CN113207101A - Information processing method based on 5G city component sensor and Internet of things cloud platform - Google Patents

Information processing method based on 5G city component sensor and Internet of things cloud platform Download PDF

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CN113207101A
CN113207101A CN202110392506.1A CN202110392506A CN113207101A CN 113207101 A CN113207101 A CN 113207101A CN 202110392506 A CN202110392506 A CN 202110392506A CN 113207101 A CN113207101 A CN 113207101A
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CN113207101B (en
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赵延军
郭念贤
王丽萍
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Shandong Shuguangzhao Information Technology Co.,Ltd.
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Shandong Dawn Shines Information Technology Co ltd
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    • G16Y40/00—IoT characterised by the purpose of the information processing
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract

The invention discloses an information processing method based on a 5G city component sensor and an Internet of things cloud platform, wherein the method comprises the steps of obtaining source data from a plurality of clustered sensors; acquiring incidence relations among description information of all clustered sensors from source data, and extracting the source data higher than a preset incidence relation threshold value to form a fusion data matrix; and extracting the data characteristic sequence of the fusion data matrix and the attribute weight corresponding to the data characteristic sequence, and performing weight distribution operation on the data characteristic sequence according to the attribute weight to form weighted fusion data. The relevance among the sensors is considered, the required data are extracted only according to the data processing rule, global fusion is not needed, the data fusion efficiency is improved, when the multi-sensor data are fused, the current scene is adaptively adapted according to the corresponding weight of each sensor, the data fusion flexibility is improved, the accuracy and the reliability of the fused data are ensured, and the problems that the multi-sensor data fusion in the prior art does not embody the unique advantages of each type of sensor, cannot guarantee the accuracy of the multi-sensor data fusion and cannot emphatically embody different weight conditions of each sensor are solved.

Description

Information processing method based on 5G city component sensor and Internet of things cloud platform
Technical Field
The invention relates to the technical field of information processing, in particular to an information processing method based on a 5G city component sensor and an Internet of things cloud platform.
Background
With the development of information technology, information of relevant data of smart cities has various forms. Due to different data sources, data of a plurality of data sources need to be fused, and a dynamic and extensible information format and content conversion capability are provided for cross-system and cross-domain information interaction. Big data items of smart cities relate to more dynamic multi-source heterogeneous data such as meteorology, pedestrian and vehicle flow, videos and the like, the multi-source dynamic data are different in structure, data sources required by different applications are different, and the fusion efficiency is low by adopting a traditional fusion method.
Due to the development of smart cities, the smart internet of things sensors are more and more popularized, and play a vital role in public location services, government department decisions, public opinion situation perception, crowd behavior characteristic analysis, epidemic disease prediction and the like. For example, fire disaster is a major disaster in modern cities, and is receiving more and more attention from people. Along with the continuous development of economy and technology, urban high-rise buildings, underground works, markets, crowded places of personnel, gas stations and the like are increased day by day, the difficulty of fire extinguishing and rescue is correspondingly increased, meanwhile, the requirement on fire fighting communication is higher and higher, at present, the real-time performance of fire fighting communication is not only guaranteed, the overall energy consumption of network nodes is reduced, and the fairness of the energy consumption of the network nodes is also required to be improved, namely the energy consumption of all the similar nodes is uniform as much as possible, so that the problem that the network cannot operate due to the fact that the energy of key nodes is exhausted and the serious loss is caused when a fire disaster occurs can be avoided.
Prior art smart city target information is typically processed using a plurality of single sensors for reflecting the target information in isolation from multiple sides. In fact, in most cases, multiple signals must be processed simultaneously, and these signals typically come from multiple signal sources, i.e., multiple sensors. However, multiple sensors also bring information redundancy and even contradiction, so that the information collected by the multiple sensors and the observation information thereof must be combined according to some optimization criteria through reasonable allocation and use of the multiple sensors and the observation information thereof to generate explanation and description of the consistency of the observation environment, and further processing of the information is urgently required. Information fusion is the fusion of data from multiple sensors or other sources to obtain a comprehensive, better estimate, with multiple aspects of processing of the data. The information fusion is an information processing technology which analyzes, synthesizes and combines data from multiple sources, namely multi-source data, so as to complete required decision and evaluation tasks, and aims to fuse originally dispersed and independent multiple data together, thereby discovering data rules and trends and improving data value.
In the traditional data fusion scheme, aiming at data needing to be fused, a mode of association of equal field values is adopted for fusion. Generally, the data fusion is performed in a large quantity and complex, multiple times of processing of the association relation of equal field values are required during data fusion, and the fusion fields in the original data table of the multi-source data need to be cleaned in the processing process, so that the processing procedure is huge and the processing data amount is large. And after the data is cleaned, the data subjected to the processing of the association relation of the equal field values needs to be stored in another data table, so that the storage occupation is increased. Or some business personnel needing experience firstly identify the incidence relation among the tables according to business requirements, so that the data fusion of a plurality of data sources is realized. Each business system used in an enterprise is designed only to meet the needs of a certain business at the beginning of design, or the foreign key relationship is not obvious, and the relevance among a plurality of business systems is not considered. Therefore, after the relevant data scattered in each business system is imported into the data warehouse, the relationship between the data cannot be obtained through a simple logic matching method, and a data developer needs to search for a new data fusion technology to mine the association relationship between the data.
