A Feature Phone Data Optimization Management System and Method Based on Cloud Applications

Abstract

The present invention discloses a data optimization management system and method for a feature machine based on cloud application, and relates to the technical field of data optimization. The present invention utilizes a 4G network to connect to a cloud server in a feature machine, extracts historical tasks of the feature machine, analyzes and extracts features from historical task data, and generates a task fingerprint library; the feature machine initiates a task request in real time, calculates the similarity of real-time tasks using task fingerprints, and determines the type of real-time tasks; performs conflict analysis on the determined real-time tasks, merges the determined tasks of the same type, and forms a task matrix; the cloud server processes the task matrix once by running an LSTM model, and outputs the processing result; extracts the common part and the difference part of the result from the output result, compresses and transmits the two results, and the feature machine reorganizes the task results locally to obtain the final result of the real-time task.

Description

Technical Field

[0001] The present invention relates to the technical field of data optimization, and particularly to a data optimization management system and method for feature phones based on cloud applications. Background Art

[0002] With the rapid development of communication technologies, feature phones still occupy a certain market share in people's lives. Although feature phones lag behind smartphones in terms of hardware performance and function richness, in some specific scenarios and user groups, their characteristics such as simplicity, ease of use, and long battery life make them irreplaceable. However, the limited local storage and processing capabilities of feature phones pose many challenges to data management. Traditional feature phones can often only store a small amount of data locally, and the efficiency of processing complex data tasks is low, making it difficult to meet users' needs for in-depth data analysis and efficient management. Therefore, in the face of ever-changing application requirements, it is necessary to utilize the powerful computing and storage capabilities of the cloud to expand their functions and application scopes. This approach not only does not require a substantial upgrade of the hardware devices of 4G feature phones but also enables flexible function expansion and dynamic allocation to meet the changing needs of users.

[0003] However, when feature phones use cloud servers to process complex tasks, in many cases where there are a large number of online feature phones, the task requests sent to the cloud server are numerous. For similar tasks, the cloud can only process them sequentially, which greatly consumes the computing resources of the cloud server. Therefore, it is crucial to reduce the number of processing times, uniformly process similar tasks, and ensure the integrity of the specific differential results of different feature phones. Summary of the Invention

[0004] The purpose of the present invention is to provide a data optimization management system and method for feature phones based on cloud applications to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A data optimization management method for feature phones based on cloud applications, the method comprising the following steps:

[0007] S100. Use a 4G network in the feature phone to connect to the cloud server, extract the historical tasks of the feature phone, analyze and extract features from the historical task data, construct a task fingerprint, and generate a task fingerprint library;

[0008] Further, the specific steps for constructing the task fingerprint are:

[0009] S101. Connect to the cloud server using the 4G network in the feature phone, record the task log at the feature phone end, compress and transmit the task data in the task log to the cloud server. The cloud server preprocesses the task data, specifically: removing the missing values in the task data and filtering the task data using a filter; finally, standardizing the task data, and the formula is:

[0010] ;

[0011] In the formula, s’ represents the standardized task data, s represents the task data received by the cloud server, sp represents the average value of the task data, and sz represents the standard deviation of the task data;

[0012] S102. Extract the timestamp of each task data, calculate the duration of each task, and the formula is: , in the formula, △t represents the task duration, t end represents the task end time, t start represents the task start time; take the duration of each task as the time feature;

[0013] Calculate the average value, variance, and peak value of each task data in the time domain as the time domain features;

[0014] In the frequency domain, use the Fourier transform to convert all task data into the frequency domain signal s[k], and calculate the amplitude spectrum of the frequency domain signal in each task data. The formula is:

[0015] ;

[0016] In the formula, |s[k]| represents the amplitude spectrum of the frequency domain signal, Re(s[k]) represents the real part of the frequency domain signal, and Im(s[k]) represents the imaginary part of the frequency domain signal;

[0017] Calculate the frequency domain features of each task data, and the formula is: , in the formula, Pt represents the frequency domain feature of each task data, argmax represents extracting the independent variable that makes the function obtain the maximum value, and k belongs to 0 to N - 1;

[0018] S103. Use the mutual information screening method to screen all the features in the time domain, frequency domain, and time to obtain the key features of each task, and construct the feature vector V from all the key features; generate the task fingerprint using the feature vector of each task, and the formula is:

[0019] ;

[0020] In the formula, H represents the task fingerprint, MinHsah represents the MinHash algorithm, and LSH represents the Locality-Sensitive Hashing algorithm; generate a 128-bit MinHash fingerprint H, generate the task fingerprint H for each task, and obtain the task fingerprint library.

