Online data traffic monitoring optimization method, apparatus, device, and medium

Abstract

The application provides an online data flow monitoring optimization method, device, equipment and medium, wherein the method comprises the following steps: through acquiring an application program, the application program is divided into multiple groups, then a first bandwidth is pre-allocated, then hot comments are acquired, a second bandwidth is set based on the hot comments, and finally the bandwidth of each group is set based on the first bandwidth and the second bandwidth.The application has the beneficial effects that the intelligent allocation and dynamic adjustment of the bandwidth are realized, the utilization efficiency of network resources is improved, and the network experience of users is improved.At the same time, through subsequent monitoring and optimization measures, the rationality and effectiveness of the bandwidth allocation are ensured.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to an online data traffic monitoring and optimization method, apparatus, equipment, and medium. Background Technology

[0002] By monitoring network traffic and understanding the bandwidth usage of various applications, optimization measures can be taken to improve network performance and user experience. Current technologies typically adjust bandwidth based on existing network traffic; however, network traffic is constantly changing, and different applications have varying bandwidth requirements at different times. Therefore, bandwidth allocation can be quite volatile, leading to slow efficiency. Summary of the Invention

[0003] The main objective of this invention is to provide an online data traffic monitoring and optimization method, apparatus, device, and medium, aiming to solve the problem that the existing bandwidth allocation range is relatively large, resulting in slow bandwidth allocation efficiency.

[0004] This invention provides an online data traffic monitoring and optimization method, comprising:

[0005] Obtain multiple applications and divide each application into n groups;

[0006] Obtain historical data traffic for each application within the group, and convert the historical data traffic into historical usage values;

[0007] A first bandwidth is pre-allocated to each group based on the historical usage data;

[0008] Retrieves multiple trending comments related to trending topics from multiple preset platforms;

[0009] Extract trending keywords from various trending comments for each application;

[0010] The application's corresponding trending comment terms are converted into trending values ​​according to a preset conversion relationship;

[0011] Calculate the sum of the hotspot values ​​of each application within the group to obtain the group's hotspot value;

[0012] Set the second bandwidth for each group based on the group hotspot values;

[0013] The bandwidth of each group is allocated based on the first bandwidth and the second bandwidth.

[0014] Furthermore, the step of acquiring multiple applications and dividing each application into n groups includes:

[0015] Obtaining application information of each of the applications, converting each of the application information into text information according to a preset conversion mode;

[0016] Classifying according to a preset classification method based on the text information, and sorting according to a category order, each of the text information being separated by a preset separator to obtain application program text;

[0017] Randomly selecting n-1 separators as preset separators;

[0018] Calculating fluctuation values between each of the subtexts separated by the preset separators;

[0019] If the fluctuation value is less than a preset fluctuation value, reselecting the separator until the fluctuation value corresponding to the selected target separator is greater than or equal to the preset fluctuation value;

[0020] Dividing the corresponding application program into n groups based on the target separator.

[0021] Further, after the step of adjusting the bandwidth of each group based on the first bandwidth and the second bandwidth, the method further comprises:

[0022] Obtaining the usage frequency of each application program in each group;

[0023] Setting a priority order of each application program in the group according to the usage frequency;

[0024] Setting a bandwidth priority satisfaction order for each application program according to the priority order.

[0025] Further, after the step of adjusting the bandwidth of each group based on the first bandwidth and the second bandwidth, the method further comprises:

[0026] Analyzing the data information of each group by a preset monitoring tool;

[0027] Determining whether the data information contains abnormal information;

[0028] If the data information contains abnormal information, stopping the bandwidth of each application program in the group.

[0029] Further, after the step of adjusting the bandwidth of each group based on the first bandwidth and the second bandwidth, the method further comprises:

[0030] Monitoring the fluctuation of the traffic data of each group;

[0031] When the fluctuation of the traffic data of the group exceeds a preset value, sending the fluctuation of the group and the corresponding application program to relevant personnel for confirmation;

[0032] When the confirmation instruction of the relevant person is received, the bandwidth of each group is recalculated and allocated.

