CN116402607A - Data processing method, device, equipment and storage medium of product resources - Google Patents
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Abstract
The application relates to the technical field of data processing and discloses a data processing method, a device, equipment and a storage medium of product resources, wherein the method comprises the steps of obtaining a plurality of product resources to be processed and determining resource types corresponding to the product resources to be processed; acquiring the data to be processed corresponding to each product resource to be processed from the corresponding multi-core server according to the resource type of each product resource to be processed; the method comprises the steps that data to be processed of each product resource to be processed are processed in a segmented mode according to corresponding multi-core servers; and carrying out data analysis processing on the segmented processed data through the multi-core servers corresponding to the product resources to be processed. Through the technical scheme provided by the application, the data processing efficiency can be provided.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for processing data of a product resource.
Background
The data processing system of the product resource can automatically read historical time, price, transaction amount, warehouse holding amount and other data according to a preset transaction rule and a transaction judgment algorithm, and judge whether to conduct transaction or not. And if the market data meets the transaction judgment algorithm, ordering and opening the warehouse, and when the market data is detected to meet the warehouse leveling condition, automatically leveling the warehouse. Meanwhile, the important information such as the time of each investment, the amount of each investment, the final profit level, the number of times of the profit, the number of times of the loss, the success rate of the transaction and the like can be automatically counted according to the transaction rules, and the graph is drawn. However, the transaction data has a large change and a slow analysis speed, so that the aim of stopping loss in time cannot be achieved rapidly when the loss is faced.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art, and provides a data processing method, device, equipment and storage medium of product resources, which aim at solving the technical problem of slower data processing speed.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned in part by the practice of the application.
According to an aspect of the embodiments of the present application, there is provided a data processing method of a product resource, including:
acquiring a plurality of product resources to be processed, and determining resource types corresponding to the product resources to be processed;
acquiring the data to be processed corresponding to each product resource to be processed from the corresponding multi-core server according to the resource type of each product resource to be processed;
the method comprises the steps that data to be processed of each product resource to be processed are processed in a segmented mode according to corresponding multi-core servers;
and carrying out data analysis processing on the segmented processed data through the multi-core servers corresponding to the product resources to be processed.
Further, the step of processing the to-be-processed data of each to-be-processed product resource in a segmented manner according to the corresponding multi-core server includes:
Acquiring the number of online processors of the multi-core server corresponding to each product resource to be processed;
and carrying out segmentation processing on the corresponding data to be processed according to the number of the online processors.
Further, the data analysis processing of the segmented processed data is performed by the multi-core server corresponding to each product resource to be processed, including:
acquiring the data quantity of to-be-processed data of to-be-processed product resources corresponding to each multi-core server;
determining the priority processing coefficient of the product resource to be processed corresponding to each multi-core server according to the data volume;
and sequentially carrying out data analysis processing on the data to be processed after the corresponding segmentation processing through the corresponding multi-core servers according to the priority processing coefficients.
Further, the determining the priority processing coefficient of the product resource to be processed corresponding to each multi-core server according to the data volume includes:
detecting whether the data volume of the product resource to be processed is larger than or equal to a first preset data volume threshold value;
if the data volume of the product resource to be processed is larger than or equal to a first preset data volume threshold value, determining that the priority processing coefficient of the product resource to be processed is a first priority processing coefficient;
If the data volume of the product resource to be processed is smaller than a first preset data volume threshold, detecting whether the data volume of the product resource to be processed is larger than or equal to a second preset data volume threshold;
if the data volume of the product resource to be processed is larger than or equal to the second preset data volume threshold, determining that the priority processing coefficient of the product resource to be processed is a second priority processing coefficient;
if the data volume of the product resource to be processed is smaller than the second preset data volume threshold, determining that the priority processing coefficient of the product resource to be processed is a third priority processing coefficient; wherein the priorities characterized by the first priority processing coefficient, the second priority processing coefficient and the third priority processing coefficient are sequentially reduced.
Further, the obtaining the data to be processed corresponding to each product resource to be processed from the corresponding multi-core server according to the resource type of each product resource to be processed includes:
determining a corresponding priority acquisition coefficient according to the resource type of the product resource to be processed;
and sequentially acquiring corresponding data to be processed from the corresponding multi-core servers according to the priority acquisition coefficients.
Further, the sequentially obtaining the corresponding data to be processed from the corresponding multi-core server according to the priority obtaining coefficient includes:
if the priority acquisition coefficient is a first priority acquisition coefficient representing the stock resource type, acquiring corresponding data to be processed from a first multi-core server corresponding to the first distance coefficient;
if the priority acquisition coefficient is a second priority acquisition coefficient representing the type of the foundation resource, acquiring corresponding data to be processed from a second multi-core server corresponding to the second distance coefficient;
if the priority acquisition coefficient is a third priority acquisition coefficient representing the gold resource type, acquiring corresponding data to be processed from a third multi-core server corresponding to a third distance coefficient; wherein the distances characterized by the first, second, and third distance coefficients gradually increase.
