Data parsing method, system, device and storage medium

Abstract

The present invention discloses a data parsing method, system, device and storage medium. The method includes: obtaining a historical image set captured by a photographing device; determining each target sub-region of the photographing device and corresponding target image elements for each target sub-region according to the historical image set; and determining a data parsing rule corresponding to the target sub-region according to the target image element. In this application, the photographing area of the photographing device is divided into multiple target sub-regions through the historical data set. Different target sub-regions can correspond to different image elements and data parsing rules, so that the pictures captured by the photographing device can be parsed specifically, avoiding the waste of server computing power caused by the undifferentiated full-attribute ability parsing of existing cameras. At the same time, it can also avoid the storage of pictures in unnecessary scenarios and save storage space.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and particularly to a data parsing method, system, device, and storage medium. Background Art

[0002] With the rapid development of the network, network products have gradually covered all corners of our lives. The network camera (IPC) is a new generation of camera produced by the combination of traditional cameras and network technology. Compared with traditional cameras, network cameras can more easily achieve remote monitoring; among many network camera products, especially for some shooting devices with a wide shooting angle, due to their wide shooting angle and high clarity, they have gradually been applied to fields such as education, commerce, medical care, community, and public utilities in recent years.

[0003] However, for the pictures captured and reported by the shooting device, there are multiple regions (such as sidewalks, motor vehicle lanes, commercial areas, waters, forests, etc.) within the perspective of the entire picture. Generally, the traditional engine background will preset the parsing capabilities of this camera in advance (such as humans, faces, motor vehicles, non-motor vehicles, etc.), and then rigidly perform things within the parsing capabilities on multiple regions within the perspective of the entire picture; perform non-discriminatory feature parsing on humans, faces, motor vehicles, non-motor vehicles, etc. that appear in each region of the picture. For example, if there is a scrapped vehicle parked in the sidewalk area within the picture perspective, the attributes of this vehicle in the sidewalk area will be continuously extracted, which will cause a great waste of server computing power and occupy unnecessary disk space.

[0004] Therefore, the existing technology still needs to be improved. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a data parsing method, system, device, and storage medium for the above-mentioned defects of the existing technology, aiming to solve the technical problem of waste of server computing power and occupation of unnecessary disk space caused by non-discriminatory full-attribute ability parsing of cameras in the existing technology.

[0006] In a first aspect, the present application provides a data parsing method, and the method includes:

[0007] Obtain a historical image set captured by a shooting device;

[0008] Determine each target sub-region of the shooting device and the target image elements corresponding to each target sub-region according to the historical image set;

[0009] Determine a data parsing rule corresponding to the target sub-region according to the target image elements.

[0010] In a second aspect, an embodiment of the present application provides a data parsing system, including:

[0011] An acquisition module, configured to acquire a historical image set captured by a photographing device;

[0012] A first determination module, configured to determine each target sub-region of the photographing device and the target image elements corresponding to each target sub-region according to the historical image set;

[0013] A second determination module, configured to determine a data parsing rule corresponding to the target sub-region according to the target image element.

[0014] In a third aspect, the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in any of the above technical solutions is implemented.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any of the above technical solutions is implemented.

[0016] Advantageous effects: The present invention provides a data parsing method, system, device, and storage medium, including: acquiring a historical image set captured by a photographing device; determining each target sub-region of the photographing device and the target image elements corresponding to each target sub-region according to the historical image set; determining a data parsing rule corresponding to the target sub-region according to the target image element. In the present application, the photographing area of the photographing device is divided into multiple target sub-regions through the historical data set, and different target sub-regions can correspond to different image elements and data parsing rules, so that the pictures captured by the photographing device can be parsed by region, instead of performing undifferentiated parsing on the entire picture, avoiding the waste of server computing power caused by the undifferentiated full-attribute ability parsing of existing cameras. At the same time, it also avoids the storage of pictures in unnecessary scenarios and saves storage space. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background art, the drawings required to be used in the embodiments of the present application or the background art will be described below.

[0018] The drawings here are incorporated into the specification and constitute a part of this specification. These drawings show embodiments consistent with the present application and are used together with the specification to illustrate the technical solutions of the present application.

