CN112541635A - Service data statistical prediction method and device, computer equipment and storage medium - Google Patents
Service data statistical prediction method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a business data statistical prediction method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: obtaining historical service data corresponding to the statistical request information in a historical service database, integrating the historical service data to obtain historical service integrated data, slicing the newly added service data according to a slicing rule to obtain newly added service slice data, counting the newly added service slice data and the historical service integrated data according to the statistical rule to obtain service statistical information, obtaining growth prediction information corresponding to the service statistical information according to a growth prediction model, and feeding back the service statistical information and the growth prediction information to a user terminal for real-time display. The invention is based on the information statistical prediction technology, belongs to the technical field of data analysis, can analyze newly added service data in time and acquire growth prediction information corresponding to the newly added service data based on historical service integration data, can greatly improve the efficiency of performing statistics and prediction on the service data, and reduce the time of statistical prediction processing.
Description
Technical Field
The invention relates to the technical field of data analysis, belongs to an application scene for counting and predicting service data, and particularly relates to a service data counting and predicting method, a device, computer equipment and a storage medium.
Background
In recent years, with the rapid development of internet finance, the sales models and operation modes of products are becoming internet. The real-time performance report is a common tool in internet operation, and in marketing activities, the sales performance of products is counted in real time and displayed on a large screen, so that managers of enterprises can know the current sales target achievement condition in time and adjust marketing strategies. Because the quantity of the service data in a short time is huge, the information contained in the insurance service data is numerous and complex for insurance services, the traditional technical mode cannot analyze massive insurance service data in a short time in real time, if corresponding analysis data needs to be calculated, the time is long, the obtained analysis data is not updated timely, and the purposes of obtaining the current sales situation in real time and predicting the future service trend are difficult to achieve. Therefore, the method in the prior art needs a long time for carrying out statistical analysis and prediction on insurance service data.
Disclosure of Invention
The embodiment of the invention provides a business data statistical prediction method, a business data statistical prediction device, computer equipment and a storage medium, and aims to solve the problem that the time required for statistical analysis and prediction of insurance business data is long in the prior art.
In a first aspect, an embodiment of the present invention provides a method for statistical prediction of service data, where the method includes:
if receiving the statistical request information from the user terminal, acquiring historical service data matched with the statistical type of the statistical request information in a historical service database;
integrating the historical service data to obtain historical service integrated data;
slicing the newly added service data received in real time according to a slicing rule in the statistical request information to obtain newly added service slicing data matched with the statistical request information;
counting the newly added service slice data and the historical service integration data according to the statistical rule of the statistical request information to obtain service statistical information;
obtaining growth prediction information corresponding to the business statistical information according to a preset growth prediction model;
and feeding back the service statistical information and the growth prediction information to the user terminal for real-time display.
In a second aspect, an embodiment of the present invention provides a service data statistics prediction apparatus, which includes:
a historical service data obtaining unit, configured to obtain historical service data in a historical service database, where the historical service data is matched with a statistical type of statistical request information if the statistical request information from the user terminal is received;
the data integration unit is used for integrating the historical service data to obtain historical service integration data;
a newly added service slice data acquisition unit, configured to slice newly added service data received in real time according to a slice rule in the statistics request information to obtain newly added service slice data matched with the statistics request information;
a service statistical information obtaining unit, configured to perform statistics on the newly added service slice data and the historical service integration data according to a statistical rule of the statistical request information to obtain service statistical information;
a growth prediction information obtaining unit, configured to obtain growth prediction information corresponding to the service statistical information according to a preset growth prediction model;
and the information feedback unit is used for feeding the service statistical information and the growth prediction information back to the user terminal for real-time display.
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 statistical prediction method for traffic data 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 statistical prediction method according to the first aspect.
The embodiment of the invention provides a business data statistical prediction method, a business data statistical prediction device, computer equipment and a storage medium. Obtaining historical service data corresponding to the statistical request information in a historical service database, integrating the historical service data to obtain historical service integrated data, slicing the newly added service data according to a slicing rule to obtain newly added service slice data, counting the newly added service slice data and the historical service integrated data according to the statistical rule to obtain service statistical information, obtaining growth prediction information corresponding to the service statistical information according to a growth prediction model, and feeding back the service statistical information and the growth prediction information to a user terminal for real-time display. By the method, the newly added service data can be analyzed in time, the growth prediction information corresponding to the newly added service data is obtained based on the historical service integration data, the efficiency of counting and predicting the service data can be greatly improved, and the time required for counting, analyzing and predicting the insurance service data is reduced.