[0021] By connecting to the cloud server through a 4G network, the powerful computing and storage resources of the cloud can be fully utilized, without the need to equip the feature phone locally with a large-capacity storage and a high-performance processor to process and store a large amount of historical task data, reducing the hardware cost and energy consumption of the feature phone. Storing the historical task data in the cloud server facilitates centralized management and maintenance, and functions such as regular data backup and disaster recovery can be realized, improving the security and reliability of the data and avoiding data loss due to reasons such as the loss or damage of the feature phone.

[0022] Constructing task fingerprints can generate unique identifiers for each task, helping to quickly identify and distinguish different tasks. The establishment of the task fingerprint library facilitates the classification, retrieval, and comparison of tasks. For example, similar tasks can be quickly found through task fingerprints, providing a reference and decision-making basis for users, and can also be used to detect abnormal tasks and timely discover potential problems or risks.

[0023] S200. The feature phone initiates a task request in real time. After receiving the task request, the cloud server determines the number of feature phones, calculates the similarity of the real-time task using the task fingerprint, and determines the real-time task type; updates the task fingerprint library using the new task type;

[0024] Further, the specific steps for determining whether it is the same type of task are as follows:

[0025] S201. All feature phones regularly send heartbeat packets to the cloud at time t1. The heartbeat packet contains the feature phone ID. The cloud server maintains an online device list based on the regular heartbeat packets. When a device does not send a heartbeat packet beyond the regular time t1, the cloud server removes the corresponding feature phone ID from the online device list and obtains the number of feature phones based on the real-time online device list;

[0026] S202. The online feature phone sends a task request data packet to the cloud server. The data packet format is Ta_request={ID, t_request, F i}, where Ta_request represents the task request data packet, t_request represents the task request time, and F i represents the task key feature; normalize the task key feature to F i '; The cloud server receives the real-time task requests sent by all feature phones;

[0027] S203. The cloud server compares the received real-time task request with each historical task fingerprint, calculates the similarity, and the formula is:

[0028] ;

[0029] In the formula, Sim k represents the similarity between the real-time task and the k-th task fingerprint, and C k represents the k-th task fingerprint; the similarity threshold Sy is set artificially, and the similarity is judged using the similarity threshold. When Sim k ≥Sy, it is judged as the same type of task; otherwise, the real-time task is marked as a new task type; the real-time task types of all online functional machines are judged in turn.

[0030] By judging the number of functional machines, the cloud server can reasonably allocate tasks according to the system resource load situation, avoid the situation that some functional machines have too many tasks while some functional machines are idle, and improve the overall system operation efficiency and resource utilization rate; after accurately judging the real-time task type, the cloud server can call the corresponding processing module or algorithm to process the task, improving the accuracy and efficiency of task processing. For example, for different types of tasks, different priority settings, data processing methods or resource allocation schemes can be adopted to better meet user needs and system requirements.

[0031] Updating the task fingerprint library with the new task type can enable the system to continuously learn and adapt to new task patterns and characteristics, enrich the content of the task fingerprint library, and improve the system's recognition and processing capabilities for various tasks. Over time, the task fingerprint library will become more and more perfect, and the system's judgment and processing of tasks will become more and more accurate and efficient.

[0032] S300. Conduct conflict analysis on the judged real-time tasks, and merge the judged same type of tasks to form a task matrix;

[0033] Furthermore, the specific steps for conducting conflict analysis on the judged real-time tasks are as follows:

[0034] S301. Count the task types of all real-time tasks to generate a task type distribution set Ta_Distribution={Type1: n1, Type2: n2,..., TypeK: nK}, where Type1, Type2,..., TypeK represent the 1st, 2nd,..., K-th real-time task types, and n1, n2,..., nK represent the number of real-time tasks in the 1st, 2nd,..., K-th real-time task types;

[0035] S302. In the same real-time task type, calculate the difference in the similarity of each real-time task, and the formula is:

[0036] ;

[0037] In the formula, Sim c (a, b) represents the similarity difference between real-time task a and real-time task b, and Sim a represents the similarity of real-time task a, and Sim b represents the similarity of real-time task b; Calculate the similarity differences of all real-time tasks under all real-time task types in the same way, calculate the average value and standard deviation of all similarity differences, and subtract the standard deviation from the average value to obtain the merging threshold By;

[0038] When Sim c (a, b) ≤ By, it is judged that real-time task a and real-time task b can be merged. When Sim c (a, b) > By, it is judged that real-time task a and real-time task b cannot be merged; Merge the real-time tasks that can be judged to be merged into a task matrix.

