CN116468213B - Personnel information matching and calling method based on urban brain - Google Patents

Personnel information matching and calling method based on urban brain Download PDF

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CN116468213B
CN116468213B CN202310207243.1A CN202310207243A CN116468213B CN 116468213 B CN116468213 B CN 116468213B CN 202310207243 A CN202310207243 A CN 202310207243A CN 116468213 B CN116468213 B CN 116468213B
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申永生
陈冲杰
宋王杰
方谊诚
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Hangzhou City Brain Co ltd
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Abstract

The application discloses a personnel information matching and calling method based on urban brains. The application comprises the following steps: s1: dividing service areas according to administrative planning, and calculating the component proportion of each component area in each service area; s2: respectively predicting personnel density of each service area in different time periods according to the component proportion, and comprehensively making a volunteer scheduling plan by combining the volunteer demand conditions and the demand quantity; s3: visual scheduling is carried out on volunteers matched with corresponding conditions in the service area; s4: each service area respectively compares the expected scheduling data with the actual scheduling data and judges whether to schedule among the service areas; s5: and scheduling among the service areas according to the difference value between the expected scheduling data and the actual scheduling data of each service area. According to the supply and demand of the service area to the volunteers, the distribution plan of the volunteers is dynamically adjusted by combining the personnel density change of different component areas in the area in different time periods, and the calling efficiency of the volunteers is improved.

Description

Personnel information matching and calling method based on urban brain
Technical Field
The application relates to the field of personnel information matching, in particular to a personnel information matching method based on urban brains.
Background
With the improvement of national quality, more and more people are willing to throw into the volunteer service. Currently, there are also various areas where more volunteer strength is needed.
For certain specific volunteer tasks, more specialized, special skills are required to accomplish this, such as medical personnel who understand medical protection knowledge, drivers who understand driving vehicles, and personnel who will use special work tools. However, these volunteers with special skills are often more difficult to match and call, and searching for volunteers by publicity publication is often time-consuming and inefficient. And no matter the number or the capacity, the situation that the supply and the demand of the volunteers do not correspond exists, and the call efficiency of the volunteer personnel is low.
An existing volunteer management method, for example, a "volunteer matching method and server" disclosed in chinese patent literature, the publication number CN108391230B of which includes the steps of: the server receives help seeking information sent by a help seeker through the first Lora module; judging the types of volunteers needed by the help seeker according to the help seeking information; matching volunteers in a volunteer information base according to preset rules and the volunteer categories to obtain matched volunteers; forwarding the help information to the terminals of the matched volunteers. The matching mode of the scheme has long matching period and lower personnel calling efficiency.
Disclosure of Invention
The application mainly solves the problems of long matching period, non-correspondence between volunteer supply and demand and lower personnel calling efficiency in the volunteer matching process in the prior art; the personnel information matching and calling method based on the urban brain is provided, and the distribution planning of the volunteers is dynamically adjusted according to the supply and demand requirements of the service area on the volunteers and the personnel density changes of different component areas in the area in different time periods, so that the calling efficiency of the volunteers is improved.
The technical problems of the application are mainly solved by the following technical proposal:
a personnel information matching calling method based on urban brains comprises the following steps:
s1: dividing service areas according to administrative planning, and calculating the component proportion of each component area in each service area;
s2: respectively predicting personnel density of each service area in different time periods according to the component proportion, and comprehensively making a volunteer scheduling plan by combining the volunteer demand conditions and the demand quantity;
s3: visual scheduling is carried out on volunteers matched with corresponding conditions in the service area;
s4: each service area respectively compares the expected scheduling data with the actual scheduling data and judges whether to schedule among the service areas;
s5: and scheduling among the service areas according to the difference value between the expected scheduling data and the actual scheduling data of each service area.
Preferably, the component ratio is a ratio of an area of each component area to a total area of the service area.
Preferably, the composition area includes an industrial area, a business area, a residential area, and a landscape area.
