Data privacy protection system and method based on block chain

Abstract

The invention discloses a block chain-based data privacy protection system and method, and the system comprises a block chain storage node, a picture analysis module and a pedestrian privacy data protection module, the block chain storage node is used for collecting and storing vehicle monitoring picture information, the picture analysis module is used for analyzing the collected and stored vehicle monitoring picture, and the pedestrian privacy data protection module is used for protecting the pedestrian privacy data. The pedestrian privacy protection module is used for collecting protection measures for pedestrian privacy data in a monitoring picture according to an analysis result of the picture analysis module to avoid pedestrian privacy leakage, and the block chain storage node and the pedestrian privacy data protection module are both in network connection with the picture analysis module. The block chain storage node comprises a monitoring picture collection module and a picture cutting setting module, the monitoring picture collection module is used for calling picture data monitored and collected in the vehicle parking period, and the system has the advantages of being low in leakage risk and good in use experience.

Description

Technical Field

[0001] The present invention relates to the field of data protection technology, and specifically to a data privacy protection system and method based on blockchain. Background Art

[0002] In recent years, with the rapid development of smart electric vehicles, the ownership, possession and use rights of automobile data have undergone complex changes. The data generated by smart electric vehicles are complex and diverse, including vehicle data, driving environment data, user driving behavior data, driving automation system operation status and related privacy data. Among them, the "Sentinel" mode launched by some car companies, that is, monitoring the surrounding environment during parking and providing monitoring data to the owner, thereby greatly reducing the situation where the vehicle is damaged by criminals during parking and the criminals cannot be found, protecting the safety of the vehicle, and therefore has been widely praised. However, it is also controversial because it arbitrarily collects information about the surrounding environment, resulting in the risk of leakage of the portraits, behaviors and whereabouts of passers-by. As a result, smart vehicles are restricted in many areas and parking is prohibited. In order to comply with relevant laws and regulations, car companies are unable to further open up functions such as remote real-time viewing of the vehicle environment using the "Sentinel" mode. This has restricted the development of smart electric vehicles. Therefore, it is necessary to design a blockchain-based data privacy protection system and method with low leakage risk and good user experience. Summary of the invention

[0003] The purpose of the present invention is to provide a data privacy protection system and method based on blockchain to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a data privacy protection system and method based on blockchain, including a blockchain storage node, a picture analysis module and a pedestrian privacy data protection module. The blockchain storage node is used to collect and store vehicle monitoring picture information, the picture analysis module is used to analyze the collected and stored vehicle monitoring pictures, and the pedestrian privacy protection module is used to collect protection measures for pedestrian privacy data in the monitoring picture according to the analysis results of the picture analysis module to avoid pedestrian privacy leakage. The blockchain storage node and the pedestrian privacy data protection module are both connected to the picture analysis module network.

[0005] According to the above technical solution, the blockchain storage node includes a monitoring screen acquisition module and a screen cropping setting module. The monitoring screen acquisition module is used to retrieve the screen data collected during the parking of the vehicle, and the screen cropping setting module is used to perform screen edge cropping settings on the collected monitoring screen.

[0006] According to the above technical solution, the image analysis module includes a dynamic portrait recognition and tracking module, a pedestrian facial position prediction module and a pedestrian image activity index analysis module. The dynamic portrait recognition and tracking module is used to continuously identify and track pedestrian portraits in the monitoring image. The pedestrian facial position prediction module is used to predict the changing trend of the pedestrian facial position based on the recognition and tracking data of the dynamic portrait recognition and tracking module. The pedestrian image activity index analysis module is used to analyze the activity of the current pedestrian based on the monitored pedestrian image.

[0007] According to the above technical solution, the dynamic portrait recognition and tracking module further includes a portrait picture ratio recognition submodule and a facial recognition submodule. The portrait picture ratio recognition submodule is used to dynamically output the portrait picture ratio according to the ratio of the pedestrian picture selected by the tracking frame to the monitoring picture, and obtain the change in the range of "coding" of the pedestrian according to the output portrait picture ratio. The facial recognition submodule is used to identify the facial area of ​​the pedestrian in the pedestrian portrait picture.