[0039] By merging the same type of tasks, redundant tasks can be removed, avoiding the functional machine or server from repeating the processing of the same tasks, saving computing resources and time, and thus being able to complete the task processing more quickly and improving the overall efficiency. After forming the task matrix, the resources of the functional machine and the server can be uniformly allocated and optimized according to the characteristics and resource requirements of the tasks. The task matrix can intuitively display the classification and distribution of tasks, making task management clearer and more straightforward. The administrator can clearly understand the quantity, status and mutual relationship of various tasks at a glance, facilitating effective task scheduling and management.

[0040] The S400 and the cloud server run the LSTM model once to process the task matrix and output the processing result; Extract the common part and the different part of the result from the output result, and perform compressed transmission on the two results;

[0041] Further, the specific steps for extracting the common part and the different part of the result from the output result are as follows:

[0042] S401. Input the task matrix into the LSTM model in the cloud server for single processing, and output the result matrix, Results = {Y1, Y2, Y3,..., Y d}; Y1, Y2, Y3,..., Y d represent the results of the 1st, 2nd, 3rd,..., dth tasks in the result matrix; Calculate the common part of the results, and the formula is:

[0043] ;

[0044] In the formula, Y common represents the common part of the results, Yj Represents the result of the j-th task in the result matrix;

[0045] Calculate the difference part of the results, and the formula is:

[0046] ;

[0047] In the formula, Y diff j Represents the difference part of the j-th task result; the cloud server compresses the common part and the difference part of the results separately. For the common part of the results, the cloud server makes a global transmission to all online functional machines at once, and all online functional machines receive the common part of the results simultaneously;

[0048] The cloud server transmits the difference part of the results to the online functional machines with corresponding IDs in sequence according to the task request data packets.

[0049] By extracting the common part and the difference part of the results and compressing them, the amount of data to be transmitted can be significantly reduced. In network transmission, the size of the data volume directly affects the transmission speed and bandwidth occupancy. Compressed transmission can speed up the data transmission speed and reduce the network bandwidth pressure. Especially in the case of unstable network environment or limited bandwidth, it can improve the stability and response speed of the system, ensuring that the processing results can be transmitted to the functional machines or other terminal devices in a timely and efficient manner.

[0050] S500. The functional machine receives the task results sent by the cloud server, and the functional machine reorganizes the task results locally to obtain the final result of the real-time task.

[0051] Furthermore, the specific steps for the functional machine to reorganize the task results locally to obtain the final result of the real-time task are as follows:

[0052] S501. The online functional machine receives the two parts of the results transmitted by the cloud server respectively. The functional machine decompresses the two parts of the results separately, and reorganizes the common part and the difference part of the results. The formula is:

[0053] ;

[0054] In the formula, Y j final Represents the final result of the j-th task; similarly, all online functional machines decompress and reorganize the common part and the difference part to obtain the final result.

[0055] After the receiving end (such as a feature phone) receives the compressed result, it only needs to decompress and simply merge it to restore the complete processing result. Compared with directly receiving a large amount of unprocessed data, this method greatly saves the storage resources and computing resources of the terminal device, reduces the processing burden of the terminal device, enables it to run other tasks more smoothly, and improves the user experience.

[0056] A feature phone data optimization management system based on cloud applications, the feature phone data optimization management system includes a data acquisition module, a task fingerprint library module, a real-time task type judgment module, a task conflict judgment module, a task processing module, and a result generation module;

[0057] The data acquisition module is used to collect task data in the history of the feature phone;

[0058] The task fingerprint library module is used to extract the features of the task data in the history, screen the key features, construct the task fingerprint of each task, and generate a task fingerprint library;

[0059] The real-time task type judgment module is used to calculate the similarity of real-time tasks using task fingerprints and judge the real-time task type;

[0060] The task conflict judgment module is used to perform conflict analysis on the judged real-time tasks, merge the same tasks obtained by the judgment, and form a task matrix;

[0061] The task processing module is used for the cloud server to input the task matrix into the LSTM model for single processing, output a result matrix, and divide the result into a common part and a difference part in the result matrix;

[0062] The result generation module is used for the online feature phone to receive the common part and the difference part, decompress and reorganize them to obtain the final result.

[0063] The task fingerprint library module includes a feature screening unit and a task fingerprint construction unit;

[0064] The feature screening unit is used to screen all features in the time domain, frequency domain, and time using the mutual information screening method;

[0065] The task fingerprint construction unit is used to obtain the task fingerprint through minhash using the screened key features.