Preferably, the estimated calculation process of the personnel density of each service area in different time periods is as follows:
according to the historic personnel flow measurement and calculation of each component area, constructing a time period-component area personnel flow coefficient table;
according to the time period of the current time, table lookup is performed to obtain personnel flow coefficients corresponding to each component area;
the process of estimating the personnel density of each component area in the t-th time period comprises the following steps:wherein, the liquid crystal display device comprises a liquid crystal display device,for the personnel density of the kth constituent region during the t-th time period; />For the personnel flow coefficient corresponding to the kth component area in the t time period;
the number of people in the kth component area in the t-1 time period; />The area of the kth component region;
the process of estimating the personnel density of the service area in the t-th time period comprises the following steps:
wherein K is the total number of component areas; />Is the area of the service area.
Preferably, the volunteer scheduling planning formulation process is as follows:
d1: each service area provides volunteer capacity requirements and corresponding people number requirements according to service matters;
d2: judging the rationality of the provided volunteer demands according to the personnel density distribution of the service area and each component area; if the judgment result is that the demand is reasonable, the step D3 is entered, otherwise, the step D1 is returned, and the demand is provided again;
d3: retrieving the volunteer information registered in the service area, and judging whether the requirements are met; if yes, entering a step D5 to distribute volunteers; otherwise, enter step D4;
d4: respectively calculating the personnel density variance of each component area in each time period, and if the personnel density variance of the corresponding component area is larger than a variance threshold and the personnel density of the component area is smaller than the average value, eliminating the component area in the time period;
d5: and planning and distributing volunteers according to the personnel density of each component area at the current moment and the estimated personnel density change.
Preferably, the rationality judging process is as follows:
judging whether the service matters are matched with the capacity demands or not; if yes, the following judgment is carried out; otherwise, returning to the step D1 to provide the requirement again;
judging whether the ratio of the number of the volunteer demands to the total number of the service area is within a preset ratio threshold range, if so, carrying out the next judgment; otherwise, returning to the step D1 to provide the requirement again;
judging whether the configuration of the people demand corresponding to the volunteer capacity demand is reasonable or not according to the distribution degree of the personnel density of each component area;
calculating a demand index for each volunteer's capacity demand：/>Wherein (1)>Is the nth volunteerA demand index of capacity demand; />Importance factor for the nth volunteer capacity demand for the service matters; />The number of the required people corresponding to the capacity requirement of the nth volunteer;
sorting according to the size of the requirement indexes, and taking the number of the required persons corresponding to the volunteer capacity requirement with the minimum requirement index; the number of people in need is rounded up and distributed according to the proportion of the personnel density of each component area; judging whether the number of the required people distributed in the component area with the maximum personnel density reaches the minimum number preset for the corresponding capacity requirement or not; if yes, judging that the number of people is reasonable in demand configuration, and entering a step D3; otherwise, returning to the step D1 to provide the requirement again.
Preferably, the procedure for planning allocation of volunteers is:
for different service matters, volunteers are randomly combined according to capability requirements;
traversing the component area distribution mode of the volunteer combination, and calculating the matching coefficient of the distribution mode according to the personnel density and the change of the personnel density of the component area in the current time period；
Alpha is a current personnel density coefficient, and is calculated according to the current personnel density and the number of matched volunteers; beta is a person density change coefficient, and is obtained by comparing and calculating the person density change and a change threshold value; />Taking 1 if the capacity requirements are matched, otherwise taking 0; r is the total number of volunteers in the random combination; />Absolute value of difference between number of volunteers and number of required persons in random combination; and sorting the matching coefficients, and selecting the allocation mode with the highest matching coefficient as the planning allocation mode of the volunteer.
Preferably, the current personnel density coefficient α is expressed as:
wherein (1)>For the personnel density of the kth constituent region during the t-th time period; />Receiving the density for preset standard people; />Is a rounding operation;
the personnel density change coefficient beta is expressed as:
wherein (1)>The variation of the density of the personnel; />A threshold value is changed for a preset standard person; />Is rounded upwards; />Is rounded downwards.
