CN114266248B - Word cloud processing method, device and system - Google Patents
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Abstract
The disclosure relates to a word cloud processing method, device and system. The word cloud processing method comprises the steps of obtaining first frequency data of words in word clouds in a first period and second frequency data of words in a plurality of second periods, wherein the first period is a complete period before the current time, the plurality of second periods are complete periods of a preset number before the first period, inputting the second frequency data into a frequency prediction model to obtain predicted frequency data of the words in the first period, obtaining abnormal difference values of the words based on the predicted frequency data and the first frequency data, and performing abnormal processing when the abnormal difference values are larger than a preset alarm threshold value.
Description
Technical Field
The disclosure relates to the field of data processing, and in particular relates to a word cloud processing method, device and system.
Background
Word cloud is an important natural language processing technology, as a main method and tool for visual display of text content, the main content and information contained in a text set can be visualized through keywords and the size of the keywords being concise, and an example of the word cloud is given in fig. 1.
Currently, word cloud processing mainly includes that business personnel determine whether an abnormality exists by checking the condition of words in the word cloud. However, this word cloud processing method has the following disadvantages:
1) The labor cost is high. The judgment of abnormal conditions completely depends on the experience of service personnel, and because the user needs are very extensive, the manual judgment workload is large, and the judgment is easy to be wrong;
2) The alarm is not timely. Word clouds are generally statistics of user demands in the past period of time, such as 10 minutes, 30 minutes, etc., and when an abnormal problem is found manually, the abnormal problem exists for a period of time, so that the problem is not solved timely.
Disclosure of Invention
The disclosure provides a word cloud processing method, device and system, which at least solve the problems of high labor cost, untimely alarm and the like of the word cloud processing method in the related technology.
According to a first aspect of the embodiment of the disclosure, a word cloud processing method is provided, which includes obtaining first frequency data of words in a word cloud in a first period and second frequency data of words in a plurality of second periods, wherein the first period is a complete period before the current time, the plurality of second periods are complete periods of a preset number before the first period, inputting the second frequency data into a frequency prediction model to obtain predicted frequency data of the words in the first period, obtaining abnormal difference values of the words based on the predicted frequency data and the first frequency data, and performing abnormal processing when the abnormal difference values are larger than a preset alarm threshold.
Optionally, before the abnormality processing, the method further comprises the steps of fitting the second frequency data and the first frequency data to obtain fitted frequencies, obtaining residual frequencies based on the second frequency data, the first real frequencies and the fitted frequencies, obtaining normal fluctuation values based on standard deviations of the residual frequencies, and obtaining preset alarm thresholds of words based on the normal fluctuation values and preset multiplying power parameters.
Optionally, before acquiring the first frequency data of the words in the word cloud in the first period and the second frequency data of the words in the second periods, the method further comprises the step of storing the frequency data of the words in the corresponding storage units, wherein each word in the word cloud corresponds to one storage unit, and the frequency data comprises the first frequency data and the second frequency data.
Optionally, the second frequency data is input into a frequency prediction model to obtain the predicted frequency data of the words in the first period, and the method comprises the step of inputting the second frequency data in the storage unit corresponding to the words into the frequency prediction model to obtain the predicted frequency data of the words in the first period.
Optionally, after the exception processing is performed under the condition that the exception difference value is larger than a preset alarm threshold value, the method further comprises the steps of receiving a word segmentation processing result sent by the word segmentation module and updating a storage unit corresponding to the word based on the word segmentation processing result.
Optionally, updating the storage unit corresponding to the word comprises the steps of storing the frequency data of the word in the new period in the word segmentation processing result into the storage unit corresponding to the word, and deleting the frequency data of the word in the forefront period in the storage unit in sequence until the length of the storage unit meets the preset maximum length under the condition that the length of the storage unit exceeds the preset maximum length.
According to a second aspect of the embodiment of the present disclosure, a word cloud processing device is provided, which includes a frequency data obtaining unit configured to obtain first frequency data of words in a word cloud in a first period and second frequency data of words in a plurality of second periods, wherein the first period is a complete period before a current time, the plurality of second periods is a predetermined number of complete periods before the first period, a predicted frequency data obtaining unit configured to input the second frequency data into a frequency prediction model to obtain predicted frequency data of the words in the first period, an anomaly difference obtaining unit configured to obtain an anomaly difference value of the words based on the predicted frequency data and the first frequency data, and a processing unit configured to perform anomaly processing if the anomaly difference value is greater than a preset alarm threshold.
