CN113807553A - Method, device, equipment and storage medium for analyzing number of reservation services - Google Patents
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Abstract
The invention relates to the field of artificial intelligence, and discloses a method for analyzing the quantity of reservation services, which comprises the following steps: acquiring historical data of the online reservation service, cleaning the data and splitting a time dimension to obtain target data of a plurality of time periods; according to the number of the reserved users and the number of the visiting users of the target data of each time period, calculating the historical performance rate and the historical income weight of the reserved service on each time period line; acquiring historical bookable service quantity of booked services on each time slot line, and constructing a bookable service quantity decision model according to the historical bookable quantity, the historical performance rate and the historical income weight; and inputting the current number of the reserved services of the online reserved service into the reservation decision service model so as to output the current number of the reserved services of the online reserved service. In addition, the invention also relates to digital medical technology, and the on-line reservation service can be a medical service storable block chain. The invention can intelligently decide the optimal reservation quantity of the reservation service.
Description
Technical Field
The present invention relates to the field of artificial intelligence, and in particular, to a method and an apparatus for analyzing the number of subscribed services, an electronic device, and a computer-readable storage medium.
Background
The over-sale refers to a service providing mode in which the sales volume (the reserved volume) can be larger than the service available volume, and can be applied to online reservation services in industries such as medical beauty, dental care, genetic testing, traditional Chinese medicine care, and physical examination. At present, the number of online booking services is usually determined based on the acceptable number of different organizations or enterprises, but in an actual business scene, due to uncertainty of user time, a phenomenon that a user makes online booking and does not perform offline can easily occur, so that the online booking services can fully utilize manpower and material resources through over-sale, but the over-sale number is not easily controlled, the over-sale number is too large, the shortage of manpower and material resources can be caused, the over-sale number is less, and the effect of fully utilizing the manpower and material resources cannot be achieved, so that how to determine the optimal number of booking services becomes more important.
Disclosure of Invention
The invention provides a method and a device for analyzing the quantity of reservation services, electronic equipment and a computer readable storage medium, and mainly aims to intelligently decide the optimal reservation quantity of the reservation services.
In order to achieve the above object, the present invention provides a method for analyzing the number of subscribed services, comprising:
acquiring historical data of on-line reservation service, and performing data cleaning on the historical data to obtain target data;
splitting the target data according to a time dimension to obtain target data of a plurality of time periods;
calculating the historical performance rate of the online reservation service in each time period according to the number of the reservation users and the number of the visiting users in the target data of each time period in the plurality of time periods;
calculating historical revenue weights of the online booking service in each time period;
acquiring historical bookable service quantity of the online booked service in each time period, and constructing a bookable service quantity decision model according to the historical bookable quantity, the historical performance rate and the historical income weight;
and analyzing the current reserved service quantity of the online reserved service by utilizing the reserved service quantity decision model according to the current reserved service quantity of the online reserved service.
Optionally, the splitting the target data according to a time dimension to obtain target data of multiple time periods includes:
acquiring the total data volume of the target data and the acquisition time of each data in the target data;
generating a timestamp of each data in the target data according to the total data volume and the acquisition time;
and generating target data of a plurality of time periods according to the time stamp of each data.
Optionally, the generating a timestamp of each data in the target data according to the total data amount and the acquisition time includes:
acquiring the data sub-amount of the target data in each acquisition time, and calculating the data proportion of each data sub-amount in the total data amount;
and updating the time stamp of each sub data volume according to the data proportion so as to obtain the time stamp of each data in the target data.
Optionally, the calculating the historical profit weight of the online booking service in each time period comprises:
inquiring service income and service loss of the online booking service in each time period;
calculating the marginal profit of the on-line reservation service in each time period according to the service profit;
calculating the marginal loss and the extra cost of the on-line reservation service in each time period according to the service loss;
and summarizing the marginal income, the marginal loss and the extra cost to be used as historical income weight of the online reservation service in each time period.
Optionally, the subscription service quantity decision model includes:
wherein, S represents the optimal reservation quantity of the on-line reservation service, N represents the historical reservable service quantity, omega represents the marginal profit in the historical profit weight, rho represents the marginal loss in the historical profit weight, phi represents the extra cost in the historical profit weight, and sigma represents the historical performance rate.