[0066] The task conflict judgment module includes a merge judgment unit and a merge unit;

[0067] The merge judgment unit is used to calculate the similarity difference and judge whether the two real-time tasks corresponding to the similarity difference can be merged using the merge threshold;

[0068] The merging unit is used to merge real-time tasks that can be merged into a task matrix.

[0069] The task processing module includes a common part calculation unit and a difference part calculation unit;

[0070] The common part calculation unit is used to obtain the intersection of all results to get the common part of the results;

[0071] The difference part calculation unit is used to subtract the common part from each result to get the difference part.

[0072] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0073] 1. In the present invention, data processing and analysis are performed in the cloud, and the functional machine is mainly responsible for data upload and result display, reducing the local computing burden, thereby improving the running speed and response ability of the functional machine and enhancing the user experience. Users can obtain task-related information more quickly without waiting for the functional machine to process data for a long time.

[0074] 2. By merging the same type of tasks, the present invention can remove redundant tasks, avoid the functional machine or server from repeatedly processing the same tasks, save computing resources and time, and thus can complete task processing more quickly and improve the overall efficiency.

[0075] 3. By extracting the common part and the difference part in the results and compressing them, the present invention can significantly reduce the amount of data to be transmitted. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a module distribution diagram of a functional machine data optimization management system based on cloud applications according to the present invention;

[0077] Figure 2 It is a step schematic diagram of a functional machine data optimization management method based on cloud applications according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0078] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0079] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a technical solution,

[0080] A functional machine data optimization management method based on cloud applications, the method includes the following steps:

[0081] S100. In the feature phone, use the 4G network to connect to the cloud server, extract the historical tasks of the feature phone, analyze the historical task data to extract features, construct task fingerprints, and generate a task fingerprint library;

[0082] The specific steps for constructing task fingerprints are as follows:

[0083] S101. In the feature phone, use the 4G network to connect to the cloud server, record task logs at the feature phone end, compress and transmit the task data in the task logs to the cloud server. The cloud server preprocesses the task data, specifically: removing missing values in the task data, filtering the task data using a filter; finally, standardizing the task data, and the formula is:

[0084] ;

[0085] In the formula, s’ represents the standardized task data, s represents the task data received by the cloud server, sp represents the average value of the task data, and sz represents the standard deviation of the task data;

[0086] S102. Extract the timestamp of each task data, calculate the duration of each task, and the formula is: , in the formula, △t represents the task duration, t end represents the task end time, t start represents the task start time; use the duration of each task as a time feature;

[0087] Calculate the average value, variance, and peak value of each task data in the time domain as time domain features;

[0088] In the frequency domain, use the Fourier transform to convert all task data into frequency domain signals s[k], and calculate the amplitude spectrum of the frequency domain signals in each task data. The formula is:

[0089] ;

[0090] In the formula, |s[k]| represents the amplitude spectrum of the frequency domain signal, Re(s[k]) represents the real part of the frequency domain signal, and Im(s[k]) represents the imaginary part of the frequency domain signal;

[0091] Calculate the frequency domain features of each task data, and the formula is: , in the formula, Pt represents the frequency domain feature of each task data, argmax represents extracting the independent variable that makes the function reach the maximum value, and k belongs to 0 to N - 1;

[0092] S103. Use the mutual information screening method to screen all features in the time domain, frequency domain, and time to obtain the key features of each task, and construct a feature vector V for all key features; generate task fingerprints using the feature vector of each task, and the formula is:

[0093] ;

[0094] In the formula, H represents the task fingerprint, MinHsah represents the min - hash algorithm, and LSH represents the locality - sensitive hashing algorithm; generate a 128 - bit MinHash fingerprint H, generate the task fingerprint H for each type of task, and obtain the task fingerprint library.

[0095] By connecting to the cloud server through the 4G network, the powerful computing and storage resources of the cloud can be fully utilized, without the need to equip the feature phone locally with a large - capacity storage and high - performance processor to process and store a large amount of historical task data, reducing the hardware cost and energy consumption of the feature phone. Storing the historical task data in the cloud server is convenient for centralized management and maintenance, and functions such as regular data backup and disaster recovery can be realized, improving the security and reliability of the data, and avoiding data loss caused by reasons such as the loss or damage of the feature phone.

[0096] Constructing task fingerprints can generate unique identifiers for each task, which helps to quickly identify and distinguish different tasks. The establishment of the task fingerprint library facilitates the classification, retrieval, and comparison of tasks. For example, similar tasks can be quickly found through task fingerprints, providing a reference and decision - making basis for users, and it can also be used to detect abnormal tasks and timely discover potential problems or risks.

