A video-based flow measurement method and an intelligent flow measurement system

Abstract

This application relates to a video-based flow measurement method and an intelligent flow measurement system. The method includes extracting a flow velocity analysis reference image from a recorded video; establishing analysis points on the first flow velocity analysis reference image in a time series; determining the change vectors of each analysis point according to other flow velocity analysis reference images in the time series; selecting an analysis area on the first flow velocity analysis reference image in the time series according to the length and direction concentration degree of the change vectors; extracting an analysis image from the flow velocity analysis reference image corresponding to the analysis area and performing feature extraction on the analysis image to obtain flow velocity features; calculating the average moving speed of the flow velocity features to obtain the flow measurement speed. The video-based flow measurement method and the intelligent flow measurement system disclosed in this application determine the capture area based on local feature analysis, perform feature capture within the capture area, and then perform flow velocity measurement based on the captured features, and can obtain a relatively accurate water body flow velocity.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and in particular, to a video-based flow measurement method and an intelligent flow measurement system. Background Art

[0002] Flow velocity measurement is one of the important tasks in hydrological monitoring. Currently, the more commonly used methods are manual detection and instrument detection. Manual detection mainly relies on hydrological workers. For example, experienced hydrological workers can even estimate the flow velocity of the river water relatively accurately. However, this method can only be used for estimation, and a more accurate method still needs to rely on the release of target objects to achieve.

[0003] The instrument detection methods include the current meter measurement method and the acoustic detection method, etc. These methods are mainly used for measuring ships, and there are problems of large equipment and manpower investment and high cost. At the same time, the measurement range is limited and it is not suitable for the river flow velocity measurement scenario in a large range.

[0004] A relatively new method uses machine vision for analysis. This analysis can capture the water body characteristics and then analyze them to obtain the water body flow velocity. With the maturity of high-precision digital cameras, unmanned aerial vehicles and related algorithms, the advantages of this method in measurement coverage and measurement cost have become obvious.

[0005] However, for some river flow velocity measurement scenarios with relatively lacking surface characteristics, the machine vision method has the problem of difficult feature capture. Summary of the Invention

[0006] This application provides a video-based flow measurement method and an intelligent flow measurement system. Based on local feature analysis, the capture area is determined and feature capture is performed within the capture area, and then the flow velocity is measured based on the captured features, and a relatively accurate water body flow velocity can be obtained.

[0007] The above object of this application is achieved through the following technical solutions:

[0008] In a first aspect, this application provides a video-based flow measurement method, including:

[0009] Extract flow velocity analysis reference images from the recorded video. The number of flow velocity analysis reference images is multiple, and the multiple flow velocity analysis reference images are distributed at intervals in the time series;

[0010] Establish analysis points on the first flow velocity analysis reference image in the time series. The analysis points are distributed in the form of a matrix of MxN, where both M and N are natural numbers greater than zero;

[0011] Determine the change vector of each analysis point according to the other flow velocity analysis reference images in the time series;

[0012] Select an analysis area on the first flow velocity analysis reference image of the time series according to the length and direction concentration degree of the change vector;

[0013] Extract an analysis image from the flow velocity analysis reference image corresponding to the analysis area and perform feature extraction on the analysis image to obtain flow velocity features;

[0014] Calculate the average moving speed of the flow velocity features to obtain the measured flow velocity.

[0015] In a possible implementation manner of the first aspect, determining the change vector of each analysis point according to other flow velocity analysis reference images of the time series includes:

[0016] Create a change area based on the analysis point, and the analysis point is located at the center position of the change area;

[0017] Obtain local flow velocity analysis reference images corresponding to the change area, and the number of local flow velocity analysis reference images is multiple;

[0018] Use the same extraction interval to extract all local flow velocity analysis reference images to obtain local change features;

[0019] Match the local change features and determine the change vector of the corresponding analysis point according to the movement of the local change features.

[0020] In a possible implementation manner of the first aspect, matching the local change features includes:

[0021] Convert the local change features into points and line segments for representation, the points have area features, and the line segments have length features;

[0022] Construct a feature grid using the obtained points and line segments;

[0023] Match different feature grids and determine the movement of the feature grids, and the movement of the feature grids includes direction and moving speed.

[0024] In a possible implementation manner of the first aspect, when the local change features cannot be obtained, replace with a new extraction interval.

