresponding to the maximum value in the distance is determined as the main influencing scenario of the first quality detection result.

[0065] In one possible implementation, the quality inspection module 23 is specifically used for: Calculate the similarity between the basic features of the observation station and the basic features of the standard observation station corresponding to the standard geographic and climatic scenario, and use the similarity as the distance between the geographic and climatic scenario and the standard geographic and climatic scenario.

[0066] In one possible implementation, the sunshine duration observation scenario includes geographical and climatic scenarios; the quality inspection module 24 is specifically used for: Spatial consistency checks are performed on suspicious data, including: Identify similar spatial regions for the target observation station based on geographical and climatic scenarios.

[0067] The automatic sunshine duration of each observation station within a similar spatial region is obtained and recorded as the reference automatic sunshine duration.

[0068] Spatial consistency checks were performed on the suspicious data based on the automatically observed sunshine duration data from each reference.

[0069] In one possible implementation, the quality inspection module 24 is specifically used for: Obtain the sunshine images corresponding to the suspicious data.

[0070] Determine whether there is sunlight in the photos.

[0071] Compare the results of the sunshine presence / absence judgment with the suspicious data to obtain the test results of sunshine image consistency test on the suspicious data.

[0072] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 3 As shown, the electronic device 3 of this embodiment includes a processor 30 and a memory 31. The memory 31 stores a computer program 32. When the processor 30 executes the computer program 32, it implements the steps in the various method embodiments described above. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the various device embodiments described above.

[0073] For example, computer program 32 may be divided into one or more modules / units, which are stored in memory 31 and executed by processor 30 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 32 in electronic device 3.

[0074] Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 3 may also include input / output devices, network access devices, buses, etc.

[0075] The processor 30 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0076] The memory 31 can be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 31 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 3. Furthermore, the memory 31 can include both internal and external storage units of the electronic device 3. The memory 31 is used to store the computer program 32 and other programs and data required by the electronic device 3. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0077] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0078] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0079] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0080] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0081] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for automatically observing and detecting the quality of sunshine duration, characterized in that, include: Acquire the basic characteristics, environmental characteristics, and sunshine image capture characteristics of the target observation station; the target observation station is the observation station where the sunshine duration to be automatically observed is located. Based on the basic characteristics of the observation station, the environmental characteristics, and the characteristics of the sunshine images, sunshine duration is predicted and scene verification is performed to obtain the predicted sunshine duration and sunshine duration observation scene of the target observation station. The first quality detection is performed on the automatically observed sunshine duration to be detected based on the predicted sunshine duration, and the confidence level of the first quality detection result is determined based on the sunshine duration observation scenario. Based on the confidence level, suspicious data in the first quality inspection result is identified, and spatial consistency test and solar image consistency test are performed on the suspicious data. The first quality inspection result and the confidence level are updated based on the test results.

2. The automatic observation method for detecting sunshine duration quality according to claim 1, characterized in that, The sunshine duration observation scenarios include geographical and climatic scenarios, time-of-day illumination scenarios, weather interference scenarios, and equipment operating condition scenarios; Based on the basic characteristics of the observation station, the environmental characteristics, and the characteristics of the sunshine duration images, a scene verification is performed to obtain the sunshine duration observation scene of the target observation station, including: Based on the basic characteristics of the observation station, scene verification is performed to obtain the geographical and climatic scenes of the target observation station; Scene verification is performed based on the basic characteristics of the observation station and the characteristics of the sunshine images to obtain the time-based illumination scene and equipment condition scene of the target observation station. Based on the environmental characteristics, scene verification is performed to obtain weather interference scenarios for the target observation station.

3. The automatic observation method for detecting sunshine duration quality according to claim 2, characterized in that, The confidence level of the first quality detection result is determined based on the sunshine duration observation scenario, including: The main influencing scenarios of the first quality inspection result are determined based on the geographical and climatic scenarios, the time period and illumination scenarios, the weather interference scenarios, and the equipment operating condition scenarios. The confidence level of the first quality detection result is determined based on the distance between the main influencing scenario and the corresponding standard scenario. The standard scenario is one of the following corresponding to the main influencing scenario: standard geographical and climatic scenario, standard time period illumination scenario, standard weather interference scenario, and standard equipment operating condition scenario.

4. The automatic observation method for detecting sunshine duration quality according to claim 3, characterized in that, Based on the geographical and climatic scenarios, the time-of-day illumination scenarios, the weather interference scenarios, and the equipment operating condition scenarios, the main influencing scenarios for the first quality inspection result are determined, including: Calculate the distance between the geographic climate scenario and the standard geographic climate scenario, the distance between the time period illumination scenario and the standard time period illumination scenario, the distance between the weather interference scenario and the standard weather interference scenario, and the distance between the equipment operating condition scenario and the standard equipment operating condition scenario. The scenario corresponding to the maximum value of the distance is determined as the main influencing scenario of the first quality detection result.

5. The automatic observation method for detecting sunshine duration quality according to claim 4, characterized in that, Calculating the distance between the geographic climate scenario and the standard geographic climate scenario includes: Calculate the similarity between the basic features of the observation station and the basic features of the standard observation station corresponding to the standard geographical and climatic scenario, and use the similarity as the distance between the geographical and climatic scenario and the standard geographical and climatic scenario.

6. The automatic observation method for detecting sunshine duration quality according to claim 1, characterized in that, The sunshine duration observation scenarios include geographical and climatic scenarios; Spatial consistency checks are performed on the suspicious data, including: Based on the aforementioned geographical and climatic scenarios, determine the similar spatial regions of the target observation station; The automatic observation sunshine duration of each observation station within the similar spatial area is obtained and recorded as the reference automatic observation sunshine duration; Spatial consistency checks are performed on the suspicious data based on the automatically observed sunshine duration data of each reference.

7. The automatic observation method for detecting sunshine duration quality according to claim 1, characterized in that, The suspicious data is subjected to a consistency check of sunshine images, including: Obtain the sunshine images corresponding to the suspicious data; The presence or absence of sunlight is determined from the aforementioned sunshine images; The results of the sunshine presence / absence judgment are compared with the suspicious data to obtain the sunshine image consistency test results for the suspicious data.

8. An automatic device for detecting sunshine duration and quality, characterized in that, include: The acquisition module is used to acquire the basic characteristics, environmental characteristics, and sunshine image capture characteristics of the target observation station; the target observation station is the observation station where the sunshine duration to be automatically observed is located. The processing module is used to predict sunshine duration and verify the scene based on the basic characteristics of the observation station, the environmental characteristics and the characteristics of the sunshine image, so as to obtain the predicted sunshine duration and sunshine duration observation scene of the target observation station. The quality detection module is used to perform a first quality detection on the automatically observed sunshine duration to be detected based on the predicted sunshine duration, and to determine the confidence level of the first quality detection result based on the sunshine duration observation scenario; The quality inspection module is used to determine suspicious data in the first quality inspection result based on the confidence level, and to perform spatial consistency inspection and solar image consistency inspection on the suspicious data, and update the first quality inspection result and the confidence level based on the inspection results.

9. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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