CN114861092A

CN114861092A - Personnel flow analysis method, system, electronic device and storage medium

Info

Publication number: CN114861092A
Application number: CN202210807369.8A
Authority: CN
Inventors: 许良锋; 陈嵩; 李惟聪; 师雪娇; 王红亮; 张彬; 荆华; 杨韬; 张倾城
Original assignee: State Post Bureau Postal Industry Security Center
Current assignee: State Post Bureau Postal Industry Security Center
Priority date: 2022-07-11
Filing date: 2022-07-11
Publication date: 2022-08-05

Abstract

The embodiment of the disclosure discloses a personnel flow analysis method and system based on consignment data, electronic equipment and a storage medium. Wherein the method comprises the following steps: the method comprises the steps of obtaining consignment data of a preset area in a preset time period and basic personnel data in the preset area; matching and analyzing the personnel information contained in the consignment data and the personnel information contained in the basic personnel data; determining that a person other than the basic person data is an incoming person when the person other than the basic person data exists among the persons included in the posting data; and acquiring the number of the receipt lists of the inflow persons, and determining the preset area as the standing address of the inflow persons when the number of the receipt lists is larger than a preset threshold value.

Description

Personnel flow analysis method, system, electronic device and storage medium

Technical Field

The present disclosure relates to the field of big data analysis, and in particular, to a method and system for analyzing a flow of people based on posted data, an electronic device, and a storage medium.

Background

The population mobility is a complex social phenomenon caused by the comprehensive influences of social economy, resource environment, policy and system and the like. The mining of the space-time law and the mode characteristics of population flow has important significance for regional human resource optimization configuration, social and economic equilibrium development, traffic system optimization and the like.

The floating population can be divided into inflow population and outflow population, and the existing population floating investigation still depends on public security policemen or staff to check at home in various places, mainly still depends on manual investigation and manual input, and has the disadvantages of large workload, lower efficiency, obvious conditions such as mistakes and omissions, repeated confirmation and check, and high time cost.

Disclosure of Invention

In view of this, the embodiments of the present disclosure provide a method, a system, an electronic device, and a storage medium for analyzing staff mobility based on posted data, which at least partially solve the technical problems in the prior art that staff mobility and manual troubleshooting are large in workload, low in efficiency, and prone to errors and omissions.

In a first aspect, an embodiment of the present disclosure provides a method for analyzing staff flow based on consignment data, including: the method comprises the steps of obtaining consignment data of a preset area in a preset time period and basic personnel data in the preset area; matching and analyzing the personnel information contained in the consignment data and the personnel information contained in the basic personnel data; determining that a person other than the basic person data is an incoming person when the person other than the basic person data exists among the persons included in the posting data; and acquiring the number of the addressee lists of each inflow person, and determining the preset area as the constant-living address of the inflow person when the number of the addressee lists is larger than a preset threshold value.

Optionally, after determining that the predetermined area is a standing address of the inflow person, the method further includes: determining a standing address type of the influent person; the determining the standing address type of the inflow person comprises: acquiring address type keywords of the mail sending address data appearing in the mail sending bill of the inflow personnel; matching the address type keywords with an address type label library, wherein the address type label library is preset with a corresponding relation between the address type labels and the address type keywords, and the address type labels comprise home addresses and unit addresses; and determining the standing address type of the inflow personnel according to the matched address type label.

Optionally, the method further comprises: when the basic personnel data in the preset area cannot be acquired, acquiring personnel information of which the addressee single number is greater than a preset threshold value; encrypting and sending the personnel information of which the singular number of the receiving surface is greater than the reservation threshold to a comparison platform; comparing the personnel information with the single number of addressees larger than a preset threshold value with national basic personnel data through the comparison platform; and determining whether the reserved area has inflow personnel according to the comparison result.

Optionally, the method further comprises: when a first person other than the person included in the consignment data exists in the basic person data, performing comparison analysis on the nationwide consignment data by the first person, and judging whether the first person has consignment activities; determining that the first person is an outgoing person for the predetermined area when a consignment activity exists.

