CN113256366A - Order data processing method and system based on big data and cloud computing - Google Patents
Order data processing method and system based on big data and cloud computing Download PDFInfo
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- CN113256366A CN113256366A CN202110435162.8A CN202110435162A CN113256366A CN 113256366 A CN113256366 A CN 113256366A CN 202110435162 A CN202110435162 A CN 202110435162A CN 113256366 A CN113256366 A CN 113256366A
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Abstract
The application provides an order data processing method and system based on big data and cloud computing, and relates to the technical field of order processing. In the application, first, target service order data sent by an order initiating device is obtained. Secondly, whether the order initiating equipment can withdraw the target business order data or not is judged. And then, if the order initiating device is judged not to withdraw the target business order data, the target business order data is sent to the order receiving device. Based on the method, the problem that resource waste is easy to occur in the process of processing the business order based on the existing order processing technology can be solved.
Description
Technical Field
The application relates to the technical field of order processing, in particular to an order data processing method and system based on big data and cloud computing.
Background
With the continuous development of computer technology and internet technology, the application range of the computer technology and the internet technology is continuously expanded. For example, information interaction is performed through network equipment, so that orders (such as financial transactions, goods transactions and the like) for forming transactions on the network are formed, and the convenience of transactions performed by users is greatly improved.
However, the inventor researches and finds that in the prior art, resources are easily wasted in the process of processing a business order.
Disclosure of Invention
In view of this, an object of the present application is to provide an order data processing method and system based on big data and cloud computing, so as to solve the problem that resource waste is likely to occur in the process of processing a business order based on the existing order processing technology.
In order to achieve the above purpose, the embodiment of the present application adopts the following technical solutions:
an order data processing method based on big data and cloud computing is applied to an order data processing platform based on big data and cloud computing, and comprises the following steps:
acquiring target service order data sent by order initiating equipment, wherein the target service order data is generated on the basis of order generating operation performed by the order initiating equipment in response to a first target user;
judging whether the order initiating equipment can withdraw the target service order data or not;
and if the order initiating device does not cancel the target business order data, sending the target business order data to the order receiving device.
In a possible embodiment, in the order data processing method based on big data and cloud computing, the order data processing method further includes:
if the order initiating device is judged to cancel the target service order data, starting to perform timing waiting operation based on the current time information to obtain timing duration;
judging whether the timing time length is greater than or equal to a preset time length threshold value or not;
and if the timing duration is greater than or equal to the duration threshold, sending the target service order data to the order receiving equipment.
In a possible embodiment, in the order data processing method based on big data and cloud computing, the order data processing method further includes:
if the timing duration is smaller than the duration threshold, judging whether order cancellation notification information sent by the order initiating equipment is acquired;
and if the order cancellation notification information is acquired, determining not to send the target service order data to the order receiving equipment.
In a possible embodiment, in the order data processing method based on big data and cloud computing, the order data processing platform is connected to a computer device, the computer device is deployed with a target database, and the step of determining whether the order initiating device can withdraw the target service order data includes:
acquiring order cancellation configuration information sent by the order initiating device from the target database, wherein the order cancellation configuration information is generated based on an order cancellation configuration operation performed by the order initiating device in response to a first target user;
and determining whether the order initiating equipment can cancel the target service order data or not based on the order canceling configuration information.
In a possible embodiment, in the order data processing method based on big data and cloud computing, the step of determining whether the order initiating device will withdraw the target service order data based on the order withdrawal configuration information includes:
analyzing the order cancellation configuration information to obtain an order cancellation configuration strategy corresponding to the order cancellation configuration information;
and if the order cancellation configuration strategy does not need to cancel the order, determining that the order initiating equipment does not cancel the target service order data.
In a possible embodiment, in the order data processing method based on big data and cloud computing, the order data processing platform is connected to a computer device, the computer device is deployed with a target database, and the step of determining whether the order initiating device can withdraw the target service order data includes:
obtaining historical service order data from the target database, wherein the historical service order data carries order identification information for identifying whether the corresponding service order is cancelled or not;
and determining whether the order initiating equipment can withdraw the target service order data or not based on the order identification information in the historical service order data.
In a possible embodiment, in the order data processing method based on big data and cloud computing, the step of determining whether the order initiating device will withdraw the target service order data based on the order identification information in the historical service order data includes:
screening the historical service order data to obtain target historical service order data;
and determining whether the order initiating equipment can cancel the target business order data or not based on the order identification information in the target historical business order data.