[0025] In a possible implementation manner of the first aspect, after obtaining the feature grid, it further includes calculating the sparsity of the feature grid and adjusting the start point and / or end point of the extraction interval according to the sparsity of the feature grid.

[0026] In a possible implementation manner of the first aspect, adjusting the start point and / or end point of the extraction interval according to the sparsity of the feature grid includes:

[0027] Calculate the individual area and area mean of each grid in the feature grid;

[0028] Calculate the dispersion of the individual area according to the individual area and the average area of each grid;

[0029] Adjust the start point and end point of the extraction interval according to the dispersion so that the dispersion is within the set interval range;

[0030] Wherein, when adjusting the start point and / or end point of the extraction interval cannot make the dispersion within the set interval range, a new extraction interval is replaced.

[0031] In a possible implementation manner of the first aspect, an analysis image is extracted from the flow velocity analysis reference image corresponding to the analysis area and feature extraction is performed on the analysis image, and the obtained flow velocity features include:

[0032] Use the same extraction interval to extract the analysis image to obtain local change features;

[0033] Convert the local change features into points and line segments for representation. The points have area features and the line segments have length features;

[0034] Use the obtained points and line segments to construct a feature grid;

[0035] Use the feature grid as the flow velocity feature.

[0036] In a second aspect, the present application provides a flow measurement device based on a video, including:

[0037] An image extraction unit for extracting a flow velocity analysis reference image from the recorded video. The number of flow velocity analysis reference images is multiple, and the multiple flow velocity analysis reference images are distributed at intervals in the time series;

[0038] An image processing unit for establishing analysis points on the first flow velocity analysis reference image in the time series. The analysis points are distributed in the form of an MxN matrix, where both M and N are natural numbers greater than zero;

[0039] A change vector processing unit for determining the change vector of each analysis point according to the other flow velocity analysis reference images in the time series;

[0040] An analysis area processing unit for selecting an analysis area on the first flow velocity analysis reference image in the time series according to the length and direction concentration degree of the change vector;

[0041] A feature extraction unit for extracting an analysis image from the flow velocity analysis reference image corresponding to the analysis area and performing feature extraction on the analysis image to obtain flow velocity features;

[0042] A result calculation unit for calculating the average moving speed of the flow velocity features to obtain the flow measurement speed.

[0043] In a third aspect, the present application provides an intelligent flow measurement system, which includes:

[0044] One or more memories for storing instructions; and

[0045] One or more processors for calling and running the instructions from the memory and executing the method described in the first aspect and any possible implementation manners of the first aspect.

[0046] In a fourth aspect, the present application provides a computer-readable storage medium, which includes:

[0047] A program that, when run by a processor, executes the method described in the first aspect and any possible implementation manners of the first aspect.

[0048] In a fifth aspect, the present application provides a computer program product, including program instructions that, when run by a computing device, execute the method described in the first aspect and any possible implementation manners of the first aspect.

[0049] In a sixth aspect, the present application provides a chip system, which includes a processor for implementing the functions involved in the above aspects, for example, generating, receiving, sending, or processing the data and / or information involved in the above method.

[0050] The chip system may be composed of chips or may include chips and other discrete devices.

[0051] In a possible design, the chip system further includes a memory for storing necessary program instructions and data. The processor and the memory may be decoupled and disposed on different devices, connected by wire or wirelessly, or the processor and the memory may also be coupled on the same device. Description of the Drawings

[0052] Figure 1 is a schematic block diagram of the steps of a flow measurement method provided by the present application.

[0053] Figure 2 is a schematic diagram of obtaining a reference image for flow velocity analysis provided by the present application.

[0054] Figure 3 is a schematic diagram of establishing an analysis point provided by the present application.

[0055] Figure 4 is a schematic diagram of a change vector belonging to an analysis point provided by the present application.

[0056] Figure 5It is a schematic diagram of the positions of analysis points and change regions provided by this application.

[0057] Figure 6 It is a schematic diagram of a feature grid provided by this application.

[0058] Figure 7 It is a schematic diagram of the principle for obtaining motion vectors provided by this application. Detailed implementation manners

[0059] The technical solutions in this application will be further described in detail below with reference to the accompanying drawings.

