CN109741227B - Processing method and system for predicting human-room consistency based on nearest neighbor algorithm - Google Patents
Processing method and system for predicting human-room consistency based on nearest neighbor algorithm Download PDFInfo
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Abstract
The disclosure provides a processing method and a system for predicting human-room consistency based on a nearest neighbor algorithm. The human-room consistency prediction processing method based on the nearest neighbor algorithm comprises the steps of storing household data and vehicle data in a preset geographic area in a correlation mode, and storing the vehicle data and vehicle passing data at a gate in a correlation mode; inquiring the household registration data of the target person according to the identification card number of the target person to obtain the vehicle data of the target person and the relationship person thereof; obtaining vehicle passing data of corresponding card ports, and obtaining the card ports with the number of foot falling times exceeding a preset threshold value; respectively and correspondingly converting the bayonet coordinates with the quantity of foot-falling times exceeding a preset threshold value and the residential address data in the household registration data into bayonet plane coordinates and residential address plane coordinates, and constructing a k-d tree; finding a bayonet coordinate point which is closest to the coordinate point of the current residential address plane in the k-d tree by using a nearest neighbor search algorithm, and if the distance between the bayonet coordinate point and the coordinate point of the current residential address plane is within a preset range, judging that the rooms are consistent; otherwise, judging that the human rooms are inconsistent.
Description
Technical Field
The disclosure belongs to the field of data processing, and particularly relates to a processing method and a system for predicting human room consistency based on a nearest neighbor algorithm.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the development of economic society and the acceleration of life rhythm, the change of eating, wearing and going in living elements is very frequent, and the change of living elements is relatively small. At present, however, the 'pipe house' and the 'pipe person' are disconnected all the time, and the pipe person is not in the pipe house, and the pipe house is not in the pipe house. The inventor finds that the existing data query has the following problems: (1) a data chimney problem, wherein the query is isolated, and result data is generated according to the identity card number; (2) based on the public security data, the searched household registration address and vehicle registration address need to manually judge whether the permanent location is close to the public registration address, which consumes manpower and material resources.
Disclosure of Invention
According to one aspect of one or more embodiments of the present disclosure, a nearest neighbor algorithm-based human-room consistency prediction processing method is provided, which can quickly analyze the permanent addresses of target people, and improve the efficiency of public security policemen in performing human-room consistency analysis, permanent address investigation, vacant house investigation and house lease investigation on target people.
The invention discloses a processing method for predicting human room consistency based on a nearest neighbor algorithm, which comprises the following steps:
storing household registration data and vehicle data in a preset geographic area in a correlation manner, and storing the vehicle data and vehicle passing data at a gate in a correlation manner; the household registration data comprises living address data, names and identification numbers of the household owner and the family-sharing relationship person;
inquiring household registration data of the target person according to the identification card number of the target person, and further obtaining vehicle data of the target person and the relationship person of the target person;
acquiring corresponding bayonet vehicle passing data according to vehicle data of a target person and a relation person, and performing track analysis on a vehicle through the bayonet vehicle passing data to obtain a bayonet of which the number of foot-falling times exceeds a preset threshold;
respectively and correspondingly converting the bayonet coordinates with the quantity of foot-falling times exceeding a preset threshold value and the residential address data in the household registration data into bayonet plane coordinates and residential address plane coordinates, and further constructing a k-d tree;
searching a bayonet coordinate point which is closest to the current residential address plane coordinate point in the k-d tree by utilizing a nearest neighbor search algorithm, and if the distance between the current residential address plane coordinate point and the nearest bayonet coordinate point is within a preset range, judging that the rooms are consistent; otherwise, judging that the human rooms are inconsistent.
In one or more embodiments, the process of analyzing the trajectory of the vehicle through the bayonet passing data is as follows:
obtaining bayonet position information which appears last time every day in a preset time period through a bayonet track;
and establishing a bayonet set with the identity card as a key and the bayonet information as a value, wherein the bayonet information comprises the name of the bayonet and the GPS position information of the bayonet.