Preferably, the visual scheduling includes:
displaying a service area map, dividing different component areas, and displaying personnel density distribution of each component area in colors of different depths;
each component area positions the volunteer distribution plan for the current period and the volunteer distribution plan for the next period, displaying the volunteer dispatch direction.
Preferably, the scheduling data is the number of people served per hour; when the difference value between the actual scheduling data and the expected scheduling data is within the difference value threshold value, judging that the service interval scheduling is not needed, and ending; otherwise, judging that the scheduling in the service interval is needed.
The beneficial effects of the application are as follows:
1. according to the supply and demand of the service area to the volunteers, the distribution plan of the volunteers is dynamically adjusted by combining the personnel density change of different component areas in the area in different time periods, and the calling efficiency of the volunteers is improved.
2. When the volunteer is insufficient in hands, the area with small personnel density is removed, and the full utilization of the volunteer resources is ensured.
3. And the scheduling data is visually displayed, so that the data is more visual.
Drawings
FIG. 1 is a flow chart of a personnel information matching call method of the present application.
Fig. 2 is a flow chart of a volunteer dispatch plan of the present application.
Detailed Description
The technical scheme of the application is further specifically described below through examples and with reference to the accompanying drawings.
Examples:
the personnel information matching and calling method based on the urban brain in the embodiment, as shown in fig. 1, comprises the following steps:
s1: dividing the service areas according to the administrative plan, and calculating the component proportion of each component area in each service area.
Component areas include industrial areas, business areas, residential areas, landscape areas, other areas, and the like. The composition ratio is the ratio of the area of each composition region to the total area of the service region.
For example, when a street is used as a service area, the residential area of a certain street is about 60% of the total area of the street, the area of commercial buildings and office buildings is about 15% of the total area of the street, the area of parks and scenic spots is about 20% of the total area of the street, and the rest area is facilities such as roads, the commercial area proportion of the street is 15%, the residential area proportion is 60%, the scenic area proportion is 20%, and the other area proportion is 5%.
S2: and respectively estimating the personnel density of each service area in different time periods according to the component proportion, and comprehensively making a volunteer scheduling plan by combining the volunteer demand conditions and the demand quantity.
The estimated calculation process of the personnel density of each service area in different time periods comprises the following steps:
1) And (5) constructing a time period-component area personnel flow coefficient table according to the historical personnel flow measurement and calculation of each component area.
The people flow coefficient is the ratio of the number of people at the next moment to the number of people at the next moment. The day is divided into twenty-four time periods with one hour as one time period. The personnel flow changes of different component areas between time periods of the history are counted, the maintenance is represented by 1, the coefficient is larger than 1 when the personnel flow changes are increased, and the figure within 0-1 when the personnel flow changes are reduced.
Taking the example of the individual component area personnel flow of a street, the section of the established time period-component area personnel flow coefficient table is shown in table 1.
TABLE 1 time period-component area personnel flow Table for a street
Industrial area Business area Residential area Scenic region Other areas
0:00-1:00 0 0.99 1.08 1.01 0.51
…… …… …… …… …… ……
7:00-8:00 0 2.12 0.74 1.28 2.33
…… …… …… …… …… ……
17:00-18:00 0 0.83 1.16 1.37 2.12
…… …… …… …… …… ……
21:00-22:00 0 1.35 1.72 1.11 0.88
…… …… …… …… …… ……
As shown in the table, since the street does not have an industrial area, the personnel flow coefficient of the industrial area is 0. The resident area obviously flows out of the on-duty peak personnel, the off-duty peak personnel obviously flows in, the other periods also have the flow of the personnel, the business area obviously flows in at the off-duty peak time, and the off-duty peak personnel flows out.
The calculation base of the personnel flow in each period is different, and the flow change of the personnel can be more objectively reflected by adopting a proportional form.
2) And according to the time period of the current time, table lookup is performed to obtain the personnel flow coefficients corresponding to the component areas.
