CN113592196A - Flow data prediction system, method, computer equipment and medium - Google Patents
Flow data prediction system, method, computer equipment and medium Download PDFInfo
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Abstract
The invention relates to the technical field of data monitoring, and particularly discloses a flow data prediction system, a flow data prediction method, computer equipment and a medium, wherein the system comprises the following components: the flow rate correction system comprises a time interval determination module, a basic flow rate generation module, a floating proportion determination module and a correction module, wherein the basic flow rate generation module is used for inputting different time intervals into a trained flow rate model in sequence to obtain basic flow rate; the floating proportion determining module is used for generating basic flow with a floating value; the correction module is used for correcting the basic flow with the floating value according to weather. The invention inputs different time intervals into the trained flow model in sequence to obtain the basic flow, the floating proportion is determined by the floating proportion determining module, and the basic flow with the floating value is determined based on the basic flow and the floating proportion.
Description
Technical Field
The invention relates to the technical field of data monitoring, in particular to a flow data prediction system, a flow data prediction method, computer equipment and a medium.
Background
With the continuous development of computer application technologies, traffic data prediction technologies are also increasingly applied to various different scenes to obtain people traffic data in different application scenes in advance, such as scenic spots, stations, large conference sites, sports sites, and other various large activity sites, so that in different application scenes, work arrangement of field workers is performed in advance according to people traffic, a field control scheme is formulated, and the like, to prevent emergency events, and in various application scenes, people traffic data are often influenced by various factors such as holidays, weather, and the like, and therefore it is very meaningful to provide a traffic prediction system and a traffic prediction method with high prediction accuracy.
Disclosure of Invention
An object of the present invention is to provide a traffic data prediction system, method, computer device and medium, so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
a traffic data prediction system, the system comprising:
a time interval determining module for acquiring the vacation information and generating different time intervals (t) according to the vacation information0,t1)，t0Denotes the start date, t1Indicating an end date;
a basic flow generation module for inputting different time periods into the trained flow model F in sequence to obtain basic flow F (t)0,t1)；
The floating proportion determining module is used for acquiring the access amount and the operation amount of the promotion file in unit time, the operation amount at least comprises a collection amount, a sharing amount and a searching amount, the floating proportion is determined according to the access amount and the operation amount, and the basic flow with the floating value is determined based on the basic flow and the floating proportion
The number of persons with intention
Effective value
Theta' represents the number of historical intentions, eta represents the return on promotion, x1Representing the amount of access, ω1Representing the number of valid values, x, for each access quantity2～xn，ω2～ωnRespectively representing the operation quantities of different types of operations and the number of effective values corresponding to each operation quantity;
a correction module, configured to obtain weather state information in a preset time period, generate a correction parameter based on the weather state information, correct the basic flow with the floating value according to the correction parameter, and obtain a predicted flow N' ═ N + λ (N),
wherein λ represents a calculation model used in determining the correction parameters;
the time period determination module specifically includes:
the position determining unit is used for acquiring vacation information, determining a central time according to a vacation length in the vacation information, and calculating the position proportion of the central time within one year;
the radius determining unit is used for determining the light and busy season information and determining the influence radius of each vacation based on the light and busy season information;
the first execution unit is used for generating a vacation table based on the position proportion and the influence radius of the vacation and determining different time intervals according to the vacation table;
the period determination module further comprises:
the retrieval unit is used for determining keywords, inputting the keywords into a search App and acquiring retrieval contents;
the file classification unit is used for classifying the retrieval contents to obtain a text file, an image file and a video file;
the conversion unit is used for converting the video file into an audio file and an image file and converting the audio file into a text file when the retrieval content is the video file;
and the second execution unit is used for respectively carrying out content identification on the acquired text file and the acquired image file and updating the light and busy season information according to the identification result.