In one or more embodiments, the process of finding the nearest bayonet coordinate point in the k-d tree from the coordinate point of the current residential address plane by using the nearest neighbor search algorithm is as follows:
starting from a root node of the k-d tree, through nearest neighbor search, if the partition dimension value of the node is smaller than the dimension value of the search point, the search point is positioned in the space of the left sub-tree, then entering the left sub-tree, if the partition dimension value of the node is larger than the dimension value of the search point, then entering the right sub-tree, and adding each node on the search path into the path until reaching a leaf node;
and then backtracking the search path, judging whether nodes closer to the search point are possible in other sub-node spaces which are not added with the path, traversing the sub-node spaces if the nodes closer to the search point are possible, adding the traversed nodes into the search path, and repeating the process until the search path is empty.
In one or more embodiments, the processing method for predicting human room consistency based on nearest neighbor algorithm further includes:
when the people rooms are inconsistent, acquiring payment data associated with the household registration data;
judging whether the payment data exist or not, if so, judging that the house is rented; otherwise, the house is judged to be vacant.
In one or more embodiments, the payment data includes water payment data, electricity payment data, natural gas payment data, and heating payment data.
According to another aspect of one or more embodiments of the present disclosure, a nearest neighbor algorithm-based system for predicting human room consistency is provided, which can quickly analyze the permanent addresses of target people, and improve the efficiency of public security policemen in performing human room consistency analysis, permanent address investigation, vacant house investigation and house lease investigation on target people.
The invention discloses a human room consistency prediction processing system based on a nearest neighbor algorithm, which comprises a memory and a processor;
the memory is used for storing household data, vehicle data associated with the household data and vehicle passing data associated with the vehicle;
the processor includes:
the data acquisition module is used for acquiring household data, vehicle data associated with the household data and vehicle passing data associated with a vehicle; the household registration data comprises residence address data, names and identity card numbers of the household owner and the same-household relations;
the target person data query module is used for querying the household registration data of the target person according to the identification card number of the target person so as to obtain the vehicle data of the target person and the relationship person of the target person;
the system comprises a foot-falling bayonet data acquisition module, a foot-falling bayonet data acquisition module and a foot-falling bayonet data acquisition module, wherein the foot-falling bayonet data acquisition module is used for acquiring corresponding bayonet vehicle-passing data according to vehicle data of a target person and a related person, and carrying out track analysis on a vehicle according to the bayonet vehicle-passing data to obtain a bayonet of which the number of foot-falling times exceeds a preset threshold;
the k-d tree construction module is used for correspondingly converting the bayonet coordinates with the quantity of foot-falling times exceeding a preset threshold value and the residential address data in the household registration data into bayonet plane coordinates and residential address plane coordinates respectively so as to construct a k-d tree;
the human-room consistency judging module is used for searching a bayonet coordinate point which is closest to the coordinate point of the current residential address plane in the k-d tree by utilizing a nearest neighbor search algorithm, and judging that the human rooms are consistent if the distance between the coordinate point of the current residential address plane and the nearest bayonet coordinate point is within a preset range; otherwise, judging that the human rooms are inconsistent.
In one or more embodiments, in the foot-drop gate data obtaining module, the process of analyzing the trajectory of the vehicle through the gate vehicle-passing data is as follows:
obtaining bayonet position information which appears last time every day in a preset time period through a bayonet track;
and establishing a bayonet set with the identity card as a key and the bayonet information as a value, wherein the bayonet information comprises the name of the bayonet and the GPS position information of the bayonet.
In one or more embodiments, in the human-room consistency determination module, the process of finding the bayonet coordinate point closest to the coordinate point of the current residential address plane in the k-d tree by using a nearest neighbor search algorithm is as follows:
starting from a root node of the k-d tree, through nearest neighbor search, if the partition dimension value of the node is smaller than the dimension value of the search point, the search point is positioned in the space of the left sub-tree, then entering the left sub-tree, if the partition dimension value of the node is larger than the dimension value of the search point, then entering the right sub-tree, and adding each node on the search path into the path until reaching a leaf node;
and then backtracking the search path, judging whether nodes closer to the search point are possible in other sub-node spaces which are not added with the path, traversing the sub-node spaces if the nodes closer to the search point are possible, adding the traversed nodes into the search path, and repeating the process until the search path is empty.
