CN112070463A - Data processing method, device, equipment and storage medium - Google Patents
Data processing method, device, equipment and storage medium Download PDFInfo
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- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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- G01S19/10—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing dedicated supplementary positioning signals
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Abstract
The invention discloses a data processing method, a device, equipment and a storage medium, wherein the method comprises the following steps: acquiring positioning data of users in an area to be analyzed, wherein the positioning data comprises the positioning data of each working day in a first statistical period before an epidemic situation occurs and the positioning data of each working day in a second statistical period after the epidemic situation occurs; determining the travel intensity of the area before the occurrence of the epidemic and the travel intensity of the area after the occurrence of the epidemic according to the acquired positioning data; and determining the rework state of the region according to the trip intensity of the region before the occurrence of the epidemic situation and the trip intensity of the region after the occurrence of the epidemic situation. The invention can calculate the rework state of the area more accurately in real time and improve the efficiency and the accuracy of determining the rework state.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a data processing method, apparatus, device, and storage medium.
Background
Production operations are critical to the development of a region. When the area is exposed to an epidemic situation, the production activities in the area are also affected, even the area is shut down. After the epidemic situation is controlled, each area can gradually start to repeat the production again so as to keep the stable and orderly development of the society. How to determine the rework status of each area becomes a relatively important issue.
Currently, a tool for analyzing and processing data is needed to determine the rework status of each area in time.
Disclosure of Invention
The invention mainly aims to provide a data processing method, a data processing device, data processing equipment and a data processing storage medium, and aims to solve the technical problem that the rework state of each area cannot be determined in time.
In order to achieve the above object, the present invention provides a data processing method, including:
acquiring positioning data of users in an area to be analyzed, wherein the positioning data comprises the positioning data of each working day in a first statistical period before an epidemic situation occurs and the positioning data of each working day in a second statistical period after the epidemic situation occurs;
determining the travel intensity of the area before the occurrence of the epidemic and the travel intensity of the area after the occurrence of the epidemic according to the acquired positioning data;
and determining the rework state of the region according to the trip intensity of the region before the occurrence of the epidemic situation and the trip intensity of the region after the occurrence of the epidemic situation.
In one possible implementation, after determining the rework status of the area, the method further includes:
controlling the public equipment in the area according to the rework state of the area;
and/or sending indication information corresponding to the rework state to a government service platform or terminal equipment in the area.
In a possible implementation manner, determining the trip intensity of the region before the occurrence of the epidemic and the trip intensity of the region after the occurrence of the epidemic according to the obtained positioning data includes:
calculating the travel intensity corresponding to each working day according to the acquired positioning data of each working day;
calculating the normal travel intensity of the region according to the travel intensity of each working day before the epidemic situation occurs;
correspondingly, according to the trip intensity of the region before the epidemic situation occurs and the trip intensity of the region after the epidemic situation occurs, the rework state of the region is determined, which includes:
and determining the re-work state of the region on each working day after the epidemic situation occurs according to the travel intensity of each working day after the epidemic situation occurs and the normal travel intensity.
In one possible implementation, the method further includes:
calculating the basic travel intensity required for maintaining basic operation in the region according to the travel intensity of the user in at least one holiday in the region;
correspondingly, according to the trip intensity of each workday after the epidemic situation occurs and the normal state trip intensity, confirm the reworking state of each workday after the epidemic situation occurs in the region, include:
and aiming at each working day after the epidemic situation occurs, determining the rework state of the working day according to the trip intensity of the working day, the normal trip intensity and the basic trip intensity.
In a possible implementation manner, calculating the travel intensity corresponding to each working day according to the obtained positioning data of each working day includes:
determining resident users in the area according to the acquired positioning data of the users in the area;
determining the residential site of each resident user according to the positioning data of each resident user;
and for each working day, determining the travel intensity of the working day through the living places of all resident users in the area and the positioning data of all resident users in the working day.
In one possible implementation manner, determining the intensity of the trip on the working day through the residence place of each resident user in the area and the positioning data of each resident user on the working day includes:
for each resident user in the area, determining whether the resident user has a trip behavior on the working day according to the positioning data of the resident user on the working day and the place where the resident user resides;
and calculating the travel intensity of the working day according to the number of resident users having travel behaviors in the working day and the number of all resident users in the area.
In a possible implementation manner, the obtained positioning data includes mobile phone signaling data of the user collected by the base station;
determining whether the resident user has a trip behavior on the workday according to the positioning data of the resident user on the workday and the place where the resident user resides, including:
and if the resident user is determined to be connected to a base station outside the residential place on the working day and not connected to a base station outside the area according to the mobile phone signaling data of the resident user on the working day, determining that the resident user has a trip behavior on the working day.
In one possible implementation manner, determining whether the resident user has a travel behavior on the working day according to the positioning data of the resident user on the working day and the residential place of the resident user includes:
and if the resident user is judged to be present at a place beyond the preset distance of the residential place for a preset duration according to the positioning data of the resident user on the working day, determining that the resident user has a trip behavior on the working day.
In a possible implementation manner, determining a rework state of the workday according to the trip intensity of the workday, the normal trip intensity, and the basic trip intensity includes:
calculating a first difference value between the trip intensity of the working day and the basic trip intensity, and a second difference value between the normal trip intensity and the basic trip intensity;
and determining a rework index of the working day according to the ratio of the first difference to the second difference, wherein the rework index is used for reflecting the rework state.
