CN112215735A - Floating population intelligent analysis system based on cloud computing and analysis method thereof - Google Patents
Floating population intelligent analysis system based on cloud computing and analysis method thereof Download PDFInfo
- Publication number
- CN112215735A CN112215735A CN202011065406.XA CN202011065406A CN112215735A CN 112215735 A CN112215735 A CN 112215735A CN 202011065406 A CN202011065406 A CN 202011065406A CN 112215735 A CN112215735 A CN 112215735A
- Authority
- CN
- China
- Prior art keywords
- information
- tenant
- client
- floating population
- fingerprint lock
- 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.)
- Pending
Links
- 238000007667 floating Methods 0.000 title claims abstract description 155
- 238000004458 analytical method Methods 0.000 title claims abstract description 26
- 238000004364 calculation method Methods 0.000 title description 3
- 238000000034 method Methods 0.000 claims abstract description 10
- 238000013507 mapping Methods 0.000 claims description 43
- 230000005484 gravity Effects 0.000 claims description 29
- 230000011218 segmentation Effects 0.000 claims description 6
- 230000000694 effects Effects 0.000 claims description 4
- 230000001174 ascending effect Effects 0.000 claims description 3
- 230000007774 longterm Effects 0.000 claims description 3
- 238000011160 research Methods 0.000 claims description 3
- 238000011161 development Methods 0.000 abstract description 4
- 230000007246 mechanism Effects 0.000 abstract description 2
- 238000012544 monitoring process Methods 0.000 abstract description 2
- 230000009466 transformation Effects 0.000 description 6
- 230000005012 migration Effects 0.000 description 4
- 238000013508 migration Methods 0.000 description 4
- 230000018109 developmental process Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 2
- 230000001052 transient effect Effects 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000009133 cooperative interaction Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000007115 recruitment Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0645—Rental transactions; Leasing transactions
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Finance (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The utility model provides a floating population intelligence analytic system based on cloud belongs to information management technical field, includes: an intelligent fingerprint lock; managing a client; a landlord client; a tenant client; and (4) a server. The method comprises the following steps: step S1, recording basic information of the rented house; step S2, recording information of landlord; step S3, inputting tenant information; step S4, authorizing the tenant client to obtain the access right of the intelligent fingerprint lock by the landlord client; and step S5, the tenant client obtains the access right of the intelligent fingerprint lock, and then the intelligent fingerprint lock is controlled and operated. According to the scheme, the characteristics and types of the floating population are analyzed, an information working mechanism of 'people come to register and people go to cancel' is implemented, the information is ensured to be timely, accurate and comprehensive, the accuracy of the information is improved, the monitoring and analysis of the floating condition of the population are perfected, the development rule and trend of the floating population are accurately mastered, and a decision basis is provided for formulating a public service management policy.
Description
Technical Field
The invention belongs to the technical field of information management, and particularly relates to a floating population intelligent analysis system based on cloud computing and an analysis method thereof.
Background
Population migration and mobility are inevitable trends in economic development and social progress. In the modernization process of any country from the traditional agricultural society to the modern industrial society, the history process of transferring agricultural labor force to non-agricultural industry and rural population to cities is necessarily experienced. With the development of urbanization in China, the number of flowing population in cities is rapidly increased, and it is expected that large-scale population flowing migration is still a major social phenomenon in the periods of social transformation, economic transformation and population transformation in China for a long period of time in the future.
How the floating population is managed after it arrives at the city has attracted a high degree of attention from government and social communities. With the transformation of government functions, social transformation and enterprise transformation, more unit people gradually transition to social people, two basic functions of government social management and public service are gradually released, and the function of community 'bottom of the book' is continuously enhanced. The community is a basic unit for realizing social management, and only when the floating population is brought into the community management and service, the real breakthrough of the floating population management mode can be realized, which is also a target of innovation of a floating population management system.
However, the floating population number is a dynamic variable, and how to count the floating population number and grasp the dynamic rule of the floating population is the basis for providing accurate services for the community. At present, the information of the floating population is mainly registered by visiting every family by a community manager, and various loopholes such as unclear base number, unclear condition and the like exist in the management aspect. How to track and collect information of floating population is a problem which needs to be solved urgently at present.
Disclosure of Invention
In view of the shortcomings of the prior art, the invention aims to provide an intelligent analysis system for floating population based on cloud computing.
The invention also aims to provide a floating population intelligent analysis method based on cloud computing
In order to achieve the above object, the present invention adopts the following technical solutions.
