CN117560700B

CN117560700B - Control method and system for monitoring network data based on intelligent equipment

Info

Publication number: CN117560700B
Application number: CN202410045862.XA
Authority: CN
Inventors: 韦加新; 许伟坡; 陈汉育; 段春新
Original assignee: Shenzhen Xinkeyun Technology Co ltd
Current assignee: Shenzhen Xinkeyun Technology Co ltd
Priority date: 2024-01-12
Filing date: 2024-01-12
Publication date: 2024-03-29
Anticipated expiration: 2044-01-12
Also published as: CN117560700A

Abstract

The invention provides a control method and a system for monitoring network data based on intelligent equipment, which are applied to the field of network data monitoring; the invention efficiently utilizes system resources by adopting asynchronous processing and parallel processing of network tasks of each processing stage aiming at the congestion network data, improves the data processing speed and throughput, simultaneously combines the self-adaptive data synchronization according to the preset network index configuration monitored in real time and the user behavior mode, optimizes the transmission efficiency of the network tasks, is beneficial to improving the efficiency and accuracy of the data synchronization, calculates the network tasks of active users by adopting the preset stream processing priority, responds and processes the requests of the active users more rapidly under the condition of limited resources, and improves the instantaneity and the user satisfaction of the data processing.

Description

Control method and system for monitoring network data based on intelligent equipment

Technical Field

The invention relates to the field of network data monitoring, in particular to a control method and a system for monitoring network data based on intelligent equipment.

Background

The smart wearable device is one of hot high-tech products in recent years, the smart watch is the most mature product developed in the smart wearable device, and the main research and development directions of functions of the smart watch are the same as those of the smart mobile phone at present, namely, the smart watch can synchronously operate telephones, short messages, mails, photos, music and the like in the mobile phone, and meanwhile, the smart watch is also often used for protecting special groups such as old people and children.

At present, although the smart watch has corresponding processing capability and storage space, real-time performance of network data monitoring can not be ensured under the condition of keeping a small data transmission amount, data are collected, the data are required to be transmitted to the watch through a connection mode (such as Bluetooth or Wi-Fi) of the watch, transmission delay depends on network speed, signal strength and connection stability, and under the condition of unstable signals or network congestion, the transmission delay may be increased.

Disclosure of Invention

The invention aims to solve the problem of ensuring the real-time performance of monitoring network data of an intelligent watch, and provides a control method and a control system based on monitoring network data of intelligent equipment.

The invention adopts the following technical means for solving the technical problems:

the invention provides a control method for monitoring network data based on intelligent equipment, which comprises the following steps:

detecting flow information of network monitoring data based on mobile equipment pre-connected with an intelligent watch, and displaying congestion network data to be processed corresponding to the flow information from the intelligent watch;

judging whether the congestion network data exceeds a preset processing period;

if yes, carrying out asynchronous processing on the congestion network data, processing network tasks of each processing stage in parallel, monitoring the transmission efficiency of preset network index configuration in real time, identifying the behavior mode of a network user on the network tasks, executing self-adaptive data synchronization on the network tasks according to the transmission efficiency and the behavior mode, and collecting the processing progress of the congestion network data, wherein the network index configuration specifically comprises measurement delay, measurement packet loss rate and measurement bandwidth index;

Judging whether the processing progress is greater than a preset progress or not;

if not, acquiring the active response state of the network user, intelligently scheduling the network user according to the active response state, screening active users and standby users from the network user, calculating the network tasks of the active users by adopting preset streaming processing priority, and regenerating network monitoring data of each network task.

Further, the step of asynchronously processing the congestion network data and processing the network tasks of each processing stage in parallel further includes:

resetting an output path of the congestion network data based on a preset message queue as a communication bridge of the congestion network data, and sending output content of the congestion network data to the message queue;

judging whether the output efficiency of the output content is lower than a preset efficiency or not;

if so, dividing the congestion network data into a plurality of network subtasks, respectively constructing corresponding execution threads according to a preset synchronization mechanism to independently process the network subtasks, and adopting synchronous execution or asynchronous execution according to the task properties of the network subtasks, wherein the task properties comprise blocking tasks, intensive tasks and interactive tasks.

Further, the step of calculating the network task of the active user by using a preset streaming priority includes:

detecting a network data stream of the active user, marking task request information of the active user from the network data stream, and taking the task request information as a single liveness label;

judging whether the number of the liveness labels is larger than a preset label number threshold value or not;

if yes, dynamically adjusting the network task of the active user based on the liveness tag, dividing preset streaming data into a plurality of continuous time windows, and setting network data streams capable of flowing in a fixed period for each continuous time window.