Optionally, before performing matching analysis on the personnel information included in the consignment data and the personnel information included in the basic personnel data, the method further includes: performing data cleaning on the consignment data; the data cleaning of the consignment data comprises: acquiring name information, telephone number information and address information in the consignment data; deleting the forwarding data when the telephone number information has a null value or an illegal format and at least one of the name information and the address information has a null value or an illegal format; when the name information and the address information are normal and the telephone number information has a null value or is illegal in format, restoring the telephone number information by comparing and matching through a name-address library, taking the telephone number information as personnel information contained in the consignment data, and taking the name information and/or the address information as auxiliary information; and when the telephone number information is normal and at least one of the name information and the address information has a null value or is illegal in format, using the telephone number information as the personnel information contained in the consignment data.

Optionally, before performing matching analysis on the personnel information included in the consignment data and the personnel information included in the basic personnel data, the method further includes: and performing aggregate statistics on element data related to actual population in the consignment data.

Optionally, the performing matching analysis on the personnel information included in the consignment data and the personnel information included in the basic personnel data includes: acquiring telephone number information in the consignment data and the basic personnel data; and performing data collision on the telephone number information in the consignment data and the basic personnel data to realize matching between the personnel contained in the consignment data and the personnel contained in the basic personnel data.

In a second aspect, an embodiment of the present disclosure provides a people flow analysis system based on consignment data, including: the data acquisition module is used for acquiring consignment data of a preset area in a preset time period and basic personnel data in the preset area; the matching analysis module is used for performing matching analysis on the personnel corresponding to the consignment data and the basic personnel data; an inflow person determination module, configured to determine, when a person other than the basic person data exists in the persons corresponding to the posting data, that the person other than the basic person data is an inflow person; and the standing address determining module is used for acquiring the number of the receiving orders of each inflow person, and determining the preset area as the standing address of the inflow person when the number of the receiving orders is greater than a preset threshold value.

In a third aspect, an embodiment of the present disclosure provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method for logistics analysis based on consignment data of any of the first aspects above.

In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, where the computer-readable storage medium stores computer instructions for causing a computer to execute the method for analyzing staff flow based on consignment data according to any one of the above first aspects.

The personnel flow analysis method based on the consignment data, the system, the electronic equipment and the storage medium are provided by the embodiment of the disclosure, wherein the personnel flow analysis method based on the consignment data performs matching analysis by utilizing the consignment data of express used routinely in daily life of people and the basic personnel data in a certain predetermined area, so that the normally-living personnel, the inflow personnel and the outflow personnel in the predetermined area can be determined, the working efficiency of personnel flow investigation can be improved, the occurrence of error and leakage conditions is reduced, and the workload and labor cost of manual investigation are greatly reduced.

Further, in the method for analyzing the flow of people based on the consignment data, the contact way of the corresponding real population can be determined through the corresponding relation between the people in the consignment data and the telephone number information; through the corresponding relation between the people and the household addressees with large addressees, the corresponding permanent addresses can be determined, namely the real house is determined; the corresponding real unit can be determined through the corresponding relation between the person and the unit receiving address, so that the consignment data is used as a supplement and check basis of 'one standard three real'.

The foregoing is a summary of the present disclosure, and for the purposes of promoting a clear understanding of the technical means of the present disclosure, the present disclosure may be embodied in other specific forms without departing from the spirit or essential attributes thereof.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a flow chart of a method for analyzing flow of people based on consignment data according to an embodiment of the present disclosure;

FIG. 2 is a database diagram of a partitioned storage forwarding data provided by an embodiment of the present disclosure;

FIG. 3 is a diagram illustrating the personnel information contained in the consignment data and the personnel information contained in the base personnel data according to an embodiment of the disclosure;

FIG. 4 is a flow chart of a method for analyzing flow of people based on consignment data according to another embodiment of the present disclosure;

FIG. 5 is a flow chart of a method for analyzing flow of people based on consignment data according to yet another embodiment of the present disclosure;

FIG. 6 is a schematic diagram of a people flow analysis system based on consignment data provided by an embodiment of the present disclosure;

fig. 7 is a functional block diagram of an electronic device provided by an embodiment of the disclosure.

Detailed Description

The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

It is to be understood that the embodiments of the present disclosure are described below by specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure herein. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.

It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.

It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.

In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.

