An image data screening method and system based on weight values and a medium

Abstract

The application discloses a kind of based on weight value's image data screening method, system and medium, obtain first data set;Select the time series environmental data set collected at any one time point, average calculation is carried out to time series environmental data set, obtain average data value, calculate the weight value of this environmental data set, traverse first data set, obtain several weight values;According to the order from low to high, several weight values are sorted, and a weight curve chart is obtained;Weight curve chart is compared with standard weight curve chart, and the time period corresponding to the environmental data set meeting standard weight curve chart is extracted;Based on the time period, the image data of the region to be screened is screened;The beneficial effects of the present application are that by calculating the weight value of the collected image data, the image data of the corresponding time period is selected for processing according to the size of the weight value, the accuracy of image data collection for the region to be screened is improved, thereby improving the accuracy of the analysis result of the regional image data.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and more specifically, to a method, system, and medium for image data filtering based on weight values. Background Technology

[0002] In the field of geological disaster monitoring, imagery is often used to record the changes and damage to the soil and rock mass at the landslide site during the disaster process. Further analysis and assessment of secondary geological disasters and their scale based on the images are then conducted. Landslide bodies can become unstable under the influence of prolonged rainfall, snowfall, and deformation, especially under severe weather conditions such as heavy rainfall and snowfall, which significantly increases the probability of instability. Image acquisition technology provides on-site image information of geological disasters, enabling remote assessment of the development status of the landslide body. Furthermore, when a landslide occurs, image information can be used to remotely analyze and assess the scale of secondary geological disasters, ultimately protecting people's lives and property.

[0003] In the current field of geological disaster monitoring, image data transmission systems are a crucial technical means for acquiring geological disaster image information. However, current image transmission systems lack in-depth, on-demand acquisition capabilities, leading to several shortcomings. The primary purpose of image data transmission systems in acquiring geological disaster image information is to reliably and stably upload the acquired image data to a designated cloud server. Traditional image data transmission systems only consider whether the image upload was successful and retransmission strategies for failed uploads, without considering climatic factors to assess the importance of each upload. Therefore, these methods have the following drawbacks: under harsh weather conditions, the communication signal quality at complex mountain geological disaster sites is prone to deterioration, resulting in inaccurate image data; and blindly analyzing the acquired image data leads to inaccurate analysis results.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] The technical problem to be solved by this invention is that in the prior art, the images collected for geological disaster monitoring do not take into account the influence of the surrounding environment on the images, resulting in inaccurate transmitted images. The purpose is to provide an image data filtering method, system and medium based on weight values, which can achieve accurate acquisition of images of geological disaster sites and improve the accuracy of image analysis results.

[0006] This invention is achieved through the following technical solution:

[0007] A weight-based image data filtering method, comprising the following steps:

[0008] Obtain the first dataset, which is a time-series environmental dataset collected at different time points in the area to be filtered;

[0009] Select any time point to collect a time series environmental dataset, and perform an average calculation on the time series environmental dataset to obtain an average data value. Then, calculate the weight value of the environmental dataset based on the average data value. Iterate through the first dataset to obtain several weight values.

[0010] The weight values ​​are sorted in ascending order to obtain a weight curve.

[0011] The weighted curve is compared with the standard weighted curve to extract the time period corresponding to the environmental dataset that conforms to the standard weighted curve.

[0012] Based on this time period, the image data of the region to be filtered is selected.

[0013] Traditionally, geological disaster image processing involves directly processing the acquired images to obtain analytical results. However, this method often overlooks the blurring effect of surrounding environmental factors on the site, leading to inaccurate analysis results. This invention provides a weighted image data filtering method. By calculating weights for the acquired image data and selecting image data from corresponding time periods based on the magnitude of the weight values, the accuracy of image data acquisition for the filtered area is improved, thereby enhancing the accuracy of the regional image data analysis results.

[0014] Preferably, the method further includes: updating the standard weight curve using the weight curve graph, specifically the following sub-steps:

[0015] Extract the weight values ​​that conform to the standard weight curve;

[0016] The standard weight curve is updated using the extracted weight values.

[0017] Preferably, the sub-step for calculating the average data value includes:

[0018] In the first dataset, the largest data value, the smallest data value, and the most recently collected dataset are removed to obtain the second dataset;

[0019] The second dataset is averaged to obtain the average data value.

[0020] Preferably, the specific expression for the weight value is:

[0021]

[0022] P is the weight value, D N D represents the latest collected data value, and D represents the average data value.