Drawings
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 statistical prediction method for service data according to an embodiment of the present invention;
fig. 2 is a schematic view of an application scenario of a statistical prediction method for service data according to an embodiment of the present invention;
fig. 3 is a schematic sub-flow diagram of a statistical prediction method for service data according to an embodiment of the present invention;
fig. 4 is another sub-flow diagram of the statistical prediction method for service data according to the embodiment of the present invention;
fig. 5 is another sub-flow diagram of a statistical prediction method for service data according to an embodiment of the present invention;
fig. 6 is another schematic flow chart of a statistical prediction method of service data according to an embodiment of the present invention;
fig. 7 is another sub-flow diagram of a statistical prediction method for service data according to an embodiment of the present invention;
fig. 8 is another schematic flow chart of a statistical prediction method of service data according to an embodiment of the present invention;
fig. 9 is a schematic block diagram of a service data statistical prediction apparatus according to an embodiment of the present invention;
FIG. 10 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 chart of a statistical prediction method of business data according to an embodiment of the present invention, and fig. 2 is a schematic application scenario diagram of the statistical prediction method of business data according to the embodiment of the present invention; the business data statistical prediction method is applied to a management server 10, the business data statistical prediction method is executed through application software installed in the management server 10, the management server 10 is in network connection with at least one user terminal 20 to realize data information transmission, the management server 10 is a server end used for executing the business data statistical prediction method to realize business data statistics and prediction, the management server 10 can 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 S160.
S110, if receiving the statistical request information from the user terminal, obtaining historical service data matched with the statistical type of the statistical request information in a historical service database.
And if the statistical request information from the user terminal is received, acquiring historical service data matched with the statistical type of the statistical request information in a historical service database. The user can send the statistic request information to the management server through the user terminal, the statistic request information includes statistic type, the statistic type is the specific information for recording the service type to be counted, the statistic type can include one or more service types, then historical service data matched with the statistical type can be obtained from a historical service database, the historical service database is a database which is configured in advance in the management server and is used for storing service handling information, the historical service database can contain a plurality of data tables for storing the service handling information, each data table stores a plurality of pieces of service handling information, a user handles a service in an enterprise, a service transaction information corresponding to the service is stored in a data table, or correspondingly storing a plurality of pieces of service handling information corresponding to the service in a plurality of data tables.
For example, for an insurance enterprise, an insurance product containing a plurality of business types, if the statistical type of the statistical request information comprises school risk (safety insurance of elementary and middle school students) and serious disease risk, historical business data matched with the school risk and the serious disease risk can be obtained from a historical business database.
And S120, integrating the historical service data to obtain historical service integrated data.
And integrating the historical service data to obtain historical service integrated data. Because multiple pieces of service handling information may exist in the historical service data of the same service handled, in order to avoid repeated statistics, the multiple pieces of service handling information corresponding to the same service handled can be integrated to obtain historical service integrated data.
In an embodiment, as shown in fig. 3, step S120 includes sub-steps S121, S122 and S123.
S121, judging whether a plurality of pieces of service handling information with the same service codes exist in the historical service data; s122, if a plurality of pieces of service handling information with the same service codes exist in the historical service data, integrating field information of the plurality of pieces of service handling information with the same service codes to obtain a piece of service handling integration information; and S123, acquiring the service handling integration information and the service handling information without the same service code to obtain historical service integration data.
Specifically, each piece of service handling information comprises a service code, whether the service code of one piece of service handling information is the same as that of other pieces of service handling information is judged, that is, whether a plurality of pieces of service handling information with the same service codes exist in historical service data is judged, if the service code of one piece of service handling information is the same as that of other pieces of service handling information, integrating the field information of a plurality of pieces of service handling information with the same service code, wherein each piece of service handling information comprises a plurality of pieces of field information, if the plurality of pieces of service handling information with the same service code comprise repeated field information, performing deduplication processing on the repeated field information, only keeping one piece of field information, after integrating a plurality of pieces of service handling information with the same service code, a piece of service handling integration information can be obtained, and the dimension number of data fields in the service handling integration information is increased.