[0019] Figure 1 It is the first flow diagram of a data parsing method according to an embodiment of the present application;

[0020] Figure 2It is a schematic diagram of the second process of a data parsing method according to an embodiment of the present application;

[0021] Figure 3 It is a schematic diagram of the shooting area division of a shooting device according to an embodiment of the present application;

[0022] Figure 4 It is a schematic structural diagram of a data parsing system according to an embodiment of the present application;

[0023] Figure 5 It is a schematic hardware structure diagram of a computer device according to an embodiment of the present application. Detailed implementation manners

[0024] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0025] Terms such as "first" and "second" in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0026] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

[0027] Currently, due to the wide shooting angle range of the shooting device, for the pictures captured and reported by the shooting device, if there are multiple regions in the entire picture's perspective (such as sidewalks, motor vehicle lanes, commercial areas, water areas, forests, etc.), while the traditional engine background generally pre-sets the parsing capabilities of this camera in advance (such as humans, faces, motor vehicles, non-motor vehicles, etc.), and then rigidly does things within the parsing capabilities for multiple regions within the entire picture's perspective, that is, performs non-discriminatory feature parsing on humans, faces, motor vehicles, non-motor vehicles, etc. that appear in each region of the picture. For example, if there is a scrapped vehicle parked in the sidewalk area within the picture's perspective, the attributes of this vehicle in the sidewalk area will be continuously extracted, which will cause a great waste of server computing power and occupy unnecessary disk space.

[0028] Based on this, the present application provides a data parsing method, which can divide the shooting angle range of the shooting device into multiple target sub-regions, and set different data parsing rules for each target sub-region. Thus, when performing feature parsing on the pictures taken by the shooting device, only the target sub-regions in the picture and the corresponding data parsing rules of the target sub-regions need to be extracted to complete the parsing of the picture.

[0029] Next, the embodiments of the present application will be described in conjunction with the accompanying drawings in the embodiments of the present application. The execution subject of the embodiments of the present application is a data parsing system, and the data parsing system can be the background server or computer of the shooting device.

[0030] Please refer to Figure 1 , Figure 1 which is a schematic flow chart of a data parsing method provided by an embodiment of the present application.

[0031] S100. Obtain the historical image set captured by the shooting device;

[0032] In the embodiments of the present application, the shooting device refers to a device with a camera function, which can be various cameras, smart phones or other electronic devices.

[0033] In a preferred embodiment, the shooting device is a camera with a wide-angle lens, and its shooting angle range is large, usually the shooting range is between 60 degrees and 180 degrees. Due to the large shooting angle range, usually a picture can contain multiple image elements. For example, a picture can contain: human attribute elements, vehicle attribute elements, building attribute elements, etc. Optionally, the shooting device can be set outdoors or indoors according to the actual application scenario.

[0034] The historical image set contains one or more images captured by the imaging device. Most of the time, it contains multiple images captured by the imaging device during a certain historical time period, so as to more accurately determine each target sub-region of the imaging device and the target image elements corresponding to each target sub-region based on the historical image set later.

[0035] In a preferred embodiment, the historical image set is a series of continuously captured images of the imaging device within the most recent time period, so as to more truthfully and accurately reflect the status of each image element within the current viewing range of the imaging device.

[0036] In one implementation of obtaining the historical image set captured by the imaging device, a data parsing system can be communicatively connected to the imaging device to obtain the historical image set captured by the imaging device.

[0037] S200. Determine each target sub-region of the imaging device and the target image elements corresponding to each target sub-region according to the historical image set;

[0038] In a preferred embodiment of the present application, the shootable viewing range of the imaging device can be divided into multiple target sub-regions according to the historical image set, and the target image elements corresponding to each target sub-region are determined. Each target sub-region can be one or more of a sidewalk region, a motor vehicle lane region, a commercial region, a water region, a forest region, etc. The target image elements can be one or more of a human body image element, a human face image element, a motor vehicle image element, a non-motor vehicle image element, a water image element, a forest image element;

[0039] Specifically, for an image captured by the imaging device, there can be multiple target sub-regions,

[0040] For each target sub-region, it generally corresponds to one or more target image elements. For example, for an image captured by the imaging device, it has a first target sub-region and a second target sub-region.

[0041] When the first target sub-region is a sidewalk region, the corresponding target image elements can be a human face image element and a human body image element. When the second target sub-region is a motor vehicle lane region, the corresponding target image element is a motor vehicle image element.