Optionally, the processing unit is further configured to fit the second frequency data and the first frequency data to obtain a fitted frequency before performing the abnormality processing if the abnormality difference is greater than a preset alarm threshold, obtain a residual frequency based on the second frequency data, the first frequency data and the fitted frequency data, obtain a normal fluctuation value based on a standard deviation of the residual frequency, and obtain a preset alarm threshold of the word based on the normal fluctuation value and a preset multiplying factor parameter.
Optionally, the frequency data obtaining unit is further configured to store the frequency data of the words to the corresponding storage unit before obtaining the first frequency data of the words in the word cloud in the first period and the second frequency data of the words in the plurality of second periods, where each word in the word cloud corresponds to one storage unit, and the frequency data includes the first frequency data and the second frequency data.
Optionally, the predicted frequency data obtaining unit is further configured to input the second frequency data in the storage unit corresponding to the word into the frequency prediction model to obtain predicted frequency data of the word in the first period.
Optionally, the processing unit is further configured to receive the word segmentation processing result sent by the word segmentation module after performing the exception processing when the exception difference value is greater than a preset alarm threshold value, and update the storage unit corresponding to the word based on the word segmentation processing result.
Optionally, the processing unit is further configured to store the frequency data of the words in the new period in the word segmentation processing result to the storage unit corresponding to the words, and delete the frequency data of the words in the forefront period in the storage unit in sequence until the length of the storage unit meets the preset maximum length under the condition that the length of the storage unit exceeds the preset maximum length.
According to a third aspect of the embodiment of the present disclosure, a word cloud processing system is provided, which includes a word segmentation module configured to receive a demand text fed back by a user and perform word segmentation processing on the demand text, and a word cloud processing module configured to perform the word cloud processing method as described above.
According to a fourth aspect of embodiments of the present disclosure, there is provided an electronic device comprising a processor, a memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement a word cloud processing method according to the present disclosure.
According to a fifth aspect of embodiments of the present disclosure, there is provided a computer-readable storage medium, which when executed by at least one processor, causes the at least one processor to perform a word cloud processing method as above according to the present disclosure.
According to a sixth aspect of embodiments of the present disclosure, there is provided a computer program product comprising computer instructions which, when executed by a processor, implement a word cloud processing method according to the present disclosure.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:
According to the word cloud processing method, device and system, the predicted frequency data of the words in the first period before the current time are predicted through the frequency prediction model, and then based on the difference value between the predicted frequency data and the first frequency data, the abnormal processing is carried out by combining with the preset alarm threshold, so that automatic alarm can be realized, participation of service personnel is not needed, labor cost is saved, and compared with the case of manually finding the abnormality in the related art, the situation of finding the abnormality can be shortened through automatic alarm, and alarm processing can be timely carried out. Therefore, the method solves the problems of high labor cost, untimely alarm and the like of the word cloud processing method in the related technology.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure and do not constitute an undue limitation on the disclosure.
FIG. 1 is a diagram showing a word cloud in the related art;
FIG. 2 is an implementation scenario diagram illustrating a word cloud processing method according to an exemplary embodiment of the present disclosure;
FIG. 3 is a flowchart illustrating a word cloud processing method according to an exemplary embodiment;
FIG. 4 is a diagram of a bi-directional queue shown in accordance with an exemplary embodiment;
FIG. 5 is a flowchart illustrating a word anomaly alarm operation according to an exemplary embodiment;
FIG. 6 is a data flow diagram illustrating a word anomaly alarm in accordance with an exemplary embodiment;
FIG. 7 is a block diagram of a word cloud processing apparatus, according to an example embodiment;
fig. 8 is a block diagram of an electronic device 800 according to an embodiment of the disclosure.
Detailed Description
In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The embodiments described in the examples below are not representative of all embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
It should be noted that, in this disclosure, "at least one of the items" refers to a case where three types of juxtaposition including "any one of the items", "a combination of any of the items", "an entirety of the items" are included. For example, "comprising at least one of A and B" includes the case of juxtaposition of three of (1) comprising A, (2) comprising B, and (3) comprising A and B. For example, "at least one of the first and second steps is executed", that is, three cases are shown in parallel, namely (1) execute the first step, (2) execute the second step, and (3) execute the first and second steps.