[0033] Further, the step of converting each of the hot comment words corresponding to the application into a hot value according to a preset conversion relationship comprises:

[0034] The hot comment words are analyzed by using a sentiment analysis tool to obtain a word representing sentiment tendency and a sentiment polarity value thereof;

[0035] The probability of occurrence of the positive emotion word is calculated; wherein the word with a sentiment polarity value greater than a set value is a positive emotion word, and the word with a sentiment polarity value less than or equal to the set value is a negative emotion word;

[0036] The hot value of the application is calculated according to the probability of occurrence of the positive emotion word.

[0037] Further, the step of pre-allocating a first bandwidth to each group based on the historical usage value comprises:

[0038] A basic bandwidth is set for each group;

[0039] An additional bandwidth is pre-allocated to each group based on the historical usage value;

[0040] The basic bandwidth and the additional bandwidth are added to obtain the first bandwidth pre-allocated to each group.

[0041] The application also provides an online data flow monitoring and optimization device, comprising:

[0042] A first acquisition module is configured to acquire a plurality of applications and divide each application into n groups;

[0043] A second acquisition module is configured to acquire historical data flow of each application in a group and convert the historical data flow into a historical usage value;

[0044] An allocation module is configured to pre-allocate a first bandwidth to each group based on the historical usage value;

[0045] A third acquisition module is configured to acquire a plurality of hot comments related to platform hotspots from a preset platform;

[0046] A fourth acquisition module is configured to acquire hot comment words of each application from each hot comment;

[0047] A conversion module is configured to convert each of the hot comment words corresponding to the application into a hot value according to a preset conversion relationship;

[0048] A computing module is configured to calculate a sum of the hotspot values of the application programs in each group to obtain a group hotspot value.

[0049] A setting module is configured to set a second bandwidth of each group according to the group hotspot value.

[0050] A deployment module is configured to deploy the bandwidth of each group based on the first bandwidth and the second bandwidth.

[0051] The application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method according to any one of the preceding embodiments when executing the computer program.

[0052] The application further provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the method according to any one of the preceding embodiments when executed by a processor.

[0053] The application has the following beneficial effects: the application programs are divided into groups, the first bandwidth is pre-allocated, the hotspot comments are obtained, the second bandwidth is set based on the hotspot comments, and finally the bandwidth of each group is set based on the first bandwidth and the second bandwidth, so that the intelligent allocation and dynamic adjustment of the bandwidth are realized. This method not only improves the utilization efficiency of network resources, but also improves the network experience of users. Meanwhile, through subsequent monitoring and optimization measures, the rationality and effectiveness of the bandwidth allocation are ensured. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 is a flowchart of an online data flow monitoring and optimization method according to an embodiment of the application;

[0055] Figure 2 is a structural schematic block diagram of an online data flow monitoring and optimization device according to an embodiment of the application;

[0056] Figure 3 is a structural schematic block diagram of a computer device according to an embodiment of the application.

[0057] The implementation, functional features and advantages of the application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the accompanying drawings of the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the application.

[0059] It should be noted that all the direction indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present application are only used to explain the relative position relationship, movement condition, etc. between components in a certain posture (as shown in the drawings), and if the certain posture changes, the direction indications will also change accordingly. The connection can be direct connection or indirect connection.

[0060] The term "and / or" herein is only used to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and B can represent three cases of A alone, A and B together, and B alone.

[0061] In addition, the descriptions such as "first", "second" and the like in the present application are only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions of various embodiments can be combined with each other, but it must be based on the realization of ordinary skilled in the art, when the combination of technical solutions appears contradictory or unachievable, it should be considered that the combination of technical solutions does not exist, nor within the protection scope required by the present application.

