CN113157984A

CN113157984A - Processing method, terminal device and storage medium

Info

Publication number: CN113157984A
Application number: CN202110430348.4A
Authority: CN
Inventors: 刘沙沙
Original assignee: Shanghai Chuanying Information Technology Co Ltd
Current assignee: Shanghai Chuanying Information Technology Co Ltd
Priority date: 2021-04-21
Filing date: 2021-04-21
Publication date: 2021-07-23

Abstract

The application discloses a processing method, terminal equipment and a storage medium. The method comprises the following steps: s1, constructing a neural network model based on the collected data; s2, outputting an abnormal report based on the neural network model; and S3, acquiring the input information of the user and outputting a report of the service inquired based on the input information. According to the method and the device, the judgment threshold value of index abnormity is set without depending on manual experience, the judgment threshold value is obtained based on big data analysis, the method and the device are more objective, a report can be obtained by combining with a natural language processing technology when a user inputs information, such as voice, and the convenience is high, and the associated report is output based on a data mining technology, so that information with more dimensions can be provided.

Description

Processing method, terminal device and storage medium

Technical Field

The present application relates to the field of information push and intelligent reporting technologies, and in particular, to a processing method, a terminal device, and a storage medium.

Background

The purpose of a Business Intelligence (BI) system method or product is to: through data extraction, sorting and analysis, the data are converted into useful information to assist enterprises in making business decisions. In the course of conceiving and implementing the present application, the inventors found that at least the following problems existed: the existing BI system often sets a judgment threshold value of index abnormity by depending on manual experience; the intelligent interaction degree with the user is low, and the convenience and the relevance of report output are low.

The foregoing description is provided for general background information and is not admitted to be prior art.

Disclosure of Invention

In view of this, the present application provides a processing method, a terminal device, and a storage medium, so as to solve the problems that the conventional BI system relies on manual setting of an abnormal judgment threshold and the convenience and the relevance of report output are low.

The application provides a processing method, which comprises the following steps:

s1, constructing a neural network model, optionally constructing the neural network model based on the collected data;

s2, outputting an abnormal report based on the neural network model;

and S3, acquiring the input information of the user and outputting a report of the service inquired based on the input information.

Optionally, the source of the data comprises at least one of: file logs, database logs, associated databases, application programs and cloud ends.

Optionally, the step of S2 includes: updating data and adjusting parameters of the neural network model; and outputting an abnormal report based on the neural network model after parameter adjustment.

Optionally, the step S2 further includes:

acquiring at least one dimension associated with indexes of various services;

and determining or generating reports for part or all dimensions associated with the service with abnormal indexes, and combining the reports determined or generated for part or all dimensions and outputting the combined reports as abnormal reports. Optionally, a report is determined or generated for part or all dimensions associated with the service with abnormal indexes, and the reports determined or generated for part or all dimensions are combined and then output as an abnormal report.

Optionally, the step S2 further includes: and pushing the abnormal report to the related personnel of the abnormal index service according to a preset rule.

Optionally, the preset rule includes at least one of the following:

the neural network model determines the associated person based on the collected data;

and acquiring the associated personnel through input operation after the abnormal report is determined or generated.

Optionally, the step S3 further includes: analyzing the input information to obtain the dimensionality associated with the inquired service; optionally, the dimension associated with the service queried by the user corpus is obtained by mining based on a data mining technology, and a report of the associated dimension is output.

Optionally, outputting the associated report based on the dimension; optionally, the service queried by the user corpus is identified based on a natural language processing technology.

Optionally, when the input information is text information, determining the queried service based on the keyword and/or the associated word; and/or when the input information is voice information, identifying the voice information based on voice identification and determining the inquired service.

The terminal device comprises a memory and a processor, wherein the memory stores a processing program which is used for executing any one of the processing methods when the processor runs.

The application provides a readable storage medium, which stores a processing program, and the processing program is used for being executed by a processor to execute any one of the processing methods.

