CN112148749B - Data analysis method, computing device and storage medium - Google Patents
Data analysis method, computing device and storage medium Download PDFInfo
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/242—Query formulation
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24564—Applying rules; Deductive queries
Abstract
The invention discloses a data analysis method which is suitable for being executed in an analysis server, wherein the analysis server is in communication connection with a data server and one or more clients, and an early warning rule set is stored in the analysis server, and the method comprises the following steps: receiving a data analysis task from a client, wherein the data analysis task comprises a task taking an analysis tree as a structure, a root node of the analysis tree is an early warning task, and task information comprises a data query statement and a calculation formula; acquiring a first data set from a data server according to the data query statement, wherein the first data set comprises a plurality of initial parameters; calculating the initial parameters according to a calculation formula to obtain first calculation parameters; and matching with the early warning rules in the early warning rule set according to the first calculation parameters, and sending early warning contents to a client for submitting a data analysis task according to the matched early warning rules. The invention also discloses a computing device and a computer readable storage medium.
Description
Technical Field
The present invention relates to the field of data detection and analysis, and in particular, to a data analysis method, a computing device, and a storage medium.
Background
With the development of information technology and internet technology, massive data and information are generated in the internet, and it becomes very important how to effectively analyze the data and the information to obtain an effective conclusion to help study and life.
When such generated data and information is stored at a location, useful data needs to be separated and extracted therefrom, processed and analyzed. When the data analysis method works in the step, a data analyst inquires data from the data storage through data inquiry sentences, and the obtained data is used for judging whether the data is abnormal or not by applying practical experience, so that an analysis conclusion is obtained. When the data is monitored, a threshold value is set for one or more items in the data memory, and once the data value exceeds the threshold value, a preset prompting program is triggered to prompt data analysis and monitoring personnel to check and process in time.
In the above prior art scheme, data analysts need to manually extract data from the data storage and manually determine whether the data is in problem, the workload is high, abnormal data cannot be analyzed in time, and often the obtained data analysis result has great delay. In the aspect of monitoring data, after a data item with abnormal data is given, the reason behind the data item cannot be directly known, and the data needs to be further retrieved to find the reason of the abnormal data.
For this reason, a new data analysis method is required.
Disclosure of Invention
To this end, the present invention provides a data analysis method in an attempt to solve or at least alleviate the above-presented problems.
According to one aspect of the present invention, there is provided a data analysis method adapted to be executed in an analysis server, the analysis server being in communication connection with a data server and one or more clients, the analysis server having an early warning rule set stored therein, the method comprising: receiving a data analysis task from a client, wherein the data analysis task comprises a task taking an analysis tree as a structure, a root node of the analysis tree is an early warning task, and task information comprises a data query statement and a calculation formula; acquiring a first data set from a data server according to the data query statement, wherein the first data set comprises a plurality of initial parameters; calculating the initial parameters according to a calculation formula to obtain first calculation parameters; and matching with the early warning rules in the early warning rule set according to the first calculation parameters, and sending early warning contents to a client for submitting a data analysis task according to the matched early warning rules.
Optionally, in the method according to the present invention, the early warning rule includes early warning content, one or more early warning indicators, and a determination condition of each early warning indicator, and the matching with the early warning rule in the early warning rule set according to the first calculation parameter includes: calculating the first calculation parameter according to a calculation formula of the early warning index to obtain an early warning index value; judging whether the early warning index value reaches the early warning index according to the judgment condition of the early warning index; and if the early warning index value reaches the early warning index in one early warning rule in the early warning rule set, matching the first calculation parameter with the early warning rule, and extracting the early warning content in the early warning rule.
Optionally, in the method according to the present invention, the early warning rule includes a priority, and the matching with the early warning rule in the early warning rule set according to the first calculation parameter further includes: and if the first calculation parameter is matched with a plurality of early warning rules in the early warning rule set, selecting the early warning rule with the highest priority as the early warning rule matched with the first calculation parameter.
Optionally, in the method according to the present invention, an analysis rule set is further stored in the analysis server, child nodes of the analysis tree are analysis tasks, task information of the analysis tasks includes a data query statement and a calculation formula, and the method includes: acquiring a second data set from the first data set according to the data query statement, wherein the second data set is a subset of the first data set; calculating the initial parameters to obtain second calculation parameters according to a calculation formula; and matching with the analysis rule in the analysis rule set according to the second calculation parameter, and sending the analysis content and the early warning content to the client for submitting the data analysis task together according to the matched analysis rule.
Optionally, in the method according to the present invention, the step of matching the analysis rule with the analysis rule in the analysis rule set according to the second calculation parameter includes: calculating the second calculation parameter according to a calculation formula of the analysis index to obtain an analysis index value; judging whether the analysis index value reaches the analysis index according to the judgment condition of the analysis index; and if the analysis index value reaches the analysis index in a certain analysis rule in the analysis rule set, matching the second calculation parameter with the analysis rule, and extracting the analysis content in the analysis rule.
Optionally, in the method according to the present invention, the analysis rule includes a priority, and the matching with the analysis rule in the analysis rule set according to the second calculation parameter further includes: if the second calculation parameter matches with a plurality of analysis rules in the analysis rule set, the analysis rule with the highest priority is selected as the analysis rule matching with the second calculation parameter.