Further, in the step of determining whether the congestion network data exceeds a preset processing period, the method further includes:

identifying the congestion times of the congestion network data in a preset period;

judging that the congestion times reach a preset congestion upper limit;

if yes, a preset real-time load sensing mechanism is introduced, a cache strategy is established according to load content corresponding to the congestion network data, and the cache strategy is divided into areas based on different cache contents, wherein the cache strategy specifically comprises data cache, page cache and result cache.

Further, in the step of determining whether the processing progress is greater than a preset progress, the method further includes:

collecting log information of the congestion network data, and identifying other users which are not matched with the network task, wherein the log information specifically comprises network task indexes, network task time stamps and network task corresponding users;

judging whether the proportion of the other users exceeds a preset proportion or not;

if yes, providing guidance content related to the network task for the preset equipment of the other users through the intelligent watch, and receiving output content of the user on the network task, wherein the guidance content specifically comprises common answer content preset for the network task, and the output content specifically comprises task query and cooperation information.

Further, the step of detecting the flow information of the network monitoring data by the mobile device based on the pre-connection of the smart watch and presenting the congestion network data to be processed corresponding to the flow information from the smart watch includes:

identifying the bearing capacity of the current network monitoring data;

judging whether the bearing capacity exceeds preset bearable content or not;

If yes, defining the network data with capacity overflow as the congestion network data, pre-downloading the congestion network data by using a preset buffer plate, and displaying the pre-downloaded congestion network data in a display screen preset by the intelligent watch.

Further, before the step of detecting the flow information of the network monitoring data by the mobile device based on the pre-connection of the smart watch, the method further comprises:

identifying a network task type corresponding to the network monitoring data;

judging whether the network task type can be matched with the pre-recorded executable network task;

if not, the network monitoring data is moved out of a preset queue to be processed, the executable network task is output to an abnormal user who has moved out of the network task, and the network monitoring data is collected again to the abnormal user.

The invention also provides a control system based on intelligent equipment monitoring network data, which comprises:

the detection module is used for detecting flow information of network monitoring data based on mobile equipment pre-connected with the intelligent watch, and displaying congestion network data to be processed corresponding to the flow information from the intelligent watch;

The judging module is used for judging whether the congestion network data exceeds a preset processing period;

the execution module is used for carrying out asynchronous processing on the congestion network data, processing network tasks of each processing stage in parallel, monitoring the transmission efficiency of preset network index configuration in real time, identifying the behavior mode of a network user on the network tasks, executing self-adaptive data synchronization on the network tasks according to the transmission efficiency and the behavior mode, and collecting the processing progress of the congestion network data, wherein the network index configuration specifically comprises measurement delay, measurement packet loss rate and measurement bandwidth index;

the second judging module is used for judging whether the processing progress is greater than a preset progress or not;

and the second execution module is used for acquiring the active response state of the network user if not, intelligently scheduling the network user according to the active response state, screening active users and standby users from the network user, calculating the network tasks of the active users by adopting preset streaming processing priority, and regenerating network monitoring data of each network task.

Further, the execution module further includes:

A resetting unit, configured to reset an output path of the congestion network data based on a preset message queue as a communication bridge of the congestion network data, and send output content of the congestion network data to the message queue;

a judging unit for judging whether the output efficiency of the output content is lower than a preset efficiency;

and the execution unit is used for dividing the congestion network data into a plurality of network subtasks, respectively constructing corresponding execution threads according to a preset synchronization mechanism to independently process the network subtasks, and synchronously executing or asynchronously executing according to the task properties of the network subtasks, wherein the task properties comprise a blocking task, a dense task and an interactive task.

Further, the second execution module further includes:

the detection unit is used for detecting a network data stream of the active user, marking task request information of the active user from the network data stream and taking the task request information as a single liveness label;

the second judging unit is used for judging whether the number of the liveness labels is larger than a preset label number threshold value or not;

And the second execution unit is used for dynamically adjusting the network tasks of the active users based on the liveness labels if the network tasks are yes, dividing preset streaming data into a plurality of continuous time windows, and setting network data streams capable of flowing in a fixed period for each continuous time window.