In the above content, the output of the time period determination module is different time periods, the time period of the present invention is different from the conventional time period determination, and we can recall through common sense that the conventional time period determination is a simple weak season, the time of one year is divided into two segments, because the number of the divided segments is too small, the span of one time period is very large, and this is also related to the purpose of segmenting the time period, for example, for scenic spots, the conventional segmentation is only for the purpose of rating a fare, and for the technical solution of the present invention, the segmentation is for the purpose of better predicting the flow rate. The segmentation process is performed on the nodes according to the holidays, the time intervals among different holidays are different, for example, summer holidays of students between noon and mid-autumn are higher than those among other holidays, and therefore, the time of one year is segmented according to the holiday nodes, and then follow-up operation is performed according to the segmented time intervals.
The purpose of the floating proportion generating module is to generate a floating proportion, and then determine a basic flow with a floating value according to the floating proportion to form a data form of a +/-b.
The correction module corrects the basic flow with the floating value as the name implies, the correction is based on weather, and the weather prediction information is related to the prediction time, in other words, the weather prediction in the tomorrow is more accurate than the weather prediction in the afterday, so that the weather prediction information continuously changes, the correction parameters also continuously change, and the correction process is a dynamic process, but the change range is not large.
Firstly, converting vacation information into nodes to generate each time period, and then adding light and busy season information in each time period, such as a scenic spot with a snow scene as a main part, wherein the passenger flow is less in summer, so that the passenger flow is less in the vacation period of a labor section, a specific influence mode is an influence radius, and if the radius is zero at an extreme point, the influence factor of the vacation period is reduced to zero actually; however, in a scene mainly based on a snow scene, the holiday time is important, and is 3 days, but in reality, many visitors "extend" the holiday time by asking for a vacation or the like, and reflect the extended holiday time in the above-mentioned module, so that the radius is increased. It is worth mentioning that the radius of influence is typically in days.
As a further limitation of the technical solution of the present invention, the floating ratio determining module specifically includes:
the effective value determining unit is used for acquiring the access amount and the operation amount in the promotion file and determining the effective value of the promotion file according to the access amount and the operation amount;
the return rate generating unit is used for reading the calculated promotion return rate and correcting the return rate based on the effective value;
the comparison unit is used for determining the number of the intentions according to the corrected return rate and comparing the number of the intentions with the corresponding historical number of the intentions;
and the floating proportion determining unit is used for determining the floating proportion according to the comparison result.
As a further limitation of the technical solution of the present invention, the effective value determining unit specifically includes:
the weight value determining subunit is used for acquiring the operation amount in the promotion file and determining the weight values of different operations in the operation amount;
the interest value operator unit is used for calculating interest values of corresponding operations according to the weight values;
and the accumulation subunit is used for accumulating the interest values of different operations and determining the effective value of the promotion file based on the accumulated interest values and the access amount and the operation amount of the promotion file.
As a further limitation of the technical solution of the present invention, the system further comprises:
the receiving module is used for receiving a user access request and setting the number of times of the request as one;
the identity confirmation module is used for acquiring login information containing a user ID and determining user registration information corresponding to the login information;
the first judgment module is used for judging whether login information containing a user ID is the same as user registration information corresponding to the login information or not, and if the login information containing the user ID is the same as the user registration information corresponding to the login information, the authentication is passed;
the second judgment module is used for judging the request times and the threshold value if the login information containing the user ID is different from the user registration information corresponding to the login information, and repeatedly receiving the user access request and increasing the request times if the request times are smaller than the threshold value; and if the request times are larger than the threshold value, stopping receiving the user access request.
The above content is an auxiliary function, which is an additional function of the technical solution of the present invention, that is, for a simple authority judgment of a person using the system, anyone does not have access authority, and the person having access authority may be a manager or a partner, because the determination of the traffic model in the technical solution of the present invention may use sample data of other partners.