And if the service code of a certain piece of service handling information is not the same as that of other service handling information, the service handling information is not integrated. Obtaining all the service handling integration information and the service handling information without the same service code, so as to obtain historical service integration data, wherein the number of the data of the historical service integration data is obviously less than that of the historical service data.
S130, slicing the newly added service data received in real time according to the slicing rule in the statistical request information to obtain the newly added service slicing data matched with the statistical request information.
And slicing the newly added service data received in real time according to a slicing rule in the statistical request information to obtain the newly added service slicing data matched with the statistical request information. Specifically, the statistical request information further includes a slicing rule, and the newly added service data received in real time can be sliced according to the slicing rule to obtain the newly added service slicing data matched with the statistical request information. Specifically, the slicing rule may include a slicing interval time, the current time is used as a slicing start time, a slicing end time is obtained according to the slicing interval time, new service data that is located between the slicing start time and the slicing end time and matches with the statistical type of the statistical request information is obtained as new service slicing data that matches with the statistical request information, and the currently obtained slicing end time is used as a slicing start time for performing the next slicing. The slice rule may be the number of slice data or the amount of slice information, among others.
For example, if the current time is 2020-10-1,10:05:01 and the slicing interval time is 1 minute, the slicing start time is 2020-10-1,10:05:01 and the slicing end time is 2020-10-1,10:06:01, and newly added service data which is between 10:05:01 and 10:06:01 and is matched with the statistical type is obtained to obtain newly added service slicing data.
S140, counting the newly added service slice data and the historical service integration data according to the statistical rule of the statistical request information to obtain service statistical information.
And counting the newly added service slice data and the historical service integration data according to the statistical rule of the statistical request information to obtain service statistical information. The statistical rule comprises a plurality of statistical time periods and a plurality of tag groups; the newly added service slice data and the historical service integration data can be used as service data to be counted, the statistical rule is rule information for counting the service data to be counted, specifically, the statistical time period is time period information for grouping the service data to be counted according to time periods, and the tag group is tag information for performing classification statistics on the service data to be counted.
For example, a part of the information in a group of service statistics information obtained by performing statistics according to a tag group is shown in table 1.
TABLE 1
In an embodiment, as shown in fig. 4, step S140 includes sub-steps S141 and S142.
S141, grouping the newly added service slice data and the historical service integration data in a time interval according to a plurality of statistical time intervals to obtain service grouping data; s142, carrying out classified statistics on the service grouping data according to each label group to obtain service statistical information matched with each label group.
Grouping the newly added service slice data and the historical service integration data according to the statistical time period to obtain service grouped data according to the time period, wherein each time period corresponds to one group in the service grouped data, the time lengths of a plurality of time periods can be equal or unequal, and each group comprises a plurality of pieces of service data. Each tag group can contain a plurality of tags, each tag group corresponds to a data field of one dimension in the service data, a plurality of pieces of service data contained in each group in the service packet data are respectively counted according to the plurality of tags contained in one tag group, so that service statistical information matched with the tag group can be obtained, the service statistical information matched with the tag group comprises statistical values corresponding to each tag in each time period, and the change condition of the service data in the plurality of dimensions corresponding to each tag in the tag group along with time can be obtained from the service statistical information of a certain tag group. The service data contained in a plurality of groups in the service grouping data can be classified and counted according to each label group to obtain corresponding service counting information.
And S150, obtaining growth prediction information corresponding to the business statistical information according to a preset growth prediction model.
And obtaining growth prediction information corresponding to the business statistical information according to a preset growth prediction model. Each label in the label group corresponds to one dimension, the service statistical information includes a plurality of statistical values corresponding to each dimension and a plurality of time periods, a dimension growth coefficient corresponding to each dimension can be obtained through prediction based on a growth prediction model and the plurality of statistical values corresponding to each dimension and a plurality of time periods, and the growth prediction information includes a dimension growth coefficient corresponding to each dimension. The growth prediction model is an intelligent prediction model constructed based on a neural network, and can be used for predicting the growth trend corresponding to each dimension.
In one embodiment, as shown in FIG. 5, step S150 includes sub-steps S151 and S152.