In a possible embodiment, in the order data processing method based on big data and cloud computing, the step of performing screening processing on the historical service order data to obtain target historical service order data includes:
determining associated order terminal equipment corresponding to the order initiating equipment in each terminal equipment in communication connection with the order data processing platform, wherein the associated order terminal equipment is terminal equipment for performing information interaction with the order initiating equipment based on the order data processing platform, is the same as the order initiating equipment in type and is different from the order receiving equipment in type, and is used for sending service order data to the order data processing platform;
determining at least one target associated order terminal device which forms historical business order data with the order data processing platform in each associated order terminal device corresponding to the order initiating device;
determining time association degree information of historical business order data formed by the order initiating equipment between each target associated order terminal equipment and the order data processing platform, wherein the time association degree information is used for representing the association degree of the target business order data and the historical business order data which are sent by the order initiating equipment in generating time dimension;
determining equipment association degree information between the order initiating equipment and each target associated order terminal equipment based on the time association degree information to obtain at least one piece of equipment association degree information, wherein for the target associated order terminal equipment with a plurality of pieces of time association degree information between the order initiating equipment, the equipment association degree information between the target associated order terminal equipment and the order initiating equipment is determined based on the plurality of pieces of time association degree information, so that a one-to-one correspondence relationship is formed between the equipment association degree information and the target associated order terminal equipment;
determining target equipment relevance information in the at least one piece of equipment relevance information, wherein the target equipment relevance information is an average value of equipment relevance corresponding to the at least one piece of equipment relevance information;
screening at least one piece of representative equipment relevance degree information from the at least one piece of equipment relevance degree information based on the target equipment relevance degree information, wherein each piece of representative equipment relevance degree information is greater than or equal to the target equipment relevance degree information;
in the at least one target associated order terminal device, determining the target associated order terminal device corresponding to each representative device association degree information in the at least one representative device association degree information as a representative associated order terminal device to obtain at least one representative associated order terminal device;
and screening out historical service order data corresponding to the order initiating equipment and each representative associated order terminal equipment from a plurality of pieces of historical service order data, and taking the screened historical service order data as target historical service order data.
In a possible embodiment, in the order data processing method based on big data and cloud computing, the step of performing screening processing on the historical service order data to obtain target historical service order data includes:
analyzing and processing a plurality of pieces of historical service order data, and determining target terminal equipment corresponding to each piece of historical service order data, wherein each target terminal equipment is used for sending service order data to the order data processing platform and comprises the order initiating equipment;
for each target terminal device, attributing all historical service order data corresponding to the target terminal device into a set to form a historical service order data set corresponding to the target terminal device, wherein the historical service order data set corresponding to the order initiating device is a first historical service order data set, and the historical service order data sets corresponding to other target terminal devices except the order initiating device are a second historical service order data set;
acquiring generation time information of each piece of historical service order data in the first historical service order data set to obtain at least one piece of generation time information;
for each second historical service order data set, determining whether the second historical service order data set belongs to a target second historical service order data set or not based on the generation time information of each piece of historical service order data included in the second historical service order data set and the at least one piece of generation time information, wherein at least one piece of generation time information in the at least one piece of generation time information belongs to a generation time interval of the historical service order data included in the target second historical service order data set;
acquiring the quantity of historical service order data included in the first historical service order data set to obtain a first quantity;
acquiring set identification information of each target second historical service order data set, wherein the number of the historical service order data is greater than or equal to the first number;
taking each target second historical service order data set corresponding to the set identification information as a candidate second historical service order data set;
for each candidate second historical service order data set, acquiring a first quantity of pieces of historical service order data from the candidate second historical service order data set, and forming an order data ordered subset corresponding to the candidate second historical service order data set based on the first quantity of pieces of historical service order data;
for each ordered subset of order data, performing order identification information matching on each piece of historical service order data included in the ordered subset of order data and historical service order data at a corresponding position in the first ordered subset of historical service order data to obtain a corresponding identification information matching result, wherein the ordered subsets of order data and the historical service order data in the first ordered subset of order data are arranged according to the sequence of generation time to form an ordered set;
for each ordered subset of the order data, obtaining matched historical service order data matched with the order identification information between the ordered subset of the order data and the first historical service order data set according to the identification information matching result corresponding to the ordered subset of the order data, wherein the matched historical service order data is the historical service order data successfully matched with the order identification information in the ordered subset of the order data, and the content represented by the two corresponding order identification information is the same when the order identification information is successfully matched;
for each ordered subset of the order data, determining whether a corresponding order data continuous unit exists according to the matching historical service order data corresponding to the ordered subset of the order data, wherein the order data continuous unit comprises a plurality of matching historical service order data with continuous positions;
determining a candidate second historical service order data set corresponding to each ordered subset of order data with the order data continuous units as a representative second historical service order data set;
and taking each piece of historical service order data included in the first historical service order data set and each piece of historical service order data included in the representative second historical service order data set as target historical service order data.
The application also provides an order data processing system based on big data and cloud computing, which comprises an order data processing platform based on big data and cloud computing, wherein the order data processing platform is used for:
acquiring target service order data sent by order initiating equipment, wherein the target service order data is generated on the basis of order generating operation performed by the order initiating equipment in response to a first target user;
judging whether the order initiating equipment can withdraw the target service order data or not;
and if the order initiating device does not cancel the target business order data, sending the target business order data to the order receiving device.
According to the order data processing method and system based on big data and cloud computing, after the target service order data sent by the order initiating device is obtained, whether the order initiating device cancels the target service order data or not can be judged, and when the order initiating device is judged not to cancel the target service order data, the target service order data is sent to the order receiving device. Based on this, compared with the technical scheme of directly sending the obtained target service order data to the order receiving device, the problem that resources (such as network resources or computing resources and storage resources of the order receiving device and the like) are wasted due to the fact that the target service order data needs to be cancelled after being sent to the order receiving device can be avoided to a certain extent, that is, the problem that resources are wasted easily in the process of processing the service order based on the existing order processing technology can be improved, and the method has high practical value.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a structural block diagram of an order data processing platform based on big data and cloud computing according to an embodiment of the present application.