[0062] Referring to Figure 1 The present application provides an online data flow monitoring optimization method, comprising:

[0063] S1: obtaining a plurality of application programs, and dividing each application program into n groups;

[0064] S2: obtaining the historical data flow of each application program in the group, and converting the historical data flow into a historical usage value;

[0065] S3: pre-allocating a first bandwidth to each group based on the historical usage value;

[0066] S4: obtaining a plurality of hot comment words related to a plurality of platform hotspots from a preset platform;

[0067] S5: obtaining the hot comment words of each application program from each hot comment word;

[0068] S6: converting each hot comment word corresponding to the application program into a hot value according to a preset conversion relationship;

[0069] S7: calculating the sum of the hot values of each application program in the group to obtain a group hot value;

[0070] S8: setting a second bandwidth for each group according to the group hot value;

[0071] S9: allocating bandwidths of each group based on the first bandwidth and the second bandwidth.

[0072] As described in step S1, a plurality of application programs are obtained, and each application program is divided into n groups; wherein the number of application programs should be more than the number of groups, wherein the grouping manner is not predetermined, which can be based on the category of each application program, or can be randomly classified, or can be classified according to the use of data flow, preferably classified by category, since the same category may be consistent with the use of cache content, which is divided into a group to optimize the bandwidth.

[0073] As described in step S2, the historical data flow of each application program in the group is obtained, and the historical data flow is converted into a historical usage value. Wherein the historical data flow is the recent data flow, for example, it can be set to one day, or the previous few hours, etc. The historical data flow of each application program can be obtained, and the historical data flow is converted into a historical usage value, which can be a pre-established corresponding relationship table between historical flow data and historical usage value, and the historical usage value is obtained based on the corresponding relationship table.

[0074] As described in step S3, a first bandwidth is pre-allocated to each group based on the historical usage value. Wherein the allocation of the first bandwidth can be based on the ratio of each historical usage value, in a preferred embodiment, a basic bandwidth can be pre-allocated to each group, and then the remaining bandwidth is allocated according to the historical usage value.

[0075] As described in steps S4-S7, a plurality of hot comment words related to platform hotspots are obtained from a plurality of platform hotspots, wherein the preset platform is a related platform, for example, a platform for car comments, so that a plurality of platform hotspots can be obtained, and then hot comment words are obtained from the comment area. Each of the hot comment words corresponding to the application program is converted into a hot value according to a preset conversion relationship, wherein the conversion manner can be based on the part of speech of each word, and the positive comment word can increase the hot value, and the negative comment word can reduce the hot value, so that the hot value of each application program can be obtained, and the sum of the hot values of each group is obtained.

[0076] The second bandwidth of each group is set according to the group hotspot value as described in steps S8-S9, and the setting method can be based on the ratio of the hotspot value of each group. The bandwidth of each group is adjusted based on the first bandwidth and the second bandwidth, and the adjustment method can be to take the average of the first bandwidth and the second bandwidth, or to modify the first bandwidth based on the second bandwidth. Specifically, a portion of the first bandwidth can be extracted as a backup bandwidth according to the proportion, and then the backup bandwidth is redistributed to each group based on the ratio of the second bandwidth, so as to adjust the bandwidth of each group. This can comprehensively consider the hotspots on the basis of the original, and can greatly improve the utilization rate of bandwidth. In a preferred embodiment, a basic bandwidth can be allocated to each group in advance, and then the remaining bandwidth is allocated according to the ratio of the hotspot value. Through comprehensive consideration of the historical data traffic of the application program and the current platform hotspot, intelligent allocation and dynamic adjustment of bandwidth are realized. This method not only improves the utilization efficiency of network resources, but also improves the network experience of users. At the same time, through subsequent monitoring and optimization measures, the rationality and effectiveness of bandwidth allocation are ensured.

[0077] In one embodiment, the step S1 of obtaining a plurality of application programs and dividing each application program into n groups includes:

[0078] S101: Obtain application information of each application program, and convert each application information into text information according to a preset conversion method;

[0079] S102: Classify based on the text information according to a preset classification method, and sort according to the category order. Each text information is separated by a preset delimiter to obtain an application text;

[0080] S103: Randomly select n-1 delimiters as preset delimiters;

[0081] S104: Calculate the fluctuation value between each subtext separated by the preset delimiters;

[0082] S105: If the fluctuation value is less than a preset fluctuation value, the delimiters are reselected until the fluctuation value corresponding to the target delimiter is greater than or equal to the preset fluctuation value;

[0083] S106: Divide the corresponding application program into n groups based on the target delimiter.