At present, the discrimination of real population and floating population on the public security side mostly depends on manual work, express delivery plays a key role in the aspect of people's life in recent years, the development of local economy is indirectly promoted, employment opportunities of multiple industries are driven, express delivery collection becomes an indispensable daily event for most of people, consumption habits and activity tracks of different people can be reflected by the express delivery collection, the embodiment discloses a personnel floating analysis method based on delivery data, a supplement basis of public security population information is completed by means of express delivery big data in an auxiliary mode, and the working efficiency of the personnel floating analysis method is improved. As shown in fig. 1, the method for analyzing staff flow based on consignment data of the present embodiment may include the following steps:

s101, consignment data of a preset area in a preset time period and basic personnel data in the preset area are obtained.

The skilled person can select the mailing data within the predetermined time period according to the actual situation, for example, the mailing data of about 3 months can be selected, and the mailing data can include name information, telephone number information, address information and the like, wherein the name information can be addressee name information and sender name information, the telephone number information can be mobile phone number information, and the address information can be addressee address information and mailing address information. In this embodiment, the predetermined area may be, for example, a cell, and the forwarding data of the cell is pulled by the name of the cell in the forwarding address information in the forwarding data, so that the staff flow analysis can be performed from the cell level in a grid form, and further on-door verification by security policemen or community staff is facilitated in the actual verification. When the predetermined area is a cell, the basic personnel data in the predetermined area is the basic personnel data in the cell, and may be, for example, information of a cell owner or tenant held by property, community, or public security and public security department.

As an optional implementation manner of the embodiment of the present invention, before performing the step S101, a cell-level grid database may be established, as shown in fig. 2, to store the forwarding data in a partitioned manner, so that the forwarding data can be conveniently pulled when analyzing the flow of people in a certain predetermined area. In the example of fig. 2, the consignment data are stored in different partitions with different granularities, the first level is provincial/major city level administrative unit, the second level is county level administrative unit, and the third level is cell level area, and those skilled in the art should understand that fig. 2 is only an example, and it is also possible to store the consignment data in partitions with more or less hierarchies.

And S102, carrying out matching analysis on the personnel information contained in the consignment data and the personnel information contained in the basic personnel data.

As shown in fig. 3, the person information included in the consignment data is denoted as a, and the person information included in the base person data is denoted as B, and in this step, the matching analysis is performed on the person information included in the consignment data and the person information included in the base person data to find a part where a and B overlap, that is, the person information of a ∞ B.

In this embodiment, the matching analysis may be performed on the person information included in the basic person data by using information such as name information, telephone number information, and address information included in the mail data, and as an alternative embodiment, the matching analysis may be performed on the person information included in the mail data and the person information included in the basic person data by using the telephone number information in the mail data and the basic person data. The step may specifically include:

s102a, acquiring telephone number information in the consignment data and the basic personnel data;

s102b, carrying out data collision on the telephone number information in the consignment data and the basic personnel data so as to realize matching between the personnel contained in the consignment data and the personnel contained in the basic personnel data.

Specifically, the data collision technology is to perform collision comparison on the consignment data and the basic personnel data, and perform deep analysis on the generated coincidence data and intersection data.

The data used for collision is often a data symbol with identification, which is also called as "identification data", and in the name information, the telephone number information and the address information in the mail data, there may be the condition of the same name and the same address, and the telephone number information has a unique characteristic and can directly point to the corresponding person or object, so in this embodiment, the telephone number information is used as the identification data, and the collision is performed by taking the telephone number information as a medium, so that the target information is more easily, quickly and accurately acquired.

In the above step, since the phone number information such as the phone number belongs to the citizen privacy data, the phone number information on both sides of the consignment data and the basic person data needs to be encrypted before performing the matching analysis.

S103, judging whether the personnel information contained in the consignment data is matched with the personnel information contained in the basic personnel data.

In this step, when the person information included in the posting data matches the person information included in the basic person data, step S104 is executed; if they do not match, there are two possibilities, one of them is the one other than the basic personal data, and then steps S105 to S108 are executed; another possibility is that there are persons other than the person included in the posting data in the basic person data, and steps S109 to S112 are executed.

And S104, determining the persons matched with the basic person data in the persons contained in the consignment data as the permanent persons in the preset area.

And S105, determining persons except basic person data existing in the persons contained in the consignment data as inflow persons.