The technical scheme of the invention also provides a flow data prediction method, which specifically comprises the following steps:
acquiring vacation information, and generating different time periods according to the vacation information;
inputting different time intervals into the trained flow model in sequence to obtain basic flow;
acquiring the access amount and the operation amount of the promotion file in unit time, determining a floating proportion according to the access amount and the operation amount, and determining a basic flow with a floating value based on the basic flow and the floating proportion; the operation amount at least comprises a collection amount, a sharing amount and a searching amount;
the method comprises the steps of obtaining weather state information in a preset time period, generating correction parameters based on the weather state information, and correcting basic flow with a floating value according to the correction parameters to obtain predicted flow.
As a further limitation of the technical solution of the present invention, the acquiring vacation information and generating different time periods according to the vacation information specifically includes:
acquiring vacation information, determining a central time according to a vacation length in the vacation information, and calculating a position proportion of the central time within one year;
determining light-season and strong-season information, and determining influence radius of each vacation based on the light-season and strong-season information;
generating a vacation table based on the position proportion and the influence radius of the vacation, and determining different time periods according to the vacation table.
The technical solution of the present invention further provides a computer device, where the computer device includes one or more processors and one or more memories, where at least one program code is stored in the one or more memories, and when the program code is loaded and executed by the one or more processors, the function of the traffic data prediction method is implemented.
The technical scheme of the present invention further provides a computer device, wherein at least one program code is stored in the computer storage medium, and when the program code is loaded and executed by a processor, the function of the above flow data prediction method is realized.
Compared with the prior art, the invention has the beneficial effects that:
1. the basic flow generation module is used for inputting different time periods into the trained flow model in sequence to obtain basic flow; the floating proportion determining module is used for acquiring the access amount and the operation amount of the promotion file in unit time, determining a floating proportion according to the access amount and the operation amount, and determining a basic flow with a floating value based on the basic flow and the floating proportion; the correction module is used for acquiring weather state information in a preset time period, generating correction parameters based on the weather state information, and correcting the basic flow with the floating value according to the correction parameters to obtain predicted flow.
2. The method comprises the steps of inputting different time intervals into a trained flow model in sequence to obtain basic flow, determining a floating proportion through a floating proportion determining module, and determining the basic flow with a floating value based on the basic flow and the floating proportion.
3. The invention also corrects the basic flow with the floating value according to weather information, has high prediction accuracy and is convenient to popularize.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 shows a first composition diagram of a flow data prediction system.
Fig. 2 shows a first component block diagram of a period determination module in a traffic data prediction system.
Fig. 3 shows a second component block diagram of a period determination module in the traffic data prediction system.
FIG. 4 is a block diagram illustrating the components of the float ratio determination module in the flow data prediction system.
Fig. 5 is a block diagram showing the composition of an effective value determination unit in the floating ratio determination module.
Fig. 6 shows a second constitutional view of the flow data prediction system.
Fig. 7 shows a flow diagram of a traffic data prediction method.
Fig. 8 shows a sub-flow block diagram of a traffic data prediction method.
Fig. 9 shows a graph of the vacation periods of months 4 to 6 of 2021.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, etc. may be used herein to describe various modules in embodiments of the invention, these modules should not be limited by these terms. These terms are only used to distinguish one type of module from another. For example, a first determination module may also be referred to as a second determination module without necessarily requiring or implying any such actual relationship or order between such entities or operations without departing from the scope of embodiments of the present invention. Similarly, the second determination module may also be referred to as the first determination module. Also, 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.
Example 1
Fig. 1 shows a first composition structure diagram of a traffic data prediction system, and in an embodiment of the present invention, a traffic data prediction system, a method, a computer device, and a medium are provided, where the system 10 specifically includes:
a time interval determining module 11 for obtaining the vacation information and generating different time intervals (t) according to the vacation information0,t1)，t0Denotes the start date, t1Indicating the end date.