[0097] S200. The feature phone initiates a task request in real - time. After receiving the task request, the cloud server judges the number of feature phones, calculates the similarity of the real - time task using the task fingerprint, and judges the real - time task type; update the task fingerprint library using the new task type;

[0098] The specific steps to judge whether it is the same type of task are as follows:

[0099] S201. All feature phones regularly send heartbeat packets to the cloud at time t1. The heartbeat packet contains the feature phone ID. The cloud server maintains an online device list based on the regular heartbeat packets. When a device does not send a heartbeat packet beyond the regular time t1, the cloud server removes the corresponding feature phone ID from the online device list and obtains the number of feature phones according to the real - time online device list;

[0100] S202. The online feature phone sends a task request data packet to the cloud server. The data packet format is Ta_request={ID, t_request,F i}, where Ta_request represents the task request data packet, t_request represents the task request time, and F i represents the task key feature; the task key feature is normalized to F i ’; the cloud server receives real-time task requests sent by all feature phones;

[0101] S203. The cloud server compares the received real-time task requests with each historical task fingerprint, calculates the similarity, and the formula is:

[0102] ;

[0103] In the formula, Sim k represents the similarity between the real-time task and the k-th task fingerprint, and C k represents the k-th task fingerprint; a similarity threshold Sy is set artificially, and the similarity is judged using the similarity threshold. When Sim k ≥Sy, it is judged as the same type of task; otherwise, the real-time task is marked as a new task type; the real-time task types of all online feature phones are judged in turn.

[0104] By judging the number of feature phones, the cloud server can reasonably allocate tasks according to the system resource load situation, avoid the situation that some feature phones have too many tasks while some feature phones are idle, and improve the overall system operation efficiency and resource utilization rate; after accurately judging the real-time task type, the cloud server can call the corresponding processing module or algorithm to process the task, improving the accuracy and efficiency of task processing. For example, for different types of tasks, different priority settings, data processing methods or resource allocation schemes can be adopted to better meet user needs and system requirements.

[0105] Updating the task fingerprint library with the new task type can enable the system to continuously learn and adapt to new task patterns and characteristics, enrich the content of the task fingerprint library, and improve the system's recognition and processing capabilities for various tasks. As time goes by, the task fingerprint library will become more and more perfect, and the system's judgment and processing of tasks will become more and more accurate and efficient.

[0106] S300. Conduct conflict analysis on the judged real-time tasks, and merge the judged same type of tasks to form a task matrix;

[0107] The specific steps for conducting conflict analysis on the judged real-time tasks are as follows:

[0108] S301. Count the task types of all real-time tasks to generate a task type distribution set Ta_Distribution = {Type1: n1, Type2: n2,..., TypeK: nK}, where Type1, Type2,..., TypeK represent the 1st, 2nd,..., Kth real-time task types, and n1, n2,..., nK represent the number of real-time tasks in the 1st, 2nd,..., Kth real-time task types;

[0109] S302. In the same real-time task type, calculate the difference in similarity for each real-time task. The formula is:

[0110] ;

[0111] In the formula, Sim c (a, b) represents the similarity difference between real-time task a and real-time task b, Sim a represents the similarity of real-time task a, and Sim b represents the similarity of real-time task b; Calculate the similarity differences for all real-time tasks under all real-time task types in the same way, calculate the average value and standard deviation of all similarity differences, and subtract the standard deviation from the average value to obtain the merging threshold By;

[0112] When Sim c (a, b) ≤ By, it is determined that real-time task a and real-time task b can be merged. When Sim c (a, b) > By, it is determined that real-time task a and real-time task b cannot be merged; Merge the real-time tasks that can be determined to be merged into a task matrix.

[0113] By merging the same tasks, redundant tasks can be removed, avoiding the functional machine or server from repeatedly processing the same tasks, saving computing resources and time, and thus being able to complete task processing more quickly and improving the overall efficiency. After forming the task matrix, the resources of the functional machine and the server can be uniformly allocated and optimized according to the characteristics and resource requirements of the tasks. The task matrix can intuitively display the classification and distribution of tasks, making task management clearer. The administrator can clearly understand the quantity, status, and mutual relationship of various tasks at a glance, facilitating effective task scheduling and management.