S106, acquiring the number of the receipt lists of each inflow person, and determining the preset area as the standing address of the inflow person when the number of the receipt lists is larger than a preset threshold value.

In this embodiment, the forwarding data may further include a receipt data. From analysis of a large amount of delivery data and consumption habits, most people are basically based on receiving except for some special professions such as e-commerce and the like engaged in internet commercial trade activities, so that whether the people are the standing people of the predetermined area can be judged by using the characteristics of the receiving, for example, when the number of receiving faces is larger than a predetermined threshold value, the predetermined area is determined as the standing address of the inflowing people.

And S107, determining the standing address type of the inflow person.

In this embodiment, the step S107 may specifically include:

s107a, acquiring address type keywords of the mail address data appearing in the mail list of the inflowing personnel.

The address type key may be, for example, contents such as a cell, a garden, a house, a city, a bay, a banker, an apartment, a villa, a coast, a court, a village, a district, a house, a center, an pavilion, a first street, a castle, a house, a bridge, a hall, a table, a royal, a pavilion, a house, a mansion, a company, a unit, a studio, a building, a bureau, a institute, a court, a school, a hospital, etc., appearing in the contents of the address data.

S107b, matching the address type keywords with an address type label library, wherein the address type label library is preset with a corresponding relation between address type labels and the address type keywords, and the address type labels comprise home addresses and unit addresses.

And S107c, determining the standing address type of the inflow personnel according to the matched address type label.

In the above steps S107b and S107c, an address organization tag library may be established from the address data to analyze the address as a home address or a unit address. For example, when contents such as a cell, a garden, a house, a city, a bay, a manor, an apartment, a villa, a coast, a court, a village, a district, a house, a center, an pavilion, a first street, a castle, a house, a bridge, a hall, a table, a gyi, a hall, a house, a mansion, etc. appear in the address data contents, the address is determined to be a home address; when contents such as a company, a unit, a studio, a building, an office, a institute, a court, a school, a hospital, etc. appear in the address data contents, the address is determined to be a unit address. It should be understood by those skilled in the art that the data content for distinguishing the home address from the unit address is not exhaustive, and those skilled in the art can set the address type tag library appropriately according to the actual situation of the area to be analyzed.

Further, in this embodiment, when the mail address is a home address, it is determined that the home address is an actual house; when the mail address is a unit address, the unit address is determined to be an actual unit.

When the standing address type of the influent person is determined through step 107, it can be supplemented into the data corresponding to the influent person in the "one-label-three-real" correlation database.

And S108, comparing and analyzing the relevant data of the first person except the person contained in the consignment data in the basic person data in the national consignment data. For the sake of clarity, the person other than the person included in the consignment data present in the base person data will be referred to as the first person hereinafter.

In this embodiment, the telephone number information of the person can be used to perform the comparison collision in the nationwide consignment data.

S109, judging whether the consignment activity exists, if so, executing step S110, and if not, executing step S111.

And S110, determining the first person as the person who flows out of the preset area.

For example, the addressee address of the first person is not matched with the predetermined area by comparing the addressee bill data of the first person in the national consignment data, so that the person can be determined to be an outgoing person in the predetermined area, and the flow direction of the outgoing person can be determined through addressee address information, for example, the outgoing person flows to other districts of a foreign province or a local area.

And S111, determining that the first person is a person without a consignment activity.

The person who does not have the consignment activity is usually the old or the child, and under the condition, the public security policeman or the community staff can further check the situation, so that the workload of manual investigation can be greatly reduced.

In the method for analyzing the flow of the people based on the delivery data, the delivery data of express deliveries which are used routinely in daily life of people and the basic personnel data in a certain predetermined area are used for matching analysis, so that the normally-living people, the inflow people and the outflow people in the predetermined area can be determined, the working efficiency of the flow investigation of the people can be improved, the occurrence of error and leakage conditions is reduced, and the workload and labor cost of manual investigation are greatly reduced.

Further, in the method for analyzing the flow of people based on the consignment data, the contact way of the corresponding real population can be determined through the corresponding relation between the people in the consignment data and the telephone number information; through the corresponding relation between the people and the household addressees with large addressees, the corresponding permanent addresses can be determined, namely the real house is determined; the corresponding real unit can be determined through the corresponding relation between the person and the unit receiving address, so that the consignment data is used as a supplement and verification basis for ' one mark and three real ', wherein the ' one mark ' refers to a standard address, and the ' three real ' refers to real population, real house and real unit '.

