CN115658946B

CN115658946B - 5G (generation of graph) internet-based monitoring data visualization method and system

Info

Publication number: CN115658946B
Application number: CN202211344170.2A
Authority: CN
Inventors: 欧阳�; 万吉梅; 蒲福春; 张志强
Original assignee: Sichuan Port Investment Southern Sichuan Port Operation Group Co ltd
Current assignee: Sichuan Port Investment Southern Sichuan Port Operation Group Co ltd
Priority date: 2022-10-31
Filing date: 2022-10-31
Publication date: 2023-05-09
Anticipated expiration: 2042-10-31
Also published as: CN115658946A

Abstract

The invention provides a monitoring data visualization method and a monitoring data visualization system based on a 5G internet, and relates to the technical field of data processing. In the invention, under the condition of receiving monitoring data visualization request information initiated by a first 5G internet terminal device, a plurality of initial monitoring images acquired by a plurality of second 5G internet terminal devices are acquired; classifying the plurality of initial monitoring images according to the corresponding image content to form at least one corresponding monitoring image classification set; and respectively carrying out data pushing processing on each monitoring image classification set so as to push each monitoring image classification set to the first 5G internet terminal equipment, so that the first 5G internet terminal equipment carries out visualization processing on the initial monitoring images included in the received monitoring image classification sets. Based on the foregoing, the problem of poor effect of the visualization processing of the monitoring data in the prior art can be improved.

Description

5G (generation of graph) internet-based monitoring data visualization method and system

Technical Field

The invention relates to the technical field of data processing, in particular to a monitoring data visualization method and system based on a 5G internet.

Background

Mobile communication continues to develop every ten years of technology, and has undergone the development of 1G, 2G, 3G and 4G. Each transition between generations, each technological progress greatly promotes industry upgrades and economic and social developments. From 1G to 2G, the transition from analog communication to digital communication is realized, and mobile communication enters thousands of households; from 2G to 3G and 4G, the conversion from voice service to data service is realized, the transmission rate is improved by hundreds of times, and the popularization and prosperity of mobile Internet application are promoted. Currently, mobile networks have been integrated into aspects of social life, which has profoundly changed people's communication, communications, and even the entire lifestyle. The 4G network creates prosperous Internet economy, solves the problem of communication between people at any time and any place, and with the rapid development of the mobile Internet, new services and new services are continuously emerging, the mobile data traffic is exploded, the 4G mobile communication system is difficult to meet the requirement of future mobile data traffic explosion, and the development of the next generation mobile communication (5G) system is urgently needed.

5G is as a novel mobile communication network, not only is the problem of person-to-person communication solved, but also the problem of person-to-object communication is solved, and the application requirements of the Internet of things such as mobile medical treatment, internet of vehicles, intelligent home, industrial control, environmental monitoring and the like are met, wherein the user is provided with augmented reality, virtual reality, ultra-high definition (3D) video and the like to be more immersive and extreme business experience. Finally, 5G is permeated into various fields of various industries of economy and society, and becomes a key novel infrastructure for supporting digitization, networking and intelligent transformation of economy and society. For example, 5G internet-based image monitoring has applications in many fields. In addition, in image monitoring, visualization of an image is an important link, but in the prior art, there is a problem that the effect is poor for visualization of a large number of monitoring images.

Disclosure of Invention

Accordingly, the present invention is directed to a method and a system for visualizing monitoring data based on 5G internet, so as to solve the problem of poor effect of the visualization processing of the monitoring data.

In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme:

a monitoring data visualization method based on 5G Internet comprises the following steps:

under the condition that monitoring data visualization request information initiated by a first 5G internet terminal device is received, acquiring a plurality of initial monitoring images acquired by a plurality of second 5G internet terminal devices;

classifying the plurality of initial monitoring images according to the corresponding image content to form at least one monitoring image classification set corresponding to the plurality of initial monitoring images, wherein each monitoring image classification set comprises at least one initial monitoring image;

and respectively carrying out data pushing processing on each monitoring image classification set in the at least one monitoring image classification set so as to push each monitoring image classification set to the first 5G internet terminal equipment, so that the first 5G internet terminal equipment carries out visualization processing on initial monitoring images included in the received monitoring image classification sets.

In some preferred embodiments, in the method for visualizing monitoring data based on the 5G internet, the step of acquiring a plurality of initial monitoring images acquired by a plurality of second 5G internet terminal devices when receiving monitoring data visualization request information initiated by the first 5G internet terminal device includes:

under the condition that monitoring data visualization request information initiated by first 5G internet terminal equipment is received, analyzing the monitoring data visualization request information to determine a target monitoring area corresponding to the monitoring data visualization request information;

and determining each second 5G Internet terminal device deployed in the target monitoring area according to the target monitoring area to determine a plurality of second 5G Internet terminal devices, and acquiring a plurality of initial monitoring images acquired by the second 5G Internet terminal devices respectively, wherein each second 5G Internet terminal device acquires at least one initial monitoring image.