Further, after the multi-core server corresponding to each product resource to be processed performs data analysis processing on the segmented data to be processed, the method further includes:
acquiring a data analysis processing result;
and if the data analysis data result represents that the data analysis processing is unsuccessful, skipping and executing the step of carrying out data analysis processing on the segmented processed data through the multi-core servers corresponding to the product resources to be processed to reach the preset times.
According to an aspect of the embodiments of the present application, there is provided a data processing apparatus for a product resource, including:
the first acquisition module is configured to acquire a plurality of product resources to be processed and determine resource types corresponding to the product resources to be processed;
the second acquisition module is configured to acquire the data to be processed corresponding to each product resource to be processed from the corresponding multi-core server according to the resource type of each product resource to be processed;
the segmentation processing module is configured to segment the data to be processed of each product resource to be processed according to the corresponding multi-core server;
the data analysis processing module is configured to perform data analysis processing on the segmented processed data through the multi-core servers corresponding to the product resources to be processed.
According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; and storage means for storing one or more programs which, when executed by the one or more processors, cause the electronic device to implement a data processing method for a product resource as described above.
According to an aspect of the embodiments of the present application, there is provided a computer-readable storage medium having stored thereon computer-readable instructions, which when executed by a processor of a computer, cause the computer to perform a data processing method of a product resource as described above.
According to an aspect of embodiments of the present application, there is provided a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the data processing method of the product resource provided in the above-mentioned various alternative embodiments.
In the technical scheme provided by the embodiment of the application, different multi-core servers are arranged in advance according to different resource types, the to-be-processed data of the to-be-processed product resources are processed in a segmented mode according to the corresponding multi-core servers, the to-be-processed data are reassigned to the corresponding multi-core processors for processing, the multi-core processors are provided with a plurality of central processors, each central processor is used for respectively processing the to-be-processed data after the segmented processing, the resources of the single central processor for processing the data can be effectively used in the data analysis processing, and the analysis rate of the to-be-processed data can be effectively accelerated.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is apparent that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
FIG. 1 is a schematic illustration of one implementation environment to which the present application relates;
FIG. 2 is a flow chart of a method of data processing of a product resource in accordance with the present application;
FIG. 3 is a flow chart of step S230 in one embodiment contemplated by the present application;
FIG. 4 is a flow chart of step S240 in one embodiment contemplated by the present application;
FIG. 5 is a flow chart of step S420 in one embodiment contemplated by the present application;
FIG. 6 is a flow chart of step S220 in one embodiment contemplated by the present application;
FIG. 7 is a flow chart of step S620 in one embodiment contemplated by the present application;
FIG. 8 is a flow chart of another method of data processing of product resources according to the present application;
FIG. 9 is a block diagram of a data processing apparatus for a product resource in accordance with the present application; fig. 10 shows a schematic diagram of a computer system suitable for use in implementing the electronic device of the embodiments of the present application.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Also to be described is: reference to "a plurality" in this application means two or more than two. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., a and/or B may represent: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
With reference now to FIG. 1, FIG. 1 is a pictorial representation of a data processing system of a product resource. The implementation environment includes a determine resource type module 110, an adjust data order module, and an adjust analysis and carding module 130, where the determine resource type module 110, the adjust data order module 120, and the adjust analysis and carding module 130 communicate over a wired or wireless network.
The data processing system of the product resource starts the thread 1 to create the resource type determining module 110, carries out data analysis urgency according to different product resources to be processed, sets priority order, and places the product resources to be processed with high priority in a multi-core server nearest to the data processing system of the product resource, so that the speed of transmitting the data analysis processing result can be accelerated by shortening the transmission distance, and the purpose of accelerating the transaction data processing is achieved. The determine resource type module 110 sets the product resource to be processed with three resource types, including stock resource type, fund resource type, and gold resource type, each of which is described in detail as follows:
The data processing system of the product resource acquires the data of the product resource of the stock resource type, the data is marked as Shares and stored in the file temporary storage area 01, the data processing system of the product resource reads the data of the file temporary storage area 01 for screening the same product resource name, the data of the same product resource name is stored in a secondary folder, the secondary folder is stored in the file storage 01, the file storage 01 contains a plurality of stored data as the secondary folders, the system can automatically generate the names of the secondary files according to different stock names, the keywords are added at the end of the naming of the secondary files, the data processing system of the product resource sets the priority acquisition coefficient of the resource type as 1, namely the secondary folder containing the keywords Shares is read, the system judges that the data is the resource type with the priority acquisition coefficient of 1, the data is processed in the nearest server, the secondary folder is stored in the digital register 01, and the data processing system of the product resource automatically adjusts the data sequence of each secondary folder according to the data quantity of the data of each product resource. Since the subsequent processing of the file data is performed in the multi-core server, the file memory 01 with the priority acquisition coefficient of 1 is stored in the multi-core server closest to the multi-core server, so that the time required for data transmission can be reduced, and the purpose of accelerating data analysis can be achieved.