[0060] This application discloses a video-based flow measurement method. Please refer to Figure 1 , in some examples, the video-based flow measurement method disclosed in this application includes the following steps:

[0061] S101. Extract flow velocity analysis reference images from the recorded video. The number of flow velocity analysis reference images is multiple, and the multiple flow velocity analysis reference images are distributed at intervals in the time series;

[0062] S102. Establish analysis points on the first flow velocity analysis reference image in the time series. The analysis points are distributed in the form of an MxN matrix, where both M and N are natural numbers greater than zero;

[0063] S103. Determine the change vectors of each analysis point according to the other flow velocity analysis reference images in the time series;

[0064] S104. Select an analysis region on the first flow velocity analysis reference image in the time series according to the length and direction concentration degree of the change vectors;

[0065] S105. Extract an analysis image from the flow velocity analysis reference image corresponding to the analysis region and perform feature extraction on the analysis image to obtain flow velocity features;

[0066] S106. Calculate the average moving speed of the flow velocity features to obtain the flow measurement speed.

[0067] In step S101, first, flow velocity analysis reference images are extracted from the recorded video. The number of flow velocity analysis reference images is multiple, and these flow velocity analysis reference images are distributed at intervals in the time series. The flow velocity analysis reference images are generated by an image acquisition device carried by a drone or by an image acquisition device deployed at a fixed position.

[0068] Please refer to Figure 2 , the flow velocity analysis reference images are distributed at intervals in the time series. The interval time can be fixed or not fixed, and no limitation is imposed here.

[0069] In step S102 in China, analysis points are established on the first flow velocity analysis reference image in the time series. The analysis points are distributed in the form of an MxN matrix, where both M and N are natural numbers greater than zero. For example, Figure 3 As shown, the role of the analysis points is to determine the flow direction at the positions where the analysis points are located.

[0070] In step S103, the change vector of each analysis point is determined according to other flow velocity analysis reference images in the time series. Here, the change vector includes two reference dimensions: direction and length. Then, in step S104, the analysis area is selected on the first flow velocity analysis reference image in the time series according to the length and direction concentration degree of the change vector. The number of analysis areas obtained here can be one or more.

[0071] The criterion for selecting the analysis area is that the analysis points in the analysis area are traceable, and at the same time, the change vectors corresponding to the analysis area can maintain consistency in direction. Generally, the direction error is controlled within 10°. Of course, the direction error can also be adjusted according to the actual detection accuracy.

[0072] For example, in Figure 4 , the analysis points within the dotted line area need to be discarded.

[0073] In step S105, the analysis image is extracted from the flow velocity analysis reference image corresponding to the analysis area and feature extraction is performed on the analysis image to obtain the flow velocity feature. Here, the flow velocity feature refers to the solid located below the water surface and in a flowing state.

[0074] Finally, in step S106, the average moving speed of the flow velocity feature is calculated to obtain the measured flow velocity. The purpose of calculating the average moving speed of the flow velocity feature is to make the calculation result more accurate.

[0075] In some examples, the specific method for determining the change vector of each analysis point according to other flow velocity analysis reference images in the time series is as follows:

[0076] S201, create a change area based on the analysis point, and the analysis point is located at the center position of the change area;

[0077] S202, obtain the local flow velocity analysis reference images corresponding to the change area, and the number of local flow velocity analysis reference images is multiple;

[0078] S203, extract all the local flow velocity analysis reference images using the same extraction interval to obtain local change features;

[0079] S204, match the local change features and determine the change vector of the corresponding analysis point according to the movement of the local change features.

[0080] In steps S201 to S204, a change region is first created based on the analysis point, and the analysis point is located at the center of the change region, as Figure 5 shown. Then, a local flow velocity analysis reference image corresponding to the change region is obtained, and the number of local flow velocity analysis reference images is multiple.

[0081] The local flow velocity analysis reference image is cropped from the flow velocity analysis reference image, and the basis for cropping is the size of the change region. Combining the content mentioned above, each flow velocity analysis reference image in the time series needs to be cropped using the change region to obtain the local flow velocity analysis reference image.

[0082] After obtaining the local flow velocity analysis reference image, the same extraction interval is used to extract all the local flow velocity analysis reference images to obtain local change features. The extraction interval is a numerical range. For example, 34 - 68 is used as the extraction interval.

[0083] At this time, some content can be extracted from the local flow velocity analysis reference image. These contents are part of the local flow velocity analysis reference image but not all of it. The purpose of using the extraction interval is to reduce the subsequent matching difficulty because the local change features obtained at this time have a certain degree of recognition.