In some preferred embodiments, in the above 5G internet-based monitoring data visualization method, the step of classifying the plurality of initial monitoring images according to corresponding image contents to form at least one monitoring image classification set corresponding to the plurality of initial monitoring images, each monitoring image classification set including at least one of the initial monitoring images includes:

Respectively mining out a direction gradient histogram characteristic representation corresponding to each initial monitoring image in the plurality of initial monitoring images, wherein the direction gradient histogram characteristic representation is used for representing direction gradient histogram data corresponding to the initial monitoring images;

constructing a corresponding first gradient histogram matching relation distribution set based on the directional gradient histogram characteristic representation corresponding to each initial monitoring image, wherein the first gradient histogram matching relation distribution set is formed based on characteristic representation matching coefficients between the directional gradient histogram characteristic representations corresponding to each two initial monitoring images, and in the first gradient histogram matching relation distribution set, the characteristic representation matching coefficients form a plurality of rows and a plurality of columns;

updating the first gradient histogram matching relation distribution set based on target updating parameters corresponding to each set distribution column data in the first gradient histogram matching relation distribution set to form an updated gradient histogram matching relation distribution set corresponding to the first gradient histogram matching relation distribution set;

analyzing and outputting the number of users of the image monitoring users in the plurality of initial monitoring images based on the updated gradient histogram matching relation distribution set;

Grouping the plurality of initial monitoring images based on the number of users of the image monitoring users to form initial monitoring images corresponding to each image monitoring user;

classifying the plurality of initial monitoring images based on whether the corresponding image monitoring users are the same or not to form at least one monitoring image classification set corresponding to the plurality of initial monitoring images.

In some preferred embodiments, in the above-mentioned 5G internet-based monitoring data visualization method, before the step of updating the first gradient histogram matching relation distribution set based on the target update parameter corresponding to each set distribution column data in the first gradient histogram matching relation distribution set to form an updated gradient histogram matching relation distribution set corresponding to the first gradient histogram matching relation distribution set, the step of classifying the plurality of initial monitoring images according to the corresponding image content to form at least one monitoring image classification set corresponding to the plurality of initial monitoring images, each monitoring image classification set including at least one of the initial monitoring images further includes:

Aiming at one set distribution column data in the first gradient histogram matching relation distribution set, carrying out sequential arrangement processing on characteristic representation matching coefficients belonging to a reference interval in a plurality of characteristic representation matching coefficients included in the set distribution column data based on initial precedence relation information so as to form corresponding initial sequential arrangement matching coefficient combinations;

calculating and outputting feature representation matching coefficient distances between every two feature representation matching coefficients adjacent to each other in the arrangement sequence number in each feature representation matching coefficient belonging to the reference interval based on the initial sequence arrangement matching coefficient combination to form a plurality of feature representation matching coefficient distances;

screening out the characteristic representation matching coefficient distances matched with the configured initial screening conditions from the characteristic representation matching coefficient distances;

and analyzing and outputting target update parameters corresponding to the set distribution column data based on the characteristic representation matching coefficient distance matched with the configured initial screening condition.

In some preferred embodiments, in the above method for visualizing monitoring data based on the 5G internet, the step of updating the first gradient histogram matching relationship distribution set based on the target update parameter corresponding to each set distribution column data in the first gradient histogram matching relationship distribution set to form an updated gradient histogram matching relationship distribution set corresponding to the first gradient histogram matching relationship distribution set includes:

Updating the characteristic representation matching coefficients of the target updating parameters corresponding to each set of distribution data in the first gradient histogram matching relation distribution set to form corresponding updated gradient histogram matching relation distribution sets; or alternatively

And performing fusion processing on the characteristic representation matching coefficient of the target updating parameter corresponding to each set distribution data and the second updating reference matching coefficient which are not exceeded in the characteristic representation matching coefficient included in each set distribution data in the first gradient histogram matching relation distribution set to form an updating gradient histogram matching relation distribution set corresponding to the first gradient histogram matching relation distribution set.

In some preferred embodiments, in the above-mentioned 5G internet-based monitoring data visualization method, the step of analyzing and outputting the number of users of the image monitoring users included in the plurality of initial monitoring images based on the updated gradient histogram matching relation distribution set includes:

Based on each target screening quantity in a plurality of preset target screening quantities, adjusting the updated gradient histogram matching relation distribution set to form an adjusted gradient histogram matching relation distribution set corresponding to each target screening quantity;

and analyzing and outputting the number of users of the image monitoring users in the plurality of initial monitoring images based on the plurality of target screening numbers and the distribution set of the matching relation of the adjustment gradient histograms corresponding to each target screening number.

In some preferred embodiments, in the above method for visualizing monitoring data based on the 5G internet, the step of adjusting the updated gradient histogram matching relationship distribution set based on each of a plurality of target filtering numbers configured in advance to form an adjusted gradient histogram matching relationship distribution set corresponding to each target filtering number includes:

aiming at one target screening quantity in the target screening quantities, carrying out coefficient updating processing on the updated gradient histogram matching relation distribution set based on the target screening quantity so as to form a corresponding primary gradient histogram matching relation distribution set;

Performing set distribution adjustment operation on the primary gradient histogram matching relation distribution set to form a corresponding intermediate gradient histogram matching relation distribution set, so that two feature representation matching coefficients included in each feature representation matching coefficient combination in the intermediate gradient histogram matching relation distribution set are consistent, and the set distribution relation of the two feature representation matching coefficients included in each feature representation matching coefficient combination is a target set distribution relation;

performing related adjustment processing on the set distribution column data and the set distribution row data on the medium-level gradient histogram matching relation distribution set to form a corresponding high-level gradient histogram matching relation distribution set;

performing coefficient mapping processing on the high-level gradient histogram matching relation distribution sets to form corresponding mapping gradient histogram matching relation distribution sets, wherein the coefficient mapping processing is used for mapping characteristic representation matching coefficients included in each set distribution data in the high-level gradient histogram matching relation distribution sets to target coefficient intervals;

and carrying out set distribution adjustment operation on the mapping gradient histogram matching relation distribution set to form an adjustment gradient histogram matching relation distribution set corresponding to the target screening quantity.