[0042] S300. Determine the data parsing rule corresponding to the target sub-region according to the target image element.

[0043] In the embodiments of the present application, the data parsing rule is a rule for extracting and parsing feature attributes of one or more image elements in an image. For example, the data parsing rule can be a face image element parsing rule, a motor vehicle image parsing rule, a non-motor vehicle image element parsing rule, or a full-attribute parsing rule, etc. Specifically, the face image element parsing rule is to extract and parse the feature attributes of the face image attributes appearing in the image; the full-attribute parsing rule is to extract and parse the feature attributes of all the image elements appearing in the picture. After determining the target sub-region and the target image element corresponding to the target sub-region, the data parsing rule corresponding to the target sub-region can be determined.

[0044] Still taking the above example for illustration, when the first target sub-region is the sidewalk area, the corresponding target image elements can be face image elements and human body image elements. At this time, it can be determined that the data parsing rule corresponding to the sidewalk area is to perform feature parsing on the face image elements and human body image elements appearing in the sidewalk area; when the second target sub-region is the motor vehicle lane area, the corresponding target image element is the motor vehicle image element. At this time, it can be determined that the data parsing rule corresponding to the motor vehicle lane area is to perform feature parsing on the motor vehicle image elements appearing in the motor vehicle lane area.

[0045] It can be seen that for the above example, when the first target sub-region is the sidewalk area, only the face image elements and human body image elements in the sidewalk area need to be parsed, and there is no need to parse the motor vehicle image elements, non-motor vehicle image elements, water area image elements, etc. appearing in the sidewalk area. When the second target sub-region is the motor vehicle lane area, only the motor vehicle image elements need to be parsed, and there is no need to parse other image elements appearing in the motor vehicle lane area. Therefore, in the embodiments of the present application, the image captured by the camera is divided into multiple target sub-regions in the above manner, and the corresponding data parsing rule is determined for the image elements of each target sub-region, so that the server can parse each target sub-region according to the corresponding data parsing rule. This method can greatly reduce the operating pressure of the server and improve the processing efficiency of the server for the images captured by the imaging device.

[0046] Please refer to Figure 2 , as a preferred embodiment, step S200: determining each target sub-region of the imaging device and the target image element corresponding to each target sub-region according to the historical image set includes the following steps:

[0047] S201. Obtain each image element in the historical image set based on the historical image set;

[0048] In the embodiments of the present application, the historical image set is the images captured by the imaging device within a preset time period. Preferably, for the images reported by the imaging device to the system, as time goes by, when a certain amount of time and a certain number of captured images are accumulated, the historical image set is obtained. Depending on the historical image set, the image elements may include, but are not limited to, human body image elements, face image elements, motor vehicle image elements, non-motor vehicle image elements, water area image elements, forest image elements, etc.

[0049] In a preferred embodiment, the step S201: obtaining each image element in the historical image set based on the historical image set includes the steps of:

[0050] A1. Pre-establish an image recognition model for identifying each image element in the historical image set;

[0051] A2. Input the historical image set into the image recognition model to obtain each image element in the historical image set.

[0052] Specifically, the image recognition model can be used to identify each image element that appears in the historical image set. After the image recognition model is established, each picture in the historical image set is input into the image recognition model in turn, and thus each image element existing in the historical image set can be determined.

[0053] S202. Determine each coordinate area where the image element appears in the historical image set;

[0054] In the embodiments of the present application, after each image element existing in the historical image set is obtained, further determine each coordinate area where each image element appears in the historical image set, that is to say, count the position where each image element appears in each historical image.

[0055] In a preferred embodiment, a coordinate system of the imaging device's viewing area can be pre-established. Since the viewing angle of each image captured by the imaging device is the same, after establishing this coordinate system, the coordinate positions where each image element appears in each historical picture can be determined. In the embodiments of the present application, the coordinate area where each image element appears can be one or multiple, and the coordinate areas that appear in each historical image can also be one or multiple; taking the face image element as an example, the face image element may only appear in the A coordinate area in the first historical image, or only appear in the B coordinate position in the second historical image, or may appear in the A coordinate area in both the first historical image and the second historical image, and so on.