In order to solve the above situation, the invention provides an information processing method based on a 5G city component sensor and an Internet of things cloud platform, which can effectively improve the prior art and overcome the defects of the prior art.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an information processing method based on a 5G city component sensor and an Internet of things cloud platform, aiming at solving the problems in the prior art, and the specific scheme is as follows:
in a first aspect, the invention provides an information processing method based on a 5G city component sensor, which includes:
clustering the sensors, and acquiring source data from a plurality of different clustered sensors;
acquiring incidence relations among the description information of the clustered sensors from the source data, and extracting the source data higher than a preset incidence relation threshold value to form a fusion data matrix;
and extracting a data characteristic sequence of the fusion data matrix and attribute weights corresponding to the data characteristic sequence, and performing weight distribution operation on the data characteristic sequence according to the attribute weights to form weighted fusion data.
Preferably, the extracting the data feature sequence of the fused data matrix and the attribute weight corresponding to the data feature sequence includes:
extracting a data attribute confidence coefficient characteristic sequence corresponding to each phase joint point of the fusion data matrix in a preset fusion transformation matrix;
and inputting all the data attribute confidence coefficient characteristic sequences into a pre-constructed logic classification model to obtain the attribute weights corresponding to the data characteristic sequences.
Preferably, the method includes inputting all the data attribute confidence coefficient feature sequences into a pre-constructed logic classification model to obtain attribute weights corresponding to the data feature sequences, and performing weight distribution operation on the data feature sequences according to the attribute weights to form a weighted fusion data matrix, where the method includes:
acquiring the sum of the data feature similarity between each feature sequence in the same cluster and other feature sequences in the cluster, and further solving the weighted sum of the sums of the data feature similarities of all feature sequences in different clusters;
and calculating the weight ratio of the sum of the data feature similarity corresponding to each feature sequence to the weighted sum, and performing weighted operation on the feature sequences by using the attribute weights corresponding to the feature sequences to obtain weighted fusion data.
Preferably, the data feature similarity obtaining method includes:
Figure BDA0003017300420000021
wherein, the data attributes of the data A and the data B are (u)A,SA) And (u)B,SB) Con (a, B) is a data feature similarity function of data a and data B, u and S are coordinate similarity and direction similarity, respectively, N is the total number of data, and α and β are initialization parameters.
Preferably, the obtaining of the association relationship between the pieces of clustered sensor description information from the source data and the extracting of the source data higher than a preset association relationship threshold form a fused data matrix, includes:
and respectively counting the occurrence probability of the equipment description information carried by the source data of each cluster sensor, taking each cluster sensor with the occurrence probability larger than a preset occurrence probability threshold value as the incidence relation, and extracting the source data higher than the preset occurrence probability threshold value to form a fusion data matrix.
Preferably, the sensor is clustered, and source data is obtained from a plurality of different clustered sensors, the method comprising:
acquiring the average value of the data quantity in the sensor fusion range with the residual computing power, and calculating the difference value between the data quantity and the average value of the data quantity to obtain the determined value of the data quantity in the sensor fusion range with the residual computing power;
clustering all fusion range regions associated with the data volume determination values having remaining computing power to generate a plurality of clustered fusion data range regions, and acquiring source data from sensors of a plurality of different region clustered fusion data ranges.
Preferably, the method further comprises:
and according to the data quantity in the fusion range of each sensor with the residual computing power, constructing a minimization constraint polygon topological graph of each cluster of sensors by the following formula:
Figure BDA0003017300420000022
wherein:
Figure BDA0003017300420000023
the D (A, A)i) And D (A, A)j) Two points (A, A) for polygon topologyi) And (A, A)j) European distance between, EiAnd EjAs a coordinate AiSensor and coordinate AjThe remaining computing power of the sensor, i, j ═ (1, 2, 3 …, n).
And calculating the time difference of the current weighted fusion data frames of the sensors in different regional clustering fusion data ranges, and selecting the latest time weighted fusion data frame with the time difference larger than a preset time difference threshold value to route the latest time weighted fusion data frame to the data processing center platform by the minimized constraint polygon topological graph path.
In a second aspect, the present invention provides a 5G city component sensor-based information processing system, comprising:
an acquisition module for clustering the sensors and acquiring source data from a plurality of different clustered sensors;
the extraction module is used for acquiring the incidence relation among the description information of each clustered sensor from the source data and extracting the source data higher than a preset incidence relation threshold value to form a fusion data matrix;
and the weighting module is used for extracting the data characteristic sequence of the fusion data matrix and the attribute weight corresponding to the data characteristic sequence, and performing weight distribution operation on the data characteristic sequence according to the attribute weight to form weighted fusion data.
In a third aspect, the invention provides an internet of things cloud platform based on a 5G city component sensor, and the platform comprises:
the communication bus is used for realizing the connection communication between the processor and the memory;
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of:
clustering the sensors, and acquiring source data from a plurality of different clustered sensors;
acquiring incidence relations among the description information of the clustered sensors from the source data, and extracting the source data higher than a preset incidence relation threshold value to form a fusion data matrix;
and extracting a data characteristic sequence of the fusion data matrix and attribute weights corresponding to the data characteristic sequence, and performing weight distribution operation on the data characteristic sequence according to the attribute weights to form weighted fusion data.
Has the advantages that: according to the information processing method based on the 5G city component sensor and the Internet of things cloud platform, the sensor clustering target number is determined through the residual computing capacity, the target sensors with the residual computing capacity are clustered, and then source data are obtained from a plurality of different clustering sensors, so that secondary sensors without or with little residual computing capacity are excluded, only the sensors with good processing capacity are extracted for processing, and the respective advantages of the sensors are reflected; secondly, extracting source data with the occurrence probability higher than the occurrence probability threshold of the preset incidence relation from the occurrence probability of the incidence relation between the description information of each clustered sensor obtained from the source data to form a fused data matrix, so that the incidence between the sensors is considered, only required data are extracted according to data processing rules, the global data are not required to be fused, and the data fusion efficiency can be improved; then extracting a data feature sequence of the fusion data matrix and attribute weights corresponding to the data feature sequence, and performing weight distribution operation on the data feature sequence according to the attribute weights to form weighted fusion data, so that the problem that when multi-sensor-based data is subjected to feature fusion, the weight corresponding to each sensor is fixed and cannot be adaptive to the current scene, so that the data fusion effect is poor is solved, the flexibility of data fusion can be improved, and the accuracy and reliability of the fusion data are ensured; and finally, forming a minimum constraint polygon topological graph of each cluster of sensors according to the data volume in the fusion range of each sensor with the residual computing capacity, and transmitting the fusion data by a latest weighted fusion data frame through a minimum path route, so that the sensors are ensured to still maintain real-time performance and energy consumption fairness when different distributions are common, and the energy consumption is reduced.