The data processing system of the product resource acquires the data of the product resource of the foundation resource type, the data is marked as Fund and stored in the file temporary storage area 02, the data processing system of the product resource reads the data of the file temporary storage area 02 for screening the same product resource name, the data of the same product resource name is stored in a secondary folder, the secondary folder is stored in the file memory 02, the file memory 01 contains a plurality of stored data as the secondary folders, the system automatically generates the names of the secondary files according to different product resource names, and adds a keyword as Fund at the end of the naming of the secondary files, the data processing system of the product resource automatically adjusts the data sequence of the secondary folders according to the data quantity of the data of each product resource, the data processing system of the product resource sets the priority acquisition coefficient of the resource type as 2, namely the secondary folder containing the keyword Fund is read, the system judges that the data is the resource type with the priority acquisition coefficient of 2, the data is stored in a closer server and is stored in the digital register 02, and the processing of the file data is the server with the priority acquisition coefficient of 2 is assumed to be the closer server 02. Because the priority acquisition coefficient is 2, that is, the real-time performance of the data processing does not have the priority acquisition coefficient of 1, the data can be placed in a nearer multi-core server for processing.
The data processing system of the product resource acquires the data of the product resource of the Gold resource type, the data is marked as Gold and stored in the file temporary storage area 03, the data processing system of the product resource reads the data of the file temporary storage area 03, the data of the same product resource name is screened and stored in a secondary folder, the secondary folder is stored in the file memory 03, the file memory 03 contains a plurality of stored data as secondary folders, the system can automatically generate the names of the secondary files according to the names of the product resources of different Gold resource types, the keywords are added as Gold at the end of the names of the secondary files, the data processing system of the product resource automatically adjusts the data sequence of the secondary folders according to the data quantity of the data of each product resource, the data processing system of the product resource sets the priority of the resource type as 3, namely the secondary folder containing the keywords is read, the system judges that the data is of the resource type with the priority of 3, the data is stored in a multi-core server with the distance of 3, and the data is stored in the digital register 03. Because the priority acquisition coefficient is 3, that is, the real-time performance of representing data processing is not high in the resource types with priority acquisition coefficients of 1 and 2, the resource types can be placed in a far-away multi-core server for processing.
The data processing system of the product resource starts the thread 2 to create the data sequence adjusting module 120, the data processing system of the product resource starts the resource type module, the priority acquisition coefficient of the resource type is read, and the data of different resource types are read according to the priority acquisition, for example: the reading sequence of the priority acquisition coefficients is sequentially from 1 to 3, it is assumed that the stock resource type with the priority acquisition coefficient of 1 is read, the processing modes of the resource types of other priority acquisition coefficients are the same, the data sequence adjustment module 120 can automatically acquire the volume of the exchanges and the volume of the exchanges in all secondary folders, then count the volume of the exchanges and the volume of the data of the volume of the exchanges, and order the secondary folders from large to small according to the volume of the data. Since the volume of the transaction is relatively large compared with the volume of data contained in the secondary folders with large volume of the transaction, the data sequence adjustment module 120 will perform priority analysis on these secondary folders and set the priority sequence of 1 to 3.
The data processing system of the product resource judges the stock resource type and the fund resource type, and the system reads the file memory 02; the data processing system of the product resource judges that the data quantity stored in the secondary folder is large, determines the data quantity stored in the secondary folder as a first priority processing coefficient, sets the first priority processing coefficient as 1, and stores the first priority processing coefficient in a temporary storage area 21;
A secondary folder with data volume more than or equal to X2 and less than X1 (which is more than or equal to 20GB and less than 50 GB) is judged by a data processing system of the product resource to be larger in data volume, and the secondary folder is determined to be a second priority processing coefficient, wherein the second priority processing coefficient is marked as 2 and is stored in a temporary storage area 22;
the data processing system of the product resource judges that the data amount stored in the secondary folder is small, determines the secondary folder as a third priority processing coefficient, sets the third priority processing coefficient as 3, and stores the third priority processing coefficient in the temporary storage area 23.
The CPU resources which can be allocated when a single CPU processes data are limited, so that the data are distributed to a plurality of CPUs in a segmented way, further processing is performed, the resources of the single CPU for processing the data can be effectively used in data processing analysis, and the analysis rate of transaction data can be effectively accelerated.