[0056] S203. Calculate the appearance frequency of the image element in each coordinate area within the preset time period;

[0057] After determining the coordinate regions of each image element in the historical image set, the frequency of the appearance of the image element in each coordinate region can be counted. For example, in a historical image set with 100 historical images, the face image element appears in the A coordinate region in 90 historical images, so it can be determined that the appearance frequency of the face image element in the A coordinate region is 90%.

[0058] S204. When the appearance frequency of the image element in the coordinate region is greater than the preset frequency threshold corresponding to the image element, take the coordinate region as the target sub-region and take the image element as the target image element corresponding to the target sub-region.

[0059] After calculating the appearance frequencies of each image element in each coordinate region, compare the appearance frequency with the preset frequency corresponding to the image element. For example, when the appearance rate of the face image element in the A coordinate region is calculated to be 90%, and the preset frequency threshold corresponding to the face image element is 80%, it can be determined that the A coordinate region is the target sub-region and the face image element is the target image element of the A coordinate region.

[0060] It should be noted that the preset frequency thresholds corresponding to each image element can be the same or different, and those skilled in the art can set them according to actual needs. After determining the target sub-region and the target image element corresponding to the target sub-region, the data parsing rule corresponding to the target sub-region can be determined. For the images captured by the subsequent imaging device, directly parse the images according to the data parsing rule, without the need for full-attribute parsing.

[0061] As an optional implementation manner, the determining of each target sub-region of the imaging device and the target image element corresponding to each target sub-region according to the historical image set includes:

[0062] S210. Determine each scene category information within the viewing angle region of the imaging device according to the historical image set;

[0063] In this implementation manner, image recognition can be performed on one or more historical images in the historical image set to identify each scene category that appears in the image. Specifically, each scene category can be a sidewalk area, a motor vehicle area, a non-motor vehicle area, a commercial area, etc.

[0064] S220. Establish a relationship mapping table between the scene category information and the target image element in advance;

[0065] Specifically, the relationship mapping table between the scene category information and the target image elements can be set by the user according to actual application requirements. For example, if the user only needs to perform face image recognition on the sidewalk area, the scene category information in the relationship mapping table only needs to include the sidewalk area, and the corresponding target image element is the face image element. If the user needs to perform face recognition on the sidewalk area and also needs to perform motor vehicle image element recognition on the motor vehicle area, the target scene in the relationship mapping table needs to include the sidewalk area and the motor vehicle area, and the corresponding target image elements are the face image element and the motor vehicle image element, respectively.

[0066] S230. Determine each target sub-region and the target image element corresponding to each target sub-region according to the respective scene category information and the relationship mapping table.

[0067] In a preferred embodiment, determining each target sub-region and the target image element corresponding to each target sub-region according to the respective scene category information and the relationship mapping table includes the following steps:

[0068] B1. Perform regional division on the perspective area of the photographing device according to the respective scene category information to determine each sub-region; wherein, each sub-region corresponds one-to-one with the respective scene category information;

[0069] In one embodiment, after identifying each scene category in the perspective area of the photographing device through image recognition, the wide-angle image perspective area can be divided according to each scene category. For example, as Figure 3 shown, in Figure 3 there are three scenes including the sidewalk area, the motor vehicle lane, and the commercial area. Therefore, the perspective area of the photographing device can be divided into the sidewalk area, the motor vehicle lane area, and the commercial area. That is, at this time, the sidewalk area, the motor vehicle lane area, and the commercial area are three sub-regions within the perspective area of the photographing device.

[0070] B2. Determine the target sub-regions in each of the sub-regions according to the relationship mapping table;

[0071] Specifically, since the relationship mapping table reflects the actual usage requirements of the user, for different usage requirements, there can be different target sub-regions. For example, if the user only needs to perform face image recognition on the sidewalk area and the scene category information in the relationship mapping table only includes the sidewalk area, only the sidewalk area is determined as the target sub-region, while the motor vehicle lane area and the commercial area are not target sub-regions. If the user needs to perform face recognition on the sidewalk area and also needs to perform recognition on the motor vehicle images in the motor vehicle area, the target scene in the relationship mapping table needs to include the sidewalk area and the motor vehicle area. At this time, both the sidewalk area and the motor vehicle lane area are target sub-regions, and the commercial area is a non-target sub-region.

[0072] B3. Determine the target image element corresponding to each target sub-region according to the relationship mapping table and each target sub-region.

