CN110728534A - Method for detecting block throwing property internet - Google Patents
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Abstract
The invention relates to a block throwing property detection method which comprises the step of using a block throwing property detection system to monitor the density of shared bicycles of each real block in a monitoring range in real time so as to draw a throwing-allowed block capable of being continuously thrown and a throwing-prohibited block with saturated vehicles.
Description
Technical Field
The invention relates to the field of internet sharing, in particular to a block jettability internet detection method.
Background
The network sharing is that a computer and other terminal equipment are used as carriers, information exchange and resource sharing are carried out by means of the public-facing social organization of the internet, and other people are allowed to share the work fruits of the people.
The internet was originally designed to provide a communication network that would work well even if some locations were destroyed by nuclear weapons. If most of the direct paths do not pass, the router directs the communication to propagate through the network via intermediate routers.
In 1978 UUCP (UNIX and UNIX copy protocol) was proposed in Bell laboratories. In 1979, a newsgroup networking system was developed based on UUCP. The development of news groups (discussion groups focusing on a topic) has provided a new approach to exchanging information worldwide. However, the newsgroup is not considered part of the internet because it does not share the TCP/IP protocol, it connects UNIX systems throughout the world, and many internet sites make full use of the newsgroup. Newsgroups are a very significant part of the development of the network world.
Learning and using a network becomes very easy for non-engineering technicians when commands for e-mail (electronic mail), FTP (file download) and telnet (remote login) are all specified as standardized. Although not so easy today anyway, the use of the internet is certainly greatly expanded in universities and in special fields. Other sectors, including computer, physical and engineering technology sectors, have also found ways to take advantage of the internet, i.e., to communicate with and share files and resources with universities around the world. Libraries are also moving forward one step, making their search directories world-wide.
Disclosure of Invention
The invention needs to have the following two key points:
(1) monitoring the density of the shared bicycle of each actual block in the monitoring range in real time to draw blocks which can be continuously released and allow releasing and blocks which are forbidden to release when vehicles are saturated, thereby realizing directional releasing of the shared bicycle;
(2) and respectively executing sharpening processing mechanisms with different strategies on the target area and the non-target area in the image so as to avoid executing too complex multiple sharpening processing on the whole image.
According to an aspect of the present invention, there is provided a neighborhood castability detection method including using a neighborhood castability detection system to monitor a density of shared bicycles for each real neighborhood within a monitoring range in real time to delineate permitted-to-throw neighborhoods where throwing can be continued and prohibited-to-throw neighborhoods where vehicles are saturated, the neighborhood castability detection system comprising: the density extraction equipment is arranged in a ground monitoring room of a shared bicycle operator, is connected with the re-identification equipment and is used for determining the shared bicycle density of the actual block corresponding to each image block based on the number of the shared bicycle sub-images in each image block; the information notification equipment is connected with the density extraction equipment and is used for uploading the actual block with the shared bicycle density being greater than or equal to the preset density threshold value to a server of a shared bicycle operator as a block prohibited to be released; the information notification equipment is also used for taking the actual block with the shared bicycle density smaller than the preset density threshold value as a server allowing the block to be released to be uploaded to a shared bicycle operator; the data receiving equipment is arranged in a ground monitoring room of a shared bicycle operator and used for receiving a remote sensing image obtained by a remote sensing satellite performing remote sensing shooting on a preset monitoring area to be output as a monitoring area image; the statistical sorting filtering equipment is connected with the data receiving equipment and used for executing statistical sorting filtering processing on the received monitoring area images so as to obtain and output corresponding statistical sorting filtering images; and the signal analysis equipment is connected with the statistical sorting filtering equipment and used for receiving the statistical sorting filtering images, searching corresponding shared bicycle sub-images from the statistical sorting filtering images based on shared bicycle image characteristics, and taking the images except the shared bicycle sub-images in the statistical sorting filtering images as residual sub-images, wherein the shared bicycle image characteristics are patterns with bicycle shapes and with the preset color mean value exceeding the limit.
The block throwing detection method is compact in design and has certain pertinence. The density of the shared single cars of each actual block in the monitoring range is monitored in real time to determine the allowed block and the prohibited block with saturated cars, which can be continuously thrown, so that the directional throwing of the shared single cars is realized.
Detailed Description
Embodiments of the present invention will be described in detail below.
The sharing of the single bicycle refers to that an enterprise provides bicycle sharing service in a campus, a subway station, a bus station, a residential area, a commercial area, a public service area and the like, and the sharing mode is a time-sharing rental mode. The shared bicycle is a novel environment-friendly and economical shared bicycle.