The data processing system of the product resource starts the thread 1 to create the adjustment analysis and carding module 130, the data processing system of the product resource reads the temporary storage area 21, namely, the secondary folder ordered according to the data quantity is obtained, the reading is not empty, the data processing system of the product resource judges that the priority of the secondary folder is 1, and then the current secondary folder is judged to store large data quantity; the data processing system of the product resource detects the number of online processors of the online multi-core server, which is marked as M (4 are assumed to be respectively named A, B, C and D), the number is stored in a digital register 31, a temporary storage area 21 is read, the data processing system of the product resource sequentially reads a first secondary folder, all data of the secondary folder are divided into M parts of data segments in pairs, namely, the number of online processors of the multi-core server is divided into the number of online processors, the M parts of data segments are sequentially distributed to the online processors of the multi-core server, and 4 CPUs are assumed to be provided in the prior art, so that the 4 parts of data segments are respectively distributed to A, B, C, D for data analysis processing; the on-line processor of the multi-core server processes M data segments, the data segments allocated to A are marked with a transmission sequence 1, the data segments allocated to B are marked with a transmission sequence 2, the data segments allocated to C are marked with a transmission sequence 3, the data segments allocated to D are marked with a transmission sequence 4, the multi-core server sequentially transmits the processing results to the data processing system of the product resource according to the sequence from 1 to 4, and the data processing system of the product resource draws a K line graph according to the processing results.
FIG. 2 is a flowchart illustrating a method of data processing for a product resource, according to an example embodiment. The method may be applied to the implementation environment shown in fig. 1 and is specifically performed by the information extraction server 20 in the embodiment environment shown in fig. 1.
As shown in fig. 2, in an exemplary embodiment, the data processing method of the product resource may include steps S210 to S240, which are described in detail as follows:
step S210, a plurality of product resources to be processed are obtained, and resource types corresponding to the product resources to be processed are determined.
In this embodiment, a plurality of product resources to be processed are obtained, and resource types corresponding to the product resources to be processed are determined, where the corresponding resource types include, but are not limited to, virtual product resources, such as stocks, futures, options, securities, virtual currency, funds, or foreign exchange.
Step S220, obtaining the data to be processed corresponding to each product resource to be processed from the corresponding multi-core server according to the resource type of each product resource to be processed.
In this embodiment of the present application, each resource type corresponds to a multi-core server, where the multi-core server is provided with a plurality of cores of a central processing unit (Central Processing Unit/Processor, CPU), and the multi-core server can bring more powerful computing performance to a user, and can meet the requirement of the user on simultaneous multitasking and multitasking computing environments. And acquiring the data to be processed of the corresponding product resources to be processed from the corresponding multi-core server according to the resource types of the product resources to be processed, wherein the data to be processed comprises historical time, price, transaction amount, warehouse holding amount and other data.
Step S230, the data to be processed of each product resource to be processed is processed in a segmented mode according to the corresponding multi-core server.
In the embodiment of the application, the multi-core processors corresponding to each data to be processed are used for carrying out the sectional processing, and the central processor resources which can be allocated when a single central processor processes the data are limited, so that the data to be processed are distributed to a plurality of central processors after being subjected to the sectional processing, and each central processor is respectively processed, so that the resources for processing the data of the single central processor can be effectively used in the data analysis processing, and the analysis rate of the data to be processed can be effectively accelerated.
Step S240, data analysis processing is carried out on the segmented processed data through the multi-core servers corresponding to the product resources to be processed.
In the embodiment of the application, the data analysis processing is performed on the data to be processed after the segmentation processing by each multi-core server, so that important information such as the time of the transaction, the amount of the transaction, the final profit level, the times of the transaction profit, the times of the loss, the success rate of the transaction and the like of each investment of the product resource to be processed is analyzed according to the transaction rules.
In the embodiment of the application, different multi-core servers are arranged in advance according to different resource types, the to-be-processed data of the to-be-processed product resources are processed in a segmented mode according to the corresponding multi-core servers, the to-be-processed data are reassigned to the corresponding multi-core processors to be processed, and the multi-core processors are provided with a plurality of central processors, so that each central processor can process the to-be-processed data after the segmented processing respectively, the resources of the single central processor for processing the data can be effectively used in data analysis processing, and the analysis rate of the to-be-processed data can be effectively accelerated.
In an exemplary embodiment of the present application, referring to fig. 3, in step S230, the processing of the to-be-processed data of each to-be-processed product resource according to the corresponding multi-core server includes step S310 and step S320, which are described in detail as follows:
step S310, the number of online processors of the multi-core server corresponding to each product resource to be processed is obtained.
In the embodiment of the application, the number of the online central processing units of the multi-core server corresponding to each product resource to be processed is obtained.
Step S320, performing segment processing on the corresponding data to be processed according to the number of the online processors.
In this embodiment of the present application, the corresponding data to be processed is processed in segments according to the number of online central processing units, that is, the data to be processed is equally divided according to the number of previous processors corresponding to the corresponding multi-core server, so as to obtain multiple segments of data segments, for example, the number of online processors of a certain multi-core server is 4, these 4 central processing units are respectively named as A, B, C, D, the corresponding data to be processed is divided into 4 data segments, and then these 4 data segments are sequentially allocated to 4 online processors when data analysis processing is performed.
In an exemplary embodiment of the present application, referring to fig. 4, in step S240, the data analysis processing is performed on the segmented processed data by the multi-core server corresponding to each product resource to be processed, including steps S410 to S430, which are described in detail below:
step S410, obtaining the data amount of the to-be-processed data of the to-be-processed product resources corresponding to each multi-core server.