The present invention also provides a data processing apparatus comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring positioning data of users in an area to be analyzed, and the positioning data comprises the positioning data of each working day in a first statistical period before an epidemic situation occurs and the positioning data of each working day in a second statistical period after the epidemic situation occurs;
the computing module is used for determining the travel intensity of the region before the occurrence of the epidemic situation and the travel intensity of the region after the occurrence of the epidemic situation according to the obtained positioning data;
and the determining module is used for determining the rework state of the region according to the trip intensity of the region before the occurrence of the epidemic situation and the trip intensity of the region after the occurrence of the epidemic situation.
The present invention also provides a data processing apparatus, comprising: memory, a processor and a data processing program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the data processing method according to any of the preceding claims.
The invention also provides a computer readable storage medium having stored thereon a data processing program which, when executed by a processor, implements the steps of the data processing method as claimed in any one of the preceding claims.
According to the method and the device, the location data of the users in the area to be analyzed are obtained, the location data comprise location data of all working days in a first statistic period before the occurrence of the epidemic situation and location data of all working days in a second statistic period after the occurrence of the epidemic situation, the trip intensity of the area before the occurrence of the epidemic situation and the trip intensity of the area after the occurrence of the epidemic situation are determined according to the obtained location data, the rework state of the area can be calculated in real time and accurately according to the trip intensity of the area before the occurrence of the epidemic situation and the trip intensity of the area after the occurrence of the epidemic situation, and the efficiency and the accuracy of determining the rework state are improved.
Drawings
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present invention;
fig. 2 is a schematic flow chart of a data processing method according to an embodiment of the present invention;
FIG. 3 is a flow chart illustrating another data processing method according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating a principle of determining a rework index according to an embodiment of the present invention;
fig. 5 is a schematic flow chart of determining a rework index after spring festival according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a rework index change according to an embodiment of the invention;
FIG. 7 is a schematic diagram illustrating a comparison of rework index changes in different areas according to an embodiment of the present invention;
FIG. 8 is a comparative illustration of the rework index change for different years according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
After an epidemic situation occurs, determining the rework state of the area has important significance for the life, economy and the like of the area. In some schemes, the rework status of an area may be evaluated by visiting local businesses, gathering rework scenarios of the businesses. However, this solution is time consuming, laborious and less accurate.
In order to solve the problem, an embodiment of the present invention provides a scheme, which may collect positioning data of a user in an area, determine a trip intensity change of the area before and after an epidemic situation according to the positioning data, and determine a rework state of the area after the epidemic situation occurs according to the trip intensity change, where the rework state may be used to represent a rework situation of each unit, such as a government department, an enterprise, and a merchant in the area.
Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present invention. As shown in fig. 1, a user may move with a terminal device, such as a mobile phone, in daily life, and a base station may obtain positioning data of the user by communicating with the terminal device, where the positioning data may be, for example, mobile phone signaling data of the user. For a particular area, the number of base stations, users and terminal devices in the area may be multiple. The mobile phone signaling data records that a certain user appears in the coverage area of a certain base station at a certain moment, so that the user is positioned.
The data processing equipment can be used as a data analysis processing tool, the positioning data of the user can be obtained from the base station, and after the positioning data is obtained, the corresponding rework state can be calculated through a set program and output.
In addition, the data processing equipment can further interact with other equipment after the obtained rework state is obtained. Optionally, the data processing device may be communicatively connected to a terminal device, a government service platform, a public device, and the like.
The data processing equipment can encapsulate the rework state in the indication information and send the rework state to the terminal equipment or the government service platform, so that a user or a government system can timely know the rework state of the area, life or work is guided according to the rework state, and convenience is provided for the user and the government.
Or, according to the rework state, control information may be sent to the public equipment in the area to control the public equipment, for example, adjusting the number of travel vehicles of the public transportation equipment, so that the scheduling condition of the public transportation meets the rework requirement, and resource waste is avoided.
Some embodiments of the invention are described in detail below with reference to the accompanying drawings. The features of the embodiments and examples described below may be combined with each other without conflict between the embodiments.
Fig. 2 is a schematic flowchart of a data processing method according to an embodiment of the present invention. The execution subject of the method in this embodiment may be a data processing device. The data processing device may be any device having data processing capabilities, such as a computer or the like. The method in this embodiment may be implemented by software, hardware, or a combination of software and hardware. As shown in fig. 2, the method may include:
The region may refer to any region in which rework state analysis is required. Alternatively, the area may be a city, or a city area within a city, or an area divided in any other way.
The location data of the users in the area may be determined in a number of ways. Alternatively, the positioning data may be directly obtained from the base station in the manner shown in fig. 1. Alternatively, the base station may transmit the positioning data to an operator server communicatively connected to the base station, and the data processing apparatus may acquire the positioning data from the operator server.
Optionally, a plurality of base stations may be disposed in the area, and the positioning data of each user appearing in the area may be determined by counting the positioning data acquired by all the base stations in the area.
In other alternative implementations, the positioning data may also be obtained by a positioning module of the terminal device. For example, the terminal device may perform Positioning through a GPS (Global Positioning System) module, and the data processing device may obtain GPS Positioning data of each user terminal, and determine an area where the user is located through an address fence or the like.