Aiming at the problems, the disclosure provides a word cloud processing method, which does not need participation of service personnel, saves labor cost, can shorten the situation of finding abnormality, can give an alarm in time, and is described below by taking a problem scene of web page blocking as an example.
Fig. 2 is a schematic diagram illustrating an implementation scenario of a word cloud processing method according to an exemplary embodiment of the present disclosure, as shown in fig. 2, where the implementation scenario includes a server 100, a user terminal 110, and a user terminal 120, where the user terminals are not limited to 2, including but not limited to devices such as a mobile phone, a personal computer, and the like, and the user terminal may be installed with an application program that may obtain a user requirement, where the server may be one server, or several servers form a server cluster, or may be a cloud computing platform or a virtualization center.
The user terminal 110 and the user terminal 120 acquire a user demand text and transmit the user demand text to the server 100, the server 100 can segment the received user demand text and put a word segmentation result into a word cloud, the server 100 can acquire first frequency data of words in the word cloud in a first period and second frequency data of words in a plurality of second periods, wherein the first period is a complete period before the current time, the plurality of second periods are a preset number of complete periods before the first period, then the server 100 inputs the second frequency data into a frequency prediction model to obtain predicted frequency data of the words in the first period, abnormal difference values of the words are obtained based on the predicted frequency data and the first frequency data, and abnormal processing is performed under the condition that the abnormal difference values are larger than a preset alarm threshold.
Next, a word cloud processing method, apparatus, and system according to an exemplary embodiment of the present disclosure will be described in detail with reference to fig. 3 to 7.
Fig. 3 is a flowchart illustrating a word cloud processing method according to an exemplary embodiment, and as shown in fig. 3, the word cloud processing method includes the steps of:
In step S301, first frequency data of words in a word cloud in a first period and second frequency data of words in a plurality of second periods are obtained, wherein the first period refers to a complete period before a current time, and the plurality of second periods refers to a predetermined number of complete periods before the first period.
According to the exemplary embodiment of the disclosure, before the first frequency data of the words in the word cloud in the first period and the second frequency data of the words in the second periods are acquired, the method further comprises the step of storing the frequency data of the words in the corresponding storage units, wherein each word in the word cloud corresponds to one storage unit, and the frequency data comprises the first frequency data and the second frequency data. According to the embodiment, each word has a corresponding storage unit to store the frequency of the corresponding word in a certain number of periods, so that the frequency can be conveniently obtained later and used for subsequent exception processing, such as alarm processing.
For example, the historical frequency of each word may be saved, for example, each word maintains a bidirectional queue (i.e. the storage unit described above), where frequency data corresponding to a time window of the word is saved in the bidirectional queue, as shown in fig. 4, time stamp indicates the current time, predict _idx is the time index closest to timestap, that is, the time index idx needed to make an alarm judgment, retain _idx indicates the longest data needed to make an alarm judgment, and the data before retain _idx may be cleared to save space, where frequency data corresponding to the time index between retain _idx and predict _idx may be used as data inside the bidirectional queue corresponding to the word at the time of alarm prediction. It should be noted that the frequency of the words is not limited to the bidirectional queue storage mode, and other modes having the same function may be used, and the disclosure is not limited thereto.
Returning to fig. 3, in step S302, the second frequency data is input to the frequency prediction model, and the predicted frequency data of the words in the first period is obtained. For example, the frequency prediction model may be a holt-winters algorithm, or may be a deep learning algorithm such as LSTM, which is not limited in this disclosure.
In step S303, an abnormal difference of the word is obtained based on the predicted frequency data and the first frequency data.
In step S304, if the anomaly difference is greater than the preset alarm threshold, anomaly processing is performed.
According to the exemplary embodiment of the disclosure, before performing the exception processing, if the exception difference value is greater than the preset alarm threshold value, the method further comprises obtaining the preset alarm threshold value of the word based on the second frequency data and the first frequency data. According to the embodiment, the corresponding preset alarm threshold value can be obtained according to the corresponding frequency data of the current word, namely, the threshold value is dynamically set at different time points, and different words adopt different preset alarm threshold values, so that false alarm and missing alarm of the problem can be effectively reduced, and the alarm accuracy is improved.