In the embodiment of the present application, in each multi-core server, the data amount of each piece of data to be processed is obtained, that is, the total byte number of the data to be processed of each piece of product resource to be processed is obtained.
Step S420, determining the priority processing coefficient of the product resource to be processed corresponding to each multi-core server according to the data volume.
In the embodiment of the application, in each multi-core server, a priority processing coefficient of the product resource to be processed in the multi-core server is determined according to the data volume of the data to be processed in the multi-core server. Specifically, the product resources to be processed corresponding to the multi-core server are arranged in a descending order according to the data volume of the corresponding data to be processed, and then the priority processing coefficients of the product resources to be processed corresponding to the multi-core server are obtained.
Step S430, according to the priority processing coefficients, the data analysis processing is carried out on the data to be processed after the corresponding segmentation processing through the corresponding multi-core server.
In the embodiment of the application, in each multi-core server, data analysis processing is sequentially performed on the segmented data to be processed according to the priority processing coefficients of the corresponding product resources to be processed. I.e. the multi-core server prioritizes the processing of the data to be processed for which the priority handling coefficients characterize the preceding.
In an exemplary embodiment of the present application, referring to fig. 5, in step S420, determining, according to the data amount, a priority processing coefficient of a product resource to be processed corresponding to each multi-core server includes steps S510 to S550, which are described in detail below:
Step S510, detecting whether the data amount of the product resource to be processed is greater than or equal to a first preset data amount threshold.
In this embodiment of the present application, in each multi-core server, it is detected whether the data amount of the corresponding product resource to be processed is greater than or equal to a first preset data amount threshold, and specifically, the first preset data amount threshold may be set to 50GB.
Step S520, if the data amount of the product resource to be processed is greater than or equal to the first preset data amount threshold, determining that the priority processing coefficient of the product resource to be processed is the first priority processing coefficient.
In this embodiment of the present application, if the data size of the product resource to be processed is greater than or equal to the first preset data size threshold, it is determined that the priority processing coefficient of the product resource to be processed is the first priority processing coefficient. Specifically, when data analysis processing is performed, if at least two to-be-processed product resources corresponding to the multi-core server have the same priority processing coefficient, descending order is performed according to respective data volume to obtain a corresponding processing sequence.
In step S530, if the data amount of the product resource to be processed is smaller than the first preset data amount threshold, it is detected whether the data amount of the product resource to be processed is greater than or equal to the second preset data amount threshold.
In this embodiment, if the data amount of the product resource to be processed is smaller than the first preset data amount threshold, whether the data amount of the product resource to be processed is greater than or equal to the second preset data amount threshold is detected. The second preset data amount threshold may be set to 20GB.
Step S540, if the data amount of the product resource to be processed is greater than or equal to the second preset data amount threshold, determining that the priority processing coefficient of the product resource to be processed is a second priority processing coefficient.
In this embodiment of the present application, if the data amount of the product resource to be processed is greater than or equal to the second preset data amount threshold and less than the first preset data amount threshold, the priority processing coefficient of the corresponding product resource to be processed is determined to be the second priority processing coefficient.
Step S550, if the data amount of the product resource to be processed is smaller than the second preset data amount threshold, determining that the priority processing coefficient of the product resource to be processed is a third priority processing coefficient; wherein the priorities characterized by the first priority processing coefficient, the second priority processing coefficient and the third priority processing coefficient are sequentially reduced.
In this embodiment of the present application, if the data amount of the product resource to be processed is smaller than the second preset data amount threshold, it is determined that the priority processing coefficient of the corresponding product resource to be processed is the third priority processing coefficient. The priorities represented by the first priority processing coefficient, the second priority processing coefficient and the third priority processing coefficient are sequentially reduced, namely, the data corresponding to the first processing coefficient is processed preferentially.
In an exemplary embodiment of the present application, referring to fig. 6, in step S220, the obtaining, according to the resource type of each product resource to be processed, the data to be processed corresponding to each product resource to be processed from the corresponding multi-core server includes step S610 and step S620, which are described in detail below:
step S610, corresponding priority acquisition coefficients are determined according to the resource types of the product resources to be processed.
In the embodiment of the application, different priority acquisition coefficients are set for different resource types in advance, and when data analysis processing is needed, the corresponding priority acquisition coefficients are directly acquired according to the resource type corresponding to the product resource to be processed.
Step S620, sequentially obtaining corresponding data to be processed from the corresponding multi-core servers according to the priority obtaining coefficients.
In this embodiment of the present application, corresponding to-be-processed data is sequentially obtained from corresponding multi-core servers according to the priority obtaining coefficients, if the to-be-processed data includes a to e, and the corresponding priority obtaining coefficients of the to-be-processed data are sequentially 1, 3, 2, 3, and 2, then corresponding priority obtains corresponding to-be-processed data from the multi-core server corresponding to the to-be-processed data a, obtains corresponding to-be-processed data from the multi-core servers corresponding to the to-be-processed data c and e, and finally obtains corresponding to-be-processed data from the multi-core servers corresponding to the to-be-processed data b and d.