[0084] For the obtained local change features, judgment is still needed, and the specific method is as follows:

[0085] Quantity judgment: Count the quantity of the local change features. The counted quantity needs to be greater than or equal to the set quantity, and the value range of the set quantity is generally 5 - 8;

[0086] Distance judgment: Count the distance between adjacent local change features, and then calculate the average value of the obtained distances. The obtained average distance needs to be greater than or equal to the set distance, and the set distance is generally 10 - 15 pixel points;

[0087] Coverage area judgment: The ratio of the area of the region surrounded by the local change features to the area of the local flow velocity analysis reference image generally needs to be greater than or equal to 5%.

[0088] When the above conditions cannot be met, the extraction interval needs to be adjusted. The specific method is to replace it with a new extraction interval or use multiple extraction intervals for content extraction.

[0089] In some possible implementation manners, when the local change features cannot be obtained, a new extraction interval is replaced.

[0090] The specific method for matching the local change features is as follows:

[0091] S301. Represent the local change features as points and line segments. The points have area features, and the line segments have length features.

[0092] S302. Construct a feature grid using the obtained points and line segments.

[0093] S303. Match different feature grids to determine the movement of the feature grid. The movement of the feature grid includes direction and moving speed.

[0094] In steps S301 to S303, the local change features will be converted into a feature grid. As Figure 6 shown, the specific method is to represent the local change features as points and line segments. The points have area features, and the line segments have length features.

[0095] The specific method of representing the local change features as points and line segments is determined according to the area and length of the local features. Here, the length has a reference value. For local change features with a length less than the length reference value, they are represented by points and area, otherwise, they are represented by line segments and length.

[0096] Then, construct a feature grid using the obtained points and line segments. The specific construction method is to connect the points and line segments in a certain way. For the construction of multiple feature grids at the same analysis point, the same connection method needs to be adopted.

[0097] In some possible implementation manners, after obtaining the feature grid, it further includes calculating the sparsity of the feature grid. The specific method is as follows:

[0098] S401. Calculate the individual area and area mean of each grid in the feature grid.

[0099] S402. Calculate the dispersion of the individual area according to the individual area and area mean of each grid.

[0100] S403. Adjust the start point and end point of the extraction interval according to the dispersion so that the dispersion is within the set interval range.

[0101] Among them, when adjusting the start point and / or end point of the extraction interval cannot make the dispersion within the set interval range, replace with a new extraction interval.

[0102] The sparsity of the feature grid refers to the average area value of the grids in the feature grid. The calculation method of the average area value is the ratio of the total area of the feature grid to the total number of grids, and this ratio is obtained through a calculation method.

[0103] Next, calculate the ratio of the number of grids smaller than this ratio to the total number of grids. This ratio generally needs to be less than 0.2 - 0.25, that is, the number of small grids in the feature grid needs to be controlled within a small range. Because too many small grids in the feature grid will increase the difficulty of feature grid matching later.

[0104] When the sparsity of the feature grid does not meet the requirements, adjust the starting point and / or the ending point of the extraction interval according to the sparsity of the feature grid. At this time, some local change features will disappear and / or new local change features will be added. This process stops until the sparsity of the feature grid meets the requirements.

[0105] In some examples, the specific way to extract the analysis image from the flow velocity analysis reference image corresponding to the analysis area and perform feature extraction on the analysis image to obtain the flow velocity feature is as follows:

[0106] S501, extract the analysis image using the same extraction interval to obtain local change features;

[0107] S502, convert the local change features into points and line segments for representation. The points have area features and the line segments have length features;

[0108] S503, use the obtained points and line segments to construct a feature grid;

[0109] S504, use the feature grid as the flow velocity feature.

[0110] The content in steps S501 to S504 is the same as the method for obtaining the feature grid described in the foregoing content, and will not be elaborated here.

[0111] After obtaining the flow velocity feature, it is necessary to match the flow velocity feature. The matching method is to calculate the similarity of two feature grids. When the two feature grids are successfully matched, calculate the moving speed of the feature grid. The moving speed has two dimensions: numerical value and direction.