In one or more embodiments, the processor further comprises:
the house renting and vacancy judging module is used for acquiring payment data associated with the household registration data when the houses are inconsistent;
judging whether the payment data exist or not, if so, judging that the house is rented; otherwise, the house is judged to be vacant.
In one or more embodiments, the payment data includes water payment data, electricity payment data, natural gas payment data, and heating payment data.
The beneficial effects of this disclosure are:
(1) according to the method, nearest neighbor analysis is carried out on the vehicle foot falling address, the household registration address and the house address, the permanent address of the target crowd can be rapidly analyzed, and the efficiency of carrying out human-room consistency analysis, permanent address investigation and vacant house and rented house investigation on the target people by public security policemen is improved.
(2) The method and the system achieve the service management effect of inquiring the house and knowing the people and inquiring the people to live by approving the actual living places or foothold of the people in the city or the coming city, improve the striking and analyzing capacity of the public security for specific people through the consistent analysis of the human rooms, and provide a good idea for the centralized investigation and code registration of the rented house and the realization of full-coverage admission management.
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The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flowchart of an embodiment of a processing method for predicting human room consistency based on a nearest neighbor algorithm according to the present disclosure.
Fig. 2 is a flowchart of a specific embodiment of a processing method for predicting human room consistency based on a nearest neighbor algorithm according to the present disclosure.
FIG. 3 is an exemplary graph of a k-d tree.
Fig. 4 is a schematic structural diagram of an embodiment of a processing system for predicting human room consistency based on a nearest neighbor algorithm according to the present disclosure.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
By the method and the system for processing the consistency of the predicted housing, the household register address of the target person can be retrieved, the data of the housing building provided by the government interface are combined, coordinate conversion is carried out through a one-standard three-real system, nearest neighbor algorithm analysis is established, a better idea is provided for comprehensive research and judgment analysis application, and a direction is provided for dynamic management of real population.
The first standard is a standard address, and compared with the current part of non-uniform and non-standard address information, the definite new address standard of the superior department consists of elements such as administrative division, township streets, street lanes, house numbers, cells (groups), building numbers, unit numbers and family rooms;
the three entities refer to the real population, real house and real unit under the standard address. The standard address, the real population, the real house and the real unit are called as the first label three real.
The k-d tree is developed from bst (binary search tree), is a high-dimensional index tree data structure, and is commonly used in a large-scale high-dimensional data-intensive search and comparison use scenario, mainly including Nearest Neighbor search (Nearest Neighbor) and Approximate Nearest Neighbor search (Approximate Nearest Neighbor). In Computer Vision (CV) is mainly the search and alignment of high-dimensional feature vectors in image retrieval and recognition.
The real population is a concept of a relatively resident person, embodies a dynamic concept of managing population, and is a leap compared with a static population management mode mainly based on household registration management in the past.
As shown in fig. 1 and fig. 2, a processing method for predicting human room consistency based on a nearest neighbor algorithm of this embodiment includes:
s101: storing household registration data and vehicle data in a preset geographic area in a correlation manner, and storing the vehicle data and vehicle passing data at a gate in a correlation manner; the household registration data comprises living address data, names and identification numbers of the household owner and the family-sharing relationship person.
Specifically, household data, vehicle data associated with the household data, and vehicle-associated passing-by-vehicle data are obtained.
The payment data comprises water payment data, electricity payment data, natural gas payment data and heating payment data.
In a specific implementation, the household data can be obtained from a public security internal database;
the payment data may be obtained from a government database.
S102: and inquiring household registration data of the target person according to the identification number of the target person, and further obtaining vehicle data of the target person and the relationship person.