According to the exemplary embodiment of the disclosure, before performing exception processing, the method further comprises the steps of fitting second frequency data and first frequency data to obtain fitted frequencies, obtaining residual frequencies based on the second frequency data, the first real frequencies and the fitted frequencies, obtaining normal fluctuation values based on standard deviations of the residual frequencies, and obtaining preset alarm thresholds of words based on the normal fluctuation values and preset multiplying power parameters. According to the embodiment, the preset alarm threshold of the current word can be conveniently and quickly obtained.
For example, a dynamic threshold algorithm for obtaining a preset alarm threshold may use a bi-directional queue as an input, and if the preset alarm threshold is greater than an anomaly difference value, output alarm information for anomaly alarm. The dynamic threshold algorithm may use a holt-winters algorithm to fit the input frequency data yi of the bidirectional queue (the input frequency data of the bidirectional queue may be data corresponding to the time indexes idx2-idx 5) to obtain a fitting value sequence ypi, subtracting the fitting value sequence value from the actual value of each time index to obtain a residual sequence di, then calculating the standard deviation of the residual sequence as a normal fluctuation value delta, and inputting an alarm multiplying factor parameter r, where the alarm threshold K (i.e. the preset alarm threshold) is the product of the normal fluctuation value and the alarm multiplying factor parameter. The detailed algorithm is as follows:
residual di=yi-ypi (1)
Residual standard deviation δ=std (di) (2)
Threshold k=δ×r (3)
According to the exemplary embodiment of the disclosure, the second frequency data is input into the frequency prediction model to obtain the predicted frequency data of the words in the first period, and the method comprises the steps of inputting the second frequency data in the storage unit corresponding to the words into the frequency prediction model to obtain the predicted frequency data of the words in the first period. According to the embodiment, the second frequency data to be input into the frequency prediction model can be quickly obtained from the storage unit, and further the predicted frequency data can be quickly obtained.
According to the exemplary embodiment of the disclosure, after performing exception processing when the exception difference is greater than a preset alarm threshold, the method further comprises the steps of receiving a word segmentation processing result sent by a word segmentation module and updating a storage unit corresponding to a word based on the word segmentation processing result. According to the embodiment, the storage unit corresponding to the word can be updated in real time based on the word segmentation processing result.
For example, the word segmentation module may receive the required text fed back by the user in real time, perform word segmentation on the required text, send the word segmentation result to the server, and when the server receives the word segmentation result transmitted by the word segmentation module, update the frequency data corresponding to the idx in the bidirectional queue in real time according to the word segmentation result.
According to the exemplary embodiment of the disclosure, updating the storage unit corresponding to the word includes storing the frequency data of the word in a new period in the word segmentation processing result to the storage unit corresponding to the word, and deleting the frequency data of the word in the forefront period in the storage unit until the length of the storage unit meets the preset maximum length under the condition that the length of the storage unit corresponding to the word exceeds the preset maximum length. According to this embodiment, when the updated bidirectional queue exceeds the allowed length, it is deleted from the forefront so as to maintain the latest data.
For a better understanding of the above embodiments, the following description is made in conjunction with fig. 5 and 6, fig. 5 is a workflow diagram illustrating a word abnormality alarm according to an exemplary embodiment, and fig. 6 is a data flow diagram illustrating a word abnormality alarm according to an exemplary embodiment.