In some cases, it may be difficult to obtain basic personnel data in a certain predetermined area, in this case, a personnel flow analysis method based on consignment data according to another embodiment of the present invention is shown in fig. 4, and may include the following steps:

s201, acquiring the personal information of which the addressee number is larger than a preset threshold.

The mail receiving characteristic can be used for judging whether the person is a standing person of the predetermined area, for example, when the mail receiving face singular number is larger than a predetermined threshold value, the predetermined area is determined as the standing address of the inflow person.

S202, encrypting and sending the personnel information of which the receiving face singular number is larger than a preset threshold value to a comparison platform.

For example, it may be sent to a comparison platform of a community or public security and public security department.

And S203, comparing the personnel information with the single number of addressees larger than the preset threshold value with national basic personnel data through a comparison platform.

In this embodiment, the telephone number information of the person may be compared with the national basic person data in a data collision manner.

And S204, determining whether inflow personnel exist in the preset area according to the comparison result.

If the address information of the person in the national basic person data is not in the preset area, indicating that the person belongs to the inflow person in the preset area; if the address information of the personnel in the national basic personnel data belongs to the preset area, indicating that the personnel belongs to the standing personnel in the preset area.

Fig. 5 shows a flow chart of a method for analyzing flow of people based on consignment data according to another embodiment of the invention, which may include the following steps, as shown in fig. 5:

s301, consignment data of a preset area in a preset time period and basic personnel data in the preset area are obtained. The detailed contents can refer to the corresponding description of step S101.

And S302, performing data cleaning on the consignment data.

For example, after the mailing data of a cell is pulled by the name of the cell in the mailing address information in the mailing data for about three months, due to various reasons, the name information, the telephone number information, and the address information in the mailing data may have null values, illegal formats with logical errors, for example, the mobile phone number is insufficient, there are special characters, and the like, and it is necessary to eliminate these unusable data, so before performing the subsequent analysis, data cleaning is necessary for the mailing data, and a specific data cleaning method is shown in the following table:

in the above table, "√" indicates that the information is normal, and "X" indicates that the information has a null value or the format is illegal. As shown in the above table, when the telephone number information has a null value or an illegal format, and at least one of the name information and the address information has a null value or an illegal format, the posting data is deleted; when the name information and the address information are normal and the telephone number information has a null value or is illegal in format, the telephone number information can be restored through comparing and matching the name information and the address information by a name-address library, the mail information is restored to be normal after the telephone number information is restored, subsequent analysis can be performed, the telephone number information can be used as personnel information contained in the mail data to be matched and analyzed, the name information or the address information can be used as auxiliary information, and the matching and analyzing result can be further verified through telephone or visiting subsequently; when the telephone number information is normal and at least one of the name information and the address information has a null value or is illegal in format, the telephone number information is used as the personnel information contained in the consignment data for matching analysis, and on the basis, the name and the address can be restored by using the name and address library to prepare for subsequent real address supplement.

The name and address library is constructed on the basis of the consignment data to realize the correlation query of the real name information and the address information, and can comprise a name field, a telephone number field, a provincial code field, a city code field, a district and county code field, a detailed address field and the like. Therefore, when the name information and the address information are normal, and the telephone number information has a null value or is illegal in format, the name and address library can be used for carrying out correlation query to restore the telephone number information of the corresponding personnel, if the condition that the personnel information exists in the name and address library is provided.

Through the data cleaning steps, the data which cannot be used are removed, and the consignment data with defects are recovered as far as possible, so that the follow-up analysis is facilitated.

And S303, carrying out aggregate statistics on element data related to the actual population in the consignment data.

In the aggregate statistical analysis, some useless data such as express delivery waybill number, express delivery enterprise, sent article, weight and the like can be filtered, and only the real population related element data is subjected to aggregate arrangement, such as name information, telephone number information, address information and number of orders in the delivery data, more specifically, the name information can be more specifically referred to addressee name information and sender name information, the telephone number information can be more specifically referred to addressee telephone number information and sender telephone number information, the address information can be more specifically referred to addressee address information and sender address information, and the number of orders can be more specifically referred to addressee number and number of sent orders.