[0084] As described in the above step S101, obtain the application information of each application, and convert each application information into text information according to a preset conversion method. The system can obtain relevant information (such as name, size, function description, etc.) from each application, and convert these information into text information through a certain preset method (such as encoding, formatting, etc.) for subsequent processing.

[0085] As described in the above step S102, classify the text information according to a preset classification method, and sort according to the category order, and separate each text information with a preset separator to obtain the application text. The system will classify the text information according to a preset classification method (such as based on function, size, user rating, etc.), and sort according to the category order. The sorted text information will be separated by a preset separator (such as comma, line break, etc.) to form a text containing all application information.

[0086] As described in the above step S103, randomly select n-1 separators as preset separators; randomly select n-1 separators (from the separators used in the above step), which will be used for subsequent subtext division.

[0087] As described in the above step S104, calculate the fluctuation value between each subtext separated by the preset separator; calculate the fluctuation value between each subtext (i.e. the text information of each application) separated by these preset separators. This fluctuation value may be based on the difference, similarity or other metrics of the text content, it should be noted that the larger the fluctuation value, the better the separation effect.

[0088] As described in the above step S105, if the fluctuation value is less than the preset fluctuation value, then reselect the separator until the fluctuation value corresponding to the selected target separator is greater than or equal to the preset fluctuation value. If the calculated fluctuation value is less than the preset fluctuation value, it means that the currently selected separator has not effectively separated the application text into subtexts with obvious differences. Therefore, the system will reselect the separator and repeat the calculation of the fluctuation value until a set of separators is found, which makes the fluctuation value between the subtexts separated by them greater than or equal to the preset fluctuation value.

[0089] As described in the above step S106, divide the corresponding application into n groups based on the target separator. According to the selected target separator, the application text is divided into n groups. Each group contains a set of applications with similar or related characteristics.

[0090] The application is automatically classified and grouped to better manage and analyze the applications. By adjusting the preset classification method, separator and fluctuation value, flexible processing of different types of applications can be achieved.

[0091] In one embodiment, after the step S9 of allocating bandwidth to each group based on the first bandwidth and the second bandwidth, the method further comprises:

[0092] S1001: obtaining the usage frequency of each application in each group;

[0093] S1002: setting a priority order of each application in a group according to the usage frequency;

[0094] S1003: setting a bandwidth priority satisfaction order for each application according to the priority order.

[0095] As described in step S1001, the usage frequency of each application in each group is obtained. The usage data of each application in each group is collected and analyzed to determine their frequency of use by users. This frequency can be based on the number of times the user opens the application, the length of time the application is used, or the usage within a certain time period, such as a day or a week.

[0096] As described in step S1002, a priority order of each application in a group is set according to the usage frequency; once the usage frequency of each application is obtained, the system sets a priority order of each application in a group according to this frequency. Generally, applications with higher usage frequency are given higher priority because they are more important or commonly used by users.

[0097] As described in step S1003, a bandwidth priority satisfaction order is set for each application according to the priority order. According to the previously set priority order, each application is assigned a bandwidth priority satisfaction order. This means that in the case of limited network resources or bandwidth competition, higher priority applications will have priority in obtaining the required bandwidth resources to ensure their normal operation and provide good user experience. Optimizing the allocation of network resources ensures that the most important applications can be supported with sufficient bandwidth in high-load or bandwidth-limited environments. By dynamically adjusting the priority and bandwidth allocation of applications, the overall system performance and user satisfaction can be improved.

[0098] In one embodiment, after the step S9 of allocating bandwidth to each group based on the first bandwidth and the second bandwidth, the method further comprises:

[0099] S1101: analyzing data information of each group through a pre-set monitoring tool;

[0100] S1102: determining whether the data information contains abnormal information;

[0101] S1103: If there is abnormal information, stop the bandwidth of each application in the group.