From the time dimension, the positioning data can include the positioning data of each weekday in the first statistics period before the epidemic situation takes place and the positioning data of each weekday in the second statistics period after the epidemic situation takes place.
Wherein the start time of the epidemic may be entered by the staff. Or, the data processing equipment can be accessed to a platform for epidemic situation statistics, and the starting time of the epidemic situation is directly obtained through the platform.
The first statistical period and the second statistical period may be set according to actual needs. The first statistical period may include a plurality of days, or may include only one day. The first statistical period may be a continuous period or a discontinuous period. The first statistical period may be immediately adjacent to the start time of the epidemic or may be spaced from the start time of the epidemic.
For example, assuming that the epidemic situation starts at 12 months and 1 day, and the first statistical period is two months, the first statistical period may be 10 months and 1 day to 11 months and 30 days, or may be 9 months and 1 day to 10 months and 31 days. Of course, the first statistical period should not be too long apart from the time of the onset of the epidemic in view of the timeliness of the data.
Similarly, the second statistical period may include a plurality of days, or may include only one day. When the rework status of a certain day or a certain number of days needs to be counted, the second counting period may be the corresponding day or a number of days. The second statistical period may also be immediately adjacent to the onset of the epidemic or at a time interval from the onset of the epidemic.
The travel intensity may be used to indicate how much proportion of users travel each day in the area, that is, a ratio of the number of users traveling each day to the total number of users in the area. According to the positioning data of each user every day, whether the user goes out or not in the same day can be determined, and therefore the outgoing intensity of the area is counted.
And 203, determining the rework state of the region according to the trip intensity of the region before the occurrence of the epidemic situation and the trip intensity of the region after the occurrence of the epidemic situation.
Optionally, if the travel intensity of the area on a certain day or more after the occurrence of the epidemic situation reaches or approaches the average travel intensity of the area before the occurrence of the epidemic situation, it may be determined that the area has been completely reworked or is close to being completely reworked.
The representation form of the rework state can be various. Optionally, the rework state may be divided into several types: not reworking and reworking; or not reworked, nearly reworked and reworked completely. The travel intensity of the region can be analyzed to determine which of the current rework states of the region is specific, so that qualitative analysis of the rework states is realized.
Alternatively, to enable a more detailed analysis of the rework status, the rework index may be used to characterize the rework status. The rework index may be a continuously changing numerical value, for example, a ratio of the trip intensity after the occurrence of the epidemic situation to the trip intensity before the occurrence of the epidemic situation, and the quantitative analysis of the rework state may be realized through the rework index.
In practical application, the scheme provided by this embodiment may be used to collect positioning data of a user in an area, and output the rework state of the area by analyzing and processing the positioning data. The rework state can reflect the economic recovery condition of the area to a certain extent, so that the evaluation of the economic recovery condition by analyzing the acquired data becomes possible.
According to the data processing method provided by the embodiment, the positioning data of the users in the area to be analyzed is acquired, the positioning data comprises the positioning data of each working day in the first statistical period before the occurrence of the epidemic situation and the positioning data of each working day in the second statistical period after the occurrence of the epidemic situation, the trip intensity of the area before the occurrence of the epidemic situation and the trip intensity of the area after the occurrence of the epidemic situation are determined according to the acquired positioning data, the trip intensity of the area before the occurrence of the epidemic situation and the trip intensity of the area after the occurrence of the epidemic situation can be calculated accurately in real time, and the efficiency and the accuracy of determining the rework state are improved.
On the basis of the technical solution provided by the above embodiment, optionally, after determining the rework state of the area, the method may further include: controlling the public equipment in the area according to the rework state of the area; and/or sending indication information corresponding to the rework state to a government service platform or terminal equipment in the area.
The public equipment can comprise public transport equipment such as buses, subways and the like, and scheduling control over the public transport equipment can be achieved according to the reworking state of the area, for example, when the reworking index is increased, the number of shifts of the public transport equipment in the area is increased, and when the reworking index is decreased, the number of shifts of the public transport equipment in the area is decreased. Or the public equipment can comprise other equipment such as charging piles in an urban area, and when the rework index is low, part of the charging piles can be indicated to be closed, the charging function is not provided any more, and the standby energy consumption is reduced.
The common equipment in the area is controlled through the rework state, so that the common equipment can be matched with the current rework state, and the resource waste is reduced on the basis of meeting the rework requirement.
In addition, after the rework state is determined, indication information corresponding to the rework state can be sent to a government service platform or terminal equipment in the area. The terminal device may be a terminal device of a general user or a terminal device of a company or a merchant. The government service platform may be any platform offered by the government.
The indication information may include the rework state, or the indication information may include suggestion information corresponding to the rework state, and the like. For example, the directive sent to the government service platform may be: the rework index reaches 80%, the restaurant is recommended to be opened, or the rework index reaches 95%, the cinema is recommended to be opened, and the like.
After the rework status is determined, the corresponding indication information is sent to a government service platform or terminal equipment, so that governments, enterprises and users can know the current rework situation in time, convenience is provided for the enterprises and the users, and the orderly progress of the rework and rework is ensured.