[0112] The matching method of two feature grids is to place the two obtained feature grids in the same coordinate system, and then determine whether the corresponding points and line segments can coincide. Movement is involved in this process. The movement here is represented by a movement vector, and the movement vector has two ways: movement and rotation when moving.

[0113] To determine whether two corresponding points coincide, the judgment method is to calculate the distance between the two points, which needs to be less than or equal to a set value. To determine whether two corresponding line segments coincide, the judgment method is to calculate the average value of the distances between the two ends corresponding to the two line segments, and this average value needs to be less than or equal to another set value.

[0114] In the above process, one of the feature grids needs to be moved and rotated.

[0115] After successful matching, the movement vector is used as the movement speed of the feature grid. After all the feature grids are successfully matched, multiple movement vectors will be obtained. As Figure 7 shown, at this time, it is required that the number of matched feature grids is greater than or equal to a set value or a set ratio.

[0116] At this point, multiple movement vectors are obtained. At this time, it is necessary to calculate the average movement speed of the flow velocity feature to obtain the flow measurement speed. The specific method is to merge the movement vectors recorded in the above content to obtain a composite vector, and then use the length value of the composite vector as the flow measurement speed.

[0117] This application also provides a flow measurement device based on video, including:

[0118] An image extraction unit, configured to extract a flow velocity analysis reference image from the recorded video. The number of flow velocity analysis reference images is multiple, and the multiple flow velocity analysis reference images are distributed at intervals in the time series;

[0119] An image processing unit, configured to establish analysis points on the first flow velocity analysis reference image in the time series. The analysis points are distributed in the form of an MxN matrix, where both M and N are natural numbers greater than zero;

[0120] A change vector processing unit, configured to determine the change vector of each analysis point according to other flow velocity analysis reference images in the time series;

[0121] An analysis area processing unit, configured to select an analysis area on the first flow velocity analysis reference image in the time series according to the length and direction concentration degree of the change vector;

[0122] A feature extraction unit, configured to extract an analysis image from the flow velocity analysis reference image corresponding to the analysis area and perform feature extraction on the analysis image to obtain a flow velocity feature;

[0123] A result calculation unit, configured to calculate the average movement speed of the flow velocity feature to obtain the flow measurement speed.

[0124] Further, determining the change vector of each analysis point according to other flow velocity analysis reference images in the time series includes:

[0125] Creating a change area based on the analysis point, and the analysis point is located at the center position of the change area;

[0126] Obtaining multiple local flow velocity analysis reference images corresponding to the change area;

[0127] Using the same extraction interval to extract all the local flow velocity analysis reference images to obtain local change features;

[0128] Match the local change features and determine the change vector of the corresponding analysis point according to the movement of the local change features.

[0129] Further, matching the local change features includes:

[0130] Convert the local change features into points and line segments for representation. The points have area features, and the line segments have length features.

[0131] Use the obtained points and line segments to construct a feature grid.

[0132] Match different feature grids to determine the movement of the feature grid. The movement of the feature grid includes direction and movement speed.

[0133] Further, when the local change features cannot be obtained, replace it with a new extraction interval.

[0134] Further, after obtaining the feature grid, it also includes calculating the sparsity of the feature grid and adjusting the start point and / or end point of the extraction interval according to the sparsity of the feature grid.

[0135] Further, adjusting the start point and / or end point of the extraction interval according to the sparsity of the feature grid includes:

[0136] Calculate the individual area and the average area of each grid in the feature grid.

[0137] Calculate the dispersion of the individual area according to the individual area and the average area of each grid.

[0138] Adjust the start point and end point of the extraction interval according to the dispersion so that the dispersion is within the set interval range.

[0139] Wherein, when adjusting the start point and / or end point of the extraction interval cannot make the dispersion within the set interval range, replace it with a new extraction interval.

[0140] Further, extract the analysis image on the flow velocity analysis reference image corresponding to the analysis area and perform feature extraction on the analysis image. The obtained flow velocity features include:

[0141] Use the same extraction interval to extract the analysis image to obtain local change features.

[0142] Convert the local change features into points and line segments for representation. The points have area features, and the line segments have length features.

[0143] Use the obtained points and line segments to construct a feature grid.

[0144] Use the feature grid as the flow velocity feature.