A cloud computing-based floating population intelligent analysis system, comprising:
intelligence fingerprint lock: the intelligent fingerprint latches and stores index information of basic information of rented houses, including address house number information and room number information; a wireless connection module is arranged in the intelligent fingerprint lock and is connected to the Internet through the wireless connection module; the wireless connection module uses short-distance data exchange standard of Bluetooth and/or wifi;
managing a client: the management client is in wireless signal connection with the intelligent fingerprint lock, and the management client inputs index information of basic rented house information into the intelligent fingerprint lock;
the landlord client side: the landlord client is in wireless signal connection with the intelligent fingerprint lock; after the landlord client is in signal connection with the intelligent fingerprint lock, reading index information of basic renting house information stored by the intelligent fingerprint lock; the landlord client is in signal connection with a server, the landlord client sends landlord information and rented house basic information to the server, and the server establishes index information with mapping relation between the landlord information and the rented house basic information and stores the index information;
the tenant client side: the tenant client is in wireless signal connection with the intelligent fingerprint lock; after the tenant client is in signal connection with the intelligent fingerprint lock, reading index information of basic rented house information stored by the intelligent fingerprint lock; the server establishes index information with mapping relation between the tenant information and the basic rented house information and stores the index information;
a server: the server is in signal connection with the intelligent fingerprint lock, the management client, the landlord client and the tenant client through a network; the server establishes index information with mapping relation for the basic rented house information, the landlord information and the tenant information and stores the index information, so that the basic rented house information, the landlord information and the tenant information are intelligently integrated; the server receives unlocking information of the intelligent fingerprint lock, including unlocking tenants and unlocking time, and synchronously maps and records the unlocking information in indexes of mapping relations corresponding to the unlocking tenants.
A floating population intelligent analysis method based on cloud computing comprises the following steps:
step S1, recording basic information of the rental housing: the management client is in wireless signal connection with the intelligent fingerprint lock; authorized management personnel enter basic rented house information into the intelligent fingerprint lock through the management client, and the intelligent fingerprint lock stores the basic rented house information and establishes an index;
step S2, inputting landlord information, and establishing index information with mapping relation between landlord information and basic rented house information: the landlord client is in wireless signal connection with the intelligent fingerprint lock, and after the landlord client is in signal connection with the intelligent fingerprint lock, the index information of basic renting house information stored by the intelligent fingerprint lock is read; the landlord client is in signal connection with the server, the landlord client sends landlord information and basic rented house information to the server, and the server establishes index information with mapping relation between the landlord information and the basic rented house information and stores the index information;
step S3, inputting tenant information, and establishing index information with mapping relation between the tenant information and the basic rented house information: after the tenant client is in signal connection with the intelligent fingerprint lock, reading index information of basic rented house information stored by the intelligent fingerprint lock; the server establishes index information with mapping relation between the tenant information and the basic rented house information and stores the index information;
step S4, authorizing the tenant client to obtain the access right of the intelligent fingerprint lock by the landlord client: the landlord client reads tenant information which has a mapping relation with the basic renting house information and is on the server, if the tenant information is true, the landlord client authorizes the tenant client to obtain the access right of the intelligent fingerprint lock, and otherwise, the tenant client cannot obtain the access right of the intelligent fingerprint lock;
step S5, the tenant client obtains the access right of the intelligent fingerprint lock, and then controls the intelligent fingerprint lock: a tenant inputs a fingerprint into the intelligent fingerprint lock, and then the tenant can open the intelligent fingerprint lock by using the input fingerprint; the intelligent fingerprint lock establishes and stores the fingerprint information and the index information corresponding to the fingerprint information and having a mapping relation with the tenant information; the server establishes index information with mapping relation for the basic rented house information, the landlord information and the tenant information and stores the index information, so that the basic rented house information, the landlord information and the tenant information are intelligently integrated.
Further, in step S1, after the management client is connected with the intelligent fingerprint lock via wireless signal and before information is entered, the authorized administrator mark needs to be verified; the authorized manager mark is at least one of the name, the telephone, the mailbox, the unit name and the preset manager account password of the manager.
Further, the floating population intelligent analysis method based on cloud computing further comprises the following steps:
step S6, acquiring unlocking information: the intelligent fingerprint lock is connected with the server through a network signal, and each time a tenant opens the intelligent fingerprint lock, the intelligent fingerprint lock sends unlocking time, unlocking frequency and tenant information to the server through the index relationship between the fingerprint information and the tenant information corresponding to the fingerprint information; the server inquires basic house information and landlord information of a rented house which have a mapping relation with the tenant information through the tenant information, and stores the basic house information, the landlord information, the tenant information, unlocking time and unlocking frequency of the rented house into a storage path after indexes of the mapping relation are established.