Fig. 2 is a schematic flowchart of steps included in an order data processing method based on big data and cloud computing according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides an order data processing system based on big data and cloud computing, which can comprise an order data processing platform based on big data and cloud computing.
The order data processing system can further comprise a computer device, the computer device can be connected with the order data processing platform, and the computer device is provided with a target database.
As shown in FIG. 1, the order data processing platform may include a memory and a processor.
In detail, the memory and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, they may be electrically connected to each other via one or more communication buses or signal lines. The memory can have stored therein at least one software function (computer program) which can be present in the form of software or firmware. The processor may be configured to execute the executable computer program stored in the memory, so as to implement the order data processing method based on big data and cloud computing provided by the embodiment of the present application (described later).
The big data and cloud computing based order data processing platform may be configured to:
acquiring target service order data sent by order initiating equipment, wherein the target service order data is generated on the basis of order generating operation performed by the order initiating equipment in response to a first target user;
judging whether the order initiating equipment can withdraw the target service order data or not;
and if the order initiating device does not cancel the target business order data, sending the target business order data to the order receiving device.
Alternatively, the Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), a System on Chip (SoC), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
Moreover, the structure shown in fig. 1 is only an illustration, and the order data processing platform based on big data and cloud computing may further include more or fewer components than those shown in fig. 1, or have a different configuration from that shown in fig. 1, for example, may include a communication unit for information interaction with other devices.
In an alternative example, the order data processing platform based on big data and cloud computing may be one or more servers with data processing capability. The computer device may also be a server having a data processing capability, and may be a cloud computing system configured by a plurality of servers, so that cloud computing can be performed in cooperation with the order data processing platform.
With reference to fig. 2, an embodiment of the present application further provides an order data processing method based on big data and cloud computing, which is applicable to the order data processing platform based on big data and cloud computing. The order data processing method based on big data and cloud computing can be implemented by the order data processing platform based on big data and cloud computing.
The specific process shown in FIG. 2 will be described in detail below.
Step S110, obtain the target service order data sent by the order initiating device.
In this embodiment, the order data processing platform based on big data and cloud computing may obtain the target service order data after the order initiating device sends the target service order data.
The target business order data is generated based on an order generation operation performed by the order initiating device (such as a mobile phone or a computer) in response to a first target user (such as a buyer of goods).
Step S120, determining whether the order initiating device will cancel the target service order data.
In this embodiment, after obtaining the target service order data based on step S110, the order data processing platform based on big data and cloud computing may first determine whether the order initiating device will withdraw the target service order data.
If it is determined that the order initiating device does not withdraw the target service order data, step S130 may be executed.
Step S130, sending the target service order data to the order receiving device.
In this embodiment, after determining that the order initiating device does not cancel the target service order data based on step S120, the order data processing platform based on big data and cloud computing may send the target service order data to the order receiving device (such as a mobile phone or a computer of a seller of goods), so that the order receiving device may receive the target service order data, and process the target service order data to complete an order.
Based on the method, after the target service order data sent by the order initiating device is obtained, whether the order initiating device cancels the target service order data or not can be judged, and when the order initiating device is judged not to cancel the target service order data, the target service order data is sent to the order receiving device. Based on this, compared with the technical scheme of directly sending the obtained target service order data to the order receiving device, the problem that resources (such as network resources or computing resources and storage resources of the order receiving device and the like) are wasted due to the fact that the target service order data needs to be cancelled after being sent to the order receiving device can be avoided to a certain extent, that is, the problem that resources are wasted easily in the process of processing the service order based on the existing order processing technology can be improved, and the method has high practical value.
In the first aspect, it should be noted that, in step S110, the order initiating device may first establish a communication connection, such as a TCP connection, with the order data processing platform based on big data and cloud computing, and then send the generated target business order data to the order data processing platform based on big data and cloud computing based on the communication connection.
In the second aspect, it should be noted that, in step S120, a specific manner for determining whether the order initiating device will withdraw the target service order data is not limited, and may be selectively configured according to actual application requirements.
For example, in an alternative example, the order data processing platform is connected to a computer device deployed with a target database, and whether the order initiating device will withdraw the target service order data may be determined based on the following steps:
firstly, obtaining order cancellation configuration information sent by the order initiating device from the target database, wherein the order cancellation configuration information is generated based on an order cancellation configuration operation performed by the order initiating device in response to a first target user;
secondly, determining whether the order initiating equipment can cancel the target service order data or not based on the order cancellation configuration information.
In a possible implementation manner, the specific manner of determining whether the order initiating device will withdraw the target service order data based on the order withdrawal configuration information may include:
firstly, analyzing and processing the order cancellation configuration information to obtain an order cancellation configuration strategy corresponding to the order cancellation configuration information;
secondly, if the order cancellation configuration policy is that the order is not required to be cancelled, it is determined that the order initiating device does not cancel the target service order data (where, if the order cancellation configuration policy is that whether the order is required to be cancelled is determined according to a certain rule, a determination is made based on the rule, and if it is determined that the same order exists, that is, when the same order exists, it is determined that the order is required to be cancelled).