In the specific implementation, the household registration information, the name, the identification number, the household registration address and the information of the co-family relationship person (filtering the group family) are obtained according to the query of the target person.
Inquiring the vehicle information under the inquiry name of the target person and the relationship person thereof to obtain the vehicle information:
s103: and acquiring corresponding bayonet vehicle passing data according to the vehicle data of the target person and the relationship person, and performing track analysis on the vehicle through the bayonet vehicle passing data to obtain the bayonets with the number of foot-falling times exceeding a preset threshold value.
In specific implementation, the process of analyzing the track of the vehicle through the bayonet vehicle passing data comprises the following steps:
obtaining bayonet position information which appears last time every day in a preset time period through a bayonet track;
establishing a bayonet set with an identity card as a key and bayonet information as a value, wherein the bayonet information comprises the name of the bayonet and the GPS position information of the bayonet, and the format is as follows:
{ (vehicle 1, (bayonet 1, bayonet 2, bayonet n.)) }.
For example: and sequencing the quantity of the bayonets appearing in each vehicle to obtain the bayonets with the number of the first three feet falling times.
S104: and respectively and correspondingly converting the bayonet coordinates with the quantity of foot-falling times exceeding a preset threshold value and the residential address data in the household registration data into bayonet plane coordinates and residential address plane coordinates, and further constructing a k-d tree.
The GPS coordinates are composed of longitude, latitude and altitude, the precision and the latitude are angles, the altitude is height, the coordinates of the GPS cannot be directly used when the K-d tree is constructed, and the longitude and the latitude are converted into plane coordinates to be used for constructing the K-d tree, as shown in figure 3.
Specifically, building a k-d tree may follow the following steps:
1) and establishing a one-dimensional array, storing the index of each point, and randomly scrambling.
2) And defining proper k-d tree function definition to facilitate recursive tree building.
3) And writing a segmentation dimension function.
4) Writing and selecting a segmentation node function.
5) The k-d tree function is realized as follows: selecting a segmentation dimension, selecting a segmentation node, recursively establishing a left sub-tree by using data on the left side of the node, and recursively establishing a right sub-tree by using data on the right side of the node.
S105: searching a bayonet coordinate point which is closest to the current residential address plane coordinate point in the k-d tree by utilizing a nearest neighbor search algorithm, and if the distance between the current residential address plane coordinate point and the nearest bayonet coordinate point is within a preset range, judging that the rooms are consistent; otherwise, judging that the human rooms are inconsistent.
Specifically, the process of finding out the bayonet coordinate point closest to the coordinate point of the current residential address plane in the k-d tree by using the nearest neighbor search algorithm is as follows:
starting from a root node of the k-d tree, through nearest neighbor search, if the partition dimension value of the node is smaller than the dimension value of the search point, the search point is positioned in the space of the left sub-tree, then entering the left sub-tree, if the partition dimension value of the node is larger than the dimension value of the search point, then entering the right sub-tree, and adding each node on the search path into the path until reaching a leaf node;
and then backtracking the search path, judging whether nodes closer to the search point are possible in other sub-node spaces which are not added with the path, traversing the sub-node spaces if the nodes closer to the search point are possible, adding the traversed nodes into the search path, and repeating the process until the search path is empty.
For example: if the distance between the current living address plane coordinate point and the nearest bayonet coordinate point is within a preset range (two kilometers), judging that the rooms are consistent; and if the distance between the current living address plane coordinate point and the nearest bayonet coordinate point exceeds a preset range (two kilometers), judging that the rooms are inconsistent.
In another embodiment, the processing method for predicting human room consistency based on the nearest neighbor algorithm further includes:
when the people rooms are inconsistent, acquiring payment data associated with the household registration data;
judging whether the payment data exist or not, if so, judging that the house is rented; otherwise, the house is judged to be vacant.