The invention provides a control method and a control system for monitoring network data based on intelligent equipment, which have the following beneficial effects:

the invention efficiently utilizes system resources by adopting asynchronous processing and parallel processing of network tasks of each processing stage aiming at the congestion network data, improves the data processing speed and throughput, simultaneously combines the self-adaptive data synchronization according to the preset network index configuration monitored in real time and the user behavior mode, optimizes the transmission efficiency of the network tasks, is beneficial to improving the efficiency and accuracy of the data synchronization, calculates the network tasks of active users by adopting the preset stream processing priority, responds and processes the requests of the active users more rapidly under the condition of limited resources, and improves the instantaneity and the user satisfaction of the data processing.

Drawings

FIG. 1 is a flow chart of one embodiment of a control method based on intelligent device monitoring network data according to the present invention;

Fig. 2 is a block diagram of a control system according to an embodiment of the present invention for monitoring network data based on intelligent devices.

Detailed Description

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present invention, as the achievement, functional features, and advantages of the present invention are further described with reference to the embodiments, with reference to the accompanying drawings.

The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Referring to fig. 1, a control method based on intelligent device monitoring network data according to an embodiment of the present invention includes:

s1: detecting flow information of network monitoring data based on mobile equipment pre-connected with an intelligent watch, and displaying congestion network data to be processed corresponding to the flow information from the intelligent watch;

s2: judging whether the congestion network data exceeds a preset processing period;

S3: if yes, carrying out asynchronous processing on the congestion network data, processing network tasks of each processing stage in parallel, monitoring the transmission efficiency of preset network index configuration in real time, identifying the behavior mode of a network user on the network tasks, executing self-adaptive data synchronization on the network tasks according to the transmission efficiency and the behavior mode, and collecting the processing progress of the congestion network data, wherein the network index configuration specifically comprises measurement delay, measurement packet loss rate and measurement bandwidth index;

s4: judging whether the processing progress is greater than a preset progress or not;

s5: if not, acquiring the active response state of the network user, intelligently scheduling the network user according to the active response state, screening active users and standby users from the network user, calculating the network tasks of the active users by adopting preset streaming processing priority, and regenerating network monitoring data of each network task.

In this embodiment, the system detects traffic information of network monitoring data based on a mobile device connected in advance to the smart watch, and then displays to-be-processed network congestion data corresponding to the traffic information on a display screen of the smart watch, and then the system judges whether the congestion network data exceeds a preset to-be-processed period to execute corresponding steps; for example, when the system determines that the congestion network data do not exceed a preset waiting period, the system considers that the current processing capacity is enough to process the congestion network data and complete tasks within a preset period, the system continues to process the congestion network data according to a normal processing flow, network monitoring and task processing operations are ensured to be executed according to a preset mode, and real-time monitoring is continuously performed, so that the processing state of the network data still accords with expectations in the processing period, and users are encouraged to actively participate in network data processing, and the users are encouraged to actively cooperate with the processing process of the network tasks to provide real-time feedback and support; for example, when the system determines that the congestion network data exceeds a preset waiting period, the system actively carries out asynchronous processing on the congestion network data, processes different network tasks of each processing stage in parallel, and actively carries out asynchronous processing and parallel processing on the congestion network data to network tasks of different stages, so that the real-time performance of the system can be remarkably improved, the network tasks can respond to the user demands more rapidly, then the transmission efficiency of preset network index configuration is monitored in real time, the data synchronization strategy is dynamically adjusted according to the network conditions, the network efficiency is optimized, the data transmission is ensured to be more efficient and stable, the user demands are better understood by identifying the behavior mode of the network users set on the network tasks, the adaptive data synchronization is carried out on each network task according to the change of the transmission efficiency and different behavior modes, the processing of the network tasks is adjusted according to the user behavior mode by adopting the adaptive data synchronization, the user experience and satisfaction degree are improved, the data processing strategy is adjusted according to the actual demands more accurately, the quality and accuracy of data processing are improved, the progress of the congestion network data processing is acquired again, and whether the congestion network data processing measures are effective or not is confirmed by the system; then the system judges whether the processing progress of the congestion network data is larger than the preset processing progress or not so as to execute the corresponding steps; for example, when the system determines that the processing progress of the congestion network data is greater than a preset processing progress, the system considers that the processing speed is higher after adopting new processing measures, the task is more rapid than expected, the system establishes a real-time collaborative decision mechanism, so that different nodes in the system can collaboratively decide the processing priority and allocation of the task in real time to adapt to the change of the network environment, and meanwhile, task processing is distributed to a plurality of processing nodes to construct a distributed processing system, thereby being beneficial to improving the transverse expansibility of the system and meeting the requirement of larger-scale task processing; for example, when the system determines that the processing progress of the congestion network data is not greater than the preset processing progress, the system acquires an active response state of the network user when the network user performs cooperation with the network task, intelligently schedules the network user according to the active response state, screens out the active cooperation user and the inactive standby user from the network users, calculates the network task priority of the active cooperation user by adopting the preset streaming processing, and can improve the priority of the network task of the active cooperation user by intelligently scheduling according to the active response state, thereby being beneficial to more quickly responding to the active participation and cooperation of the system, simultaneously, preferentially calculating the network task of the active cooperation user by streaming processing, realizing optimal allocation of resources in the network congestion state, effectively utilizing limited computing resources by the system, improving the efficiency of the whole system, and being capable of helping the system to uniformly allocate the network tasks among the active users, preventing the task backlog of certain users, ensuring the whole network load balance, and regenerating the network monitoring data of each network task after the processing measures of the congestion network data.