[0023] Preferably, the first dataset is rainfall data or wind and sand data.

[0024] Preferably, in the first dataset, the time interval between two adjacent environmental data collections is 1 minute.

[0025] The present invention also provides an image data filtering system based on weight values, the system including a data acquisition module, a calculation module, a curve module, a comparison module and a filtering module;

[0026] The data acquisition module is used to acquire a first dataset, which is a time-series environmental dataset collected at different time points in the area to be screened.

[0027] The calculation module is used to select a time series environmental dataset collected at any time point, perform an average calculation on the time series environmental dataset to obtain an average data value, and calculate a weight value for the environmental dataset based on the average data value. It then iterates through the first dataset to obtain several weight values.

[0028] The curve module is used to sort the weight values ​​in ascending order to obtain a weight curve.

[0029] The comparison module is used to compare the weight curve with the standard weight curve and extract the time period corresponding to the environmental dataset that conforms to the standard weight curve.

[0030] The filtering module is used to filter the image data of the region to be filtered based on the time period.

[0031] Preferably, the system further includes a weight value extraction module and an update module;

[0032] The weight value extraction module is used to extract weight values ​​that conform to the standard weight curve.

[0033] The update module is used to update the standard weight curve using the extracted weight values.

[0034] Preferably, the calculation module includes a data removal module and an average data value calculation module;

[0035] The data removal module is used to remove the maximum data value, the minimum data value, and the most recently collected data from the first dataset to obtain the second dataset;

[0036] The update module is used to perform an average calculation on the second dataset to obtain an average data value.

[0037] The present invention also provides a computer storage medium having a computing program stored thereon, which, when executed by a processor, implements the screening method described above.

[0038] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0039] The present invention provides an image data filtering method, system, and medium based on weight values. By calculating weight values ​​for the collected image data and selecting image data of the corresponding time period for processing according to the magnitude of the weight values, the accuracy of image data collection in the area to be filtered is improved, thereby improving the accuracy of the analysis results of the regional image data. Attached Figure Description

[0040] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a schematic diagram of the screening method steps;

[0042] Figure 2 This is a flowchart illustrating the screening method. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0044] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other embodiments, well-known structures, circuits, materials, or methods have not been specifically described in order to avoid obscuring the invention.

[0045] Throughout this specification, references to "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "an embodiment," "an example," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0046] In the description of this invention, the terms "front", "rear", "left", "right", "up", "down", "vertical", "horizontal", "high", "low", "inner", and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the scope of protection of this invention.

[0047] Example 1

[0048] Traditionally, geological disaster image processing involves directly processing the acquired images to obtain analytical results. However, this method often ignores the blurring effect of surrounding environmental factors on the site, leading to inaccurate analysis results.

[0049] This embodiment discloses a weighted image data filtering method. By calculating weights on the collected image data and selecting image data from corresponding time periods for processing based on the magnitude of the weights, the accuracy of image data collection for the region to be filtered is improved, thereby enhancing the accuracy of the regional image data analysis results. A schematic diagram of the specific method in this embodiment is shown below. Figure 1 , Figure 2 As shown, the method steps include:

[0050] S1: Obtain the first dataset, which is a time-series environmental dataset collected at different time points in the area to be filtered; the first dataset is rainfall data or wind and sand data. In the first dataset, the time interval between two adjacent environmental data collections is 1 minute.

[0051] In this embodiment, because the risk of geological disasters varies depending on whether a sandstorm or rainfall occurs in the area, this embodiment mainly considers the impact of surrounding environmental factors on the occurrence of geological disasters in the area. This allows for refined screening of the collected images, enabling accurate analysis and processing of geological disasters in the area based on the collected images. In the first dataset, environmental factor data is acquired at regular intervals, with the intervals between the acquired factor data being the same. This embodiment takes a 1-minute acquisition as an example. Furthermore, the first dataset contains datasets from different time series, meaning that the data acquired at each time point includes data acquired at all previous time points to form a dataset, which is a sequential dataset.

[0052] S2: Select a time series environmental dataset collected at any time point, and perform an average calculation on the time series environmental dataset to obtain an average data value. Calculate the weight value of the environmental dataset based on the average data value, and iterate through the first dataset to obtain several weight values. The sub-step of calculating the average data value includes: removing the maximum data value, the minimum data value, and the most recently collected dataset from the first dataset to obtain a second dataset; and performing an average calculation on the second dataset to obtain the average data value.