On the basis of the technical solution provided in the above embodiment, optionally, determining the trip intensity of the area before the occurrence of the epidemic and the trip intensity of the area after the occurrence of the epidemic according to the obtained positioning data may include: calculating the travel intensity corresponding to each working day according to the acquired positioning data of each working day; and calculating the normal travel intensity of the region according to the travel intensity of each working day before the epidemic situation occurs. Correspondingly, determining the rework state of the region according to the trip intensity of the region before the occurrence of the epidemic situation and the trip intensity of the region after the occurrence of the epidemic situation, which may include: and determining the re-work state of the region on each working day after the epidemic situation occurs according to the travel intensity of each working day after the epidemic situation occurs and the normal travel intensity.
Specifically, for the first statistical period before the epidemic situation occurs, the travel intensity of each working day is integrated, and a normal travel intensity is determined. And for the second statistical period after the epidemic situation occurs, respectively calculating the rework states corresponding to the working days aiming at each working day. Or, the trip intensity of each working day in the second statistical period can be integrated to determine the overall rework state of the area in the second statistical period after the epidemic situation occurs.
Confirm normality trip intensity according to the location data before the epidemic situation takes place to with the trip intensity after the epidemic situation takes place and normality trip intensity comparison, can confirm accurately fast whether the trip intensity after the epidemic situation takes place resumes the level before the epidemic situation takes place, improve the efficiency of calculation.
The normal trip intensity may be an average value of trip intensities of respective working days in a first statistical period before occurrence of an epidemic situation. The specific calculation of the normal trip intensity will be described in detail in the following examples.
In other alternative implementations, the normal trip intensity may not be calculated, for example, the trip intensity of each working day after the epidemic situation occurs may be compared with the trip intensity of the working day of the same period of the previous year, and the rework state of the working day may be determined.
Fig. 3 is a schematic flow chart of another data processing method according to an embodiment of the present invention. As shown in fig. 3, the method includes:
In this embodiment, the specific implementation principle and process of step 301 may refer to the foregoing embodiments, and are not described herein again.
And 302, calculating the travel intensity corresponding to each working day according to the acquired positioning data of each working day.
The travel intensity may include a ratio of the number of resident users having travel behaviors to the number of all resident users in the area.
Optionally, calculating the travel intensity corresponding to each working day according to the obtained positioning data of each working day may include: determining resident users in the area according to the acquired positioning data of the users in the area; determining the residential site of each resident user according to the positioning data of each resident user; and for each working day, determining the travel intensity of the working day through the living places of all resident users in the area and the positioning data of all resident users in the working day.
Specifically, resident users of the area may be determined according to the positioning data of the users in the area, and the resident users may refer to residents of the area and non-residents living in the area for a long time.
Optionally, the resident user may be a user whose total duration in the area reaches the dwell duration threshold within the statistical period, so that users who temporarily enter the area may be eliminated, and the accuracy of calculation is improved.
Wherein, the statistical period can be set according to actual needs. Optionally, the statistical period may include a first statistical period before the occurrence of the epidemic situation and a second statistical period after the occurrence of the epidemic situation, so that the resident user is calculated by using the existing data, and the utilization rate of the data is improved.
After determining the resident users of the area, the residence location of each resident user, i.e., the user's residence, may be further determined. For a single resident user, the place with the longest occurrence time in the statistical period can be taken as the residence of the user.
Finally, aiming at each working day, the travel intensity of the working day can be determined through the living places of all resident users in the area and the positioning data of all resident users in the working day.
Optionally, determining the travel intensity of the working day through the living places of the resident users in the area and the positioning data of the resident users on the working day may include: for each resident user in the area, determining whether the resident user has a trip behavior on the working day according to the positioning data of the resident user on the working day and the place where the resident user resides; and calculating the travel intensity of the working day according to the number of resident users having travel behaviors in the working day and the number of all resident users in the area.
In one example, the acquired positioning data may include cell phone signaling data of the user acquired by the base station. Correspondingly, determining whether the resident user has a trip behavior on the working day according to the positioning data of the resident user on the working day and the residential place of the resident user may include: and if the resident user is determined to be connected to a base station outside the residential place on the working day and not connected to a base station outside the area according to the mobile phone signaling data of the resident user on the working day, determining that the resident user has a trip behavior on the working day.
Specifically, the mobile phone signaling data may indicate which base station the mobile phone of the user is connected to at which moment, the living location of the user may be a location covered by the base station with the longest mobile phone connection time, and if the mobile phone of the user is connected to a base station other than the living location, it may be considered that the user has a trip on the same day, so as to simply and quickly determine whether the user has a trip behavior. In addition, the mobile phone can be further limited from being connected to the base station outside the area, namely the user does not walk out of the area on the same day, and the user is determined to have the travel behavior on the same day, and the travel behavior under the limitation is related to the re-work state, so that the re-work state can be calculated more accurately.
Besides, whether the user has a trip behavior or not may be determined by determining whether the user is present outside the place of residence on the weekday through GPS positioning data or the like.
After whether each resident user has a travel behavior in a certain working day is obtained, the ratio of the number of the resident users having the travel behavior to the number of all resident users can be used as the travel intensity of the area in the working day.
And 303, calculating the normal trip intensity according to the trip intensity of each working day before the epidemic situation occurs.
