CN111949840A - Topological graph structure construction method and device based on Internet of things data - Google Patents
Topological graph structure construction method and device based on Internet of things data Download PDFInfo
- Publication number
- CN111949840A CN111949840A CN201910403879.7A CN201910403879A CN111949840A CN 111949840 A CN111949840 A CN 111949840A CN 201910403879 A CN201910403879 A CN 201910403879A CN 111949840 A CN111949840 A CN 111949840A
- Authority
- CN
- China
- Prior art keywords
- data
- node
- internet
- things
- nodes
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000010276 construction Methods 0.000 title claims description 10
- 238000000034 method Methods 0.000 claims abstract description 54
- 238000012545 processing Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 10
- 238000004140 cleaning Methods 0.000 claims description 9
- 230000015654 memory Effects 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 4
- 238000012163 sequencing technique Methods 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- 230000009471 action Effects 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 3
- 238000013178 mathematical model Methods 0.000 description 3
- 230000004927 fusion Effects 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 230000002123 temporal effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000004931 aggregating effect Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000006403 short-term memory Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/9038—Presentation of query results
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the invention provides a method and a device for constructing a topological graph structure based on data of the Internet of things. Wherein the method comprises the following steps: acquiring a plurality of internet of things data, wherein each internet of things data comprises score data in a plurality of dimensions and at least comprises score data in a time dimension and score data in a space dimension; constructing a plurality of nodes according to the score data of the plurality of internet of things data in the spatial dimension, wherein each node corresponds to at least one internet of things data in the spatial dimension; determining the correlation between every two nodes in the plurality of nodes, wherein the correlation is used for representing the correlation degree between the data of the internet of things corresponding to one node in the two nodes and the data of the internet of things corresponding to the other node in the spatial dimension and the time dimension; and according to the correlation among the nodes, constructing edges among the nodes to obtain a topological graph structure. The data amount of the data to be utilized can be effectively increased.
Description
Technical Field
The invention relates to the technical field of data mining, in particular to a method and a device for constructing a topological graph structure based on data of the Internet of things.
Background
In some application scenarios, a large amount of collected data of the internet of things may need to be analyzed to obtain information required by a user. For example, in intelligent transportation, a traffic flow model may need to be constructed according to a large amount of traffic data to predict the traffic flow in a future period, so that traffic planning can be performed in a targeted manner.
However, the data of the internet of things may be acquired from a plurality of places, that is, may be discrete in spatial dimension, and therefore, when analyzing the data of the internet of things acquired from one place, it is difficult to refer to the data of the internet of things acquired from other places, so that the data of the internet of things is not fully utilized. How to fully utilize the data of the internet of things becomes a technical problem to be solved urgently.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a device for constructing a topological graph structure based on internet of things data so as to more fully utilize the internet of things data. The specific technical scheme is as follows:
in a first aspect of the embodiments of the present invention, a method for constructing a topology graph structure based on data of an internet of things is provided, where the method includes:
acquiring a plurality of internet of things data, wherein each internet of things data comprises score data in a plurality of dimensions and at least comprises score data in a time dimension and score data in a space dimension;
constructing a plurality of nodes according to the score data of the plurality of internet of things data in the spatial dimension, wherein each node corresponds to at least one internet of things data in the spatial dimension;
determining the correlation between every two nodes in the plurality of nodes, wherein the correlation is used for representing the correlation degree between the data of the internet of things corresponding to one node in the two nodes and the data of the internet of things corresponding to the other node in the spatial dimension and the time dimension;
and according to the correlation among the nodes, constructing edges among the nodes to obtain a topological graph structure.
In one possible embodiment, the determining the correlation between each two nodes of the plurality of nodes comprises:
for each two nodes in the plurality of nodes, determining the interval of the data of the internet of things corresponding to one node in the two nodes and the data of the internet of things corresponding to the other node in the space dimension and/or the time dimension;
and if the interval is smaller than a preset interval threshold value, determining that the correlation between the two nodes is related.
In a possible embodiment, after the constructing edges between the plurality of nodes according to the correlations between the plurality of nodes to obtain the topology, the method further includes:
for each node in the topological graph structure, determining topological graph structure characteristics of the node, wherein the topological graph structure characteristics are used for representing the communication relationship between the node and other nodes in the topological graph structure;
for each piece of Internet of things data, combining the topological graph structure characteristics of the node corresponding to the piece of Internet of things data with the piece data of the piece of Internet of things data in other dimensions except the space dimension and the target dimension, and taking the piece data of the piece of Internet of things data in the target dimension as a marking value to obtain sample data of the node;
and constructing a prediction model according to the sample data of each node in the topological graph structure, wherein the prediction model is used for predicting the fractional data of the target space position on the target dimension.
In a possible embodiment, the constructing a prediction model according to the sample data of each node in the topological graph structure includes:
for each node in the topological graph structure, sequencing sample data of the node according to the time sequence to obtain a sample sequence;
performing sliding window processing on the sample sequence to obtain a plurality of time sequence data;
and constructing a prediction model according to the time sequence data of each node in the topological graph.
In a possible embodiment, the constructing a prediction model according to the sample data of each node in the topological graph structure includes:
respectively training a plurality of basic models according to data of different dimensions in the sample data of each node in the topological graph structure;
and fusing the trained basic models to obtain a prediction model.
In a possible embodiment, the obtaining a plurality of internet of things data includes:
acquiring data aiming at a plurality of spatial positions to obtain a plurality of multi-dimensional data to be processed, wherein each piece of data to be processed at least comprises fractional data in a time dimension and fractional data in a spatial dimension;
and carrying out data cleaning on the plurality of data to be processed to obtain a plurality of Internet of things data.
In a second aspect of the embodiments of the present invention, there is provided an apparatus for constructing a topology structure of base internet of things data, the apparatus including:
the data acquisition module is used for acquiring a plurality of Internet of things data, wherein each Internet of things data comprises score data on a plurality of dimensions and at least comprises score data on a time dimension and score data on a space dimension;
the node construction module is used for constructing a plurality of nodes according to the fractional data of the plurality of internet of things data on the spatial dimension, wherein each node corresponds to at least one piece of internet of things data on the spatial dimension;
the correlation determination module is used for determining the correlation between every two nodes in the plurality of nodes, wherein the correlation is used for representing the correlation degree of the data of the internet of things corresponding to one node in the two nodes and the data of the internet of things corresponding to the other node in the space dimension and the time dimension;
and the edge construction module is used for constructing edges among the nodes according to the correlation among the nodes to obtain the topological graph structure.