[0102] As described in step S1101, analyze the data information of each group through the preset monitoring tool; use the preset monitoring tool (such as network monitoring software, performance analysis tool, etc.) to collect and analyze the data information of each group. These information may include the running state of the application, the network bandwidth usage, user behavior data, etc.

[0103] As described in step S1102, determine whether the data information contains abnormal information. Analyze the collected data information to determine whether it contains abnormal information. Abnormal information may indicate application failure, network attack, resource abuse, etc. The system will identify these abnormal information according to the preset abnormal detection rules or algorithms.

[0104] As described in step S1103, if there is abnormal information, stop the bandwidth of each application in the group. If the system detects that the data information contains abnormal information, in order to protect the safety and stability of the network, the system will take emergency measures to stop the bandwidth of each application in the group. This means that the system will temporarily cut off the network connection of these applications to prevent the further spread of abnormal information or cause greater impact on the network.

[0105] This monitoring and response mechanism can be used to protect the safety and stability of the network and prevent abnormal behavior of the application from causing damage to the network. By detecting and responding to abnormal information in a timely manner, the system can quickly take measures to reduce potential risks and losses. However, in actual application, stopping the bandwidth may be an extreme measure, which is usually taken after other more moderate solutions (such as restarting the application, adjusting the configuration, etc.) fail, i.e., in some embodiments, the application can also be restarted or the configuration can be adjusted.

[0106] In one embodiment, after the step S9 of adjusting the bandwidth of each group based on the first bandwidth and the second bandwidth, it further comprises:

[0107] S1201: Monitor the fluctuation of the traffic data of each group;

[0108] S1202: When the fluctuation of the traffic data of a group exceeds the preset value, send the fluctuation of the group and the corresponding application to the relevant personnel for confirmation;

[0109] S1203: After receiving the confirmation instruction of the relevant personnel, recalculate the bandwidth of each group and distribute it.

[0110] As described in step S1201 above, the fluctuation of the traffic data of each group is monitored, and the fluctuation of the traffic data of each group is continuously monitored and analyzed. The fluctuation of the traffic data may reflect changes in network activity of applications within the group, such as an increase in user access, the online of new functions, etc.

[0111] As described in step S1202 above, when the fluctuation of the traffic data of a group exceeds a preset value, the fluctuation of the group and the corresponding application are sent to the relevant personnel for confirmation. If the fluctuation of the traffic data of a group exceeds the preset threshold (which may be set based on historical data, network capacity planning, etc.), the system will trigger an alarm mechanism. The alarm information will contain the traffic fluctuation of the group and the corresponding application information, and will be sent to the relevant personnel (such as network administrators, application maintenance personnel, etc.) for confirmation.

[0112] As described in step S1203 above, when the confirmation instruction of the relevant personnel is received, the bandwidth of each group is recalculated and allocated. After receiving the confirmation instruction of the relevant personnel, the system will recalculate the bandwidth requirements of each group based on the current traffic data and network conditions. The recalculation method is the same as described above, and will not be repeated here. After the calculation is completed, the system will reallocate the bandwidth of each group according to the new bandwidth allocation scheme, to ensure the rational use of network resources and the smooth running of applications. It helps to discover and handle network traffic anomalies in a timely manner, and prevents network congestion and application performance degradation. Through monitoring, alarm and bandwidth reallocation, the system can dynamically adjust network resources to adapt to changes in application and user demand.

[0113] In one embodiment, the step S6 of converting each of the hot comment words corresponding to the application into a hot value according to a preset conversion relationship comprises:

[0114] S601: using a sentiment analysis tool to analyze the hot comment words to obtain words representing sentiment polarity and their sentiment polarity values;

[0115] S602: calculating the probability of occurrence of positive emotional words; wherein the words with sentiment polarity values greater than a set value are positive emotional words, and the words with sentiment polarity values less than or equal to a set value are negative emotional words;

[0116] S603: calculating the hot value of the application according to the probability of occurrence of positive emotional words.