A basic flow generating module 12, configured to input the trained flow model F at different time intervals in sequence to obtain a basic flow F (t)0,t1)；
A floating proportion determining module 13, configured to obtain an access amount and an operation amount of the promotion file in unit time, where the operation amount at least includes a collection amount, a sharing amount, and a search amount, determine a floating proportion according to the access amount and the operation amount, and determine a basic flow with a floating value based on the basic flow and the floating proportion
The number of persons with intention
Effective value
Theta' represents the number of historical intentions, eta represents the promotion return rate,x1representing the amount of access, ω1Representing the number of valid values, x, for each access quantity2～xn，ω2～ωnRespectively representing the operation quantities of different types of operations and the number of effective values corresponding to each operation quantity;
a correction module 14, configured to obtain weather state information in a preset time period, generate a correction parameter based on the weather state information, correct the basic flow with the floating value according to the correction parameter, and obtain a predicted flow N' ═ N + λ (N),
where λ represents the computational model for determining the correction parameters.
In the above content, the output of the time period determination module is different time periods, the time period of the present invention is different from the conventional time period determination, and we can recall through common sense that the conventional time period determination is a simple weak season, the time of one year is divided into two segments, because the number of the divided segments is too small, the span of one time period is very large, and this is also related to the purpose of segmenting the time period, for example, for scenic spots, the conventional segmentation is only for the purpose of rating a fare, and for the technical solution of the present invention, the segmentation is for the purpose of better predicting the flow rate. The segmenting process is performed by segmenting the nodes according to the holidays, the time intervals among different holidays are different, for example, summer holidays of students between noon and mid-autumn are higher than the summer holidays among other holidays, and therefore, the segmenting process is meaningful in that the time of one year is segmented according to the holiday nodes, and then subsequent operations are performed according to the segmented time intervals.
A simple description needs to be made on the flow model in the basic flow generation module, and the generation method of the flow model is most commonly generated by a sample fitting method, so that the method is very suitable for practical engineering and is effective, and the accuracy of the method is higher and higher as the number of samples is increased. It should be noted that the sample is not only historical data of the scene itself, but also a feasible scheme for generating the model according to the sample of the scene with similar scale.
The purpose of the floating proportion generating module is to generate a floating proportion, and then determine a basic flow with a floating value according to the floating proportion to form a data form of a +/-b.
A correction module, configured to correct the basic traffic with the floating value, where the correction is based on weather, and first needs to obtain a weather state in a certain time period, and the weather state directly affects the increase or decrease of the passenger traffic, so that the influence of the weather state on the passenger traffic needs to be evaluated according to the weather state in the certain time period, and the passenger traffic affected by the weather state, i.e. a correction parameter, is used to further correct the basic traffic with the floating value, and a calculation model is needed in the determination process of the correction parameter, for example, different machine learning models are trained for different weather states, and by inputting the basic traffic with the floating value into the machine learning model trained in advance, the model can predict the passenger traffic affected by the weather state, and it needs to be stated that weather prediction information is related to prediction time, in other words, the weather forecast in tomorrow is more accurate than the weather forecast in the afterweather, so the weather forecast information changes continuously, the correction parameters also change continuously, and the correction process is a dynamic process.
Fig. 2 shows a first component structure diagram of a time interval determination module in a flow data prediction system, where the time interval determination module 11 specifically includes:
the position determining unit 111 is used for acquiring vacation information, determining a central time according to a vacation length in the vacation information, and calculating a position proportion of the central time within one year;
a radius determining unit 112, configured to determine light season and high season information, and determine each holiday influence radius based on the light season and high season information;
a first execution unit 113 for generating a vacation table based on the position proportion and the influence radius of the vacation and determining different periods according to the vacation table.
Firstly, converting vacation information into nodes to generate each time period, and then adding light and busy season information in each time period, for example, a scenic spot mainly based on a snow scene, wherein the passenger flow is less in summer, so that the passenger flow is less in the vacation in labor section, the specific influence mode is the influence radius, and the radius is zero at an extreme point, so that the influence factor of the vacation is reduced to zero actually; however, in a scene mainly based on a snow scene, the holiday time is important, and is 3 days, but in reality, many visitors "extend" the holiday time by asking for a vacation or the like, and reflect the extended holiday time in the above-mentioned module, so that the radius is increased. It is worth mentioning that the radius of influence is typically in days.