It should be noted that, a specific example of performing adaptive data synchronization on a network task is as follows:

assume, for example, an image processing system, demonstrates how adaptive data synchronization is performed in processing image data uploaded by a network user,

firstly, a system needs to monitor network conditions in real time, including bandwidth, delay and packet loss rate, because image data uploaded by network users have different processing requirements, some tasks may need to be processed with higher real-time performance, other tasks have lower requirements on real-time performance, secondly, the activity of the users needs to be analyzed to know which network users frequently upload images and need to be processed quickly, and which network users upload less and have lower requirements on real-time performance; the adaptive synchronization strategy can be determined: for tasks requiring higher real-time performance, the system adopts smaller synchronization intervals, such as synchronization once per minute; for the processing task of large-scale image data, a system adopts a block synchronization strategy to synchronize data blocks as required so as to reduce network transmission load; for users with fewer uploaded images, the system adopts a delay synchronization strategy and synchronizes once per hour so as to save system resources; for synchronization of image data, the system selects an incremental synchronization algorithm, only transmits a changed part to reduce data transmission quantity, monitors network conditions and user liveness in real time, dynamically adjusts a synchronization strategy, increases a synchronization interval to avoid network congestion, continuously monitors a synchronization effect including synchronization time, transmission efficiency and user response time when the network is abnormal, temporarily stores unsynchronized image data by adopting a buffer mechanism, and synchronizes after the network is recovered to be normal.

In this embodiment, the step S3 of asynchronously processing the congestion network data and processing the network tasks of each processing stage in parallel further includes:

s31: resetting an output path of the congestion network data based on a preset message queue as a communication bridge of the congestion network data, and sending output content of the congestion network data to the message queue;

s32: judging whether the output efficiency of the output content is lower than a preset efficiency or not;

s33: if so, dividing the congestion network data into a plurality of network subtasks, respectively constructing corresponding execution threads according to a preset synchronization mechanism to independently process the network subtasks, and adopting synchronous execution or asynchronous execution according to the task properties of the network subtasks, wherein the task properties comprise blocking tasks, intensive tasks and interactive tasks.

In this embodiment, after resetting the output path of the congestion network data based on the preset message queue as the communication bridge of the congestion network data, the system makes the congestion network data send the output data content thereof to the message queue instead of directly transmitting the output data content to the next task, and then the system judges whether the output efficiency of the output data content is still lower than the preset efficiency, so as to execute the corresponding steps; for example, when the system determines that the output efficiency of the output data content is not lower than the preset efficiency, the system considers that the introduction of the message queue successfully improves the data transmission efficiency, so that the system can better process the congestion network data, and the system optimizes the configuration parameters of the message queue according to the actual use condition, including the queue capacity, the number of consumers and the message transmission mode, thereby being beneficial to further improving the performance of the message queue, optimizing the message processing logic for processing the congestion network data, reducing the processing delay to the greatest extent, and implementing the backup and recovery strategy aiming at the reliability of the message queue, so as to ensure that the system can quickly recover and keep high-efficiency operation even if abnormal conditions occur; for example, when the system determines that the output efficiency of the output data content is lower than the preset efficiency, the system divides the congestion network data into a plurality of network subtasks, respectively constructs corresponding execution threads according to a preset synchronization mechanism to independently process the network subtasks, adopts synchronous execution or asynchronous execution according to different task properties of the network subtasks, and can adapt to the characteristics of various tasks more flexibly by selecting synchronous execution or asynchronous execution according to different task properties of the network subtasks, the synchronous execution is suitable for tasks needing sequential processing, and the asynchronous execution is suitable for tasks which can be executed in parallel, thereby improving the flexibility and adaptability of the system; for example, the obstructive task usually needs to wait for the completion or response of the external resource, which may cause the blocking of the whole system, so that the obstructive task is distributed to independent execution threads, so that the normal execution of other tasks is not affected when the obstructive task waits for the external resource, and an asynchronous execution strategy can be adopted, so that the other tasks can be continuously executed in the waiting process; for example, the intensive task consumes a large amount of computing resources, which may cause overload of the system, so that the intensive task is divided into a plurality of independent subtasks and executed in parallel through multithreading, so as to fully utilize the multi-core processor and improve the computing performance of the whole system; for example, the response time of the interactive task to the user is high, and the real-time performance of the system needs to be maintained, so that a synchronous execution strategy can be selected to ensure the sequence and real-time performance of the task, and an independent execution thread is adopted, so that the interactive task can be executed independently of other tasks and respond to the request of the user in time.

