Intelligent adjusting system and method based on mobile phone application data analysis

Abstract

The invention discloses an intelligent adjusting system and method based on mobile phone application data analysis, and relates to the technical field of data analysis, the adjusting method comprises the following steps: step S100: the system collects mobile phone application data according to a preset time interval, and stores the mobile phone application data; step S200, analyzing the mobile phone application data to obtain a priority score of each mobile phone application; step S300, analyzing the mobile phone application data to obtain a data analysis speed and a data volume threshold value of each mobile phone application; s400, distributing the data resources for the mobile phone applications applying for the data resources according to the obtained priority scores; and step S500, carrying out data analysis on the mobile phone application data for acquiring the data resources through the data analysis speed and the data volume threshold.

Description

Technical Field

[0001] The present invention relates to the technical field of data parsing, and particularly to an intelligent adjustment system and method based on mobile application data parsing. Background Art

[0002] With the rapid development of mobile Internet technology, smart phones have become an indispensable and important tool in people's daily lives. Various applications have emerged like mushrooms after rain, greatly enriching the user experience. However, during the use of mobile phones, especially in the management of mobile applications, there are still some challenges. These challenges not only affect the user experience but also limit the full play of the functions of smart phones.

[0003] The following are the main challenges in mobile application data parsing: Application data conflicts lead to parsing failures. When multiple applications run simultaneously, existing mobile operating systems often cannot effectively manage data interactions between applications, resulting in data conflicts. Such conflicts may manifest as data reading errors, data overwriting, or data acquisition interruptions. Especially when using cloud services, it may lead to unstable or even interrupted data synchronization, having a negative impact on data integrity. Such situations of data connection interruption often cause trouble and inconvenience to customers. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent adjustment system and method based on mobile application data parsing to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An intelligent adjustment method based on mobile application data parsing, the method comprising:

[0006] Step S100: The system collects mobile application data according to a preset time interval and stores the mobile application data;

[0007] Step S200: Analyze the mobile application data to obtain the priority scores of each mobile application;

[0008] Step S300: Analyze the mobile application data to obtain the data parsing speed and data volume threshold of each mobile application;

[0009] Step S400: Allocate data resources to the mobile applications applying for data resources according to the obtained priority scores;

[0010] Step S500: Parse the mobile application data of the mobile applications obtaining data resources through the data parsing speed and data volume threshold.

[0011] Further, step S100 includes:

[0012] Step S101: Set a timer which is used to control the time interval of data collection. The time interval of the timer is set according to the performance and requirements of the system.

[0013] Step S102: When the data resource listener detects a data resource application request, trigger the timer and call the data collection interface of the system. The collection interface collects the application data of the currently running mobile applications.

[0014] Step S103: Store the collected application data into the corresponding data structure and perform backup processing on the application data.

[0015] In the above steps, the time interval of the timer is not fixed, but is flexibly set according to the performance and requirements of the system. If the system performance is strong and the real-time requirement for data collection is high, the time interval can be set shorter. If the system performance is limited or the real-time requirement for data collection is not high, the time interval can be appropriately extended to reduce the system burden. The above data structure is a container specifically used to store data, and it can be reasonably designed according to the characteristics and usage requirements of the data, such as arrays, linked lists, hash tables, etc. By storing the data into a suitable data structure, it is convenient to manage and use the data later.

[0016] Further, step S200 includes:

[0017] Step S201: Read the application data from the stored data structure. The application data includes all usage logs of each mobile application. The usage logs include the unique identifier, usage date, usage times, usage duration, and data interaction information of the mobile application.

[0018] Step S202: Obtain the number of days between the current usage date and the last usage date of the mobile application according to the usage date.

[0019] Step S203: Accumulate the usage times recorded in each usage log of the mobile application to obtain the total usage times of the mobile application.

[0020] Step S204: Accumulate the usage durations recorded in each usage log of the mobile application to obtain the total usage duration of the mobile application. Obtain the average usage duration of the mobile application through the total usage times and the total usage duration.

[0021] Step S205: Calculate the corresponding priority score for the mobile application according to the number of days difference, the total usage times, and the average usage duration. The calculation method of the priority score is as follows:

[0022]

[0023] Among them, Q is the priority score of the mobile application, A is the number of days between the current usage date and the last usage date of the mobile application, ω1 is the weight factor corresponding to the number of days, B is the average usage duration of the mobile application, ω2 is the weight factor corresponding to the average usage duration, C is the total number of times the mobile application is used, and ω3 is the weight factor corresponding to the total number of times of use;

[0024] Step S206: Associate the calculated priority score with the unique identifier of the mobile application to construct a priority mapping table;

[0025] In the above steps, by comparing the current usage date and the last usage date, the number of days between these two time points can be obtained. This number of days can reflect the usage activity of the mobile application. The smaller the number of days, the more frequently the application has been used recently; perform an accumulation operation on the number of times of use recorded in each usage log of the mobile application to obtain the total number of times of use, which can reflect the popularity or user dependence of the mobile application; constructing a priority mapping table can facilitate quickly finding the corresponding priority score according to the unique identifier of the mobile application later.