For each working day before and after the epidemic situation occurs, the travel intensity of the day can be calculated by the method provided in the previous step. The normal state travel intensity can be calculated according to the travel intensity of each working day before the epidemic situation occurs. The normal trip intensity reflects the normal trip intensity of the region in the absence of an epidemic.
Optionally, the average value of the trip intensity of all working days in the first statistical period before the occurrence of the epidemic situation may be used as the normal trip intensity. Of course, other designs may be introduced, for example, after removing the too high or too low travel intensity, the remaining travel intensity is averaged, which is not limited in this embodiment.
And 304, calculating the basic travel intensity required for maintaining basic operation in the region according to the travel intensity of the user in at least one holiday in the region.
In this embodiment, the base travel intensity may be calculated from the travel intensity of a holiday, which may be part or all of the holidays within the statistical period. The base trip intensity may reflect the trip level required to maintain basic operation in the area, and is a relatively stable value that does not vary greatly with time and events.
When determining the basic travel intensity, the travel intensity corresponding to each day of the holiday can be calculated, and the basic travel intensity can be an average value or a lowest value of the travel intensities corresponding to the holidays. The method for calculating the travel intensity of the holiday is similar to the method for calculating the travel intensity of the working day, and reference may be specifically made to step 303, which is not described herein again.
Of course, the basic travel intensity may also be calculated in other manners, for example, the basic travel intensity of the area may be determined by a manual investigation method. Or, the lowest travel intensity of the region in the last year before the epidemic situation occurs may be calculated as the region basic travel intensity.
And 305, determining the rework state of the working day according to the trip intensity of the working day, the normal trip intensity and the basic trip intensity aiming at each working day after the epidemic situation occurs.
In this embodiment, the reworking state of the area on each working day after the occurrence of the epidemic situation can be determined according to the travel intensity on each working day after the occurrence of the epidemic situation and the normal travel intensity in step 305.
As previously mentioned, the rework status may be characterized in particular by a rework index. Fig. 4 is a schematic diagram of a principle of determining a rework index according to an embodiment of the present invention. As shown in fig. 4, in order to determine the rework state of the area, the normal trip intensity of the area may be determined by the positioning data of the working day before the occurrence of the epidemic situation, the trip intensity of each day after the occurrence of the epidemic situation may be determined by the positioning data of the working day after the occurrence of the epidemic situation, and the basic trip intensity of the area may be determined by the positioning data of the holiday and the location data of the holiday and the trip intensity of each day after the occurrence of.
Determining the rework state of the working day according to the trip intensity of the working day, the normal trip intensity and the basic trip intensity, which may include: calculating a first difference value between the trip intensity of the working day and the basic trip intensity, and a second difference value between the normal trip intensity and the basic trip intensity; and determining a rework index of the working day according to the ratio of the first difference to the second difference, wherein the rework index is used for reflecting the rework state. The larger the rework index is, the better the rework state of the area is, and correspondingly, the better the economic recovery condition of the area is. Specifically, the following formula can be referred to:
S＝(D-M)/(A-M) (1)
wherein S is the rework index of the working day, D is the trip intensity of the working day, M is the basic trip intensity of the region, and A is the normal trip intensity of the region. The larger the value of S is, the better the rework condition of the area is. If the value of S is 1, it represents that the region has completely returned to normal. The calculation of the rework index can be quickly realized through the formula, and calculation resources are saved.
Optionally, the rework index may be a value between 0 and 1, if the rework index obtained through formula calculation is greater than 1, the rework index of the current day is taken as 1, and if the rework index obtained through formula calculation is less than 0, the rework index of the current day is taken as 0, so that the rework index is limited between 0 and 1, and the user can check the rework index conveniently.
According to the data processing method provided by the embodiment, the normal trip intensity of the area can be determined through the positioning data of the working day before the occurrence of the epidemic situation, the basic trip intensity of the area is determined through the positioning data of the holidays, the trip intensity of each working day after the occurrence of the epidemic situation is comprehensively calculated according to the normal trip intensity and the basic trip intensity, and the basic trip intensity required for maintaining the basic operation is considered during calculation, so that the calculated ratio is more consistent with the actual rework index, and the accuracy of the result is further improved.
On the basis of the technical solution provided in the foregoing embodiment, optionally, determining whether the resident user has a travel behavior on the working day according to the positioning data of the resident user on the working day and the residential site of the resident user may include: and if the resident user is judged to be present at a place beyond the preset distance of the residential place for a preset duration according to the positioning data of the resident user on the working day, determining that the resident user has a trip behavior on the working day.
The preset distance and the preset duration can be set according to actual needs. Optionally, the preset distance may be 1km, and the preset time may be half an hour. If a resident user stays in a place 1km away from the residential place for half an hour in a certain day, the resident user can be judged to have a trip behavior in the same day. Therefore, the effective travel behavior of the user can be detected quickly, and the accuracy of data processing is improved.
A specific example of calculating the rework index is given below in conjunction with the above implementation flow. Supposing that the first day of the spring festival is the time when the epidemic starts, in order to resist the epidemic, the shutdown and production stoppage are performed at each dispute during the spring festival and for a long time after the spring festival, and the whole people are isolated. With the gradual control of the epidemic situation, after the orderly epidemic prevention preparation work, the different places start to carry out the re-work and the re-production.