Optionally, in the method according to the invention, the calculation formula comprises one or more operators for calculating the parameters of the input calculation formula.
Optionally, in the method according to the present invention, the early warning content includes an early warning template, the early warning template has embedded therein initial parameters and first calculation parameters, the analysis content includes an analysis template, and the analysis template has embedded therein initial parameters and second calculation parameters.
Optionally, in the method according to the present invention, further comprising: the analysis server provides a data analysis interface for the client and sends a task configuration page to the client, wherein the task configuration page comprises a data query statement configuration frame and a first calculation parameter configuration frame of the early warning task, so that the early warning task can receive the data query statement and calculate a calculation formula of a first calculation parameter through the data analysis interface.
Optionally, in the method according to the present invention, the task configuration page further includes an early warning rule configuration box and an analysis rule configuration box, so as to receive the early warning rule and the analysis rule through the data analysis interface.
According to yet another aspect of the present invention, there is provided a computing device comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of a data analysis method according to the present invention.
According to a further aspect of the present invention, there is provided a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of a data analysis method according to the present invention.
In the invention, the analysis server is in communication connection with the data server and one or more clients, a user can configure a data analysis task at the client, the data analyzer extracts a data query statement from an early warning task in the data analysis task after receiving the data analysis task, and a first data set is obtained from the data server according to the data query statement. The first data set includes a plurality of initial parameters. After a person needing to acquire data configures a data analysis task at a client, an analysis server automatically acquires corresponding data, namely a first data set, according to a data query statement in the data analysis task without manually searching and extracting the data.
After the further analysis server obtains the first data set from the data server, the initial parameters in the first data set are calculated according to a calculation formula in the task information of the early warning task, and the first calculation parameters are obtained through calculation. And matching the early warning rules in the early warning rule set according to the first calculation parameter, and sending early warning contents to the client after matching the corresponding early warning rules, thereby realizing automatic monitoring and early warning of data. And the early warning rule set comprises a plurality of early warning rules, and the first calculation parameter can be matched with different early warning rules, so that a plurality of data are monitored and matched with the corresponding early warning rules, and corresponding early warning contents are pushed to the client.
Drawings
To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings, which are indicative of various ways in which the principles disclosed herein may be practiced, and all aspects and equivalents thereof are intended to be within the scope of the claimed subject matter. The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description read in conjunction with the accompanying drawings. Throughout this disclosure, like reference numerals generally refer to like parts or elements.
FIG. 1 shows a schematic diagram of an analytics server in communication with a data server and a client;
FIG. 2 illustrates a block diagram of a computing device 200, according to an exemplary embodiment of the invention;
FIG. 3 shows a flow diagram of a data analysis method 300 according to an embodiment of the invention;
FIG. 4a illustrates a partial schematic diagram of a task configuration page according to one embodiment of the invention;
FIG. 4b illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention;
FIG. 4c illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention;
FIG. 4d illustrates a partial schematic diagram of a task configuration page in accordance with yet another embodiment of the present invention;
FIG. 4e illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention;
FIG. 4f illustrates a partial schematic view of a task configuration page according to yet another embodiment of the invention;
FIG. 4g illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention;
FIG. 5a illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention;
FIG. 5b illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention;
fig. 6 shows a schematic diagram of transmitting analysis content and early warning content according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Like reference numerals generally refer to like parts or elements.
Fig. 1 shows a schematic diagram of an analysis server in communication with a data server and a client. As shown in FIG. 1, the analysis server 110 is communicatively coupled to the data server 120 and to the clients 131-133. The connection shown in fig. 1 is only exemplary, and the number of clients connected to the analysis server 110 is not limited by the present invention.
For any of the clients 131-133 shown in FIG. 1, a user may configure data analysis tasks on the client. The user is specifically a data analyst or a data monitoring person, and data is acquired, monitored and analyzed by configuring a data analysis task on the client. The analysis server 110 provides a data analysis interface to the clients 131 to 133, and sends a task configuration page to the clients 131 to 133 through the data analysis interface. A user configures task data on task configuration pages of the clients 131-133, and the analysis server 110 receives a data analysis task comprising the configured task data through a data analysis interface and executes the data analysis task.
The data analysis task comprises an early warning task and an analysis task. The early warning task and the analysis task are associated in the form of an analysis tree. The analysis tree is configured on a task configuration page of the client by a user. The analytical tree is a tree-like structure and comprises a root node and a child node. The root node is only one, the child nodes are one or more, and the child nodes are indirectly or directly connected to the root node through other child nodes. The user configures the number of the child nodes of the data analysis task and the connection mode of the root node and the child nodes according to the specific needs, and the number of the child nodes and the connection mode included in the root node are not limited by the invention.
In the analysis tree, the early warning task is a root node of the analysis tree, and other analysis tasks are child nodes of the analysis tree. And configuring all nodes including the root node and the child nodes in the analysis tree, namely configuring the early warning task and the analysis task. The early warning task and the analysis task both have task information, and the task information comprises a data query statement and a calculation formula.
The data query statement of the early warning task is used to obtain a first data set from a data server 130 connected to the analysis server 110. The data server 130 has stored therein a plurality of initial parameters. The data server 120 in the present invention interfaces with a service system that provides services to other clients, and the initial parameters include service system data of the service system and other user behavior data. The data query statement of the early warning task selects all or part of the stored initial parameters from the data server as a first data set, namely the first data set is a full set or a non-empty subset of the initial data stored in the data server. The data query statement of the specific early warning task queries and acquires initial data from the data server, the method is not limited in this respect, and the user can set the data query statement at the client according to actual analysis needs to acquire corresponding data from the data server.