As shown in fig. 5, a time sequence alarm model and a dynamic alarm threshold model may be built in the word cloud monitoring system, and the workflow of word anomaly alarm may be as follows:
1. the user demands of different users are fed back to an analysis system (the system comprises a word segmentation module in the embodiment) from various channels such as a mobile phone end, a webpage end and the like;
2. The analysis system performs word segmentation processing on the received user demands, and the word segmentation processing result is sent to a word cloud monitoring system (which can be arranged in a server);
3. Dividing words of the word cloud into continuous time indexes idx according to a fixed period (for example, 5 minutes is a period), namely, in the embodiment, each word is mentioned to maintain a bidirectional queue, as shown in fig. 6, abnormality judgment is carried out on the frequency of a single word on each time index by using a time sequence alarm algorithm, an abnormal difference value is obtained, specifically, the bidirectional queue of the word is input into the time sequence alarm algorithm to obtain a predicted frequency of the word, and the predicted frequency is compared with an actual frequency of the word to obtain the abnormal difference value. For example, the time series alarm algorithm may be the holt-winters algorithm, although the disclosure is not limited in this regard;
4. As shown in fig. 6, inputting the two-way queue of the word into a dynamic threshold algorithm to obtain an alarm threshold (i.e. the preset alarm threshold) of the word, comparing the abnormal difference value obtained in the third step with the alarm threshold, and if the abnormal difference value is greater than the alarm threshold, carrying out abnormal alarm through an alarm channel 2;
5. The business personnel 2 intervenes in the alarm information.
In sum, the automatic word cloud monitoring alarm scheme based on time sequence prediction is provided, and has the advantages that the automatic word cloud monitoring alarm scheme based on time sequence prediction is high in labor cost, insufficient in timeliness, easy in false alarm and omission, difficult in threshold setting and the like: 1) automatic alarm, no participation of business personnel is needed, namely, the user can be found timely without depending on experience of the business personnel, and the user problem can be found manually in the related technology. 2) The automatic alarm accuracy and recall rate are high, the alarm is comprehensive, even if the frequency of an abnormal problem is not high enough, the abnormal problem cannot be easily found in word clouds, the abnormal problem can be detected by the method, the alarm threshold is modified in real time by the dynamic threshold algorithm, and false alarm can be effectively reduced. 3) Modeling the frequency characteristics of each word, calculating different alarm thresholds by different words, and preventing the alarm from being influenced by the frequency of the word. 4) The alarm threshold varies with time. The time sequence based alarm algorithm can timely capture the time-varying frequency of each word. 5) And alarming in time. The alarm algorithm based on the time sequence can find abnormal problems and alarm in a single time window, and the abnormal finding time is shortened.
Fig. 7 is a block diagram illustrating a word cloud processing apparatus according to an exemplary embodiment. Referring to fig. 7, the apparatus includes a frequency data acquisition unit 70, a predicted frequency data acquisition unit 72, an abnormality difference acquisition unit 74, and a processing unit 76.
The frequency data obtaining unit 70 is configured to obtain first frequency data of words in the word cloud in a first period and second frequency data of words in a plurality of second periods, wherein the first period is one complete period before the current time, the plurality of second periods are a preset number of complete periods before the first period, the predicted frequency data obtaining unit 72 is configured to input the second frequency data into the frequency prediction model to obtain predicted frequency data of the words in the first period, the abnormal difference obtaining unit 74 is configured to obtain abnormal difference values of the words based on the predicted frequency data and the first frequency data, and the processing unit 76 is configured to perform abnormal processing when the abnormal difference values are larger than a preset alarm threshold.
According to an exemplary embodiment of the present disclosure, the processing unit 76 is further configured to obtain a preset alarm threshold value of the word based on the second frequency data and the first frequency data before performing the abnormality processing in case that the abnormality difference value is larger than the preset alarm threshold value.
According to an exemplary embodiment of the present disclosure, the processing unit 76 is further configured to fit the second frequency data and the first frequency data to obtain a fitted frequency, obtain a residual frequency based on the second frequency data, the first frequency data and the fitted frequency data, obtain a normal fluctuation value based on a standard deviation of the residual frequency, and obtain a preset alarm threshold of the word based on the normal fluctuation value and a preset magnification parameter.
According to an exemplary embodiment of the present disclosure, the frequency data obtaining unit 70 is further configured to store the frequency data of the words to the corresponding storage unit before obtaining the first frequency data of the words in the word cloud in the first period and the second frequency data of the words in the plurality of second periods, wherein each word in the word cloud corresponds to one storage unit, and the frequency data includes the first frequency data and the second frequency data.
According to an exemplary embodiment of the present disclosure, the predicted frequency data obtaining unit 72 is further configured to input the second frequency data in the storage unit corresponding to the word into the frequency prediction model, to obtain the predicted frequency data of the word in the first period.
According to an exemplary embodiment of the present disclosure, the processing unit 76 is further configured to receive the word segmentation result sent by the word segmentation module after performing the exception processing if the exception difference value is greater than the preset alarm threshold value, and update the storage unit corresponding to the word based on the word segmentation result.