For example, as shown in fig. 9, taking a time interval from 2021, 4/1/2021, 6/30/2021 as an example, where the time interval includes a clearness section, a labor section, and an afternoon section, the central time is determined according to the length of the holiday, the influence radius of each holiday is determined in units of days based on the thin season information, and a holiday schedule is generated based on the central time and the influence radius of the holiday, so that the holiday from 2021, 4/1/2021, 6/30/2021 is divided into a plurality of time periods and output according to different holidays.
Fig. 3 shows a second component structure diagram of a period determination module in the traffic data prediction system, wherein the period determination module 11 further includes:
a retrieval unit 114, configured to determine a keyword, input the keyword into a search App, and obtain a retrieval content;
a file classification unit 115, configured to classify the search content to obtain a text file, an image file, and a video file;
a conversion unit 116, configured to, when the retrieval content is a video file, convert the video file into an audio file and an image file, and convert the audio file into a text file;
and a second executing unit 117, configured to perform content recognition on the acquired text file and image file, respectively, and update the light and heavy season information according to the recognition result.
The above-mentioned light season information is used to determine the influence radius of a certain holiday, wherein the light season information is changeable, for example, if a promotional article finds that a scenic region mainly including a snowscape is also preferable in summer holiday, the season time of the scenic region will increase, and accordingly, the influence radius of the summer holiday of the scenic region will increase.
Fig. 4 is a structural diagram illustrating a floating ratio determining module in the flow data prediction system, where the floating ratio determining module 13 specifically includes:
an effective value determining unit 131, configured to obtain an access amount and an operation amount in the promotion file, and determine an effective value of the promotion file according to the access amount and the operation amount;
a return rate generating unit 132, configured to read the calculated promotion return rate, and correct the return rate based on the effective value;
a comparison unit 133, configured to determine the number of the intended people according to the modified return rate, and compare the number of the intended people with the corresponding historical number of the intended people;
and a floating ratio determining unit 134, configured to determine a floating ratio according to the comparison result.
The floating scale workflow is specifically described in the above, so that online popularization is easier since media technology arises, and it is also described from the side that a self-media file affects traffic, and for convenience of description, the invention is described by specific examples: for example, a promotion document is an article, and the system reads the access amount and the operation amount of the article to determine the effective value of the article, where the effective value is a value reflecting the promotion effect.
Fig. 5 is a structural diagram illustrating a composition of an effective value determining unit in the floating ratio determining module, where the effective value determining unit 131 specifically includes:
a weight value determining subunit 1311, configured to obtain the operation amount in the promotion file, and determine weight values of different operations in the operation amount;
an interest value operator unit 1312 for calculating interest values of the corresponding operations according to the weight values;
an accumulation subunit 1313, configured to accumulate interest values of different operations, and determine an effective value of the promotion file based on the accumulated interest values and the access amount and operation amount of the promotion file.
The effective value determining process is important, and as can be known from the above, the effective value is used for obtaining the promotion effect of the promotion file, for example, in the case of the article, if a user likes the article, the user is interested in the article, and if the user shares the article, the interest value of the user is higher, so that the interest value of the user can be determined based on the operation amount; it should be noted that one access amount can be regarded as an interest value, and it can be stated that a user is interested as long as a promotion file is accessed, because in the big data era, personal interests are easily obtained, and many existing promotion software are promoted according to the interests of the user, in other words, as long as the user accesses the promotion file, it is stated that the corresponding software considers that the user belongs to the interests, so that one access amount can be regarded as an interest value.
The content process is relatively complete, and it needs to be explained that the share amount in the operation amount is more important than the access amount, for convenience of calculation, a unit is set, that is, an interest value, for example, one share amount is equivalent to 10 interest values, and one interest value is equivalent to 1 effective value, so that the operation amount can be distinguished, and the proportion determination process is more accurate.