[0053] In step S2, the specific implementation process is explained using the amount of rainfall as an example:

[0054] Suppose we now have N rainfall data sequences, each with a 1-minute interval, and let them be D. i Where i = 1, 2, 3…N, and D N D1 represents the latest rainfall data acquired by the rain gauge sensor, and D2 represents the historical rainfall data acquired in the previous N-1 minutes. In the rainfall data sequence [D1, D2, D3, ..., D...] N-1 The maximum historical rainfall data obtained through cyclic comparison is D. max The minimum value is D min The average rainfall data D, after removing the maximum, minimum, and latest values, can be calculated using the following formula:

[0055]

[0056] The purpose of obtaining the mean D is to measure the rainfall data D. N Based on the data fluctuation status, further evaluation of the data upload weight is conducted. The weight value P obtained by the data upload weight evaluation method used in this system can be expressed by the following formula:

[0057]

[0058] P is the weight value, D NThe latest collected data value is denoted as D, and the average data value is denoted as D. The weight value P ranges from 0 to 1. The weight value P can represent the fluctuation state of the current rainfall data compared with the historical rainfall data.

[0059] S3: Sort the weight values ​​in ascending order to obtain a weight curve.

[0060] In step S3, the weight values ​​P calculated from each piece of data are statistically analyzed, and the statistically analyzed weight values ​​are sorted in descending order to form a new sequence P. i Where i = 1, 2, 3..., the resulting sequence P i The data to be uploaded is then associated with its importance; that is, the dataset composed of data collected at each time point has a corresponding importance weight. When no data is being collected, the sequence dataset collected at the last time point is used as the cutoff point, starting from sequence P. i The system identifies the data corresponding to the maximum value and prioritizes its upload. This means that the changing trend of rainfall in the environment is used as the basis for calculating the priority weight value of data upload, thereby enabling the control of data upload priority during the transmission of geological disaster image data.

[0061] S4: Compare the weighted curve with the standard weighted curve, and extract the time period corresponding to the environmental dataset that conforms to the standard weighted curve.

[0062] In step S4, the obtained weighted curve is compared with the standard weighted curve. Through intuitive data comparison, it is possible to visually observe which time period or point in time corresponds to more accurate image data, making the final analysis results closer to the real situation. This embodiment analyzes environmental data to detect the impact of surrounding environmental data on geological areas, thereby determining which time period or point in time the image data collected is more accurate for geological disaster analysis, and can obtain analysis results in real time and accurately.

[0063] S5: Based on this time period, filter the image data of the area to be filtered. In step S5, determine the required weight value, extract the time period corresponding to the relevant weight value, and obtain the corresponding image data according to the specific time period. This allows us to determine which time period the image data obtained is the most accurate and best reflects the geological disaster situation in the area.

[0064] The method further includes updating the standard weight curve using the weight curve, specifically by extracting weight values ​​that conform to the standard weight curve and updating the standard weight curve using the extracted weight values.

[0065] To improve the accuracy of image data acquisition, after each evaluation of relevant data, the accurate weight values ​​are updated to the corresponding standard weight curve. The specific update method is to add the latest obtained weight values ​​to the corresponding time nodes of the standard weight curve sequence, that is, to replace the original values ​​at the time nodes of the standard weight curve sequence with the latest obtained weight values, so as to facilitate the next threshold comparison.

[0066] This embodiment discloses a weighted image data filtering method. It calculates the data upload weight value using historical rainfall data in the environment and sorts the image data to be uploaded based on this weight value. This prioritizes the upload of more critical and important image data. Compared with existing technologies, this embodiment's method of controlling image data transmission based on rainfall data can prioritize the transmission of image data with large rainfall fluctuations under complex mountainous and harsh weather conditions and when communication signal quality deteriorates at geological disaster sites. It allows image data to be transmitted in an orderly manner according to rainfall trends, reducing the transmission latency of critical or important image data. It avoids the additional power consumption caused by fixed retransmission strategies and solves the problem of increased latency for important image data due to long-term occupation of communication channels by retransmission strategies for unimportant image data. This method has strong practical value in monitoring landslide geological disasters in complex mountainous areas.

[0067] Example 2

[0068] This embodiment discloses an image data filtering system based on weight values. This embodiment is to implement the filtering method as in Embodiment 1. The system includes a data acquisition module, a calculation module, a curve module, a comparison module, and a filtering module.

[0069] The data acquisition module is used to acquire a first dataset, which is a time-series environmental dataset collected at different time points in the area to be screened.