[0008] According to the above technical solution, the pedestrian screen activity index analysis module further includes a pedestrian torso fitting submodule, a category judgment submodule and an output submodule. The pedestrian torso fitting submodule is used to fit the pedestrian torso contour and obtain the ratio of the pedestrian head contour length value to the torso contour length value. The category judgment submodule is used to judge the pedestrian category. The output submodule is used to output the pedestrian activity index according to the pedestrian category.

[0009] According to the above technical solution, the pedestrian privacy data protection module includes a screen image processing module, an abnormal behavior judgment module and a privacy protection module. The screen image processing module is used to code the pedestrian's face according to the screen analysis results, the abnormal behavior judgment module is used to judge the pedestrian's abnormal behavior based on the pedestrian's face prediction results and real-time dynamic tracking conditions, and the privacy protection module is used to strengthen the privacy protection processing of the pedestrian's image when it is judged that the pedestrian has abnormal behavior.

[0010] The operation method of the data privacy protection system based on blockchain comprises the following steps:

[0011] Step S1: obtaining a monitoring picture collected during the parking of the vehicle, and cropping the edge of the monitoring picture;

[0012] Step S2: After a pedestrian enters the monitoring screen, the dynamic portrait recognition and tracking module is used to dynamically identify and track the new person and frame the pedestrian;

[0013] Step S3: Identify the basic range area value m of the pedestrian portrait to be coded according to the proportion of the portrait frame to the monitoring screen,

[0014] Step S4: identifying the pedestrian's facial area in the pedestrian portrait image through the facial recognition submodule, and performing real-time dynamic tracking;

[0015] Step S5: recording the trajectory of the pedestrian's face in the picture that is dynamically tracked, and predicting the position of the pedestrian's face in the picture;

[0016] Step S6: The pedestrian image activity index analysis module further analyzes the pedestrian image, determines its activity index, and gives an additional area value n for coding according to the activity index;

[0017] Step S7: according to the given picture processing reaction cycle T, the image processing module linearly takes the position after the cycle T as the center and performs coding processing on a circle with an area of ​​m+n, wherein the mosaic processing depth of the circle with an area of ​​m in the middle of the circle is greater than the mosaic processing depth of the circular ring with an area of ​​n on the outside;

[0018] Step S8: When the deviation between the pedestrian face prediction result and the real-time dynamic tracking position exceeds the preset threshold, the pedestrian behavior is judged to be abnormal, and the privacy protection module is activated to perform coding on the entire pedestrian frame area.

[0019] According to the above technical solution, step S6 further includes:

[0020] Step S61: When a pedestrian enters the edge area of ​​the cropped surveillance image, the pedestrian image is locked, the image contour is fitted and the contour line is drawn;

[0021] Step S62: obtaining a ratio b between the length of the pedestrian's head contour line and the length of the newcomer's torso contour line;

[0022] Step S63: According to the judgment standard of the category judgment submodule, a critical value B of the ratio between children and adults is preset in advance. When the ratio b>B, it is judged to be a child, otherwise it is an adult;

[0023] Step S64: outputting a fixed pedestrian image activity index Z according to the determined category;

[0024] Step S65: When the pedestrian activity index Z is larger, the additional area value n for coding is larger.

[0025] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention, through the setting of cropping the screen, can perform identification and coding position processing in advance when the face does not appear on the monitoring screen, and can predict the pedestrian position and facial dynamic picture in real time within the monitoring screen, analyze the screen activity index of the newcomer, and code the picture. When the instantaneous change distance exceeds the threshold, the whole body is directly coded, thereby effectively avoiding the problem of mosaic detachment caused by the processing delay caused by the complex changes in the external environment and a certain delay in the picture processing when the picture data privacy processing of the prior art is performed, resulting in the omission of coding when the newcomer just enters the monitoring screen and the sudden irregular activities of the behavior in the screen causing processing delays, thereby greatly improving the protection effect of pedestrian privacy data, and at the same time solving the problem of limiting the development of the "sentinel" mode, thereby promoting social progress. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0027] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] See also Figure 1 The present invention provides a technical solution: a data privacy protection system and method based on blockchain, including a blockchain storage node, a picture analysis module and a pedestrian privacy data protection module. The blockchain storage node is used to collect and store vehicle monitoring picture information, the picture analysis module is used to analyze the collected and stored vehicle monitoring pictures, and the pedestrian privacy protection module is used to collect protection measures for pedestrian privacy data in the monitoring picture according to the analysis results of the picture analysis module to avoid pedestrian privacy leakage. The blockchain storage node and the pedestrian privacy data protection module are both connected to the picture analysis module network.