Specifically, the effective value p may be represented by a formula
Performing a calculation of where x1Representing the amount of access, ω1Representing the number of valid values, x, for each access quantity2Represents the reserve, omega2Representing the number of valid values, x, corresponding to each of the reserves3Representing the share amount, ω3Represents the number of the effective values corresponding to each sharing amount, x4Representing the amount of search, ω4Representing the number of valid values corresponding to each search quantity, and so on, xn，ωnRespectively represent itThe operation amount of other types of operation and the number of effective values corresponding to each operation amount.
After the effective value is determined, reading a promotion return rate η, wherein the promotion return rate η is an existing data, and what articles are sent by different platforms in different ways has a prediction thereof in advance, which is very high in accuracy today in the big data era, but there are exceptions to the high accuracy, so that the return rate needs to be corrected through the effective value.
Specifically, the correction method is to calculate the effective value p and the historical average effective value
Comparing to obtain an offset rate, and then correcting the above-mentioned return rate according to the offset rate, where the corrected return rate can be expressed as
The number of the intentions can be determined by directly multiplying the corrected rate of return by the sum of the visit amount and the operation amount
Wherein x is1Representing the amount of access, x2Represents the collection amount, x3Represents the share amount, x4Represents the amount of search, and so on, xnAn operation amount representing other types of operations; comparing the number of intentions theta with the corresponding historical number of intentions theta' in the unified time period, and then determining the floating proportion according to the comparison result
The final process of the above process is to generate a ratio γ, which may appear somewhat redundant since the number of the intended persons is not directly used as a predicted value since it is calculated; on one hand, the self-media is only one factor influencing the flow and cannot be approximated, the real prediction process of the invention is completed by the basic flow generation module, and on the other hand, the data of the self-media isThe fluctuation is large, and the obtained number of the intentions is actually contained in the basic flow. Therefore, the above-mentioned contents are only intended to generate a ratio, for example, how the degree of fire explosion in the scenic spot today compares with the last year, determine the floating ratio according to the comparison result, and then correct the basic flow to obtain a basic flow with floating flow
Wherein F represents the trained flow model, (t)0,t1) Represents a period of time, t0Denotes the start date, t1Indicating the end date.
After the basic flow N with the floating value is determined, the influence of the weather state in the time period on the passenger flow needs to be considered, so that the passenger flow influenced by the weather state in the time period needs to be determined, that is, a correction parameter, and the correction parameter is used to further correct the basic flow N with the floating value to obtain a predicted flow N' ═ N + λ (N),
where λ represents the computational model used in determining the correction parameters.
Fig. 6 shows a second component block diagram of the flow data prediction system, the system 10 further comprising:
the receiving module 15 is used for receiving the user access request and setting the number of times of the request as one;
the identity confirmation module 16 is configured to obtain login information including a user ID, and determine user registration information corresponding to the login information;
the first judging module 17 is configured to judge whether login information including a user ID is the same as user registration information corresponding to the login information, and if the login information including the user ID is the same as the user registration information corresponding to the login information, pass authentication;
the second judging module 18 is configured to judge the number of times of the request and the size of the threshold if the login information including the user ID is different from the user registration information corresponding to the login information, and repeatedly receive the user access request and increase the number of times of the request if the number of times of the request is smaller than the threshold; and if the request times are larger than the threshold value, stopping receiving the user access request.
The above content is an auxiliary function, which is an additional function of the technical solution of the present invention, that is, for a simple authority judgment of a person using the system, anyone does not have access authority, and the person having access authority may be a manager or a partner, because the determination of the traffic model in the technical solution of the present invention may use sample data of other partners.