In summary, through the above series of data fusion measures, compared with the prior art, the method improves the data fusion rate on the premise of ensuring the data fusion accuracy, and solves the problems that most of multi-sensor data fusion methods in the prior art focus on data fusion of a plurality of sensors of the same type without considering relevance, or data fusion is performed only on two or a few simple sensors, the unique advantages of each type of sensor are not reflected, the accuracy of the multi-sensor data fusion cannot be ensured, and different weight conditions of each sensor cannot be reflected in a highlighted manner.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, the embodiments in the drawings do not constitute any limitation to the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of an embodiment of the information processing method based on a 5G city component sensor.
FIG. 2 is a schematic flow chart of an embodiment of the information processing method based on the 5G city component sensor.
FIG. 3 is a schematic structural diagram of an embodiment of the information processing system based on the 5G city component sensor.
FIG. 4 is a schematic structural diagram of an embodiment of an information processing platform based on a 5G city component sensor according to the invention.
Detailed Description
The technical solution of the present invention will be further described in detail with reference to the accompanying drawings and embodiments, which are preferred embodiments of the present invention. It is to be understood that the described embodiments are merely a subset of the embodiments of the invention, and not all embodiments; it should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The main idea of the technical scheme of the embodiment of the invention is as follows: clustering the sensors, and acquiring source data from a plurality of different clustered sensors; acquiring the incidence relation among the description information of each clustering sensor from the source data, and extracting the source data higher than a preset incidence relation threshold value to form a fusion data matrix; and extracting a data characteristic sequence of the fusion data matrix and an attribute weight corresponding to the data characteristic sequence, and performing weight distribution operation on the data characteristic sequence according to the attribute weight to form weighted fusion data.
In order to better understand the technical solutions, the technical solutions will be described in detail below with reference to the drawings and specific embodiments.
Example one
An embodiment of the present invention provides an information processing method based on a 5G city component sensor, and as shown in fig. 1, the data processing method may specifically include the following steps:
s101, clustering the sensors and acquiring source data from a plurality of different clustered sensors.
As an example, the execution main body in this embodiment may be obtained by intelligent hardware such as a fixed fire sensor, a mobile fire sensor, a fixed or mobile sensor controller, a smart watch, a Leap Motion somatosensory controller, or a camera in a wired or wireless communication manner, and the execution main body in each step in this embodiment may also be other devices that can implement the same or similar functions, for example: mobile phone, personal computer, PAD, etc., which are not limited in this embodiment.
Specifically, in the embodiment of the present invention, the sensor controller first obtains the average value of the data amount in each sensor fusion range with the remaining computing power through wired or wireless transmission, and obtains the difference between the obtained data amount and the average value of the data amount, so as to obtain the determined value of the data amount in the sensor fusion range with the remaining computing power, and then clusters all fusion range areas associated with the determined value of the data amount in the preset range with the remaining computing power, so as to generate a plurality of clustered fusion data areas with different ranges, so that the sensor controller can obtain the raw data from the sensors in the clustered fusion data ranges with the different areas for the subsequent processing operation.
In summary, the sensor clustering target number is determined through the residual computing capacity, the target sensors with the residual computing capacity are clustered, and then the source data are acquired from the plurality of different clustering sensors, so that secondary sensors without or with little residual computing processing capacity are excluded, only those sensors with good processing capacity are extracted for processing, and the respective advantages of each sensor are reflected.
S102, acquiring the incidence relation among the description information of each clustering sensor from the source data, and extracting the source data higher than a preset incidence relation threshold value to form a fusion data matrix.
In the embodiment of the invention, specifically, the sensor controller respectively counts the occurrence probability of the device description information carried by the source data of each cluster sensor, each cluster sensor with the occurrence probability larger than a preset occurrence probability threshold value is taken as a defined incidence relation, and the source data higher than the preset occurrence probability threshold value is extracted to form a fusion data matrix.
In summary, the occurrence probability of the incidence relation between the description information of each clustered sensor obtained from the source data is extracted to form a fused data matrix, and thus, the incidence relation between the sensors is considered, only the required data needs to be extracted according to the data processing rule, the global data does not need to be fused, and the data fusion efficiency can be improved.
It should be noted that the association relationship is determined by an association rule, and a fusion data matrix with different association degrees can be obtained for different association rules, which is not limited herein. Other association rules are well within the purview of one skilled in the art and are included within the scope of the present invention.
S103, extracting the data characteristic sequence of the fusion data matrix and the attribute weight corresponding to the data characteristic sequence, and performing weight distribution operation on the data characteristic sequence according to the attribute weight to form weighted fusion data.
In the embodiment of the invention, the data attribute confidence coefficient characteristic sequences corresponding to all the joint points of the fusion data matrix in the preset fusion transformation matrix are firstly extracted, and then all the data attribute confidence coefficient characteristic sequences are input into a logic classification model which is constructed in advance to obtain the attribute weights corresponding to the data characteristic sequences.