[0145] In one example, the units in any of the above devices may be one or more integrated circuits configured to implement the above methods. For example: one or more application specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0146] For another example, when the units in the device can be implemented in the form of a processing element scheduler, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processors that can call programs. For another example, these units can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0147] In this application, names may be assigned to various objects such as various messages / information / devices / network elements / systems / devices / actions / operations / processes / concepts, etc. It can be understood that these specific names do not constitute a limitation on the relevant objects, and the assigned names may change with factors such as scenarios, contexts, or usage habits. The understanding of the technical meanings of the technical terms in this application should be mainly determined from the functions and technical effects they embody / perform in the technical solutions.

[0148] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0149] In several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0150] The unit described as a separation component may or may not be physically separated. The component displayed as a unit may or may not be a physical unit, that is, it may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0152] It should also be understood that in various embodiments of the present application, the first, second, etc. are only used to indicate that multiple objects are different. For example, the first time window and the second time window are only used to indicate different time windows. And it should not have any impact on the time window itself. The above first, second, etc. should not impose any restrictions on the embodiments of the present application.

[0153] It should also be understood that in various embodiments of the present application, if there is no special description and logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other. The technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0154] If the above functions are implemented in the form of software function units and sold or used as an independent product, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a computer-readable storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned computer-readable storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0155] This application also provides an intelligent flow measurement system, and the system includes:

[0156] One or more memories for storing instructions; and

[0157] One or more processors, configured to call and run the instructions from the memory and execute the method described above.

[0158] This application also provides a computer program product, which includes instructions that, when executed, cause the terminal device and the network device to perform the operations of the terminal device and the network device corresponding to the above method.

[0159] This application also provides a chip system, which includes a processor for implementing the functions involved in the above, for example, generating, receiving, sending, or processing the data and / or information involved in the above method.

[0160] The chip system may be composed of chips or may include chips and other discrete devices.

[0161] The processor mentioned anywhere above may be a CPU, a microprocessor, an ASIC, or an integrated circuit for controlling the execution of one or more programs for the method of transmitting the above feedback information.

[0162] In a possible design, the chip system further includes a memory for storing necessary program instructions and data. The processor and the memory may be decoupled and separately disposed on different devices, and connected by wired or wireless means to support the chip system to implement various functions in the above embodiments. Alternatively, the processor and the memory may also be coupled on the same device.

[0163] Optionally, the computer instructions are stored in the memory.

[0164] Optionally, the memory is a storage unit within the chip, such as a register, a cache, etc., and the memory may also be a storage unit outside the chip within the terminal, such as a ROM or other types of static storage devices that can store static information and instructions, a RAM, etc.

[0165] It can be understood that the memory in this application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories.

[0166] The non-volatile memory may be a ROM, a programmable ROM (PROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), or a flash memory.

[0167] The volatile memory can be a RAM, which is used as an external cache. There are various different types of RAM, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synch link DRAM (SLDRAM), and direct rambus random access memory.

[0168] The embodiments of this specific implementation manner are all preferred embodiments of this application, and do not limit the protection scope of this application accordingly. Therefore, all equivalent changes made according to the structure, shape, and principle of this application shall be covered within the protection scope of this application.

Claims

1. A video-based flow measurement method, characterized in that: include: Extracting a flow velocity analysis reference image from the recorded video, the number of the flow velocity analysis reference images is multiple, and the multiple flow velocity analysis reference images are distributed at intervals in a time series; Establish analysis points on the first velocity analysis reference image of the time series. The analysis points are distributed in the form of an MxN matrix, where M and N are both natural numbers greater than zero. Determine the change vector of each analysis point based on other flow velocity analysis reference images of the time series; An analysis area is selected on the first velocity analysis reference image of the time series according to the length and directional concentration of the change vector; Extracting an analysis image on a velocity analysis reference image corresponding to the analysis area and performing feature extraction on the analysis image to obtain a velocity feature, wherein the velocity feature refers to a solid that is below the water surface and in a flowing state; Calculate the average moving speed of the flow velocity characteristics to obtain the measured flow velocity; Among them, the analysis image is extracted from the flow velocity analysis reference image corresponding to the analysis area and features are extracted from the analysis image to obtain flow velocity features including: The analysis image is extracted using the same extraction interval to obtain local change features; The local variation characteristics are converted into points and line segments for representation, wherein the points have the area characteristics of the solid and the line segments have the length characteristics of the solid; Use the obtained points and line segments to construct a feature grid; Use the characteristic grid as the velocity feature; Determining the change vector of each analysis point based on other flow velocity analysis reference images of the time series includes: Creating a change region based on the analysis point, where the analysis point is located at the center of the change region; Obtaining a local flow velocity analysis reference image corresponding to the changed area, wherein the number of the local flow velocity analysis reference images is multiple; Use the same extraction interval to extract all local velocity analysis reference images to obtain local change features; Matching the local change features and determining the change vector of the corresponding analysis point according to the movement of the local change features; Different feature grids are matched to determine the movement of the feature grids. The movement of the feature grids includes direction and speed. The movement of the feature grids is represented by movement vectors. The movement vectors are merged to obtain a composite vector. The length value of the composite vector is used as the measured flow velocity.