And S304, matching and analyzing the personnel information contained in the consignment data and the personnel information contained in the basic personnel data. The detailed contents can refer to the corresponding description of step S102.

S305, judging whether the personnel information contained in the consignment data is matched with the personnel information contained in the basic personnel data.

In this step, when the person information included in the posting data matches the person information included in the basic person data, step S306 is executed; if they do not match, there are two possibilities, one of them is the one other than the basic personal data, and then steps S307 to S309 are executed; another possibility is that there are persons other than the person included in the posting data in the basic person data, and steps S310 to S313 are executed. The specific content can refer to the corresponding description of step S103.

And S306, determining the persons matched with the basic person data in the persons contained in the consignment data as the permanent persons in the preset area. The detailed contents can refer to the corresponding description of step S104.

And S307, determining persons except basic person data existing in the persons contained in the consignment data as inflow persons. The detailed contents may refer to the corresponding description of step S305.

S308, acquiring the number of the addressee lists of each inflow person, and determining the preset area as the constant-living address of the inflow person when the number of the addressee lists is larger than a preset threshold value. The detailed description may refer to the corresponding description of step S106.

S309, determining the standing address type of the inflow person. The detailed contents can refer to the corresponding description of step S107.

And S310, comparing and analyzing the relevant data of the first person except the person contained in the consignment data in the national consignment data. The detailed description may refer to the corresponding description of step S108.

S311, determine whether there is a consignment activity, if there is a consignment activity, go to step S312, and if there is no consignment activity, go to step S313. The detailed contents may refer to the corresponding description of step S109.

And S312, determining the first person as the person who flows out of the preset area. The detailed contents can refer to the corresponding description of step S110.

S313, the first person is determined to be a person without consignment activity. The detailed contents may refer to the corresponding description of step S111.

In the method for analyzing the flow of the person based on the consignment data, the method has the same beneficial effects as the previous embodiment, namely the normally-living person, the inflow person and the outflow person in the predetermined area can be determined, the working efficiency of the flow investigation of the person can be improved, the occurrence of error and leakage can be reduced, the workload and the labor cost of manual investigation can be greatly reduced, the consignment data can be used as a supplement and check basis of 'one mark three real', and besides, the data cleaning is carried out on the consignment data, the unusable data can be removed, and the defective consignment data can be recovered as much as possible, so that the follow-up analysis is facilitated; and by carrying out aggregate statistics on the consignment data, useless data are filtered, and aggregate arrangement is carried out on the actual population related element data.

Accordingly, fig. 6 shows a schematic diagram of a personnel flow analysis system based on consignment data according to an embodiment of the invention, which may include:

the data obtaining module 401 is configured to obtain the consignment data of the predetermined area and the basic staff data in the predetermined area within a predetermined time period, and the specific content may refer to the corresponding description in step S101.

The matching analysis module 402 is configured to perform matching analysis on the data of the person corresponding to the consignment data and the basic person data, and the specific content may refer to the corresponding description in step S102.

The inflow person determining module 403 is configured to determine, when there are persons other than the basic person data in the persons corresponding to the consignment data, that the persons other than the basic person data are inflow persons, and the specific content may refer to the corresponding description in step S105.

The standing address determining module 404 is configured to obtain the number of the addressees of each of the inflows, and determine that the predetermined area is the standing address of the inflows when the number of the addressees is greater than a predetermined threshold, where specific contents may refer to corresponding descriptions in step S106.

The personnel flow analysis system may further comprise:

a standing person determining module, configured to determine, as a standing person in the predetermined area, a person in the persons included in the consignment data that matches the basic person data, and the specific content may refer to the corresponding description in step S104.

And an address type determining module, configured to determine a standing address type of the inflow person, where specific contents may refer to corresponding descriptions in step S107.

A nationwide consignment data comparison module, configured to compare and analyze, in the nationwide consignment data, relevant data of a first person other than the person included in the consignment data in the base person data, and the specific content may refer to the corresponding description in step S108.

And an egress person determining module, configured to determine that the first person is an egress person of the predetermined area when the first person has a consignment activity, and the specific content may refer to the corresponding description in step S110.