As described above, according to the processing method, the terminal device and the storage medium of the application, the abnormal report is automatically output through the constructed neural network model, that is, the judgment of the index abnormality does not depend on artificial experience, but is obtained by analyzing the neural network model based on big data; in addition, the report of the service to be inquired is determined or generated through the information input by the user, for example, the report of the service to be inquired is determined or generated through the voice information, and the method has a voice inquiry function and is high in convenience.

Optionally, a report related to the service queried by the user is mined based on a data mining technology, so that more dimensional information can be provided for the user, and the relevance of report output is improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, 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 only some embodiments of the present application, 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 diagram of a hardware structure of a terminal device for implementing various embodiments of the present application;

fig. 2 is a communication network system architecture diagram according to an embodiment of the present application;

FIG. 3 is a schematic flow chart of a processing method according to a first embodiment of the present application;

FIG. 4 is a schematic interface diagram of an embodiment of obtaining associated persons according to the present application;

FIG. 5 is a schematic interface diagram of another embodiment of obtaining associated persons according to the present application;

FIG. 6 is a schematic flow chart of a processing method according to a second embodiment of the present application;

FIG. 7 is a schematic flow chart of a processing method according to a third embodiment of the present application;

fig. 8 is a schematic flow chart of a processing method according to a fourth embodiment of the present application.

With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the recitation of an element by the phrase "comprising an … …" does not exclude the presence of additional like elements in the process, method, article, or apparatus that comprises the element, and further, where similarly-named elements, features, or elements in different embodiments of the disclosure may have the same meaning, or may have different meanings, that particular meaning should be determined by their interpretation in the embodiment or further by context with the embodiment.

It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope herein. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context. Also, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. Further, the terms "comprises," "comprising," or any other variation thereof, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, items, species, and/or groups. The terms "or," "and/or," "including at least one of the following," and the like, as used herein, are to be construed as inclusive or mean any one or any combination. For example, "includes at least one of: A. b, C "means" any of the following: a; b; c; a and B; a and C; b and C; a and B and C ", again for example," A, B or C "or" A, B and/or C "means" any of the following: a; b; c; a and B; a and C; b and C; a and B and C'. An exception will occur only when a combination of elements, functions, steps or operations are inherently mutually exclusive in some manner.

Although the steps in the flowcharts in the embodiments of the present application are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in a strict order unless explicitly stated herein, but may be performed in other orders. Moreover, at least a portion of the steps may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, may be performed at different times, are not necessarily performed in the same order, and may be performed in turn or alternatingly with other steps or at least a portion of the sub-steps or stages of other steps.

The words "if", as used herein, may be interpreted as "at … …" or "at … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.

In addition, step numbers such as S1 and S2 are used herein for the purpose of more clearly and briefly describing the corresponding content, and do not constitute a substantial limitation on the sequence, and those skilled in the art may perform S2 first and then S1 in specific implementation, but these steps are all within the scope of protection of the present application.

It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.

In the following description, suffixes such as "module", "component", or "unit" used to indicate elements are merely for convenience of description of the present application, and have no specific meaning in themselves. Thus, "module", "component" or "unit" may be used in a mixture.

The terminal device may be implemented in various forms. For example, the terminal devices described herein may include mobile terminals such as mobile phones, tablet computers, notebook computers, palmtop computers, Personal Digital Assistants (PDAs), Portable Media Players (PMPs), navigation devices, wearable devices, smart bands, pedometers, and fixed terminal devices such as Digital TVs, desktop computers, and the like.

The following description will be given taking a mobile terminal as an example, and it will be understood by those skilled in the art that the configuration according to the embodiment of the present application can be applied to a fixed type terminal device in addition to elements particularly used for mobile purposes.