And S151, calculating to obtain dimension ratio information corresponding to each label in the label group according to the service statistical information.
The method includes the steps that firstly, dimension ratio information corresponding to each label can be obtained through calculation according to service statistical information, specifically, the service statistical information comprises a plurality of statistical values corresponding to each dimension, a statistical value which is the longest from the current time in the dimension is used as a reference value, proportion values between other statistical values corresponding to the dimension and the reference value are obtained through calculation, and the obtained proportion values are dimension ratio information of the label corresponding to the dimension.
For example, the reference value corresponding to the label "premium amount 0-1000" is 2316; the three proportional values 0.1183, 0.2465, 0.6792 are calculated in turn according to table 1.
And S152, respectively inputting the dimension ratio information corresponding to each label into the growth prediction model for prediction to obtain a dimension growth coefficient corresponding to each label.
The dimension ratio information corresponding to each label is respectively input into the growth prediction model, so that a dimension growth coefficient corresponding to each label can be calculated, the dimension growth coefficient can be one or more, each dimension growth coefficient can be used for representing the growth trend of a future time point relative to the current time point, the dimension growth coefficient can be larger than zero, smaller than zero or equal to zero, if the dimension growth coefficient is larger than zero, the future statistical value of the label will be increased, if the dimension growth coefficient is smaller than zero, the future statistical value of the label will be reduced, if the dimension growth coefficient is equal to zero, the future statistical value of the label will not be increased, and the absolute value of the dimension growth coefficient is the amplitude of increase or reduction of the future statistical value of the label.
For example, the dimension ratio information corresponding to a certain label is input into a growth prediction model to obtain three dimension growth coefficients, the first dimension growth coefficient can predict a growth trend after 10 minutes, the second dimension growth coefficient can predict a growth trend after 25 minutes, and the third dimension growth coefficient can predict a growth trend after 60 minutes.
Specifically, the growth prediction model comprises a plurality of input nodes, one or more intermediate layers and one or more output nodes, each intermediate layer comprises a plurality of characteristic units, and each characteristic unit is connected with the input nodes or other characteristic units through input formulasEach feature unit is also connected with the output node through an output formula. Wherein, the input formula or the output formula can be expressed as: a is as aX+ b; wherein a and b are parameter values in a formula, y is a calculated value, and x is an input value; the calculation formula of the output value of any one output node can be expressed as:
wherein, ajThe weighted value h of the jth characteristic unit of the last middle layer in the full connection layerjThe calculated value of the jth characteristic unit of the last middle layer in the full connection layer is N, and N is the number of the characteristic units contained in the last middle layer in the full connection layer. Each input node corresponds to one proportion value in the dimension ratio information, a plurality of proportion values contained in the dimension proportion information of one label are used as input node values of corresponding input nodes to be input, an output value corresponding to each output node can be calculated through an input formula, an output formula and a calculation formula of the output value, and the output value of each output node is the obtained dimension growth coefficient.
In one embodiment, as shown in fig. 6, step S150 is preceded by step S150 a.
S150a, training the growth prediction model according to preset model training rules and the historical business integration data.
In order to enable the growth prediction model to have higher accuracy in the growth prediction process, the growth prediction model can be subjected to iterative training before the growth prediction model is used, namely parameter values in an input formula, an output formula and a calculation formula of an output value of the growth prediction model are adjusted, and the accuracy of the growth prediction can be greatly improved by the growth prediction model obtained after training. The model training rule is a specific rule for training the growth prediction model, the model training rule comprises a loss value calculation formula and a gradient calculation formula, and historical service integration data can be used as training sample data and the growth prediction model can be trained by combining the model training rule. For example, a time point before 60 minutes may be used as the virtual current time, and the historical service integration data is counted according to the statistical rule to obtain training dimension ratio information corresponding to the historical service integration data and each label, a ratio between the statistical value of the current time and the statistical value of the virtual current time is used as a 60-minute target dimension growth coefficient, a ratio between the statistical value 35 minutes before the current time and the statistical value of the virtual current time is used as a 25-minute target dimension growth coefficient, and a ratio between the statistical value 50 minutes before the current time and the statistical value of the virtual current time is used as a 10-minute target dimension growth coefficient. Each proportional value contained in the training dimension ratio information of one label corresponds to one input node, the training dimension ratio information of one label is input into a growth prediction model to obtain a prediction dimension growth coefficient corresponding to the label, a corresponding loss value can be obtained by calculating according to a loss value calculation formula and a target dimension growth coefficient corresponding to the label, an updated value corresponding to each parameter in the calculation formulas of the input formula, the output formula and the output value can be obtained by calculating according to a loss value and a gradient calculation formula, a parameter value corresponding to each parameter can be updated through the updated value, and the process of updating the parameter values is a specific process of training the growth prediction model.