[0070] The calculation module is used to select a time series environmental dataset collected at any time point, perform an average calculation on the time series environmental dataset to obtain an average data value, and calculate a weight value for the environmental dataset based on the average data value. It then iterates through the first dataset to obtain several weight values.

[0071] The curve module is used to sort the weight values ​​in ascending order to obtain a weight curve.

[0072] The comparison module is used to compare the weight curve with the standard weight curve and extract the time period corresponding to the environmental dataset that conforms to the standard weight curve.

[0073] The filtering module is used to filter the image data of the region to be filtered based on the time period.

[0074] The system further includes a weight value extraction module and an update module; the weight value extraction module is used to extract weight values ​​that conform to the standard weight curve; the update module is used to update the standard weight curve using the extracted weight values.

[0075] The calculation module includes a data removal module and an average data value calculation module;

[0076] The data removal module is used to remove the maximum data value, the minimum data value, and the most recently collected data from the first dataset to obtain a second dataset; the update module is used to perform an average calculation on the second dataset to obtain an average data value.

[0077] Example 3

[0078] This embodiment discloses a computer storage medium storing a computing program, which, when executed by a processor, implements the method described in Embodiment 1.

[0079] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program issuing instructions. These computer program issuing instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the issuing instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce implementations of the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0081] These computer program issuing instructions may also be stored in a computer-readable storage medium capable of directing a computer or other programmable data processing device to function in a particular manner, such that the issuing instructions stored in the computer-readable storage medium produce an article of manufacture including an issuing instruction means, the issuing instruction means being implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing the instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0083] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of screening image data based on weight values, characterized by, The method steps include: obtaining a first data set, the first data set being a time series of environmental data sets collected at different time points in a region to be screened, and the first data set being rainfall data or wind-sand data; selecting a time series of environmental data set collected at any time point, and performing average calculation on the time series of environmental data set to obtain an average data value, the calculation sub-step of the average data value including: removing the maximum data value, the minimum data value and the latest collected data set in the first data set to obtain a second data set; performing average calculation on the second data set to obtain an average data value; calculating the weight value of the environmental data set based on the average data value, traversing the first data set to obtain a plurality of weight values, and the specific expression of the weight value being: ； P is a weight value, D N is a newly collected data value, average data value; sorting the plurality of weight values in order from low to high to obtain a weight curve; comparing the weight curve with a standard weight curve to extract a time period corresponding to the environmental data set conforming to the standard weight curve; based on the time period, screening image data of the region to be screened.

2. The method of claim 1, wherein, The method steps further include updating the standard weight curve through the weight curve, and the specific sub-steps being: extracting the weight value conforming to the standard weight curve; updating the standard weight curve through the extracted weight value.

3. The method of claim 1, wherein the weight value is determined based on a distance between the image data and a center of the image data. In the first data set, the time interval between the collection of adjacent two environmental data is 1 min.

4. An image data screening system based on weight values, characterized by, The system includes a data acquisition module, a calculation module, a curve module, a comparison module, an updating module and a screening module; The data acquisition module is used to obtain a first data set, the first data set including rainfall data or wind-sand data, and the first data set being a time series of environmental data sets collected at different time points in a region to be screened; The calculation module is used to select a time series of environmental data set collected at any time point, and perform average calculation on the time series of environmental data set to obtain an average data value, and calculate the weight value of the environmental data set based on the average data value, traverse the first data set to obtain a plurality of weight values, and the specific expression of the weight value being: ； P is a weight value, D N is the most recently collected data value, is the average data value; The calculation module includes a data removal module and an average data value calculation module; The data removal module is used to remove the maximum data value, the minimum data value and the latest collected data set in the first data set to obtain a second data set; The updating module is used to perform average calculation on the second data set to obtain an average data value; The curve module is used to sort the plurality of weight values in order from low to high to obtain a weight curve; The comparison module is used to compare the weight curve with a standard weight curve to extract a time period corresponding to the environmental data set conforming to the standard weight curve; The screening module is used to screen image data of the region to be screened based on the time period.

5. The image data screening system based on weight values according to claim 4, characterized in that, The system further includes a weight value extraction module; The weight value extraction module is used to extract the weight value conforming to the standard weight curve; The updating module is used to update the standard weight curve through the extracted weight value.

6. A computer storage medium having stored thereon a computer program, characterized in that The computer program, when executed by a processor, implements the screening method as claimed in any one of claims 1 to 3.

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