Specifically, positioning data of N1 working days before the spring festival, 7 holidays in the spring festival, and N2 working days after the spring festival in a certain area can be collected. The positioning data records the presence of a certain user at a certain base station at a certain moment.
Through the positioning data, the ratio of the number of the resident users with the trip in the area to the total number of the resident users in the area can be calculated once a day, and the trip intensity in the area is defined. Wherein, the users who appear in the area within (N1+7+ N2) days of the statistical period and reach a certain threshold value are counted as the resident users of the area, so as to eliminate some users who temporarily enter the area. For a single user, the place with the longest occurrence time in the time range is evaluated as the residence place of the user, and whether the user has a trip is judged according to whether each user appears in other places except the residence place in the day.
Fig. 5 is a schematic flow chart of determining a rework index after spring festival according to an embodiment of the present invention. As shown in fig. 5, the regional trip intensity of N1 working days before the spring festival is taken, an average value is calculated and defined as the normal trip intensity, and the value is used to reflect the normal level before the regional festival. The average or lowest value of the travel intensity of the region in spring festival holiday 7 days is taken and defined as the basic travel intensity, and the value reflects the travel intensity required by the region to maintain basic operation.
For a certain day T1 after the spring festival holiday, the daily trip intensity is calculated, and then the rework index of the day T1 can be obtained according to the daily trip intensity, the normal trip intensity and the basic trip intensity obtained in the previous step, so that the rework condition of the area of the day T1 is reflected.
For example, assuming that N1 is 90 and N2 is 60, the area has 10000 resident users, and if 2000 of 10000 people have a trip on a certain day, the intensity of the trip on the day is 20%. For 90 working days before the spring festival, the trip intensity of each day is calculated to obtain the average trip intensity of the first 90 working days, which is recorded as the normal trip intensity and is assumed to be 50%. And calculating the travel intensity every day for 7 days in spring festival to obtain the lowest value of the travel intensity, recording as the basic travel intensity, and assuming that the value is 20%.
Then, the travel intensity of one or several days in 60 working days after the spring festival is calculated, if the travel intensity reaches 50%, the corresponding rework index can be determined to be 1 according to the formula (1), and then the rework state of the area can be considered to be recovered to the normal state, and if the rework index is only 35%, then the rework state can be considered to be recovered to half. According to the rework index of the area, the method can interact with the equipment in the area, and provides convenience for users and enterprises in the area.
Fig. 6 is a schematic diagram of a rework index change according to an embodiment of the invention. As shown in fig. 6, the horizontal axis represents time (format: month-day), the vertical axis represents the rework index, during the spring festival (1-26 to 2-2), the rework index reaches the lowest level, and after the spring festival, the rework index gradually increases, wherein the rework index increases and decreases periodically, because the working day trip intensity increases, and the weekend trip intensity falls. As can be seen from fig. 6, after the spring festival, the rework situation is gradually recovered as the epidemic situation is gradually controlled.
Furthermore, on the basis, a comparison graph of the rework indexes of each region can be manufactured. Fig. 7 is a comparative illustration of the rework index change of different areas according to an embodiment of the invention. As shown in fig. 7, the dotted line represents the rework index change of city a, the solid line represents the rework index change of city B, and the rework index between different areas can be visually checked through the curve shown in the figure.
In addition, a comparison graph of the rework index of each year may be created. Fig. 8 is a comparative illustration of the rework index change of different years according to an embodiment of the present invention. In the figure, the vertical axis represents the rework index, and the horizontal axis represents time. Since the calendar dates corresponding to the spring festival in different years are different, 0 on the horizontal axis in the figure represents the day of the spring festival, and the subsequent numbers represent the days after the current day of the spring festival. In the figure, the dotted line represents the re-work index change trend of the year without the occurrence of the epidemic, and the solid line represents the re-work index change trend of the year with the occurrence of the epidemic. As can be seen from fig. 8, under the condition that no epidemic situation occurs, the rework index quickly rises to 1 after the spring festival holiday, and under the condition that an epidemic situation occurs, the rework index slowly rises after the spring festival holiday, so that the comparison between the year with the epidemic situation and the year without the epidemic situation is visually reflected.
Fig. 9 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention. As shown in fig. 9, the data processing apparatus may include:
an obtaining module 901, configured to obtain positioning data of a user in an area to be analyzed, where the positioning data includes positioning data of each working day in a first statistical period before an epidemic situation occurs and positioning data of each working day in a second statistical period after the epidemic situation occurs;
the calculating module 902 is configured to determine, according to the obtained positioning data, the travel intensity of the area before the occurrence of the epidemic and the travel intensity of the area after the occurrence of the epidemic;
the determining module 903 is configured to determine a rework state of the area according to the trip intensity of the area before the occurrence of the epidemic situation and the trip intensity of the area after the occurrence of the epidemic situation.
The data processing apparatus provided in this embodiment may be configured to execute the technical solution provided in any of the foregoing method embodiments, and the implementation principle and technical effect are similar.
In a possible implementation manner, the determining module 903 is further configured to:
controlling the public equipment in the area according to the rework state of the area;
and/or sending indication information corresponding to the rework state to a government service platform or terminal equipment in the area.