Further, the floating population intelligent analysis method based on cloud computing further comprises the following steps:
step S7, target analysis: the method comprises the following steps of dividing tenants into a regular population and a floating population, and further dividing the floating population into a long-term floating population, a short-term floating population and a cross floating population so as to facilitate the research and application of activity characteristics of different types of tenants; the method comprises the following specific steps:
s7a, establish a time-slice sequence for all tenants: the mapping index information of each tenant is arranged according to the ascending order of unlocking time to obtain the time segmentation sequence { A ] of each tenantn（p，tn-tn-1）、An-1（p，tn-1-tn-2）、An-2（p，tn-2-tn-3）...A1（p，t1-t0) Therein ofP is the community information of the address room number, tnTime point of sequence number n, tn-tn-1For time segments of sequence number n, AnThe tenant's demographic value, when t, for a time segment of sequence number nn-tn-1When the unlocking time A with the number not less than 1 exists betweennIs 1, otherwise AnIs 0;
s7b, setting a time segmentation threshold value delta Tn = tn-tn-1=3 days, 90 days is taken as a completed statistical period, then n is 30, when A is1+A2+A3+...+An-1+AnIf the number n is not less than the preset number n, the current tenant is a permanent population and is not included in the category of the study object;
when A is1+A2+A3+...+An-1+An< n, and Ax+Ax+1+Ax+2=3, x traversal [1, m]If n and x exist at the same time, the current tenant is a floating population and is included in the category of the study object;
when the tenant is not the permanent population or the floating population, the tenant is classified as a short-term cross-border population and is not included in the category of the study object;
s8, floating population judgment based on community positions:
s8a, judging the density spatial distribution equilibrium of the community floating population:
and queuing the floating population of each community in a sequence from low to high, and dividing the floating population into n groups. Let Wi be the proportion of the number of floating population from 1 st to ith community to the total number of floating population, the Lorentz curve passes through the point (i/n, Wi). W0 =0 and Wn =1 are specified, so that i =0,1, 2. The area B can then be found by integrating the lorentz curve using the trapezoidal rule, and a + B =1/2 is the area of the right triangle. The final density coefficient G = a/(a + B) = 1-2B. Is formulated as follows:
wherein G represents a density coefficient, and n represents the number of communities; sorting the total floating population of each community from low to high, wherein Wi represents the percentage of the floating population accumulated from the 1 st community to the ith community to the total floating population;
the density coefficient G represents the space distribution equilibrium of the floating population, and the maximum is 1, and the minimum is 0. The closer the density coefficient G is to 0, the more balanced the distribution of the floating population density is, and 0.2-0.3 is regarded as the average of the floating population density; 0.3-0.4 is considered to be a relatively reasonable floating population density; 0.4-0.5 is regarded as that the density difference of the floating population is large, and when the density coefficient G reaches more than 0.5, the density distribution of the floating population is very different;
s8b, judging the gravity center movement trend of the community floating population:
the gravity center of the regional floating population indicates that a certain point exists on the region, and the population densities of the certain point in the front, the back, the left and the right directions are relatively balanced; assuming that the rest is composed of n communities, the center coordinate of the ith community is (x)i,yi) And Pi is the number of floating population of the ith community, the gravity center of the floating population of the area is calculated as follows:
in the formula, X represents the abscissa of the gravity center point of the regional floating population, Y represents the ordinate of the gravity center point of the regional floating population, Pi is the floating population number of the ith community,
moving distance of center of gravity of community floating population:
in the formula, i and j represent two different years, D represents the moving distance of the gravity center of the community floating population between the two different years, and (xi, yi) and (xj, yj) represent the geographical coordinates of the gravity center of the regional floating population in the ith and jth years respectively; r is constant, typically 111.111 km.
The scheme improves the defects and shortcomings that the work of related functional departments of the government is not available, the basic public service of the floating population is equal, the cooperative interaction of a multivariate management main body is lacked, and the design of policy and regulation is inconsistent with the actual implementation.
The scheme analyzes the characteristics and types of the floating population, implements an information working mechanism of 'people come to register and people go to cancel', ensures the timeliness, accuracy and comprehensiveness of information, improves the accuracy of the information, perfects the monitoring and analysis of the floating condition of the population, accurately grasps the development law and trend of the floating population, provides a decision basis for making a public service management policy, and also provides basic data for the social security service of the community.
Drawings
FIG. 1 is an architectural diagram of the present invention;
in the figure: the system comprises an intelligent fingerprint lock 1, a management client 2, a landlord client 3, a tenant client 4 and a server 5.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The floating population is closely related to the traditional household registration system in China. Currently, the academic world has no clear and broad definition of "floating population". Depending on the purpose of the study and the needs of the management of the actual functional sector, there are different understandings of floating populations:
first, from a demographic perspective: floating population is that portion of the population where transient floating behavior exists in a certain geographic area, and is one of the special forms of spatial migration and movement of the population.
Secondly, from the economic point of view, there are two definitions: the first is the reason of population migration, which is the part of population that flows into a certain area to carry out socioeconomic activities but is not changed when the population lives in the house; the second is that the difference in industry is taken as a standard, and those populations which depart from the first industry and cannot enter the regular units or departments of the city are defined as floating populations.
And thirdly, from the perspective of administration, the floating population is defined by whether the resident with one place lives the house or not, and the floating population is generally defined as the part of residents staying in a place but not having the resident with the place staying at the house.
The term "living registration regulation of floating population in Zhejiang province" refers to the people who live at the non-local city region or county (city) nationality in the local province. And regulation of Zhejiang province floating population living registration regulation: the registration condition is reported to the public security organization or community living (village) committee in compliance with laws in three working days after the work of handling relevant procedures such as lodging registration, recruitment, house lease and the like to the mobile population. For the convenience of analysis, the person whose family member does not move to the local but has a residence time longer than 3 days is the subject of the study.
Renting houses is the main foothold of floating population. The collection of basic information of the floating population of the rented house is the premise and the basis for establishing a floating population comprehensive information platform.