For another example, in another alternative example, the order data processing platform is connected to a computer device, the computer device is deployed with a target database, and it may be determined whether the order initiating device will withdraw the target service order data based on the following steps:
firstly, obtaining historical service order data from the target database, wherein the historical service order data carries order identification information for identifying whether a service order corresponding to an identifier is cancelled or not;
secondly, determining whether the order initiating equipment can withdraw the target business order data or not based on the order identification information in the historical business order data.
Optionally, in the above example, a specific manner of determining whether the order initiating device will withdraw the target service order data based on the order identification information in the historical service order data is not limited, and may be selected and configured according to actual application requirements.
For example, in an alternative example, to make the determination of whether the order initiating device will withdraw the targeted business order data more accurate, it may be determined whether the order initiating device will withdraw the targeted business order data based on the order identifying information in the historical business order data by:
firstly, screening the historical service order data to obtain target historical service order data (that is, irrelevant data in the historical service order data can be removed first to retain valid data, that is, the target historical service order data is obtained, so that when determining whether the order initiating device can cancel the target service order data or not based on the target historical service order data, the target historical service order data can be obtained with higher accuracy);
then, based on the order identification information in the target historical service order data, it is determined whether the order initiating device will cancel the target service order data (for example, it may be determined whether the target service order data will be cancelled based on a ratio of the target historical service order data corresponding to the order identification information identifying that the service order is cancelled in the target historical service order data, and if the ratio is greater than 0.5 or a preset threshold value, it is determined that the target service order data will be cancelled).
It can be understood that, in the above example, the specific manner of obtaining the target historical service order data by performing the screening processing on the historical service order data is not limited, and the target historical service order data may be selected and configured according to actual application requirements.
For example, in an alternative example, in order to make the filtered target historical service order data have higher reliability, the filtered historical service order data may be filtered based on the following steps (i.e., a data filtering method based on big data is provided):
firstly, determining associated order terminal equipment corresponding to the order initiating equipment in each terminal equipment in communication connection with the order data processing platform, wherein the associated order terminal equipment is the terminal equipment performing information interaction (such as mutually sending private messages or commenting on published information of a counterpart) with the order initiating equipment based on the order data processing platform, is the same as the order initiating equipment in type and different from the order receiving equipment in type, and is used for sending service order data to the order data processing platform;
secondly, in each associated order terminal device corresponding to the order initiating device, determining at least one target associated order terminal device which has formed historical service order data with the order data processing platform (that is, the target associated order terminal device has historically sent service order data to the order data processing platform and formed historical service order data, and the historical service order data can be generated based on the service order data and order identification information for identifying whether the service order data is cancelled);
a third step of determining time association degree information of historical service order data formed by the order initiating device for each target associated order terminal device and the order data processing platform, where the time association degree information is used to represent an association degree of a generation time dimension between the target service order data sent by the order initiating device and the historical service order data (for example, in an alternative example, the time association degree may be obtained based on a difference between the generation time information of the target service order data and the generation time information of the service order data corresponding to the historical service order data, where the larger the difference is, the smaller the corresponding time association degree is);
and fourthly, determining equipment association degree information between the order initiating equipment and each target associated order terminal equipment based on the time association degree information to obtain at least one piece of equipment association degree information, wherein for the target associated order terminal equipment with a plurality of pieces of time association degree information between the order initiating equipment, the equipment association degree information between the target associated order terminal equipment and the order initiating equipment is determined based on the plurality of pieces of time association degree information, so that a one-to-one correspondence relationship is formed between the equipment association degree information and the target associated order terminal equipment (namely, if only one time association degree exists between the order initiating equipment and the target associated order terminal equipment, namely, only one piece of historical business order data exists in the target associated order terminal equipment, the time association degree information is used as the equipment association degree information, and if the order initiating equipment and the target associated order terminal equipment have only one piece of historical business order data, the time association degree information is used as the equipment association degree information, and if the order initiating equipment and if there are multiple time relevance degrees, that is, the target relevant order terminal device has multiple pieces of historical service order data, then taking the average value of multiple pieces of time relevance degree information as device relevance degree information);
fifthly, determining target equipment relevance information in the at least one piece of equipment relevance information, wherein the target equipment relevance information is an average value of equipment relevance corresponding to the at least one piece of equipment relevance information;
sixthly, screening at least one piece of representative equipment relevance degree information from the at least one piece of equipment relevance degree information based on the target equipment relevance degree information, wherein each piece of representative equipment relevance degree information is greater than or equal to the target equipment relevance degree information (namely, each piece of equipment relevance degree which is greater than or equal to the target equipment relevance degree information is taken as the representative equipment relevance degree information);
seventhly, in the at least one target associated order terminal device, determining the target associated order terminal device corresponding to each piece of representative device association degree information in the at least one piece of representative device association degree information as a representative associated order terminal device to obtain at least one representative associated order terminal device;
and eighthly, screening out historical service order data corresponding to the order initiating device and each representative associated order terminal device from a plurality of pieces of historical service order data, and taking the screened historical service order data as target historical service order data.