In a possible implementation manner, the calculating module 902 is specifically configured to:
calculating the travel intensity corresponding to each working day according to the acquired positioning data of each working day;
calculating the normal travel intensity of the region according to the travel intensity of each working day before the epidemic situation occurs;
correspondingly, the determining module 903 is specifically configured to:
and determining the re-work state of the region on each working day after the epidemic situation occurs according to the travel intensity of each working day after the epidemic situation occurs and the normal travel intensity.
In one possible implementation, the calculating module 902 is further configured to:
calculating the basic travel intensity required for maintaining basic operation in the region according to the travel intensity of the user in at least one holiday in the region;
correspondingly, the determining module 903 is specifically configured to:
and aiming at each working day after the epidemic situation occurs, determining the rework state of the working day according to the trip intensity of the working day, the normal trip intensity and the basic trip intensity.
In a possible implementation manner, when the calculating module 902 calculates the travel intensity corresponding to each working day according to the obtained positioning data of each working day, specifically configured to:
determining resident users in the area according to the acquired positioning data of the users in the area;
determining the residential site of each resident user according to the positioning data of each resident user;
and for each working day, determining the travel intensity of the working day through the living places of all resident users in the area and the positioning data of all resident users in the working day.
In a possible implementation manner, the calculating module 902 is specifically configured to, when determining the travel intensity on the working day through the residential location of each resident user in the area and the positioning data of each resident user on the working day:
for each resident user in the area, determining whether the resident user has a trip behavior on the working day according to the positioning data of the resident user on the working day and the place where the resident user resides;
and calculating the travel intensity of the working day according to the number of resident users having travel behaviors in the working day and the number of all resident users in the area.
In a possible implementation manner, the obtained positioning data includes mobile phone signaling data of the user collected by the base station;
the calculating module 902 is specifically configured to, when determining whether the resident user has a travel behavior on the workday according to the positioning data of the resident user on the workday and the place where the resident user resides:
and if the resident user is determined to be connected to a base station outside the residential place on the working day and not connected to a base station outside the area according to the mobile phone signaling data of the resident user on the working day, determining that the resident user has a trip behavior on the working day.
In a possible implementation manner, when determining whether the resident user has a travel behavior on the weekday according to the positioning data of the resident user on the weekday and the place of residence of the resident user, the calculating module 902 is specifically configured to:
and if the resident user is judged to be present at a place beyond the preset distance of the residential place for a preset duration according to the positioning data of the resident user on the working day, determining that the resident user has a trip behavior on the working day.
In a possible implementation manner, when determining the rework state of the working day according to the trip intensity of the working day, the normal trip intensity, and the basic trip intensity, the determining module 903 is specifically configured to:
calculating a first difference value between the trip intensity of the working day and the basic trip intensity, and a second difference value between the normal trip intensity and the basic trip intensity;
and determining a rework index of the working day according to the ratio of the first difference to the second difference, wherein the rework index is used for reflecting the rework state.
The data processing apparatus provided in any of the foregoing embodiments is configured to execute the technical solution of any of the foregoing method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.
Fig. 10 is a schematic structural diagram of a data processing apparatus according to an embodiment of the present invention. As shown in fig. 10, the apparatus may include: a memory 1001, a processor 1002 and a data processing program stored on the memory 1001 and executable on the processor 1002, the data processing program implementing the steps of the data processing method according to any of the previous embodiments when executed by the processor 1002.
Alternatively, the memory 1001 may be separate or integrated with the processor 1002.
For the implementation principle and the technical effect of the device provided by this embodiment, reference may be made to the foregoing embodiments, and details are not described here.
An embodiment of the present invention further provides a computer-readable storage medium, where a data processing program is stored on the computer-readable storage medium, and when the data processing program is executed by a processor, the data processing program implements the steps of the data processing method according to any of the foregoing embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules is only one logical division, and other divisions may be realized in practice, for example, a plurality of modules may be combined or integrated into another system, or some features may be omitted, or not executed.
The integrated module implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a processor to execute some steps of the methods according to the embodiments of the present invention.
It should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile storage NVM, such as at least one disk memory, and may also be a usb disk, a removable hard disk, a read-only memory, a magnetic or optical disk, etc.
The storage medium may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuits (ASIC). Of course, the processor and the storage medium may reside as discrete components in an electronic device or host device.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (12)
1. A data processing method, comprising:
acquiring positioning data of users in an area to be analyzed, wherein the positioning data comprises the positioning data of each working day in a first statistical period before an epidemic situation occurs and the positioning data of each working day in a second statistical period after the epidemic situation occurs;
determining the travel intensity of the area before the occurrence of the epidemic and the travel intensity of the area after the occurrence of the epidemic according to the acquired positioning data;
and determining the rework state of the region according to the trip intensity of the region before the occurrence of the epidemic situation and the trip intensity of the region after the occurrence of the epidemic situation.
2. The method of claim 1, after determining the rework status of the area, further comprising:
controlling the public equipment in the area according to the rework state of the area;
and/or sending indication information corresponding to the rework state to a government service platform or terminal equipment in the area.