For example, the loss value calculation formula may be expressed as
Wherein p isnThe nth predicted dimension growth coefficient output for the growth prediction model, fnFor the nth target dimension corresponding to a certain label, fpAnd fnCan be greater than zero, less than zero or equal to zero, wherein fnNot simultaneously zero.
And calculating an updated value of each parameter in the growth prediction model according to the gradient calculation formula, the loss value and the calculated value of the growth prediction model. Specifically, a calculation value obtained by calculating the training dimension ratio information by using one parameter in the growth prediction model is input into a gradient calculation formula, and an update value corresponding to the parameter can be calculated by combining the loss value, and the calculation process is also gradient descent calculation.
Specifically, the gradient calculation formula can be expressed as:
And updating the parameter values of the corresponding parameters in the growth prediction model according to the updated values of each parameter so as to train the growth prediction model. And correspondingly updating the parameter value of each parameter in the growth prediction model based on the calculated updated value, namely finishing a training process of the growth prediction model. Calculating the training dimension ratio information and the target dimension growth coefficient of another label again based on the growth prediction model obtained after one training, and repeating the training process to realize iterative training of the growth prediction model; and when the calculated loss value is smaller than a loss threshold value preset in the model training rule, terminating the training to obtain the trained growth prediction model. The growth prediction model can be trained through the method before the growth prediction model is trained each time, so that the purpose of performing rolling training on the growth prediction model by using historical service integration data is achieved.
And S160, feeding back the service statistical information and the growth prediction information to the user terminal for real-time display.
And feeding back the service statistical information and the growth prediction information to the user terminal for real-time display. When new service statistical information and growth prediction information are obtained and the obtained service statistical information and growth prediction information can be fed back to the user terminal, the user terminal can update the displayed original information after receiving the new service statistical information and growth prediction information.
In one embodiment, as shown in fig. 7, step S160 includes sub-steps S161, S162, and S163.
S161, judging whether to feed back the service statistical information and the growth prediction information to the user terminal for the first time.
And judging whether the management server feeds back the service statistical information and the growth prediction information to the user terminal for the first time, if not, directly feeding back the service statistical information and the growth prediction information to the user terminal to update the displayed original information, wherein the service statistical information and the growth prediction information only contain numerical information.
And S162, if the service statistical information and the growth prediction information are fed back to the user terminal for the first time, generating statistical display information matched with the service statistical information and the growth prediction information according to the terminal information in the statistical request information and feeding back the statistical display information to the user terminal.
If the management server feeds back data information to the user terminal for the first time, statistical display information can be generated according to the terminal information contained in the statistical request information, the statistical display information is information for visually displaying the service statistical information and the growth prediction information in a chart form, and the obtained statistical display information 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.
a. Acquiring the terminal type and the display resolution of the terminal information; b. acquiring a display template matched with the terminal type and the display resolution in a preset template library; c. filling the service statistical information and the growth prediction information in the display template to generate the statistical display information;
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 filling the obtained service statistical information and the growth prediction information into a display template matched with the terminal information, so as to generate statistical display information matched with the service statistical information and the growth prediction information.
S163, if the service statistical information and the growth prediction information are not fed back to the user terminal for the first time, feeding back the service statistical information and the growth prediction information to the user terminal in a manner of numerical information.
In an embodiment, as shown in fig. 8, step S170 is further included after step S160.
S170, integrating the newly added service slice data into the historical service integration data for storage, and returning to the step of executing the step of slicing the newly added service data received in real time according to the slicing rule in the statistical request information to obtain the newly added service slice data matched with the statistical request information.