2. The video-based flow measurement method according to claim 1, characterized in that: Matching local change features includes: The local change features are converted into points and line segments for representation. Points have area features, and line segments have length features. Use the obtained points and line segments to construct a feature grid; Different feature grids are matched to determine the movement of the feature grids, where the movement of the feature grids includes direction and movement speed.

3. The video-based flow measurement method according to claim 1 or 2, characterized in that: When the local change characteristics cannot be obtained, a new extraction interval is replaced.

4. The video-based flow measurement method according to claim 2, characterized in that: After the feature grid is obtained, the method further includes calculating the sparsity of the feature grid and adjusting the starting point and / or the end point of the extraction interval according to the sparsity of the feature grid.

5. The video-based flow measurement method according to claim 4, characterized in that: Adjusting the starting point and / or the end point of the extraction interval according to the sparsity of the feature grid includes: Calculate the individual area and area mean of each grid in the feature grid; The dispersion of the individual area is calculated based on the individual area and the area mean of each grid; Adjust the starting point and the end point of the extraction interval according to the discreteness so that the discreteness is within the set interval; When adjusting the starting point and / or the end point of the extraction interval fails to make the discreteness fall within the set interval, a new extraction interval is replaced.

6. A video-based flow measurement device, characterized in that: include: An image extraction unit is used to extract a flow velocity analysis reference image from the recorded video, wherein the number of the flow velocity analysis reference images is multiple, and the multiple flow velocity analysis reference images are distributed at intervals in a time series; An image processing unit, used for establishing analysis points on the first velocity analysis reference image of the time series, wherein the analysis points are distributed in the form of an MxN matrix, where M and N are both natural numbers greater than zero; a change vector processing unit, for determining a change vector of each analysis point according to other flow velocity analysis reference images of the time series; An analysis region processing unit, used for selecting an analysis region on the first flow velocity analysis reference image of the time series according to the length and directional concentration of the change vector; A feature extraction unit is used to extract an analysis image on a velocity analysis reference image corresponding to the analysis area and perform feature extraction on the analysis image to obtain a velocity feature, wherein the velocity feature refers to a solid that is below the water surface and in a flowing state; A result calculation unit is used to calculate the average moving speed of the flow velocity characteristics to obtain the measured flow velocity; Extract the analysis image on the velocity analysis reference image corresponding to the analysis area and perform feature extraction on the analysis image to obtain the velocity features including: The analysis image is extracted using the same extraction interval to obtain local change features; The local change features are converted into points and line segments for representation. Points have area features, and line segments have length features. Use the obtained points and line segments to construct a feature grid; Use the characteristic grid as the velocity feature; Determining the change vector of each analysis point based on other flow velocity analysis reference images of the time series includes: Creating a change region based on the analysis point, where the analysis point is located at the center of the change region; Obtaining a local flow velocity analysis reference image corresponding to the changed area, wherein the number of the local flow velocity analysis reference images is multiple; Use the same extraction interval to extract all local velocity analysis reference images to obtain local change features; Matching the local change features and determining the change vector of the corresponding analysis point according to the movement of the local change features; Different feature grids are matched to determine the movement of the feature grids. The movement of the feature grids includes direction and speed. The movement of the feature grids is represented by movement vectors. The movement vectors are merged to obtain a composite vector. The length value of the composite vector is used as the measured flow velocity.

7. An intelligent flow measurement system, characterized in that: The system comprises: one or more memories for storing instructions; and One or more processors, configured to call and execute the instructions from the memory to perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer readable storage medium comprises: The program, when the program is executed by a processor, the method according to any one of claims 1 to 5 is executed.

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