In this embodiment, the step S5 of calculating the network task of the active user with the preset streaming priority includes:

s51: detecting a network data stream of the active user, marking task request information of the active user from the network data stream, and taking the task request information as a single liveness label;

s52: judging whether the number of the liveness labels is larger than a preset label number threshold value or not;

s53: if yes, dynamically adjusting the network task of the active user based on the liveness tag, dividing preset streaming data into a plurality of continuous time windows, and setting network data streams capable of flowing in a fixed period for each continuous time window.

In this embodiment, the system marks the network task request information of the active user from the network data stream by detecting the network data stream of the active user, uses the task request information as a single liveness tag, and then the system judges whether the number of the liveness tags is greater than a preset tag number threshold value so as to execute the corresponding steps; for example, when the system determines that the number of activity tabs is not greater than a preset tab count threshold, the system may consider that the active user has not reached the system's expected activity level, the system may check whether the preset tab count threshold is reasonable, possibly the threshold is set too high, resulting in fewer users being considered active users, re-evaluating the tab count threshold to ensure that it matches the actual activity level; for example, when the system determines that the number of the activity labels is greater than a preset label number threshold, the system determines that the active content of the active user is enough to have the processing authority of the priority network task at this time, dynamically adjusts the network task of the active user based on the activity labels, divides the preset streaming data into a plurality of continuous time windows, sets the network data flow which can flow in a fixed period for each continuous time window, and is beneficial to more effectively managing and processing the data flow, the division of the time windows can enable the system to better adapt to the flow change in different time periods, the elasticity and stability of the system are improved, and sets the network data flow which can flow in the fixed period, so that the network resources are reasonably distributed, network congestion is prevented, the system load can be balanced, and the stability of network data transmission is improved.

In this embodiment, in step S2 of determining whether the congestion network data exceeds a preset processing period, the method further includes:

s21: identifying the congestion times of the congestion network data in a preset period;

s22: judging that the congestion times reach a preset congestion upper limit;

s23: if yes, a preset real-time load sensing mechanism is introduced, a cache strategy is established according to load content corresponding to the congestion network data, and the cache strategy is divided into areas based on different cache contents, wherein the cache strategy specifically comprises data cache, page cache and result cache.

In this embodiment, the system identifies the congestion times of the congestion network data in a preset period, and then the system judges whether the congestion times reach a preset congestion upper limit or not, so as to execute corresponding steps; for example, when the system determines that the congestion network data does not reach the congestion upper limit, the system continuously monitors the network data flow and the performance index in real time so as to discover congestion trend and abnormal situation in time, and adjusts the flow control strategy according to the data monitored in real time, wherein the flow control strategy comprises adjusting the priority and the speed limit strategy of the congestion network data, so that the flow control strategy can be better adapted to the actual situation of the network, and the buffer is used for relieving the burden of the server, especially for repeated request or static data, the reasonable buffer strategy can reduce the request to the network and reduce the congestion risk; for example, when the system judges that the congestion network data reaches the upper congestion limit, the system introduces a preset real-time load sensing mechanism, establishes a cache policy according to load content corresponding to the congestion network data, and performs area division on the cache policy based on different cache contents, wherein the introduction of the cache policy can reduce real-time requests for the congestion network data, thereby reducing the load on a server, quickly acquiring cached data, pages or results, being beneficial to improving the access speed of users, and simultaneously performing area division of the cache policy according to different cache contents, so that the system can be more accurately suitable for access modes of different types of data; if the caching strategies are respectively data caching, frequent access to the database is reduced, the load of the database is reduced, the performance and response speed of the database are improved, and the pressure of a database server is reduced; if the caching strategy is page caching, the page caching reduces the request to the server, because the cached pages can be directly used locally without requesting the page content from the server every time, which is helpful for reducing the pressure of the network server; if the caching strategy is result caching, the result caching reduces the calculation request to the server, because the cached result can be directly returned to the user without recalculation, the calculation load of the network server is reduced, and the performance of the system is improved.