Optionally, the performing data cleaning on the historical data to obtain target data includes:
carrying out duplicate removal operation on the historical data, and detecting whether the duplicate-removed historical data has a data missing value;
if no data missing value exists, the history data after the duplication removal is used as target data;
and if the data missing value exists, performing data filling on the data missing value to obtain target data.
Optionally, the performing a deduplication operation on the historical data includes:
calculating the similarity of any two data in the historical data;
if the similarity is not greater than the preset similarity, simultaneously keeping the two historical data;
and if the similarity is greater than the preset similarity, deleting any one of the two data.
In order to solve the above problem, the present invention also provides a device for analyzing the number of subscribed services, the device comprising:
the data cleaning module is used for collecting historical data of the on-line reservation service and cleaning the historical data to obtain target data;
the time dimension splitting module is used for splitting the target data according to a time dimension to obtain target data of a plurality of time periods;
a performance rate calculation module, configured to calculate a historical performance rate of the online reservation service in each time slot according to the number of reservation users and the number of visited users in the target data of each time slot of the multiple time slots;
the profit weight calculation module is used for calculating the historical profit weight of the online reservation service in each time period;
a decision model building module, configured to obtain a historical bookable service quantity of the online booked service in each time period, and build a booked service quantity decision model according to the historical bookable quantity, the historical fulfillment rate, and the historical revenue weight;
and the service quantity decision module is used for analyzing the current reserved service quantity of the on-line reserved service by utilizing the reserved service quantity decision model according to the current reserved service quantity of the on-line reserved service.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to implement the above-described method for analyzing the number of subscribed services.
In order to solve the above problem, the present invention also provides a computer-readable storage medium having at least one computer program stored therein, the at least one computer program being executed by a processor in an electronic device to implement the above-mentioned method for analyzing the number of subscribed services.
According to the invention, the historical data of the online reservation service is collected, and the historical data is subjected to data cleaning and time dimension splitting to obtain the target data of a plurality of time periods, so that the data volume of the historical data can be reduced, the processing speed of subsequent data is improved, and the historical data is further split according to time, so that the user behaviors of the online reservation service in different time periods can be known in more detail, and the accuracy of determining the optimal number of the subsequent reservation service can be improved; secondly, the embodiment of the invention calculates the historical performance rate and the historical profit weight of the online booking service in each time period and acquires the historical bookable service quantity of the online booking service in each time period to construct a booking service quantity decision model, so that the optimal quantity of the current booking service of the online booking service can be intelligently decided, the loss caused by canceling the online booking service due to uncertain factors of users is reduced, and the profit maximization of the online booking service is ensured. Therefore, the method, the device, the electronic device and the computer-readable storage medium for analyzing the number of the subscribed services provided by the embodiment of the invention can intelligently decide the optimal subscribed number of the subscribed services.
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Fig. 1 is a flowchart illustrating a method for analyzing the number of subscribed services according to an embodiment of the present invention;
fig. 2 is a block diagram of an apparatus for analyzing the number of subscribed services according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an internal structure of an electronic device implementing a method for analyzing the number of subscribed services 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
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 embodiment of the application provides a method for analyzing the number of reserved services. The execution subject of the method for analyzing the number of subscribed services includes, but is not limited to, at least one of a server, a terminal, and other electronic devices that can be configured to execute the method provided by the embodiments of the present application. In other words, the method for analyzing the number of subscribed services may be performed by software or hardware installed in the terminal device or the server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
Fig. 1 is a schematic flow chart illustrating a method for analyzing the number of subscribed services according to an embodiment of the present invention. In an embodiment of the present invention, the method for analyzing the number of subscribed services includes:
and S1, collecting historical data of the on-line reservation service, and performing data cleaning on the historical data to obtain target data.
In the embodiment of the present invention, the online booking service refers to a service that is pre-consulted and booked through a network platform, such as hotel booking service, traditional Chinese medicine physiotherapy booking service, medical booking service, and the like, the historical data includes service basic data and user data, the service basic data refers to product data of the online booking service, such as service price, service time, service items, service objects, and the like, and the user data refers to data that consults the online booking service, such as user name, user booking time, user visiting data, user consumption data, and the like.
Further, it should be understood that the historical data may contain a lot of useless data and/or repeated data, and therefore, the embodiment of the present invention reduces the data amount of the historical data and increases the processing speed of subsequent data by performing data cleansing on the historical data.