The early warning task also comprises a calculation formula for calculating the first calculation parameter, the calculation formula comprises one or more initial parameters and one or more preactors, and a user combines the calculation formula and an operator according to calculation needs to draw the calculation formula.
The task information of the early warning task also comprises basic information and a pushing mode. The basic information comprises the task name of the early warning task, the task description, the execution time of the task and the early warning template. The task execution time can be configured to the time that the early warning task is expected to be executed, the early warning task is executed in a timing mode by configuring the task execution time, the early warning template provides a plurality of preset task configuration templates for selection, each configuration template is configured in advance and stored, and a user can directly use one early warning template or perform further configuration based on the early warning template.
The pushing mode is a mode that after the analysis server finishes executing the early warning task, the result of executing the early warning task is pushed to the user, and the pushing mode comprises a mail mode and a data analysis interface mode. The mail mode can set the mail address of the receiving user, the mail parameters, the data name inquired by the data inquiry statement in the early warning task and the corresponding parameter values. The data analysis interface mode, namely the analysis server directly sends an execution result to the client side submitting the data analysis task.
And after the client 131-133 receives the task configuration page, displaying the task configuration page. The task configuration page comprises a basic information configuration area, a pushing mode configuration area, a data query statement configuration frame and a first calculation parameter configuration frame.
FIG. 4a illustrates a partial schematic diagram of a task configuration page according to one embodiment of the invention. The task configuration page intercepted in fig. 4a includes a basic information configuration area and a push mode configuration area. The basic information configuration area comprises a task name input box, a task description input box, an event setting configuration box and an early warning template selection box which are respectively used for inputting task names, task descriptions, execution time and selection of early warning templates. The push mode configuration area is used for configuring a push receiving mode, and an input box used for inputting receiving users, mail subjects and relevant parameters of data analysis tasks is arranged under a tab of the mail mode.
FIG. 4b illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention. The parse tree and task nodes may be configured as in the task configuration page intercepted in fig. 4 b. In the parse tree shown in FIG. 4b, the root node is the pre-warning task, and the task name is "general overview". When the early warning task is selected, a data query statement configuration frame is arranged in the task configuration page, and the data query statement of the early warning task can be input.
FIG. 4c illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention. The first calculation parameter may be configured as in the task configuration page intercepted in FIG. 4 c. The first calculation parameters can be set to be one or more, and each first calculation parameter is provided with a parameter name and a corresponding calculation formula. In fig. 4c, the first calculation parameters "the ratio of the number of people who pay attention to the competitive products in the previous 1 month" and "the ratio of the number of people who pay attention to the competitive products in the current month" have been set.
FIG. 4d illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention. The task configuration page as captured in fig. 4d includes a configuration box for "the previous 1 month competitive spotlighting people ratio" of the first calculation parameter. Initial parameters in the first data set are listed in the configuration frame, and a user can select the initial parameters in the selectable parameters and edit the calculation formula by applying operators. The calculation formula for calculating the ratio of the number of people who pay attention to the competitive products in the previous 1 month in fig. 4d is as follows: the number of people concerned with the product in the last month/the number of people concerned with the competitive product in the last month.
And analyzing the early warning rules in the early warning rule set stored in the server, and configuring the early warning rules by a task configuration page displayed by the user at the client. FIG. 4e illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention. As shown in fig. 4e, the user may configure the warning rules in the warning rule set. The early warning rules comprise priority, rule names, one or more early warning indexes, judging conditions of each early warning index and early warning content.
The priority, that is, the priority of the early warning rules matched with the first calculation parameter, is set in the early warning rule set by different early warning rules, and the early warning rules with high priority are matched with the first calculation parameter in preference to the early warning rules with low priority. The early warning index is an index for judging whether the first calculation parameter is matched with the early warning rule or not, and is obtained by calculating the first calculation parameter according to a calculation formula of the early warning index.
FIG. 4f illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention. In the task configuration page intercepted in fig. 4f, the user may configure the name of the early warning indicator and the calculation formula for calculating the early warning indicator, and may apply the initial parameter and the first calculation parameter when calculating the early warning indicator, and calculate the operator. In fig. 4f, the formula for calculating the early warning index "the product attention rate ring ratio" is "product attention rate/last 1 month product attention rate-1".
And the judgment condition of the early warning rule is used for judging whether the early warning index value obtained by calculation according to the first calculation parameter reaches the early warning index. One early warning rule may include one or more early warning indicators, and when the early warning indicator value of the first calculation parameter reaches all the corresponding judgment conditions in the early warning rule, the first calculation parameter matches the early warning rule. In a table consisting of a plurality of early warning rules and a plurality of early warning indexes, 1 (dark color cell) is used for identifying the early warning index which needs to be met by the first calculation parameter, and 0 (light color cell) is used for identifying the early warning index which does not need to be met by the first calculation parameter. And when the first calculation parameter is matched with the plurality of early warning rules in the early warning rule set, selecting the early warning rule with the highest priority as the early warning rule matched with the first calculation parameter. The user can perform operations such as adding, deleting, moving up, moving down and the like on the early warning rule.