A cloud computing-based floating population intelligent analysis system, comprising:
intelligent fingerprint lock 1: the intelligent fingerprint lock 1 stores index information of basic information of rented houses, including address house number information and room number information; the intelligent fingerprint lock 1 is internally provided with a wireless connection module and is accessed to the Internet through the wireless connection module; the wireless connection module uses short-distance data exchange standard of Bluetooth and/or wifi;
the management client 2: the management client 2 is in wireless signal connection with the intelligent fingerprint lock 1, for example, the management client 2 is connected with the intelligent fingerprint lock 1 through bluetooth and/or wifi; authorized managers such as community managers and police officers enter index information of basic rented house information into the intelligent fingerprint lock 1 through the management client 2, and can also input landlord information and floating population information. Renting house basic information including but not limited to: address room number information, room number information.
Landlord client 3: the landlord client 3 is in wireless signal connection with the intelligent fingerprint lock 1, for example, the landlord client 3 is connected with the intelligent fingerprint lock 1 through Bluetooth and/or wifi; after the landlord client 3 is in signal connection with the intelligent fingerprint lock 1, reading index information of basic renting house information stored in the intelligent fingerprint lock 1; the landlord client 3 is in signal connection with the server 5, the landlord client 3 sends landlord information and rental house basic information to the server 5, and the server 5 establishes index information with mapping relation between the landlord information and the rental house basic information and stores the index information. Landlord information, including but not limited to: the name of the landlord, the identity card of the landlord, the address of the landlord contact and the contact way of the landlord. The landlord client 3 reads tenant information which has a mapping relation with the basic renting house information and is on the server 5, if the tenant information is true, the landlord client 3 authorizes the tenant client 4 to obtain the access control authority of the intelligent fingerprint lock 1, and otherwise, the tenant client 4 cannot obtain the access control authority of the intelligent fingerprint lock 1.
The tenant client 4: the tenant client 4 is in wireless signal connection with the intelligent fingerprint lock 1, for example, the tenant client 4 is connected with the intelligent fingerprint lock 1 through Bluetooth and/or wifi; after the tenant client 4 is in signal connection with the intelligent fingerprint lock 1, the index information of the basic rented house information stored in the intelligent fingerprint lock 1 is read; the server 5 is connected with the tenant client 4 through signals, the tenant client 4 sends tenant information and basic rented house information to the server 5, and the server 5 establishes index information with mapping relation between the tenant information and the basic rented house information and stores the index information. Tenant information, including but not limited to: the name of the tenant, the identity card of the tenant, the contact way of the tenant, the age, the sex, the culture degree and the employment information. After the tenant information is confirmed at the landlord client 3, the tenant client 4 obtains the access control authority of the intelligent fingerprint lock 1, and then the intelligent fingerprint lock 1 is controlled: the tenant inputs the fingerprint into the intelligent fingerprint lock 1, and then the tenant can open the intelligent fingerprint lock 1 by using the input fingerprint.
The server 5: the server 5 is in signal connection with the intelligent fingerprint lock 1, the management client 2, the landlord client 3 and the tenant client 4 through a network; the server 5 establishes index information with mapping relation for the basic rented house information, the landlord information and the tenant information and stores the index information, so that the basic rented house information, the landlord information and the tenant information can be intelligently integrated. The server 5 receives unlocking information of the intelligent fingerprint lock 1, including unlocking tenants and unlocking time, and synchronously maps and records the unlocking information in indexes of mapping relations corresponding to the unlocking tenants.
The management client 2, the landlord client 3, and the tenant client 4 are mobile internet devices, including but not limited to: smart phones, tablet computers, and notebook computers.
A floating population intelligent analysis method based on cloud computing comprises the following steps:
s1, recording basic information of the rental housing: the management client 2 is in wireless signal connection with the intelligent fingerprint lock 1; authorized managers enter basic rented house information into the intelligent fingerprint lock 1 through the management client 2, and the intelligent fingerprint lock 1 stores the basic rented house information and establishes an index. After the management client 2 is in wireless signal connection with the intelligent fingerprint lock 1 and before information is input, the mark of an authorized manager needs to be verified. The authorized manager mark can be at least one of the name, telephone, mailbox, unit name and preset manager account password of the manager.
S2, inputting landlord information, and establishing index information with mapping relation between landlord information and basic rented house information: the landlord client 3 is in wireless signal connection with the intelligent fingerprint lock 1, and after the landlord client 3 is in signal connection with the intelligent fingerprint lock 1, the index information of basic renting house information stored in the intelligent fingerprint lock 1 is read; the landlord client 3 is in signal connection with the server 5, the landlord client 3 sends landlord information and rental house basic information to the server 5, and the server 5 establishes index information with mapping relation between the landlord information and the rental house basic information and stores the index information.
S3, inputting tenant information, and establishing index information with mapping relation between the tenant information and the basic rented house information: after the tenant client 4 is in signal connection with the intelligent fingerprint lock 1, the index information of the basic rented house information stored in the intelligent fingerprint lock 1 is read; the server 5 is connected with the tenant client 4 through signals, the tenant client 4 sends tenant information and basic rented house information to the server 5, and the server 5 establishes index information with mapping relation between the tenant information and the basic rented house information and stores the index information.