[0073] After determining the target sub-regions in the sub-region, the target image elements corresponding to each target sub-region can be determined according to the relationship mapping table. For example, if the user needs to identify whether pedestrians on the sidewalk are wearing masks, the scene category in the relationship mapping table is the sidewalk area, and the corresponding target image element is the face image element. Subsequently, for the images taken by the shooting device, it is only necessary to recognize the faces of pedestrians in the sidewalk area to confirm whether the pedestrians are wearing masks. At this time, there is no need to perform feature analysis on other areas of the images taken by the shooting device, and non-motor vehicles / billboards that appear in the sidewalk area, which reduces the operating pressure of the server and avoids the storage of images of non-essential scenes and non-essential elements.

[0074] After the data parsing rules corresponding to each target sub-area of ​​the shooting device are preset through the above implementation, the method further includes the steps of:

[0075] S400, receiving a current image captured by the shooting device;

[0076] Specifically, after the corresponding data analysis rules are preset for each target sub-region of the photographing device, the current image currently photographed by the photographing device can be analyzed specifically.

[0077] S500: parse the current image according to the target sub-region and a data parsing rule corresponding to the target sub-region.

[0078] In one embodiment, after determining the target sub-region and the data parsing rule corresponding to the target sub-region, it is only necessary to specifically extract the coordinate position of the target sub-region and the data parsing rule corresponding to the target sub-region to complete the parsing of the current image without parsing the entire image area, thereby reducing image storage of unnecessary scenes and avoiding waste of server computing power.

[0079] For example, when the user needs to detect whether there are illegally parked non-motor vehicles on the motor vehicle lane within the shooting area of ​​the camera, the corresponding target sub-area is the motor vehicle lane, and the corresponding image element is the non-motor vehicle image element. Therefore, it is only necessary to perform feature analysis on the non-motor vehicle elements in the motor vehicle lane area in the image taken by the camera, without the need to perform feature analysis on other areas or other elements of the motor vehicle lane (such as motor vehicles, billboards, etc.). This greatly reduces the operating pressure on the server and improves the efficiency of image feature analysis.

[0080] In a preferred embodiment, the method further comprises:

[0081] The S600 adds the current image captured by the capturing device to the historical image set in real time, obtains an updated historical image set, and uses the updated historical image set as the historical image set.

[0082] In one embodiment, the image captured by the capturing device can be added to the historical image set in real time. At the same time, the image in the historical image set that is relatively old in the nearest time period can be deleted accordingly, so that the historical image set is always the image set in the nearest time period, ensuring the accuracy and reliability of the historical image set to the greatest extent.

[0083] Based on the same inventive concept, an embodiment of the present invention provides a data parsing system 1. Please refer to Figure 4 , Figure 4 which is a schematic structural diagram of a data parsing system 1 provided by an embodiment of the present application. The data parsing system 1 includes the following modules: an acquisition module 11, a first determination module 12, and a second determination module 13;

[0084] The acquisition module 11 is configured to acquire the historical image set captured by the capturing device;

[0085] The first determination module 12 is configured to determine each target sub-region of the capturing device and the target image element corresponding to each target sub-region according to the historical image set;

[0086] The second determination module 13 is configured to determine the data parsing rule corresponding to the target sub-region according to the target image element.

[0087] Combined with any implementation manner of the present application, the first determination module 12 further includes the following units: a first determination unit, a second determination unit, a calculation unit, and a processing unit, where:

[0088] The first determination unit is configured to obtain each image element in the historical image set based on the historical image set;

[0089] The second determination unit is configured to determine each coordinate region where the image element appears in the historical image set;

[0090] The calculation unit is configured to calculate the appearance frequency of the image element in each coordinate region within the preset time period;

[0091] The processing unit is configured to use the coordinate region as the target sub-region and the image element as the target image element corresponding to the target sub-region when the appearance frequency of the image element in the coordinate region is greater than the preset frequency threshold corresponding to the image element.

[0092] In combination with any embodiment of the present application, the first determination unit is further configured to:

[0093] Pre - establish an image recognition model for identifying each image element of the historical image set;

[0094] Input the historical image set into the image recognition model to obtain each image element in the historical image set.