The newly added service slice data is integrated into the historical service integration data for storage, and the historical service integration data after the newly added service slice data is added can be used as historical information for subsequent analysis or data information for rolling training of the growth prediction model, specifically, the step S130 can be returned to, the management server analyzes and predicts the service statistical information again according to the above steps and feeds back the service statistical information and the growth prediction information to the user terminal, the original information displayed in the user terminal is updated iteratively until a termination statistical instruction sent by the employee to the management server through the user terminal is received, that is, after step S160, it is determined whether the termination statistical instruction from the user terminal is received, if the termination statistical instruction is received, the service data statistical prediction process is terminated, and if the termination statistical instruction is not received, step S170 is executed.
In addition, the service statistical information and the growth prediction information may also be uploaded to a block chain network for storage, specifically, corresponding digest information is obtained based on the service statistical information and the growth prediction information, specifically, the digest information is obtained by performing hash processing on the service statistical information and the growth prediction information, for example, by using the sha256s algorithm. The abstract information corresponding to the service statistical information and the growth prediction information is uploaded to the block chain, so that the safety and the fair transparency to users can be guaranteed. The ue may download the summary information from the blockchain to verify whether the traffic statistics and the growth prediction information are 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.
In the service data statistical prediction method provided by the embodiment of the invention, historical service data corresponding to statistical request information in a historical service database is obtained and integrated to obtain historical service integrated data, the newly added service data is sliced according to a slicing rule to obtain newly added service sliced data, the newly added service sliced data and the historical service integrated data are counted according to a statistical rule to obtain service statistical information, growth prediction information corresponding to the service statistical information is obtained according to a growth prediction model, and the service statistical information and the growth prediction information are fed back to a user terminal to be displayed in real time. By the method, the newly added service data can be analyzed in time, the growth prediction information corresponding to the newly added service data is obtained based on the historical service integration data, the efficiency of counting and predicting the service data can be greatly improved, and the time required for counting, analyzing and predicting the insurance service data is reduced.
The embodiment of the invention also provides a business data statistical prediction device, which is used for executing any embodiment of the business data statistical prediction method. Specifically, referring to fig. 9, fig. 9 is a schematic block diagram of a traffic data statistics prediction apparatus according to an embodiment of the present invention. The traffic data statistical prediction apparatus may be configured in the management server 10.
As shown in fig. 9, the service data statistics prediction apparatus 100 includes a historical service data acquisition unit 110, a data integration unit 120, a newly added service slice data acquisition unit 130, a service statistics information acquisition unit 140, a growth prediction information acquisition unit 150, and an information feedback unit 160.
A historical service data obtaining unit 110, configured to obtain historical service data in a historical service database, where the historical service data is matched with a statistical type of the statistical request information, if the statistical request information from the user terminal is received.
A data integration unit 120, configured to integrate the historical service data to obtain historical service integration data.
In one embodiment, the data integration unit 120 includes sub-units: the system comprises a service code judging unit, a field information integrating unit and a historical service integrating data acquiring unit.
A service code judging unit, configured to judge whether multiple pieces of service handling information with the same service code exist in the historical service data; a field information integration unit, configured to, if multiple pieces of service handling information with the same service code exist in the historical service data, integrate the field information of the multiple pieces of service handling information with the same service code to obtain a piece of service handling integration information; and the historical service integration data acquisition unit is used for acquiring the service transaction integration information and the service transaction information without the same service code to obtain historical service integration data.
A newly added service slice data obtaining unit 130, configured to slice the newly added service data received in real time according to the slice rule in the statistics request information to obtain newly added service slice data matched with the statistics request information.
A service statistical information obtaining unit 140, configured to perform statistics on the newly added service slice data and the historical service integration data according to a statistical rule of the statistical request information to obtain service statistical information.
In an embodiment, the service statistical information obtaining unit 140 includes sub-units: a service grouping data acquisition unit and a classification statistical unit.
A service grouping data obtaining unit, configured to group the newly added service slice data and the historical service integration data in time segments according to a plurality of statistical time segments to obtain service grouping data; and the classification statistical unit is used for performing classification statistics on the service grouping data according to each label group to obtain service statistical information matched with each label group.
And a growth prediction information obtaining unit 150, configured to obtain growth prediction information corresponding to the service statistical information according to a preset growth prediction model.