In some preferred embodiments, in the above 5G internet-based monitoring data visualization method, the step of analyzing and outputting the number of users of the image monitoring users in the plurality of initial monitoring images based on the plurality of target screening numbers and the set of adjusted gradient histogram matching relation distributions corresponding to each of the target screening numbers includes:

analyzing and outputting a target screening quantity ratio corresponding to each target screening quantity based on the target screening quantity and an adjustment gradient histogram matching relation distribution set corresponding to each target screening quantity, wherein the target screening quantity ratio is used for reflecting the screening quantity of the characteristic representation matching coefficient screened out from the adjustment gradient histogram matching relation distribution set corresponding to the corresponding target screening quantity;

and analyzing and outputting the number of users of the image monitoring users in the plurality of initial monitoring images based on the target screening number proportion corresponding to each target screening number.

In some preferred embodiments, in the above 5G internet-based monitoring data visualization method, the step of performing data pushing processing on each of the at least one monitoring image classification set to push each of the monitoring image classification sets to the first 5G internet terminal device, so that the first 5G internet terminal device performs visualization processing on an initial monitoring image included in the received monitoring image classification set includes:

Respectively carrying out data pushing processing on each monitoring image classification set in the at least one monitoring image classification set so as to sequentially push each monitoring image classification set to the first 5G internet terminal equipment, so that the first 5G internet terminal equipment sequentially carries out visualization processing on initial monitoring images included in the currently received monitoring image classification sets;

and sequentially pushing each monitoring image classification set to the first 5G internet terminal equipment, determining based on the image acquisition time of the included initial monitoring images, and in the process of sequentially pushing each monitoring image classification set to the first 5G internet terminal equipment, splicing a plurality of initial monitoring images included in the same monitoring image classification set to form a spliced initial monitoring image, and then pushing the spliced initial monitoring image to the first 5G internet terminal equipment.

The embodiment of the invention also provides a monitoring data visualization system based on the 5G Internet, which comprises a processor and a memory, wherein the memory is used for storing a computer program, and the processor is used for executing the computer program so as to realize the monitoring data visualization method based on the 5G Internet.

According to the monitoring data visualization method and system based on the 5G Internet, under the condition that monitoring data visualization request information initiated by the first 5G Internet terminal equipment is received, a plurality of initial monitoring images acquired by a plurality of second 5G Internet terminal equipment are acquired; classifying the plurality of initial monitoring images according to the corresponding image content to form at least one corresponding monitoring image classification set; and respectively carrying out data pushing processing on each monitoring image classification set in the at least one monitoring image classification set so as to push each monitoring image classification set to the first 5G internet terminal equipment, so that the first 5G internet terminal equipment carries out visualization processing on the initial monitoring images included in the received monitoring image classification sets. Based on the foregoing, since the monitoring images are classified and pushed, classification visualization processing can be performed, so that the effect of the visualization processing (for example, the visualized contents can be more compact through classification visualization) is provided, and the problem that the effect of the visualization processing of the monitoring data is poor in the prior art is solved.

In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

Fig. 1 is a block diagram of a monitoring data visualization system based on the 5G internet according to an embodiment of the present invention.

Fig. 2 is a schematic flow chart of each step included in the 5G internet-based monitoring data visualization method according to the embodiment of the present invention.

Fig. 3 is a schematic diagram of each module included in the 5G internet-based monitoring data visualization device according to the embodiment of the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of 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, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.

Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

As shown in fig. 1, the embodiment of the invention provides a monitoring data visualization system based on a 5G internet. The monitoring data visualization system based on the 5G internet can comprise a memory and a processor, or can also comprise other devices or components.

In detail, the memory and the processor are electrically connected directly or indirectly to realize transmission or interaction of data. For example, electrical connection may be made to each other via one or more communication buses or signal lines. The memory may store at least one software functional module (computer program) that may exist in the form of software or firmware. The processor may be configured to execute the executable computer program stored in the memory, so as to implement the 5G internet-based monitoring data visualization method provided by the embodiment of the present invention.

Alternatively, in some embodiments, the Memory may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), and the like. The processor may be a general purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a System on Chip (SoC), etc.; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

Alternatively, in some embodiments, the 5G internet-based monitoring data visualization system may be a server with data processing capabilities.

With reference to fig. 2, the embodiment of the invention further provides a monitoring data visualization method based on the 5G internet, which can be applied to the monitoring data visualization system based on the 5G internet. The method steps defined by the flow related to the monitoring data visualization method based on the 5G internet can be realized by the monitoring data visualization system based on the 5G internet.

The specific flow shown in fig. 2 will be described in detail.

Step S110, under the condition that monitoring data visualization request information initiated by the first 5G Internet terminal equipment is received, a plurality of initial monitoring images acquired by a plurality of second 5G Internet terminal equipment are acquired.

In the embodiment of the invention, the monitoring data visualization system based on the 5G internet can acquire a plurality of initial monitoring images acquired by a plurality of second 5G internet terminal devices (the plurality of initial monitoring images can be acquired synchronously or asynchronously by a plurality of second 5G internet terminal devices) under the condition of receiving the monitoring data visualization request information initiated by the first 5G internet terminal device.

Step S120, classifying the plurality of initial monitoring images according to the corresponding image content, so as to form at least one monitoring image classification set corresponding to the plurality of initial monitoring images.

In the embodiment of the present invention, the monitoring data visualization system based on the 5G internet may classify the plurality of initial monitoring images according to the corresponding image content, so as to form at least one monitoring image classification set corresponding to the plurality of initial monitoring images. Each of the monitoring image classification sets includes at least one of the initial monitoring images.