[0095] In combination with any embodiment of the present application, the first determination module 12 further includes the following units: a third determination unit and a fourth determination unit:

[0096] The third determination unit is configured to determine each scene category information within the perspective area of the photographing device according to the historical image set;

[0097] The fourth determination unit is configured to determine each target sub - area and the target image element corresponding to each target sub - area according to the scene category information.

[0098] In combination with any embodiment of the present application, the fourth determination unit is further configured to:

[0099] Pre - establish a relationship mapping table between the scene category information and the target image element;

[0100] Perform area division on the perspective area of the photographing device according to the scene category information to determine each target sub - area; wherein, each target sub - area corresponds one - to - one with the scene category information;

[0101] Determine the target image element corresponding to each target sub - area according to the relationship mapping table and each target sub - area.

[0102] In combination with any embodiment of the present application, the data parsing system further includes a receiving module and a parsing module:

[0103] The receiving module is configured to receive the current image captured by the photographing device;

[0104] The parsing module is configured to parse the current image according to the target sub - area and the data parsing rule corresponding to the target sub - area.

[0105] In combination with any embodiment of the present application, the data parsing system further includes an updating module:

[0106] The updating module is configured to add the current image captured by the photographing device to the historical image set in real - time to obtain an updated historical image set, and use the updated historical image set as the historical image set.

[0107] In some embodiments, the functions or modules included in the system provided by the embodiments of the present application can be used to execute the methods described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0108] Figure 5 FIG. 4 is a schematic hardware structure diagram of a computer device 2 provided by an embodiment of the present application. The computer device 2 can be used to execute the methods described in the above method embodiments. The computer device includes a processor 21, a memory 22, an input device 23, and an output device 24. The processor 21, the memory 22, the input device 23, and the output device 24 are coupled through a connector. The connector includes various interfaces, transmission lines, buses, etc., and the embodiments of the present application do not limit this. It should be understood that in various embodiments of the present application, coupling means being interconnected in a specific manner, including being directly connected or indirectly connected through other devices. For example, they can be connected through various interfaces, transmission lines, buses, etc.

[0109] The processor 21 can be one or more graphics processing units (GPUs). When the processor 21 is a single GPU, the GPU can be a single-core GPU or a multi-core GPU. Optionally, the processor 21 can be a processor group composed of multiple GPUs, and multiple processors are coupled to each other through one or more buses. Optionally, the processor 21 can also be other types of processors, etc., and the embodiments of the present application do not limit this.

[0110] The memory 22 can be used to store computer program instructions and various computer program codes including the program codes for executing the solution of the present application. Optionally, the memory 22 includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM). The memory is used for relevant instructions and data.

[0111] The input device 23 is used to input data and / or signals, and the output device is used to output data and / or signals. The input device 23 and the output device 24 can be independent devices or an integrated device.

[0112] It is understandable that in the embodiments of the present application, the memory 22 can be used not only to store relevant instructions, but also to store relevant data. For example, the memory 22 can be used to store the data obtained through the input device 23, or the memory 22 can also be used to store the comparison results obtained through the processor, etc. The embodiments of the present application do not limit the specific data stored in the memory.

[0113] It can be understood that in practical applications, the intelligent empowerment system in the above embodiments may further include necessary other components, including but not limited to any number of input / output devices, processors, memories, etc., and all intelligent empowerment systems that can implement the embodiments of the present application are within the protection scope of the present application.

[0114] 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 the present application.

[0115] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein. Those skilled in the art can also clearly understand that each embodiment of the present application has different focuses. For the convenience and simplicity of description, the same or similar parts may not be elaborated in different embodiments. Therefore, the parts not described or not detailedly described in a certain embodiment can refer to the descriptions of other embodiments.

[0116] In several embodiments provided by the present 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.

[0117] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed across 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.

[0118] In addition, each functional unit in various embodiments of the present application may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit.

[0119] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that the computer can access or a data storage device such as a server, data center, etc. that includes one or more available media integrated. The available medium may be a magnetic medium (for example, floppy disk, hard disk, magnetic tape), an optical medium (for example, digital versatile disc (DVD)), or a semiconductor medium (for example, solid state disk (SSD)), etc.