3) The process of estimating the personnel density of each component area in the t-th time period comprises the following steps:
wherein (1)>For the kth time period, the personnel density of the kth component area. />To be a person flow coefficient corresponding to the kth component region in the kth period. />The number of people in the kth component area in the t-1 time period. />Is the area of the kth component region.
4) The process of estimating the personnel density of the service area in the t-th time period comprises the following steps:
where K is the total number of constituent regions. />Is the area of the service area.
As shown in fig. 2, the volunteer scheduling program is formulated as follows:
d1: each service area provides volunteer capacity requirements and corresponding people number requirements according to service matters. For example, a service event requires 3 drivers who drive trucks, 10 volunteers who understand physical therapy knowledge, 3 volunteers who drive small vehicles, and 5 security guards in a service area of a certain street.
D2: judging the rationality of the provided volunteer demands according to the personnel density distribution of the service area and each component area; if the judgment result is that the demand is reasonable, the step D3 is entered, otherwise, the step D1 is returned, and the demand is provided again.
The rationality judging process is as follows:
a1: judging whether the service matters are matched with the capacity demands or not; if yes, A2 judgment is carried out; otherwise, returning to the step D1 to provide the requirement again.
A2: judging whether the ratio of the number of the volunteer demands to the total number of the service area is within a preset ratio threshold range, if so, carrying out A3 judgment; otherwise, returning to the step D1 to provide the requirement again.
A3: judging whether the configuration of the people demand corresponding to the volunteer capacity demand is reasonable or not according to the distribution degree of the personnel density of each component area;
calculating a demand index for each volunteer's capacity demand：/>Wherein (1)>A demand index that is the nth volunteer capacity demand; />Importance factor for the nth volunteer capacity demand for the service matters; />The number of people in need corresponding to the nth volunteer capacity need.
Sorting according to the size of the requirement indexes, and taking the number of the required persons corresponding to the volunteer capacity requirement with the minimum requirement index; and (5) rounding up and distributing the number of people in need according to the proportion of the personnel density of each component area.
Judging whether the number of the required people distributed in the component area with the maximum personnel density reaches the minimum number preset for the corresponding capacity requirement or not; if yes, judging that the number of people is reasonable in demand configuration, and entering a step D3; otherwise, returning to the step D1 to provide the requirement again.
D3: retrieving the volunteer information registered in the service area, and judging whether the requirements are met; if yes, entering a step D5 to distribute volunteers; otherwise, step D4 is entered.
D4: and respectively calculating the personnel density variance of each component area in each time period, and if the personnel density variance of the corresponding component area is larger than the variance threshold and the personnel density of the component area is smaller than the average value, eliminating the component area in the time period.
When the volunteer is insufficient in hands, the area with small personnel density is removed, and the full utilization of the volunteer resources is ensured.
D5: and planning and distributing volunteers according to the personnel density of each component area at the current moment and the estimated personnel density change.
The procedure for planning allocation of volunteers was:
for different service matters, volunteers are randomly combined according to capability requirements;
traversing the component area distribution mode of the volunteer combination, and calculating the matching coefficient of the distribution mode according to the personnel density and the change of the personnel density of the component area in the current time period；
And alpha is a current personnel density coefficient, and is calculated according to the current personnel density and the number of matched volunteers.
The current person density coefficient α is expressed as:wherein (1)>For the personnel density of the kth constituent region during the t-th time period; />Receiving the density for preset standard people; />To take outAnd (5) integer operation.
Beta is the coefficient of variation of the personnel density, and is calculated according to the comparison of the variation of the personnel density and the variation threshold value.
The person density change coefficient β is expressed as:
wherein (1)>The variation of the density of the personnel; />A threshold value is changed for a preset standard person; />Is rounded upwards; />Is rounded downwards. />The matching degree of the r-th volunteer and the volunteer requirement in the random combination is 1 if the capability requirement is matched, otherwise 0 is taken. R is the total number of volunteers in the random combination. />Is the absolute value of the difference between the number of volunteers and the number of consumers in the random combination.