[0114] S400. The cloud server runs the LSTM model once to process the task matrix and outputs the processing result; Extract the common part and the different part of the result from the output result, and perform compressed transmission on the two results;

[0115] The specific steps for extracting the common part and the different part of the result from the output result are:

[0116] S401. Input the task matrix into the LSTM model in the cloud server for single processing, and output the result matrix, Results = {Y1, Y2, Y3, ..., Y d}, where Y1, Y2, Y3, ..., Y d represent the results of the 1st, 2nd, 3rd, ..., dth tasks in the result matrix; calculate the common part of the results, and the formula is:

[0117] ;

[0118] In the formula, Y common represents the common part of the results, and Y j represents the result of the jth task in the result matrix;

[0119] Calculate the difference part of the results, and the formula is:

[0120] ;

[0121] In the formula, Y diff j represents the difference part of the result of the jth task; the cloud server compresses the common part and the difference part of the results separately. For the common part of the results, the cloud server makes a global transmission to all online functional machines once, and all online functional machines receive the common part of the results simultaneously;

[0122] The cloud server transmits the difference part of the results to the online functional machines corresponding to the IDs in sequence according to the task request data packets.

[0123] By extracting the common part and the difference part of the results and compressing them, the amount of data to be transmitted can be significantly reduced. In network transmission, the size of the data volume directly affects the transmission speed and bandwidth occupancy. Compressed transmission can accelerate the data transmission speed and reduce the network bandwidth pressure. Especially in the case of unstable network environment or limited bandwidth, it can improve the stability and response speed of the system, ensuring that the processing results can be transmitted to the functional machines or other terminal devices in a timely and efficient manner.

[0124] S500. The functional machine receives the task results sent by the cloud server, and the functional machine reorganizes the task results locally to obtain the final real-time task results.

[0125] The specific steps for the functional machine to reorganize the task results locally to obtain the final real-time task results are as follows:

[0126] S501. The online functional machine receives the two parts of the results transmitted by the cloud server respectively. The functional machine decompresses the two parts of the results respectively, and reorganizes the common part and the difference part of the results. The formula is:

[0127] ;

[0128] In the formula, Y j final represents the final result of the j-th task; similarly, all online functional machines decompress and reorganize the common part and the different part to obtain the final result.

[0129] After the receiving end (such as a functional machine) receives the compressed result, it only needs to decompress and simply merge it to restore the complete processing result. Compared with directly receiving a large amount of unprocessed data, this method greatly saves the storage resources and computing resources of the terminal device, reduces the processing burden of the terminal device, enables it to run other tasks more smoothly, and improves the user experience.

[0130] A functional machine data optimization management system based on cloud applications, the functional machine data optimization management system includes a data collection module, a task fingerprint library module, a real-time task type judgment module, a task conflict judgment module, a task processing module, and a result generation module;

[0131] The data collection module is used to collect task data in the history of the functional machine;

[0132] The task fingerprint library module is used to extract the characteristics of the task data in the history, screen key characteristics, construct the task fingerprint of each task, and generate a task fingerprint library;

[0133] The real-time task type judgment module is used to calculate the similarity of real-time tasks using task fingerprints and judge the real-time task type;

[0134] The task conflict judgment module is used to perform conflict analysis on the judged real-time tasks, merge the same tasks obtained by the judgment, and form a task matrix;

[0135] The task processing module is used for the cloud server to input the task matrix into the LSTM model for single processing, output a result matrix, and divide the results into a common part and a different part in the result matrix;

[0136] The result generation module is used for the online functional machine to receive the common part and the different part, decompress and reorganize them to obtain the final result.

[0137] The task fingerprint library module includes a feature screening unit and a task fingerprint construction unit;

[0138] The feature screening unit is used to screen all features in the time domain, frequency domain, and time using the mutual information screening method;

[0139] The task fingerprint construction unit is used to obtain the task fingerprint through minhash using the screened key features.

[0140] The task conflict judgment module includes a merging judgment unit and a merging unit;

[0141] The merging judgment unit is used to calculate the similarity difference and determine whether two real-time tasks corresponding to the similarity difference can be merged by using a merging threshold;

[0142] The merging unit is used to merge the real-time tasks that are judged to be mergable into a task matrix.

[0143] The task processing module includes a common part calculation unit and a difference part calculation unit;

[0144] The common part calculation unit is used to find the intersection of all results to obtain the common part of the results;

[0145] The difference part calculation unit is used to subtract the common part from each result to obtain the difference part.

[0146] Embodiment: In a rural residential area, there are 5 feature phones connected to a cloud server. A task fingerprint database is constructed according to historical task data, and the obtained task types include game type, audio-video type, and document type;

[0147] The 5 feature phones send task request data packets to the cloud server. The tasks of feature phones 1-5 are downloading music, online translation, game archiving, watching videos, and online listening to music respectively;

[0148] After receiving the task requests, the cloud server calculates the similarity and judges each real-time task type;

[0149] In the audio-video type, it includes downloading music, watching videos, and online listening to music;

[0150] Calculate the similarity difference, judge that watching videos and online listening to music can be merged, and generate a task matrix; The cloud server processes to obtain a result matrix;

[0151] Extract the common part of the results as audio parsing, and the difference part as video parsing and sound quality improvement;

[0152] They are respectively compressed and transmitted to the feature phones, and the feature phones reorganize the results to obtain the final result.