The sharing bicycle is a novel vehicle rental business-bicycle rental business, and mainly depends on a carrier as a bicycle. The method can fully utilize the running depression of the bicycle caused by rapid economic development in cities; the public road passing rate is utilized to the maximum extent.
In 2018, 5, 21, the Beijing municipal transportation all-purpose card and the ofo minibus jointly declare that strategic cooperation is achieved, and an NFC intelligent lock supporting the Beijing all-purpose card is released. 7 months and 5 days, the Mobai bicycle announces that the mortgage-free vehicle will be applied to the whole country with zero threshold.
In the prior art, operators of shared bicycles have certain blindness when releasing shared bicycles, and generally regularly release the same number of shared bicycles for each actual block within a city range by using an average release mode, however, the release mode does not consider different frequencies of using the shared bicycles for different blocks, and in addition, some adaptive release modes executed based on the number of the shared bicycles in each actual block do not consider the competition problem that the shared bicycles in the actual block have different colors from other operators.
In order to overcome the defects, the invention builds a block jettability detection method, which comprises the step of using a block jettability detection system to monitor the density of the shared bicycles of each actual block in a monitoring range in real time so as to demarcate a release-allowed block and a release-prohibited block, wherein the release-allowed block and the release-prohibited block are capable of continuing to release vehicles. The block throwing property detection system can effectively solve the corresponding technical problem.
The block throwing power detection system according to the embodiment of the invention comprises:
the density extraction equipment is arranged in a ground monitoring room of a shared bicycle operator, is connected with the re-identification equipment and is used for determining the shared bicycle density of the actual block corresponding to each image block based on the number of the shared bicycle sub-images in each image block;
the information notification equipment is connected with the density extraction equipment and is used for uploading the actual block with the shared bicycle density being greater than or equal to the preset density threshold value to a server of a shared bicycle operator as a block prohibited to be released;
the information notification equipment is also used for taking the actual block with the shared bicycle density smaller than the preset density threshold value as a server allowing the block to be released to be uploaded to a shared bicycle operator;
the data receiving equipment is arranged in a ground monitoring room of a shared bicycle operator and used for receiving a remote sensing image obtained by a remote sensing satellite performing remote sensing shooting on a preset monitoring area to be output as a monitoring area image;
the statistical sorting filtering equipment is connected with the data receiving equipment and used for executing statistical sorting filtering processing on the received monitoring area images so as to obtain and output corresponding statistical sorting filtering images;
the signal analysis equipment is connected with the statistical sorting filtering equipment and used for receiving the statistical sorting filtering images, searching corresponding shared bicycle sub-images from the statistical sorting filtering images based on shared bicycle image characteristics, and taking the images except the shared bicycle sub-images in the statistical sorting filtering images as residual sub-images, wherein the shared bicycle image characteristics are patterns with bicycle shapes and with the preset color mean value exceeding the limit;
the image enhancement device is connected with the signal analysis device and used for performing image enhancement processing on the shared bicycle sub-image to obtain a first sub-image and performing image enhancement processing on the residual sub-image to obtain a second sub-image;
the high-pass filtering sharpening device is respectively connected with the signal analysis device and the image enhancement device and is used for receiving the first sub-image and carrying out high-pass filtering sharpening processing on the first sub-image so as to obtain a third sub-image;
the data merging device is respectively connected with the image enhancement device and the high-pass filtering sharpening device and is used for respectively carrying out normalization processing operation on the second sub-image and the third sub-image so as to respectively obtain a fourth sub-image and a fifth sub-image and merging the fourth sub-image and the fifth sub-image so as to obtain an integrated processing image;
the edge sharpening device is connected with the data merging device and is used for receiving the integrated processing image and carrying out edge sharpening on the integrated processing image so as to obtain and output a corresponding edge sharpened image;
and the re-identification device is connected with the edge sharpening device and used for searching out corresponding shared bicycle sub-images from the edge sharpened image based on the shared bicycle image characteristics, performing uniform image segmentation on the edge sharpened image to obtain each image block, and counting the number of the shared bicycle sub-images in each image block.
Next, a specific configuration of the street availability detection system of the present invention will be further described.
In the block jettability detection system:
the image enhancement device is also used for directly sending the shared bicycle sub-image as a first sub-image to the high-pass filtering sharpening device when the definition of the shared bicycle sub-image is detected to be out of limit;
and the image enhancement device is also used for directly sending the residual sub-image as a second sub-image to the high-pass filtering sharpening device when the definition of the residual sub-image is detected to be out of limit.