In a possible embodiment, the association determining module is specifically configured to determine, for each two nodes of the plurality of nodes, the interval of the internet of things data corresponding to one node of the two nodes in a spatial dimension and/or a time dimension;
and if the interval is smaller than a preset interval threshold value, determining that the correlation between the two nodes is related.
In a possible embodiment, the apparatus further includes a prediction model building module, configured to determine, for each node in the topological graph structure, a topological graph structure feature of the node, where the topological graph structure feature is used to represent a connectivity relationship between the node and other nodes in the topological graph structure;
for each piece of Internet of things data, combining the topological graph structure characteristics of the node corresponding to the piece of Internet of things data with the piece data of the piece of Internet of things data in other dimensions except the space dimension and the target dimension, and taking the piece data of the piece of Internet of things data in the target dimension as a marking value to obtain sample data of the node;
and constructing a prediction model according to the sample data of each node in the topological graph structure, wherein the prediction model is used for predicting the fractional data of the target space position on the target dimension.
In a possible embodiment, the prediction model building module is specifically configured to, for each node in the topological graph structure, sort sample data of the node according to a time sequence to obtain a sample sequence;
performing sliding window processing on the sample sequence to obtain a plurality of time sequence data;
and constructing a prediction model according to the time sequence data of each node in the topological graph.
In a possible embodiment, the prediction model building module is specifically configured to train a plurality of basic models respectively according to data of different dimensions in the sample data of each node in the topological graph structure;
and fusing the trained basic models to obtain a prediction model.
In a possible embodiment, the data acquisition module is specifically configured to perform data acquisition for a plurality of spatial positions to obtain a plurality of multidimensional data to be processed, where each data to be processed at least includes data fractions in a time dimension and data fractions in a spatial dimension;
and carrying out data cleaning on the plurality of data to be processed to obtain a plurality of Internet of things data.
In a third aspect of the present invention, there is provided an electronic device comprising:
a memory for storing a computer program;
a processor adapted to perform the method steps of any of the above first aspects when executing a program stored in the memory.
In a fourth aspect of the invention, a computer-readable storage medium is provided, having stored therein a computer program which, when executed by a processor, carries out the method steps of any of the above.
According to the method and the device for constructing the topological graph structure based on the data of the Internet of things, provided by the embodiment of the invention, in the obtained topological graph structure, the data utilized by each node is more comprehensive, and the real information of the node can be more accurately reflected. Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of 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 schematic flow chart of a method for constructing a topology based on data of the internet of things according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a method for constructing a topology structure based on internet of things data in an application scenario of traffic flow prediction according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of a prediction model construction method based on a topological graph structure according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of a sample data obtaining method according to an embodiment of the present invention;
FIG. 5 is a schematic flow chart of a method for constructing a prediction model according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a device for constructing a topology based on data of the internet of things according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for constructing a topology based on data of the internet of things according to an embodiment of the present invention, which may include:
s101, obtaining a plurality of Internet of things data.
The data of the internet of things can be data collected by entity equipment, and the entity equipment can be different according to different application scenes, for example, the data of the internet of things can comprise speed data obtained by radar measurement, image data collected by a camera, electronic identification information read by an electronic identification reader-writer, and one or more of GPS positioning information collected by a GPS positioning device. The data collected by the entity devices may be multi-dimensional, for example, the dimensions of the data collected by the cameras may include a collection location (spatial dimension), a collection time (temporal dimension), personnel and/or vehicle image information.
Each internet of things data includes fractional data in multiple dimensions, and includes at least fractional data in a temporal dimension and fractional data in a spatial dimension. The dimensions of the data of different internet of things may be different or the same, and the representation forms of the data of different internet of things in the same dimension may be the same or different, which is not limited in this embodiment. For example, the fraction data of one internet of things data in the time dimension may be used for representing a time instant, such as 9:30, and the fraction data of another internet of things data in the time dimension may be used for representing a time end, such as 9:00-10: 00.
For example, one piece of internet-of-things data may be { bayonet a, 9:30, zhe AXXXXXXX, red, vendor 1, model 1}, a red vehicle representing model 1 produced by vendor 1 with a license plate number of zhe AXXXXXXX, appearing at bayonet a at 9:30, where bayonet a is point data of the internet-of-things data in a spatial dimension, and 9:30 is point data of the internet-of-things data in a time dimension.
For another example, an internet of things data may also be obtained by shooting a person in an office, and may be represented as { region a, 8:15, person 3, face image a }, where region a is a region where the person is located when the person is shot, 8:15 represents time when the person is shot, person 3 is a person identifier of the person, and face image a is a shot face image of the person.
In a possible embodiment, the data to be processed in multiple dimensions may be acquired for multiple spatial positions, where each data to be processed includes at least data in a time dimension and data in a spatial dimension. And cleaning a plurality of data to be processed to obtain a plurality of internet of things data. For example, vehicle information of passing vehicles can be collected at a plurality of checkpoints respectively to obtain passing data of the plurality of checkpoints, each passing data at least comprises time (i.e. fractional data in a time dimension) and place (i.e. fractional data in a space dimension) of collecting the vehicle information, the collected passing data is cleaned, the passing data lacking necessary information (such as license plate numbers), redundant and abnormal passing data is removed, and the remaining passing data is used as a plurality of internet of things data.
In another possible embodiment, the personnel information of personnel entering and exiting the office areas in the office building is collected through a camera, each personnel information at least comprises time (namely, fractional data in a time dimension) of collecting the personnel information and the office areas (namely, fractional data in a space dimension), the collected personnel information is cleaned, the personnel information lacking redundancy and abnormality of necessary information (such as personnel identification and face images) is removed, and the rest personnel information is used as the data of the plurality of internet of things.
S102, constructing a plurality of nodes according to the fractional data of the internet of things data in the spatial dimension.
Each node corresponds to at least one piece of internet of things data in the spatial dimension. Illustratively, still taking the internet of things data { bayonet a, 9:30, zhe AXXXXXXX, red, vendor 1, model 1} as an example, a node representing bayonet a may be constructed, which corresponds to the internet of things data in a spatial dimension since it represents bayonet a.
For another example, taking the internet of things data { region a, 8:15, person 3, face image a } as an example, a node representing the region a may be constructed, and since the node represents the region a, the node corresponds to the internet of things data in a spatial dimension.
Each node may correspond to only one piece of internet of things data or a plurality of pieces of internet of things data in the spatial dimension. The internet of things data may or may not correspond to one or more nodes of the plurality of nodes according to different application scenarios.
And S103, determining the correlation between every two nodes in the plurality of nodes.