In practical application, the attribute weight is obtained by firstly obtaining the sum of the data feature similarities between each feature sequence in the same cluster and the rest of the feature sequences in the cluster, further obtaining the weighted sum of the sums of the data feature similarities of all the feature sequences in different clusters, further calculating the weight ratio of the sum of the data feature similarities corresponding to each feature sequence to the weighted sum, and performing weighted operation on the feature sequences by using the attribute weights corresponding to the feature sequences to obtain weighted fusion data. The data feature similarity can be obtained by the following method:
Figure BDA0003017300420000051
it should be noted that the data attributes of the data a and the data B in the above formula are (u) respectivelyA,SA) And (u)B,SB) Con (a, B) is a data feature similarity function of data a and data B, u and S are coordinate similarity and direction similarity, respectively, N is the total number of data, and α and β are initialization parameters.
In summary, the data feature sequence of the fused data matrix and the attribute weight corresponding to the data feature sequence are extracted, and weight distribution operation is performed on the data feature sequence according to the attribute weight to form weighted fused data, so that the problem that when multi-sensor-based data is subjected to feature fusion, the weight corresponding to each sensor is fixed and cannot be adaptively adapted to the current scene, so that the data fusion effect is poor is solved, the flexibility of data fusion can be improved, and the accuracy and reliability of the fused data are ensured.
Example two
An embodiment of the present invention provides an information processing method based on a 5G city component sensor, and as shown in fig. 2, the information processing method may specifically include the following steps:
s201, clustering the sensors and acquiring source data from a plurality of different clustered sensors.
As an example, the execution main body in this embodiment may be obtained by intelligent hardware such as a fixed fire sensor, a mobile fire sensor, a fixed or mobile sensor controller, a smart watch, a Leap Motion somatosensory controller, or a camera in a wired or wireless communication manner, and the execution main body in each step in this embodiment may also be other devices that can implement the same or similar functions, for example: mobile phone, personal computer, PAD, etc., which are not limited in this embodiment.
Specifically, in the embodiment of the present invention, the sensor controller first obtains the average value of the data amount in each sensor fusion range with the remaining computing power through wired or wireless transmission, and obtains the difference between the obtained data amount and the average value of the data amount, so as to obtain the determined value of the data amount in the sensor fusion range with the remaining computing power, and then clusters all fusion range areas associated with the determined value of the data amount in the preset range with the remaining computing power, so as to generate a plurality of clustered fusion data areas with different ranges, so that the sensor controller can obtain the raw data from the sensors in the clustered fusion data ranges with the different areas for the subsequent processing operation.
In summary, the sensor clustering target number is determined through the residual computing capacity, the target sensors with the residual computing capacity are clustered, and then the source data are acquired from the plurality of different clustering sensors, so that secondary sensors without or with little residual computing processing capacity are excluded, only those sensors with good processing capacity are extracted for processing, and the respective advantages of each sensor are reflected.
S202, acquiring the incidence relation among the cluster sensor description information from the source data, and extracting the source data higher than a preset incidence relation threshold value to form a fusion data matrix.
In the embodiment of the invention, specifically, the sensor controller respectively counts the occurrence probability of the device description information carried by the source data of each cluster sensor, each cluster sensor with the occurrence probability larger than a preset occurrence probability threshold value is taken as a defined incidence relation, and the source data higher than the preset occurrence probability threshold value is extracted to form a fusion data matrix.
In summary, the occurrence probability of the incidence relation between the description information of each clustered sensor obtained from the source data is extracted to form a fused data matrix, and thus, the incidence relation between the sensors is considered, only the required data needs to be extracted according to the data processing rule, the global data does not need to be fused, and the data fusion efficiency can be improved.
It should be noted that the association relationship is determined by an association rule, and a fusion data matrix with different association degrees can be obtained for different association rules, which is not limited herein. Other association rules are well within the purview of one skilled in the art and are included within the scope of the present invention.
S203, extracting the data characteristic sequence of the fusion data matrix and the attribute weight corresponding to the data characteristic sequence, and performing weight distribution operation on the data characteristic sequence according to the attribute weight to form weighted fusion data.
In the embodiment of the invention, the data attribute confidence coefficient characteristic sequences corresponding to all the joint points of the fusion data matrix in the preset fusion transformation matrix are firstly extracted, and then all the data attribute confidence coefficient characteristic sequences are input into a logic classification model which is constructed in advance to obtain the attribute weights corresponding to the data characteristic sequences.
In practical application, the attribute weight is obtained by firstly obtaining the sum of the data feature similarities between each feature sequence in the same cluster and the rest of the feature sequences in the cluster, further obtaining the weighted sum of the sums of the data feature similarities of all the feature sequences in different clusters, further calculating the weight ratio of the sum of the data feature similarities corresponding to each feature sequence to the weighted sum, and performing weighted operation on the feature sequences by using the attribute weights corresponding to the feature sequences to obtain weighted fusion data. The data feature similarity can be obtained by the following method:
Figure BDA0003017300420000061
it should be noted that the data attributes of the data a and the data B in the above formula are (u) respectivelyA,SA) And (u)B,SB) Con (a, B) is a data feature similarity function of data a and data B, u and S are coordinate similarity and direction similarity, respectively, N is the total number of data, and α and β are initialization parameters.
In summary, the data feature sequence of the fused data matrix and the attribute weight corresponding to the data feature sequence are extracted, and weight distribution operation is performed on the data feature sequence according to the attribute weight to form weighted fused data, so that the problem that when multi-sensor-based data is subjected to feature fusion, the weight corresponding to each sensor is fixed and cannot be adaptively adapted to the current scene, so that the data fusion effect is poor is solved, the flexibility of data fusion can be improved, and the accuracy and reliability of the fused data are ensured.