As an embodiment of the present invention, the performing data cleansing on the historical data to obtain target data includes: carrying out duplicate removal operation on the historical data, and detecting whether the duplicate-removed historical data has a data missing value; if no data missing value exists, the history data after the duplication removal is used as target data; and if the data missing value exists, performing data filling on the data missing value to obtain target data.
Further, as an embodiment of the present invention, the performing a deduplication operation on the historical data includes: and calculating the similarity of any two data in the historical data, if the similarity is not greater than the preset similarity, simultaneously keeping the two historical data, and if the similarity is greater than the preset similarity, deleting any one data in the two data. The preset similarity may be set to 0.9, or may be set according to an actual service scenario.
It should be noted that, before calculating the similarity of the historical data, the embodiment of the present invention further includes: and converting the historical data into corresponding hash values by using a hash algorithm so as to realize the calculation of the similarity of the subsequent historical data.
In an alternative embodiment, the similarity between any two data in the history data is calculated by the following method:
where d represents the similarity of any two data, w1jAnd w2jRepresenting the hash value for any two data.
Further, as one embodiment of the present invention, the detection of the data missing value may be implemented by a detection function in a currently known data missing value detection tool, such as a mismap function detection function in an Amelia package tool.
And S2, splitting the target data according to a time dimension to obtain target data of a plurality of time periods.
According to the embodiment of the invention, the target data is split according to the time dimension to obtain the data of the online reservation service in different time periods, and if the target data is historical data in the last year, the target data can be split into the target data with four dimensions according to the seasons.
As an embodiment of the present invention, the splitting the target data according to a time dimension to obtain target data of multiple time periods includes: acquiring the total data volume of the target data and the acquisition time of each piece of data in the target data, generating a time stamp of each piece of data in the target data according to the total data volume and the acquisition time, and generating the target data of a plurality of time periods according to the time stamp of each piece of data.
Wherein the generating a timestamp for each data in the target data according to the total data volume and the acquisition time comprises: acquiring the sub data volume of the target data in each acquisition time, calculating the data proportion of each sub data volume in the total data volume, and updating the time stamp of each sub data volume according to the data proportion so as to acquire the time stamp of each data in the target data.
Based on the division of the time dimension, the target data can be further split according to time, so that the user behaviors of the online reservation service in different time periods can be known in more detail, and the accuracy of determining the optimal number of the subsequent reservation services can be improved.
And S3, calculating the historical fulfillment rate of the online reservation service in each time period according to the number of the reservation users and the number of the visiting users in the target data of each time period in the plurality of time periods.
It should be understood that, in an actual business scenario, due to the uncertainty of the time of the user or the instability of the subscribed service, a phenomenon that the user subscribes the service but does not actually consume the service may occur, and therefore, in the embodiment of the present invention, the historical fulfillment rate of the online subscribed service in each time period is calculated according to the number of subscribed users and the number of visiting users in the target data of each time period in the plurality of time periods, so as to serve as a decision factor for subsequently constructing a decision model, thereby ensuring the decision accuracy of the decision model, where the decision model is used for deciding the optimal subscribed number of the online subscribed service, and ensuring the maximum revenue rate of the service product.
In the embodiment of the invention, the number of the reserved users refers to the number of users submitting reserved orders after consulting the on-line reserved service through a network platform, and the number of the visiting users refers to the number of users who have consumed the on-line reserved service on line. It should be noted that, before calculating the historical performance rate of the online reservation service in each time period according to the number of reservation users and the number of visited users in the target data of each time period in the plurality of time periods, the embodiment of the present invention further includes: and inquiring historical performance information of users corresponding to the number of the reserved users and the number of the visited users, judging whether the users are in a blacklist user or not according to the historical performance information, if the users are in the blacklist user, deleting the reserved and/or visited numbers of the users, and if the users are not in the blacklist user, reserving the reserved and/or visited numbers of the users. The history fulfillment information may be obtained by querying browsing records of the user in a background database of the online reservation service, and the determination of the blacklist user may be set based on the history fulfillment number of the user, and if the history fulfillment number of the user reaches ten times, the user is determined to be a blacklist user, which may also be set according to an actual service scenario.