The correlation between the two nodes can be used for representing the internet of things data corresponding to one node in the two nodes, and the correlation degree in the space dimension and the time dimension between the internet of things data corresponding to the other node. The expression manner of the correlation may be different according to different application scenarios, for example, the correlation may be associated or not associated, or may be a numerical value with a value range of [0,1] for representing the degree of association between two nodes, for example, 1 represents that the two nodes are completely associated, 0 represents that the two nodes are completely unrelated, and the numerical value of (0,1) represents that the two nodes are partially associated, and the higher the numerical value is, the higher the degree of association between the two nodes is.
For example, the internet of things data corresponding to one node is determined, the interval of the internet of things data corresponding to another node in the space dimension and/or the time dimension is determined, and if the interval is smaller than a preset interval threshold, the correlation between the two nodes is determined to be associated.
For example, assume that one node is used to represent bayonet a, and bayonet a corresponds to internet of things data { bayonet a, 9:30, zhe AXXXXXXX, red, vendor 1, model 1}, and another node is used to represent bayonet B, and bayonet B corresponds to internet of things data { bayonet B, 10:00, zhe AXXXXXXX, red, vendor 1, model 1 }. It may be calculated that the interval between the two pieces of internet-of-things data in the time dimension is 30 minutes, and assuming that the preset interval threshold is 1 hour, it may be determined that the two nodes are associated. It is also possible to calculate the spatial distance between bayonet a and bayonet B, assuming 2 km, and if the preset interval threshold is 1 km, the two nodes may be considered to be uncorrelated.
For another example, assume that one node is used to represent an office area a, and the office area a corresponds to the internet of things data { area a, 8:15, person 3, face image a }, and the other node is used to represent an office area B, and the office area B corresponds to the internet of things data { area B, 6:15, person 3, face image a }. The interval of the two pieces of internet-of-things data in the time dimension may be calculated to be 2 hours, and assuming that the preset interval threshold is 1 hour, it may be determined that the two nodes are not related. It is also possible to calculate the spatial distance between the office area a and the office area B, assuming that the floor difference between the office area a and the office area B is 0 floor, that is, the two office areas are located on the same floor, and if the preset interval threshold is 1 floor, the two nodes can be considered to be associated.
In other possible embodiments, it may also be determined whether two nodes are related according to actual requirements, or by integrating the intervals in the time dimension and the space dimension. In other possible embodiments, a value with a value range of [0,1] may also be obtained by calculating according to a preset algorithm with the interval in the time dimension and/or the space dimension, as the correlation between the two nodes, for example, the inverse of the interval in the time dimension or the space dimension may be calculated as the correlation between the two nodes. The present embodiment does not limit this.
And S104, constructing edges among the nodes according to the correlation among the nodes to obtain a topological graph structure.
The method of constructing the edge may be different according to different application scenarios, and for example, the method may be to construct an edge with an edge weight, or may be to construct an edge without an edge weight, where the constructed edge may be a directional edge with a direction, or may be a non-directional edge without a direction.
For example, an edge may be used to connect associated nodes in the plurality of nodes, and for an application scenario in which a directed edge is constructed, the direction of the edge may be determined according to the sequence of the internet of things data corresponding to the two connected nodes in the time dimension. For example, assume that a node a is associated with a node B, the node a corresponds to internet of things data { bayonet a, 9:30, zhe AXXXXXXX, red, vendor 1, model 1}, the node B corresponds to internet of things data { bayonet B, 10:00, zhe AXXXXXXX, red, vendor 1, model 1}, and since the score data of the internet of things data corresponding to the node a is 9:30 and the score data of the internet of things data corresponding to the node B is 10:00 in the time dimension, the internet of things data corresponding to the node a precedes the internet of things data corresponding to the node B, and thus an edge pointing to the node B from the node a may be constructed.
For another example, assume that a node a is associated with a node B, and the node a corresponds to internet of things data { region a, 8:15, person 3, face image a }, and the node B corresponds to internet of things data { region B, 8:00, person 3, face image a }, because the score data of the internet of things data corresponding to the node a is 8:15 and the score data of the internet of things data corresponding to the node B is 8:00 in the time dimension, the internet of things data corresponding to the node a is subsequent to the internet of things data corresponding to the node B, and thus an edge pointing to the node a from the node B may be constructed.
It can be understood that, in the constructed topological graph structure, an edge may reflect a time and/or space association relationship between one node and other nodes, and a time and/or space association relationship between one node and other nodes may be regarded as information of the node, and the information is determined based on the data of the internet of things corresponding to the node and the data of the internet of things corresponding to other nodes except the node. Therefore, in the topological graph structure, the data utilized by each node is more comprehensive, and the real information of the node can be more accurately reflected.
To more clearly describe the method for constructing the topology graph mechanism based on the internet of things data according to the embodiment of the present invention, the following description is made in combination with a specific application scenario, and referring to fig. 2, fig. 2 is a schematic flow diagram of the method for constructing the topology graph mechanism in an application scenario of traffic flow prediction, including:
s201, a plurality of vehicle passing data collected by a plurality of checkpoints are obtained and serve as a plurality of data to be processed.
S202, data cleaning is carried out on the data to be processed, and a plurality of Internet of things data are obtained.
For the passing data and the data cleaning, reference may be made to the related description in the foregoing S101, and details are not described here. For convenience of description, it is assumed that the time of acquiring the passing data is the dimensional fraction data in each passing data, and the spatial fraction data is a bayonet identification of a bayonet for acquiring the passing data and further includes a vehicle identification.
And S203, constructing a plurality of nodes according to the plurality of Internet of things data.
Each node is used for representing a bayonet, and the bayonet corresponds to at least one bayonet identifier of the data of the Internet of things. For convenience of description, node a is a node corresponding to the bayonet represented by bayonet identifier a, node B is a node corresponding to the bayonet represented by bayonet identifier B, and so on.
And S204, aggregating the bayonet identifications corresponding to the vehicle identifications according to the time sequence for each vehicle identification in the data of the Internet of things to obtain a bayonet sequence.
For example, suppose that the vehicle identifier is five pieces of passing data of the vehicle 1, which are respectively recorded as passing data 1-5, wherein the time of the passing data 1 is 9:00, and the gate identifier is gate a; the time of the vehicle passing data 2 is 9:10, and the bayonet mark is a bayonet B; the time of the vehicle passing data 3 is 9:12, and the bayonet mark is a bayonet B; the time of the vehicle passing data 4 is 9:05, and the bayonet mark is a bayonet C; the time of the vehicle passing data 5 is 9:20, and the bayonet is marked as a bayonet D.