The early warning content of the early warning rule comprises an early warning template, and initial parameters and first calculation parameters are embedded in the early warning template. FIG. 4g illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention. As shown in the task configuration page captured in fig. 4g, the user can set the pre-warning content of the user rule, and push the corresponding pre-warning content to the user by editing the pre-warning template and adding the embedded parameter when the first calculation parameter matches the pre-warning rule.
The task configuration page further comprises a data query statement configuration box and a second calculation parameter configuration box of the analysis task, so that the data query statement of the analysis task and a calculation formula for calculating a second calculation parameter are received through the data analysis interface. The interface and mode for configuring the analysis task are the same as those for configuring the early warning task. It is noted that the data query statement of the analysis task obtains the initial data from the first data set, resulting in the second data set. The second data set is the full set or a subset of the first data set.
FIG. 5a illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention. As shown in fig. 5a, the task configuration page includes a data query statement configuration box of the analysis task, and the user can configure the data query statement.
And the analysis rules in the analysis rule set stored in the analysis server are configured by the task configuration page displayed by the client side by the user. FIG. 5b illustrates a partial schematic diagram of a task configuration page according to yet another embodiment of the invention. As shown in fig. 5b, the user may configure the analysis rules in the analysis rule set. The analysis rule comprises a priority, a rule name, one or more analysis indexes, a judgment condition of each analysis index and analysis content.
The priority, i.e. the priority of the different analysis rules in the analysis rule set as the analysis rule matching the second calculation parameter, the analysis rule with the higher priority matching the second calculation parameter before the analysis rule with the lower priority. The analysis index is an index for judging whether the second calculation parameter is matched with the analysis rule, and is obtained by calculating the second calculation parameter according to a calculation formula of the analysis index.
The user can configure the name of the analysis index and a calculation formula for calculating the analysis index, and the initial parameter and the second calculation parameter can be applied to calculate the analysis index, and the operator can calculate the analysis index. And the judgment condition of the analysis rule is used for judging whether the analysis index value calculated according to the second calculation parameter reaches the analysis index. One analysis rule may include one or more analysis indexes, and when the analysis index value of the second calculation parameter reaches all the corresponding judgment conditions in the analysis rule, the second calculation parameter matches with the analysis rule. In a table composed of an analysis rule and a plurality of analysis indexes, an analysis index that the second calculation parameter needs to satisfy is identified by 1, and the analysis index that the second calculation parameter does not need to satisfy is identified by 0. When the second calculation parameter matches a plurality of analysis rules in the analysis rule set, the analysis rule with the highest priority is selected as the analysis rule matching the second calculation parameter. The user can add, delete, move up, move down, and the like, to the analysis rule.
The analysis content of the analysis rule comprises an analysis template, and the initial parameter and the second calculation parameter are embedded in the analysis template. The user can set the analysis content of the analysis rule, and corresponding analysis content is pushed to the user by editing the analysis template and adding the embedded parameters when the second calculation parameter is matched with the analysis rule.
The data server 120 may be implemented as any server that provides data storage and querying, and the invention is not limited by the type of data server 120.
The analysis server 110 of fig. 1 may be implemented as a computing device. FIG. 2 illustrates a block diagram of a computing device 200, according to an exemplary embodiment of the invention. As shown in FIG. 2, in a basic configuration 202, a computing device 200 typically includes a system memory 206 and one or more processors 204. A memory bus 208 may be used for communication between the processor 204 and the system memory 206.
Depending on the desired configuration, the processor 204 may be any type of processing, including but not limited to: a microprocessor (μ P), a microcontroller (μ C), a digital information processor (DSP), or any combination thereof. The processor 204 may include one or more levels of cache, such as a level one cache 210 and a level two cache 212, a processor core 214, and registers 216. Example processor cores 214 may include Arithmetic Logic Units (ALUs), Floating Point Units (FPUs), digital signal processing cores (DSP cores), or any combination thereof. The example memory controller 218 may be used with the processor 204, or in some implementations the memory controller 218 may be an internal part of the processor 204.
Depending on the desired configuration, system memory 206 may be any type of memory, including but not limited to: volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 206 may include an operating system 220, one or more programs 222, and program data 224. In some embodiments, the program 222 may be arranged to execute the instructions 223 of the method 300 according to the invention on an operating system by one or more processors 204 using the program data 224.
Computing device 200 may also include a storage interface bus 234. The storage interface bus 234 enables communication from the storage devices 232 (e.g., removable storage 236 and non-removable storage 238) to the basic configuration 202 via the bus/interface controller 230. At least a portion of the operating system 220, applications 222, and data 224 may be stored on removable storage 236 and/or non-removable storage 238, and loaded into system memory 206 via storage interface bus 234 and executed by the one or more processors 204 when the computing device 200 is powered on or the applications 222 are to be executed.
Computing device 200 may also include an interface bus 240 that facilitates communication from various interface devices (e.g., output devices 242, peripheral interfaces 244, and communication devices 246) to the basic configuration 202 via the bus/interface controller 230. The example output device 242 includes a graphics processing unit 248 and an audio processing unit 250. They may be configured to facilitate communication with various external devices, such as a display or speakers, via one or more a/V ports 252. Example peripheral interfaces 244 can include a serial interface controller 254 and a parallel interface controller 256, which can be configured to facilitate communications with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device) or other peripherals (e.g., printer, scanner, etc.) via one or more I/O ports 258. An example communication device 246 may include a network controller 260, which may be arranged to facilitate communications with one or more other computing devices 262 over a network communication link via one or more communication ports 264.