Further, in an optional embodiment of the present invention, the historical performance rate of the online reservation service in each time period is calculated by using the following formula:
wherein, P represents the history performance rate, M represents the number of the reservation users, and N represents the number of the visiting users.
It should be noted that the number of the reservation users and the number of the visiting users may be queried through a query statement, and the query statement may be an SQL statement.
And S4, calculating the historical profit weight of the online booking service in each time period.
It should be understood that when the number of the visiting users is less than the data amount of the subscribing users, that is, when the historical performance rate is not equal to 1, the online subscribing service is caused to be in an idle state, and thus a certain service cost is brought, therefore, the present invention ensures the decision accuracy of the decision model by calculating the historical revenue weight of the online subscribing service in each time period to determine the decision factor of the subsequent decision model.
As an embodiment of the present invention, the calculating the historical profit weight of the online booking service in each time period includes: inquiring service income and service loss of the online reservation service in each time period, calculating marginal income of the online reservation service in each time period according to the service income, calculating marginal loss and extra cost of the online reservation service in each time period according to the service loss, summarizing the marginal income, the marginal loss and the extra cost, and taking the summarized marginal income, the marginal loss and the extra cost as historical income weight of the online reservation service in each time period.
The service profit and the service loss are determined based on service items and service prices of different online reservation services, for example, if the online reservation service is hotel reservation service and the service items are reservations of hotel rooms, the service profit and the service loss can be determined according to the number of the reservations of the hotel rooms and the number of the check-in persons of the hotel rooms. The marginal profit refers to a profit converted by a newly added user who reserves the on-line reservation service for consumption, the marginal loss refers to a loss converted by a user who reserves the on-line reservation service for consumption, and the marginal loss refers to a device loss caused by reserving the on-line reservation service for consumption.
Further, in order to ensure privacy and security of the historical revenue weight, the historical revenue weight may also be stored in a blockchain node.
S5, obtaining the historical bookable service quantity of the online booked service in each time period, and constructing a booked service quantity decision model according to the historical bookable quantity, the historical performance rate and the historical income weight.
It should be understood that in an actual business scenario, the number of online booking services should not be greater than the actual bookable number, for example, for a hotel room booking service, the number of hotel rooms bookable online should not be greater than the actual number of hotel rooms bookable, so the embodiment of the present invention determines the maximum number of online booking services in each time slot by obtaining the historical bookable service number of the online booking service in each time slot, and optionally, the historical bookable service number may also be obtained through the query statement.
It should be further understood that, since the reservation service is in the network platform for online transaction, in an actual service scenario, the reservation service may also perform transaction offline, and if the available reservation quantity of the online reservation service is directly opened, there is a high possibility that there is no available service under the online service, so that a certain loss may be brought.
Further, in an optional embodiment of the present invention, the reservation service quantity decision model is constructed by using the following formula:
wherein, S represents the optimal reservation quantity of the on-line reservation service, N represents the historical reservable service quantity, omega represents the marginal profit in the historical profit weight, rho represents the marginal loss in the historical profit weight, phi represents the extra cost in the historical profit weight, and sigma represents the historical performance rate.
And S6, analyzing the current reserved service quantity of the online reserved service by using the reserved service quantity decision model according to the current reserved service quantity of the online reserved service.
In the embodiment of the present invention, the current bookable service quantity refers to a remaining idle service quantity of the online booked service, which is generated based on different service scenarios, and the current bookable service quantity of the online booked service is input to the booked service quantity decision model, that is, the historical bookable service quantity in the booked service quantity decision model is replaced by the current bookable service quantity to output the current booked service quantity of the online booked service, so that an optimal quantity of the current booked service of the online booked service can be determined, and a current maximum profitability of the online booked service is ensured.
According to the invention, the historical data of the online reservation service is collected, and the historical data is subjected to data cleaning and time dimension splitting to obtain the target data of a plurality of time periods, so that the data volume of the historical data can be reduced, the processing speed of subsequent data is improved, and the historical data is further split according to time, so that the user behaviors of the online reservation service in different time periods can be known in more detail, and the accuracy of determining the optimal number of the subsequent reservation service can be improved; secondly, the embodiment of the invention calculates the historical performance rate and the historical profit weight of the online booking service in each time period and acquires the historical bookable service quantity of the online booking service in each time period to construct a booking service quantity decision model, so that the optimal quantity of the current booking service of the online booking service can be intelligently decided, the loss caused by canceling the online booking service due to uncertain factors of users is reduced, and the profit maximization of the online booking service is ensured. Therefore, the method for analyzing the number of the reservation services provided by the embodiment of the invention can intelligently decide the optimal reservation number of the reservation services.