[0117] As described in steps S601-S603, the calculation of the hotspot value is realized. Specifically, a pre-set sentiment analysis tool SentiWordNet can be used to divide the hotspot comment words into positivity and negativity according to the sentiment score. Further, the probabilities of occurrence of positive and negative emotion words in the comment content are calculated, and the hotspot value of the application program is calculated according to the probabilities of occurrence of positive and negative emotion words.

[0118] In one embodiment, the step S3 of pre-allocating a first bandwidth to each group based on the historical usage value comprises:

[0119] S301: setting a basic bandwidth for each group;

[0120] S302: pre-allocating an additional bandwidth to each group based on the historical usage value;

[0121] S303: adding the basic bandwidth and the additional bandwidth to obtain the first bandwidth pre-allocated to each group.

[0122] As described in steps S301-S303, the pre-allocation of the first bandwidth is realized, that is, a basic bandwidth is set for each group, which is the minimum network resource required to ensure that the application program in the group can run normally. The basic bandwidth is set based on factors such as the type of application program, the number of users, and business requirements. The historical usage value (such as traffic data and user access patterns in the past period of time) is used to pre-allocate an additional bandwidth to each group. The additional bandwidth is an extra network resource reserved to cope with traffic fluctuations, emergencies or future growth. The basic bandwidth and the additional bandwidth are added to obtain the first bandwidth pre-allocated to each group. The first bandwidth is the initial bandwidth resource obtained by the group in the network, which is used to support the normal operation and future development needs of the application program in the group.

[0123] Referring to Figure 2 , the present application also provides an online data traffic monitoring and optimization device, comprising:

[0124] A first acquisition module 10 is configured to acquire a plurality of application programs and divide each application program into n groups;

[0125] A second acquisition module 20 is configured to acquire historical data traffic of each application program in the group and convert the historical data traffic into a historical usage value;

[0126] A distribution module 30 is configured to pre-allocate a first bandwidth to each group based on the historical usage value;

[0127] The third obtaining module 40 is configured to obtain a plurality of hotspot comments related to a plurality of platform hotspots from a preset platform.

[0128] The fourth obtaining module 50 is configured to obtain a hotspot comment word of each application program from each hotspot comment.

[0129] The conversion module 60 is configured to convert each hotspot comment word corresponding to the application program into a hotspot value according to a preset conversion relationship.

[0130] The calculation module 70 is configured to calculate a sum of the hotspot values of each application program in a group to obtain a group hotspot value.

[0131] The setting module 80 is configured to set a second bandwidth of each group according to the group hotspot value.

[0132] The allocation module 90 is configured to allocate the bandwidth of each group based on the first bandwidth and the second bandwidth.

[0133] In an embodiment, the first obtaining module 10 comprises:

[0134] The application information obtaining submodule is configured to obtain application information of each application program, and convert each application information into text information according to a preset conversion mode.

[0135] The classification submodule is configured to classify each text information according to a preset classification method based on the text information, and sort the classified text information according to a category order, each text information is separated by a preset separator, and application program text is obtained.

[0136] The random selection submodule is configured to randomly select n-1 separators as preset separators.

[0137] The fluctuation value calculation submodule is configured to calculate a fluctuation value between each subtext separated by the preset separators.

[0138] The re-selection submodule is configured to reselect the separator if the fluctuation value is less than a preset fluctuation value, until the fluctuation value corresponding to a target separator selected is greater than or equal to the preset fluctuation value.

[0139] The division submodule is configured to divide the corresponding application program into n groups based on the target separator.

[0140] In an embodiment, the online data flow monitoring and optimization device further comprises:

[0141] The frequency of use obtaining module is configured to obtain a frequency of use of each application program in each group.

[0142] The priority order setting module is configured to set a priority order of each application program in the group according to the frequency of use.

[0143] The bandwidth priority satisfaction order setting module is configured to set a bandwidth priority satisfaction order for each application according to the priority order.

[0144] In one embodiment, the online data traffic monitoring optimization device further comprises:

[0145] The data information analysis module is configured to analyze the data information of each group by using a preset monitoring tool.