In this embodiment, in step S4 of determining whether the processing progress is greater than a preset progress, the method further includes:

s41: collecting log information of the congestion network data, and identifying other users which are not matched with the network task, wherein the log information specifically comprises network task indexes, network task time stamps and network task corresponding users;

s42: judging whether the proportion of the other users exceeds a preset proportion or not;

s43: if yes, providing guidance content related to the network task for the preset equipment of the other users through the intelligent watch, and receiving output content of the user on the network task, wherein the guidance content specifically comprises common answer content preset for the network task, and the output content specifically comprises task query and cooperation information.

In this embodiment, the system identifies other users who do not actively cooperate with the network task from the log information by collecting the log information of the congested network data, and then the system judges whether the proportion of the other users exceeds a preset proportion so as to execute the corresponding steps; for example, when the system determines that the proportion of other users does not exceed the preset proportion, the system continuously monitors the performance and load of the system, ensures that the running condition of the system is good, and knows indexes of the resource utilization rate and response time of the server in real time so as to discover abnormal conditions in time, and meanwhile, the current lighter load is utilized to improve the user experience, including ways of adding functions, optimizing interfaces and the like, so that the satisfaction degree of the user to the system is improved; for example, when the system determines that the proportion of other users exceeds the preset proportion, the system provides relevant guidance content about the network task for other user preset devices through the intelligent watch, receives output content of the user on the network task through the intelligent watch, extracts query content of the other users on the task and information for completing the task in cooperation from the output content, and can reduce misunderstanding and errors of the user in the task execution process, improve the completion quality and accuracy of the network task and reduce the possibility of network server congestion.

In this embodiment, the step S1 of detecting, based on the mobile device pre-connected to the smart watch, traffic information of network monitoring data, and presenting, from the smart watch, congestion network data to be processed corresponding to the traffic information includes:

s11: identifying the bearing capacity of the current network monitoring data;

s12: judging whether the bearing capacity exceeds preset bearable content or not;

s13: if yes, defining the network data with capacity overflow as the congestion network data, pre-downloading the congestion network data by using a preset buffer plate, and displaying the pre-downloaded congestion network data in a display screen preset by the intelligent watch.

In this embodiment, the system identifies the current network monitoring data bearing capacities, and then the system judges whether the bearing capacities exceed preset bearable contents or not, so as to execute corresponding steps; for example, when the system determines that the current bearing capacity does not exceed the preset bearable content, the system continuously monitors the load condition of the system, including indexes such as the resource utilization rate of the server, response time and the like, so as to ensure that the load level is within a controllable range, maintain and optimize operation, improve the stability of the system and ensure that the system can maintain good stability in the face of load fluctuation; for example, when the system determines that the bearing capacity exceeds the preset bearable content, the system defines the network data with the current bearable capacity overflow as the congestion network data, the preset buffer plate is used for pre-downloading the overflowed congestion network data, the congestion network data is displayed on a display screen through a smart watch after the downloading is finished, the availability of the data can be improved through pre-downloading the congestion network data, the data can be directly obtained from a local buffer when needed, the influence of network congestion is avoided, meanwhile, the dependence on the network is reduced, the system can better cope with network instability and fluctuation, and the overall stability of the system is improved.

In this embodiment, before step S1 of detecting flow information of network monitoring data based on the mobile device pre-connected to the smart watch, the method further includes:

s101: identifying a network task type corresponding to the network monitoring data;

s102: judging whether the network task type can be matched with the pre-recorded executable network task;

s103: if not, the network monitoring data is moved out of a preset queue to be processed, the executable network task is output to an abnormal user who has moved out of the network task, and the network monitoring data is collected again to the abnormal user.

In this embodiment, the system identifies network task types corresponding to the network monitoring data, and then the system determines whether the network task types can be matched with executable network tasks recorded in advance so as to execute corresponding steps; for example, when the system determines that the network task types can be matched with the executable network tasks recorded in advance, the system can identify the network tasks submitted by the user, the task types have corresponding matching items in an executable task list of the system, after the system confirms that the tasks are successfully matched, the system starts to execute the network tasks according to predefined execution logic and steps, and meanwhile, in the task execution process, the system can monitor the execution state and progress of the tasks in real time, so that possible problems or abnormal situations can be found in time; for example, when the system determines that the network task types cannot be matched with the executable network tasks recorded in advance, the system moves the network monitoring data corresponding to the network tasks out of a preset queue to be processed, outputs the executable network tasks recorded in advance to an abnormal user who has moved out of the network tasks, and collects new network monitoring data again to the abnormal user so as to identify the executable network tasks which can be matched, and the network monitoring data is moved out of the queue to be processed in time and the executable tasks are output, so that backlog of the abnormal tasks in the queue can be reduced, and efficient operation of the system can be maintained.