For another example, in another alternative example, in order to make the filtered target historical service order data have higher reliability, the filtered historical service order data may be filtered based on the following steps (i.e., a data filtering method based on big data is provided):
firstly, analyzing and processing a plurality of pieces of historical service order data, and determining target terminal equipment corresponding to each piece of historical service order data, wherein each target terminal equipment is used for sending service order data to the order data processing platform and comprises the order initiating equipment (namely, the obtained target terminal equipment comprises the order initiating equipment, and after each target terminal equipment sends the service order data to the order data processing platform in history, the service order data can be formed to form the corresponding historical service order data by combining with order identification information of whether the service order data is cancelled);
secondly, for each target terminal device, attributing all historical service order data corresponding to the target terminal device into a set to form a historical service order data set corresponding to the target terminal device, wherein the historical service order data set corresponding to the order initiating device is a first historical service order data set, and the historical service order data sets corresponding to other target terminal devices except the order initiating device are a second historical service order data set;
step three, acquiring generation time information of each piece of historical service order data in the first historical service order data set to obtain at least one piece of generation time information;
a fourth step of determining, for each of the second historical service order data sets, whether the second historical service order data set belongs to a target second historical service order data set based on the generation time information of each piece of historical service order data included in the second historical service order data set and the at least one piece of generation time information, where at least one piece of generation time information in the at least one piece of generation time information belongs to a generation time interval of the historical service order data included in the target second historical service order data set (that is, a time interval is formed based on the earliest generation time and the latest generation time of the historical service order data included in the second historical service order data set, and if at least one piece of generation time information in the at least one piece of generation time information belongs to the time interval, indicating that the second historical service order data set belongs to the target second historical service order data set);
step five, acquiring the quantity of the historical service order data included in the first historical service order data set to obtain a first quantity;
sixthly, acquiring set identification information of each target second historical service order data set, wherein the number of the historical service order data is greater than or equal to the first number;
seventhly, taking each target second historical service order data set corresponding to the set identification information as a candidate second historical service order data set;
eighthly, for each candidate second historical service order data set, obtaining a first quantity of historical service order data (for example, obtaining the first quantity of historical service order data with the latest continuous generation time) from the candidate second historical service order data set, and forming an ordered subset of order data corresponding to the candidate second historical service order data set based on the first quantity of historical service order data (that is, the first quantity of historical service order data are sorted according to the sequence of the generation time to form an ordered subset of order data);
ninthly, aiming at each ordered subset of order data, matching order identification information of each piece of historical service order data included in the ordered subset of order data with historical service order data at a corresponding position in the first ordered subset of historical service order data (namely, matching the order identification information of the first piece of historical service order data in the ordered subset of order data with the order identification information of the first piece of historical service order data in the first ordered subset of historical service order data, matching the order identification information of the second piece of historical service order data in the ordered subset of order data with the order identification information of the second piece of historical service order data in the first ordered subset of historical service order data, and so on) to obtain a corresponding identification information matching result, wherein the order identification information matching result is obtained according to the sequence of the order data in the ordered subset of order data and the historical service order data in the first ordered subset of historical service order data according to the generation time Arranging in sequence to form an ordered set;
tenth, for each ordered subset of the order data, obtaining, according to an identification information matching result corresponding to the ordered subset of the order data, matching historical service order data in which the order identification information between the ordered subset of the order data and the first historical service order data set is matched, where the matching historical service order data is the historical service order data in which the order identification information in the ordered subset of the order data is successfully matched, and the matching of the order identification information is successful, that contents of two corresponding order identification information representations are the same (for example, both representing that the corresponding service order data is cancelled, or both representing that the corresponding service order data is not cancelled);
a tenth step of determining, for each ordered subset of the order data, whether a corresponding order data continuous unit exists according to the matching historical service order data corresponding to the ordered subset of the order data, where the order data continuous unit includes matching historical service order data with a plurality of continuous locations (the number of the plurality of locations may be generated based on configuration operations of a user, and the number of the plurality of locations may be larger if the requirement for accuracy is higher);
step ten, determining a candidate second historical service order data set corresponding to each ordered subset of order data with the order data continuous units as a representative second historical service order data set;
and a tenth step of taking each piece of historical service order data included in the first historical service order data set and each piece of historical service order data included in the representative second historical service order data set as target historical service order data.