Referring to fig. 1, which is a schematic diagram of a hardware structure of a mobile terminal for implementing various embodiments of the present application, the mobile terminal 100 may include: RF (Radio Frequency) unit 101, WiFi module 102, audio output unit 103, a/V (audio/video) input unit 104, sensor 105, display unit 106, user input unit 107, interface unit 108, memory 109, processor 110, and power supply 111. Those skilled in the art will appreciate that the configuration of the mobile terminal 100 shown in fig. 1 is not intended to be limiting of the mobile terminal 100, and that the mobile terminal 100 may include more or less components than those shown, or some components may be combined, or a different arrangement of components.

The various components of the mobile terminal 100 are described in detail below with reference to fig. 1:

the rf unit 101 may be configured to receive and transmit information or receive and transmit signals during a call, and specifically, receive downlink information of a base station and send the downlink information to the processor 110 for processing, and send uplink data to the base station. Typically, radio frequency unit 101 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like. In addition, the radio frequency unit 101 can also communicate with a network and other devices through wireless communication. The wireless communication may use any communication standard or protocol, including but not limited to GSM (Global System for Mobile communications), GPRS (General Packet Radio Service), CDMA2000(Code Division Multiple Access 2000), WCDMA (Wideband Code Division Multiple Access), TD-SCDMA (Time Division-Synchronous Code Division Multiple Access), FDD-LTE (Frequency Division duplex Long Term Evolution), and TDD-LTE (Time Division duplex Long Term Evolution).

WiFi belongs to a short-distance wireless transmission technology, and the mobile terminal 100 can help a user send and receive e-mails, browse webpages, access streaming media and the like through the WiFi module 102, and provides the user with wireless broadband internet access. Although fig. 1 shows the WiFi module 102, it is understood that it does not belong to the essential constitution of the mobile terminal 100, and may be omitted as needed within the scope not changing the essence of the invention.

The audio output unit 103 converts audio data received by the radio frequency unit 101 or the WiFi module 102 or stored in the memory 109 into an audio signal and outputs as sound when the mobile terminal 100 is in a call signal reception mode, a call mode, a recording mode, a voice recognition mode, a broadcast reception mode, or the like. The audio output unit 103 may also provide audio output related to the mobile terminal 100 performing a specific function (e.g., a call signal reception sound, a message reception sound, etc.). The audio output unit 103 includes a speaker, a buzzer, and the like.

The a/V input unit 104 is used to receive audio or video signals. The a/V input Unit 104 may include a Graphics Processing Unit (GPU) 1041 and a microphone 1042, the Graphics processor 1041 Processing still pictures or video image data obtained by an image capturing device (e.g., a camera) in a video capturing mode or an image capturing mode. The processed image frames may be displayed on the display unit 106. The image frames processed by the graphics processor 1041 may be stored in the memory 109 (or other storage medium) or transmitted via the radio frequency unit 101 or the WiFi module 102. The microphone 1042 can receive sound (audio data) via the microphone 1042 in a phone call mode, a recording mode, a voice recognition mode, or the like, and can process the sound into audio data. The processed audio (voice) data may be converted into a format output that can be transmitted to a mobile communication base station via the radio frequency unit 101 in case of a phone call mode. The microphone 1042 may implement various types of noise cancellation (or suppression) algorithms to cancel (or suppress) noise or interference generated in the course of receiving and transmitting audio signals.

The mobile terminal 100 also includes at least one sensor 105, such as a light sensor, motion sensor, or other sensor. The light sensor includes an ambient light sensor that can adjust the brightness of the display panel 1061 according to the brightness of ambient light, and a proximity sensor that can turn off the display panel 1061 and/or a backlight when the mobile terminal 100 moves to the ear. As one of the motion sensors, the accelerometer sensor can detect acceleration in various directions (generally three axes), detect the magnitude and direction of gravity when stationary, and be used for applications (such as horizontal and vertical screen switching, related games, magnetometer attitude calibration) for recognizing the attitude of the mobile phone, and related functions (such as pedometer and tapping) for vibration recognition; the mobile phone may also be configured with other sensors such as a fingerprint sensor, a pressure sensor, an iris sensor, a molecular sensor, a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which are not described herein.