Example 2
Fig. 7 shows a flow chart of a traffic data prediction method, and in an embodiment of the present invention, a traffic data prediction method is further provided, where the method specifically includes:
step S200: acquiring vacation information, and generating different time periods according to the vacation information;
said step S200 is completed by the time interval determination module 11;
step S400: inputting different time intervals into the trained flow model in sequence to obtain basic flow;
step S400 is completed by the basic flow generating module 12;
step S600: acquiring the access amount and the operation amount of the promotion file in unit time, determining a floating proportion according to the access amount and the operation amount, and determining a basic flow with a floating value based on the basic flow and the floating proportion; the operation amount at least comprises a collection amount, a sharing amount and a searching amount;
the step S600 is completed by a floating proportion determining module 13;
step S800: acquiring weather state information in a preset time period, generating a correction parameter based on the weather state information, and correcting a basic flow with a floating value according to the correction parameter to obtain a predicted flow;
the step S800 is completed by the modification module 14.
Fig. 8 is a sub-flow block diagram of a traffic data prediction method, where the acquiring vacation information and generating different time periods according to the vacation information specifically include:
step S201: acquiring vacation information, determining a central time according to a vacation length in the vacation information, and calculating a position proportion of the central time within one year;
said step S201 is performed by the position determination unit 111;
step S203: determining light-season and strong-season information, and determining influence radius of each vacation based on the light-season and strong-season information;
the step S203 is completed by the radius determination unit 112;
step S205: generating a vacation table based on the position proportion and the influence radius of the vacation, and determining different time periods according to the vacation table;
the step S205 is completed by the first execution unit 113.
It should be noted that the above-mentioned description relates to a flow data prediction system and method, which is suitable for use in scenic spots, stations, large conference sites, sports event sites, and any other large activity sites where accurate prediction of human flow is required.
The functions that can be achieved by the above flow data prediction method are all performed by a computer device, which comprises one or more processors and one or more memories, wherein at least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to achieve the functions of the flow data prediction method.
The processor fetches instructions and analyzes the instructions one by one from the memory, then completes corresponding operations according to the instruction requirements, generates a series of control commands, enables all parts of the computer to automatically, continuously and coordinately act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
Illustratively, a computer program can be partitioned into one or more modules, which are stored in memory and executed by a processor to implement the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the terminal device.
Those skilled in the art will appreciate that the above description of the service device is merely exemplary and not limiting of the terminal device, and may include more or less components than those described, or combine certain components, or different components, such as may include input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal equipment and connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the terminal device by operating or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory mainly comprises a storage program area and a storage data area, wherein the storage program area can store an operating system, application programs (such as an information acquisition template display function, a product information publishing function and the like) required by at least one function and the like; the storage data area may store data created according to the use of the berth-state display system (e.g., product information acquisition templates corresponding to different product types, product information that needs to be issued by different product providers, etc.), and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The terminal device integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the modules/units in the system according to the above embodiment may be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the functions of the embodiments of the system. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like.
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 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 (8)
1. A flow data prediction system, the system comprising:
a time interval determining module for acquiring the vacation information and generating different time intervals (t) according to the vacation information0,t1)，t0Denotes the start date, t1Indicating an end date;
a basic flow generation module for inputting different time periods into the trained flow model F in sequence to obtain basic flow F (t)0,t1)；
The floating proportion determining module is used for acquiring the access amount and the operation amount of the promotion file in unit time, the operation amount at least comprises a collection amount, a sharing amount and a searching amount, the floating proportion is determined according to the access amount and the operation amount, and the basic flow with the floating value is determined based on the basic flow and the floating proportion
The number of persons with intention
Effective value
Theta' represents the number of historical intentions, eta represents the return on promotion, x1Representing the amount of access, ω1Representing the number of valid values, x, for each access quantity2～xn，ω2～ωnRespectively representing the operation quantities of different types of operations and the number of effective values corresponding to each operation quantity;
a correction module for obtaining weather state information in a preset time period, generating a correction parameter based on the weather state information, and correcting the basic flow with the floating value according to the correction parameter to obtain a predicted flowN'＝N+λ(N),
Wherein λ represents a calculation model used in determining the correction parameters;
the period determination module specifically includes the following units:
the position determining unit is used for acquiring vacation information, determining a central time according to a vacation length in the vacation information, and calculating the position proportion of the central time within one year;
the radius determining unit is used for determining the light and busy season information and determining the influence radius of each vacation based on the light and busy season information;
the first execution unit is used for generating a vacation table based on the position proportion and the influence radius of the vacation and determining different time intervals according to the vacation table;
the period determination module further includes the following units:
the retrieval unit is used for determining keywords, inputting the keywords into a search App and acquiring retrieval contents;
the file classification unit is used for classifying the retrieval contents to obtain a text file, an image file and a video file;
the conversion unit is used for converting the video file into an audio file and an image file and converting the audio file into a text file when the retrieval content is the video file;
and the second execution unit is used for respectively carrying out content identification on the acquired text file and the acquired image file and updating the light and busy season information according to the identification result.