According to the method, nearest neighbor analysis is carried out on the vehicle foot falling address, the household registration address and the house address, the permanent address of the target crowd can be rapidly analyzed, and the efficiency of carrying out human-room consistency analysis, permanent address investigation and vacant house and rented house investigation on the target people by public security policemen is improved.
The service management effect of inquiring the house and knowing the person to know the house and the person to know the living is achieved by approving the actual living places or foothold of the people in the city or the coming city.
Fig. 4 is a schematic diagram of an embodiment of a system architecture for predicting human room consistency based on a nearest neighbor algorithm according to the present disclosure.
As shown in fig. 4, a system for predicting human room consistency based on nearest neighbor algorithm of this embodiment includes a memory and a processor;
the memory is used for storing household data, vehicle data associated with the household data and vehicle passing data associated with the vehicle;
the processor includes:
(1) the data acquisition module is used for acquiring household data, vehicle data associated with the household data and vehicle passing data associated with a vehicle; the household registration data comprises living address data, names and identification numbers of the household owner and the family-sharing relationship person.
Specifically, the payment data comprises water payment data, electricity payment data, natural gas payment data and heating payment data.
In a specific implementation, the household data can be obtained from a public security internal database;
the payment data may be obtained from a government database.
(2) And the target person data query module is used for querying the household registration data of the target person according to the identification number of the target person so as to obtain the vehicle data of the target person and the relationship person of the target person.
In the specific implementation, the household registration information, the name, the identification number, the household registration address and the information of the co-family relationship person (filtering the group family) are obtained according to the query of the target person.
Inquiring the vehicle information under the inquiry name of the target person and the relationship person thereof to obtain the vehicle information:
(3) and the pin falling bayonet data acquisition module is used for acquiring corresponding bayonet vehicle passing data according to the vehicle data of the target person and the related person, and performing track analysis on the vehicle through the bayonet vehicle passing data to obtain the bayonets with the number of pin falling times exceeding a preset threshold value.
In specific implementation, the process of analyzing the track of the vehicle through the bayonet vehicle passing data comprises the following steps:
obtaining bayonet position information which appears last time every day in a preset time period through a bayonet track;
establishing a bayonet set with an identity card as a key and bayonet information as a value, wherein the bayonet information comprises the name of the bayonet and the GPS position information of the bayonet, and the format is as follows:
{ (vehicle 1, (bayonet 1, bayonet 2, bayonet n.)) }.
For example: and sequencing the quantity of the bayonets appearing in each vehicle to obtain the bayonets with the number of the first three feet falling times.
(4) And the k-d tree construction module is used for correspondingly converting the bayonet coordinates of which the number of foot-falling times exceeds a preset threshold value and the residential address data in the household registration data into bayonet plane coordinates and residential address plane coordinates respectively so as to construct a k-d tree.
The GPS coordinates are composed of longitude, latitude and altitude, the precision and the latitude are angles, the altitude is height, the coordinates of the GPS cannot be directly used when the K-d tree is constructed, and the longitude and the latitude are converted into plane coordinates to be used for constructing the K-d tree, as shown in figure 3.
Specifically, building a k-d tree may follow the following steps:
1) and establishing a one-dimensional array, storing the index of each point, and randomly scrambling.
2) And defining proper k-d tree function definition to facilitate recursive tree building.
3) And writing a segmentation dimension function.
4) Writing and selecting a segmentation node function.
5) The k-d tree function is realized as follows: selecting a segmentation dimension, selecting a segmentation node, recursively establishing a left sub-tree by using data on the left side of the node, and recursively establishing a right sub-tree by using data on the right side of the node.
(5) The human-room consistency judging module is used for searching a bayonet coordinate point which is closest to the coordinate point of the current residential address plane in the k-d tree by utilizing a nearest neighbor search algorithm, and judging that the human rooms are consistent if the distance between the coordinate point of the current residential address plane and the nearest bayonet coordinate point is within a preset range; otherwise, judging that the human rooms are inconsistent.