Step S130, performing data pushing processing on each of the at least one monitoring image classification set, so as to push each monitoring image classification set to the first 5G internet terminal device.

In the embodiment of the present invention, the 5G internet-based monitoring data visualization system may perform data pushing processing on each of the at least one monitoring image classification set, so as to push each monitoring image classification set to the first 5G internet terminal device. And enabling the first 5G internet terminal equipment to perform visualization processing on the initial monitoring images included in the received monitoring image classification set.

Based on the foregoing, since the monitoring images are classified and pushed, classification visualization processing can be performed, so that the effect of the visualization processing is provided (for example, by classification visualization, the visualized contents can be more compact, and the content as the same as or adjacent to the time visualization has higher correlation), thereby improving the problem of poor effect of the visualization processing of the monitoring data in the prior art.

Optionally, in the step S110 described above, in some embodiments, it may specifically include the following sub-steps that may be performed:

under the condition that monitoring data visualization request information initiated by first 5G internet terminal equipment is received, analyzing the monitoring data visualization request information to determine a target monitoring area corresponding to the monitoring data visualization request information (illustratively, the monitoring data visualization request information can carry area identification information of the target monitoring area);

and determining each second 5G Internet terminal device deployed in the target monitoring area according to the target monitoring area to determine a plurality of second 5G Internet terminal devices, and acquiring a plurality of initial monitoring images acquired by the second 5G Internet terminal devices respectively, wherein each second 5G Internet terminal device acquires at least one initial monitoring image (the second 5G Internet terminal device can be an image acquisition device and the like in an exemplary manner).

Optionally, in the step S120 described above, in some embodiments, it may specifically include the following sub-steps that may be performed:

respectively mining a direction gradient histogram feature representation corresponding to each initial monitoring image in the plurality of initial monitoring images, wherein the direction gradient histogram feature representation is used for representing direction gradient histogram data corresponding to the initial monitoring images (illustratively, the determination of a direction gradient histogram can be performed on each initial monitoring image first, and then, encoding processing can be performed on the direction gradient histogram through an encoding neural network to form the direction gradient histogram feature representation, namely Histogram of Oriented Gradient, HOG);

constructing a corresponding first gradient histogram matching relation distribution set based on the direction gradient histogram feature representation corresponding to each initial monitoring image, wherein the first gradient histogram matching relation distribution set is formed based on feature representation matching coefficients between the direction gradient histogram feature representations corresponding to each two initial monitoring images, and in the first gradient histogram matching relation distribution set, the feature representation matching coefficients form a plurality of rows and columns (that is, feature representation matching coefficients between the direction gradient histogram feature representations corresponding to each two initial monitoring images can be calculated firstly, and the feature representation matching coefficients between the direction gradient histogram feature representation corresponding to one initial monitoring image and the direction gradient histogram feature representation corresponding to the initial monitoring image are included, so that in the first gradient histogram matching relation distribution set, one set of distribution column data comprises feature representation matching coefficients between the direction gradient histogram feature representation corresponding to one initial monitoring image and the direction gradient histogram feature representation corresponding to each initial monitoring image in the first gradient histogram feature distribution set, and likewise, one set of distribution column data also comprises feature matching coefficients between the direction gradient histogram feature representation corresponding to one initial monitoring image and the direction gradient histogram feature representation corresponding to the initial monitoring image in the first gradient histogram feature representation corresponding to the initial monitoring image;

analyzing and outputting the number of users of the image monitoring users in the plurality of initial monitoring images based on the updated gradient histogram matching relation distribution set (in one image, the appearance and shape of a local target can be well described by gradient or edge direction density distribution, and the gradient exists mainly at the edge, so that the profile information of the image monitoring users in the initial monitoring images can be reflected through the direction gradient histogram characteristic representation, and the profile information can better represent the image monitoring users, namely different image monitoring users have different profile information);

grouping the plurality of initial monitoring images based on the number of users of the image monitoring users to form an initial monitoring image corresponding to each image monitoring user (that is, one image monitoring user may correspond to one group);

The plurality of initial monitoring images are classified based on whether the corresponding image monitoring users are the same, so as to form at least one monitoring image classification set corresponding to the plurality of initial monitoring images (that is, in the same monitoring image classification set, the image monitoring users corresponding to the initial monitoring images are the same, or one grouping of initial monitoring images can be directly used as one monitoring image classification set when the grouping is performed).

Optionally, before the step of updating the first gradient histogram matching relation distribution set based on the target update parameter corresponding to each set distribution column data in the first gradient histogram matching relation distribution set to form an updated gradient histogram matching relation distribution set corresponding to the first gradient histogram matching relation distribution set, in the above step S120, in some embodiments, the method may specifically further include the following sub-steps that may be performed:

for one set of distribution column data in the first gradient histogram matching relation distribution set (the processing mode of each set of distribution column data may be consistent), performing sequential arrangement processing on feature representation matching coefficients belonging to a reference interval in a plurality of feature representation matching coefficients included in the set of distribution column data based on initial precedence relation information (such as size before size) so as to form a corresponding initial sequential arrangement matching coefficient combination (illustratively, the specific interval of the reference interval is not limited, for example, may be-0.9 to 0.9 or-0.8 to 0.8, etc.);

Calculating and outputting feature representation matching coefficient distances between every two feature representation matching coefficients adjacent to each other in the arrangement sequence number in each feature representation matching coefficient belonging to the reference interval based on the initial in-order arrangement matching coefficient combination to form a plurality of feature representation matching coefficient distances (for example, the feature representation matching coefficient distances may be a result of subtracting the two feature representation matching coefficients, such as subtracting the latter feature representation matching coefficient from the former feature representation matching coefficient);

screening out the feature representation matching coefficient distances matching the configured initial screening condition from the plurality of feature representation matching coefficient distances (illustratively, the feature representation matching coefficient distance having the largest value among the plurality of feature representation matching coefficient distances may be used as the feature representation matching coefficient distance matching the configured initial screening condition);

and analyzing and outputting a target update parameter corresponding to the set distribution column data based on the characteristic representation matching coefficient distance matched with the configured initial screening condition (for example, a previous characteristic representation matching coefficient in two characteristic representation matching coefficients corresponding to the characteristic representation matching coefficient distance matched with the configured initial screening condition can be taken as the target update parameter).