A network communication link may be one example of a communication medium. Communication media may typically be embodied by computer readable instructions, data structures, program modules, and may include any information delivery media, such as carrier waves or other transport mechanisms, in a modulated data signal. A "modulated data signal" may be a signal that has one or more of its data set or its changes made in such a manner as to encode information in the signal. By way of non-limiting example, communication media may include wired media such as a wired network or private-wired network, and various wireless media such as acoustic, Radio Frequency (RF), microwave, Infrared (IR), or other wireless media. The term computer readable media as used herein may include both storage media and communication media.
In a computing device 200 according to the present invention, the application 222 includes program instructions that perform a data analysis method 300 that can instruct the processor 204 to perform some of the steps of the data analysis method 300 that is run in a computing device 200 according to the present invention, such that the various components in the computing device 200 perform analysis of data by performing a data analysis method 300 according to the present invention.
Computing device 200 may be implemented as a server, e.g., file server 240, database 250, a server, an application server, etc., which may be a device such as a Personal Digital Assistant (PDA), a wireless web-browsing device, an application-specific device, or a hybrid device that include any of the above functions. May be implemented as a personal computer including both desktop and notebook computer configurations, and in some embodiments, computing device 200 is configured to perform a data analysis method 300.
FIG. 3 shows a flow diagram of a data analysis method 300 according to an embodiment of the invention. The method 300 is performed in a computing device, such as the computing device 200. As shown in fig. 3, a method 300 for analyzing pickup data begins with step S310, and receives data analysis tasks from a client, where the data analysis tasks include tasks with an analysis tree as a structure, a root node of the analysis tree is an early warning task, and task information includes a data query statement and a calculation formula.
And the analysis server receives the data analysis task, determines the execution time of the data analysis task, and executes the data analysis task within the configured execution time. When the data analysis task is executed, traversal is started from the root node of the analysis tree, and the early warning task of the root node is executed firstly.
Then, step S320 is executed to obtain a first data set from the data server according to the data query statement, where the first data set includes a plurality of initial parameters. As shown in fig. 4b, the early warning task of the parse tree is "overview". According to the data query statement in the early warning task:
the number of people concerned in the last 1 month of the selection month, the number of people concerned in the last 1 month of the competition, and the number of people concerned in the current month of the competition
from table_test
where month={begin_month} and series_name in ({series_name_a},{series_name_name_b})
Acquiring a first data set from a data server, wherein the first data set comprises initial parameters: monthly, 1 month later, the people concerned by the product competitive in 1 month later and the people concerned by the product competitive in this month later.
Subsequently, step S330 is executed to calculate the initial parameter according to the calculation formula to obtain a first calculation parameter. The task information of the early warning task comprises a calculation formula of a first calculation parameter. The first calculation parameters of the early warning task comprise a ratio of the number of people concerned by the competitive products in the previous 1 month and a ratio of the number of people concerned by the competitive products in the current month.
The calculation formula for calculating the first calculation parameter of the ratio of the number of people concerned in the previous 1 month competitive products is ' the number of people concerned in the previous month/the number of people concerned in the previous month competitive products ', and the number of people concerned in the previous month competitive products are substituted into the value of the ratio of the number of people concerned in the previous 1 month competitive products ' according to the initial parameter. Similarly, the first calculation parameter of the ratio of the number of people concerned by the competitive products in this month is calculated.
And then, executing step S340, matching the first calculation parameter with the early warning rule in the early warning rule set, and sending the early warning content to the client submitting the data analysis task according to the matched early warning rule. The early warning rule set includes rule 1, rule 2 and rule 3, and the priorities are 1, 2 and 3, respectively. The early warning indexes of the rule 1 are 'the product attention number ring ratio', 'the previous 1 month competitive product attention number ratio' and 'the current month competitive product attention number ratio', and the judgment conditions are respectively less than-0.3, more than 1 and less than 1. The early warning index of rule 2 is "the product attention people number ring ratio", and the judgment condition is less than-0.3.
According to the first calculation parameter, the step of matching the early warning rule in the early warning rule set comprises the following steps: and calculating the first calculation parameter according to a calculation formula of the early warning index to obtain the early warning index value. The calculation formula of the early warning index value is set by a user at a user side in advance. According to an embodiment of the invention, the early warning index value may also be calculated from the initial parameter. The calculation formula of the early warning index value of the product attention people number ring ratio is that the product attention people number per the last 1 month is-1. And (3) carrying the initial parameters of the number of people concerned by the product in the month and the number of people concerned by the product in the last 1 month into calculation to obtain the early warning index value of the product attention number ring ratio.
And then, judging whether the early warning index value reaches the early warning index according to the judgment condition of the early warning index. And judging whether the early warning index value of the product attention number ring ratio is less than-0.3 according to the calculation, and if the early warning index value is less than-0.3, judging that the early warning index is reached.