The display unit 106 is used to display information input by the user or information provided to the user. The Display unit 106 may include a Display panel 1061, and the Display panel 1061 may be configured in a form including, but not limited to, a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), and the like.

The user input unit 107 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the mobile terminal 100. Specifically, the user input unit 107 may include a touch panel 1071 and other input devices 1072. The touch panel 1071, also referred to as a touch screen, may collect touch operations of a user (e.g., operations of the user on or near the touch panel 1071 using any suitable object or accessory such as a finger, a stylus, etc.) thereon or nearby, and drive corresponding connection devices according to a preset program. The touch panel 1071 may include two parts of a touch detection device and a touch controller. Optionally, the touch detection device detects a touch orientation of a user, detects a signal caused by a touch operation, and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 110, and can receive and execute commands sent by the processor 110. In addition, the touch panel 1071 may be implemented in various types, such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. Other input devices 1072 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like, and are not limited to these specific examples.

Alternatively, the touch panel 1071 may cover the display panel 1061, and when the touch panel 1071 detects a touch operation thereon or nearby, the touch panel 1071 transmits the touch operation to the processor 110 to determine the type of the touch event, and then the processor 110 provides a corresponding visual output on the display panel 1061 according to the type of the touch event. Although the touch panel 1071 and the display panel 1061 are illustrated as two separate components in fig. 1 to implement the input and output functions of the mobile terminal 100, in some embodiments, the touch panel 1071 and the display panel 1061 may be integrated to implement the input and output functions of the mobile terminal 100, and is not limited herein.

The interface unit 108 serves as an interface through which at least one external device is connected to the mobile terminal 100. The external device includes, for example, a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device having an identification module, an audio input/output (I/O) port, a video I/O port, an earphone port, and the like. The interface unit 108 may be used to receive input from external devices (e.g., data information, power, etc.) and transmit the input to one or more elements of the mobile terminal 100 or to transmit data between the mobile terminal 100 and the external devices.

The memory 109 is used to store software programs and various data. The memory 109 mainly includes a program storage area and a data storage area, and the program storage area can store an operating system, at least one program required by the function (such as a sound playing function and an image playing function), and the like; the storage data area may store data created according to the use of the cellular phone (such as audio data, a phonebook), and the like. Further, the memory 109 includes high speed random access memory, non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.

The processor 110 is a control center of the mobile terminal 100, connects various parts of the entire mobile terminal 100 using various interfaces and lines, and performs various functions of the mobile terminal 100 and processes data by running or executing software programs and/or modules stored in the memory 109 and calling data in the memory 109, thereby monitoring the mobile terminal 100 as a whole. Processor 110 may include one or more processing units. Preferably, the processor 110 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 110.

The mobile terminal 100 may also include a power supply 111 (e.g., a battery) to power the various components. Preferably, the power source 111 may be logically connected to the processor 110 through a power management system, and the power management system may implement functions such as charging, discharging, and power consumption management.

Although not shown in fig. 1, it is understood that the mobile terminal 100 may further include other components such as a bluetooth module, which are not described herein.

In order to facilitate understanding of the embodiments of the present application, a communication network system on which the mobile terminal 100 is based is described below.

Referring to fig. 2, an architecture diagram of a communication Network system provided in this embodiment of the present application is shown, where the communication Network system is an LTE system of a universal mobile telecommunications technology, and includes a UE (User Equipment) 201, an E-UTRAN (Evolved UMTS Terrestrial Radio Access Network) 202, an EPC (Evolved Packet Core) 203, and an IP service 204 of an operator, which are sequentially in communication connection.

Specifically, the UE 201 may be the mobile terminal 100 described above.