3. The method according to claim 1 or 2, wherein determining the travel intensity of the area before the occurrence of the epidemic and the travel intensity of the area after the occurrence of the epidemic according to the obtained positioning data comprises:
calculating the travel intensity corresponding to each working day according to the acquired positioning data of each working day;
calculating the normal travel intensity of the region according to the travel intensity of each working day before the epidemic situation occurs;
correspondingly, according to the trip intensity of the region before the epidemic situation occurs and the trip intensity of the region after the epidemic situation occurs, the rework state of the region is determined, which includes:
and determining the re-work state of the region on each working day after the epidemic situation occurs according to the travel intensity of each working day after the epidemic situation occurs and the normal travel intensity.
4. The method of claim 3, further comprising:
calculating the basic travel intensity required for maintaining basic operation in the region according to the travel intensity of the user in at least one holiday in the region;
correspondingly, according to the trip intensity of each workday after the epidemic situation occurs and the normal state trip intensity, confirm the reworking state of each workday after the epidemic situation occurs in the region, include:
and aiming at each working day after the epidemic situation occurs, determining the rework state of the working day according to the trip intensity of the working day, the normal trip intensity and the basic trip intensity.
5. The method according to claim 3, wherein calculating the travel intensity corresponding to each working day according to the acquired positioning data of each working day comprises:
determining resident users in the area according to the acquired positioning data of the users in the area;
determining the residential site of each resident user according to the positioning data of each resident user;
and for each working day, determining the travel intensity of the working day through the living places of all resident users in the area and the positioning data of all resident users in the working day.
6. The method of claim 5, wherein determining the intensity of travel on the weekday from the residence of each resident user in the area and the location data for each resident user on the weekday comprises:
for each resident user in the area, determining whether the resident user has a trip behavior on the working day according to the positioning data of the resident user on the working day and the place where the resident user resides;
and calculating the travel intensity of the working day according to the number of resident users having travel behaviors in the working day and the number of all resident users in the area.
7. The method according to claim 6, wherein the acquired positioning data includes user's mobile phone signaling data acquired by a base station;
determining whether the resident user has a trip behavior on the workday according to the positioning data of the resident user on the workday and the place where the resident user resides, including:
and if the resident user is determined to be connected to a base station outside the residential place on the working day and not connected to a base station outside the area according to the mobile phone signaling data of the resident user on the working day, determining that the resident user has a trip behavior on the working day.
8. The method of claim 6, wherein determining whether the resident user has travel behavior on the working day according to the positioning data of the resident user on the working day and the resident place of the resident user comprises:
and if the resident user is judged to be present at a place beyond the preset distance of the residential place for a preset duration according to the positioning data of the resident user on the working day, determining that the resident user has a trip behavior on the working day.
9. The method of claim 4, wherein determining the rework status of the workday according to the trip intensity of the workday, the normal trip intensity and the basic trip intensity comprises:
calculating a first difference value between the trip intensity of the working day and the basic trip intensity, and a second difference value between the normal trip intensity and the basic trip intensity;
and determining a rework index of the working day according to the ratio of the first difference to the second difference, wherein the rework index is used for reflecting the rework state.
10. A data processing apparatus, comprising:
the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring positioning data of users in an area to be analyzed, and the positioning data comprises the positioning data of each working day in a first statistical period before an epidemic situation occurs and the positioning data of each working day in a second statistical period after the epidemic situation occurs;
the computing module is used for determining the travel intensity of the region before the occurrence of the epidemic situation and the travel intensity of the region after the occurrence of the epidemic situation according to the obtained positioning data;
and the determining module is used for determining the rework state of the region according to the trip intensity of the region before the occurrence of the epidemic situation and the trip intensity of the region after the occurrence of the epidemic situation.
11. A data processing apparatus, characterized in that the data processing apparatus comprises: memory, processor and data processing program stored on the memory and executable on the processor, which when executed by the processor implements the steps of the data processing method according to any one of claims 1 to 9.
12. A computer-readable storage medium, characterized in that a data processing program is stored thereon, which when executed by a processor implements the steps of the data processing method according to any one of claims 1 to 9.
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| WO2022041905A1 (en) * | 2020-08-25 | 2022-03-03 | 深圳前海微众银行股份有限公司 | Data processing method and apparatus, device, and storage medium |
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| US20170032291A1 (en) * | 2013-12-24 | 2017-02-02 | Zte Corporation | Bus Planning Method Using Mobile Communication Data Mining |
| CN108985586A (en) * | 2018-06-28 | 2018-12-11 | 中国联合网络通信有限公司深圳市分公司 | Appraisal procedure, device and the computer readable storage medium of resident trip index |
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| CN111461677A (en) * | 2020-04-27 | 2020-07-28 | 深圳市城市公共安全技术研究院有限公司 | Method, device, terminal and storage medium for managing personnel in epidemic situation period |
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| US20170032291A1 (en) * | 2013-12-24 | 2017-02-02 | Zte Corporation | Bus Planning Method Using Mobile Communication Data Mining |
| CN108985586A (en) * | 2018-06-28 | 2018-12-11 | 中国联合网络通信有限公司深圳市分公司 | Appraisal procedure, device and the computer readable storage medium of resident trip index |
| CN111311193A (en) * | 2020-02-26 | 2020-06-19 | 百度在线网络技术（北京）有限公司 | Configuration method and device of public service resources |
| CN111461677A (en) * | 2020-04-27 | 2020-07-28 | 深圳市城市公共安全技术研究院有限公司 | Method, device, terminal and storage medium for managing personnel in epidemic situation period |
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