In an exemplary embodiment of the present application, referring to fig. 7, in step S620, the step of sequentially obtaining the corresponding data to be processed from the corresponding multi-core server according to the priority obtaining coefficient includes steps S710 to S730, which are described in detail below:
in step S710, if the priority obtaining coefficient is a first priority obtaining coefficient characterizing the stock resource type, corresponding data to be processed is obtained from a first multi-core server corresponding to the first distance coefficient.
In the embodiment of the present application, a corresponding priority acquisition coefficient is set in advance according to a resource type, where the priority acquisition coefficient is correspondingly provided with a distance coefficient, if a product resource to be processed is a stock resource type, the corresponding priority acquisition coefficient is a first priority acquisition coefficient, the corresponding distance coefficient is a first distance coefficient, the first distance coefficient corresponds to a first multi-core server, and the first multi-core server is the closest multi-core server to the data processing device. The method has the advantages that the data to be processed of the stock resource type to be processed needing to be processed preferentially is placed in the nearest multi-core server, the speed of the result of analysis processing of the transmission data can be accelerated by shortening the transmission distance, and the purpose of accelerating the processing of the data to be processed is achieved
Step S720, if the priority acquisition coefficient is a second priority acquisition coefficient representing the type of the fund resource, acquiring corresponding data to be processed from a second multi-core server corresponding to the second distance coefficient.
In this embodiment of the present application, if the product resource to be processed is a foundation resource type, the corresponding priority acquisition coefficient is a second priority acquisition coefficient, the corresponding distance coefficient is a second distance coefficient, the second distance coefficient corresponds to a second multi-core server, and the distance ranking between the second multi-core server and the data processing device is second.
Step S730, if the priority acquisition coefficient is a third priority acquisition coefficient representing the gold resource type, acquiring corresponding data to be processed from a third multi-core server corresponding to the third distance coefficient; wherein the distances characterized by the first, second, and third distance coefficients gradually increase.
In this embodiment of the present application, if the product resource to be processed is of a gold resource type, the corresponding priority acquisition coefficient is a third priority acquisition coefficient, the corresponding distance coefficient is a third distance coefficient, the third distance coefficient corresponds to a third multi-core server, and the distance between the third multi-core server and the data processing device is ranked third.
In other embodiments, the user may customize the priority acquisition coefficients corresponding to the resource types according to the needs, which is not limited herein.
In an exemplary embodiment of the present application, referring to fig. 8, after the multi-core server corresponding to each product resource to be processed performs data analysis processing on the corresponding segmented data to be processed in step S240, the method further includes step S810 and step S820, which are described in detail below:
step S810, obtaining a data analysis processing result.
In the embodiment of the application, after each online processor of the multi-core server processes a plurality of data segments, a data analysis processing result is correspondingly output.
Step S820, if the data analysis data result indicates that the data analysis processing is unsuccessful, skipping to execute the step of performing the data analysis processing on the segmented processed data corresponding to each multi-core server corresponding to the product resource to be processed to reach the preset times.
In this embodiment of the present application, if the data analysis processing result indicates that the data analysis processing is unsuccessful, repeating the data analysis processing for a plurality of times until the data analysis processing is successfully implemented, and if the number of times of repeating the data analysis processing reaches the budget number of times and the data analysis processing is not successfully implemented yet, generating error reporting information to prompt an administrator to perform error checking. The above-mentioned preset number of times may be set to 5 times.
In an exemplary embodiment of the present application, referring to fig. 9, fig. 9 is a data processing apparatus for product resources according to an exemplary embodiment, including:
a first obtaining module 910, configured to obtain a plurality of product resources to be processed, and determine a resource type corresponding to the plurality of product resources to be processed;
the second obtaining module 920 is configured to obtain, from the corresponding multi-core server, to-be-processed data corresponding to each to-be-processed product resource according to the resource type of each to-be-processed product resource;
the segment processing module 930 is configured to perform segment processing on the to-be-processed data of each to-be-processed product resource according to the corresponding multi-core server;
the data analysis processing module 940 is configured to perform data analysis processing on the segmented processed data through the multi-core servers corresponding to the product resources to be processed.
In one exemplary embodiment of the present application, the segmentation processing module 930 includes:
the first acquisition submodule is configured to acquire the number of online processors of the multi-core server corresponding to each product resource to be processed;
and the segmentation processing sub-module is configured to segment the corresponding data to be processed according to the number of the online processors.
In one exemplary embodiment of the present application, the data analysis processing module 940 includes:
the second acquisition submodule is configured to acquire the data quantity of the to-be-processed data of the to-be-processed product resources corresponding to each multi-core server;
the first determining submodule is configured to determine the priority processing coefficient of the product resource to be processed corresponding to each multi-core server according to the data volume;
and the data analysis processing sub-module is configured to sequentially perform data analysis processing on the corresponding segmented data to be processed through the corresponding multi-core server according to the priority processing coefficient.