And S204, forming a minimized constraint polygon topological graph of each cluster of sensors according to the data volume in the sensor fusion range with the residual computing capacity, and routing the latest weighted fusion data frame to the data processing center platform through a minimized constraint polygon topological graph path.
In the embodiment of the invention, according to the data volume in the fusion range of each sensor with the residual computing power, the minimization constraint polygon topological graph of each cluster of sensors is formed by the following formula:
Figure BDA0003017300420000062
wherein for each
Figure BDA0003017300420000063
The following conditions need to be satisfied:
Figure BDA0003017300420000064
in the above formula, D (A, A)i) And D (A, A)j) Respectively restraining any two points (A, A) of the polygon in the polygon topological graph for minimizationi) And (A, A)j) European distance between, EiAnd EjAre respectively coordinates AiSensor and coordinate AjThe remaining computing power of the sensor, i, j ═ (1, 2, 3 …, n).
In another alternative embodiment, the minimization of constrained polygon topology may employ the following scheme: firstly, deleting the sensor nodes from the initialized topological graph to obtain a plurality of connected components, solving a minimum spanning tree for each connected component to obtain a plurality of minimum spanning trees, and finding the edge with the minimum relevance from each connected component to obtain the constrained polygon topological graph with the minimum relevance.
Further, calculating the time difference of the current weighted fusion data frames of the sensors in different area clustering fusion data ranges, and selecting the latest time weighted fusion data frames with the time difference larger than a preset time difference threshold value to minimize the constraint polygon topological graph path and route the data frames to the data processing center platform.
In summary, according to the data volume in the fusion range of each sensor with the remaining computing power, the minimum constraint polygon topological graph of each cluster of sensors is formed, and the latest weighted fusion data frame is used for transmitting the fusion data through the minimum path route, so that the sensors are ensured to still maintain real-time performance and energy consumption fairness when different distributions are common, and energy consumption is reduced
It is noted that the above-described minimized constrained polygon topology scheme is given by way of example only, and that other topology schemes may be fully employed by those skilled in the art and are within the scope of the present invention.
EXAMPLE III
An embodiment of the present invention provides an information processing system based on a 5G city component sensor, and as shown in fig. 3, the information processing system may specifically include the following modules:
and the acquisition module is used for clustering the sensors and acquiring source data from a plurality of different clustered sensors.
As an example, the execution main body in this embodiment may be obtained by intelligent hardware such as a fixed fire sensor, a mobile fire sensor, a fixed or mobile sensor controller, a smart watch, a Leap Motion somatosensory controller, or a camera in a wired or wireless communication manner, and the execution main body in each step in this embodiment may also be other devices that can implement the same or similar functions, for example: mobile phone, personal computer, PAD, etc., which are not limited in this embodiment.
Specifically, in the embodiment of the present invention, the sensor controller first obtains the average value of the data amount in each sensor fusion range with the remaining computing power through wired or wireless transmission, and obtains the difference between the obtained data amount and the average value of the data amount, so as to obtain the determined value of the data amount in the sensor fusion range with the remaining computing power, and then clusters all fusion range areas associated with the determined value of the data amount in the preset range with the remaining computing power, so as to generate a plurality of clustered fusion data areas with different ranges, so that the sensor controller can obtain the raw data from the sensors in the clustered fusion data ranges with the different areas for the subsequent processing operation.
In summary, the sensor clustering target number is determined through the residual computing capacity, the target sensors with the residual computing capacity are clustered, and then the source data are acquired from the plurality of different clustering sensors, so that secondary sensors without or with little residual computing processing capacity are excluded, only those sensors with good processing capacity are extracted for processing, and the respective advantages of each sensor are reflected.
And the extraction module is used for acquiring the incidence relation among the description information of each clustered sensor from the source data and extracting the source data higher than a preset incidence relation threshold value to form a fusion data matrix.
In the embodiment of the invention, specifically, the sensor controller respectively counts the occurrence probability of the device description information carried by the source data of each cluster sensor, each cluster sensor with the occurrence probability larger than a preset occurrence probability threshold value is taken as a defined incidence relation, and the source data higher than the preset occurrence probability threshold value is extracted to form a fusion data matrix.
In summary, the occurrence probability of the incidence relation between the description information of each clustered sensor obtained from the source data is extracted to form a fused data matrix, and thus, the incidence relation between the sensors is considered, only the required data needs to be extracted according to the data processing rule, the global data does not need to be fused, and the data fusion efficiency can be improved.
It should be noted that the association relationship is determined by an association rule, and a fusion data matrix with different association degrees can be obtained for different association rules, which is not limited herein. Other association rules are well within the purview of one skilled in the art and are included within the scope of the present invention.
And the weighting module is used for extracting the data characteristic sequence of the fusion data matrix and the attribute weight corresponding to the data characteristic sequence, and performing weight distribution operation on the data characteristic sequence according to the attribute weight to form weighted fusion data.
In the embodiment of the invention, the data attribute confidence coefficient characteristic sequences corresponding to all the joint points of the fusion data matrix in the preset fusion transformation matrix are firstly extracted, and then all the data attribute confidence coefficient characteristic sequences are input into a logic classification model which is constructed in advance to obtain the attribute weights corresponding to the data characteristic sequences.
In practical application, the attribute weight is obtained by firstly obtaining the sum of the data feature similarities between each feature sequence in the same cluster and the rest of the feature sequences in the cluster, further obtaining the weighted sum of the sums of the data feature similarities of all the feature sequences in different clusters, further calculating the weight ratio of the sum of the data feature similarities corresponding to each feature sequence to the weighted sum, and performing weighted operation on the feature sequences by using the attribute weights corresponding to the feature sequences to obtain weighted fusion data. The data feature similarity can be obtained by the following method:
Figure BDA0003017300420000071
it should be noted that the data attributes of the data a and the data B in the above formula are (u) respectivelyA,SA) And (u)B,SB) Con (A, B) is a data feature similarity function of data A and data B, u and S are the coordinate similarity and the square, respectivelyAnd the similarity, N is the total number of data, and alpha and beta are initialization parameters.