S4, the landlord client 3 authorizes the tenant client 4 to obtain the access right of the intelligent fingerprint lock 1: the landlord client 3 reads tenant information which has a mapping relation with the basic renting house information and is on the server 5, if the tenant information is true, the landlord client 3 authorizes the tenant client 4 to obtain the access control authority of the intelligent fingerprint lock 1, and otherwise, the tenant client 4 cannot obtain the access control authority of the intelligent fingerprint lock 1.
S5, the tenant client 4 obtains the access control authority of the intelligent fingerprint lock 1, and then controls the intelligent fingerprint lock 1: a tenant inputs a fingerprint into the intelligent fingerprint lock 1, and then the tenant can open the intelligent fingerprint lock 1 by using the input fingerprint; the intelligent fingerprint lock 1 establishes and stores index information with mapping relation between fingerprint information and tenant information corresponding to the fingerprint information; the server 5 establishes index information with mapping relation for the basic rented house information, the landlord information and the tenant information and stores the index information, so that the basic rented house information, the landlord information and the tenant information can be intelligently integrated.
S6, acquiring unlocking information: the intelligent fingerprint lock 1 is connected with the server 5 through a network signal, a tenant opens the intelligent fingerprint lock 1 each time, and the intelligent fingerprint lock 1 sends unlocking time, unlocking frequency and tenant information to the server 5 through the index relation between the fingerprint information and the tenant information corresponding to the fingerprint information; the server 5 inquires the basic information of the rented house and the landlord information which have a mapping relation with the rented house information through the tenant information, and stores the basic information of the rented house, the landlord information, the tenant information, the unlocking time and the unlocking frequency into a storage path after establishing an index of the mapping relation.
According to the scheme, the visiting information of the floating population in the community and the rental house is effectively tracked through the internet of things technology, the information of the floating population is automatically recorded, the labor force of community managers is reduced, the information of the floating population can be guaranteed not to be omitted, and the supervision strength of the floating population is enhanced.
S7, target analysis: the method is characterized in that the tenants are divided into regular resident population and floating population, and the floating population is further divided into long-term floating population, short-term floating population and cross floating population, so that research and application of activity characteristics of different types of tenants can be conveniently carried out. The method comprises the following specific steps:
s7a, establish a time-slice sequence for all tenants: the mapping index information of each tenant is arranged according to the ascending order of unlocking time to obtain the time segmentation sequence { A ] of each tenantn（p，tn-tn-1）、An-1（p，tn-1-tn-2）、An-2（p，tn-2-tn-3）...A1（p，t1-t0) Where p is the community information of the address room number, tnTime point of sequence number n, tn-tn-1For time segments of sequence number n, AnThe tenant's demographic value, when t, for a time segment of sequence number nn-tn-1When the unlocking time A with the number not less than 1 exists betweennIs 1, otherwise AnIs 0.
S7b, setting a time segmentation threshold value delta Tn = tn-tn-1=3 days, 90 days is taken as a completed statistical period, then n is 30, when A is1+A2+A3+...+An-1+AnIf the number n is not less than the preset number n, the current tenant is a permanent population and is not included in the category of the study object;
when A is1+A2+A3+...+An-1+An< n, and Ax+Ax+1+Ax+2=3, x traversal [1, m]If n and x exist at the same time, the current tenant is a floating population and is included in the category of the study object;
when the tenant is neither the standing population nor the floating population, the tenant is classified as a transient border crossing population and is not included in the category of the study subject.
S8, floating population judgment based on community positions:
s8a, judging the density spatial distribution equilibrium of the community floating population:
and queuing the floating population of each community in a sequence from low to high, and dividing the floating population into n groups. Let Wi be the proportion of the number of floating population from 1 st to ith community to the total number of floating population, the Lorentz curve passes through the point (i/n, Wi). W0 =0 and Wn =1 are specified, so that i =0,1, 2. The area B can then be found by integrating the lorentz curve using the trapezoidal rule, and a + B =1/2 is the area of the right triangle. The final density coefficient G = a/(a + B) = 1-2B. Is formulated as follows:
wherein G represents a density coefficient, and n represents the number of communities; the floating population counts of the communities are ranked from low to high, and Wi represents the percentage of the floating population accumulated from the 1 st community to the ith community to the total floating population.
The density coefficient G represents the space distribution equilibrium of the floating population, and the maximum is 1, and the minimum is 0. The closer the density coefficient G is to 0, the more balanced the distribution of the floating population density is, and 0.2-0.3 is regarded as the average of the floating population density; 0.3-0.4 is considered to be a relatively reasonable floating population density; the density difference of the floating population is considered to be large when the density coefficient G is more than 0.5, and the density distribution of the floating population is very different when the density coefficient G is more than 0.5.