And sorting the matching coefficients, and selecting the allocation mode with the highest matching coefficient as the planning allocation mode of the volunteer.
S3: visual scheduling is carried out on volunteers matched with corresponding conditions in the service area.
The visual schedule comprises:
and displaying a service area map, dividing different component areas, and displaying personnel density distribution of each component area in colors of different depths. In the present embodiment, the distribution of the personnel density is represented by a light to dark blue color, and the greater the personnel density, the darker the color.
Each component area positions the volunteer distribution plan for the current period and the volunteer distribution plan for the next period, displaying the volunteer dispatch direction.
And the scheduling data is visually displayed, so that the data is more visual.
S4: and each service area respectively compares the expected scheduling data with the actual scheduling data and judges whether to schedule the service areas.
When the difference value between the actual scheduling data and the expected scheduling data is within a difference value threshold value, judging that the service interval scheduling is not needed; otherwise, judging that the scheduling in the service interval is needed.
In the present embodiment, the number of people served per hour is employed as the schedule data.
S5: and scheduling among the service areas according to the difference value between the expected scheduling data and the actual scheduling data of each service area.
The current personnel configuration service capability difference can be known according to the difference between the expected dispatching data and the actual dispatching data, namely the difference between the expected number of served persons per hour and the actual number of served persons per hour.
A corresponding number of volunteers are scheduled from the surrounding service area according to the standard per-volunteer service capacity.
Preferably, the available service capacity of each service area is displayed on the map, and the service area with the strongest available service capacity is scheduled preferentially.
According to the scheme of the embodiment, according to the supply and demand of the service area to the volunteers, the distribution plan of the volunteers is dynamically adjusted by combining the personnel density changes of different component areas in the area in different time periods, and the calling efficiency of the volunteers is improved.
It should be understood that the examples are only for illustrating the present application and are not intended to limit the scope of the present application. Furthermore, it should be understood that various changes and modifications can be made by one skilled in the art after reading the teachings of the present application, and such equivalents are intended to fall within the scope of the application as defined in the appended claims.

Claims (6)

1. A personnel information matching calling method based on urban brains is characterized by comprising the following steps:
s1: dividing service areas according to administrative planning, and calculating the component proportion of each component area in each service area; the component proportion is the proportion of the area of each component area to the total area of the service area;
s2: respectively predicting personnel density of each service area in different time periods according to the component proportion, and comprehensively making a volunteer scheduling plan by combining the volunteer demand conditions and the demand quantity;
the estimated calculation process of the personnel density of each service area in different time periods comprises the following steps:
according to the historic personnel flow measurement and calculation of each component area, constructing a time period-component area personnel flow coefficient table;
according to the time period of the current time, table lookup is performed to obtain personnel flow coefficients corresponding to each component area;
the process of estimating the personnel density of each component area in the t-th time period comprises the following steps:
wherein ρ is kt For the personnel density of the kth constituent region during the t-th time period;
α kt for the personnel flow coefficient corresponding to the kth component area in the t time period;
p kt-1 the number of people in the kth component area in the t-1 time period;
S k the area of the kth component region;
the process of estimating the personnel density of the service area in the t-th time period comprises the following steps:
wherein K is the total number of component areas;
S all is a service areaArea of the domain;
the volunteer scheduling planning making process comprises the following steps:
d1: each service area provides volunteer capacity requirements and corresponding people number requirements according to service matters;
d2: judging the rationality of the provided volunteer demands according to the personnel density distribution of the service area and each component area; if the judgment result is that the demand is reasonable, the step D3 is entered, otherwise, the step D1 is returned, and the demand is provided again;
d3: retrieving the volunteer information registered in the service area, and judging whether the requirements are met; if yes, entering a step D5 to distribute volunteers; otherwise, enter step D4;
d4: respectively calculating the personnel density variance of each component area in each time period, and if the personnel density variance of the corresponding component area is larger than a variance threshold and the personnel density of the component area is smaller than the average value, eliminating the component area in the time period;
d5: planning and distributing volunteers according to the personnel density of each component area at the current moment and the estimated personnel density change;
s3: visual scheduling is carried out on volunteers matched with corresponding conditions in the service area;
the visual scheduling comprises the following steps:
displaying a service area map, dividing different component areas, and displaying personnel density distribution of each component area in colors of different depths; each component area positions and displays a volunteer distribution plan of the current period and a volunteer distribution plan of the next period, and displays a volunteer dispatching direction;
s4: each service area respectively compares the expected scheduling data with the actual scheduling data and judges whether to schedule among the service areas;
s5: and scheduling among the service areas according to the difference value between the expected scheduling data and the actual scheduling data of each service area.