In an embodiment, the growth prediction information obtaining unit 150 includes sub-units: the device comprises a dimension ratio information acquisition unit and a dimension growth coefficient acquisition unit.
The dimension ratio information acquisition unit is used for calculating and obtaining dimension ratio information corresponding to each label in the label group according to the service statistical information; and the dimension growth coefficient acquisition unit is used for respectively inputting the dimension ratio information corresponding to each label into the growth prediction model for prediction so as to obtain a dimension growth coefficient corresponding to each label.
In an embodiment, the traffic data statistics prediction apparatus 100 further includes a sub-unit: and a growth prediction model training unit.
And the growth prediction model training unit is used for training the growth prediction model according to preset model training rules and the historical service integration data.
An information feedback unit 160, configured to feed back the service statistics information and the growth prediction information to the user terminal for real-time display.
In one embodiment, the information feedback unit 160 includes sub-units: the device comprises a feedback judgment unit, a statistical display information feedback unit and a numerical information feedback unit.
A feedback judgment unit, configured to judge whether to feed back the service statistical information and the growth prediction information to the user terminal for the first time; a statistics display information feedback unit, configured to generate, if service statistics information and the growth prediction information are fed back to the user terminal for the first time, statistics display information matched with the service statistics information and the growth prediction information according to terminal information in the statistics request information, and feed back the statistics display information to the user terminal; and the numerical value information feedback unit is used for feeding back the service statistical information and the growth prediction information to the user terminal in a numerical value information mode if the service statistical information and the growth prediction information are not fed back to the user terminal for the first time.
In an embodiment, the traffic data statistics prediction apparatus 100 further includes a sub-unit: and a data storage unit.
And the data storage unit is used for integrating the newly added service slice data into the historical service integration data for storage, and returning to execute the step of slicing the newly added service data received in real time according to the slicing rule in the statistical request information to obtain the newly added service slice data matched with the statistical request information.
The service data statistical prediction device provided by the embodiment of the invention applies the service data statistical prediction method to obtain and integrate historical service data corresponding to statistical request information in a historical service database to obtain historical service integrated data, slices the newly added service data according to a slicing rule to obtain newly added service sliced data, counts the newly added service sliced data and the historical service integrated data according to the statistical rule to obtain service statistical information, obtains growth prediction information corresponding to the service statistical information according to a growth prediction model, and feeds the service statistical information and the growth prediction information back to a user terminal for real-time display. By the method, the newly added service data can be analyzed in time, the growth prediction information corresponding to the newly added service data is obtained based on the historical service integration data, the efficiency of counting and predicting the service data can be greatly improved, and the time required for counting, analyzing and predicting the insurance service data is reduced.
The traffic data statistical prediction apparatus may be implemented in the form of a computer program, which may be run on a computer device as shown in fig. 10.
Referring to fig. 10, fig. 10 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 traffic data statistical prediction method to count and predict traffic data.
Referring to fig. 10, 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 traffic data statistical prediction 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 enabled to execute the statistical prediction method of the business data.
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. 10 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 traffic data statistical prediction method.
Those skilled in the art will appreciate that the embodiment of a computer device illustrated in fig. 10 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. 10, 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 statistical prediction 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 business data statistics prediction 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 receiving the statistical request information from the user terminal, acquiring historical service data matched with the statistical type of the statistical request information in a historical service database;
integrating the historical service data to obtain historical service integrated data;
slicing the newly added service data received in real time according to a slicing rule in the statistical request information to obtain newly added service slicing data matched with the statistical request information;
counting the newly added service slice data and the historical service integration data according to the statistical rule of the statistical request information to obtain service statistical information;
obtaining growth prediction information corresponding to the business statistical information according to a preset growth prediction model;
and feeding back the service statistical information and the growth prediction information to the user terminal for real-time display.
2. The statistical prediction method for service data according to claim 1, wherein the integrating the historical service data to obtain historical service integration data comprises:
judging whether a plurality of pieces of service handling information with the same service code exist in the historical service data;
if a plurality of pieces of service handling information with the same service code exist in the historical service data, integrating the field information of the plurality of pieces of service handling information with the same service code to obtain a piece of service handling integration information;
and acquiring the service handling integration information and the service handling information without the same service code to obtain historical service integration data.