And if the early warning index value reaches the early warning index in one early warning rule in the early warning rule set, matching the first calculation parameter with the early warning rule, and extracting the early warning content in the early warning rule. And when the early warning index value obtained by calculating the first calculation parameter meets all the early warning indexes in a certain early warning rule, judging that the first calculation parameter meets the early warning rule. According to one embodiment of the invention, the calculated early warning index value of the product attention people number ring ratio is smaller than-0.3, the first calculation parameter meets all the early warning indexes in the rule 2, and the first calculation parameter is matched with the rule 2. Because the early warning index values of the ratio of the number of people who pay attention to the competitive products in the previous 1 month and the ratio of the number of people who pay attention to the competitive products in the current month obtained by settlement of the first calculation parameter do not meet the corresponding judgment conditions, the first calculation parameter only meets one early warning index of the prediction rule 1, and does not meet other early warning indexes. Therefore, the warning indicator does not match rule 1.
According to the method and the device, the early warning rule set is set, so that after the first data set is extracted, the initial data in the first data set are processed to obtain the first calculation parameter. Comparing the first calculation parameter with the early warning rules in the early warning rule set, determining the early warning rules matched with the first calculation parameter, and sending corresponding early warning contents to the user; therefore, the data in the data server is automatically analyzed, early warning is timely made for data abnormity, and further multiple early warning modes can be realized for the data by the aid of the multiple early warning rules which are concentrated by the set early warning rules, and diversified requirements for data early warning are met.
According to one embodiment of the present invention, if the first calculation parameter matches a plurality of warning rules in the warning rule set, the warning rule with the highest priority is selected as the warning rule matching the first calculation parameter. And if the early warning index values of the first calculation parameter are calculated and the early warning indexes of the rule 1 and the rule 2 are simultaneously met, selecting the rule 1 with higher priority as the early warning rule matched with the first calculation parameter. The first calculation parameter may satisfy multiple early warning rules of different levels after calculating the early warning index value, so that the early warning rule with the highest level is selected from all the satisfied early warning rules and pushed to the client. The method and the device avoid the problem that the sending conflict of the early warning rules needing to be pushed is caused by excessive early warning rules met by the calculation parameters, so that the only priority is set for each early warning rule in advance, and the most important early warning rule with the highest priority is selected and pushed to a user.
And extracting the early warning content in the early warning rule matched with the first calculation parameter, filling the related initial parameter in the early warning content and the first calculation parameter into an early warning template in the early warning content when the early warning content is sent to the client, and sending the early warning content to the client. According to an embodiment of the present invention, the early warning content of rule 2 is "{ begin _ month } { series _ name _ a } attention people number ring ratio falls significantly { thread people number ring ratio }, lags behind { series _ name _ b }", and the relevant parameters { begin _ month } and { series _ name _ a } are filled into the early warning template, so as to obtain the complete early warning content, and the early warning content is sent to the user.
And after the early warning task is executed, executing the analysis task connected with the early warning task in the analysis tree as the analysis task of the child node. The analysis tasks under the 'general overview' of the early warning task comprise 'analysis and interpretation according to sources', 'analysis and interpretation according to regions' and 'analysis and interpretation according to columns'. And when the analysis task is executed, acquiring a second data set from the first data set according to the data query statement, wherein the second data set is a subset of the first data set. According to one embodiment of the present invention, the data query statement in the analysis task "analyze and interpret by source" is:
the source of select, the number of people concerned in the last 1 month, the number of people concerned in this month, and the number of people concerned in the month
from table_test
where month={begin_month} and series_name in ({series_name_a},{series_name_name_b})
order by this month product attention number desc
Acquiring a second data set from the first data set, wherein the second data set comprises initial parameters: the source is the number of people concerned in the last 1 month and the number of people concerned in this month.
And then, calculating the initial parameters according to a calculation formula to obtain second calculation parameters. The task information of the analysis task includes a calculation formula of the second calculation parameter. The second calculation parameter of the analysis task includes "the source of significant downslope in Top 10". The way of calculating the initial parameter according to the calculation formula to obtain the second calculation parameter is the same as the way of calculating the first calculation parameter.
And then, according to the second calculation parameter, matching with the analysis rule in the analysis rule set, and sending the analysis content and the early warning content to the client side submitting the data analysis task together according to the matched analysis rule. According to one embodiment of the present invention, the analysis rule set stored in the analysis server includes rules 1, the priorities of which are top1 levels, respectively. The early warning index of rule 1 is "circle of attention population ratio", and the judgment condition is less than-0.5.
According to the second calculation parameter, matching with the analysis rule in the analysis rule set comprises the following steps: and calculating the second calculation parameter according to the calculation formula of the analysis index to obtain the analysis index value. The calculation formula for analyzing the index value is set by the user at the user end in advance. According to an embodiment of the invention, the analysis index value may also be calculated from the initial parameter. The mode of calculating the second calculation parameter according to the calculation formula of the analysis index to obtain the analysis index value is the same as the mode of calculating the early warning index value.
And then, judging whether the analysis index value reaches the early warning index according to the judgment condition of the analysis index. And judging whether the analysis index value of the 'attention people number ring ratio' obtained by calculation is less than-0.5, and if the analysis index value is less than-0.5, judging that the analysis index is reached.