[0030] The blockchain storage node includes a monitoring screen acquisition module and a screen cropping setting module. The monitoring screen acquisition module is used to retrieve the screen data collected during the parking of the vehicle, and the screen cropping setting module is used to set the edge cropping of the collected monitoring screen.

[0031] The image analysis module includes a dynamic portrait recognition and tracking module, a pedestrian facial position prediction module and a pedestrian image activity index analysis module. The dynamic portrait recognition and tracking module is used to continuously identify and track pedestrian portraits in the monitoring image. The pedestrian facial position prediction module is used to predict the changing trend of the pedestrian facial position based on the recognition and tracking data of the dynamic portrait recognition and tracking module. The pedestrian image activity index analysis module is used to analyze the activity of the current pedestrian based on the monitored pedestrian image.

[0032] The dynamic portrait recognition and tracking module further includes a portrait picture ratio recognition sub-module and a facial recognition sub-module. The portrait picture ratio recognition sub-module is used to dynamically output the portrait picture ratio according to the ratio of the pedestrian picture selected by the tracking frame to the monitoring picture, and obtain the change in the range of "coding" of the pedestrian according to the output portrait picture ratio. The facial recognition sub-module is used to identify the facial area of ​​the pedestrian in the pedestrian portrait picture.

[0033] The pedestrian screen activity index analysis module further includes a pedestrian torso fitting submodule, a category judgment submodule and an output submodule. The pedestrian torso fitting submodule is used to fit the pedestrian torso contour and obtain the ratio of the pedestrian head contour length value to the torso contour length value. The category judgment submodule is used to judge the pedestrian category. The output submodule is used to output the pedestrian activity index according to the pedestrian category.

[0034] The pedestrian privacy data protection module includes an image processing module, an abnormal behavior judgment module and a privacy protection module. The image processing module is used to code the pedestrian's face according to the image analysis results. The abnormal behavior judgment module is used to judge the pedestrian's abnormal behavior based on the pedestrian's facial prediction results and real-time dynamic tracking. The privacy protection module is used to strengthen the privacy protection processing of the pedestrian image when it is judged that the pedestrian has abnormal behavior.

[0035] The operation method of the data privacy protection system based on blockchain comprises the following steps:

[0036] Step S1: Obtain the monitoring picture collected during the parking of the vehicle, and crop the edge of the monitoring picture; by cropping the picture in advance, the privacy of pedestrians is prevented from being leaked due to system processing delay when pedestrians directly enter the monitoring picture, thereby achieving the effect of image preprocessing;

[0037] Step S2: After a pedestrian enters the monitoring screen, the dynamic portrait recognition and tracking module is used to dynamically identify and track the new person's screen and frame it; then the approximate distance between the pedestrian and the vehicle can be reflected according to the size ratio of the framed portrait, so that the basic coding range given during the image processing is more accurate;

[0038] Step S3: Identify the basic range area value m of the pedestrian portrait to be coded according to the proportion of the portrait frame to the monitoring screen,

[0039] Step S4: identifying the pedestrian's facial area in the pedestrian portrait image through the facial recognition submodule, and performing real-time dynamic tracking;

[0040] Step S5: Record the trajectory of the pedestrian's face in the picture that is dynamically tracked, and predict the position of the pedestrian's face in the picture; the prediction method mainly obtains an accurate prediction value by predicting the direction of the trajectory, the speed of the movement, and the speed of the change in the size ratio of the frame of the portrait;