As shown in fig. 2, the present invention is a functional block diagram of a device for analyzing the number of reserved services.
The device 100 for analyzing the number of subscribed services according to the present invention may be installed in an electronic device. According to the implemented functions, the quantity analysis device of the reservation service may include a data cleaning module 101, a time dimension splitting module 102, a performance rate calculation module 103, an income weight calculation module 104, a decision model construction module 105, and a service quantity decision module 106. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform a fixed function, and is stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the data cleaning module 101 is configured to collect historical data of an online reservation service, and perform data cleaning on the historical data to obtain target data;
the time dimension splitting module 102 is configured to split the target data according to a time dimension to obtain target data of multiple time periods;
the performance rate calculation module 103 is configured to calculate a historical performance rate of the online reservation service in each time period according to the number of reservation users and the number of visited users in the target data of each time period in the plurality of time periods;
the profit weight calculation module 104 is configured to calculate a historical profit weight of the online booking service in each time period;
the decision model building module 105 is configured to obtain a historical bookable service quantity of the online booked service in each time period, and build a booked service quantity decision model according to the historical bookable quantity, the historical performance rate, and the historical revenue weight;
the service quantity decision module 106 is configured to analyze the current reserved service quantity of the online reserved service by using the reserved service quantity decision model according to the current reservable service quantity of the online reserved service.
In detail, when the modules in the device 100 for analyzing the number of subscribed services in the embodiment of the present invention are used, the same technical means as the method for analyzing the number of subscribed services described in fig. 1 above are adopted, and the same technical effect can be produced, and details are not described here.
Fig. 3 is a schematic structural diagram of an electronic device 1 that implements a method for analyzing the number of subscribed services according to the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a number analysis program of subscribed services, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device 1, connects various components of the electronic device 1 by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (for example, a quantity analysis program for executing a reservation service, etc.) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic apparatus 1 and various types of data such as codes of a quantity analysis program of a reservation service, etc., but also to temporarily store data that has been output or is to be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device 1 and other devices, and includes a network interface and an employee interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices 1. The employee interface may be a Display (Display), an input unit, such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visual staff interface.
Fig. 3 shows only the electronic device 1 with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The quantity analysis program of the reservation services stored in the memory 11 of the electronic device 1 is a combination of a plurality of computer programs, which when run in the processor 10, can implement:
acquiring historical data of on-line reservation service, and performing data cleaning on the historical data to obtain target data;
splitting the target data according to a time dimension to obtain target data of a plurality of time periods;
calculating the historical performance rate of the online reservation service in each time period according to the number of the reservation users and the number of the visiting users in the target data of each time period in the plurality of time periods;
calculating historical revenue weights of the online booking service in each time period;
acquiring historical bookable service quantity of the online booked service in each time period, and constructing a bookable service quantity decision model according to the historical bookable quantity, the historical performance rate and the historical income weight;
and analyzing the current reserved service quantity of the online reserved service by utilizing the reserved service quantity decision model according to the current reserved service quantity of the online reserved service.
Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a non-volatile computer-readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device 1, may implement:
acquiring historical data of on-line reservation service, and performing data cleaning on the historical data to obtain target data;
splitting the target data according to a time dimension to obtain target data of a plurality of time periods;
calculating the historical performance rate of the online reservation service in each time period according to the number of the reservation users and the number of the visiting users in the target data of each time period in the plurality of time periods;
calculating historical revenue weights of the online booking service in each time period;
acquiring historical bookable service quantity of the online booked service in each time period, and constructing a bookable service quantity decision model according to the historical bookable quantity, the historical performance rate and the historical income weight;
and analyzing the current reserved service quantity of the online reserved service by utilizing the reserved service quantity decision model according to the current reserved service quantity of the online reserved service.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for analyzing the number of subscribed services, the method comprising:
acquiring historical data of on-line reservation service, and performing data cleaning on the historical data to obtain target data;
splitting the target data according to a time dimension to obtain target data of a plurality of time periods;
calculating the historical performance rate of the online reservation service in each time period according to the number of the reservation users and the number of the visiting users in the target data of each time period in the plurality of time periods;
calculating historical revenue weights of the online booking service in each time period;
acquiring historical bookable service quantity of the online booked service in each time period, and constructing a bookable service quantity decision model according to the historical bookable quantity, the historical performance rate and the historical income weight;
and analyzing the current reserved service quantity of the online reserved service by utilizing the reserved service quantity decision model according to the current reserved service quantity of the online reserved service.