The block throwing power detection system can further comprise:
the channel value sequencing equipment is connected with the data receiving equipment and used for receiving the monitoring area image and sequencing the gray values of all pixel points in the monitoring area image to obtain the maximum sequence number of the sequencing queue;
and the redundancy identification device is connected with the channel value sequencing device and used for receiving the maximum sequence number and determining the redundancy of the monitoring area images in inverse proportion to the maximum sequence number.
The block throwing power detection system can further comprise:
and the minimum filtering device is connected with the redundancy identification device and is used for receiving the monitoring area image from the channel value sequencing device when the received redundancy is lower than a preset redundancy threshold value and executing minimum filtering processing on the monitoring area image to obtain a minimum filtering image.
The block throwing power detection system can further comprise:
and the matrix extraction device is used for receiving the minimum value filtering image and performing color space conversion on the minimum value filtering image to obtain a C color matrix, an M color matrix, a Y color matrix and a K color matrix in a CMYK color space of the minimum value filtering image.
The block throwing power detection system can further comprise:
and the customized sharpening device is connected with the matrix extraction device and is used for determining the intensity of sharpening processing on the C color matrix based on the mean square error of the C color matrix, determining the intensity of sharpening processing on the M color matrix based on the mean square error of the M color matrix, and not sharpening the K color matrix and the Y color matrix.
The block throwing power detection system can further comprise:
and the combined execution device is connected with the customized sharpening device and is used for carrying out combined operation on the sharpened C color matrix, the sharpened M color matrix, the unsharpened K color matrix and the unsharpened Y color matrix to obtain a corresponding combined operation image.
The block throwing power detection system can further comprise:
the bilinear interpolation device is connected with the combination execution device and is used for executing bilinear interpolation operation on the combined operation image to obtain an instant interpolation image;
the bilinear interpolation equipment is also connected with the statistical sorting filtering equipment and is used for sending the instant interpolation image to the statistical sorting filtering equipment in place of the monitoring area image;
wherein, in the custom sharpening device, determining an intensity of the sharpening process performed on the C color matrix based on a mean square error of the C color matrix comprises: the greater the mean square error of the C color matrix, the greater the intensity of sharpening performed on the C color matrix;
wherein, in the custom sharpening device, determining an intensity of the sharpening process performed on the M-color matrix based on a mean square error of the M-color matrix comprises: the greater the mean square error of the M color matrix, the greater the intensity of the sharpening process performed on the M color matrix.
In the block jettability detection system:
in the channel value sorting device, sorting the gray values of the pixels in the monitoring area image includes: pixel points with the same gray value occupy the serial number of the same queuing sequence;
wherein the channel value sorting device is connected with the redundancy identification device through a serial communication interface.
In addition, the matrix extraction device may be implemented using an MCU controller. MCUs can be classified into Harvard (Harvard) structures and Von Neumann (Von Neumann) structures according to their memory structures. Most of the current single-chip computers are based on a von Neumann structure, and the structure clearly defines four essential parts required by an embedded system: a central processor core, program memory (read only memory or flash memory), data memory (random access memory), one or more timers/timers, and input/output ports for communicating with peripherals and extended resources, all integrated on a single integrated circuit chip.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (9)
1. A neighborhood jettability detection method that includes using a neighborhood jettability detection system to monitor, in real-time, a shared bicycle density for each actual neighborhood within a monitored range to delineate allowed-delivery neighborhoods that can continue delivery and prohibited-delivery neighborhoods that are saturated with vehicles, the neighborhood jettability detection system comprising:
the density extraction equipment is arranged in a ground monitoring room of a shared bicycle operator, is connected with the re-identification equipment and is used for determining the shared bicycle density of the actual block corresponding to each image block based on the number of the shared bicycle sub-images in each image block;
the information notification equipment is connected with the density extraction equipment and is used for uploading the actual block with the shared bicycle density being greater than or equal to the preset density threshold value to a server of a shared bicycle operator as a block prohibited to be released;
the information notification equipment is also used for taking the actual block with the shared bicycle density smaller than the preset density threshold value as a server allowing the block to be released to be uploaded to a shared bicycle operator;
the data receiving equipment is arranged in a ground monitoring room of a shared bicycle operator and used for receiving a remote sensing image obtained by a remote sensing satellite performing remote sensing shooting on a preset monitoring area to be output as a monitoring area image;
the statistical sorting filtering equipment is connected with the data receiving equipment and used for executing statistical sorting filtering processing on the received monitoring area images so as to obtain and output corresponding statistical sorting filtering images;