Specifically, the process of finding out the bayonet coordinate point closest to the coordinate point of the current residential address plane in the k-d tree by using the nearest neighbor search algorithm is as follows:
starting from a root node of the k-d tree, through nearest neighbor search, if the partition dimension value of the node is smaller than the dimension value of the search point, the search point is positioned in the space of the left sub-tree, then entering the left sub-tree, if the partition dimension value of the node is larger than the dimension value of the search point, then entering the right sub-tree, and adding each node on the search path into the path until reaching a leaf node;
and then backtracking the search path, judging whether nodes closer to the search point are possible in other sub-node spaces which are not added with the path, traversing the sub-node spaces if the nodes closer to the search point are possible, adding the traversed nodes into the search path, and repeating the process until the search path is empty.
For example: if the distance between the current living address plane coordinate point and the nearest bayonet coordinate point is within a preset range (two kilometers), judging that the rooms are consistent; and if the distance between the current living address plane coordinate point and the nearest bayonet coordinate point exceeds a preset range (two kilometers), judging that the rooms are inconsistent.
In another embodiment, the processor further comprises:
the house renting and vacancy judging module is used for acquiring payment data associated with the household registration data when the houses are inconsistent;
judging whether the payment data exist or not, if so, judging that the house is rented; otherwise, the house is judged to be vacant.
According to the method, nearest neighbor analysis is carried out on the vehicle foot falling address, the household registration address and the house address, the permanent address of the target crowd can be rapidly analyzed, and the efficiency of carrying out human-room consistency analysis, permanent address investigation and vacant house and rented house investigation on the target people by public security policemen is improved.
The service management effect of inquiring the house and knowing the person to know the house and the person to know the living is achieved by approving the actual living places or foothold of the people in the city or the coming city.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), 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 will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (8)
1. A processing method for predicting human room consistency based on a nearest neighbor algorithm is characterized by comprising the following steps:
storing household registration data and vehicle data in a preset geographic area in a correlation manner, and storing the vehicle data and vehicle passing data at a gate in a correlation manner; the household registration data comprises living address data, names and identification numbers of the household owner and the family-sharing relationship person;
inquiring household registration data of the target person according to the identification card number of the target person, and further obtaining vehicle data of the target person and the relationship person of the target person;
acquiring corresponding bayonet vehicle passing data according to vehicle data of a target person and a relation person, and performing track analysis on a vehicle through the bayonet vehicle passing data to obtain a bayonet of which the number of foot-falling times exceeds a preset threshold;
respectively and correspondingly converting the bayonet coordinates with the quantity of foot-falling times exceeding a preset threshold value and the residential address data in the household registration data into bayonet plane coordinates and residential address plane coordinates, and further constructing a k-d tree;
searching a bayonet coordinate point which is closest to the current residential address plane coordinate point in the k-d tree by utilizing a nearest neighbor search algorithm, and if the distance between the current residential address plane coordinate point and the nearest bayonet coordinate point is within a preset range, judging that the rooms are consistent; otherwise, judging that the rooms are inconsistent;
the process of finding out the bayonet coordinate point closest to the coordinate point of the current residential address plane in the k-d tree by using the nearest neighbor search algorithm comprises the following steps:
starting from a root node of the k-d tree, through nearest neighbor search, if the partition dimension value of the node is smaller than the dimension value of the search point, the search point is positioned in the space of the left sub-tree, then entering the left sub-tree, if the partition dimension value of the node is larger than the dimension value of the search point, then entering the right sub-tree, and adding each node on the search path into the path until reaching a leaf node;
and then backtracking the search path, judging whether nodes closer to the search point are possible in other sub-node spaces which are not added with the path, traversing the sub-node spaces if the nodes closer to the search point are possible, adding the traversed nodes into the search path, and repeating the process until the search path is empty.
2. The human-room consistency prediction processing method based on the nearest neighbor algorithm as claimed in claim 1, wherein the process of analyzing the trajectory of the vehicle through the bayonet vehicle passing data comprises:
obtaining bayonet position information which appears last time every day in a preset time period through a bayonet track;
and establishing a bayonet set with the identity card as a key and the bayonet information as a value, wherein the bayonet information comprises the name of the bayonet and the GPS position information of the bayonet.