[0120] In summary, the present application provides a data analysis method, system, device and storage medium, including: obtaining a historical image set taken by a shooting device; determining each target sub-area of ​​the shooting device and the target image element corresponding to each target sub-area according to the historical image set; and determining the data analysis rules corresponding to the target sub-area according to the target image element. In the present application, the shooting area of ​​the shooting device is divided into multiple target sub-areas through a historical data set, and different target sub-areas may correspond to different image elements and data analysis rules, so that the pictures taken by the shooting device can be analyzed in a targeted manner by region, without the need for indiscriminate analysis of the entire picture, thereby avoiding the waste of server computing power caused by the indiscriminate full attribute capability analysis of existing cameras, and at the same time, avoiding the storage of pictures of unnecessary scenes, saving storage space.

[0121] The technical principle of the present invention is described above in conjunction with specific embodiments. These descriptions are only for explaining the principle of the present invention and cannot be interpreted as limiting the scope of protection of the present invention in any way. Based on the explanations herein, those skilled in the art can associate other specific implementations of the present invention without paying creative labor, and these methods will fall within the scope of protection of the present invention.

Claims

1. A data parsing method, characterized in that, the method includes: Obtaining a historical image set captured by a photographing device, where the historical image set is the images captured by the photographing device within a preset time period; Determining each target sub-region of the photographing device and the target image elements corresponding to each target sub-region according to the historical image set; Determining a data parsing rule corresponding to the target sub-region according to the target image element; The determining each target sub-region of the photographing device and the target image elements corresponding to each target sub-region according to the historical image set includes: Obtaining each image element in the historical image set based on the historical image set; After establishing a coordinate system for the perspective region of the photographing device, determining each coordinate region where the image element appears in the historical image set according to the coordinate position where the image element appears in each historical picture; Calculating the appearance frequency of the image element in each coordinate region within the preset time period; When the appearance frequency of the image element in the coordinate region is greater than the preset frequency threshold corresponding to the image element, taking the coordinate region as the target sub-region and taking the image element as the target image element corresponding to the target sub-region.

2. The method according to claim 1, characterized in that, The obtaining each image element in the historical image set based on the historical image set includes: Pre-establishing an image recognition model for identifying each image element of the historical image set; Inputting the historical image set into the image recognition model to obtain each image element in the historical image set.

3. The method according to claim 1, characterized in that, The determining each target sub-region of the photographing device and the target image elements corresponding to each target sub-region according to the historical image set includes: Determining each scene category information within the perspective region of the photographing device according to the historical image set; Pre-establishing a relationship mapping table between the scene category information and the target image elements; Determining each target sub-region and the target image elements corresponding to each target sub-region according to the each scene category information and the relationship mapping table.

4. The method according to claim 3, characterized in that, The determining each target sub-region and the target image elements corresponding to each target sub-region according to the each scene category information and the relationship mapping table includes: Performing regional division on the perspective region of the photographing device according to the each scene category information to determine each sub-region; where each sub-region corresponds to the each scene category information one by one; Determining the target sub-regions in each sub-region according to the relationship mapping table; Determining the target image elements corresponding to each target sub-region according to the relationship mapping table and each target sub-region.

5. The method according to claim 1, characterized in that, the method further includes: Receiving a current image captured by the photographing device; Parsing the current image according to the target sub-region and the data parsing rule corresponding to the target sub-region.

6. The method according to claim 5, characterized in that, the method further includes: Add the current image captured by the imaging device to the historical image set in real time to obtain an updated historical image set, and use the updated historical image set as the historical image set.

7. A data parsing method and system Characterized in that It includes: An acquisition module, configured to acquire a historical image set captured by an imaging device, where the historical image set is images captured by the imaging device within a preset time period; A first determination module, configured to determine each target sub-region of the imaging device and the target image elements corresponding to each target sub-region according to the historical image set; The determination of each target sub-region of the imaging device and the target image elements corresponding to each target sub-region according to the historical image set includes: obtaining each image element in the historical image set based on the historical image set; after establishing a coordinate system for the perspective region of the imaging device, determining each coordinate region where the image element appears in the historical image set according to the coordinate position where the image element appears in each historical picture; calculating the appearance frequency of the image element in each coordinate region within the preset time period; when the appearance frequency of the image element in the coordinate region is greater than the preset frequency threshold corresponding to the image element, using the coordinate region as the target sub-region and using the image element as the target image element corresponding to the target sub-region; A second determination module, configured to determine a data parsing rule corresponding to the target sub-region according to the target image element.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor Characterized in that When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program Characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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