2. The flow data prediction system of claim 1, wherein the floating proportion determination module specifically comprises:
the effective value determining unit is used for acquiring the access amount and the operation amount in the promotion file and determining the effective value of the promotion file according to the access amount and the operation amount;
the return rate generating unit is used for reading the calculated promotion return rate and correcting the return rate based on the effective value;
the comparison unit is used for determining the number of the intentions according to the corrected return rate and comparing the number of the intentions with the corresponding historical number of the intentions;
and the floating proportion determining unit is used for determining the floating proportion according to the comparison result.
3. The flow data prediction system according to claim 2, wherein the effective value determination unit specifically includes:
the weight value determining subunit is used for acquiring the operation amount in the promotion file and determining the weight values of different operations in the operation amount;
the interest value operator unit is used for calculating interest values of corresponding operations according to the weight values;
and the accumulation subunit is used for accumulating the interest values of different operations and determining the effective value of the promotion file based on the accumulated interest values and the access amount and the operation amount of the promotion file.
4. The flow data prediction system of claim 1, further comprising:
the receiving module is used for receiving a user access request and setting the number of times of the request as one;
the identity confirmation module is used for acquiring login information containing a user ID and determining user registration information corresponding to the login information;
the first judgment module is used for judging whether login information containing a user ID is the same as user registration information corresponding to the login information or not, and if the login information containing the user ID is the same as the user registration information corresponding to the login information, the authentication is passed;
the second judgment module is used for judging the request times and the threshold value if the login information containing the user ID is different from the user registration information corresponding to the login information, and repeatedly receiving the user access request and increasing the request times if the request times are smaller than the threshold value; and if the request times are larger than the threshold value, stopping receiving the user access request.
5. A traffic data prediction method implemented based on the traffic data prediction system according to any one of claims 1 to 4, the method specifically comprising:
acquiring vacation information, and generating different time periods according to the vacation information;
inputting different time intervals into the trained flow model in sequence to obtain basic flow;
acquiring the access amount and the operation amount of the promotion file in unit time, determining a floating proportion according to the access amount and the operation amount, and determining a basic flow with a floating value based on the basic flow and the floating proportion; the operation amount at least comprises a collection amount, a sharing amount and a searching amount;
the method comprises the steps of obtaining weather state information in a preset time period, generating correction parameters based on the weather state information, and correcting basic flow with a floating value according to the correction parameters to obtain predicted flow.
6. The method for predicting traffic data according to claim 5, wherein the acquiring vacation information and generating different time periods according to the vacation information specifically comprises:
acquiring vacation information, determining a central time according to a vacation length in the vacation information, and calculating a position proportion of the central time within one year;
determining light-season and strong-season information, and determining influence radius of each vacation based on the light-season and strong-season information;
generating a vacation table based on the position proportion and the influence radius of the vacation, and determining different time periods according to the vacation table.
7. A computer device comprising one or more processors and one or more memories having at least one program code stored therein, the program code when loaded and executed by the one or more processors implementing the functions of the traffic data prediction method according to any one of claims 5 to 6.
8. A computer storage medium having at least one program code stored therein, which when loaded and executed by a processor, performs the functions of the traffic data prediction method according to any one of claims 5 to 6.
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