S8b, judging the gravity center movement trend of the community floating population:
the gravity center of the regional floating population indicates that a certain point exists on the slice region, and the population densities of the certain point in the front, the back, the left and the right are relatively balanced. Assuming that the rest is composed of n communities, the center coordinate of the ith community is (x)i,yi) And Pi is the number of floating population of the ith community, the gravity center of the floating population of the area is calculated as follows:
in the formula, X represents the abscissa of the gravity center point of the regional floating population, Y represents the ordinate of the gravity center point of the regional floating population, Pi is the floating population number of the ith community,
moving distance of center of gravity of community floating population:
in the formula, i and j represent two different years, D represents the moving distance of the gravity center of the community floating population between the two different years, and (xi, yi) and (xj, yj) represent the geographical coordinates of the gravity center of the regional floating population in the ith and jth years respectively; r is constant, typically 111.111 km.
The moving distance of the center of gravity of the floating population in the community shows the moving trend of the floating population, reflects the change of the center of gravity of population aggregation and embodies the advantage of attracting the population at the center of gravity.
The floating population of the community position is judged, and the judgment on the space distribution and the gravity center movement trend of the floating population is mastered, so that the intelligent statistics on the information of the floating population is facilitated, the complexity of population density calculation is reduced, and the cost of population density early warning is effectively reduced; in addition, the judgment can be applied to the identification of the people flow congestion condition, and people flow congestion early warning service can be issued.
The invention breaks through the limitation of the traditional investigation, utilizes big data to extract objective space-time information from the behavior track for analysis and mining, breaks through the limitation of low sampling rate, can extract information for multiple times with different calibers and fully, observes and identifies the population mobility from the space dimension and the time dimension, distinguishes the population groups of the floating population, and judges the density and the space distribution equilibrium of the community floating population and the gravity center movement trend of the community floating population so that the application of the data can play a role in different fields.
Claims (5)
1. An intelligent floating population analysis system based on cloud computing, comprising:
intelligent fingerprint lock (1): the intelligent fingerprint lock (1) stores index information of basic rented house information, including address house number information and room number information; the intelligent fingerprint lock (1) is internally provided with a wireless connection module and is accessed to the Internet through the wireless connection module; the wireless connection module uses short-distance data exchange standard of Bluetooth and/or wifi;
management client (2): the management client (2) is in wireless signal connection with the intelligent fingerprint lock (1), and the management client (2) inputs index information of basic renting house information into the intelligent fingerprint lock (1);
landlord client (3): the landlord client (3) is in wireless signal connection with the intelligent fingerprint lock (1); after the landlord client (3) is in signal connection with the intelligent fingerprint lock (1), reading index information of basic renting house information stored in the intelligent fingerprint lock (1); the landlord client (3) is in signal connection with a server (5), the landlord client (3) sends landlord information and rented house basic information to the server (5), and the server (5) establishes index information with mapping relation with the landlord information and the rented house basic information and then stores the index information;
tenant client (4): the tenant client (4) is in wireless signal connection with the intelligent fingerprint lock (1); after the tenant client (4) is in signal connection with the intelligent fingerprint lock (1), the index information of the basic rented house information stored in the intelligent fingerprint lock (1) is read; the server (5) is connected with the tenant client (4) through signals, the tenant client (4) sends tenant information and basic rented house information to the server (5), and the server (5) establishes index information with a mapping relation between the tenant information and the basic rented house information and then stores the index information;
server (5): the server (5) is in signal connection with the intelligent fingerprint lock (1), the management client (2), the landlord client (3) and the tenant client (4) through a network; the server (5) establishes index information with mapping relation for the basic rented house information, the landlord information and the tenant information and stores the index information, so that the basic rented house information, the landlord information and the tenant information are intelligently integrated; the server (5) receives unlocking information of the intelligent fingerprint lock (1), including unlocking tenants and unlocking time, and synchronously maps and records the unlocking information in indexes of mapping relations corresponding to the unlocking tenants.
2. An intelligent analysis method for floating population based on cloud computing is characterized by comprising the following steps:
step S1, recording basic information of the rental housing: the management client (2) is in wireless signal connection with the intelligent fingerprint lock (1); authorized managers enter basic rented house information into the intelligent fingerprint lock (1) through the management client (2), and the intelligent fingerprint lock (1) stores the basic rented house information and establishes an index;
step S2, inputting landlord information, and establishing index information with mapping relation between landlord information and basic rented house information: the landlord client (3) is in wireless signal connection with the intelligent fingerprint lock (1), and after the landlord client (3) is in signal connection with the intelligent fingerprint lock (1), index information of basic renting house information stored in the intelligent fingerprint lock (1) is read; the landlord client (3) is in signal connection with the server (5), the landlord client (3) sends landlord information and rented house basic information to the server (5), and the server (5) establishes index information with mapping relation between the landlord information and the rented house basic information and then stores the index information;
step S3, inputting tenant information, and establishing index information with mapping relation between the tenant information and the basic rented house information: after the tenant client (4) is in signal connection with the intelligent fingerprint lock (1), the index information of the basic rented house information stored in the intelligent fingerprint lock (1) is read; the system comprises a server (5), a tenant client (4) and a server (5), wherein the tenant client (4) is in signal connection with the server (5), tenant information and basic rented house information are sent to the server (5), and the server (5) establishes index information with a mapping relation between the tenant information and the basic rented house information and stores the index information;
step S4, authorizing the tenant client (4) to obtain the access right of the intelligent fingerprint lock (1) by the landlord client (3): the landlord client (3) reads tenant information which has a mapping relation with the basic renting house information and is on the server (5), if the tenant information is true, the landlord client (3) authorizes the tenant client (4) to obtain the access control authority of the intelligent fingerprint lock (1), and otherwise, the tenant client (4) cannot obtain the access control authority of the intelligent fingerprint lock (1);
step S5, the tenant client (4) obtains the access control authority of the intelligent fingerprint lock (1), and then the intelligent fingerprint lock (1) is controlled: a tenant inputs a fingerprint into the intelligent fingerprint lock (1), and then the tenant can open the intelligent fingerprint lock (1) by using the input fingerprint; the intelligent fingerprint lock (1) establishes and stores index information with mapping relation between fingerprint information and tenant information corresponding to the fingerprint information; the server (5) establishes index information with mapping relation for the basic rented house information, the landlord information and the tenant information and stores the index information, so that the basic rented house information, the landlord information and the tenant information are intelligently integrated.