2. The method for matching and invoking personal information based on urban brain according to claim 1, wherein the composition area comprises an industrial area, a business area, a resident area and a landscape area.
3. The personnel information matching calling method based on the urban brain according to claim 1, wherein the rationality judging process is as follows:
judging whether the service matters are matched with the capacity demands or not; if yes, the following judgment is carried out; otherwise, returning to the step D1 to provide the requirement again;
judging whether the ratio of the number of the volunteer demands to the total number of the service area is within a preset ratio threshold range, if so, carrying out the next judgment; otherwise, returning to the step D1 to provide the requirement again;
judging whether the configuration of the people demand corresponding to the volunteer capacity demand is reasonable or not according to the distribution degree of the personnel density of each component area;
calculating a requirement index A of the capability requirement of each volunteer n ：
A n ＝R n ·P n
Wherein A is n A demand index that is the nth volunteer capacity demand;
R n importance factor for the nth volunteer capacity demand for the service matters;
P n the number of the required people corresponding to the capacity requirement of the nth volunteer;
sorting according to the size of the requirement indexes, and taking the number of the required persons corresponding to the volunteer capacity requirement with the minimum requirement index; the number of people in need is rounded up and distributed according to the proportion of the personnel density of each component area; judging whether the number of the required people distributed in the component area with the maximum personnel density reaches the minimum number preset for the corresponding capacity requirement or not; if yes, judging that the number of people is reasonable in demand configuration, and entering a step D3; otherwise, returning to the step D1 to provide the requirement again.
4. The personal information matching call method based on city brain according to claim 1, wherein the process of planning and distributing volunteers is:
for different service matters, volunteers are randomly combined according to capability requirements;
traversing the component area distribution mode of the volunteer combination, and calculating a matching coefficient S of the distribution mode according to the personnel density and the change of the personnel density of the component area in the current time period i ；
Alpha is a current personnel density coefficient, and is calculated according to the current personnel density and the number of matched volunteers;
beta is a person density change coefficient, and is obtained by comparing and calculating the person density change and a change threshold value;
M r taking 1 if the capacity requirements are matched, otherwise taking 0;
r is the total number of volunteers in the random combination;
the delta R is the absolute value of the difference between the number of volunteers and the number of people in the random combination;
and sorting the matching coefficients, and selecting the allocation mode with the highest matching coefficient as the planning allocation mode of the volunteer.
5. The urban brain-based personal information matching call method according to claim 4, wherein the current personal density coefficient α is expressed as:
wherein ρ is kt For the personnel density of the kth constituent region during the t-th time period;
AV ρ receiving the density for preset standard people;
[. Cndot ] is a rounding operation;
the personnel density change coefficient beta is expressed as:
wherein Δρ is the amount of change in the person density;
Δρ c a threshold value is changed for a preset standard person;
is rounded upwards;
is rounded downwards.
6. The urban brain-based personal information matching call method according to claim 1, wherein the scheduling data is the number of people served per hour; when the difference value between the actual scheduling data and the expected scheduling data is within the difference value threshold value, judging that the service interval scheduling is not needed, and ending; otherwise, judging that the scheduling in the service interval is needed.
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Citations (6)

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