Optionally, in the step of updating the first gradient histogram matching relation distribution set based on the target update parameter corresponding to each set distribution column data in the first gradient histogram matching relation distribution set to form an updated gradient histogram matching relation distribution set corresponding to the first gradient histogram matching relation distribution set, in some embodiments, the method may specifically include the following sub-steps that may be performed:

updating the feature representation matching coefficients of each set of distribution column data in the first gradient histogram matching relationship distribution set, wherein the feature representation matching coefficients do not exceed a target updating parameter corresponding to each set of distribution column data (as described above, each set of distribution column data corresponds to one target updating parameter), so as to update the feature representation matching coefficients to corresponding first updating reference matching coefficients, so as to form an updated gradient histogram matching relationship distribution set corresponding to the first gradient histogram matching relationship distribution set (that is, replacing the feature representation matching coefficients which do not exceed the target updating parameter with the first updating reference matching coefficients; in addition, for example, the specific numerical value of the first updating reference matching coefficients is not limited, such as may be 0); or alternatively

And performing fusion processing on the characteristic representation matching coefficient of the target updating parameter corresponding to each set distribution column data in the first gradient histogram matching relation distribution set and the second updating reference matching coefficient which are not exceeded in the characteristic representation matching coefficient included in each set distribution column data in the first gradient histogram matching relation distribution set to form an updating gradient histogram matching relation distribution set corresponding to the first gradient histogram matching relation distribution set (the fusion processing may be, for example, performing product calculation on the characteristic representation matching coefficient and the second updating reference matching coefficient, and the second updating reference matching coefficient may be 0.02).

Optionally, in the step of analyzing and outputting the number of users of the image monitoring users included in the plurality of initial monitoring images based on the updated gradient histogram matching relation distribution set, in some embodiments, it may specifically include the following sub-steps that may be performed:

based on each target screening quantity in a plurality of target screening quantities which are configured in advance, the updated gradient histogram matching relation distribution set is adjusted to form an adjusted gradient histogram matching relation distribution set corresponding to each target screening quantity (based on the adjusted gradient histogram matching relation distribution set, a plurality of adjusted gradient histogram matching relation distribution sets can be formed for the plurality of target screening quantities, and in addition, specific numerical values of the target screening quantities can be configured according to actual application requirements);

Optionally, in the step of adjusting the updated gradient histogram matching relation distribution set based on each target screening number of the preconfigured plurality of target screening numbers to form an adjusted gradient histogram matching relation distribution set corresponding to each target screening number, in some embodiments, the method may specifically include the following executable sub-steps:

aiming at one target screening quantity (the processing mode corresponding to each target screening quantity can be consistent to obtain an adjustment gradient histogram matching relation distribution set) in the target screening quantity, carrying out coefficient updating processing on the update gradient histogram matching relation distribution set based on the target screening quantity to form a corresponding primary gradient histogram matching relation distribution set;

performing a set distribution adjustment operation on the mapping gradient histogram matching relation distribution set to form an adjusted gradient histogram matching relation distribution set corresponding to the target screening number (a specific manner of performing a set distribution adjustment operation on the mapping gradient histogram matching relation distribution set may be identical to a specific manner of performing a set distribution adjustment operation on the primary gradient histogram matching relation distribution set, which is not described in detail herein, and may refer to related descriptions later).

Optionally, in the step of performing coefficient update processing on the updated gradient histogram matching relation distribution set to form a corresponding primary gradient histogram matching relation distribution set based on the target screening number for one target screening number of the plurality of target screening numbers, in some embodiments, the method may specifically include the following sub-steps that may be performed:

For a plurality of feature representation matching coefficients included in each set distribution column data in the updated gradient histogram matching relation distribution set, performing coefficient updating processing on the feature representation matching coefficients except for the target screening number feature representation matching coefficients (i.e., the largest target screening number feature representation matching coefficients) matched with an initial reference condition to update the feature representation matching coefficients to a first updated target matching coefficient configured in advance, so as to obtain the primary gradient histogram matching relation distribution set (illustratively, the specific value of the first updated target matching coefficient is not limited, such as may be 0); or alternatively

And performing fusion processing on the feature representation matching coefficients included in the updated gradient histogram matching relation distribution set and the feature representation matching coefficients except for the feature representation matching coefficients of the target screening number matched with the initial reference condition and a second pre-configured updated target matching coefficient to form a corresponding primary gradient histogram matching relation distribution set (the fusion processing may be exemplified by performing product operation on the other feature representation matching coefficients and the second updated target matching coefficient, and in addition, the second updated target matching coefficient may be 0.02 and other values).