Referring to fig. 2, a control system based on intelligent device monitoring network data according to an embodiment of the present invention includes:

the detection module 10 is configured to detect traffic information of network monitoring data based on a mobile device pre-connected to a smart watch, and present congestion network data to be processed corresponding to the traffic information from the smart watch;

a judging module 20, configured to judge whether the congestion network data exceeds a preset processing period;

the execution module 30 is configured to asynchronously process the congestion network data, process network tasks of each processing stage in parallel, monitor transmission efficiency of preset network index configuration in real time, identify a behavior mode of a network user on the network task, execute adaptive data synchronization on the network task according to the transmission efficiency and the behavior mode, and acquire a processing progress of the congestion network data, wherein the network index configuration specifically includes measurement delay, measurement packet loss rate and measurement bandwidth index;

a second judging module 40, configured to judge whether the processing progress is greater than a preset progress;

and the second execution module 50 is configured to obtain an active response state of the network user if not, intelligently schedule the network user according to the active response state, screen active users and standby users from the network user, and calculate network tasks of the active users by using preset streaming processing priority to regenerate network monitoring data of each network task.

In this embodiment, the detection module 10 detects traffic information of network monitoring data based on a mobile device connected to the smart watch in advance, and then displays to-be-processed network congestion data corresponding to the traffic information on a display screen of the smart watch, and then the judgment module 20 judges whether the congestion network data exceeds a preset to-be-processed period to execute corresponding steps; for example, when the system determines that the congestion network data do not exceed a preset waiting period, the system considers that the current processing capacity is enough to process the congestion network data and complete tasks within a preset period, the system continues to process the congestion network data according to a normal processing flow, network monitoring and task processing operations are ensured to be executed according to a preset mode, and real-time monitoring is continuously performed, so that the processing state of the network data still accords with expectations in the processing period, and users are encouraged to actively participate in network data processing, and the users are encouraged to actively cooperate with the processing process of the network tasks to provide real-time feedback and support; for example, when the system determines that the congestion network data exceeds a preset waiting period, the execution module 30 actively processes the congestion network data asynchronously, processes different network tasks of each processing stage in parallel, and actively processes the congestion network data asynchronously and processes the network tasks of different stages in parallel, so that the real-time performance of the system can be remarkably improved, the network tasks can respond to the user demand more rapidly, then the transmission efficiency of the preset network index configuration is monitored in real time, the data synchronization strategy is dynamically adjusted according to the network condition, so that the network efficiency is optimized, the data transmission is ensured to be more efficient and stable, the user demand is better understood by identifying the behavior mode of the network user set for the network task, the adaptive data synchronization is performed for each network task according to the change of the transmission efficiency and different behavior modes, the processing of the network task is adjusted according to the user behavior mode by adopting the adaptive data synchronization, the user experience and satisfaction degree are improved, the data processing strategy is adjusted according to the actual demand more accurately, the quality and accuracy of the data processing are improved, and the processing of the congestion network data is acquired again, so that whether the system is effective in processing the congestion network data is ensured; then the second judging module 40 judges whether the processing progress of the congestion network data is greater than a preset processing progress or not so as to execute the corresponding steps; for example, when the system determines that the processing progress of the congestion network data is greater than a preset processing progress, the system considers that the processing speed is higher after adopting new processing measures, the task is more rapid than expected, the system establishes a real-time collaborative decision mechanism, so that different nodes in the system can collaboratively decide the processing priority and allocation of the task in real time to adapt to the change of the network environment, and meanwhile, task processing is distributed to a plurality of processing nodes to construct a distributed processing system, thereby being beneficial to improving the transverse expansibility of the system and meeting the requirement of larger-scale task processing; for example, when the system determines that the processing progress of the congestion network data is not greater than the preset processing progress, the second execution module 50 obtains an active response state of the network user when the network user performs cooperation with the network task, intelligently schedules the network user according to the active response state, screens out the active cooperation user and the inactive standby user from each network user, calculates the priority of the network task of the active cooperation user by adopting the preset streaming processing, and by intelligently scheduling according to the active response state, the system can improve the priority of the network task of the active cooperation user, thereby being beneficial to more quickly responding to the active participation and cooperation of the system, and simultaneously, the streaming processing performs priority calculation on the network task of the active cooperation user, so that the optimal allocation of resources is realized in the network congestion state, the system can more effectively utilize limited calculation resources, improve the efficiency of the whole system, and the intelligent scheduling can help the system to uniformly allocate the network tasks among the active users, prevent the task from being pressed, ensure the balance of the whole network load, and regenerate the network monitoring data of each network task after the processing measures of the congestion network data.