In one exemplary embodiment of the present application, determining a sub-module includes:
the first detection unit is configured to detect whether the data volume of the product resource to be processed is larger than or equal to a first preset data volume threshold value;
the first determining unit is configured to determine that the priority processing coefficient of the product resource to be processed is a first priority processing coefficient if the data amount of the product resource to be processed is greater than or equal to a first preset data amount threshold;
the second detection unit is configured to detect whether the data volume of the product resource to be processed is larger than or equal to a second preset data volume threshold value or not if the data volume of the product resource to be processed is smaller than the first preset data volume threshold value;
The second determining unit is configured to determine that the priority processing coefficient of the product resource to be processed is a second priority processing coefficient if the data amount of the product resource to be processed is greater than or equal to the second preset data amount threshold;
a third determining unit configured to determine that the priority processing coefficient of the product resource to be processed is a third priority processing coefficient if the data amount of the product resource to be processed is smaller than the second preset data amount threshold; wherein the priorities characterized by the first priority processing coefficient, the second priority processing coefficient and the third priority processing coefficient are sequentially reduced.
In an exemplary embodiment of the present application, the second obtaining module 920 includes:
the second determining submodule is configured to determine a corresponding priority acquisition coefficient according to the resource type of the product resource to be processed;
and the third acquisition sub-module is configured to sequentially acquire corresponding data to be processed from the corresponding multi-core server according to the priority acquisition coefficient.
In an exemplary embodiment of the present application, the third acquisition sub-module includes:
the first acquisition unit is configured to acquire corresponding data to be processed from a first multi-core server corresponding to the first distance coefficient if the priority acquisition coefficient is a first priority acquisition coefficient representing the stock resource type;
The second acquisition unit is configured to acquire corresponding data to be processed from a second multi-core server corresponding to a second distance coefficient if the priority acquisition coefficient is a second priority acquisition coefficient representing the foundation resource type;
the third acquisition unit is configured to acquire corresponding data to be processed from a third multi-core server corresponding to a third distance coefficient if the priority acquisition coefficient is a third priority acquisition coefficient representing the gold resource type; wherein the distances characterized by the first, second, and third distance coefficients gradually increase.
In an exemplary embodiment of the present application, the data processing apparatus of a product resource further includes:
the third acquisition module is configured to acquire a data analysis processing result;
and the jump module is configured to jump and execute the step of carrying out data analysis processing on the corresponding segmented processed data through the multi-core server corresponding to each product resource to be processed to reach the preset times if the data analysis and data result representation data analysis processing is unsuccessful.
It should be noted that, the data processing apparatus for product resources provided in the foregoing embodiments and the data processing method for product resources provided in the foregoing embodiments belong to the same concept, and specific manners in which each module, sub-module, and unit perform operations have been described in detail in the method embodiments, which are not described herein again.
The embodiment of the application also provides electronic equipment, which comprises: one or more processors; and a storage means for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the data processing method for product resources provided in the above embodiments.
Fig. 10 shows a schematic diagram of a computer system suitable for use in implementing the electronic device of the embodiments of the present application.
It should be noted that, the computer system 1000 of the electronic device shown in fig. 10 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
As shown in fig. 10, the computer system 1000 includes a central processing unit (Central Processing Unit, CPU) 1001 that can perform various appropriate actions and processes, such as performing the method described in the above embodiment, according to a program stored in a Read-Only Memory (ROM) 1002 or a program loaded from a storage section 1008 into a random access Memory (Random Access Memory, RAM) 1003. In the RAM 1003, various programs and data required for system operation are also stored. The CPU1001, ROM 1002, and RAM 1003 are connected to each other by a bus 1004. An Input/Output (I/O) interface 1005 is also connected to bus 1004.
The following components are connected to the I/O interface 1005: an input section 1006 including a keyboard, a mouse, and the like; an output portion 1007 including a Cathode Ray Tube (CRT), a liquid crystal display (Liquid Crystal Display, LCD), and a speaker; a storage portion 1008 including a hard disk or the like; and a communication section 1009 including a network interface card such as a LAN (Local Area Network ) card, a modem, or the like. The communication section 1009 performs communication processing via a network such as the internet. The drive 1010 is also connected to the I/O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like, is installed on the drive 1010 as needed, so that a computer program read out therefrom is installed into the storage section 1008 as needed.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 1009, and/or installed from the removable medium 1011. When executed by a Central Processing Unit (CPU) 1001, the computer program performs various functions defined in the system of the present application.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-Only Memory (ROM), an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), flash Memory, an optical fiber, a portable compact disc read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where 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 or flowchart illustration, and combinations of blocks in the block diagrams 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.
The units involved in the embodiments of the present application may be implemented by means of software, or may be implemented by means of hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
Another aspect of the present application also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment or may exist alone without being incorporated in the electronic device.
Another aspect of the present application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the methods provided in the above-described respective embodiments.