A no-consignment-activity-person determining module, configured to determine that the first person is a no-consignment-activity-person when the first person has no consignment activity, and the specific content may refer to the corresponding description in step S111.

In the personnel flow analysis system based on the posting data, the posting data of express deliveries which are used routinely in daily life of people and the basic personnel data in a certain preset area are used for matching analysis through the modules, so that the frequent residents, the inflowing personnel and the outflowing personnel in the preset area can be determined, the working efficiency of personnel flow investigation can be improved, the occurrence of error and leakage conditions is reduced, and the workload and labor cost of manual investigation are greatly reduced.

Furthermore, each module in the staff flow analysis system based on the consignment data can determine the contact way of the corresponding real population through the corresponding relation between the persons and the telephone number information in the consignment data; through the corresponding relation between the people and the household addressees with large addressees, the corresponding permanent addresses can be determined, namely the real house is determined; the corresponding real unit can be determined through the corresponding relation between the person and the unit receiving address, so that the consignment data is used as a supplement and check basis of 'one standard three real'.

As an alternative embodiment, the people flow analysis system based on the consignment data may further include:

and a data cleaning module, configured to perform data cleaning on the consignment data, where specific content may refer to corresponding description in step S302.

The aggregation statistics module is configured to perform aggregation statistics on element data associated with the actual population in the consignment data, and the specific content may refer to the corresponding description in step S303.

The data cleaning module is used for cleaning the consignment data, so that the unusable data can be removed, and the consignment data with defects can be recovered as far as possible, so that the follow-up analysis is facilitated; and the aggregation statistical module is used for performing aggregation statistics on the consignment data, filtering useless data and performing aggregation arrangement on the actual population related element data.

An electronic device according to an embodiment of the present disclosure includes a memory and a processor. The memory is to store non-transitory computer readable instructions. In particular, the memory may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc.

The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is configured to execute the computer readable instructions stored in the memory, so that the electronic device performs all or part of the aforementioned steps of the method for analyzing staff flow based on consignment data according to the embodiments of the present disclosure.

Those skilled in the art should understand that, in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus, an interface, and the like, and these well-known structures should also be included in the protection scope of the present disclosure.

Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. There is shown a schematic diagram of a structure suitable for implementing an electronic device in an embodiment of the present disclosure. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 6, the electronic device may include a processing means (e.g., a central processing unit, a graphic processor, etc.) that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage means into a Random Access Memory (RAM). In the RAM, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device, the ROM, and the RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.

Generally, the following devices may be connected to the I/O interface: input means including, for example, a sensor or a visual information acquisition device; output devices including, for example, display screens and the like; storage devices including, for example, magnetic tape, hard disk, etc.; and a communication device. The communication means may allow the electronic device to communicate wirelessly or by wire with other devices, such as edge computing devices, to exchange data. While fig. 6 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means, or installed from a storage means, or installed from a ROM. When executed by the processing device, performs all or a portion of the steps of the posted data-based people flow analysis method of the disclosed embodiments.

For the detailed description of the present embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which are not repeated herein.

A computer-readable storage medium according to an embodiment of the present disclosure has non-transitory computer-readable instructions stored thereon. The non-transitory computer readable instructions, when executed by the processor, perform all or a portion of the steps of the method for personnel flow analysis based on consignment data of the embodiments of the present disclosure described above.

The computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or removable disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROMs (e.g., ROM cartridges).

The foregoing describes the general principles of the present disclosure in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present disclosure are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present disclosure. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the disclosure is not intended to be limited to the specific details so described.

In the present disclosure, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions, and the block diagrams of devices, apparatuses, devices, systems, etc. referred to in the present disclosure are used merely as illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".

Also, as used herein, "or" as used in a list of items beginning with "at least one" indicates a separate list, such that, for example, a list of "A, B or at least one of C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the described example is preferred or better than other examples.

It is also noted that in the systems and methods of the present disclosure, components or steps may be decomposed and/or re-combined. These decompositions and/or recombinations are to be considered equivalents of the present disclosure.