In summary, the data feature sequence of the fused data matrix and the attribute weight corresponding to the data feature sequence are extracted, and weight distribution operation is performed on the data feature sequence according to the attribute weight to form weighted fused data, so that the problem that when multi-sensor-based data is subjected to feature fusion, the weight corresponding to each sensor is fixed and cannot be adaptively adapted to the current scene, so that the data fusion effect is poor is solved, the flexibility of data fusion can be improved, and the accuracy and reliability of the fused data are ensured.
In an optional embodiment, the information processing system further comprises a routing module, configured to construct a minimization constraint polygon topology map for each cluster of sensors according to the data volume in the fusion range of each sensor with the remaining computing power, and route the latest weighted fusion data frame to the data processing center platform in order to minimize the constraint polygon topology map path.
In the embodiment of the invention, according to the data volume in the fusion range of each sensor with the residual computing power, the minimization constraint polygon topological graph of each cluster of sensors is formed by the following formula:
Figure BDA0003017300420000081
wherein for each
Figure BDA0003017300420000082
The following conditions need to be satisfied:
Figure BDA0003017300420000083
in the above formula, D (A, A)i) And D (A, A)j) Respectively restraining any two points (A, A) of the polygon in the polygon topological graph for minimizationi) And (A, A)j) European distance between, EiAnd EjAre respectively a seatMark AiSensor and coordinate AjThe remaining computing power of the sensor, i, j ═ (1, 2, 3 …, n).
In another alternative embodiment, the minimization of constrained polygon topology may employ the following scheme: firstly, deleting the sensor nodes from the initialized topological graph to obtain a plurality of connected components, solving a minimum spanning tree for each connected component to obtain a plurality of minimum spanning trees, and finding the edge with the minimum relevance from each connected component to obtain the constrained polygon topological graph with the minimum relevance.
Further, calculating the time difference of the current weighted fusion data frames of the sensors in different area clustering fusion data ranges, and selecting the latest time weighted fusion data frames with the time difference larger than a preset time difference threshold value to minimize the constraint polygon topological graph path and route the data frames to the data processing center platform.
In summary, according to the data volume in the fusion range of each sensor with the remaining computing power, the minimum constraint polygon topological graph of each cluster of sensors is formed, and the latest weighted fusion data frame is used for transmitting the fusion data through the minimum path route, so that the sensors are ensured to still maintain real-time performance and energy consumption fairness when different distributions are common, and energy consumption is reduced
It is noted that the above-described minimized constrained polygon topology scheme is given by way of example only, and that other topology schemes may be fully employed by those skilled in the art and are within the scope of the present invention.
Example four
An embodiment of the present invention provides an information processing system based on a 5G city component sensor, and as shown in fig. 4, the information processing system may specifically include the following modules:
the communication bus is used for realizing the connection communication between the processor and the memory;
a memory for storing a computer program; the memory may comprise high-speed RAM memory and may also comprise non-volatile memory, such as at least one disk memory. The memory may optionally comprise at least one memory device.
A processor for executing the computer program to implement the steps of:
first, the sensors are clustered, and source data is acquired from a plurality of different clustered sensors.
As an example, the execution main body in this embodiment may be obtained by intelligent hardware such as a fixed fire sensor, a mobile fire sensor, a fixed or mobile sensor controller, a smart watch, a Leap Motion somatosensory controller, or a camera in a wired or wireless communication manner, and the execution main body in each step in this embodiment may also be other devices that can implement the same or similar functions, for example: mobile phone, personal computer, PAD, etc., which are not limited in this embodiment.
Specifically, in the embodiment of the present invention, the sensor controller first obtains the average value of the data amount in each sensor fusion range with the remaining computing power through wired or wireless transmission, and obtains the difference between the obtained data amount and the average value of the data amount, so as to obtain the determined value of the data amount in the sensor fusion range with the remaining computing power, and then clusters all fusion range areas associated with the determined value of the data amount in the preset range with the remaining computing power, so as to generate a plurality of clustered fusion data areas with different ranges, so that the sensor controller can obtain the raw data from the sensors in the clustered fusion data ranges with the different areas for the subsequent processing operation.
In summary, the sensor clustering target number is determined through the residual computing capacity, the target sensors with the residual computing capacity are clustered, and then the source data are acquired from the plurality of different clustering sensors, so that secondary sensors without or with little residual computing processing capacity are excluded, only those sensors with good processing capacity are extracted for processing, and the respective advantages of each sensor are reflected.
And secondly, acquiring the incidence relation among the description information of each clustering sensor from the source data, and extracting the source data higher than a preset incidence relation threshold value to form a fusion data matrix.
In the embodiment of the invention, specifically, the sensor controller respectively counts the occurrence probability of the device description information carried by the source data of each cluster sensor, each cluster sensor with the occurrence probability larger than a preset occurrence probability threshold value is taken as a defined incidence relation, and the source data higher than the preset occurrence probability threshold value is extracted to form a fusion data matrix.
In summary, the occurrence probability of the incidence relation between the description information of each clustered sensor obtained from the source data is extracted to form a fused data matrix, and thus, the incidence relation between the sensors is considered, only the required data needs to be extracted according to the data processing rule, the global data does not need to be fused, and the data fusion efficiency can be improved.
It should be noted that the association relationship is determined by an association rule, and a fusion data matrix with different association degrees can be obtained for different association rules, which is not limited herein. Other association rules are well within the purview of one skilled in the art and are included within the scope of the present invention.