3. The intelligent analysis method for floating population based on cloud computing as claimed in claim 2,
in step S1, after the management client (2) is in wireless signal connection with the intelligent fingerprint lock (1) and before information is input, the authorized manager mark needs to be verified; the authorized manager mark is at least one of the name, the telephone, the mailbox, the unit name and the preset manager account password of the manager.
4. The intelligent analysis method for the floating population based on cloud computing as claimed in claim 2, further comprising:
step S6, acquiring unlocking information: the intelligent fingerprint lock (1) is connected with the server (5) through a network signal, a tenant opens the intelligent fingerprint lock (1) each time, and the intelligent fingerprint lock (1) sends unlocking time, unlocking frequency and tenant information to the server (5) through the index relation between the fingerprint information and the tenant information corresponding to the fingerprint information; the server (5) inquires basic house information and landlord information of a rented house which have a mapping relation with the tenant information through the tenant information, and stores the basic house information, the landlord information, the tenant information, unlocking time and unlocking frequency of the rented house into a storage path after establishing an index of the mapping relation.
5. The intelligent analysis method for the floating population based on cloud computing as claimed in claim 4, further comprising:
step S7, target analysis: the method comprises the following steps of dividing tenants into a regular population and a floating population, and further dividing the floating population into a long-term floating population, a short-term floating population and a cross floating population so as to facilitate the research and application of activity characteristics of different types of tenants; the method comprises the following specific steps:
s7a, establish a time-slice sequence for all tenants: the mapping index information of each tenant is arranged according to the ascending order of unlocking time to obtain the time segmentation sequence { A ] of each tenantn（p，tn-tn-1）、An-1（p，tn-1-tn-2）、An-2（p，tn-2-tn-3）...A1（p，t1-t0) Where p is the community information of the address room number, tnTime point of sequence number n, tn-tn-1For time segments of sequence number n, AnThe tenant's demographic value, when t, for a time segment of sequence number nn-tn-1When the unlocking time A with the number not less than 1 exists betweennIs 1, otherwise AnIs 0;
s7b, setting a time segmentation threshold value delta Tn = tn-tn-1=3 days, 90 days is taken as a completed statistical period, then n is 30, when A is1+A2+A3+...+An-1+AnIf n is not greater than n, the current tenant is the permanent population and is not included in the studyWithin the object category;
when A is1+A2+A3+...+An-1+An< n, and Ax+Ax+1+Ax+2=3, x traversal [1, m]If n and x exist at the same time, the current tenant is a floating population and is included in the category of the study object;
when the tenant is not the permanent population or the floating population, the tenant is classified as a short-term cross-border population and is not included in the category of the study object;
s8, floating population judgment based on community positions:
s8a, judging the density spatial distribution equilibrium of the community floating population:
queuing floating population of each community in a sequence from low to high, and dividing the floating population into n groups; setting the proportion of the number of floating population from the 1 st community to the ith community to the total number of floating population as Wi, and then passing through the point (i/n, Wi) by the Lorentz curve; w0 =0 and Wn =1 are provided, so that i =0,1, 2. The lorentz curve can then be integrated using the trapezoidal rule to find the area B, while a + B =1/2 is the area of the right triangle; the final density coefficient G = a/(a + B) = 1-2B; is formulated as follows:
wherein G represents a density coefficient, and n represents the number of communities; sorting the total floating population of each community from low to high, wherein Wi represents the percentage of the floating population accumulated from the 1 st community to the ith community to the total floating population;
the density coefficient G represents the space distribution equilibrium of floating population, the maximum is 1, and the minimum is 0; the closer the density coefficient G is to 0, the more balanced the distribution of the floating population density is, and 0.2-0.3 is regarded as the average of the floating population density; 0.3-0.4 is considered to be a relatively reasonable floating population density; 0.4-0.5 is regarded as that the density difference of the floating population is large, and when the density coefficient G reaches more than 0.5, the density distribution of the floating population is very different;
s8b, judging the gravity center movement trend of the community floating population:
the gravity center of the regional floating population indicates that a certain point exists on the region, and the population densities of the certain point in the front, the back, the left and the right directions are relatively balanced; assuming that the rest is composed of n communities, the center coordinate of the ith community is (x)i,yi) And Pi is the number of floating population of the ith community, the gravity center of the floating population of the area is calculated as follows:
wherein X represents the abscissa of the gravity center point of the regional floating population, Y represents the ordinate of the gravity center point of the regional floating population, Pi is the floating population number of the ith community,