The foregoing is merely a preferred exemplary embodiment of the present application and is not intended to limit the embodiments of the present application, and those skilled in the art may make various changes and modifications according to the main concept and spirit of the present application, so that the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for processing data of a product resource, comprising:
acquiring a plurality of product resources to be processed, and determining resource types corresponding to the product resources to be processed;
acquiring the data to be processed corresponding to each product resource to be processed from the corresponding multi-core server according to the resource type of each product resource to be processed;
the method comprises the steps that data to be processed of each product resource to be processed are processed in a segmented mode according to corresponding multi-core servers;
and carrying out data analysis processing on the segmented processed data through the multi-core servers corresponding to the product resources to be processed.
2. The method for processing data of product resources according to claim 1, wherein the step of processing the data to be processed of each product resource to be processed in segments according to the corresponding multi-core server includes:
acquiring the number of online processors of the multi-core server corresponding to each product resource to be processed;
and carrying out segmentation processing on the corresponding data to be processed according to the number of the online processors.
3. The method for processing data of product resources according to claim 1, wherein the performing, by the multi-core server corresponding to each product resource to be processed, data analysis processing on the segmented processed data to be processed includes:
Acquiring the data quantity of to-be-processed data of to-be-processed product resources corresponding to each multi-core server;
determining the priority processing coefficient of the product resource to be processed corresponding to each multi-core server according to the data volume;
and sequentially carrying out data analysis processing on the data to be processed after the corresponding segmentation processing through the corresponding multi-core servers according to the priority processing coefficients.
4. The method for processing data of product resources according to claim 3, wherein determining a priority processing coefficient of a product resource to be processed corresponding to each multi-core server according to the data amount comprises:
detecting whether the data volume of the product resource to be processed is larger than or equal to a first preset data volume threshold value;
if the data volume of the product resource to be processed is larger than or equal to a first preset data volume threshold value, determining that the priority processing coefficient of the product resource to be processed is a first priority processing coefficient;
if the data volume of the product resource to be processed is smaller than a first preset data volume threshold, detecting whether the data volume of the product resource to be processed is larger than or equal to a second preset data volume threshold;
if the data volume of the product resource to be processed is larger than or equal to the second preset data volume threshold, determining that the priority processing coefficient of the product resource to be processed is a second priority processing coefficient;
If the data volume of the product resource to be processed is smaller than the second preset data volume threshold, determining that the priority processing coefficient of the product resource to be processed is a third priority processing coefficient; wherein the priorities characterized by the first priority processing coefficient, the second priority processing coefficient and the third priority processing coefficient are sequentially reduced.
5. The method for processing data of product resources according to claim 1, wherein the obtaining the data to be processed corresponding to each product resource to be processed from the corresponding multi-core server according to the resource type of each product resource to be processed comprises:
determining a corresponding priority acquisition coefficient according to the resource type of the product resource to be processed;
and sequentially acquiring corresponding data to be processed from the corresponding multi-core servers according to the priority acquisition coefficients.
6. The method for processing data of product resources according to claim 5, wherein sequentially acquiring corresponding data to be processed from the corresponding multi-core server according to the priority acquisition coefficient comprises:
if the priority acquisition coefficient is a first priority acquisition coefficient representing the stock resource type, acquiring corresponding data to be processed from a first multi-core server corresponding to the first distance coefficient;
If the priority acquisition coefficient is a second priority acquisition coefficient representing the type of the foundation resource, acquiring corresponding data to be processed from a second multi-core server corresponding to the second distance coefficient;
if the priority acquisition coefficient is a third priority acquisition coefficient representing the gold resource type, acquiring corresponding data to be processed from a third multi-core server corresponding to a third distance coefficient; wherein the distances characterized by the first, second, and third distance coefficients gradually increase.
7. The method for processing data of product resources according to any one of claims 1 to 6, wherein after the data analysis processing is performed on the segmented processed data by the multi-core server corresponding to each product resource to be processed, the method further comprises:
acquiring a data analysis processing result;
and if the data analysis data result represents that the data analysis processing is unsuccessful, skipping and executing the step of carrying out data analysis processing on the segmented processed data through the multi-core servers corresponding to the product resources to be processed to reach the preset times.
8. A data processing apparatus for a product resource, comprising:
The first acquisition module is configured to acquire a plurality of product resources to be processed and determine resource types corresponding to the product resources to be processed;
the second acquisition module is configured to acquire the data to be processed corresponding to each product resource to be processed from the corresponding multi-core server according to the resource type of each product resource to be processed;
the segmentation processing module is configured to segment the data to be processed of each product resource to be processed according to the corresponding multi-core server;
the data analysis processing module is configured to perform data analysis processing on the segmented processed data through the multi-core servers corresponding to the product resources to be processed.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs which, when executed by the one or more processors, cause the electronic device to implement the data processing method of the product resource of any of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer readable instructions which, when executed by a processor of a computer, cause the computer to perform the data processing method of the product resource of any of claims 1 to 7.
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