For another example, in another alternative example, in order to make the filtered target historical service order data have higher reliability, the filtered historical service order data may be filtered based on the following steps (i.e., a data filtering method based on big data is provided):
firstly, analyzing and processing a plurality of pieces of historical service order data, and determining target terminal equipment corresponding to each piece of historical service order data, wherein each target terminal equipment is used for sending service order data to the order data processing platform, and comprises the order initiating equipment (as mentioned above, the description is not repeated one by one);
secondly, for each target terminal device, attributing all historical service order data corresponding to the target terminal device into a set to form a historical service order data set corresponding to the target terminal device, wherein the historical service order data included in the historical service order data set is an ordered set, the historical service order data set corresponding to the order initiating device is a first historical service order data set, and the historical service order data sets corresponding to other target terminal devices except the order initiating device are a second historical service order data set (as described above, the description is omitted herein);
thirdly, acquiring the quantity of the historical service order data included in the first historical service order data set to obtain a first quantity;
fourthly, determining each second historical service order data set with the quantity of the historical service order data greater than or equal to the first quantity as a candidate second historical service order data set;
a fifth step of determining a target number less than or equal to the first number (the specific value of the target number is not limited, if the precision requirement is higher, the target number may be larger, if the target number is equal to the first number);
sixthly, determining continuous target quantity bar historical service order data (such as the target quantity bar historical service order data with the latest generation time) in the first historical service order data set and each candidate second historical service order data set respectively to obtain a first order data comparison unit in the first historical service order data set and a second order data comparison unit in each candidate second historical service order data set (in this way, the quantity of the historical service data three data included by the first order data comparison unit and each candidate second order data comparison unit can be the target quantity);
seventhly, matching the order identification information of each piece of historical service order data in the second order data comparison unit with the order identification information of the historical service order data at the corresponding position in the first order data comparison unit for each second order data comparison unit (the order identification information of the historical service order data at the corresponding position is matched, which can be referred to the above) to obtain matched historical service order data between the order data comparison units (the matched historical service order data is the historical service order data in the second order data comparison unit, and the order identification information of the matched historical service order data is the same as the representation content of the order identification information of the historical service order data at the corresponding position in the first order data comparison unit);
eighthly, determining whether the corresponding second order data comparison unit is a matching order data comparison unit matched between the candidate second historical service order data set and the first historical service order data set or not according to the quantity of the matching historical service order data between the order data comparison units, wherein the quantity of the matching historical service order data included in the matching order data comparison unit is greater than a preset quantity (the preset quantity can be determined based on the target quantity, if a parameter smaller than 1 and greater than 0 can be set, such as 0.5, and the product of the parameter and the target quantity is taken as the preset quantity);
ninth, obtaining time identification information of each matching order data comparison unit, wherein the time identification information includes time length information (i.e. a difference between the earliest generation time and the latest generation time of the matching historical service order data) formed based on the generation time information of the matching historical service order data included in the matching order data comparison unit;
tenth, for each of the matching order data comparing units, determining whether the candidate second historical service order data set corresponding to the matching order data comparing unit is the representative second historical service order data set based on the time identifier information of the matching order data comparing unit and the number of the matching historical service order data included in the matching order data comparing unit (for example, calculating a ratio between the number of the matching historical service order data included in the matching order data comparing unit and the time length information of the matching order data comparing unit, then judging whether the ratio is greater than a preset ratio, and if the ratio is greater than the preset ratio, determining that the candidate second historical service order data set corresponding to the matching order data comparing unit is the representative second historical service order data set);
and step ten, taking each piece of historical service order data in the first historical service order data set and each piece of historical service order data in the representative second historical service order data set as target historical service order data.
On the basis of the above example, if it is determined that the order initiating device will withdraw the target service order data based on step S120, in order to avoid the problem that the order is withdrawn by mistake, the order data processing method based on big data and cloud computing may further include the following steps:
firstly, if the order initiating device is judged to cancel the target service order data, starting to perform timing waiting operation based on the current time information to obtain timing duration;
secondly, judging whether the timing duration is greater than or equal to a preset duration threshold (wherein the duration threshold can be generated based on configuration operation performed by a user according to an actual application scene, for example, if the order tends to be avoided being cancelled by mistake as much as possible, the duration threshold can be larger);
then, if the timing duration is greater than or equal to the duration threshold, the target service order data is sent to the order receiving equipment,
in an alternative example, if the timing duration is smaller than the duration threshold, further determining whether to acquire order cancellation notification information sent by the order initiating device; and if the order cancellation notification information is acquired, determining not to send the target service order data to the order receiving equipment.
That is, after it is determined that the order initiating device cancels the target service order data, the order initiating device may wait, or determine not to send the target service order data to the order receiving device when obtaining the order cancellation notification information; or determining to send the target service order data to the order receiving equipment if the order cancellation notification information is not acquired and the waiting time reaches the time threshold.
On the basis of the above example, in order to avoid the problem that resources are wasted in time, a big data-based order screening method is also provided, where the order screening method may include the following steps:
acquiring target service order data sent by order initiating equipment, wherein the target service order data is generated on the basis of order generating operation performed by the order initiating equipment in response to a first target user;
determining whether the order initiating device cancels the target service order data (this step is the same as the step S120 in the previous example, for example, first obtaining historical service order data from the target database, where the historical service order data carries order identification information indicating whether a service order corresponding to an identifier is cancelled;
and if the order initiating device is judged to cancel the target service order data, determining not to send the target service order data to the order receiving device, and sending notification information to the order initiating device, wherein the notification information is used for representing and determining not to send the target service order data to the order receiving device.