the signal analysis equipment is connected with the statistical sorting filtering equipment and used for receiving the statistical sorting filtering images, searching corresponding shared bicycle sub-images from the statistical sorting filtering images based on shared bicycle image characteristics, and taking the images except the shared bicycle sub-images in the statistical sorting filtering images as residual sub-images, wherein the shared bicycle image characteristics are patterns with bicycle shapes and with the preset color mean value exceeding the limit;
the image enhancement device is connected with the signal analysis device and used for performing image enhancement processing on the shared bicycle sub-image to obtain a first sub-image and performing image enhancement processing on the residual sub-image to obtain a second sub-image;
the high-pass filtering sharpening device is respectively connected with the signal analysis device and the image enhancement device and is used for receiving the first sub-image and carrying out high-pass filtering sharpening processing on the first sub-image so as to obtain a third sub-image;
the data merging device is respectively connected with the image enhancement device and the high-pass filtering sharpening device and is used for respectively carrying out normalization processing operation on the second sub-image and the third sub-image so as to respectively obtain a fourth sub-image and a fifth sub-image and merging the fourth sub-image and the fifth sub-image so as to obtain an integrated processing image;
the edge sharpening device is connected with the data merging device and is used for receiving the integrated processing image and carrying out edge sharpening on the integrated processing image so as to obtain and output a corresponding edge sharpened image;
and the re-identification device is connected with the edge sharpening device and used for searching out corresponding shared bicycle sub-images from the edge sharpened image based on the shared bicycle image characteristics, performing uniform image segmentation on the edge sharpened image to obtain each image block, and counting the number of the shared bicycle sub-images in each image block.
2. The method of claim 1, wherein:
the image enhancement device is also used for directly sending the shared bicycle sub-image as a first sub-image to the high-pass filtering sharpening device when the definition of the shared bicycle sub-image is detected to be out of limit;
and the image enhancement device is also used for directly sending the residual sub-image as a second sub-image to the high-pass filtering sharpening device when the definition of the residual sub-image is detected to be out of limit.
3. The method of claim 2, wherein the system further comprises:
the channel value sequencing equipment is connected with the data receiving equipment and used for receiving the monitoring area image and sequencing the gray values of all pixel points in the monitoring area image to obtain the maximum sequence number of the sequencing queue;
and the redundancy identification device is connected with the channel value sequencing device and used for receiving the maximum sequence number and determining the redundancy of the monitoring area images in inverse proportion to the maximum sequence number.
4. The method of claim 3, wherein the system further comprises:
and the minimum filtering device is connected with the redundancy identification device and is used for receiving the monitoring area image from the channel value sequencing device when the received redundancy is lower than a preset redundancy threshold value and executing minimum filtering processing on the monitoring area image to obtain a minimum filtering image.
5. The method of claim 4, wherein the system further comprises:
and the matrix extraction device is used for receiving the minimum value filtering image and performing color space conversion on the minimum value filtering image to obtain a C color matrix, an M color matrix, a Y color matrix and a K color matrix in a CMYK color space of the minimum value filtering image.
6. The method of claim 5, wherein the system further comprises:
and the customized sharpening device is connected with the matrix extraction device and is used for determining the intensity of sharpening processing on the C color matrix based on the mean square error of the C color matrix, determining the intensity of sharpening processing on the M color matrix based on the mean square error of the M color matrix, and not sharpening the K color matrix and the Y color matrix.
7. The method of claim 6, wherein the system further comprises:
and the combined execution device is connected with the customized sharpening device and is used for carrying out combined operation on the sharpened C color matrix, the sharpened M color matrix, the unsharpened K color matrix and the unsharpened Y color matrix to obtain a corresponding combined operation image.
8. The method of claim 7, wherein the system further comprises:
the bilinear interpolation device is connected with the combination execution device and is used for executing bilinear interpolation operation on the combined operation image to obtain an instant interpolation image;
the bilinear interpolation equipment is also connected with the statistical sorting filtering equipment and is used for sending the instant interpolation image to the statistical sorting filtering equipment in place of the monitoring area image;
wherein, in the custom sharpening device, determining an intensity of the sharpening process performed on the C color matrix based on a mean square error of the C color matrix comprises: the greater the mean square error of the C color matrix, the greater the intensity of sharpening performed on the C color matrix;
wherein, in the custom sharpening device, determining an intensity of the sharpening process performed on the M-color matrix based on a mean square error of the M-color matrix comprises: the greater the mean square error of the M color matrix, the greater the intensity of the sharpening process performed on the M color matrix.
9. The method of claim 8, wherein:
in the channel value sorting device, sorting the gray values of the pixels in the monitoring area image includes: pixel points with the same gray value occupy the serial number of the same queuing sequence;
wherein the channel value sorting device is connected with the redundancy identification device through a serial communication interface.
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