[0153] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to include all changes within the meaning and scope of the equivalent elements of the claims in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights.

Claims

1. A method for optimizing and managing function machine data based on cloud applications, characterized in that: The method comprises the following steps: S100, using the 4G network to connect to the cloud server in the feature phone, extracting historical tasks of the feature phone, analyzing and extracting features from the historical task data, constructing task fingerprints, and generating a task fingerprint library; The specific steps to build a task fingerprint are: S101, using 4G network to connect to the cloud server in the feature phone, recording the task log on the feature phone, compressing the task data in the task log and transmitting it to the cloud server, the cloud server pre-processes the task data, specifically: removing missing values ​​in the task data, filtering the task data using a filter; finally, standardizing the task data, the formula is: ； In the formula, s' represents the standardized task data, s represents the task data received by the cloud server, sp represents the average value of the task data, and sz represents the standard deviation of the task data; S102. Extract the timestamps of each task data and calculate the duration of each task. The formula is as follows: , in the formula, △t represents the task duration, and t end represents the task end time, and t start represents the task start time; Use the duration of each task as a time feature; The mean, variance and peak value of each task data are calculated in the time domain as the time domain features; In the frequency domain, Fourier transform is used to convert all task data into frequency domain signals s[k], and the amplitude spectrum of the frequency domain signal is calculated in each task data. The formula is: ； In the formula, |s[k]| represents the amplitude spectrum of the frequency domain signal, Re(s[k]) represents the real part of the frequency domain signal, and Im(s[k]) represents the imaginary part of the frequency domain signal; Calculate the frequency domain features of each task data, and the formula is: , in the formula, Pt represents the frequency domain features of each task data, argmax represents extracting the independent variable that makes the function obtain the maximum value, and k belongs to 0 to N-1; S103. Use the mutual information screening method to screen all the features in the time domain, frequency domain, and time to obtain the key features of each task, and construct the feature vector V from all the key features; generate the task fingerprint using the feature vector of each task, and the formula is: ; In the formula, H represents the task fingerprint, LSH represents the local sensitive hash algorithm; MinHash represents the minimum hash algorithm, and generates a 128-bit MinHash fingerprint; using each generated task fingerprint H, a task fingerprint library is constructed; S200, the feature machine initiates a task request in real time. After receiving the task request, the cloud server determines the number of feature machines, calculates the similarity of the real-time tasks using the task fingerprint, and determines the type of the real-time task; and updates the task fingerprint library using the new task type; S300, performing conflict analysis on the determined real-time tasks, merging the determined tasks of the same type to form a task matrix; S400, the cloud server runs the LSTM model once to process the task matrix and output the processing result; extracts the common part and the difference part of the result from the output result, and compresses and transmits the two results; S500, the feature machine receives the task results sent by the cloud server, and the feature machine reorganizes the task results locally to obtain the final result of the real-time task.

2. The functional phone data optimization management method based on cloud applications according to claim 1, characterized in that: The specific steps of determining whether the tasks are the same in S200 are as follows: S201, all feature machines send heartbeat packets to the cloud regularly at time t1. The heartbeat packets contain the feature machine ID. The cloud server maintains the online device list based on the regular heartbeat packets. When a device fails to send a heartbeat packet beyond the regular time t1, the cloud server removes the corresponding feature machine ID from the online device list and obtains the number of feature machines based on the real-time online device list; S202. The online functional machine sends a task request data packet to the cloud server. The data packet format is Ta_request = {ID, t_request, F i}, where Ta_request represents the task request data packet, t_request represents the task request time, and F i represents the task key feature; the task key feature is normalized to F i '; the cloud server receives the real-time task requests sent by all functional machines; S203, the cloud server compares the received real-time task request with each historical task fingerprint and calculates the similarity. The formula is: ； In the formula, Sim k represents the similarity between the real-time task and the k-th task fingerprint, and C k represents the k-th task fingerprint; the similarity threshold Sy is set artificially, and the similarity is judged using the similarity threshold. When Sim k ≥Sy, it is judged as the same type of task; otherwise, the real-time task is marked as a new task type. Determine the real-time task types of all online functional machines in turn.