In this embodiment, the execution module further includes:

In this embodiment, the second execution module further includes:

In this embodiment, the judging module further includes:

the identifying unit is used for identifying the congestion times of the congestion network data in a preset period;

the third judging unit is used for judging that the congestion times reach a preset congestion upper limit;

and the third execution unit is used for introducing a preset real-time load sensing mechanism if the network data is in the congestion state, establishing a cache policy according to load content corresponding to the congestion network data, and dividing the cache policy into areas based on different cache contents, wherein the cache policy specifically comprises data cache, page cache and result cache.

In this embodiment, the second judging module further includes:

the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring log information of the congestion network data, and identifying other users which are not matched with the network task from the log information, wherein the log information specifically comprises a network task index, a network task time stamp and a network task corresponding user;

a fourth judging unit, configured to judge whether the proportion of the other users exceeds a preset proportion;

and the fourth execution unit is used for providing guidance content related to the network task for the preset equipment of other users through the intelligent watch if the network task is executed, and receiving output content of the user on the network task, wherein the guidance content specifically comprises common answer content preset for the network task, and the output content specifically comprises task questions and cooperation information.

In this embodiment, the detection module further includes:

the second identification unit is used for identifying the bearing capacity of the current network monitoring data;

a fifth judging unit for judging whether the bearing capacity exceeds a preset bearable content;

and the fifth execution unit is used for defining the network data with the capacity overflow as the congestion network data if the network data with the capacity overflow is the congestion network data, pre-downloading the congestion network data by applying a preset buffer plate, and displaying the pre-downloaded congestion network data in a preset display screen of the intelligent watch.

In this embodiment, further comprising:

the identification module is used for identifying the network task type corresponding to the network monitoring data;

the third judging module is used for judging whether the network task type can be matched with the pre-recorded executable network task;

and the third execution module is used for moving the network monitoring data out of a preset queue to be processed if not, outputting the executable network task to an abnormal user who has moved out of the network task, and collecting the network monitoring data again to the abnormal user.

Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims

1. The control method for monitoring network data based on intelligent equipment is characterized by comprising the following steps:

judging whether the congestion network data exceeds a preset processing period;

if not, acquiring an active response state of the network user, intelligently scheduling the network user according to the active response state, screening active users and standby users from the network user, calculating network tasks of the active users by adopting preset streaming processing priority, and regenerating network monitoring data of each network task;

the step of calculating the network task of the active user by adopting the preset streaming processing priority includes:

2. The method for controlling network data based on intelligent device monitoring according to claim 1, wherein the step of asynchronously processing the congested network data and processing network tasks of each processing stage in parallel further comprises:

3. The method for controlling network data based on intelligent device monitoring according to claim 1, wherein the step of determining whether the congested network data exceeds a preset processing period further comprises:

judging that the congestion times reach a preset congestion upper limit;

4. The method for controlling network data based on intelligent device monitoring according to claim 1, wherein the step of determining whether the processing progress is greater than a preset progress further comprises:

5. The method for controlling network data based on intelligent device monitoring according to claim 1, wherein the step of detecting traffic information of network monitoring data based on the mobile device pre-connected to the intelligent watch and presenting the traffic information corresponding to congestion network data to be processed from the intelligent watch comprises:

Identifying the bearing capacity of the current network monitoring data;

judging whether the bearing capacity exceeds preset bearable content or not;

6. The method for controlling network data based on intelligent device monitoring according to claim 1, wherein before the step of detecting traffic information of the network monitoring data by the mobile device based on intelligent watch pre-connection, the method further comprises:

identifying a network task type corresponding to the network monitoring data;

7. A control system for monitoring network data based on intelligent devices, comprising:

the second execution module is used for acquiring the active response state of the network user if not, intelligently scheduling the network user according to the active response state, screening active users and standby users from the network user, calculating the network tasks of the active users by adopting preset streaming processing priority, and regenerating network monitoring data of each network task;

Wherein the second execution module further comprises:

8. The smart device monitoring network data based control system of claim 7, wherein the execution module further comprises: