CN112527602A - Business data statistical method and device, computer equipment and storage medium - Google Patents
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Abstract
The invention discloses a business data statistical method, a business data statistical device, computer equipment and a storage medium. The method comprises the following steps: acquiring newly added service data in a processing time period, carrying out standardization processing to obtain standard service information, carrying out statistics on the standard service information according to a service information statistical rule to obtain service statistical information, acquiring growth trend information of the service statistical information according to a historical statistical information table, storing the service statistical information and the growth trend information into the historical statistical information table, acquiring corresponding target statistical information from the historical statistical information table according to a statistical information acquisition request, and feeding the target statistical information back to a user terminal. The invention is based on a data report technology, belongs to the field of data statistics, carries out time-interval statistics on service data according to a processing time interval, stores service statistical information and growth trend information obtained by the time-interval statistics through a historical statistical information table, does not need to carry out multi-dimensional analysis processing on massive service data at the same time, and greatly improves the statistical efficiency and the statistical coverage.
Description
Technical Field
The invention relates to the technical field of data reports, belongs to an application scene for counting business data information in a smart city, and particularly relates to a business data counting method, a business data counting device, computer equipment and a storage medium.
Background
In order to master the self business development situation in real time, an enterprise generally needs to count business data to obtain a statistical result, so that the business development situation is comprehensively displayed through the statistical result, and a decision maker of the enterprise can conveniently make a decision and manage according to the statistical result. However, with the continuous development of enterprises, the number of business data of an enterprise itself becomes huge, the business data may belong to a plurality of different types, and the dimensions of the business data are numerous and complicated, so that it is difficult to perform multidimensional statistical analysis on massive business data in a short time, and if only part of the dimensions or part of the business data are counted, the obtained statistical results are not covered comprehensively. Therefore, the method in the prior art has the problem of low statistical efficiency when the business data of the enterprise are counted.
Disclosure of Invention
The embodiment of the invention provides a business data statistical method, a business data statistical device, computer equipment and a storage medium, and aims to solve the problem of low statistical efficiency in enterprise business data statistics in the prior art.
In a first aspect, an embodiment of the present invention provides a service data statistics method, including:
if the preset processing time point is reached, acquiring newly added service data in a processing time period in a prestored service data information table, wherein the processing time period is the interval time between the current processing time point and the last processing time point;
according to a preset processing cluster, carrying out standardization processing on the newly added service data to obtain standard service information corresponding to each newly added service data;
counting the standard service information according to a preset service information counting rule to obtain service counting information;
acquiring growth trend information corresponding to the service statistical information according to the service statistical information and a pre-stored historical statistical information table;
storing the service statistical information and the growth trend information to the historical statistical information table;
if a statistical information acquisition request from the user terminal is received, acquiring target statistical information matched with the statistical information acquisition request in the historical statistical information table;
and feeding back the target statistical information to the user terminal.
In a second aspect, an embodiment of the present invention provides a service data statistics apparatus, including:
a newly added service data acquisition unit, configured to acquire newly added service data in a processing time period from a pre-stored service data information table if a preset processing time point is reached, where the processing time period is an interval time between a current processing time point and a previous processing time point;
the standardization processing unit is used for carrying out standardization processing on the newly added service data according to a preset processing cluster to obtain standard service information corresponding to each newly added service data;
a service statistical information obtaining unit, configured to obtain service statistical information by performing statistics on the standard service information according to a preset service information statistical rule;
the growth trend information acquisition unit is used for acquiring growth trend information corresponding to the service statistical information according to the service statistical information and a pre-stored historical statistical information table;
the information storage unit is used for storing the service statistical information and the growth trend information into the historical statistical information table;
a target statistical information obtaining unit, configured to obtain target statistical information that is matched with the statistical information obtaining request in the history statistical information table if a statistical information obtaining request from the user terminal is received;
and the target statistical information feedback unit is used for feeding the target statistical information back to the user terminal.
In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the traffic data statistics method according to the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the traffic data statistics method according to the first aspect.
The embodiment of the invention provides a business data statistical method, a business data statistical device, computer equipment and a storage medium. Acquiring newly added service data in a processing time period, carrying out standardization processing to obtain standard service information, carrying out statistics on the standard service information according to a service information statistical rule to obtain service statistical information, acquiring growth trend information of the service statistical information according to a historical statistical information table, storing the service statistical information and the growth trend information into the historical statistical information table, acquiring corresponding target statistical information from the historical statistical information table according to a statistical information acquisition request, and feeding the target statistical information back to a user terminal. By the method, the service data are subjected to time-interval statistics according to the processing time interval, the service statistical information and the growth trend information obtained by the time-interval statistics are stored through the historical statistical information table, the massive service data do not need to be subjected to multi-dimensional analysis and processing at the same time, and the statistical efficiency and the statistical coverage are greatly improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a service data statistics method according to an embodiment of the present invention;
fig. 2 is a schematic view of an application scenario of a service data statistics method according to an embodiment of the present invention;
fig. 3 is a sub-flow diagram of a service data statistics method according to an embodiment of the present invention;
fig. 4 is another sub-flow diagram of a service data statistics method according to an embodiment of the present invention;
fig. 5 is another sub-flow diagram of a service data statistics method according to an embodiment of the present invention;
fig. 6 is another sub-flow diagram of a service data statistics method according to an embodiment of the present invention;
fig. 7 is another sub-flow diagram of a service data statistics method according to an embodiment of the present invention;
fig. 8 is a schematic block diagram of a service data statistics apparatus according to an embodiment of the present invention;
FIG. 9 is a schematic block diagram of a computer device provided by an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. 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 invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1 and fig. 2, fig. 1 is a schematic flow diagram of a business data statistical method provided by an embodiment of the present invention, fig. 2 is a schematic application scenario diagram of the business data statistical method provided by the embodiment of the present invention, the business data statistical method is applied to a management server 10, the method is executed by application software installed in the management server 10, the management server 10 is in network connection with at least one user terminal 20 to implement transmission of data information, the management server 10 is a server side for executing the business data statistical method to implement intelligent statistics on business data, the management server 10 may be a server set in an enterprise, and a user of the management server 10 is an administrator of the enterprise; the user terminal 20 is a terminal device, such as a desktop computer, a notebook computer, a tablet computer, a mobile phone, or a large screen display terminal, which establishes a network connection with the management server 10 for data information transmission, and a user of the user terminal 20 may be a general employee of an enterprise. As shown in fig. 1, the method includes steps S110 to S170.
And S110, if the preset processing time point is reached, acquiring newly added service data in a processing time period in a prestored service data information table, wherein the processing time period is the interval time between the current processing time point and the last processing time point.
And if the preset processing time point is reached, acquiring newly added service data in the processing time period in a prestored service data information table. An administrator of the management server may preset a processing time period, where the processing time period is an interval time between a current processing time point and a previous processing time point, and when the processing time period is separated from the previous processing time point, the preset processing time point is reached, and new service data in the processing time period may be obtained from the service data information table according to the processing time period and may be subsequently processed, for example, the processing interval time may be configured to be 2 hours. The management server is pre-configured with one or more service data information tables, one service data information table can be used for storing service data corresponding to one service scene acquired by an enterprise, each service data stored in the service data information table comprises corresponding handling time, and when a processing time point is reached, the service data of which the handling time is in a processing time period in the service data information table is acquired as new service data.
And S120, carrying out standardized processing on the newly added service data according to a preset processing cluster to obtain standard service information corresponding to each newly added service data.
And carrying out standardized processing on the newly added service data according to a preset processing cluster to obtain standard service information corresponding to each newly added service data. In order to improve the efficiency of counting the newly added service data, the newly added service data can be subjected to standardized processing according to a processing cluster, wherein the processing cluster can be a Hadoop processing cluster constructed in a management server, the processing cluster comprises a plurality of processing examples, the processing examples are processing program examples for standardized processing, each processing example can process one newly added service data, and the plurality of processing examples contained in the processing cluster can simultaneously perform parallel processing on a plurality of newly added service data. The newly added service data comprises field information corresponding to a plurality of fields, and the field information describing the same content may be different, so that the field information in the newly added service data can be standardized, the field information describing the same content can be kept consistent, and the statistical efficiency can be improved by subsequently counting the newly added service data.
Specifically, in an embodiment, as shown in fig. 3, the step S120 includes the sub-steps of: s121, S122, S123 and S124.
And S121, inputting each newly added service data into one processing instance in the processing cluster respectively.
And if a certain processing example finishes processing the current newly added service data and is in an idle state, continuously acquiring the next newly added service data and inputting the next newly added service data into the processing example in the idle state for processing until each newly added service data is input into the processing example for standardized processing.
And S122, judging whether the field information of the newly added service data contains non-standard field information which is not matched with the standard field information in the processing example.
Each processing example comprises a plurality of pieces of standard field information, the standard field information is not repeated, whether each piece of field information of the newly added service data is matched with any piece of standard field information in the processing example can be judged, and if the piece of field information is matched with any piece of standard field information, the piece of field information does not need to be subjected to standardized processing; if the field information is not matched with the standard field information, the field information is a non-standard field, and the field information needs to be standardized.
And S123, if the newly added service data contains non-standard field information, acquiring standard field information matched with each piece of non-standard field information according to a conversion dictionary and a neural network in the processing example and replacing the non-standard field information to obtain standard service data corresponding to the newly added service data.
If the newly added service data contains field information which is not matched with the standard field information, the field information is non-standard field information, the standard field information of the non-standard field information can be obtained according to a conversion dictionary and a neural network in the processing example, the non-standard field information is replaced by the standard field information matched with the non-standard field information, and the standard service data corresponding to each newly added service data is obtained. The conversion dictionary is a dictionary for converting characters, and each character can be matched with a corresponding feature code in the conversion dictionary; the neural network is an intelligent network for calculating the matching probability between the standard field information and the non-standard field information.
Specifically, the specific step of acquiring the standard character information matched with the certain non-standard character information includes:
a. and respectively converting the non-standard field information and the standard field information according to the conversion dictionary to obtain a first feature vector and a second feature vector.
Each character can be matched with a corresponding feature code in the conversion dictionary, then the characters contained in the non-standard field information can be converted according to the conversion dictionary, the feature codes corresponding to each character are combined to obtain a first feature vector, the obtained first feature vector represents the features of the non-standard field information in a vector mode, the size of the first feature vector is (1, L), the first feature vector represents that the first feature vector is 1 line L column, the length L of the first feature vector can be preset by an administrator, for example, the number of numerical values in the first feature vector and the second feature vector can be set to be 10(L ═ 10), the feature codes of the non-standard field information are used as numerical values to fill the first feature vector, and the numerical values which are not filled in the first feature vector are marked as "0". And converting each standard field information by adopting the same conversion mode to obtain a second feature vector.
For example, a first feature vector corresponding to "contact phone" may be represented as [3711,6452,1287,4964, … …, 0], and a second feature vector corresponding to "phone number" may be represented as [1287,4964,2473,7141, … …, 0 ].
b. And simultaneously inputting the first feature vector and the second feature vector into the neural network to obtain a matching probability corresponding to each second feature vector.
The neural network is composed of an input layer, a plurality of intermediate layers and an output layer, and the input layer is associated with the intermediate layers, the intermediate layers are associated with other intermediate layers, and the intermediate layers are associated with the output layer through association formulas, for example, a certain association formula can be expressed as y ═ r × x + t, and r and t are parameter values in the association formula. The number of input nodes contained in the input layer is equal to the sum of the lengths of the first eigenvector and the second eigenvector, the number of the input nodes is 2L, each vector value in the first eigenvector corresponds to one input node, each vector value in the second eigenvector also corresponds to one input node, the first eigenvector and the second eigenvector are calculated simultaneously, an output result can be obtained from the output layer, the output result is an output node value of the output node, the output node value can be represented by a percentage, and the value range is [0, 1 ].
c. And acquiring standard field information corresponding to the second feature vector with the highest matching probability as standard field information matched with the non-standard field information.
And according to the matching probability corresponding to each second feature vector, acquiring standard field information with the highest matching probability as standard field information matched with the current non-standard field information, replacing the non-standard field information by adopting the standard field information, and sequentially standardizing each piece of non-standard field information contained in newly added service data by using the method, namely standardizing the newly added service data to obtain a piece of corresponding standard service information.
And S124, if the newly added service data does not contain non-standard field information, taking the newly added service data as the standard service data.
And if the newly added service data does not contain the non-standard field information which is not matched with the standard field information, indicating that the field information contained in the newly added service data is the same as the standard field information, directly taking the newly added service data as the standard service data.
S130, counting the standard service information according to a preset service information counting rule to obtain service counting information.
And counting the standard service information according to a preset service information counting rule to obtain service counting information. Wherein the service information statistical rule comprises a plurality of hierarchical tag groups; after the standard service information is obtained, the standard service information can be counted according to a service information statistical rule, wherein the service information statistical rule is rule information for counting the standard service information, the service information statistical rule comprises a plurality of hierarchical label groups, and each hierarchical label group comprises a plurality of first-level labels and a plurality of second-level labels.
Specifically, in one embodiment, as shown in fig. 4, step S130 includes sub-steps S131 and S132.
S131, respectively carrying out classification statistics on the standard service information according to the primary labels contained in each hierarchical label group to obtain initial statistical information matched with each hierarchical label group; s132, counting the initial statistical information matched with the grading label group according to the secondary labels contained in each grading label group to obtain the service statistical information matched with each grading label group.
In the process of acquiring the service statistical information, standard service information can be classified and counted according to the primary labels included in each hierarchical label group, the primary label of one hierarchical label group can correspond to obtain a piece of initial statistical information, the initial statistical information is a statistical result obtained by counting all standard service information according to a plurality of primary labels of the hierarchical label group, and the statistical result corresponding to each label in the initial statistical information is counted again according to a plurality of secondary labels included in the hierarchical label group to obtain corresponding service statistical information.
For example, the service statistical information obtained by counting all the standard service information according to a hierarchical label group is shown in table 1.
TABLE 1
And S140, acquiring growth trend information corresponding to the service statistical information according to the service statistical information and a pre-stored historical statistical information table.
And acquiring growth trend information corresponding to the service statistical information according to the service statistical information and a pre-stored historical statistical information table. The method can acquire growth trend information corresponding to the business statistical information according to a historical statistical information table, the growth trend information can represent the growth trend of each item of data in the business statistical information, the historical statistical information table is a data table which is configured in advance by a management server and used for storing the business statistical information obtained before, and the historical statistical information table comprises historical business statistical information and a historical growth coefficient. The growth trend information includes a growth coefficient and a growth trend curve.
Specifically, in one embodiment, as shown in fig. 5, step S140 includes sub-steps S141, S142, and S143.
S141, calculating historical service statistical information corresponding to each hierarchical tag group in the historical statistical information table according to a preset reference calculation formula to obtain reference comparison information of each hierarchical tag group; and S142, calculating a growth coefficient of the service statistical information of each hierarchical label group relative to the reference comparison information by taking the reference comparison information as a reference.
The method comprises the steps of obtaining historical service statistical information of each hierarchical label group from a historical statistical information table, calculating the historical service statistical information of each hierarchical label group according to a reference calculation formula to obtain corresponding reference comparison information, wherein the reference calculation formula is a calculation formula for calculating the historical service statistical information of each hierarchical label group, one group of reference comparison information is information obtained by calculating a plurality of pieces of historical service statistical information of one hierarchical label group, the reference comparison information comprises a comparison value corresponding to each primary label and each secondary label in the hierarchical label group, and the reference comparison information of one hierarchical label group can be used as a reference to be compared with the service statistical information of the hierarchical label group to obtain a growth coefficient of the hierarchical label group.
Specifically, the reference calculation formula can be expressed as: fj＝p1×fj1+p2×fj2+…+pi×fji(ii) a Wherein p is1+p2+…+pi＝1，FjIs a contrast value corresponding to the jth label, fjiIs the ith statistical value, p, corresponding to the jth label in the historical service statistical informationiAre the coefficient values in the formula. If the traffic statistics in table 1 include 12 tags in total, 12 comparison values corresponding to the 12 tags in the hierarchical tag group can be calculated.
And calculating the reference comparison information of each hierarchical label group according to the reference calculation formula, wherein the reference comparison information comprises a comparison value corresponding to each label in the hierarchical label group, calculating a ratio value of the service statistical information of each hierarchical label group divided by each comparison value according to the reference comparison information, and acquiring a plurality of ratio values of one hierarchical label group to obtain the growth coefficient of the hierarchical label group.
S143, obtaining a growth trend curve corresponding to each grading label group according to the historical growth coefficient corresponding to each grading label group in the historical statistical information table and the growth coefficient.
Specifically, a historical growth coefficient of a hierarchical tag group and a currently obtained growth coefficient are fitted to obtain a fitted curve corresponding to each tag in the hierarchical tag group, a plurality of fitted curves corresponding to the hierarchical tag group are growth trend curves of the hierarchical tag group, and specific trend information of the business development condition of the enterprise can be visually reflected through the growth trend curves.
S150, storing the service statistical information and the growth trend information to the historical statistical information table.
After the service statistical information and the growth trend information are obtained, the obtained information can be stored in a historical statistical information table, and the service statistical information and the growth coefficient stored in the historical statistical information table can be used as historical service statistical information and a historical growth coefficient which are analyzed later.
Step S150 is followed by: and uploading the service statistical information and the growth coefficient to a block chain for storage.
Specifically, the integrated information obtained by integrating the service statistical information and the growth coefficient is obtained based on the integrated information, and specifically, the digest information is obtained by hashing the integrated information, for example, by using the sha256s algorithm. Uploading summary information to the blockchain can ensure the safety and the fair transparency of the user. The user equipment can download the summary information from the blockchain so as to verify whether the integrated information is tampered. The blockchain referred to in this example is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm, and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
And S160, if a statistical information acquisition request from the user terminal is received, acquiring target statistical information matched with the statistical information acquisition request in the historical statistical information table.
And if a statistical information acquisition request from the user terminal is received, acquiring target statistical information matched with the statistical information acquisition request in the historical statistical information table. The user terminal can send a statistical information acquisition request to the management server, the management server acquires corresponding target statistical information according to the statistical information acquisition request and feeds the target statistical information back to the user terminal, and a user of the user terminal can be an employee of an enterprise. The statistical information acquisition request comprises an acquisition tag, and the target statistical information comprises target service statistical information and target growth trend information.
Specifically, in one embodiment, as shown in fig. 6, step S160 includes sub-steps S161 and S162.
S161, according to the acquisition label of the statistical information acquisition request, acquiring target service statistical information matched with the acquisition label from the historical statistical information table; and S162, acquiring target growth trend information matched with the acquired label from the historical statistical information table according to the acquired label.
Specifically, the statistical value matched with each acquisition tag can be acquired from the historical business statistical information of the historical statistical information table according to the acquisition tag to obtain the target business statistical information, wherein if a certain acquisition tag is a secondary tag, the statistical value corresponding to the secondary tag is directly acquired, and if a certain acquisition tag is a primary tag, the statistical value corresponding to the primary tag is acquired, and the statistical values corresponding to a plurality of secondary tags contained in the primary tag are acquired. The historical statistical information table also comprises a historical growth coefficient, the historical growth coefficient comprises a plurality of processing time points, a growth coefficient and a fitting curve corresponding to each label, and the growth coefficient and the fitting curve matched with the current time point and each obtained label are obtained from the historical growth coefficient and serve as corresponding target growth trend information.
And S170, feeding the target statistical information back to the user terminal.
And feeding back the obtained target statistical information to the user terminal, and displaying the target statistical information after the user terminal receives the target statistical information.
Specifically, in an embodiment, the step S170 may include: and generating statistical display information matched with the target statistical information according to the terminal information in the statistical information acquisition request and feeding the statistical display information back to the user terminal.
In order to enable the user terminal to adaptively display the obtained target statistical information so as to improve the display effect of the target statistical information, the statistical display information matched with the target statistical information can be generated according to the terminal information contained in the statistical information acquisition request, and the statistical display information is the display information which can be adaptively displayed in the user terminal. The terminal information is specific information describing characteristics of the user terminal, and includes a terminal type and a display resolution, the terminal type is information distinguishing the type of the user terminal, and the display resolution is information recording the resolution of a display screen of the user terminal. For example, the terminal type may be a mobile phone, a laptop computer, a desktop computer, a large screen display terminal, or the like.
In one embodiment, as shown in fig. 7, the step of generating statistical display information matching the target statistical information according to the terminal information in the statistical information acquisition request and feeding back the statistical display information to the user terminal includes substeps S171, S172 and S173.
S171, acquiring the terminal type and the display resolution of the terminal information; and S172, acquiring a display template matched with the terminal type and the display resolution in a preset template library.
The template library comprises a plurality of display templates corresponding to different terminal types and different display resolutions, one display template matched with the terminal type can be obtained from the template library according to the terminal information, the content displayed by the display template corresponding to the mobile phone is relatively less, the content displayed by the display template corresponding to the large-screen display terminal is relatively more, the display font is relatively smaller when the display resolution is smaller, and the display font is relatively larger when the display resolution is larger.
And S173, filling the target statistical information in the display template to generate statistical display information matched with the target statistical information.
And filling the obtained target statistical information into a display template matched with the terminal information, so as to generate statistical display information matched with the target statistical information.