Optionally, in the step of performing a set distribution adjustment operation on the primary gradient histogram matching relation distribution set to form a corresponding intermediate gradient histogram matching relation distribution set, in some embodiments, the method may specifically include the following sub-steps that may be performed:

performing a swap operation on the set distribution column data and the set distribution line data in the primary gradient histogram matching relation distribution set to form a swap gradient histogram matching relation distribution set corresponding to the primary gradient histogram matching relation distribution set (that is, after the set distribution column data in the primary gradient histogram matching relation distribution set is swapped, after the swap gradient histogram matching relation distribution set belongs to the set distribution line data, and after the set distribution line data in the primary gradient histogram matching relation distribution set is swapped, after the swap gradient histogram matching relation distribution set belongs to the set distribution line data);

and carrying out mean value fusion of characteristic representation matching coefficients on the primary gradient histogram matching relation distribution set and the opposite gradient histogram matching relation distribution set corresponding to the primary gradient histogram matching relation distribution set (that is, carrying out mean value calculation on two characteristic representation matching coefficients with the same set position between the two gradient histogram matching relation distribution sets to obtain elements with the same set position in the intermediate gradient histogram matching relation distribution set) so as to form the intermediate gradient histogram matching relation distribution set corresponding to the primary gradient histogram matching relation distribution set.

screening each feature representation matching coefficient combination from the primary gradient histogram matching relation distribution set, wherein the set distribution relation of the two feature representation matching coefficients included in each feature representation matching coefficient combination is a target set distribution relation (illustratively, the target set distribution relation may refer to that in the primary gradient histogram matching relation distribution set, a perpendicular bisector of a connecting line between two feature representation matching coefficients included in each feature representation matching coefficient combination coincides with a set diagonal of the primary gradient histogram matching relation distribution set);

for each of the feature-representation-matching-coefficient combinations, one of the two feature-representation-matching coefficients included in the feature-representation-matching-coefficient combination is replaced with the other feature-representation-matching coefficient to form a medium-level gradient-histogram-matching-relationship distribution set corresponding to the primary gradient-histogram-matching-relationship distribution set (illustratively, a smaller one of the two feature-representation-matching coefficients may be updated or replaced with a larger one of the two feature-representation-matching coefficients).

Optionally, in the step of performing the correlation adjustment processing of the set distribution column data and the set distribution row data on the medium-level gradient histogram matching relation distribution set to form a corresponding high-level gradient histogram matching relation distribution set, in some embodiments, the method may specifically include the following sub-steps that may be performed:

forming a matching relation distribution set of the opposite gradient histogram corresponding to the matching relation distribution set of the intermediate gradient histogram based on the matching relation distribution set of the intermediate gradient histogram;

based on the medium-level gradient histogram matching relation distribution set and the opposite gradient histogram matching relation distribution set corresponding to the medium-level gradient histogram matching relation distribution set, a set distribution position of the characteristic representing matching coefficient in the medium-level gradient histogram matching relation distribution set, such as the number of set distribution columns and the number of set distribution rows, is formed, each characteristic in the medium-level gradient histogram matching relation distribution set represents a matching coefficient, data of the set distribution columns corresponding to the medium-level gradient histogram matching relation distribution set are correlated with data of the set distribution columns corresponding to the medium-level gradient histogram matching relation distribution set, data of the set distribution columns corresponding to the opposite gradient histogram matching relation distribution set are correlated with each other (illustratively, for each characteristic representing a matching coefficient in the high-level gradient histogram matching relation distribution set, the set distribution position of the characteristic representing matching coefficient in the high-level gradient histogram matching relation distribution set can be determined first, the number of set distribution columns and the number of set distribution rows can be extracted, data of the set distribution columns corresponding to the medium-level gradient matching relation distribution sets can be extracted as a first data sequence, the corresponding set distribution columns corresponding to the medium-level gradient matching relation distribution sets can be extracted, the first data sequence and the first sequence data sequence and the second sequence data corresponding to the first sequence data can be obtained, and the product of the first sequence and the product of the first data and the first sequence data and the second sequence data can be obtained.

Optionally, in the step of performing coefficient mapping processing on the set of advanced gradient histogram matching relation distributions to form a corresponding set of mapped gradient histogram matching relation distributions, in some embodiments, the method may specifically include the following sub-steps that may be performed:

screening out target feature representation matching coefficients corresponding to each set of distribution column data based on a plurality of feature representation matching coefficients included in each set of distribution column data in the high-level gradient histogram matching relation distribution set (the target feature representation matching coefficients may be maximum values in the plurality of feature representation matching coefficients, for example);

for each set distribution column data in the set of advanced gradient histogram matching relation distribution sets, performing a coefficient mapping process (which may be referred to as, for example, dividing the feature representation matching coefficient by the corresponding target feature representation matching coefficient) on a plurality of feature representation matching coefficients included in the set distribution column data based on the target feature representation matching coefficient corresponding to the set distribution column data to form a corresponding set of mapped gradient histogram matching relation distribution sets.

Optionally, in the step of analyzing and outputting the number of users of the image monitoring users included in the plurality of initial monitoring images based on the plurality of target screening numbers and the set of adjusted gradient histogram matching relation distributions corresponding to each of the target screening numbers, in some embodiments, the method may specifically include the following sub-steps that may be performed:

analyzing and outputting a target screening quantity ratio corresponding to each target screening quantity based on the target screening quantity and an adjustment gradient histogram matching relation distribution set corresponding to each target screening quantity, wherein the target screening quantity ratio is used for reflecting the screening quantity of the characteristic representation matching coefficient screened out from the adjustment gradient histogram matching relation distribution set corresponding to the corresponding target screening quantity; and analyzing and outputting the number of users of the image monitoring users in the plurality of initial monitoring images based on the target screening number ratio corresponding to each target screening number.