On the basis of the above example, in order to avoid the problem of resource waste in time and also consider the problem of order false revocation, a big data-based order data confirmation method is also provided, where the order data confirmation method may include the following steps:
acquiring target service order data sent by order initiating equipment, wherein the target service order data is generated on the basis of order generating operation performed by the order initiating equipment in response to a first target user;
determining whether the order initiating device cancels the target service order data (this step is the same as the step S120 in the previous example, for example, first obtaining historical service order data from the target database, where the historical service order data carries order identification information indicating whether a service order corresponding to an identifier is cancelled;
if the order initiating equipment is judged to cancel the target business order data, generating order cancellation confirmation notification information and sending the order cancellation confirmation information to the order initiating equipment;
judging whether to acquire confirmation information sent by the order initiating equipment based on the order cancellation confirmation notification information, wherein the confirmation information is generated based on the confirmation operation of the order initiating equipment responding to the order cancellation confirmation notification information by the first target user;
if the confirmation information is acquired, determining not to send the target service order data to the order receiving equipment;
and if the confirmation information is not acquired, determining to send the target service order data to the order receiving equipment.
To sum up, after the order data of the target service sent by the order initiating device is obtained, the order initiating device may first determine whether to cancel the order data of the target service, and when it is determined that the order initiating device does not cancel the order data of the target service, the order data of the target service may be sent to the order receiving device. Based on this, compared with the technical scheme of directly sending the obtained target service order data to the order receiving device, the problem that resources (such as network resources or computing resources and storage resources of the order receiving device and the like) are wasted due to the fact that the target service order data needs to be cancelled after being sent to the order receiving device can be avoided to a certain extent, that is, the problem that resources are wasted easily in the process of processing the service order based on the existing order processing technology can be improved, and the method has high practical value.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. 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 identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. The order data processing method based on big data and cloud computing is applied to an order data processing platform based on big data and cloud computing, and comprises the following steps:
acquiring target service order data sent by order initiating equipment, wherein the target service order data is generated on the basis of order generating operation performed by the order initiating equipment in response to a first target user;
judging whether the order initiating equipment can withdraw the target service order data or not;
and if the order initiating device does not cancel the target business order data, sending the target business order data to the order receiving device.
2. The order data processing method based on big data and cloud computing according to claim 1, further comprising:
if the order initiating device is judged to cancel the target service order data, starting to perform timing waiting operation based on the current time information to obtain timing duration;
judging whether the timing time length is greater than or equal to a preset time length threshold value or not;
and if the timing duration is greater than or equal to the duration threshold, sending the target service order data to the order receiving equipment.
3. The order data processing method based on big data and cloud computing according to claim 2, wherein the order data processing method further comprises:
if the timing duration is smaller than the duration threshold, judging whether order cancellation notification information sent by the order initiating equipment is acquired;
and if the order cancellation notification information is acquired, determining not to send the target service order data to the order receiving equipment.
4. The order data processing method based on big data and cloud computing according to any one of claims 1 to 3, wherein the order data processing platform is connected with a computer device, the computer device is deployed with a target database, and the step of determining whether the order initiating device can withdraw the target service order data includes:
acquiring order cancellation configuration information sent by the order initiating device from the target database, wherein the order cancellation configuration information is generated based on an order cancellation configuration operation performed by the order initiating device in response to a first target user;
and determining whether the order initiating equipment can cancel the target service order data or not based on the order canceling configuration information.
5. The big data and cloud computing based order data processing method according to claim 4, wherein the step of determining whether the order initiating device will withdraw the target service order data based on the order withdrawal configuration information includes:
analyzing the order cancellation configuration information to obtain an order cancellation configuration strategy corresponding to the order cancellation configuration information;
and if the order cancellation configuration strategy does not need to cancel the order, determining that the order initiating equipment does not cancel the target service order data.
6. The order data processing method based on big data and cloud computing according to any one of claims 1 to 3, wherein the order data processing platform is connected with a computer device, the computer device is deployed with a target database, and the step of determining whether the order initiating device can withdraw the target service order data includes:
obtaining historical service order data from the target database, wherein the historical service order data carries order identification information for identifying whether the corresponding service order is cancelled or not;
and determining whether the order initiating equipment can withdraw the target business order data or not based on the order identification information in the historical business order data.
7. The big data and cloud computing based order data processing method according to any one of claim 6, wherein the step of determining whether the order initiating device will withdraw the target business order data based on the order identification information in the historical business order data comprises:
screening the historical service order data to obtain target historical service order data;
and determining whether the order initiating equipment can cancel the target business order data or not based on the order identification information in the target historical business order data.