3. The method according to claim 1, wherein the statistical rules include a plurality of statistical time periods and a plurality of tag sets, and the statistical rules according to the statistical request information are used to perform statistics on the newly added service slice data and the historical service integration data to obtain service statistical information, including:
grouping the newly added service slice data and the historical service integration data at different time periods according to a plurality of statistical time periods to obtain service grouping data;
and carrying out classified statistics on the service grouping data according to each label group to obtain service statistical information matched with each label group.
4. The statistical prediction method of business data according to claim 3, wherein the growth prediction information includes a dimension growth coefficient corresponding to each label, and the obtaining of growth prediction information corresponding to the business statistical information according to a preset growth prediction model includes:
calculating to obtain dimension ratio information corresponding to each label in the label group according to the service statistical information;
and respectively inputting the dimension ratio information corresponding to each label into the growth prediction model for prediction to obtain a dimension growth coefficient corresponding to each label.
5. The statistical prediction method of traffic data according to claim 1, before obtaining growth prediction information corresponding to the traffic statistical information according to a preset growth prediction model, further comprising:
and training the growth prediction model according to preset model training rules and the historical service integration data.
6. The method of claim 1, wherein the feeding back the service statistics information and the growth prediction information to the user terminal for real-time display comprises:
judging whether the service statistical information and the growth prediction information are fed back to the user terminal for the first time;
if the service statistical information and the growth prediction information are fed back to the user terminal for the first time, generating statistical display information matched with the service statistical information and the growth prediction information according to the terminal information in the statistical request information and feeding back the statistical display information to the user terminal;
and if the service statistical information and the growth prediction information are not fed back to the user terminal for the first time, feeding back the service statistical information and the growth prediction information to the user terminal in a numerical information mode.
7. The method of claim 1, wherein after the feeding back the service statistics information and the growth prediction information to the user terminal for real-time display, the method further comprises:
integrating the newly added service slice data into the historical service integration data for storage, and returning to execute the step of slicing the newly added service data received in real time according to the slicing rule in the statistical request information to obtain the newly added service slice data matched with the statistical request information.
8. A traffic data statistical prediction apparatus, comprising:
a historical service data obtaining unit, configured to obtain historical service data in a historical service database, where the historical service data is matched with a statistical type of statistical request information if the statistical request information from the user terminal is received;
the data integration unit is used for integrating the historical service data to obtain historical service integration data;
a newly added service slice data acquisition unit, configured to slice newly added service data received in real time according to a slice rule in the statistics request information to obtain newly added service slice data matched with the statistics request information;
a service statistical information obtaining unit, configured to perform statistics on the newly added service slice data and the historical service integration data according to a statistical rule of the statistical request information to obtain service statistical information;
a growth prediction information obtaining unit, configured to obtain growth prediction information corresponding to the service statistical information according to a preset growth prediction model;
and the information feedback unit is used for feeding the service statistical information and the growth prediction information back to the user terminal for real-time display.
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 statistical prediction method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, causes the processor to carry out the traffic data statistical prediction method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113344282A (en) * | 2021-06-23 | 2021-09-03 | 中国光大银行股份有限公司 | Method, system and computer readable medium for capacity data processing and allocation |
| CN115242630A (en) * | 2021-04-23 | 2022-10-25 | 中国移动通信集团四川有限公司 | Arranging method and device for 5G network slices and electronic equipment |
| CN117349345A (en) * | 2023-12-05 | 2024-01-05 | 南京研利科技有限公司 | Data statistics method and device and data statistics acquisition method and device thereof |
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2020
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115242630A (en) * | 2021-04-23 | 2022-10-25 | 中国移动通信集团四川有限公司 | Arranging method and device for 5G network slices and electronic equipment |
| CN115242630B (en) * | 2021-04-23 | 2023-10-27 | 中国移动通信集团四川有限公司 | 5G network slice arrangement method and device and electronic equipment |
| CN113344282A (en) * | 2021-06-23 | 2021-09-03 | 中国光大银行股份有限公司 | Method, system and computer readable medium for capacity data processing and allocation |
| CN117349345A (en) * | 2023-12-05 | 2024-01-05 | 南京研利科技有限公司 | Data statistics method and device and data statistics acquisition method and device thereof |
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