Then, in one possible embodiment, the obtained bayonet sequence may be { a, C, B, D }, and in another possible embodiment, since the bayonet identifications of the vehicle passing data 2 and the vehicle passing data 3 are the same and are adjacent in the time dimension and have small intervals (the smaller determination condition may be different according to different application scenarios), the vehicle passing data 2 and the vehicle passing data 3 may be considered as two vehicle passing data collected by a vehicle identified as "vehicle 1" when the vehicle passes through the bayonet B at one time, and therefore, the bayonet identification of one of the vehicle passing data 2 and the vehicle passing data 3 may be retained in the bayonet sequence, that is, the obtained bayonet sequence is { a, C, B, D }.
And S205, sliding on the track sequence according to the step 1 by using a window with the width of 2 so as to split the bayonet row into topological point pairs.
Illustratively, taking the sequence of bayonets { A, C, B, D } as an example, for convenience of description, the bayonets covered by the window are represented in the form of underline, and then the sequence of bayonets and the window are aA，CAnd B, D, obtaining the topological point pair AC. It will be appreciated that although A, C represents two bayonets, there are bayonets and nodes between themCorrespondence, therefore A, C may also represent two nodes, so the AC may be considered a topological point pair.
When the window slides for the first time, the bayonet sequence and the window are { A,C，Bd, obtaining a topological point pair CB, wherein when the window slides for the second time, the bayonet sequence and the window are (A, C,B，Dand obtaining a topological point pair BD, wherein the window slides to the tail of the bayonet sequence and does not slide any more. Thus, a total of three topological point pairs, AC, CB, and BD, are obtained.
And S206, connecting a plurality of nodes according to the topological point pairs to obtain a topological graph structure.
In a possible embodiment, it may be considered that one topological point pair can represent that two checkpoints are spatially communicated, and thus two nodes in the topological point pair may be considered to be associated, for example, assuming that a topological point pair AC is obtained in S205, node a and node C are connected by an edge, and the direction of the edge may be pointed to by node a to node C. In other possible embodiments, it may be considered that one pair of topological points represents that two checkpoints may be spatially communicated, and when there are more than a preset number of pairs of the same topological points, it is determined that two nodes in the pairs of topological points are associated.
In a possible embodiment, whether to connect two nodes may also be determined in combination with the geographical position relationship between two checkpoints represented by the two nodes. For example, assuming that the spatial distance between the checkpoints represented by the nodes a and C is greater than the preset distance threshold, and the topological point pair AC is obtained in S205, it can be considered that the nodes a and C are possibly in communication, but are too far away, so that the nodes a and C are not associated with each other, and therefore, the nodes a and C are not connected by using an edge.
In one possible embodiment, the edge weights of each edge may be considered to be 1, and the edge may not have an edge weight. In another possible embodiment, the edge weight of the edge between two nodes may also be determined according to the number of connections between two nodes. For example, taking an edge between node a and node B as an example, the edge weight of the edge may be calculated according to the following formula:
where P (B | a) represents the edge weight of the edge between node a and node B, count (a, B) represents the number of connections between node a and node B, count (a, I) represents the number of connections between node a and the I-th neighbor node of node a, and neighbor represents the total number of neighbor nodes of node a. The number of connections between two nodes may refer to the number of topology point pairs formed by the two nodes obtained in the split of the bayonet sequence. For example, assuming that the number of connections between node a and node B is 10, node a is also adjacent to node C and node D, the number of connections between node a and node C is 20, and the number of connections between node a and node D is 20, the edge weight between node a and node B may be 0.2.
It can be understood that fig. 2 is a schematic flow diagram in a possible application scenario of the method for constructing a topology graph structure based on data of the internet of things provided in the embodiment of the present invention, and the method for constructing a topology graph structure based on data of the internet of things provided in the embodiment of the present invention may also be applied to other application scenarios, which is not limited in this embodiment. For example, the method can also be applied to application scenarios such as people flow rate prediction, personnel trajectory prediction, unmanned aerial vehicle trajectory prediction, and the like, and the process is similar to the process shown in fig. 2, and the difference is that the manner of obtaining the data of the internet of things, the spatial positions represented by the nodes in the topological graph structure, and the format of the data of the internet of things are different, so details are not repeated here.
The topological graph structure constructed by the method for constructing the topological graph structure based on the internet of things data provided by the embodiment of the invention can be subsequently used for different downstream tasks according to different application scenes, and can be exemplarily used for traffic flow prediction, track prediction, fake plate detection and the like, which is not limited by the embodiment.
For example, referring to fig. 3, fig. 3 is a schematic flowchart illustrating a data prediction method based on a topological structure according to an embodiment of the present invention, where the method includes:
s301, a plurality of Internet of things data are obtained.
This step is the same as S101, and reference may be made to the related description in S101 described previously.
S302, constructing a plurality of nodes according to the fractional data of the internet of things data in the space dimension.
This step is the same as S102, and reference may be made to the related description in S102 described above.
S303, determining a correlation between each two nodes in the plurality of nodes.
This step is the same as S103, and reference may be made to the related description in S103 described earlier.
S304, according to the correlation among the nodes, edges among the nodes are constructed, and a topological graph structure is obtained.
This step is the same as S104, and reference may be made to the related description in S104 described above.
S305, determining the topological graph structure characteristics of each node in the topological graph structure.
The topological graph structure feature is used for representing the connection relationship between the node and other nodes in the topological graph structure. It is understood that two nodes may be considered similar in their positions in the topology if the topology features of the two nodes are similar.
S306, aiming at each piece of Internet of things data, combining the topological graph structure characteristics of the node corresponding to the piece of Internet of things data with the piece data of the Internet of things data in other dimensions except the space dimension and the target dimension, and taking the piece data of the plurality of pieces of dimensional data in the target dimension as marking data to obtain the sample data of the node.
According to different application scenarios, the dimensionality of the internet of things data is different, and in one possible application scenario, taking the application scenario of traffic flow prediction as an example, the ith internet of things data can be represented as Di＝{xi1，xi2，xi3，xi4，xi5，xi6，xi7，yiIn which xi1Represents the monitoring point position, x, of the collected data of the Internet of thingsi2Representing the flow characteristics, x, of the monitoring point location in the historical time periodi3Time information, x, representing the current time of the point being monitoredi4Geographical location information, x, representing the location of the monitoring pointi5Weather information, x, representing the current time of the point locationi6Event information x representing an event occurring within the point site history periodi7Other information indicating the current time of the point being monitored, yiAnd the traffic flow of the current time of the monitoring point is shown. The number of dimensions of the data of the internet of things in other application scenarios may also be different, which is not limited in this embodiment. .