Specifically, if the terminal type in the terminal information is a mobile phone, the target statistical information in the statistical display information may be displayed in a paging manner, information corresponding to each tag in the target statistical information is used as one paging in the statistical display information, and when the statistical display information is displayed in the user terminal, the user may click the user terminal switching tag to perform switching display on the display information corresponding to a plurality of pairs of pages.
The technical method can be applied to application scenes including business data information statistics, such as intelligent government affairs, intelligent city management, intelligent communities, intelligent security protection, intelligent logistics, intelligent medical treatment, intelligent education, intelligent environmental protection and intelligent traffic, and accordingly construction of intelligent cities is promoted.
In the service data statistical method provided by the embodiment of the invention, newly added service data in a processing time period is obtained and standardized to obtain standard service information, the standard service information is counted according to a service information statistical rule to obtain service statistical information, the growth trend information of the service statistical information is obtained according to a historical statistical information table, the service statistical information and the growth trend information are stored in the historical statistical information table, and corresponding target statistical information is obtained from the historical statistical information table according to a statistical information obtaining request and is fed back to a user terminal. By the method, the service data are subjected to time-interval statistics according to the processing time interval, the service statistical information and the growth trend information obtained by the time-interval statistics are stored through the historical statistical information table, the massive service data do not need to be subjected to multi-dimensional analysis and processing at the same time, and the statistical efficiency and the statistical coverage are greatly improved.
The embodiment of the invention also provides a business data statistical device, which is used for executing any embodiment of the business data statistical method. Specifically, referring to fig. 8, fig. 8 is a schematic block diagram of a service data statistics apparatus according to an embodiment of the present invention. The traffic data statistics apparatus may be configured in the management server 10.
As shown in fig. 8, the service data statistics apparatus 100 includes a new service data acquisition unit 110, a normalization processing unit 120, a service statistics information acquisition unit 130, a growth trend information acquisition unit 140, an information storage unit 150, a target statistics information acquisition unit 160, and a target statistics information feedback unit 170.
The new service data obtaining unit 110 is configured to obtain new service data in a processing time period from a pre-stored service data information table if a preset processing time point is reached, where the processing time period is an interval time between a current processing time point and a previous processing time point.
The normalization processing unit 120 is configured to perform normalization processing on the newly added service data according to a preset processing cluster to obtain standard service information corresponding to each newly added service data.
In one embodiment, the normalization processing unit 120 includes sub-units: a newly added service data input unit, a field information judgment unit, a non-standard field information replacement unit and a standard service data determination unit.
A newly added service data input unit, configured to input each newly added service data into one processing instance in the processing cluster; a field information judging unit, configured to judge whether field information of the newly added service data includes non-standard field information that is not matched with standard field information in the processing example; a nonstandard field information replacing unit, configured to, if the newly added service data includes nonstandard field information, obtain, according to a conversion dictionary and a neural network in the processing example, standard field information that matches each piece of the nonstandard field information and replace the nonstandard field information, to obtain standard service data corresponding to the newly added service data; and the standard service data determining unit is used for taking the newly added service data as the standard service data if the newly added service data does not contain non-standard field information.
A service statistical information obtaining unit 130, configured to perform statistics on the standard service information according to a preset service information statistical rule to obtain service statistical information.
In an embodiment, the service statistical information obtaining unit 130 includes sub-units: the device comprises a first statistical unit and a second statistical unit.
The first statistical unit is used for respectively carrying out classification statistics on the standard service information according to the primary labels contained in each hierarchical label group to obtain initial statistical information matched with each hierarchical label group; and the second statistical unit is used for carrying out statistics on the initial statistical information matched with the grading label group according to the secondary labels contained in each grading label group to obtain the service statistical information matched with each grading label group.
The growth trend information obtaining unit 140 is configured to obtain growth trend information corresponding to the service statistical information according to the service statistical information and a pre-stored historical statistical information table.
In one embodiment, the growth trend information obtaining unit 140 includes sub-units: the device comprises a reference comparison information acquisition unit, a growth coefficient acquisition unit and a growth trend curve acquisition unit.
A reference comparison information obtaining unit, configured to calculate, according to a preset reference calculation formula, historical service statistical information corresponding to each hierarchical tag group in the historical statistical information table, to obtain reference comparison information of each hierarchical tag group; a growth coefficient obtaining unit, configured to calculate a growth coefficient of the service statistical information of each hierarchical tag group with respect to the reference comparison information, with the reference comparison information as a reference; and the growth trend curve acquisition unit is used for acquiring a growth trend curve corresponding to each hierarchical label group according to the historical growth coefficient corresponding to each hierarchical label group in the historical statistical information table and the growth coefficient.