3. The method of claim 1, wherein the nearest neighbor algorithm-based human room consistency prediction processing method further comprises:
when the people rooms are inconsistent, acquiring payment data associated with the household registration data;
judging whether the payment data exist or not, if so, judging that the house is rented; otherwise, the house is judged to be vacant.
4. The nearest neighbor algorithm-based human-room consistency prediction processing method as claimed in claim 3, wherein the payment data comprises water payment data, electricity payment data, natural gas payment data and heating payment data.
5. A human room consistency prediction processing system based on a nearest neighbor algorithm is characterized by comprising a memory and a processor;
the memory is used for storing household registration data and vehicle data in a preset geographic area in an associated manner, and storing the vehicle data and vehicle passing data at a gate in an associated manner;
the processor includes:
the data acquisition module is used for acquiring household data, vehicle data associated with the household data and vehicle passing data associated with a vehicle; the household registration data comprises residence address data, names and identity card numbers of the household owner and the same-household relations;
the target person data query module is used for querying the household registration data of the target person according to the identification card number of the target person so as to obtain the vehicle data of the target person and the relationship person of the target person;
the system comprises a foot-falling bayonet data acquisition module, a foot-falling bayonet data acquisition module and a foot-falling bayonet data acquisition module, wherein the foot-falling bayonet data acquisition module is used for acquiring corresponding bayonet vehicle-passing data according to vehicle data of a target person and a related person, and carrying out track analysis on a vehicle according to the bayonet vehicle-passing data to obtain a bayonet of which the number of foot-falling times exceeds a preset threshold;
the k-d tree construction module is used for correspondingly converting the bayonet coordinates with the quantity of foot-falling times exceeding a preset threshold value and the residential address data in the household registration data into bayonet plane coordinates and residential address plane coordinates respectively so as to construct a k-d tree;
the human-room consistency judging module is used for searching a bayonet coordinate point which is closest to the coordinate point of the current residential address plane in the k-d tree by utilizing a nearest neighbor search algorithm, and judging that the human rooms are consistent if the distance between the coordinate point of the current residential address plane and the nearest bayonet coordinate point is within a preset range; otherwise, judging that the rooms are inconsistent;
in the human-room consistency judging module, a nearest neighbor searching algorithm is utilized to find a bayonet coordinate point which is closest to a coordinate point of a current residential address plane in a k-d tree, and the process comprises the following steps:
starting from a root node of the k-d tree, through nearest neighbor search, if the partition dimension value of the node is smaller than the dimension value of the search point, the search point is positioned in the space of the left sub-tree, then entering the left sub-tree, if the partition dimension value of the node is larger than the dimension value of the search point, then entering the right sub-tree, and adding each node on the search path into the path until reaching a leaf node;
and then backtracking the search path, judging whether nodes closer to the search point are possible in other sub-node spaces which are not added with the path, traversing the sub-node spaces if the nodes closer to the search point are possible, adding the traversed nodes into the search path, and repeating the process until the search path is empty.
6. The system for processing human-room consistency prediction based on the nearest neighbor algorithm as claimed in claim 5, wherein in the data acquisition module of the foot-drop gate, the process of analyzing the trajectory of the vehicle through the data of the passing vehicle through the gate comprises:
obtaining bayonet position information which appears last time every day in a preset time period through a bayonet track;
and establishing a bayonet set with the identity card as a key and the bayonet information as a value, wherein the bayonet information comprises the name of the bayonet and the GPS position information of the bayonet.
7. The system of claim 5, wherein the processor further comprises:
and the house renting and vacancy judging module is used for acquiring payment data associated with the household registration data when the houses are inconsistent.
8. The nearest neighbor algorithm-based human-room consistency prediction processing system of claim 7, wherein the payment data comprises water payment data, electricity payment data, natural gas payment data and heating payment data.
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