Various changes, substitutions and alterations to the techniques described herein may be made without departing from the techniques of the teachings as defined by the appended claims. Moreover, the scope of the claims of the present disclosure is not limited to the particular aspects of the process, machine, manufacture, composition of matter, means, methods and acts described above. Processes, machines, manufacture, compositions of matter, means, methods, or acts, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein may be utilized. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or acts.

The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the disclosure to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.

Claims

1. A method for analyzing flow of people based on consignment data, comprising:

the method comprises the steps of obtaining consignment data of a preset area in a preset time period and basic personnel data in the preset area;

matching and analyzing the personnel information contained in the consignment data and the personnel information contained in the basic personnel data;

determining that a person other than the basic person data is an incoming person when the person other than the basic person data exists among the persons included in the posting data;

and acquiring the number of the receipt lists of the inflow persons, and determining the preset area as the standing address of the inflow persons when the number of the receipt lists is larger than a preset threshold value.

2. The method of claim 1, wherein after determining that the predetermined area is a standing address of the influent person, the method further comprises:

determining a standing address type of the influent person;

the determining the standing address type of the inflow person comprises:

acquiring address type keywords of the mail sending address data appearing in the mail sending bill of the inflow personnel;

matching the address type keywords with an address type label library, wherein the address type label library is preset with a corresponding relation between the address type labels and the address type keywords, and the address type labels comprise home addresses and unit addresses;

and determining the standing address type of the inflow personnel according to the matched address type label.

3. The method of claim 1, further comprising:

when the basic personnel data in the preset area cannot be acquired, acquiring personnel information of which the addressee face singular number is larger than a preset threshold value;

encrypting and sending the personnel information of which the singular number of the receiving surface is greater than a preset threshold value to a comparison platform;

comparing the personnel information with the receiving face odd number larger than a preset threshold value with national basic personnel data through the comparison platform;

and determining whether inflow personnel exist in the preset area or not according to the comparison result.

4. The method of claim 1, further comprising:

when a first person other than the person included in the consignment data exists in the basic person data, performing comparison analysis on the nationwide consignment data by the first person, and judging whether the first person has consignment activities;

determining that the first person is an outgoing person for the predetermined area when a consignment activity exists.

5. The method of claim 1, further comprising, prior to performing a matching analysis of the personal information contained in the consignment data with the personal information contained in the base personal data: performing data cleaning on the consignment data;

the data cleaning of the consignment data comprises:

acquiring name information, telephone number information and address information in the consignment data;

deleting the forwarding data when the telephone number information has a null value or an illegal format and at least one of the name information and the address information has a null value or an illegal format;

when the name information and the address information are normal and the telephone number information has a null value or is illegal in format, restoring the telephone number information by comparing and matching through a name-address library, taking the telephone number information as personnel information contained in the consignment data, and taking the name information and/or the address information as auxiliary information;

and when the telephone number information is normal and at least one of the name information and the address information has a null value or is illegal in format, using the telephone number information as the personnel information contained in the consignment data.

6. The method of claim 1, further comprising, prior to performing a matching analysis of the personal information contained in the consignment data with the personal information contained in the base personal data:

and performing aggregate statistics on element data related to actual population in the consignment data.

7. The method according to any one of claims 1-6, wherein said matching analysis of the personnel information contained in the consignment data with the personnel information contained in the base personnel data comprises:

acquiring telephone number information in the consignment data and the basic personnel data;

and performing data collision on the telephone number information in the consignment data and the basic personnel data to realize matching between the personnel contained in the consignment data and the personnel contained in the basic personnel data.

8. A people flow analysis system based on consignment data, comprising:

the data acquisition module is used for acquiring consignment data of a preset area in a preset time period and basic personnel data in the preset area;

the matching analysis module is used for performing matching analysis on the personnel corresponding to the consignment data and the basic personnel data;

an inflow person determination module, configured to determine, when a person other than the basic person data exists in the persons corresponding to the posting data, that the person other than the basic person data is an inflow person;

and the standing address determining module is used for acquiring the number of the receiving orders of each inflow person, and determining the preset area as the standing address of the inflow person when the number of the receiving orders is greater than a preset threshold value.

9. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method for trucking flow analysis based on consignment data as set forth in any one of claims 1-7.

10. A computer-readable storage medium storing computer instructions for causing a computer to perform the method for logistics analysis based on consignment data of any of claims 1-7.