[0041] Step S6: The pedestrian screen activity index analysis module further analyzes the pedestrian screen, determines its activity index, and gives the additional coding area value n according to the activity index; by cutting the screen in advance, the pedestrian activity index can also be analyzed in advance when the pedestrian enters the cut screen. The activity index is mainly related to age. Under the same external factors, children are more likely to unexpectedly perform behaviors such as "jumping", "squatting", "running", etc. when walking. Then, by further adding a preventive additional coding range on top of the basic coding range, and dynamically adjusting the additional coding range according to different target types, high security and high data privacy protection are achieved to prevent leakage, and prevent the malicious dissemination and profit-seeking by criminals, while reducing the impact of excessive coding on the perception of the monitoring screen, achieving a balanced effect of safety and picture quality;

[0042] Step S7: according to the given picture processing reaction cycle T, the image processing module linearly takes the position after the cycle T as the center and performs coding processing on a circle with an area of ​​m+n, wherein the mosaic processing depth of the circle with an area of ​​m in the middle of the circle is greater than the mosaic processing depth of the circular ring with an area of ​​n on the outside;

[0043] Step S8: When the deviation between the pedestrian face prediction result and the real-time dynamic tracking position exceeds the preset threshold, the pedestrian behavior is judged to be abnormal, and the privacy protection module is activated to perform coding on the entire pedestrian frame area.

[0044] Step S6 further comprises:

[0045] Step S61: When a pedestrian enters the edge area of ​​the cropped surveillance image, the pedestrian image is locked, the image contour is fitted and the contour line is drawn;

[0046] Step S62: obtaining a ratio b between the length of the pedestrian's head contour line and the length of the newcomer's torso contour line;

[0047] Step S63: According to the judgment standard of the category judgment submodule, a critical value B of the ratio between children and adults is preset in advance. When the ratio b>B, it is judged to be a child, otherwise it is an adult;

[0048] Step S64: outputting a fixed pedestrian image activity index Z according to the determined category;

[0049] Step S65: The larger the pedestrian screen activity index Z is, the larger the additional range area value n for coding is. By setting the cropped screen, when the face does not appear on the monitoring screen, the recognition and coding position processing are done in advance, and in the monitoring screen, the pedestrian position and facial dynamic screen are predicted in real time, the newcomer screen activity index is analyzed, and the screen is coded. When the instantaneous change distance exceeds the threshold, the whole body is directly coded, thereby effectively avoiding the problem of mosaic detachment caused by the complex changes in the external environment and a certain delay in the screen processing when the screen data privacy processing of the prior art is avoided, resulting in the omission of coding at the moment when the newcomer just enters the monitoring screen and the processing delay caused by the sudden irregular activities of the behavior in the screen, thereby greatly improving the protection effect of pedestrian privacy data, while solving the problem of limiting the development of the "sentinel" mode and promoting social progress.

[0050] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0051] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A data privacy protection system based on blockchain, comprising a blockchain storage node, a picture analysis module and a pedestrian privacy data protection module, characterized in that: The blockchain storage node is used to collect and store vehicle monitoring screen information, the screen analysis module is used to analyze the collected and stored vehicle monitoring screen, and the pedestrian privacy protection module is used to collect protection measures for pedestrian privacy data in the monitoring screen according to the analysis results of the screen analysis module to avoid pedestrian privacy leakage. The blockchain storage node and the pedestrian privacy data protection module are both connected to the screen analysis module network.

2. The data privacy protection system based on blockchain according to claim 1 is characterized in that: The blockchain storage node includes a monitoring screen acquisition module and a screen cropping setting module. The monitoring screen acquisition module is used to retrieve the screen data collected during the parking of the vehicle, and the screen cropping setting module is used to set the cropping of the screen edge of the collected monitoring screen.