And then, extracting a data characteristic sequence of the fusion data matrix and an attribute weight corresponding to the data characteristic sequence, and performing weight distribution operation on the data characteristic sequence according to the attribute weight to form weighted fusion data.
In the embodiment of the invention, the data attribute confidence coefficient characteristic sequences corresponding to all the joint points of the fusion data matrix in the preset fusion transformation matrix are firstly extracted, and then all the data attribute confidence coefficient characteristic sequences are input into a logic classification model which is constructed in advance to obtain the attribute weights corresponding to the data characteristic sequences.
In practical application, the attribute weight is obtained by firstly obtaining the sum of the data feature similarities between each feature sequence in the same cluster and the rest of the feature sequences in the cluster, further obtaining the weighted sum of the sums of the data feature similarities of all the feature sequences in different clusters, further calculating the weight ratio of the sum of the data feature similarities corresponding to each feature sequence to the weighted sum, and performing weighted operation on the feature sequences by using the attribute weights corresponding to the feature sequences to obtain weighted fusion data. The data feature similarity can be obtained by the following method:
Figure BDA0003017300420000091
it should be noted that the data attributes of the data a and the data B in the above formula are (u) respectivelyA,SA) And (u)B,SB) Con (a, B) is a data feature similarity function of data a and data B, u and S are coordinate similarity and direction similarity, respectively, N is the total number of data, and α and β are initialization parameters.
In summary, the data feature sequence of the fused data matrix and the attribute weight corresponding to the data feature sequence are extracted, and weight distribution operation is performed on the data feature sequence according to the attribute weight to form weighted fused data, so that the problem that when multi-sensor-based data is subjected to feature fusion, the weight corresponding to each sensor is fixed and cannot be adaptively adapted to the current scene, so that the data fusion effect is poor is solved, the flexibility of data fusion can be improved, and the accuracy and reliability of the fused data are ensured.
And finally, forming a minimized constraint polygon topological graph of each cluster of sensors according to the data volume in the fusion range of each sensor with the residual computing power, and routing the latest weighted fusion data frame to the data processing center platform by the path of the minimized constraint polygon topological graph.
In the embodiment of the invention, according to the data volume in the fusion range of each sensor with the residual computing power, the minimization constraint polygon topological graph of each cluster of sensors is formed by the following formula:
Figure BDA0003017300420000092
wherein for each
Figure BDA0003017300420000093
The following conditions need to be satisfied:
Figure BDA0003017300420000101
in the above formula, D (A, A)i) And D (A, A)j) Respectively restraining any two points (A, A) of the polygon in the polygon topological graph for minimizationi) And (A, A)j) European distance between, EiAnd EjAre respectively coordinates AiSensor and coordinate AjThe remaining computing power of the sensor, i, j ═ (1, 2, 3 …, n).
In another alternative embodiment, the minimization of constrained polygon topology may employ the following scheme: firstly, deleting the sensor nodes from the initialized topological graph to obtain a plurality of connected components, solving a minimum spanning tree for each connected component to obtain a plurality of minimum spanning trees, and finding the edge with the minimum relevance from each connected component to obtain the constrained polygon topological graph with the minimum relevance.
Further, calculating the time difference of the current weighted fusion data frames of the sensors in different area clustering fusion data ranges, and selecting the latest time weighted fusion data frames with the time difference larger than a preset time difference threshold value to minimize the constraint polygon topological graph path and route the data frames to the data processing center platform.
In summary, according to the data volume in the fusion range of each sensor with the remaining computing power, the minimum constraint polygon topological graph of each cluster of sensors is formed, and the latest weighted fusion data frame is used for transmitting the fusion data through the minimum path route, so that the sensors are ensured to still maintain real-time performance and energy consumption fairness when different distributions are common, and energy consumption is reduced
It is noted that the above-described minimized constrained polygon topology scheme is given by way of example only, and that other topology schemes may be fully employed by those skilled in the art and are within the scope of the present invention.
The processor in this embodiment may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The processor described above may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. The processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
EXAMPLE five
An embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored, the computer program implementing the information processing method when executed by a processor.
In summary, according to the information processing method based on the 5G city component sensor and the internet of things cloud platform provided by the embodiment of the invention, the sensor clustering target number is determined through the residual computing capacity, the target sensors with the residual computing capacity are clustered, and then the source data is acquired from a plurality of different clustering sensors, so that secondary sensors without or with less residual computing capacity are excluded, only those sensors with good processing capacity are extracted for processing, and the respective advantages of each sensor are reflected; secondly, extracting source data with the occurrence probability higher than the occurrence probability threshold of the preset incidence relation from the occurrence probability of the incidence relation between the description information of each clustered sensor obtained from the source data to form a fused data matrix, so that the incidence between the sensors is considered, only required data are extracted according to data processing rules, the global data are not required to be fused, and the data fusion efficiency can be improved; then extracting a data feature sequence of the fusion data matrix and attribute weights corresponding to the data feature sequence, and performing weight distribution operation on the data feature sequence according to the attribute weights to form weighted fusion data, so that the problem that when multi-sensor-based data is subjected to feature fusion, the weight corresponding to each sensor is fixed and cannot be adaptive to the current scene, so that the data fusion effect is poor is solved, the flexibility of data fusion can be improved, and the accuracy and reliability of the fusion data are ensured; and finally, forming a minimum constraint polygon topological graph of each cluster of sensors according to the data volume in the fusion range of each sensor with the residual computing capacity, and transmitting the fusion data by a latest weighted fusion data frame through a minimum path route, so that the sensors are ensured to still maintain real-time performance and energy consumption fairness when different distributions are common, and the energy consumption is reduced.