According to an exemplary embodiment of the present disclosure, the processing unit 76 is further configured to store the frequency data of the word in the new period in the word segmentation processing result to the storage unit corresponding to the word, and if the length of the storage unit exceeds the preset maximum length, delete the frequency data of the word in the forefront period in the storage unit in sequence until the length of the storage unit meets the preset maximum length.
According to another aspect of the embodiment of the disclosure, a word cloud processing system is provided, which comprises a word segmentation module and a word cloud processing module, wherein the word segmentation module is configured to receive a demand text fed back by a user and perform word segmentation processing on the demand text, and the word cloud processing module is configured to execute the word cloud processing method.
According to embodiments of the present disclosure, an electronic device may be provided. Fig. 8 is a block diagram of an electronic device 800 including at least one memory 801 having stored therein a set of computer-executable instructions that, when executed by the at least one processor, perform a word cloud processing method according to an embodiment of the present disclosure, and at least one processor 802, according to an embodiment of the present disclosure.
By way of example, electronic device 800 may be a PC computer, tablet device, personal digital assistant, smart phone, or other device capable of executing the above-described set of instructions. Here, the electronic device 1000 is not necessarily a single electronic device, but may be any apparatus or a collection of circuits capable of executing the above-described instructions (or instruction sets) individually or in combination. The electronic device 800 may also be part of an integrated control system or system manager, or may be configured as a portable electronic device that interfaces with either locally or remotely (e.g., via wireless transmission).
In electronic device 800, processor 802 may include a Central Processing Unit (CPU), a Graphics Processor (GPU), a programmable logic device, a special purpose processor system, a microcontroller, or a microprocessor. By way of example, and not limitation, the processor 802 may also include an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, and the like.
The processor 802 may execute instructions or code stored in the memory, wherein the memory 801 may also store data. The instructions and data may also be transmitted and received over a network via a network interface device, which may employ any known transmission protocol.
The memory 801 may be integrated with the processor 802, for example, RAM or flash memory disposed within an integrated circuit microprocessor or the like. In addition, the memory 801 may include a stand-alone device, such as an external disk drive, storage array, or other storage device usable by any database system. The memory 801 and the processor 802 may be operatively coupled or may communicate with each other, for example, through an I/O port, network connection, etc., such that the processor 802 is able to read files stored in the memory 801.
In addition, the electronic device 800 may also include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, mouse, touch input device, etc.). All components of the electronic device may be connected to each other via a bus and/or a network.
According to an embodiment of the present disclosure, there may also be provided a computer-readable storage medium, wherein the instructions in the computer-readable storage medium, when executed by at least one processor, cause the at least one processor to perform the word cloud processing method of the embodiments of the present disclosure. Examples of computer readable storage media herein include read-only memory (ROM), random-access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), dynamic random-access memory (DRAM), static random-access memory (SRAM), flash memory, nonvolatile memory, CD-ROM, CD-R, CD + R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD + R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, blu-ray or optical disk memory, hard Disk Drive (HDD), solid State Disk (SSD), card memory (such as a multimedia card, secure Digital (SD) card or ultra-fast digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, hard disk, solid state disk, and any other device configured to non-temporarily store a computer program and any associated data, data files and data structures and to cause the computer program and any associated data, data file and data structures to be provided to a processor or processor to execute the computer program. The computer programs in the computer readable storage media described above can be run in an environment deployed in a computer device, such as a client, host, proxy device, server, etc., and further, in one example, the computer programs and any associated data, data files, and data structures are distributed across networked computer systems such that the computer programs and any associated data, data files, and data structures are stored, accessed, and executed in a distributed fashion by one or more processors or computers.
According to an embodiment of the present disclosure, there is provided a computer program product comprising computer instructions which, when executed by a processor, implement a word cloud processing method of an embodiment of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101296128A (en) * | 2007-04-24 | 2008-10-29 | 北京大学 | A method for monitoring abnormal state of Internet information |
| CN113420202A (en) * | 2021-07-15 | 2021-09-21 | 上海明略人工智能(集团)有限公司 | Method and device for predicting keyword search times, electronic equipment and storage medium |
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