3. The data privacy protection system based on blockchain according to claim 2 is characterized in that: The image analysis module includes a dynamic portrait recognition and tracking module, a pedestrian facial position prediction module and a pedestrian image activity index analysis module. The dynamic portrait recognition and tracking module is used to continuously recognize and track pedestrian portraits in the monitoring image. The pedestrian facial position prediction module is used to predict the changing trend of the pedestrian facial position based on the recognition and tracking data of the dynamic portrait recognition and tracking module. The pedestrian image activity index analysis module is used to analyze the activity of the current pedestrian based on the monitored pedestrian image.

4. The data privacy protection system based on blockchain according to claim 3 is characterized in that: The dynamic portrait recognition and tracking module further includes a portrait picture ratio recognition submodule and a facial recognition submodule. The portrait picture ratio recognition submodule is used to dynamically output the portrait picture ratio according to the ratio of the pedestrian picture selected by the tracking frame to the monitoring picture, and obtain the change in the range of "coding" of the pedestrian according to the output portrait picture ratio. The facial recognition submodule is used to identify the facial area of ​​the pedestrian in the pedestrian portrait picture.

5. The data privacy protection system based on blockchain according to claim 4 is characterized in that: The pedestrian screen activity index analysis module further includes a pedestrian torso fitting submodule, a category judgment submodule and an output submodule. The pedestrian torso fitting submodule is used to fit the pedestrian torso contour and obtain the ratio of the pedestrian head contour length value to the torso contour length value. The category judgment submodule is used to judge the pedestrian category. The output submodule is used to output the pedestrian activity index according to the pedestrian category.

6. The data privacy protection system based on blockchain according to claim 5 is characterized in that: The pedestrian privacy data protection module includes a screen image processing module, an abnormal behavior judgment module and a privacy protection module. The screen image processing module is used to code the pedestrian's face according to the screen analysis result, the abnormal behavior judgment module is used to judge the pedestrian's abnormal behavior according to the pedestrian face prediction result and real-time dynamic tracking situation, and the privacy protection module is used to strengthen the privacy protection processing of the pedestrian image when it is judged that the pedestrian has abnormal behavior.

7. A method for operating a data privacy protection system based on blockchain according to claim 6, characterized in that: The method comprises the following steps: Step S1: obtaining a monitoring picture collected during the parking of the vehicle, and cropping the edge of the monitoring picture; Step S2: After a pedestrian enters the monitoring screen, the dynamic portrait recognition and tracking module is used to dynamically identify and track the new person and frame the pedestrian; Step S3: Identify the basic range area value m of the pedestrian portrait to be coded according to the proportion of the portrait frame to the monitoring screen, Step S4: identifying the pedestrian's facial area in the pedestrian portrait image through the facial recognition submodule, and performing real-time dynamic tracking; Step S5: recording the trajectory of the pedestrian's face in the picture that is dynamically tracked, and predicting the position of the pedestrian's face in the picture; Step S6: The pedestrian image activity index analysis module further analyzes the pedestrian image, determines its activity index, and gives an additional area value n for coding according to the activity index; Step S7: according to the given picture processing reaction cycle T, the image processing module linearly takes the position after the cycle T as the center and performs coding processing on a circle with an area of ​​m+n, wherein the mosaic processing depth of the circle with an area of ​​m in the middle of the circle is greater than the mosaic processing depth of the circular ring with an area of ​​n on the outside; Step S8: When the deviation between the pedestrian face prediction result and the real-time dynamic tracking position exceeds the preset threshold, the pedestrian behavior is judged to be abnormal, and the privacy protection module is activated to perform coding on the entire pedestrian frame area.

8. The data privacy protection method based on blockchain according to claim 7 is characterized in that: The step S6 further comprises: Step S61: When a pedestrian enters the edge area of ​​the cropped surveillance image, the pedestrian image is locked, the image contour is fitted and the contour line is drawn; Step S62: obtaining a ratio b between the length of the pedestrian's head contour line and the length of the newcomer's torso contour line; Step S63: According to the judgment standard of the category judgment submodule, a critical value B of the ratio between children and adults is preset in advance. When the ratio b>B, it is judged to be a child, otherwise it is an adult; Step S64: outputting a fixed pedestrian image activity index Z according to the determined category; Step S65: When the pedestrian activity index Z is larger, the additional area value n for coding is larger.

Images