The sample data of the ith node may be represented as Xi＝{xid1，xid2，xid3，xid4，xid5，xid6，xid7|yiIn which XiSample data for the ith node, xid1Is a topological graph structure characteristic of the node, xid2Data of the internet of things for the node is in xi2Characteristic of (1), xid3Data of the internet of things for the node is in xi3The above feature, analogized in turn, yiMay be the annotation data of the sample data of the node.
In another possible application scenario, taking the application scenario of people flow prediction as an example, the ith physical network data may be represented as Di＝{xi1，xi2，xi3，xi4，xi5，yiIn which xi1Represents the office area, x, of the collected data of the internet of thingsi2Representing the flow characteristics, x, over the historical time period of the office areai3Time information, x, indicating the current time of the office areai4Position information in the office building, x, indicating the office areai5Other information indicating the current time of the office area, yiIndicating the current time of traffic in the office area.
The sample data of the ith node may be represented as Xi＝{xid1，xid2，xid3，xid4，xid5|yiIn which XiSample data for the ith node, xid1Is a topological graph structure characteristic of the node, xid2Data of the internet of things for the node is in xi2Characteristic of (1), xid3Data of the internet of things for the node is in xi3The above feature, analogized in turn, yiMay be the annotation data of the sample data of the node.
S307, according to the sample data of each node in the topological graph structure, a prediction model is constructed.
With Xi＝{xid1，xid2，xid3，xid4，xid5，xid6，xid7|yiFor convenience of description, if f () is used to represent the prediction model to be constructed, the following relationship exists:
f(xid1，xid2，xid3，xid4，xid5，xid6，xid7)＝yi
according to the sample data of a plurality of nodes and the relationship, f () can be constructed and obtained in a machine learning and/or deep learning mode. Taking the application scenario of traffic flow prediction as an example, after f () is constructed, a set of { x } can be inputd1，xd2，xd3，xd4，xd5，xd6，xd7And d, predicting to obtain the traffic flow information y. After the prediction model is constructed, the prediction model can be used for predicting the fractional data on the target dimension corresponding to the fractional data on the spatial dimension based on the structural features of the topological graph and the features on other dimensions.
By adopting the embodiment, the plurality of internet of things data can be associated in the space dimension and/or the time dimension by utilizing the topological graph structure, and when the fraction data of the target dimension corresponding to one fraction data in the space dimension is predicted, the internet of things data corresponding to other fraction data in the space dimension can be based on, so that the data volume of the data which can be referred to when the prediction model is built is effectively improved, and the built prediction model is more accurate. For example, with this embodiment, when predicting the traffic flow of the gate a, the traffic data of other gates except the monitoring gate a may be referred to, so the reference data is more comprehensive. For another example, this embodiment is selected so that, when predicting the traffic of the office area a, the information of the persons in the office areas other than the office area a can be referred to.
The process of constructing the prediction model can be considered to be based on f (x)id1，xid2，xid3，xid4，xid5，xid6，xid7)＝yiF () is fitted, so the more sample data, the more accurate f () is theoretically obtained.
In view of this, in an alternative embodiment, referring to fig. 4, fig. 4 shows a sample data processing method provided in an embodiment of the present invention, including:
s401, aiming at each node in the topological graph structure, sequencing sample data of the node according to the time sequence to obtain a sample sequence.
For convenience of description, assume that the ith node is at tjSample data of time XijAnd assume t1＜t2…＜tnThen the sample data for that node may be arranged into a sample sequence { X }i1，Xi2，…Xin}。
S402, performing sliding window processing on the sample sequence to obtain a plurality of time sequence data.
The width of the window selected in the sliding window processing may be different according to the application scenario. For example, assuming that the window is four sample sequences wide, the sample data included in the initial position of the window is Xi1、Xi2、Xi3And Xi4After the window slides by one unit, the sample data included in the window is Xi2、Xi3、Xi4And Xi5And so on until the window slides to the end of the sequence. Taking the sample data included after each window sliding and the sample data initially included as a time sequence data, and obtaining the time sequence data as follows:
{[Xi1,Xi2,Xi3.Xi4],[Xi2,Xi3,Xi4.Xi5],[Xin-3,Xin-2,Xin-1.Xin]}
each time series data can be regarded as a sub-sample data of the node sample data before the sliding window processing, so that the time series data can also be used for constructing the model, and the principle of constructing the prediction model according to the node sample data is the same as that of constructing the prediction model according to the node sample data. By adopting the embodiment, more data for constructing the prediction model can be obtained through sliding window processing, so that the obtained prediction model is more accurate.
For example, taking the predicted traffic flow as an example, assuming that sample data of a node may include characteristics of traffic data acquired within 10 days from a monitoring point represented by the node, if a prediction model is directly constructed based on the sample data, the constructed prediction model is a model for predicting the traffic flow based on the traffic data within 10 days. And each time series data obtained through sliding window processing may include the characteristics of traffic data acquired within 4 days from a monitoring point represented by a node, and if a prediction model is constructed based on the time series data, the constructed prediction model is a model for predicting the traffic flow based on the traffic data within 4 days.
For another example, taking the predicted human traffic as an example, assuming that sample data of a node may include features of the person information acquired within 10 days from the monitoring point represented by the node, if a prediction model is directly constructed based on the sample data, the constructed prediction model is a model for predicting the human traffic based on the person information within 10 days. And each time series data obtained through sliding window processing may include the characteristics of the personnel information acquired within 4 days of the office area represented by one node, and if a prediction model is built based on the time series data, the built prediction model is a model for predicting the flow of people based on the personnel information within 4 days.
As described in relation to S307 above, the process of constructing the prediction model can be considered to be based on f (x)id1，xid2，xid3，xid4，xid5，xid6，xid7)＝yiTo f () proceedsAnd (5) line fitting. In an alternative embodiment, a mathematical model of f () may be selected, and the parameters of the mathematical model may be determined by fitting. However, in some application scenarios, because the difference of the data of the internet of things in different dimensions may be large, it is difficult to determine a suitable mathematical model, and then a prediction model is obtained through fitting.
In view of this, referring to fig. 5, fig. 5 is a schematic flow chart of a prediction model constructing method according to an embodiment of the present invention, which may include:
s501, training a plurality of basic models respectively according to data with different dimensions in the sample data of each node in the topological graph structure.
The basic model may include KNN (K-Nearest Neighbor classification), randomtrees, LightGBM (learning algorithm based on decision tree), etc., and in an alternative embodiment, CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory Network) may be used as the basic model.
Still with Xi＝{xid1，xid2，xid3，xid4，xid5，xid6，xid7|yiFor example, assuming that the base model of CNN is u (), and the base model of LSTM is v (), the respective training may be to adjust the parameters in u () and v () respectively based on the following relations:
u(xid1，xid2，xid3，xid4)＝y，v(xid5，xid6，xid7)＝y。
and S502, fusing the trained basic models to obtain a prediction model.