Optionally, in the step of analyzing and outputting the target screening number ratio corresponding to each target screening number based on the plurality of target screening numbers and the set of gradient histogram matching relation distribution adjustment corresponding to each target screening number, in some embodiments, the method may specifically include the following sub-steps that may be performed:

For one target screening quantity (for each target screening quantity, the corresponding processing mode may be consistent to form a target screening quantity ratio corresponding to each target screening quantity), carrying out a Laplace integral mapping process (for example, the Laplace integral mapping process is an integral transformation commonly used in engineering mathematics and belongs to linear transformation, and a function with a factor of a real number t (t is greater than or equal to 0) can be converted into a function with a factor of a complex number s) on an adjustment gradient histogram matching relation distribution set corresponding to the target screening quantity so as to form an integral gradient histogram matching relation distribution set corresponding to the target screening quantity;

performing a set element principal component decomposition operation on the integral gradient histogram matching relation distribution set (the processing principle of the set element principal component decomposition operation is that for a matrix A (m×n), namely the integral gradient histogram matching relation distribution set, U (m×m), V (n×n) and S (m×n) exist, so that singular vectors of A in A=U×S×V ' are satisfied, and the singular values of A in U and V are satisfied, the orthogonal unit feature vectors of AA ' form U, the orthogonal unit feature vectors of the target key parameters form S ' S, the orthogonal unit feature vectors of A ' form V and the target key parameters form SS ') to form a plurality of corresponding target key parameters;

Screening a first key parameter and a specified number of second key parameters from the target key parameters, wherein the first key parameter is the largest in the target key parameters, each second key parameter belongs to target key parameters (such as the largest specified number, the specified number can be 2, 4 and the like) which are determined after the target key parameters are sequentially arranged based on target precedence relation information (such as the big before small) and are matched with preset related configuration conditions;

performing difference calculation on every two adjacent second key parameters of the arrangement sequence numbers in the appointed number of second key parameters to output a corresponding plurality of key parameter difference calculation results;

performing numerical mapping processing on a first key parameter difference result based on the first key parameter to obtain a mapped key parameter difference result (the first key parameter difference result may be divided by the first key parameter, for example), where the first key parameter difference result belongs to a key parameter difference result having a maximum value among the plurality of key parameter difference results;

and calculating and outputting a target screening quantity ratio corresponding to the target screening quantity based on the mapping key parameter difference result and the target screening quantity (the target screening quantity ratio may be equal to the mapping key parameter difference result in an exemplary manner).

Optionally, in the step of analyzing and outputting the number of users of the image monitoring users in the plurality of initial monitoring images based on the target screening number ratio corresponding to each of the target screening numbers, in some embodiments, the method specifically includes the following sub-steps that may be performed:

analyzing and outputting a corresponding first target screening quantity from the target screening quantities based on the target screening quantity ratio corresponding to each target screening quantity, wherein the first target screening quantity belongs to a target screening quantity with the minimum value of the target screening quantity ratio corresponding to the target screening quantity in the target screening quantities; obtaining a plurality of key parameter difference results corresponding to the first target screening quantity; and analyzing and outputting the number of users of the image monitoring users in the plurality of initial monitoring images based on a plurality of key parameter differencing results corresponding to the first target screening number (illustratively, the key parameter differencing results corresponding to the first target screening number can be formed into a key parameter differencing result characteristic representation corresponding to the first target screening number, and then, analysis processing is performed on the key parameter differencing result characteristic representation to obtain the number of users of the specific image monitoring users in the plurality of initial monitoring images, wherein the analysis processing can be to search parameters with the maximum score.

Optionally, in the step of grouping the plurality of initial monitoring images based on the number of users of the image monitoring user to form an initial monitoring image corresponding to each image monitoring user, in some embodiments, the method may specifically include the following sub-steps that may be performed:

performing set element principal component decomposition operation (as described in the foregoing related description) on the distribution set of the matching relation of the gradient histogram corresponding to the first target screening quantity to obtain a plurality of representative key parameters;

screening out the representative key parameters of the number of users of the image monitoring user (illustratively, the minimum representative key parameters of the number of users of the image monitoring user can be screened out); extracting a characteristic representation corresponding to each representative key parameter of the number of users of the image monitoring user;

constructing a corresponding feature representation distribution set (for example, the size of each feature representation may be 1*a, and for b representative key parameters, the size of the feature representation distribution set may be b×a), based on a feature representation corresponding to each representative key parameter of the number of users of the image monitoring user, the number of set row data of the feature representation distribution set is equal to the number of users of the image monitoring user, and the number of set column data of the feature representation distribution set is equal to the number of initial monitoring images;

Analyzing and outputting a feature representation corresponding to each of the plurality of initial monitoring images based on the feature representation distribution set (for example, one set column data in the feature representation distribution set may be used as a feature representation corresponding to one initial monitoring image);

the plurality of initial monitoring images are grouped based on the number of users of the image monitoring users and the feature representation corresponding to each of the plurality of initial monitoring images (illustratively, the plurality of initial monitoring images may be clustered based on a matching coefficient or a distance between the feature representations, wherein a specific clustering manner is not limited, and the number of clustering centers may be the number of users of the image monitoring users) so as to form an initial monitoring image corresponding to each image monitoring user.