8. The order data processing method based on big data and cloud computing according to claim 7, wherein the step of performing screening processing on the historical business order data to obtain target historical business order data comprises:
determining associated order terminal equipment corresponding to the order initiating equipment in each terminal equipment in communication connection with the order data processing platform, wherein the associated order terminal equipment is terminal equipment for performing information interaction with the order initiating equipment based on the order data processing platform, is the same as the order initiating equipment in type and is different from the order receiving equipment in type, and is used for sending service order data to the order data processing platform;
determining at least one target associated order terminal device which forms historical business order data with the order data processing platform in each associated order terminal device corresponding to the order initiating device;
determining time association degree information of historical business order data formed by the order initiating equipment between each target associated order terminal equipment and the order data processing platform, wherein the time association degree information is used for representing the association degree of the target business order data and the historical business order data which are sent by the order initiating equipment in generating time dimension;
determining equipment association degree information between the order initiating equipment and each target associated order terminal equipment based on the time association degree information to obtain at least one piece of equipment association degree information, wherein for the target associated order terminal equipment with a plurality of pieces of time association degree information between the order initiating equipment, the equipment association degree information between the target associated order terminal equipment and the order initiating equipment is determined based on the plurality of pieces of time association degree information, so that a one-to-one correspondence relationship is formed between the equipment association degree information and the target associated order terminal equipment;
determining target equipment relevance information in the at least one piece of equipment relevance information, wherein the target equipment relevance information is an average value of equipment relevance corresponding to the at least one piece of equipment relevance information;
screening at least one piece of representative equipment relevance degree information from the at least one piece of equipment relevance degree information based on the target equipment relevance degree information, wherein each piece of representative equipment relevance degree information is greater than or equal to the target equipment relevance degree information;
in the at least one target associated order terminal device, determining the target associated order terminal device corresponding to each representative device association degree information in the at least one representative device association degree information as a representative associated order terminal device to obtain at least one representative associated order terminal device;
and screening out historical service order data corresponding to the order initiating equipment and each representative associated order terminal equipment from a plurality of pieces of historical service order data, and taking the screened historical service order data as target historical service order data.
9. The order data processing method based on big data and cloud computing according to claim 7, wherein the step of performing screening processing on the historical business order data to obtain target historical business order data includes:
analyzing and processing a plurality of pieces of historical service order data, and determining target terminal equipment corresponding to each piece of historical service order data, wherein each target terminal equipment is used for sending service order data to the order data processing platform and comprises the order initiating equipment;
for each target terminal device, attributing all historical service order data corresponding to the target terminal device into a set to form a historical service order data set corresponding to the target terminal device, wherein the historical service order data set corresponding to the order initiating device is a first historical service order data set, and the historical service order data sets corresponding to other target terminal devices except the order initiating device are a second historical service order data set;
acquiring generation time information of each piece of historical service order data in the first historical service order data set to obtain at least one piece of generation time information;
for each second historical service order data set, determining whether the second historical service order data set belongs to a target second historical service order data set or not based on the generation time information of each piece of historical service order data included in the second historical service order data set and the at least one piece of generation time information, wherein at least one piece of generation time information in the at least one piece of generation time information belongs to a generation time interval of the historical service order data included in the target second historical service order data set;
acquiring the quantity of historical service order data included in the first historical service order data set to obtain a first quantity;
acquiring set identification information of each target second historical service order data set, wherein the number of the historical service order data is greater than or equal to the first number;
taking each target second historical service order data set corresponding to the set identification information as a candidate second historical service order data set;
for each candidate second historical service order data set, acquiring a first quantity of pieces of historical service order data from the candidate second historical service order data set, and forming an order data ordered subset corresponding to the candidate second historical service order data set based on the first quantity of pieces of historical service order data;
for each ordered subset of order data, performing order identification information matching on each piece of historical service order data included in the ordered subset of order data and historical service order data at a corresponding position in the first ordered subset of historical service order data to obtain a corresponding identification information matching result, wherein the ordered subsets of order data and the historical service order data in the first ordered subset of order data are arranged according to the sequence of generation time to form an ordered set;
for each ordered subset of the order data, obtaining matched historical service order data matched with the order identification information between the ordered subset of the order data and the first historical service order data set according to the identification information matching result corresponding to the ordered subset of the order data, wherein the matched historical service order data is the historical service order data successfully matched with the order identification information in the ordered subset of the order data, and the content represented by the two corresponding order identification information is the same when the order identification information is successfully matched;
for each ordered subset of the order data, determining whether a corresponding order data continuous unit exists according to the matching historical service order data corresponding to the ordered subset of the order data, wherein the order data continuous unit comprises a plurality of matching historical service order data with continuous positions;
determining a candidate second historical service order data set corresponding to each ordered subset of order data with the order data continuous units as a representative second historical service order data set;
and taking each piece of historical service order data included in the first historical service order data set and each piece of historical service order data included in the representative second historical service order data set as target historical service order data.
10. The order data processing system based on big data and cloud computing is characterized by comprising an order data processing platform based on big data and cloud computing, wherein the order data processing platform is used for:
acquiring target service order data sent by order initiating equipment, wherein the target service order data is generated on the basis of order generating operation performed by the order initiating equipment in response to a first target user;
judging whether the order initiating equipment can withdraw the target service order data or not;
and if the order initiating device does not cancel the target business order data, sending the target business order data to the order receiving device.
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| CN113836402A (en) * | 2021-09-08 | 2021-12-24 | 金勋杰 | Order screening method based on data processing |
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| CN113836402A (en) * | 2021-09-08 | 2021-12-24 | 金勋杰 | Order screening method based on data processing |
| CN113836402B (en) * | 2021-09-08 | 2023-11-03 | 广州欧派创意家居设计有限公司 | Order screening method based on data processing |
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