In summary, through the above series of data fusion measures, compared with the prior art, the method improves the data fusion rate on the premise of ensuring the data fusion accuracy, and solves the problems that most of multi-sensor data fusion methods in the prior art focus on data fusion of a plurality of sensors of the same type without considering relevance, or data fusion is performed only on two or a few simple sensors, the unique advantages of each type of sensor are not reflected, the accuracy of the multi-sensor data fusion cannot be ensured, and different weight conditions of each sensor cannot be reflected in a highlighted manner.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that the acts and modules illustrated are not necessarily required to practice the invention.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions described in accordance with the embodiments of the present application are generated in whole or in part when the computer program instructions are loaded or executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more collections of available media. The available media may be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. An information processing method based on a 5G city component sensor is characterized by comprising the following steps:
clustering the sensors, and acquiring source data from a plurality of different clustered sensors;
acquiring incidence relations among the description information of the clustered sensors from the source data, and extracting the source data higher than a preset incidence relation threshold value to form a fusion data matrix;
and extracting a data characteristic sequence of the fusion data matrix and attribute weights corresponding to the data characteristic sequence, and performing weight distribution operation on the data characteristic sequence according to the attribute weights to form weighted fusion data.
2. The method according to claim 1, wherein the extracting of the data feature sequences of the fused data matrix and the attribute weights corresponding to the data feature sequences comprises:
extracting a data attribute confidence coefficient characteristic sequence corresponding to each phase joint point of the fusion data matrix in a preset fusion transformation matrix;
and inputting all the data attribute confidence coefficient characteristic sequences into a pre-constructed logic classification model to obtain the attribute weights corresponding to the data characteristic sequences.
3. The method according to claim 2, wherein the all data attribute confidence coefficient feature sequences are input into a pre-constructed logic classification model to obtain attribute weights corresponding to the data feature sequences, and weight distribution operation is performed on the data feature sequences according to the attribute weights to form a weighted fusion data matrix, and the method comprises:
acquiring the sum of the data feature similarity between each feature sequence in the same cluster and other feature sequences in the cluster, and further solving the weighted sum of the sums of the data feature similarities of all feature sequences in different clusters;
and calculating the weight ratio of the sum of the data feature similarity corresponding to each feature sequence to the weighted sum, and performing weighted operation on the feature sequences by using the attribute weights corresponding to the feature sequences to obtain weighted fusion data.
4. The method according to claim 2, wherein the data feature similarity obtaining method comprises:
Figure FDA0003017300410000011
wherein, the data attributes of the data A and the data B are (u)A,SA) And (u)B,SB) Con (a, B) is a data feature similarity function of data a and data B, u and S are coordinate similarity and direction similarity, respectively, N is the total number of data, and α and β are initialization parameters.
5. The method according to claim 1, wherein the obtaining of the association between the clustered sensor description information from the source data, and the extracting of the source data above a preset association threshold value form a fused data matrix, comprises:
and respectively counting the occurrence probability of the equipment description information carried by the source data of each cluster sensor, taking each cluster sensor with the occurrence probability larger than a preset occurrence probability threshold value as the incidence relation, and extracting the source data higher than the preset occurrence probability threshold value to form a fusion data matrix.
6. The method of any one of claims 1-5, wherein the clustering the sensors, obtaining source data from a plurality of different clustered sensors, comprises:
acquiring the average value of the data quantity in the sensor fusion range with the residual computing power, and calculating the difference value between the data quantity and the average value of the data quantity to obtain the determined value of the data quantity in the sensor fusion range with the residual computing power;
clustering all fusion range regions associated with the data volume determination values having remaining computing power to generate a plurality of clustered fusion data range regions, and acquiring source data from sensors of a plurality of different region clustered fusion data ranges.
7. The method of claim 6, further comprising:
and according to the data quantity in the fusion range of each sensor with the residual computing power, constructing a minimization constraint polygon topological graph of each cluster of sensors by the following formula:
Figure FDA0003017300410000012
wherein:
Figure FDA0003017300410000013
the D (A, A)i) And D (A, A)j) Two points (A, A) for polygon topologyi) And (A, A)j) European distance between, EiAnd EjAs a coordinate AiSensor and coordinate AjThe remaining computing power of the sensor, i, j ═ (1, 2, 3 …, n).
And calculating the time difference of the current weighted fusion data frames of the sensors in different regional clustering fusion data ranges, and selecting the latest time weighted fusion data frame with the time difference larger than a preset time difference threshold value to route the latest time weighted fusion data frame to the data processing center platform by the minimized constraint polygon topological graph path.
8. An information processing system based on 5G city component sensors, characterized in that the system comprises:
an acquisition module for clustering the sensors and acquiring source data from a plurality of different clustered sensors;
the extraction module is used for acquiring the incidence relation among the description information of each clustered sensor from the source data and extracting the source data higher than a preset incidence relation threshold value to form a fusion data matrix;
and the weighting module is used for extracting the data characteristic sequence of the fusion data matrix and the attribute weight corresponding to the data characteristic sequence, and performing weight distribution operation on the data characteristic sequence according to the attribute weight to form weighted fusion data.
9. The utility model provides a thing networking cloud platform based on 5G city part sensor which characterized in that, the platform includes:
the communication bus is used for realizing the connection communication between the processor and the memory;
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of:
clustering the sensors, and acquiring source data from a plurality of different clustered sensors;
acquiring incidence relations among the description information of the clustered sensors from the source data, and extracting the source data higher than a preset incidence relation threshold value to form a fusion data matrix;
and extracting a data characteristic sequence of the fusion data matrix and attribute weights corresponding to the data characteristic sequence, and performing weight distribution operation on the data characteristic sequence according to the attribute weights to form weighted fusion data.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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