And if the analysis index value reaches the analysis index in a certain analysis rule in the analysis rule set, matching the second calculation parameter with the analysis rule, and extracting the analysis content in the analysis rule. And when the analysis index value obtained by calculating the second calculation parameter meets all the analysis indexes in a certain analysis rule, judging that the second calculation parameter meets the analysis rule. According to one embodiment of the invention, the calculated analysis index value of the product attention people number ring ratio is smaller than-0.5, and the second calculation parameter meets all the early warning indexes in the rule 1, so that the second calculation parameter is matched with the analysis rule 1.
According to the method and the device, the analysis rule set is set, so that after the second data set is extracted from the first data set, the initial data in the second data set are processed to obtain the second calculation parameter. Comparing the second calculation parameter with the analysis rule in the analysis rule set, determining the analysis rule matched with the second calculation parameter, and sending the corresponding analysis content to the user; therefore, the data in the data server is automatically analyzed, the data abnormity is analyzed in time, furthermore, the data can be analyzed in a variety way by the aid of a plurality of analysis rules which are concentrated by the set analysis rules, and diversified requirements for data analysis are met.
According to an embodiment of the present invention, if the second calculation parameter matches a plurality of analysis rules in the analysis rule set, the analysis rule with the highest priority is selected as the analysis rule matching the second calculation parameter. And if the analysis index values calculated by the second calculation parameters simultaneously meet the early warning indexes of the rule 1 and other rules, selecting the analysis rule with higher priority as the early warning rule matched with the second calculation parameters. The second calculation parameter may satisfy a plurality of analysis rules of different levels after the analysis index value is calculated, so that the analysis rule with the highest level is selected from all the satisfied analysis rules and pushed to the client. And avoiding the sending conflict of the analysis rules needing to be pushed caused by excessive analysis rules met by the second calculation parameters, so that a unique priority is set for each analysis rule in advance, and the most important analysis rule with the highest priority is selected and pushed to the user.
And extracting the analysis content in the analysis rule matched with the second calculation parameter, filling the related initial parameter in the analysis content and the second calculation parameter into an analysis template in the analysis content when the analysis content is sent to the client, and sending the analysis content to the client. According to an embodiment of the present invention, the analysis content of rule 1 is "from the source, the number of people concerned is significantly more than 50% than the number of people concerned, the downward-sliding significant source in { Top10 } is obviously dropped", the relevant parameter { Top10 is filled in the analysis template, i.e. the complete analysis content is obtained, and the analysis content is sent to the user.
After completing the analysis task of 'analyzing and reading by source', traversing other child nodes of the root node in the analysis tree: and analyzing and reading according to regions and analyzing and reading according to columns, and executing the analysis tasks of all the child nodes to complete the whole data analysis task. Particularly, when a child node or a leaf node is further included below a certain child node, all child nodes and leaf nodes below the child node are preferentially traversed, and after all analysis tasks under the analysis task are executed, the analysis tasks of other child nodes are executed. According to one embodiment of the invention, the analysis task "geographical analysis interpretation" further comprises the child nodes: "intersect with column according to region". After the analysis task is executed, namely the analysis task is crossed with the columns according to regions, the analysis task is continuously executed, namely the analysis and the reading according to the columns.
And finally, sending the analysis content obtained by executing all the analysis tasks and the early warning content obtained by executing the early warning task to a client for submitting the data analysis task. And sending the analysis content and the early warning content to a client for submitting the data analysis task in a pushing mode comprising an email mode and a data analysis interface mode.
When the analysis content and the early warning content are sent in a data analysis interface mode of the client, related business suggestions can be provided according to the analysis content and the early warning content, so that business personnel can be helped to improve related business operation modes. Fig. 6 shows a schematic diagram of transmitting analysis content and early warning content according to an embodiment of the present invention.
When the mail is passed through the mail mode, the analysis server writes the mail including the analysis content and the early warning content, and sends the mail to the user according to the preset mail title and the preset mail address. The mail also comprises related parameters of the data analysis content and related service suggestions according to the analysis content and the early warning content.
In the invention, the analysis server is in communication connection with the data server and one or more clients, a user can configure a data analysis task at the client, the data analyzer extracts a data query statement from an early warning task in the data analysis task after receiving the data analysis task, and a first data set is obtained from the data server according to the data query statement. The first data set includes a plurality of initial parameters. After a person needing to acquire data configures a data analysis task at a client, an analysis server automatically acquires corresponding data, namely a first data set, according to a data query statement in the data analysis task without manually searching and extracting the data.
After the further analysis server obtains the first data set from the data server, the initial parameters in the first data set are calculated according to a calculation formula in the task information of the early warning task, and the first calculation parameters are obtained through calculation. And matching the early warning rules in the early warning rule set according to the first calculation parameter, and sending early warning contents to the client after matching the corresponding early warning rules, thereby realizing automatic monitoring and early warning of data. And the early warning rule set comprises a plurality of early warning rules, and the first calculation parameter can be matched with different early warning rules, so that a plurality of data are monitored and matched with the corresponding early warning rules, and corresponding early warning contents are pushed to the client.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules or units or groups of devices in the examples disclosed herein may be arranged in a device as described in this embodiment, or alternatively may be located in one or more devices different from the devices in this example. The modules in the foregoing examples may be combined into one module or may be further divided into multiple sub-modules.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. Modules or units or groups in embodiments may be combined into one module or unit or group and may furthermore be divided into sub-modules or sub-units or sub-groups. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Furthermore, some of the described embodiments are described herein as a method or combination of method elements that can be performed by a processor of a computer system or by other means of performing the described functions. A processor having the necessary instructions for carrying out the method or method elements thus forms a means for carrying out the method or method elements. Further, the elements of the apparatus embodiments described herein are examples of the following apparatus: the apparatus is used to implement the functions performed by the elements for the purpose of carrying out the invention.
The various techniques described herein may be implemented in connection with hardware or software or, alternatively, with a combination of both. Thus, the methods and apparatus of the present invention, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium, wherein, when the program is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the invention.
In the case of program code execution on programmable computers, the computing device will generally include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. Wherein the memory is configured to store program code; the processor is configured to execute the method for determining the apparatus shutdown state of the present invention according to instructions in the program code stored in the memory.
By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer-readable media includes both computer storage media and communication media. Computer storage media store information such as computer readable instructions, data structures, program modules or other data. Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. Combinations of any of the above are also included within the scope of computer readable media.
As used herein, unless otherwise specified the use of the ordinal adjectives "first", "second", "third", etc., to describe a common object, merely indicate that different instances of like objects are being referred to, and are not intended to imply that the objects so described must be in a given sequence, either temporally, spatially, in ranking, or in any other manner.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as described herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense, and the scope of the present invention is defined by the appended claims.
Claims (12)
1. A data analysis method adapted to be executed in an analysis server, the analysis server being in communication with a data server and one or more clients, the analysis server having stored therein an early warning rule set and an analysis rule set, the method comprising:
receiving a data analysis task from a client, wherein the data analysis task comprises a task taking an analysis tree as a structure, the analysis tree comprises a root node and one or more child nodes connected to the root node, the root node is an early warning task, the child nodes are analysis tasks, and task information of the early warning task and the analysis tasks comprises a data query statement and a calculation formula;
acquiring a first data set from a data server according to a data query statement of the early warning task, wherein the first data set comprises a plurality of initial parameters;
calculating initial parameters according to a calculation formula of the early warning task to obtain first calculation parameters;
matching with the early warning rules in the early warning rule set according to the first calculation parameter;
acquiring a second data set from the first data set according to a data query statement of the analysis task, wherein the second data set is a subset of the first data set;
calculating the initial parameters according to a calculation formula of the analysis task to obtain second calculation parameters;
and matching with the analysis rule in the analysis rule set according to the second calculation parameter, and sending the early warning content and the analysis content to a client for submitting a data analysis task together according to the matched early warning rule and the matched analysis rule.
2. The method of claim 1, wherein the pre-warning rules include pre-warning content, one or more pre-warning indicators, and a determination condition for each pre-warning indicator, and the matching with the pre-warning rules in the pre-warning rule set according to the first calculation parameter includes:
calculating the first calculation parameter according to the calculation formula of the early warning index to obtain an early warning index value;
judging whether the early warning index value reaches the early warning index according to the judgment condition of the early warning index;
and if the early warning index value reaches the early warning index in one early warning rule in an early warning rule set, matching the first calculation parameter with the early warning rule, and extracting the early warning content in the early warning rule.
3. The method of claim 2, wherein the early warning rules include a priority, and the matching with early warning rules in the early warning rule set according to the first calculation parameter further comprises:
and if the first calculation parameter is matched with the plurality of early warning rules in the early warning rule set, selecting the early warning rule with the highest priority as the early warning rule matched with the first calculation parameter.
4. The method of claim 1, wherein the analysis rule includes analysis content, one or more analysis indicators, and a determination condition for each analysis indicator, and the matching with the analysis rule in the set of analysis rules according to the second calculation parameter includes:
calculating the second calculation parameter according to the calculation formula of the analysis index to obtain an analysis index value;
judging whether the analysis index value reaches the analysis index according to the judgment condition of the analysis index;
and if the analysis index value reaches the analysis index in a certain analysis rule in the analysis rule set, matching the second calculation parameter with the analysis rule, and extracting the analysis content in the analysis rule.
5. The method of claim 4, wherein the analysis rule includes a priority, and said matching with the analysis rule in the set of analysis rules based on the second calculation parameter further comprises the steps of:
and if the second calculation parameter is matched with a plurality of analysis rules in the analysis rule set, selecting the analysis rule with the highest priority as the analysis rule matched with the second calculation parameter.
6. The method of any one of claims 1-5, wherein the computational formula includes one or more operators to compute parameters input to the computational formula.
7. The method of claim 4 or 5, wherein the early warning content comprises an early warning template having embedded therein initial parameters and first calculation parameters, and the analysis content comprises an analysis template having embedded therein initial parameters and second calculation parameters.
8. The method of claim 4 or 5, further comprising: the analysis server provides a data analysis interface for a client and sends a task configuration page to the client, wherein the task configuration page comprises a data query statement configuration frame and a first calculation parameter configuration frame of the early warning task, so that the data query statement of the early warning task and a calculation formula for calculating a first calculation parameter are received through the data analysis interface.
9. The method of claim 8, wherein the task configuration page further comprises a data query configuration box and a second calculation parameter configuration box of the analysis task, so as to receive a data query statement of the analysis task and a calculation formula for calculating a second calculation parameter through the data analysis interface.
10. The method of claim 9, wherein the task configuration page further comprises an alert rule configuration box and an analysis rule configuration box to receive alert rules and analysis rules through the data analysis interface.
11. A computing device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-10.
12. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-10.
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