An information storage unit 150, configured to store the service statistical information and the growth trend information in the historical statistical information table.
A target statistical information obtaining unit 160, configured to obtain target statistical information in the history statistical information table matching with the statistical information obtaining request if the statistical information obtaining request from the user terminal is received.
In one embodiment, the target statistic information obtaining unit 160 includes sub-units: a target service statistical information acquisition unit and a target growth trend information acquisition unit.
A target service statistical information obtaining unit, configured to obtain, according to an obtaining tag of the statistical information obtaining request, target service statistical information matched with the obtaining tag from the historical statistical information table; and the target growth trend information acquisition unit is used for acquiring target growth trend information matched with the acquisition label from the historical statistical information table according to the acquisition label.
A target statistic information feedback unit 170, configured to feed back the target statistic information to the user terminal.
In an embodiment, the target statistical information feedback unit 170 may be configured to generate statistical display information matched with the target statistical information according to the terminal information in the statistical information obtaining request, and feed the statistical display information back to the user terminal.
In one embodiment, the target statistical information feedback unit 170 includes sub-units: the device comprises a terminal information acquisition unit, a display template matching unit and a statistic display information generation unit.
The terminal information acquisition unit is used for acquiring the terminal type and the display resolution of the terminal information; the display template matching unit is used for acquiring a display template matched with the terminal type and the display resolution in a preset template library; and the statistical display information generating unit is used for filling the target statistical information in the display template to generate statistical display information matched with the target statistical information.
The service data statistical device provided by the embodiment of the invention applies the service data statistical method to obtain newly added service data in a processing time period and carry out standardized processing to obtain standard service information, the standard service information is counted according to a service information statistical rule to obtain service statistical information, the growth trend information of the service statistical information is obtained according to a historical statistical information table, the service statistical information and the growth trend information are stored in the historical statistical information table, and corresponding target statistical information is obtained from the historical statistical information table according to a statistical information obtaining request and is fed back to a user terminal. By the method, the service data are subjected to time-interval statistics according to the processing time interval, the service statistical information and the growth trend information obtained by the time-interval statistics are stored through the historical statistical information table, the massive service data do not need to be subjected to multi-dimensional analysis and processing at the same time, and the statistical efficiency and the statistical coverage are greatly improved.
The traffic data statistics apparatus may be implemented in the form of a computer program, which may be run on a computer device as shown in fig. 9.
Referring to fig. 9, fig. 9 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device may be a management server for performing a business data statistics method to perform statistics on business data information.
Referring to fig. 9, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a non-volatile storage medium 503 and an internal memory 504.
The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer programs 5032, when executed, cause the processor 502 to perform a business data statistics method.
The processor 502 is used to provide computing and control capabilities that support the operation of the overall computer device 500.
The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 can be caused to execute the business data statistical method.
The network interface 505 is used for network communication, such as providing transmission of data information. Those skilled in the art will appreciate that the configuration shown in fig. 9 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing device 500 to which aspects of the present invention may be applied, and that a particular computing device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The processor 502 is configured to run a computer program 5032 stored in the memory to implement the corresponding functions in the service data statistics method.
Those skilled in the art will appreciate that the embodiment of a computer device illustrated in fig. 9 does not constitute a limitation on the specific construction of the computer device, and that in other embodiments a computer device may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may only include a memory and a processor, and in such embodiments, the structures and functions of the memory and the processor are consistent with those of the embodiment shown in fig. 9, and are not described herein again.
It should be understood that, in the embodiment of the present invention, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
In another embodiment of the invention, a computer-readable storage medium is provided. The computer readable storage medium may be a non-volatile computer readable storage medium. The computer readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps included in the traffic data statistics method described above.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only a logical division, and there may be other divisions when the actual implementation is performed, or units having the same function may be grouped into one unit, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit 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 invention essentially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a computer-readable storage medium, which includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage media comprise: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A service data statistical method is applied to a management server, the management server is in network connection with at least one user terminal, and the method is characterized by comprising the following steps:
if the preset processing time point is reached, acquiring newly added service data in a processing time period in a prestored service data information table, wherein the processing time period is the interval time between the current processing time point and the last processing time point;
according to a preset processing cluster, carrying out standardization processing on the newly added service data to obtain standard service information corresponding to each newly added service data;
counting the standard service information according to a preset service information counting rule to obtain service counting information;
acquiring growth trend information corresponding to the service statistical information according to the service statistical information and a pre-stored historical statistical information table;
storing the service statistical information and the growth trend information to the historical statistical information table;
if a statistical information acquisition request from the user terminal is received, acquiring target statistical information matched with the statistical information acquisition request in the historical statistical information table;
and feeding back the target statistical information to the user terminal.