3. The method for optimizing and managing the function machine data based on cloud applications according to claim 2, characterized in that: The specific steps of performing conflict analysis on the determined real-time tasks in S300 are: S301. Count the task types of all real-time tasks, and generate a task type distribution set Ta_Distribution = {Type1: n1, Type2: n2,..., TypeK: nK}, where Type1, Type2,..., TypeK represent the 1st, 2nd,..., Kth real-time task types, and n1, n2,..., nK represent the number of real-time tasks in the 1st, 2nd,..., Kth real-time task types; S302. Calculate the difference in similarity for each real-time task within the same type of real-time task. The formula is: ; In the formula, Sim c represents the similarity difference between real-time task a and real-time task b, and Sim a represents the similarity of real-time task a, and Sim b represents the similarity of real-time task b; calculate the similarity differences of all real-time tasks under all real-time task types in the same way, calculate the average value and standard deviation of all similarity differences, and subtract the standard deviation from the average value to obtain the merging threshold By; When Sim c (a, b) ≤ By, it is determined that the real-time tasks a and b can be merged. When Sim c (a, b) > By, it is determined that the real-time tasks a and b cannot be merged; the real-time tasks that can be merged are combined into a task matrix.

4. A method for optimizing and managing function machine data based on cloud applications according to claim 3, characterized in that: The specific steps for extracting the result common part and the result difference part from the output result in S400 are as follows: S401. Input the task matrix into the LSTM model in the cloud server for single processing, and output the result matrix, Results = {Y1, Y2, Y3, ..., Y d}, where Y1, Y2, Y3, ..., Y d represent the results of the 1st, 2nd, 3rd, ..., dth tasks in the result matrix; calculate the common part of the results, and the formula is: ; In the formula, Y common represents the common part of the results, and Y j represents the j-th task result in the result matrix; The part of the calculation result difference, the formula is: ; In the formula, Y diff j represents the difference part of the j-th task result; the cloud server compresses the result common part and the difference part separately, and for the result common part, the cloud server makes a global broadcast to all online functional machines, and all online functional machines receive the result common part simultaneously; The cloud server transmits the result difference part to the online functional machine corresponding to the ID in sequence according to the task request data packet.

5. A method for optimizing and managing function machine data based on cloud applications according to claim 4, characterized in that: The specific steps for the functional machine to reorganize the task result locally to obtain the final result of the real-time task in S500 are as follows: S501. The online functional machine receives the two parts of the result transmitted by the cloud server respectively, decompresses the two parts of the result respectively, and reorganizes the result common part and the difference part. The formula is: ； In the formula, Y j final represents the final result of the j-th task; similarly, all online functional machines decompress and reorganize the common part and the different part to obtain the final result.

6. A cloud application-based functional phone data optimization management system for applying the cloud application-based functional phone data optimization management method described in any one of claims 1-5, characterized in that: The functional machine data optimization management system includes a data collection module, a task fingerprint library module, a real-time task type judgment module, a task conflict judgment module, a task processing module, and a result generation module; The data collection module is used to collect the task data in the history of the functional machine; The task fingerprint library module is used to extract the features of the task data in the history, screen the key features, construct the task fingerprint of each task, and generate a task fingerprint library; The real-time task type judgment module is used to calculate the similarity of the real-time task by using the task fingerprint and judge the real-time task type; The task conflict judgment module is used to perform conflict analysis on the judged real-time tasks, merge the same tasks judged, and form a task matrix; The task processing module is used to input the task matrix into the LSTM model by the cloud server for single processing, output a result matrix, and divide the result into a common part and a difference part in the result matrix; The result generation module is used for the online functional machine to receive the common part and the difference part, decompress and reorganize them to obtain the final result.

7. A function phone data optimization management system based on cloud applications according to claim 6, characterized in that: The task fingerprint library module includes a feature screening unit and a task fingerprint construction unit; The feature screening unit is used to screen all features in the time domain, frequency domain, and time by using the mutual information screening method; The task fingerprint construction unit is used to obtain the task fingerprint by using the screened key features through minhash.

8. The functional phone data optimization management system based on cloud applications according to claim 6, wherein: The task conflict judgment module includes a merging judgment unit and a merging unit; The merging judgment unit is used to calculate the similarity difference and judge whether the two real-time tasks corresponding to the similarity difference can be merged by using the merging threshold; The merging unit is used to merge the real-time tasks that are judged to be mergable into a task matrix.

9. The functional phone data optimization management system based on cloud applications according to claim 6, characterized in that: The task processing module includes a common part calculation unit and a difference part calculation unit; The common part calculation unit is used to obtain the intersection of all results to get the common part of the results; The difference part calculation unit is used to subtract the common part from each result to get the difference part.

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