[0146] The data information judgment module is configured to judge whether the data information contains abnormal information.

[0147] The bandwidth stopping module is configured to stop the bandwidth of each application in the group if the data information contains abnormal information.

[0148] In one embodiment, the online data traffic monitoring optimization device further comprises:

[0149] The fluctuation condition monitoring module is configured to monitor the fluctuation condition of the traffic data of each group.

[0150] The information sending module is configured to send the fluctuation condition of the group and the corresponding application to a relevant person for confirmation when the fluctuation condition of the traffic data of the group exceeds a preset value.

[0151] The bandwidth recalculation module is configured to recalculate the bandwidth of each group and allocate the bandwidth after receiving a confirmation instruction from the relevant person.

[0152] In one embodiment, the conversion module 60 comprises:

[0153] The analysis submodule is configured to analyze the hot comment words by using a sentiment analysis tool to obtain words representing sentiment tendencies and sentiment polarity values of the words.

[0154] The probability statistics submodule is configured to calculate the probability of occurrence of positive sentiment words, wherein the words with sentiment polarity values greater than a set value are positive sentiment words, and the words with sentiment polarity values less than or equal to the set value are negative sentiment words.

[0155] The hot value calculation submodule is configured to calculate the hot value of the application according to the probability of occurrence of the positive sentiment words.

[0156] In one embodiment, the allocation module 30 comprises:

[0157] The basic bandwidth setting submodule is configured to set a basic bandwidth for each group.

[0158] The additional bandwidth setting submodule is configured to pre-allocate an additional bandwidth to each group based on the historical use value.

[0159] The first bandwidth setting submodule is configured to add the basic bandwidth and the additional bandwidth to obtain a first bandwidth pre-allocated to each group.

[0160] The application has the following beneficial effects: by acquiring the application, dividing the application into multiple groups, pre-allocating a first bandwidth to each group, acquiring hot comments, setting a second bandwidth based on the hot comments, and finally setting the bandwidth of each group based on the first bandwidth and the second bandwidth, the application realizes intelligent allocation and dynamic adjustment of the bandwidth. This method not only improves the utilization efficiency of network resources, but also improves the network experience of users. At the same time, through subsequent monitoring and optimization measures, the rationality and effectiveness of bandwidth allocation are ensured.

[0161] Reference Figure 3 The application also provides a computer device, which can be a server, and the internal structure of the computer device can be as shown in Figure 3 The computer device includes a processor, a memory, a network interface and a database connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store various hot comments and the like. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program, when executed by the processor, can implement the online data flow monitoring optimization method described in any of the above embodiments.

[0162] Those skilled in the art can understand that Figure 3 The structure shown in the above embodiment is only a block diagram of part of the structure related to the scheme of the application, and does not constitute a limitation on the computer device to which the scheme of the application is applied.

[0163] The application also provides a computer readable storage medium having a computer program stored thereon, and the computer program, when executed by the processor, can implement the online data flow monitoring optimization method described in any of the above embodiments.

[0164] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments can be included. Any reference to memory, storage, databases, or other media in this application and in examples provided herein can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM can be available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0165] It should be noted that in this paper, the term "including", "containing" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, device, article or method including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, device, article or method. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0166] The embodiments of the present application can acquire and process related data based on artificial intelligence technology. Among them, artificial intelligence (AI) is to use digital computers or digital computer controlled machines to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. Theory, method, technology and application system.

[0167] The basic technology of artificial intelligence generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. Artificial intelligence software technology mainly includes computer vision technology, robot technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc. Several major directions.

[0168] The above merely provides the preferred embodiments of the present application, and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the principles and technical scope of the present application shall fall into the scope of claims of the present application.

Claims

1. An online data traffic monitoring optimization method, characterized in that, The method comprises the following steps: obtaining a plurality of applications, and dividing each application into n groups; obtaining historical data traffic of each application in a group, and converting the historical data traffic into a historical usage value; pre-allocating a first bandwidth to each group based on the historical usage value; obtaining a plurality of hot comment words related to a plurality of platform hotspots from a preset platform; obtaining hot comment words of each application from each hot comment word; converting each hot comment word corresponding to the application into a hot value according to a preset conversion relationship; calculating the sum of the hot values of each application in the group to obtain a group hot value; setting a second bandwidth for each group according to the group hot value; allocating the bandwidth of each group based on the first bandwidth and the second bandwidth.