2. The service data statistical method according to claim 1, wherein the processing cluster includes a plurality of processing instances, and the normalizing the newly added service data according to a preset processing cluster to obtain standard service information corresponding to each newly added service data includes:
inputting each newly added service data into a processing example in the processing cluster respectively;
judging whether the field information of the newly added service data contains non-standard field information which is not matched with the standard field information in the processing example;
if the newly added service data contains non-standard field information, standard field information matched with each piece of non-standard field information is obtained according to a conversion dictionary and a neural network in the processing example and replaces the non-standard field information, and standard service data corresponding to the newly added service data is obtained;
and if the newly added service data does not contain non-standard field information, taking the newly added service data as the standard service data.
3. The method of claim 1, wherein the service information statistics rule comprises a plurality of hierarchical tag groups, and the performing statistics on the standard service information according to a preset service information statistics rule to obtain service statistics information comprises:
classifying and counting the standard service information respectively according to a primary label contained in each hierarchical label group to obtain initial statistical information matched with each hierarchical label group;
and counting the initial statistical information matched with the grading label group according to the secondary labels contained in each grading label group to obtain the service statistical information matched with each grading label group.
4. The traffic data statistical method according to claim 1, wherein the growth trend information includes a growth coefficient and a growth trend curve, and the obtaining of the growth trend information corresponding to the traffic statistical information according to the traffic statistical information and a pre-stored historical statistical information table includes:
calculating historical service statistical information corresponding to each hierarchical tag group in the historical statistical information table according to a preset reference calculation formula to obtain reference comparison information of each hierarchical tag group;
calculating a growth coefficient of the service statistical information of each hierarchical label group relative to the reference comparison information by taking the reference comparison information as a reference;
and acquiring a growth trend curve corresponding to each grading label group according to the historical growth coefficient corresponding to each grading label group in the historical statistical information table and the growth coefficient.
5. The traffic data statistical method according to claim 1, wherein the target statistical information includes target traffic statistical information and target growth tendency information, and the acquiring the target statistical information in the historical statistical information table that matches the statistical information acquisition request includes:
acquiring target service statistical information matched with the acquired label from the historical statistical information table according to the acquired label of the statistical information acquisition request;
and acquiring target growth trend information matched with the acquired label from the historical statistical information table according to the acquired label.
6. The traffic data statistical method according to claim 1, wherein the feeding back the target statistical information to the user terminal comprises:
and generating statistical display information matched with the target statistical information according to the terminal information in the statistical information acquisition request and feeding the statistical display information back to the user terminal.
7. The method of claim 6, wherein the generating statistical display information matched with the target statistical information according to the terminal information in the statistical information acquisition request and feeding back the statistical display information to the user terminal comprises:
acquiring the terminal type and the display resolution of the terminal information;
acquiring a display template matched with the terminal type and the display resolution in a preset template library;
and filling the target statistical information in the display template to generate statistical display information matched with the target statistical information.
8. A traffic data statistics apparatus, comprising:
a newly added service data acquisition unit, configured to acquire newly added service data in a processing time period from a pre-stored service data information table if a preset processing time point is reached, where the processing time period is an interval time between a current processing time point and a previous processing time point;
the standardization processing unit is used for carrying out standardization processing on the newly added service data according to a preset processing cluster to obtain standard service information corresponding to each newly added service data;
a service statistical information obtaining unit, configured to obtain service statistical information by performing statistics on the standard service information according to a preset service information statistical rule;
the growth trend information acquisition unit is used for acquiring growth trend information corresponding to the service statistical information according to the service statistical information and a pre-stored historical statistical information table;
the information storage unit is used for storing the service statistical information and the growth trend information into the historical statistical information table;
a target statistical information obtaining unit, configured to obtain target statistical information that is matched with the statistical information obtaining request in the history statistical information table if a statistical information obtaining request from the user terminal is received;
and the target statistical information feedback unit is used for feeding the target statistical information back to the user terminal.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the traffic data statistics method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to perform the traffic data statistical method according to any one of claims 1 to 7.
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