The fusion mode may be different according to different application scenarios. Illustratively, still taking the example in S301 as an example, assuming that the prediction model obtained after the fusion is f (), f () can be determined in any one of the following manners:
the first method is as follows: f () + α u (), + β v ();
the second method comprises the following steps: f (), u (v ()).
By adopting the embodiment, the constructed prediction model can be well fitted with the sample data at a plurality of different latitudes by fusing a plurality of different technical models, namely, the constructed prediction model is more accurate.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a device for constructing a topology based on data of the internet of things according to an embodiment of the present invention, where the device may include:
a data obtaining module 601, configured to obtain multiple pieces of internet of things data, where each piece of internet of things data includes multiple pieces of data in multiple dimensions, and at least includes a piece of data in a time dimension and a piece of data in a space dimension;
a node constructing module 602, configured to construct a plurality of nodes according to the fractional data of the plurality of internet of things data in the spatial dimension, where each node corresponds to at least one piece of internet of things data in the spatial dimension;
the association determining module 603 is configured to determine a correlation between each two nodes in the plurality of nodes, where the correlation is used to represent the correlation degree between the data of the internet of things corresponding to one node of the two nodes and the data of the internet of things corresponding to another node in a spatial dimension and a time dimension;
the edge constructing module 604 is configured to construct an edge between multiple nodes according to the correlation between the multiple nodes, so as to obtain a topology structure.
In a possible embodiment, the association determining module 603 is specifically configured to determine, for each two nodes of the multiple nodes, the interval of the internet of things data corresponding to one node of the two nodes in the spatial dimension and/or the time dimension, and the interval of the internet of things data corresponding to another node in the spatial dimension and/or the time dimension;
and if the interval is smaller than a preset interval threshold, determining that the correlation between the two nodes is related.
In a possible embodiment, the apparatus further includes a prediction model building module, configured to determine, for each node in the topological graph structure, a topological graph structure feature of the node, where the topological graph structure feature is used to represent a connectivity relationship between the node and other nodes in the topological graph structure;
for each piece of Internet of things data, combining the topological graph structure characteristics of the node corresponding to the piece of Internet of things data with the piece data of the piece of Internet of things data in other dimensions except the space dimension and the target dimension, and taking the piece data of the piece of Internet of things data in the target dimension as a marking value to obtain sample data of the node;
and constructing a prediction model according to the sample data of each node in the topological graph structure, wherein the prediction model is used for predicting the fractional data of the target space position on the target dimension.
In a possible embodiment, the prediction model construction module is specifically configured to, for each node in the topological graph structure, sort sample data of the node according to a time sequence to obtain a sample sequence;
performing sliding window processing on the sample sequence to obtain a plurality of time sequence data;
and constructing a prediction model according to a plurality of time sequence data of each node in the topological graph.
In a possible embodiment, the prediction model construction module is specifically configured to train a plurality of basic models respectively according to data of different dimensions in sample data of each node in the topological graph structure;
and fusing the trained basic models to obtain a prediction model.
In a possible embodiment, the data obtaining module 601 is specifically configured to perform data acquisition for a plurality of spatial positions to obtain a plurality of multidimensional data to be processed, where each data to be processed at least includes data points in a time dimension and data points in a spatial dimension;
and carrying out data cleaning on the data to be processed to obtain a plurality of Internet of things data.
An embodiment of the present invention further provides an electronic device, as shown in fig. 7, including:
a memory 701 for storing a computer program;
the processor 702 is configured to implement the following steps when executing the program stored in the memory 701:
acquiring a plurality of internet of things data, wherein each internet of things data comprises score data in a plurality of dimensions and at least comprises score data in a time dimension and score data in a space dimension;
constructing a plurality of nodes according to the fractional data of the plurality of internet of things data in the spatial dimension, wherein each node corresponds to at least one internet of things data in the spatial dimension;
determining the correlation between every two nodes in the plurality of nodes, wherein the correlation is used for representing the data of the internet of things corresponding to one node in the two nodes, and the correlation degree between the data of the internet of things corresponding to the other node in the space dimension and the time dimension;
and according to the correlation among the nodes, constructing edges among the nodes to obtain the topological graph structure.
In one possible embodiment, determining a correlation between each two of the plurality of nodes comprises:
determining the interval of the data of the internet of things corresponding to one node in the two nodes and the data of the internet of things corresponding to the other node in the space dimension and/or the time dimension aiming at every two nodes in the plurality of nodes;
and if the interval is smaller than a preset interval threshold, determining that the correlation between the two nodes is related.
In a possible embodiment, after constructing edges between a plurality of nodes according to the correlations between the plurality of nodes to obtain the topology, the method further includes:
for each node in the topological graph structure, determining topological graph structure characteristics of the node, wherein the topological graph structure characteristics are used for representing the communication relationship between the node and other nodes in the topological graph structure;
for each piece of Internet of things data, combining the topological graph structure characteristics of the node corresponding to the piece of Internet of things data with the piece data of the piece of Internet of things data in other dimensions except the space dimension and the target dimension, and taking the piece data of the piece of Internet of things data in the target dimension as a marking value to obtain sample data of the node;
and constructing a prediction model according to the sample data of each node in the topological graph structure, wherein the prediction model is used for predicting the fractional data of the target space position on the target dimension.
In a possible embodiment, constructing the prediction model according to the sample data of each node in the topology structure includes:
for each node in the topological graph structure, sequencing sample data of the node according to the time sequence to obtain a sample sequence;
performing sliding window processing on the sample sequence to obtain a plurality of time sequence data;
and constructing a prediction model according to a plurality of time sequence data of each node in the topological graph.
In a possible embodiment, constructing the prediction model according to the sample data of each node in the topology structure includes:
respectively training a plurality of basic models according to data with different dimensions in the sample data of each node in the topological graph structure;
and fusing the trained basic models to obtain a prediction model.
In one possible embodiment, obtaining a plurality of internet of things data comprises:
acquiring data aiming at a plurality of spatial positions to obtain a plurality of multi-dimensional data to be processed, wherein each piece of data to be processed at least comprises fractional data in a time dimension and fractional data in a spatial dimension;
and carrying out data cleaning on the data to be processed to obtain a plurality of Internet of things data.
The aforementioned electronic device may include a Random Access Memory (RAM) and a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; 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.
In another embodiment of the present invention, a computer-readable storage medium is further provided, where instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, the computer is caused to execute any one of the above-mentioned methods for constructing a topology based on data of the internet of things.