Optionally, in the step S130 described above, in some embodiments, it may specifically include the following sub-steps that may be performed:

And in the process of pushing each monitoring image classification set to the first 5G internet terminal device in turn, determining based on the image acquisition time of the included initial monitoring images (for example, pushing the average value of the image acquisition time of the included initial monitoring images earlier and pushing the average value of the image acquisition time of the included initial monitoring images later), and in the process of pushing each monitoring image classification set to the first 5G internet terminal device in turn, forming a spliced initial monitoring image by splicing a plurality of initial monitoring images included in the same monitoring image classification set, and pushing the spliced initial monitoring image to the first 5G internet terminal device (so that the first 5G internet terminal device can directly perform visualization processing on the spliced initial monitoring image).

With reference to fig. 3, the embodiment of the invention further provides a monitoring data visualization device based on the 5G internet, which can be applied to the monitoring data visualization system based on the 5G internet. Wherein, the monitoring data visualization device based on the 5G internet can comprise:

the monitoring image obtaining module is configured to obtain a plurality of initial monitoring images collected by a plurality of second 5G internet terminal devices when receiving the monitoring data visualization request information initiated by the first 5G internet terminal device (refer to the step S110 for specific content);

The monitoring image classification module is configured to classify the plurality of initial monitoring images according to the corresponding image content, so as to form at least one monitoring image classification set corresponding to the plurality of initial monitoring images, where each monitoring image classification set includes at least one initial monitoring image (the specific content may refer to step S120 described above);

the monitoring image pushing module is configured to perform data pushing processing on each of the at least one monitoring image classification set, so as to push each of the monitoring image classification sets to the first 5G internet terminal device, so that the first 5G internet terminal device performs visualization processing on an initial monitoring image included in the received monitoring image classification set (for details, refer to step S130).

In summary, according to the monitoring data visualization method and system based on the 5G internet provided by the invention, under the condition that the monitoring data visualization request information initiated by the first 5G internet terminal device is received, a plurality of initial monitoring images acquired by a plurality of second 5G internet terminal devices are acquired; classifying the plurality of initial monitoring images according to the corresponding image content to form at least one corresponding monitoring image classification set; and respectively carrying out data pushing processing on each monitoring image classification set in the at least one monitoring image classification set so as to push each monitoring image classification set to the first 5G internet terminal equipment, so that the first 5G internet terminal equipment carries out visualization processing on the initial monitoring images included in the received monitoring image classification sets. Based on the foregoing, since the monitoring images are classified and pushed, classification visualization processing can be performed, so that the effect of the visualization processing (for example, the visualized contents can be more compact through classification visualization) is provided, and the problem that the effect of the visualization processing of the monitoring data is poor in the prior art is solved.

The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. The monitoring data visualization method based on the 5G Internet is characterized by comprising the following steps of:

respectively carrying out data pushing processing on each monitoring image classification set in the at least one monitoring image classification set so as to push each monitoring image classification set to the first 5G internet terminal equipment, so that the first 5G internet terminal equipment carries out visualization processing on initial monitoring images included in the received monitoring image classification sets;

The step of classifying the plurality of initial monitoring images according to the corresponding image content to form at least one monitoring image classification set corresponding to the plurality of initial monitoring images, wherein each monitoring image classification set comprises at least one initial monitoring image comprises the following steps:

2. The method for visualizing monitoring data based on the 5G internet according to claim 1, wherein the step of acquiring a plurality of initial monitoring images acquired by a plurality of second 5G internet terminal devices in the case of receiving the monitoring data visualization request information initiated by the first 5G internet terminal device comprises:

3. The 5G internet-based monitoring data visualization method of claim 1, wherein before the step of updating the first gradient histogram matching relation distribution set based on the target update parameter corresponding to each set distribution column data in the first gradient histogram matching relation distribution set to form an updated gradient histogram matching relation distribution set corresponding to the first gradient histogram matching relation distribution set, the step of classifying the plurality of initial monitoring images according to the corresponding image content to form at least one monitoring image classification set corresponding to the plurality of initial monitoring images, each monitoring image classification set including at least one of the initial monitoring images, further comprises:

4. The method for visualizing monitored data based on the 5G internet as set forth in claim 1, wherein said step of updating said first set of gradient histogram matching relation distribution based on said target update parameter corresponding to each set of distribution column data in said first set of gradient histogram matching relation distribution to form an updated set of gradient histogram matching relation distribution corresponding to said first set of gradient histogram matching relation distribution comprises:

5. The 5G internet-based monitoring data visualization method of claim 1, wherein the step of analyzing and outputting the number of users of the image monitoring users included in the plurality of initial monitoring images based on the updated gradient histogram matching relation distribution set comprises:

6. The method for visualizing data on a 5G internet as recited in claim 5, wherein said step of adjusting said updated gradient histogram matching relationship distribution set based on each of a plurality of pre-configured target screening amounts, respectively, to form an adjusted gradient histogram matching relationship distribution set corresponding to each target screening amount, comprises:

7. The 5G internet-based monitoring data visualization method of claim 5, wherein the step of analyzing and outputting the number of users of the image monitoring users in the plurality of initial monitoring images based on the plurality of target screening numbers and the set of adjusted gradient histogram matching relation distributions corresponding to each of the target screening numbers comprises:

8. The method for visualizing data on a 5G internet-based monitoring system according to any one of claims 1-7, wherein said step of performing data pushing processing on each of said at least one monitoring image classification set to push each of said monitoring image classification sets to said first 5G internet terminal device, respectively, so that said first 5G internet terminal device performs visualization processing on an initial monitoring image included in said received monitoring image classification set, comprises:

9. A 5G internet based monitoring data visualization system comprising a processor and a memory, the memory for storing a computer program, the processor for executing the computer program to implement the method of any of claims 1-8.