The E-UTRAN 202 may include eNodeB 2021 and other eNodeBs 2022, among others. Alternatively, the eNodeB 2021 may be connected with other enodebs 2022 through a backhaul (e.g., X2 interface), the eNodeB 2021 is connected to the EPC 203, and the eNodeB 2021 may provide the UE 201 access to the EPC 203.

The EPC 203 may include an MME (Mobility Management Entity) 2031, an HSS (Home Subscriber Server) 2032, other MMEs 2033, an SGW (Serving gateway) 2034, a PGW (PDN gateway) 2035, and a PCRF (Policy and Charging Rules Function) 2036, and the like. Optionally, the MME 2031 is a control node that handles signaling between the UE 201 and the EPC 203, providing bearer and connection management. HSS 2032 is used to provide registers to manage functions such as home location register (not shown) and holds subscriber specific information about service characteristics, data rates, etc. All user data may be sent through SGW 2034, PGW 2035 may provide IP address assignment for UE 201 and other functions, and PCRF 2036 is a policy and charging control policy decision point for traffic data flow and IP bearer resources, which selects and provides available policy and charging control decisions for a policy and charging enforcement function (not shown).

The IP services 204 may include the internet, intranets, IMS (IP Multimedia Subsystem), or other IP services, etc.

Although the LTE system is described as an example, it should be understood by those skilled in the art that the embodiments of the present application are not limited to the LTE system, but may also be applied to other current or future new wireless communication systems, such as GSM, CDMA2000, WCDMA, TD-SCDMA, and future new network systems.

Based on the hardware structure of the mobile terminal 100 depicted in fig. 1 and the communication network system depicted in fig. 2, the present application proposes the following embodiments.

Fig. 3 is a schematic flow chart of a processing method according to an embodiment of the present application. Referring to fig. 3, the processing method of the present embodiment may include the following steps S1 to S3.

And S1, constructing a neural network model.

Optionally, a neural network model is constructed based on the collected data.

In the case of data from various sources, the step S1 is to integrate these scattered data together to form a large database, and analyze the database by integrating. In one scenario, the source of the data includes at least one of: file logs, database logs, associated databases, application programs and cloud ends.

The file log may be understood as data information recorded when all actions in the current business field are executed, such as daily information of each level department in a mobile phone sales business scenario, test equipment and parameters of each test stage and each tester in an APP (application program) test scenario, and the like.

The database log includes, but is not limited to, records of related personnel, equipment, and/or procedures calling the database.

An associated database is understood to be a database related to the current business sector, including its own databases and associated databases, such as those shared by upstream and downstream suppliers of the product supply chain.

The cloud data is related data obtained from the internet, and whether the data is related or not is determined according to the present application, which is not limited in the embodiments, for example, data of different manufacturers in the same business field, data of the business uploaded to the internet by personnel in the related department, data sequence of the same APP, and/or data opened by upstream and downstream suppliers in the product supply chain may be considered as related data.

In one scenario, in the embodiment, a neural network model may be constructed in a foreground of the information push system by using a feature extraction algorithm of an Artificial Neural Network (ANN) technology according to a large number of samples provided by collected big data (e.g., an AWS cloud, which is a data management tool commonly used in the art).

And S2, outputting an abnormal report based on the neural network model.

Before determining or generating the abnormal report, step S2 may perform iterative optimization on the neural network model, for example, refer to the neural network model (also referred to as adjusting a feature function), and the abnormal report is not output until the neural network model tends to converge. Step S2 may automatically determine or generate a report, or output an abnormal report according to a preset rule. The preset rules include, but are not limited to: and receiving and responding to at least one of a user instruction, a preset time point and a preset period.

Optionally, the user instruction may identify part or all of the exception reports actually required, or alternatively, the user instruction may be only used for instructing to output the exception reports, and does not identify which types of exception reports are input. The user command issuing mode can be determined according to actual needs, and in some scenarios, the user command issuing mode includes but is not limited to: at least one of the actions of the limbs and the voice is generated through the keys.

Alternatively, the data is updated continuously, and the step S2 may refer to the neural network model after each data update until the neural network model tends to converge.

In the step S2, the abnormal report is a visual chart that determines or generates all the information of the data index abnormality, and the visual chart is adaptive to the information push system (e.g., BI system). The abnormal report is realized in a form including, but not limited to, a table and/or a statistical chart in the shape of a column, a cake and the like.

And if the data index exceeds a preset threshold value, defining the data index as abnormal. The preset threshold is obtained based on big data and neural network model fitting. In a scene, for example, in the business field of monitoring of the mobile phone application starting time, if the mobile phone application starting time exceeds a preset threshold, the mobile phone application starting time is abnormal, the mobile phone application starting time is drawn into a visual chart, the abnormal report is determined or generated and pushed to the related personnel of the business, and the related personnel can quickly locate the reason of the abnormal occurrence through the information displayed by the abnormal report.

The step S2 may determine the associated person according to a preset rule.

Optionally, the preset rule may be at least one of:

first, the neural network model determines the associated person based on the collected data.

During data acquisition, the information push system can obtain the responsible person of the service and the persons of each operation, each stage, inputting each item of data and the like, and the neural network model can select part or all of the responsible person as the associated persons. For example, only the person in charge is selected as the associated person.

And secondly, acquiring the associated personnel through input operation after determining or generating an abnormal report.

In one implementation, as shown in fig. 4, after the abnormal report is determined or generated, a foreground of the information push system (for example, a display interface of a mobile phone) is switched to a person selection interface, all related persons including a business person in charge are listed on the interface, and a user determines related persons from the list by clicking selection.

In another implementation, please refer to fig. 5, a user-defined input option may be displayed on an interface provided by a foreground of the information push system, and after the user clicks the user-defined input option, the interface displays an input box, and obtains the associated person according to the input operation of the user. The input operation manner of the user is not limited in this embodiment, and for example, the input manner is touch input (including but not limited to pinyin input and stroke input), or voice input.

When the associated person is not preset, the person can be pushed to the person responsible for the service by default.

Thirdly, combining the two modes, the neural network model firstly confirms the personnel to be pushed, then displays the personnel to the user, and finally confirms the associated personnel according to the input operation of the user.

Optionally, this embodiment may further include: and S3, acquiring the input information of the user and outputting a report of the service inquired based on the input information.

Alternatively, the input information is text information, and step S3 determines the queried service based on the keywords and/or associated words. For example, the input information of the boss includes sales performance of the B department in the a market that the boss wants to know, and the "a market" and the "B department" can be used as keywords. The information pushing system can determine or generate a sales report of the department A, the department B and the market, and the boss can achieve the purpose through the report.

The associated word is a word having a preset relationship with the input information, and the preset relationship includes, but is not limited to: at least one of synonyms, membership between employee names and company departments, and business related personnel. For example, the boss inputs the text message "sales achievement of zhang san", the related word may be "sales achievement of one south china sales" of the department responsible for zhang san, and the information push system may output the sales report of one south china sales and the sales report of three people.

Alternatively, the input information may be voice information. Optionally, the information push system may integrate an artificial intelligent question and answer function to obtain the user corpus, and perform word understanding and text processing on the voice-converted words based on a Natural Language Processing (NLP) technology, so as to obtain the service queried by the user corpus, and then output a report of the queried service.

Of course, step S3 may also be used to determine the queried service by combining text information and voice information.

The method does not depend on artificial experience to set the judgment threshold value of the index abnormity, and is obtained through the constructed neural network model based on big data analysis, so that the method is more objective.

In addition, in the scene that the input information is voice information, the natural language processing technology is combined, the user can obtain the report to be inquired by speaking, and the convenience is high.

For example, the boss receives a sales report of a company delivery mobile phone, finds that the sales of the market a is abnormally low, and the boss wants to know the sales performance of a certain department or even a certain employee in the market a. The embodiment can provide a BI system combining NLP technology and neural network technology, and the boss finds that the sales volume of the a market is low, and only needs to directly inquire the BI system about "how is the sales information of the X department of the a market? The BI system can automatically determine or generate a sales report of the X department of the A market, and the boss can achieve the purpose through the report. Therefore, the boss can quickly know the information only by one natural language, and the labor and time consumption is low.

On the basis of the above embodiments, fig. 6 is a schematic flow chart of a processing method according to another embodiment of the present application. Referring to fig. 6, the method includes the following steps S21 to S24.

And S21, constructing a neural network model, and optionally constructing the neural network model based on the collected data.

S22, acquiring at least one dimension associated with the indexes of each service.

And S23, determining or generating a report for part or all dimensions associated with the abnormal-index business, and outputting the report as an abnormal report. Optionally, a report is determined or generated for part or all dimensions associated with the service with abnormal indexes, and the reports determined or generated for part or all dimensions are combined and then output as an abnormal report.

Optionally, after step S23, the method further comprises: and S24, acquiring the input information of the user and outputting a report of the service inquired based on the input information.

Optionally, please refer to the description of the foregoing embodiment for constructing a data source of the neural network model, an obtaining method of user input information, and a method of outputting a service report, which are not described herein again.

For implementing the foregoing step S2, in steps S22 and S23 of this embodiment, the business indexes are analyzed from at least one dimension, and the report of the dimensions is pushed to the associated person.

In a scene, for example, in the business field of monitoring of mobile phone application starting time, if the mobile phone application starting time exceeds a preset threshold, the mobile phone application starting time is abnormal, and the information push system not only draws self information of the index, such as actual application starting time, average application starting time in the industry and the like, into a visual chart, but also analyzes the self information from each relevant dimension (namely, various factors affecting the index, namely the mobile phone application starting time).

For example, the dimension associated with the service index, i.e. the starting time of the mobile phone application, includes at least one of the following: the method comprises the steps of application cold start time, application hot start time, equipment activation time, cold start times of various machine types, machine types and application versions. Optionally, the output report may further include at least one of the following information: the method comprises the steps of applying cold/hot start time summation, average cold start time according to the equipment activation time decline trend, average hot start time according to the equipment activation time decline trend, the cold start times of all models according to the time trend, the hot start times of all models according to the time trend, APP cold start times according to the model comparison, the cold and hot start times of all models according to the distribution, the cold and hot start times of all versions according to the distribution, the cold start time summation according to the time trend of all models, the APP hot start time summation according to the time trend of all models, the APP cold start time summation according to the time trend of all versions according to the time trend, the APP cold start average time, the APP cold start abnormal percentage and the APP hot start abnormal percentage.

In step S24, the system may also determine or generate a report of the service to be queried from at least one dimension, and in one implementation, please refer to fig. 7, the dimension associated with the queried service is mined based on a data mining (DW) technique, and the report of the associated dimension is output.

For example, the boss receives a sales volume report of a mobile phone for company delivery, finds that the sales volume of market a is abnormally low, and the boss only needs to ask the BI system directly through natural language "why is market a sales volume abnormal? ", the BI system may determine or generate a technical report for market a from multiple dimensions (e.g., from technical dimensions), through which the boss may directly see that various indicators of the mobile phone are below the industry average, and the reason that the boss ultimately gets is that the application holdover rate of market a is high. Therefore, the boss can quickly know the product sale condition and the reason of low sale of the company only by combining one abnormal report with one natural language, and the method has low labor and time consumption and high objectivity.

Therefore, the embodiment of the application learns the abnormal threshold of each type of service index based on big data combined with neural network model fitting, obtains all dimensions influencing the abnormal indexes through association rule mining aiming at the abnormal indexes, and determines or generates the corresponding report. In some scenarios, after receiving the abnormal report, the user may speak the information desired to be obtained through natural language, and the information push system (e.g., BI system) identifies the information queried by the user according to NLP technology, and automatically outputs the associated report.

On the basis of the above embodiment, fig. 8 is a schematic flow chart of a processing method according to another embodiment of the present application. Referring to fig. 8, the method includes the following steps S11 to S14.

And S11, constructing a neural network model, and optionally constructing the neural network model based on the collected data.

And S12, outputting an abnormal report based on the neural network model.

And S13, analyzing the input information of the user and acquiring the dimension associated with the inquired service.

And S14, outputting the associated report based on the dimension.

Optionally, please refer to the description of the foregoing embodiment for constructing a data source of the neural network model, an obtaining method of the input information, and an outputting method of the business report, which are not described herein again.

Alternatively, step S13 may determine the queried service based on the keywords and/or associated words in the input information, which may be text information and/or voice information.

Optionally, the dimensionality associated with the queried service is obtained according to a preset rule, the preset rule may be all or part of preset factors affecting the service index, the preset rule may be preset by a user, and the preset rule may also be obtained by analyzing the big data by an information recommendation system. For example, the service area of monitoring the starting time of the mobile phone application, and the associated dimension includes at least one of the following: the method comprises the steps of application cold start time, application hot start time, equipment activation time, cold start times of various machine types, machine types and application versions.

In the embodiment, all the abnormal reports are output, and then the report with the associated dimension is selected from the abnormal reports according to the requirement of a user and then output. In the embodiment described in fig. 6, the dimension is obtained first, then the abnormal report associated with the dimension is determined or generated according to the dimension, and then optionally, the report of the associated dimension is selected from the abnormal reports according to the user's needs, and finally, the report is output.

Embodiments of the present application also provide a computer program product, which includes computer program code, when the computer program code runs on a computer, causes the computer to execute the method as described in the above various possible embodiments.

Embodiments of the present application further provide a chip, which includes a memory for storing a computer program and a processor for calling and executing the computer program from the memory, so that a device in which the chip is installed performs the method in the above various possible embodiments.

The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.

In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.

The technical features of the technical solution of the present application may be arbitrarily combined, and for brevity of description, all possible combinations of the technical features in the embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, the scope of the present application should be considered as being described in the present application.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, a controlled terminal, or a network device) to execute the method of each embodiment of the present application.

The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.

Claims

1. A method of processing, comprising:

s1, constructing a neural network model;

s2, outputting an abnormal report based on the neural network model;

and S3, acquiring user input information, and outputting a report of the service inquired based on the input information.

2. The method of claim 1, wherein the source of the data comprises at least one of: file logs, database logs, associated databases, application programs and cloud ends.

3. The method of claim 1, wherein the step of S2 includes:

updating data and adjusting parameters of the neural network model;

and outputting an abnormal report based on the neural network model after parameter adjustment.

4. The method of claim 1, wherein the step of S2 further comprises:

acquiring at least one dimension associated with indexes of various services;

and determining or generating a report for part or all dimensions associated with the service with the abnormal index, and outputting the report as an abnormal report.

5. The method of claim 4, wherein the step of S2 further comprises: and pushing the abnormal report to the associated personnel of the service with the abnormal index according to a preset rule.

6. The method of claim 5, wherein the preset rules comprise at least one of:

7. The method according to any one of claims 1 to 6, wherein the step S3 further comprises:

analyzing the input information to obtain the dimensionality associated with the inquired service;

outputting the associated report based on the dimension.

8. The method according to any one of claims 1 to 6,

when the input information is character information, determining the inquired service based on the key words and/or the associated words; and/or the presence of a gas in the gas,

and when the input information is voice information, identifying the voice information based on voice identification to determine the inquired service.

9. A terminal device comprising a memory and a processor, the memory storing a processing program for execution by the processor to perform the steps of the processing method of any one of claims 1 to 8.

10. A readable storage medium, in which a processing program is stored, the processing program being configured to be executed by a processor to perform the steps of the processing method according to any one of the claims 1 to 8.