2. The method for analyzing the number of subscribed services as claimed in claim 1, wherein the splitting the target data according to the time dimension to obtain the target data of a plurality of time periods comprises:
acquiring the total data volume of the target data and the acquisition time of each data in the target data;
generating a timestamp of each data in the target data according to the total data volume and the acquisition time;
and generating target data of a plurality of time periods according to the time stamp of each data.
3. The reservation service quantity analysis method according to claim 2, wherein the generating a time stamp of each data in the target data according to the total data amount and the collection time comprises:
acquiring the data sub-amount of the target data in each acquisition time, and calculating the data proportion of each data sub-amount in the total data amount;
and updating the time stamp of each sub data volume according to the data proportion so as to obtain the time stamp of each data in the target data.
4. The method for analyzing the number of subscribed services as claimed in claim 1, wherein said calculating the historical profit weight for said online subscribed service in said each time period comprises:
inquiring service income and service loss of the online booking service in each time period;
calculating the marginal profit of the on-line reservation service in each time period according to the service profit;
calculating the marginal loss and the extra cost of the on-line reservation service in each time period according to the service loss;
and summarizing the marginal income, the marginal loss and the extra cost to be used as historical income weight of the online reservation service in each time period.
5. The method of analyzing the number of subscribed services as claimed in claim 4, wherein the subscribed service number decision model comprises:
wherein, S represents the optimal reservation quantity of the on-line reservation service, N represents the historical reservable service quantity, omega represents the marginal profit in the historical profit weight, rho represents the marginal loss in the historical profit weight, phi represents the extra cost in the historical profit weight, and sigma represents the historical performance rate.
6. The method for analyzing the number of subscribed services as claimed in claim 1, wherein the performing data cleansing on the historical data to obtain target data comprises:
carrying out duplicate removal operation on the historical data, and detecting whether the duplicate-removed historical data has a data missing value;
if no data missing value exists, the history data after the duplication removal is used as target data;
and if the data missing value exists, performing data filling on the data missing value to obtain target data.
7. The method for analyzing the number of subscribed services as claimed in claim 6, wherein said performing a deduplication operation on said historical data comprises:
calculating the similarity of any two data in the historical data;
if the similarity is not greater than the preset similarity, simultaneously keeping the two historical data;
and if the similarity is greater than the preset similarity, deleting any one of the two data.
8. An apparatus for analyzing the number of subscribed services, the apparatus comprising:
the data cleaning module is used for collecting historical data of the on-line reservation service and cleaning the historical data to obtain target data;
the time dimension splitting module is used for splitting the target data according to a time dimension to obtain target data of a plurality of time periods;
a performance rate calculation module, configured to calculate a historical performance rate of the online reservation service in each time slot according to the number of reservation users and the number of visited users in the target data of each time slot of the multiple time slots;
the profit weight calculation module is used for calculating the historical profit weight of the online reservation service in each time period;
a decision model building module, configured to obtain a historical bookable service quantity of the online booked service in each time period, and build a booked service quantity decision model according to the historical bookable quantity, the historical fulfillment rate, and the historical revenue weight;
and the service quantity decision module is used for analyzing the current reserved service quantity of the on-line reserved service by utilizing the reserved service quantity decision model according to the current reserved service quantity of the on-line reserved service.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform a method of analyzing the quantity of subscribed services according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for analyzing the number of subscribed services according to any one of claims 1 to 7.
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| CN202111148141.4A CN113807553A (en) | 2021-09-29 | 2021-09-29 | Method, device, equipment and storage medium for analyzing number of reservation services |
| PCT/CN2022/087790 WO2023050779A1 (en) | 2021-09-29 | 2022-04-20 | Quantity analysis method and apparatus for reservation service, device and storage medium |
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