2. The online data traffic monitoring optimization method of claim 1, wherein, The step of obtaining a plurality of applications and dividing each application into n groups comprises the following steps: obtaining application information of each application, and converting each application information into text information according to a preset conversion method; classifying and sorting according to the text information according to a preset classification method, and separating each text information by using a preset separator to obtain an application text; randomly selecting n-1 separators as preset separators; calculating the fluctuation value between each subtext separated by the preset separators; if the fluctuation value is less than a preset fluctuation value, reselecting the separator until the fluctuation value corresponding to the target separator is greater than or equal to the preset fluctuation value; dividing the corresponding application into n groups based on the target separator.

3. The online data traffic monitoring optimization method of claim 1, wherein, After the step of allocating the bandwidth of each group based on the first bandwidth and the second bandwidth, the method further comprises the following steps: obtaining the usage frequency of each application in each group; setting the priority order of each application in the group according to the usage frequency; setting the bandwidth priority satisfaction order for each application according to the priority order.

4. The online data traffic monitoring optimization method of claim 1, wherein, After the step of allocating the bandwidth of each group based on the first bandwidth and the second bandwidth, the method further comprises the following steps: analyzing the data information of each group by using a preset monitoring tool; determining whether the data information contains abnormal information; if there is abnormal information, stopping the bandwidth of each application in the group.

5. The online data traffic monitoring optimization method of claim 1, wherein, After the step of allocating the bandwidth of each group based on the first bandwidth and the second bandwidth, the method further comprises the following steps: monitoring the fluctuation of the traffic data of each group; when the fluctuation of the traffic data of a group exceeds a preset value, sending the fluctuation of the group and the corresponding application to relevant personnel for confirmation; after receiving the confirmation instruction of the relevant personnel, recalculating the bandwidth of each group and allocating the bandwidth.

6. The online data traffic monitoring optimization method of claim 1, wherein, The step of converting each hot comment word corresponding to the application into a hot value according to a preset conversion relationship comprises the following steps: using a sentiment analysis tool to analyze the hot comment word to obtain a word representing emotional tendency and its emotional polarity value; statistically analyzing the probability of occurrence of positive emotional words; wherein, the words with emotional polarity values greater than a set value are positive emotional words, and the words with emotional polarity values less than or equal to a set value are negative emotional words; The hot value of the application program is calculated according to the probability of the appearance of the words of the positive emotion.

7. The online data traffic monitoring optimization method of claim 1, wherein, The step of pre-allocating the first bandwidth for each group based on the historical usage value comprises: setting a basic bandwidth for each group; pre-allocating an additional bandwidth for each group based on the historical usage value; adding the basic bandwidth and the additional bandwidth to obtain the first bandwidth pre-allocated for each group.

8. An online data traffic monitoring optimization apparatus, characterized by, comprises: a first acquisition module configured to acquire a plurality of application programs and divide the application programs into n groups; a second acquisition module configured to acquire historical data traffic of each application program in a group and convert the historical data traffic into a historical usage value; an allocation module configured to pre-allocate a first bandwidth for each group based on the historical usage value; a third acquisition module configured to acquire a plurality of hot comments related to platform hot spots from a preset platform; a fourth acquisition module configured to acquire hot comment words of each application program from each hot comment; a conversion module configured to convert each hot comment word corresponding to an application program into a hot value according to a preset conversion relationship; a calculation module configured to calculate a sum of the hot values of each application program in a group to obtain a group hot value; a setting module configured to set a second bandwidth of each group according to the group hot value; an adjustment module configured to adjust the bandwidth of each group based on the first bandwidth and the second bandwidth. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to realize the steps of the method in any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the steps of the method in any one of claims 1 to 7.

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