moving distance of center of gravity of community floating population:
in the formula, i and j represent two different years, D represents the moving distance of the gravity center of the community floating population between the two different years, and (xi, yi) and (xj, yj) represent the geographical coordinates of the gravity center of the regional floating population in the ith and jth years respectively; r is constant, typically 111.111 km.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011065406.XA CN112215735A (en) | 2020-09-30 | 2020-09-30 | Floating population intelligent analysis system based on cloud computing and analysis method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011065406.XA CN112215735A (en) | 2020-09-30 | 2020-09-30 | Floating population intelligent analysis system based on cloud computing and analysis method thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112215735A true CN112215735A (en) | 2021-01-12 |
Family
ID=74051674
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011065406.XA Pending CN112215735A (en) | 2020-09-30 | 2020-09-30 | Floating population intelligent analysis system based on cloud computing and analysis method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112215735A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113010578A (en) * | 2021-03-22 | 2021-06-22 | 华南理工大学 | Community data analysis method and device, community intelligent interaction platform and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104156799A (en) * | 2014-07-11 | 2014-11-19 | 广东建邦计算机软件有限公司 | Floating population information management method and system |
| CN106096631A (en) * | 2016-06-02 | 2016-11-09 | 上海世脉信息科技有限公司 | A kind of recurrent population's Classification and Identification based on the big data of mobile phone analyze method |
| CN110458048A (en) * | 2019-07-23 | 2019-11-15 | 南京林业大学 | Take population distribution Spatio-temporal Evolution and the cognition of town pattern feature into account |
-
2020
- 2020-09-30 CN CN202011065406.XA patent/CN112215735A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104156799A (en) * | 2014-07-11 | 2014-11-19 | 广东建邦计算机软件有限公司 | Floating population information management method and system |
| CN106096631A (en) * | 2016-06-02 | 2016-11-09 | 上海世脉信息科技有限公司 | A kind of recurrent population's Classification and Identification based on the big data of mobile phone analyze method |
| CN110458048A (en) * | 2019-07-23 | 2019-11-15 | 南京林业大学 | Take population distribution Spatio-temporal Evolution and the cognition of town pattern feature into account |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113010578A (en) * | 2021-03-22 | 2021-06-22 | 华南理工大学 | Community data analysis method and device, community intelligent interaction platform and storage medium |
| CN113010578B (en) * | 2021-03-22 | 2024-03-15 | 华南理工大学 | Community data analysis method and device, community intelligent interaction platform and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106096631B (en) | A kind of floating population's Classification and Identification analysis method based on mobile phone big data | |
| CN110324787B (en) | Method for acquiring occupational sites of mobile phone signaling data | |
| Zhang et al. | Understanding urban dynamics from massive mobile traffic data | |
| Wang | Why police and policing need GIS: an overview | |
| Demissie et al. | Analysis of the pattern and intensity of urban activities through aggregate cellphone usage | |
| CN108717676A (en) | Evaluation space method and system are lived in duty under different scale based on multi-data fusion | |
| CN112802611A (en) | Visual area prevention and control method based on epidemic situation risk model | |
| CN110288261A (en) | Estate management management system based on big data platform | |
| CN110956188A (en) | Population behavior track digital coding method based on mobile communication signaling data | |
| Orford | Identifying and comparing changes in the spatial concentrations of urban poverty and affluence: a case study of inner London | |
| CN115809378A (en) | Medical shortage area identification and layout optimization method based on mobile phone signaling data | |
| CN111242352A (en) | Parking aggregation effect prediction method based on vehicle track | |
| Yuan et al. | Recognition of functional areas based on call detail records and point of interest data | |
| CN112766878A (en) | Digital country population information management system based on big data technology processing mode | |
| Gu et al. | Geography of talent in China during 2000–2015: An eigenvector spatial filtering negative binomial approach | |
| CN112215735A (en) | Floating population intelligent analysis system based on cloud computing and analysis method thereof | |
| CN115017244A (en) | Geographic information big data and population data fusion service community management method | |
| CN110288125A (en) | It is a kind of based on the commuting method for establishing model of mobile phone signaling data and application | |
| Du et al. | The temporal network of mobile phone users in Changchun Municipality, Northeast China | |
| CN112218052A (en) | Comprehensive security centralized supervision platform and application thereof | |
| CN116089448A (en) | Real-time population management system for establishing population portraits based on multidimensional perception | |
| Salat et al. | Analysing the impact of electrification on rural attractiveness in Senegal with mobile phone data | |
| Sun et al. | Build smart campus using human behavioral data | |
| CN113642976A (en) | Information system based on enterprise policy and regulation acquisition | |
| Chen et al. | Privacy protection method for vehicle trajectory based on VLPR data |
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 |