In another embodiment of the present invention, a computer program product containing instructions is further provided, which when run on a computer, causes the computer to execute any one of the above-mentioned methods for constructing a topology based on data of the internet of things.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, 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. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. 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 in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (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, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
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.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the embodiments of the apparatus, the electronic device, the computer-readable storage medium, and the computer program product, since they are substantially similar to the method embodiments, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiments.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (14)
1. A method for constructing a topological graph structure based on data of the Internet of things is characterized by comprising the following steps:
acquiring a plurality of internet of things data, wherein each internet of things data comprises score data in a plurality of dimensions and at least comprises score data in a time dimension and score data in a space dimension;
constructing a plurality of nodes according to the score data of the plurality of internet of things data in the spatial dimension, wherein each node corresponds to at least one internet of things data in the spatial dimension;
determining the correlation between every two nodes in the plurality of nodes, wherein the correlation is used for representing the correlation degree between the data of the internet of things corresponding to one node in the two nodes and the data of the internet of things corresponding to the other node in the spatial dimension and the time dimension;
and according to the correlation among the nodes, constructing edges among the nodes to obtain a topological graph structure.
2. The method of claim 1, wherein determining the correlation between each two of the plurality of nodes comprises:
for each two nodes in the plurality of nodes, determining the interval of the data of the internet of things corresponding to one node in the two nodes and the data of the internet of things corresponding to the other node in the space dimension and/or the time dimension;
and if the interval is smaller than a preset interval threshold value, determining that the correlation between the two nodes is related.
3. The method of claim 1, wherein after the constructing edges between the plurality of nodes according to the dependencies between the plurality of nodes to obtain a topology structure, the method further comprises:
for each node in the topological graph structure, determining topological graph structure characteristics of the node, wherein the topological graph structure characteristics are used for representing the communication relationship between the node and other nodes in the topological graph structure;
for each piece of Internet of things data, combining the topological graph structure characteristics of the node corresponding to the piece of Internet of things data with the piece data of the piece of Internet of things data in other dimensions except the space dimension and the target dimension, and taking the piece data of the piece of Internet of things data in the target dimension as a marking value to obtain sample data of the node;
and constructing a prediction model according to the sample data of each node in the topological graph structure, wherein the prediction model is used for predicting the fractional data of the target space position on the target dimension.
4. The method according to claim 3, wherein said constructing a prediction model from said sample data of each node in said topology structure comprises:
for each node in the topological graph structure, sequencing sample data of the node according to the time sequence to obtain a sample sequence;
performing sliding window processing on the sample sequence to obtain a plurality of time sequence data;
and constructing a prediction model according to the time sequence data of each node in the topological graph.
5. The method according to claim 3, wherein said constructing a prediction model from said sample data of each node in said topology structure comprises:
respectively training a plurality of basic models according to data of different dimensions in the sample data of each node in the topological graph structure;
and fusing the trained basic models to obtain a prediction model.
6. The method of claim 1, wherein the obtaining the plurality of internet of things data comprises:
acquiring data aiming at a plurality of spatial positions to obtain a plurality of multi-dimensional data to be processed, wherein each piece of data to be processed at least comprises fractional data in a time dimension and fractional data in a spatial dimension;
and carrying out data cleaning on the plurality of data to be processed to obtain a plurality of Internet of things data.
7. A topological graph structure construction device based on data of the Internet of things is characterized by comprising the following components:
the data acquisition module is used for acquiring a plurality of Internet of things data, wherein each Internet of things data comprises score data on a plurality of dimensions and at least comprises score data on a time dimension and score data on a space dimension;
the node construction module is used for constructing a plurality of nodes according to the fractional data of the plurality of internet of things data on the spatial dimension, wherein each node corresponds to at least one piece of internet of things data on the spatial dimension;
the correlation determination module is used for determining the correlation between every two nodes in the plurality of nodes, wherein the correlation is used for representing the correlation degree of the data of the internet of things corresponding to one node in the two nodes and the data of the internet of things corresponding to the other node in the space dimension and the time dimension;
and the edge construction module is used for constructing edges among the nodes according to the correlation among the nodes to obtain the topological graph structure.
8. The apparatus according to claim 7, wherein the association determining module is specifically configured to determine, for each two nodes of the plurality of nodes, an interval of the internet of things data corresponding to one node of the two nodes in a spatial dimension and/or a time dimension;
and if the interval is smaller than a preset interval threshold value, determining that the correlation between the two nodes is related.
9. The apparatus according to claim 7, further comprising a prediction model building module, configured to determine, for each node in the topological graph structure, a topological graph structure feature of the node, where the topological graph structure feature is used to represent a connectivity relationship between the node and other nodes in the topological graph structure;
for each piece of Internet of things data, combining the topological graph structure characteristics of the node corresponding to the piece of Internet of things data with the piece data of the piece of Internet of things data in other dimensions except the space dimension and the target dimension, and taking the piece data of the piece of Internet of things data in the target dimension as a marking value to obtain sample data of the node;
and constructing a prediction model according to the sample data of each node in the topological graph structure, wherein the prediction model is used for predicting the fractional data of the target space position on the target dimension.
10. The apparatus according to claim 9, wherein the prediction model building module is specifically configured to, for each node in the topological graph structure, sort sample data of the node in a time sequence to obtain a sample sequence;
performing sliding window processing on the sample sequence to obtain a plurality of time sequence data;
and constructing a prediction model according to the time sequence data of each node in the topological graph.
11. The apparatus according to claim 9, wherein the prediction model building module is specifically configured to train a plurality of basic models respectively according to data of different dimensions in the sample data of each node in the topological graph structure;
and fusing the trained basic models to obtain a prediction model.
12. The apparatus according to claim 7, wherein the data acquisition module is specifically configured to perform data acquisition for a plurality of spatial locations to obtain a plurality of multidimensional data to be processed, where each data to be processed at least includes data fractions in a time dimension and data fractions in a spatial dimension;
and carrying out data cleaning on the plurality of data to be processed to obtain a plurality of Internet of things data.
13. An electronic device, comprising:
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1-6 when executing a program stored in the memory.
14. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910403879.7A CN111949840B (en) | 2019-05-15 | 2019-05-15 | Method and device for constructing topological graph structure based on data of Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910403879.7A CN111949840B (en) | 2019-05-15 | 2019-05-15 | Method and device for constructing topological graph structure based on data of Internet of things |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111949840A true CN111949840A (en) | 2020-11-17 |
| CN111949840B CN111949840B (en) | 2024-05-31 |
Family
ID=73336551
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910403879.7A Active CN111949840B (en) | 2019-05-15 | 2019-05-15 | Method and device for constructing topological graph structure based on data of Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111949840B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112770256A (en) * | 2021-01-06 | 2021-05-07 | 重庆邮电大学 | Node track prediction method in unmanned aerial vehicle self-organizing network |
| CN113824643A (en) * | 2021-11-25 | 2021-12-21 | 中国科学院信息工程研究所 | Ubiquitous network topological graph construction method and network security protection method |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090141028A1 (en) * | 2007-11-29 | 2009-06-04 | International Business Machines Corporation | Method to predict edges in a non-cumulative graph |
| US20090189777A1 (en) * | 2008-01-25 | 2009-07-30 | Monte Johnson | Topology maintenance and discovery facility for downhole networks |
| CN103175513A (en) * | 2013-03-01 | 2013-06-26 | 戴会超 | System and method for monitoring hydrology and water quality of river basin under influence of water projects based on Internet of Things |
| CN103531024A (en) * | 2013-10-28 | 2014-01-22 | 武汉旭云科技有限公司 | Dynamic traffic network urban road feature model and modeling method thereof |
| US20170131721A1 (en) * | 2015-11-06 | 2017-05-11 | Samsung Electronics Co., Ltd. | Robot cleaner and method for controlling the same |
| CN107277827A (en) * | 2017-06-30 | 2017-10-20 | 中国矿业大学 | The network structure and network node dispositions method of a kind of Mine Wireless Sensor Networks |
| CN108985309A (en) * | 2017-05-31 | 2018-12-11 | 腾讯科技（深圳）有限公司 | A kind of data processing method and device |
| CN109151042A (en) * | 2018-09-06 | 2019-01-04 | 广州知弘科技有限公司 | Internet of Things perception data Intelligent planning method |
| CN109729171A (en) * | 2019-01-10 | 2019-05-07 | 七彩安科智慧科技有限公司 | A kind of construction method of small town cognition matrix Internet of Things |
-
2019
- 2019-05-15 CN CN201910403879.7A patent/CN111949840B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090141028A1 (en) * | 2007-11-29 | 2009-06-04 | International Business Machines Corporation | Method to predict edges in a non-cumulative graph |
| US20090189777A1 (en) * | 2008-01-25 | 2009-07-30 | Monte Johnson | Topology maintenance and discovery facility for downhole networks |
| CN103175513A (en) * | 2013-03-01 | 2013-06-26 | 戴会超 | System and method for monitoring hydrology and water quality of river basin under influence of water projects based on Internet of Things |
| CN103531024A (en) * | 2013-10-28 | 2014-01-22 | 武汉旭云科技有限公司 | Dynamic traffic network urban road feature model and modeling method thereof |
| US20170131721A1 (en) * | 2015-11-06 | 2017-05-11 | Samsung Electronics Co., Ltd. | Robot cleaner and method for controlling the same |
| CN108985309A (en) * | 2017-05-31 | 2018-12-11 | 腾讯科技（深圳）有限公司 | A kind of data processing method and device |
| CN107277827A (en) * | 2017-06-30 | 2017-10-20 | 中国矿业大学 | The network structure and network node dispositions method of a kind of Mine Wireless Sensor Networks |
| CN109151042A (en) * | 2018-09-06 | 2019-01-04 | 广州知弘科技有限公司 | Internet of Things perception data Intelligent planning method |
| CN109729171A (en) * | 2019-01-10 | 2019-05-07 | 七彩安科智慧科技有限公司 | A kind of construction method of small town cognition matrix Internet of Things |
Non-Patent Citations (2)
| Title |
|---|
| FULVIO CORNO, ET.AL: "An Unsupervised and Noninvasive Model for Predicting Network Resource Demands", 《IEEE INTERNET OF THINGS JOURNAL 》, vol. 5, no. 6, pages 4342, XP011705691, DOI: 10.1109/JIOT.2018.2860681 * |
| 李彦瑾;罗霞;王莹;: "考虑多节点拥堵的城市道路网级联失效仿真", 工业工程, no. 04 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112770256A (en) * | 2021-01-06 | 2021-05-07 | 重庆邮电大学 | Node track prediction method in unmanned aerial vehicle self-organizing network |
| CN113824643A (en) * | 2021-11-25 | 2021-12-21 | 中国科学院信息工程研究所 | Ubiquitous network topological graph construction method and network security protection method |
| CN113824643B (en) * | 2021-11-25 | 2022-02-22 | 中国科学院信息工程研究所 | Ubiquitous network topological graph construction method and network security protection method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111949840B (en) | 2024-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zheng | Trajectory data mining: an overview | |
| Gupte et al. | A guide to pre‐processing high‐throughput animal tracking data | |
| Wang et al. | The spatio-temporal modeling for criminal incidents | |
| US20170286845A1 (en) | Automatic extraction of user mobility behaviors and interaction preferences using spatio-temporal data | |
| CN111212383B (en) | Method, device, server and medium for determining number of regional permanent population | |
| EP4233003A1 (en) | Centralized tracking system with distributed fixed sensors | |
| CN112309126B (en) | License plate detection method and device, electronic equipment and computer readable storage medium | |
| CN111598610A (en) | Abnormal event detection method and device | |
| Weng et al. | Real-time bus travel speed estimation model based on bus GPS data | |
| CN111444294A (en) | Track completion method and device and electronic equipment | |
| CN114398462B (en) | Destination recommendation method and system based on multi-source heterogeneous information network | |
| Liu et al. | Modeling the interaction coupling of multi-view spatiotemporal contexts for destination prediction | |
| CN111949840A (en) | Topological graph structure construction method and device based on Internet of things data | |
| CN116046008A (en) | Situation awareness-based route planning method, system and efficiency evaluation device | |
| Corcoran et al. | New technologies in the mix: Assessing N‐mixture models for abundance estimation using automated detection data from drone surveys | |
| CN116257663A (en) | Abnormality detection and association analysis method and related equipment for unmanned ground vehicle | |
| Ma et al. | Active pointillistic pattern search | |
| Loglisci | Using interactions and dynamics for mining groups of moving objects from trajectory data | |
| CN115099561A (en) | Similarity measurement method, device, equipment and medium for process model | |
| CN111540202B (en) | Similar bayonet determining method and device, electronic equipment and readable storage medium | |
| CN111325255B (en) | Specific crowd delineating method and device, electronic equipment and storage medium | |
| RU2740708C1 (en) | Radio monitoring results processing method | |
| CN112884190B (en) | Flow prediction method and device | |
| Baratchi et al. | Finding frequently visited paths: dealing with the uncertainty of spatio-temporal mobility data | |
| WO2023051085A1 (en) | Object recognition method and apparatus, device, storage medium and program product |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |