CN111432080A - Ticket data processing method, electronic equipment and computer readable storage medium - Google Patents
Ticket data processing method, electronic equipment and computer readable storage medium Download PDFInfo
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- CN111432080A CN111432080A CN201811583902.7A CN201811583902A CN111432080A CN 111432080 A CN111432080 A CN 111432080A CN 201811583902 A CN201811583902 A CN 201811583902A CN 111432080 A CN111432080 A CN 111432080A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M3/00—Automatic or semi-automatic exchanges
- H04M3/42—Systems providing special services or facilities to subscribers
- H04M3/436—Arrangements for screening incoming calls, i.e. evaluating the characteristics of a call before deciding whether to answer it
Abstract
The application relates to the technical field of application software, in particular to a call ticket data processing method, electronic equipment and a computer readable storage medium, wherein the call ticket data processing method comprises the following steps: acquiring various types of signaling data corresponding to a telephone number to be processed; after each type of signaling data is specifically processed, call ticket data to be processed corresponding to the telephone number is constructed based on a specific processing result; extracting characteristic information corresponding to the ticket data to be processed; and identifying the characteristic information based on a preset data identification model to obtain a corresponding identification result, wherein the identification result is used for representing whether the call ticket data is the data of a specific type. The processing provided by the application realizes effective identification of the crank calls, and ensures the identification accuracy rate based on the introduction of the data identification model.
Description
Technical Field
The present application relates to the field of application software technologies, and in particular, to a method for processing ticket data, an electronic device, and a computer-readable storage medium.
Background
With the rapid development of science and technology, a communication mode based on a telephone has become an indispensable contact mode in people's life.
Although the communication mode based on the telephone is convenient for our life, a great number of harassing calls such as advertising promotion calls, fraud calls impersonating bank workers, intentional telephone harassments and the like are carried along with the communication mode, so that many negative influences are brought to our life, and the normal life of people is influenced. Of course, in the prior art, when identifying a harassing call, a user usually actively identifies the harassing call, and performs a corresponding harassing label based on the identification of the user. Therefore, on the basis of ensuring the identification accuracy of the crank calls, how to effectively identify the crank calls becomes a technical problem to be solved urgently at present.
Disclosure of Invention
The application provides a call ticket data processing method, electronic equipment and a computer readable storage medium, which are used for realizing effective identification of crank calls on the basis of ensuring identification accuracy of crank calls.
In a first aspect, a method for processing call ticket data is provided, including:
acquiring various types of signaling data corresponding to a telephone number to be processed;
after each type of signaling data is specifically processed, call ticket data to be processed corresponding to the telephone number is constructed based on a specific processing result;
extracting characteristic information corresponding to the ticket data to be processed;
and identifying the characteristic information based on a preset data identification model to obtain a corresponding identification result, wherein the identification result is used for representing whether the call ticket data is the data of a specific type.
In one possible implementation, the specific processing includes at least one of:
carrying out deduplication processing on various types of signaling data;
and (5) sorting and processing each type of signaling data.
In one possible implementation manner, the call ticket data is used for characterizing specific call characteristic information corresponding to each telephone number,
the specific call feature information includes at least one of:
calling and called call accounts for a ratio;
dispersion of calling and called calls.
In a possible implementation manner, the extracting feature information corresponding to the to-be-processed ticket data includes:
and performing semantic recognition on the ticket data to be processed to obtain corresponding semantic feature information.
In one possible implementation manner, before identifying the feature information based on a preset data identification model, the method further includes:
acquiring sample data; the sample data comprises ticket data to be trained and identification information corresponding to the ticket data, and the identification information is used for representing that the ticket data to be trained is data of a specific type;
extracting semantic feature information corresponding to the sample data;
and performing model training based on the semantic feature information of the sample data to obtain the data identification model.
In one possible implementation, the method further includes:
when a preset condition is met, updating sample data of the data identification model;
wherein the updating of the sample data of the data identification model comprises:
acquiring to-be-processed call ticket data for updating;
and updating sample data in the data identification model on line based on the to-be-processed call ticket data for updating.
In one possible implementation, the preset condition includes any one of:
the recognition result is different from the actual result;
the frequency that the recognition result and the actual result are different results meets a preset threshold value;
a preset time period.
In a second aspect, a device for processing call ticket data is provided, which includes:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring various types of signaling data corresponding to a telephone number to be processed;
the processing unit is used for constructing the to-be-processed call ticket data corresponding to the telephone number based on a specific processing result after performing specific processing on each type of signaling data;
the extraction unit is used for extracting the characteristic information corresponding to the ticket data to be processed;
and the identification unit is used for identifying the characteristic information based on a preset data identification model to obtain a corresponding identification result, and the identification result is used for representing whether the ticket data is the data of a specific type.
In one possible implementation, the specific processing includes at least one of:
carrying out deduplication processing on various types of signaling data;
and (5) sorting and processing each type of signaling data.
In one possible implementation manner, the call ticket data is used for characterizing specific call characteristic information corresponding to each telephone number,
the specific call feature information includes at least one of:
calling and called call accounts for a ratio;
dispersion of calling and called calls.
In a possible implementation manner, the extraction unit is configured to perform semantic identification on the ticket data to be processed to obtain corresponding semantic feature information.
In one possible implementation, the method further includes:
an acquisition unit configured to acquire sample data; the sample data comprises ticket data to be trained and identification information corresponding to the ticket data, and the identification information is used for representing that the ticket data to be trained is data of a specific type;
the extraction unit is used for extracting semantic feature information corresponding to the sample data;
and the training unit is used for carrying out model training based on the semantic feature information of the sample data to obtain the data identification model.
In one possible implementation, the method further includes:
the updating unit is used for updating the sample data of the data identification model when a preset condition is met;
the updating unit is used for acquiring the to-be-processed call ticket data for updating; and updating sample data in the data identification model on line based on the to-be-processed call ticket data for updating.
In one possible implementation, the preset condition includes any one of:
the recognition result is different from the actual result;
the frequency that the recognition result and the actual result are different results meets a preset threshold value;
a preset time period.
In a third aspect, an electronic device is provided, including: a processor and a memory;
the memory is used for storing operation instructions;
and the processor is used for executing the call ticket data processing method by calling the operation instruction.
In a fourth aspect, a computer-readable storage medium is provided, which is used for storing computer instructions, and when the computer-readable storage medium is run on a computer, the computer is caused to execute the above-mentioned ticket data processing method.
By means of the technical scheme, the technical scheme provided by the application at least has the following advantages:
in the method, various types of signaling data corresponding to the telephone number to be processed are obtained; after each type of signaling data is specifically processed, call ticket data to be processed corresponding to the telephone number is constructed based on a specific processing result; extracting characteristic information corresponding to the ticket data to be processed; and identifying the characteristic information based on a preset data identification model to obtain a corresponding identification result, wherein the identification result is used for representing whether the call ticket data is the data of a specific type. The processing provided by the application realizes effective identification of the crank calls, and ensures the identification accuracy rate based on the introduction of the data identification model.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the embodiments of the present application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic flow diagram of a call ticket data processing method provided in an embodiment of the present application;
fig. 2 is a schematic processing flow diagram of a possible implementation manner of the call ticket data processing method provided in the embodiment of the present application;
fig. 3 is a schematic structural diagram of a ticket data processing apparatus provided in the embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device of a call ticket data processing method provided in an embodiment of the present application.
Detailed Description
The present application provides a method and an apparatus for processing call ticket data, an electronic device, and a computer-readable storage medium, and the following describes in detail embodiments of the present application with reference to the accompanying drawings.
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
It will be understood by those within the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. In this application, the electronic device may be a terminal device or a server, and is not particularly limited in this application.
As shown in fig. 1, a schematic flow chart of a call ticket data processing method provided by the present application is shown, and the method includes the following steps:
step S101, acquiring various types of signaling data corresponding to the telephone number to be processed;
step S102, after each type of signaling data is specifically processed, call ticket data to be processed corresponding to the telephone number is constructed based on a specific processing result;
step S103, extracting characteristic information corresponding to the ticket data to be processed;
and step S104, identifying the characteristic information based on a preset data identification model to obtain a corresponding identification result, wherein the identification result is used for representing whether the ticket data is the data of a specific type.
In the method, various types of signaling data corresponding to the telephone number to be processed are obtained; after each type of signaling data is specifically processed, call ticket data to be processed corresponding to the telephone number is constructed based on a specific processing result; extracting characteristic information corresponding to the ticket data to be processed; and identifying the characteristic information based on a preset data identification model to obtain a corresponding identification result, wherein the identification result is used for representing whether the call ticket data is the data of a specific type. The processing provided by the application realizes effective identification of the crank calls, and ensures the identification accuracy rate based on the introduction of the data identification model.
Based on the technical solution provided by the present application, the following explains the technical solution in detail, as shown in fig. 2, a specific processing flow chart of a possible implementation manner of the call ticket data processing method provided by the present application is provided.
In one possible implementation, the processing of the foregoing step S101 specifically includes the processing of the following step S201.
Step S201, obtaining signaling data of each type corresponding to the phone number to be processed.
For the application, the processing for acquiring the signaling data of each type corresponding to the phone number to be processed may be data directly obtained by the electronic device from an operator, or may be data directly extracted from the local of the electronic device. These types of signaling data may include origination signaling data, ring signaling data, call signaling data, answer signaling data, reject signaling data, and so on.
In one possible implementation, the aforementioned processing of step S102 specifically includes the processing of step S202 described below.
Step S202, call ticket data to be processed is constructed based on various types of signaling data.
For the present application, when constructing the ticket data based on each type of signaling data, it is further required to perform specific processing on each type of signaling data first, and then construct the to-be-processed ticket data corresponding to the telephone number according to a specific processing result, where the specific processing may include:
1. and (5) carrying out deduplication processing.
In the application, for each type of acquired signaling data, because the data volume is large and the data is complicated, some repeated data may exist inevitably, and therefore, in order to improve the efficiency of subsequent processing and increase the accuracy of the subsequent processing, it is necessary to perform corresponding deduplication processing.
2. And (6) finishing.
In the present application, the above-described finishing process may occur after the deduplication process, or the above-described process may be performed directly without the deduplication process. Of course, whether the processing occurs after the deduplication processing or the processing directly, it is necessary to sort each type of signaling data, so that each type of signaling data is structured into the type to which it belongs.
For the above-mentioned call ticket data, it is specifically used to characterize specific call feature information corresponding to each phone number, and the specific call feature information may include:
the calling and called calling accounts for the ratio of the active call to the passive call within a period of time as the name suggests;
the dispersion of the calling and called calls, as the name implies, refers to whether the telephone number calls are targeted to several or many people.
Of course, the specific call feature information is not limited to the feature information.
In one possible implementation, the processing of the foregoing step S103 specifically includes the processing of the step S203 described below.
Step S203, performing semantic identification on the ticket data to be processed to obtain corresponding semantic feature information.
For the application, when performing semantic recognition, a corresponding semantic recognition module may be constructed by performing Deep learning process processing on semantic features through Neural Network training in advance, where the Neural Network may be a CNN (Convolutional Neural Network), a DNN (Deep Neural Network), or an RNN (Recurrent Neural Network).
In one possible implementation, the processing of the foregoing step S104 specifically includes the processing of the following step S204.
And step S204, identifying semantic feature information based on a preset data identification model to obtain a corresponding identification result.
In this step, for the constructed to-be-processed call ticket data, corresponding recognition is performed on semantic feature information corresponding to the to-be-processed call ticket data through a preset data recognition model, so that a recognition result corresponding to the to-be-processed call ticket data of the telephone number is determined.
For the application, for example, the received to-be-processed call ticket data is a "telephone number 1850 xxxxxx", the dispersion of the call in the corresponding call ticket data includes several frequently-called objects A, B and C ", after the semantic recognition processing, the semantic feature information obtained by the semantic recognition processing is correspondingly recognized through a preset data recognition model, and the recognition result corresponding to the to-be-processed call ticket data is determined to be" the telephone number is a non-harassing call ".
In a possible implementation manner, before the electronic device performs the identification processing on the to-be-processed ticket data, the training of the data identification model for the ticket data needs to be performed first, the training process may be performed in the electronic device, and a large amount of sample data is used for continuously and cyclically training, so that the identification result obtained when the data identification model is used for performing the to-be-processed ticket data identification tends to be more accurate, and the processing accuracy is improved.
For the present application, the training process of the data recognition model may include the following processes:
1. obtaining sample data
The sample data can comprise call ticket data to be trained and corresponding identification information; the identification information is used for representing the call ticket data to be trained as the data of a specific type. And each piece of identification information is labeled by means of artificial analysis according to each piece of corresponding call ticket data to be trained. If the user determines that a certain telephone is a promotional telephone based on answering, the identification information is 'the telephone number is a harassing call' when the manual analysis is marked.
For the present application, before the training of the data recognition model, a large amount of sample data is acquired, where the sample data may be manually input, may be extracted from a local storage, may be provided by an operator, and may be acquired by sending an acquisition request of the sample data to a server, and the acquisition path of the sample data is not limited to this.
2. Training process for data recognition model
According to the method, a large amount of acquired sample data is sequentially input into the model to be trained, the model is continuously improved through a large amount of training, and therefore the data identification model is obtained.
For the application, the obtained identification result cannot be accurate every time, and the situation that the identification result is different from the actual result may occur, for example, when a person pranks a friend to make a call by using the telephone number of the friend as a telephone call, the telephone may be marked as a harassing call, but the actual telephone is a private number, namely a non-harassing call. Therefore, in order to avoid the occurrence of the similar situation, the data recognition model needs to be continuously trained to perfect the optimization, and based on this, the sample data of the data recognition model needs to be updated when the preset condition is met.
Wherein, the preset condition may include:
P1and updating the sample data of the data identification model in real time.
And once the identification result is determined to be a result different from the actual result, indicating that the to-be-processed call ticket data corresponding to the identification result is possibly not stored in the sample data of the data identification model, directly updating the sample data of the data identification model by using the to-be-processed call ticket data corresponding to the identification result.
P2And updating the sample data of the data identification model at intervals.
At the moment, whether the identification result is the same as the actual result or not is not concerned, and the sample data of the data identification model is updated by using the ticket data to be processed acquired in the preset time period as long as the preset time period is reached, so that the sample data base of the data identification model is enriched and enlarged.
The preset time period may be preset in advance, or may be set immediately as needed.
P3And when the times that the identification result and the actual result are different results reach a certain threshold value, updating the sample data of the data identification model, thereby enriching and enlarging the sample database of the data identification model.
And recording the condition that the identification result and the actual result are different results every time, and updating the sample data of the data identification model by using the ticket data to be processed corresponding to each processing result when the frequency of the condition reaches a preset threshold value, thereby enriching and expanding the sample database of the data identification model.
For the method and the device, the to-be-processed call ticket data used for updating are obtained, and the sample data in the data identification model is updated on line based on the obtained to-be-processed call ticket data. The to-be-processed call ticket data used for updating may be the above-mentioned to-be-processed call ticket data corresponding to the identification result of the result that the actual result is different from the actual result, or may be all the to-be-processed call ticket data received within a period of time.
For the application, when the sample data in the data identification model is updated on line based on the acquired to-be-processed call ticket data, the method can be divided into two situations:
in case of a first situation, if the electronic device is a terminal device, the online updating process includes:
1. and updating sample data in the data identification model on line based on locally stored to-be-processed call ticket data.
In the processing process, the to-be-processed ticket data for updating can be stored in a local storage, and when the ticket data needs to be updated, corresponding sample data is directly extracted from the local storage to perform online updating processing.
2. And updating the sample data in the data identification model on line based on the sample data sent by the server for updating.
During the processing, the data identification model is updated by receiving sample data for updating sent by the server for the data identification model.
In case of the second situation, if the electronic device is a server, the online updating process includes:
1. and updating sample data in the data identification model on line based on locally stored to-be-processed call ticket data.
In the processing process, the to-be-processed ticket data for updating can be stored in a local storage, and when the ticket data needs to be updated, corresponding sample data is directly extracted from the local storage to perform online updating processing.
2. And updating sample data in the data identification model on line based on the to-be-processed call ticket data which is sent by the terminal equipment and used for updating.
In the processing process, the data identification model is updated by receiving the to-be-processed call ticket data which is sent by the terminal equipment and is used for updating the data identification model.
For the application, the updating process of the sample data in the data identification model based on the locally stored to-be-processed call ticket data, or based on the sample data sent by the server, or based on the to-be-processed call ticket data uploaded by the terminal device, may be an updating process actively initiated by the terminal device or the server.
The data recognition model used in the present application may be a decision tree (DecisionTree) model, and as the name suggests, the decision tree is a decision analysis method that, on the basis of the occurrence probability of various known situations, obtains the probability that the expected value of the net present value is greater than or equal to zero by forming the decision tree, evaluates the risk of the project, and determines the feasibility of the project, and is a graphical method that intuitively uses probability analysis. This decision branch is called a decision tree because it is drawn to resemble a branch of a tree. In machine learning, a decision tree is a predictive model that represents a mapping between object attributes and object values.
A decision tree is a tree-like structure in which each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a category. Classification trees (decision trees) are a very common classification method. It is a supervised learning, which is to say that given a stack of samples, each sample having a set of attributes and a class, which are determined in advance, a classifier is obtained by learning, which classifier is able to give the correct classification to the newly emerging object. Such machine learning is called supervised learning.
In machine learning, a decision tree is a prediction model; he represents a mapping between object properties and object values. Each node in the tree represents an object and each divergent path represents a possible attribute value, and each leaf node corresponds to the value of the object represented by the path traveled from the root node to the leaf node. The decision tree has only a single output, and if a plurality of outputs are desired, independent decision trees can be established to handle different outputs. Decision trees in data mining are a frequently used technique that can be used to analyze data and also to make predictions.
For the decision tree model, when the decision tree model is adopted for data processing, the decision tree model has the following advantages:
1. the decision tree is easy to understand and implement, people do not need to know much background knowledge in the learning process, and the decision tree can directly reflect the characteristics of data and has the ability to understand the meaning expressed by the decision tree only through interpretation.
2. For decision trees, data preparation is often simple or unnecessary and can handle both data-type and conventional-type attributes simultaneously, enabling feasible and effective results for large data sources in a relatively short time.
3. The model is easy to evaluate through static test, and the reliability of the model can be measured; given an observed model, the corresponding logical expression is easily deduced from the generated decision tree.
In the method, various types of signaling data corresponding to the telephone number to be processed are obtained; after each type of signaling data is specifically processed, call ticket data to be processed corresponding to the telephone number is constructed based on a specific processing result; extracting characteristic information corresponding to the ticket data to be processed; and identifying the characteristic information based on a preset data identification model to obtain a corresponding identification result, wherein the identification result is used for representing whether the call ticket data is the data of a specific type. The processing provided by the application realizes effective identification of the crank calls, and ensures the identification accuracy rate based on the introduction of the data identification model.
The present application provides a schematic structural diagram of a ticket data processing apparatus, as shown in fig. 3, a ticket data processing apparatus 30 of the present application may include: an acquisition unit 31, a processing unit 32, an extraction unit 33, a recognition unit 34, a training unit 35, an update unit 36, wherein,
an obtaining unit 31, configured to obtain various types of signaling data corresponding to a phone number to be processed;
the processing unit 32 is configured to construct to-be-processed ticket data corresponding to the telephone number based on a specific processing result after performing specific processing on each type of signaling data;
the extraction unit 33 is used for extracting the characteristic information corresponding to the ticket data to be processed;
and the identifying unit 34 is configured to identify the feature information based on a preset data identification model to obtain a corresponding identification result, where the identification result is used to characterize whether the ticket data is a specific type of data.
In one possible implementation, the specific processing includes at least one of:
carrying out deduplication processing on various types of signaling data;
and (5) sorting and processing each type of signaling data.
In one possible implementation, the call ticket data is used to characterize the specific call feature information corresponding to each telephone number,
the call characteristic-specific information includes at least one of:
calling and called call accounts for a ratio;
dispersion of calling and called calls.
In a possible implementation manner, the extracting unit 33 is configured to perform semantic recognition on the ticket data to be processed to obtain corresponding semantic feature information.
In one possible implementation, the method further includes:
an acquisition unit 31 configured to acquire sample data; the sample data comprises the ticket data to be trained and the corresponding identification information, and the identification information is used for representing the ticket data to be trained as the data of a specific type;
the extracting unit 33 is configured to extract semantic feature information corresponding to the sample data;
and the training unit 35 is configured to perform model training based on the semantic feature information of the sample data to obtain a data recognition model.
In one possible implementation, the method further includes:
an updating unit 36, configured to update sample data of the data identification model when a preset condition is met;
the updating unit 36 is configured to obtain to-be-processed ticket data for updating; and updating sample data in the data identification model on line based on the to-be-processed call ticket data for updating.
In one possible implementation, the preset condition includes any one of:
the recognition result is different from the actual result;
the frequency that the recognition result and the actual result are different results meets a preset threshold value;
a preset time period.
In the method, various types of signaling data corresponding to the telephone number to be processed are obtained; after each type of signaling data is specifically processed, call ticket data to be processed corresponding to the telephone number is constructed based on a specific processing result; extracting characteristic information corresponding to the ticket data to be processed; and identifying the characteristic information based on a preset data identification model to obtain a corresponding identification result, wherein the identification result is used for representing whether the call ticket data is the data of a specific type. The processing provided by the application realizes effective identification of the crank calls, and ensures the identification accuracy rate based on the introduction of the data identification model.
Referring now to fig. 4, a schematic diagram of an electronic device (e.g., the terminal device or the server in fig. 1) 400 suitable for implementing embodiments of the present application is shown. The terminal device in the embodiments of the present application may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 4, electronic device 400 may include a processing device (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage device 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
In general, input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc., output devices 407 including, for example, a liquid crystal display (L CD), speaker, vibrator, etc., storage devices 408 including, for example, magnetic tape, hard disk, etc., and communication devices 409 may allow electronic device 400 to communicate wirelessly or wiredly with other devices to exchange data although FIG. 4 illustrates electronic device 400 with various means, it is to be understood that not all of the illustrated means are required to be implemented or provided.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 409, or from the storage device 408, or from the ROM 402. The computer program, when executed by the processing device 401, performs the above-described functions defined in the methods of the embodiments of the present application.
It should be noted that the computer readable medium mentioned above in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising the at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; wherein the obtained internet protocol address indicates an edge node in the content distribution network.
Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from the at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.
Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including AN object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving at least two internet protocol addresses".
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the disclosure. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
The electronic device provided by the present application is applicable to any embodiment of the above method, and is not described herein again.
In the method, various types of signaling data corresponding to the telephone number to be processed are obtained; after each type of signaling data is specifically processed, call ticket data to be processed corresponding to the telephone number is constructed based on a specific processing result; extracting characteristic information corresponding to the ticket data to be processed; and identifying the characteristic information based on a preset data identification model to obtain a corresponding identification result, wherein the identification result is used for representing whether the call ticket data is the data of a specific type. The processing provided by the application realizes effective identification of the crank calls, and ensures the identification accuracy rate based on the introduction of the data identification model.
The application provides a computer-readable storage medium for storing computer instructions, which when run on a computer, make the computer execute the above-mentioned call ticket data processing method.
The computer-readable storage medium provided in the present application is applicable to any embodiment of the foregoing method, and is not described herein again.
In the method, various types of signaling data corresponding to the telephone number to be processed are obtained; after each type of signaling data is specifically processed, call ticket data to be processed corresponding to the telephone number is constructed based on a specific processing result; extracting characteristic information corresponding to the ticket data to be processed; and identifying the characteristic information based on a preset data identification model to obtain a corresponding identification result, wherein the identification result is used for representing whether the call ticket data is the data of a specific type. The processing provided by the application realizes effective identification of the crank calls, and ensures the identification accuracy rate based on the introduction of the data identification model.
It will be understood by those within the art that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by computer program instructions. Those skilled in the art will appreciate that the computer program instructions may be implemented by a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, implement the aspects specified in the block or blocks of the block diagrams and/or flowchart illustrations disclosed herein.
The modules of the device can be integrated into a whole or can be separately deployed. The modules can be combined into one module, and can also be further split into a plurality of sub-modules.
Those skilled in the art will appreciate that the drawings are merely schematic representations of one preferred embodiment and that the blocks or flow diagrams in the drawings are not necessarily required to practice the present application.
Those skilled in the art will appreciate that the modules in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, and may be correspondingly changed in one or more devices different from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
The above application serial numbers are for descriptive purposes only and do not represent the merits of the embodiments.
The disclosure of the present application is only a few specific embodiments, but the present application is not limited to these, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present application.
Claims (10)
1. A call ticket data processing method is characterized by comprising the following steps:
acquiring various types of signaling data corresponding to a telephone number to be processed;
after each type of signaling data is specifically processed, call ticket data to be processed corresponding to the telephone number is constructed based on a specific processing result;
extracting characteristic information corresponding to the ticket data to be processed;
and identifying the characteristic information based on a preset data identification model to obtain a corresponding identification result, wherein the identification result is used for representing whether the call ticket data is the data of a specific type.
2. The method of claim 1, wherein the specific processing comprises at least one of:
carrying out deduplication processing on various types of signaling data;
and (5) sorting and processing each type of signaling data.
3. The method of claim 1 or 2, wherein the call ticket data is used to characterize specific call feature information corresponding to each telephone number,
the specific call feature information includes at least one of:
calling and called call accounts for a ratio;
dispersion of calling and called calls.
4. The method according to any one of claims 1-3, wherein the extracting the feature information corresponding to the ticket data to be processed comprises:
and performing semantic recognition on the ticket data to be processed to obtain corresponding semantic feature information.
5. The method according to any one of claims 1-4, wherein before identifying the feature information based on a preset data identification model, further comprising:
acquiring sample data; the sample data comprises ticket data to be trained and identification information corresponding to the ticket data, and the identification information is used for representing that the ticket data to be trained is data of a specific type;
extracting semantic feature information corresponding to the sample data;
and performing model training based on the semantic feature information of the sample data to obtain the data identification model.
6. The method of claim 5, further comprising:
when a preset condition is met, updating sample data of the data identification model;
wherein the updating of the sample data of the data identification model comprises:
acquiring to-be-processed call ticket data for updating;
and updating sample data in the data identification model on line based on the to-be-processed call ticket data for updating.
7. The method of claim 6, wherein the preset condition comprises any one of:
the recognition result is different from the actual result;
the frequency that the recognition result and the actual result are different results meets a preset threshold value;
a preset time period.
8. A ticket data processing apparatus, comprising:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring various types of signaling data corresponding to a telephone number to be processed;
the processing unit is used for constructing the to-be-processed call ticket data corresponding to the telephone number based on a specific processing result after performing specific processing on each type of signaling data;
the extraction unit is used for extracting the characteristic information corresponding to the ticket data to be processed;
and the identification unit is used for identifying the characteristic information based on a preset data identification model to obtain a corresponding identification result, and the identification result is used for representing whether the ticket data is the data of a specific type.
9. An electronic device, comprising: a processor and a memory;
the memory is used for storing operation instructions;
the processor is configured to execute the ticket data processing method according to any one of the claims 1 to 7 by calling the operation instruction.
10. A computer-readable storage medium for storing computer instructions which, when executed on a computer, cause the computer to perform the ticket data processing method of any one of the preceding claims 1 to 7.
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| CN201811583902.7A CN111432080A (en) | 2018-12-24 | 2018-12-24 | Ticket data processing method, electronic equipment and computer readable storage medium |
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| CN201811583902.7A CN111432080A (en) | 2018-12-24 | 2018-12-24 | Ticket data processing method, electronic equipment and computer readable storage medium |
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