[0026] Further, step S300 includes:

[0027] Step S301: Calculate the current data parsing volume of the mobile application according to the data interaction information in the mobile application data, where the data interaction information includes the data volume and the time stamp corresponding to each data volume;

[0028] Step S302: Count the number of times each data volume appears according to the data volume and the time stamp corresponding to each data volume, and calculate the total parsing duration corresponding to each data volume according to the number of times and the time stamp, and set the data volume with the longest total parsing duration as the data volume threshold;

[0029] Step S303: Calculate the data parsing speed according to the data volume and the time stamp corresponding to each data volume. The data parsing speed calculation method is as follows:

[0030]

[0031] Among them, V represents the data parsing speed, S i represents the i-th data volume, S i+1 represents the (i + 1)-th data volume, t i represents the time stamp corresponding to the i-th data volume, t i+1 represents the time stamp corresponding to the (i + 1)-th data volume, and N represents the total number of data volumes;

[0032] The data parsing speed in the above steps represents the speed at which the system processes the amount of data within a certain period of time; for example, if the amount of data changes significantly within a short period of time, the data parsing speed will be relatively fast. On the contrary, if the amount of data changes little over a long period of time, the data parsing speed will be relatively slow.

[0033] Further, step S400 includes:

[0034] Step S401: Monitor the data resource application requests of mobile applications through a data resource listener. The data resource listener is provided with a first data resource and a second data resource, and the priority of the first data resource is higher than that of the second data resource;

[0035] Step S402: When the data resource listener monitors that there is only one data resource application request from a mobile application, allocate the first data resource to the mobile application;

[0036] Step S403: When the data resource listener monitors that there are application requests from multiple mobile applications for data resources at the same time, retrieve in the priority mapping table according to the unique identifier of the mobile application, and sort the mobile applications in descending order of priority scores;

[0037] Step S404: Allocate the first data resource to the mobile application with the highest priority score, and allocate the second data resource to the mobile application with the priority score second only to the highest priority score;

[0038] Step S405: Set the mobile applications that have not obtained data resources to the waiting state, and at the same time record the waiting order of each mobile application according to the priority score sorting. When the first data resource or the second data resource is released, re-allocate the data resources according to the waiting order;

[0039] The above steps set the first data resource and the second data resource to avoid data processing conflicts when two mobile applications request data resources at the same time, and set the priority of the first data resource to be higher than that of the second data resource to avoid data chaos when parsing the data of two mobile applications at the same time, enabling the two mobile applications to be independent and non-interfering during the data parsing process.

[0040] Further, step S500 includes:

[0041] Step S501: For the mobile applications that have obtained data resources, process the data according to the data parsing speed and the data volume threshold;

[0042] Step S502: When the data resource is transferred, according to the system's data parsing strategy, process the mobile application data of the newly obtained data resource at a data parsing speed not exceeding the data volume threshold;

[0043] Step S503: For the mobile application that releases the data resource, stop parsing the data of the mobile application according to the system's data parsing strategy; if the data of the mobile application is not parsed completely, set the mobile application to the waiting state, and temporarily store the unparsed data in the corresponding data structure;

[0044] The above steps are to enable the system to parse the mobile application data at an appropriate speed, avoiding problems such as data chaos caused by sudden interruption of data parsing.

[0045] Furthermore, to better implement the above method, an intelligent adjustment system based on mobile application data parsing is also provided. The system includes a data acquisition and storage module, a resource allocation module, and a data parsing adjustment module;

[0046] The data acquisition and storage module is responsible for collecting mobile application data at preset time intervals. The time interval is controlled by a timer and set according to system performance and requirements. When the data resource listener detects a data resource application request, it triggers the data acquisition action. The acquisition interface obtains the mobile application data of the currently running mobile applications, stores the mobile application data in the corresponding data structure, and performs backup processing;

[0047] The resource allocation module monitors data resource application requests through a data resource listener. Different priority first data resources and second data resources are set in the data resource listener. After the data resource listener detects a data resource application request, if only one mobile application applies, allocate the first data resource to the mobile application. If multiple mobile applications apply simultaneously, sort the mobile applications according to the priority mapping table, allocate the first data resource to the mobile application with the highest priority, allocate the second data resource to the mobile application with the priority second only to the highest priority mobile application. The mobile applications that do not obtain the data resource enter the waiting state and record the waiting order. When the first data resource or the second data resource is released, re-allocate the data resource according to the waiting order;

[0048] The data parsing adjustment module analyzes the mobile application data, calculates the data parsing speed and data volume threshold of each mobile application. The data volume threshold is calculated by counting the number of occurrences of the data volume to calculate the corresponding total duration, and the data volume corresponding to the maximum value of the total duration is selected as the data volume threshold. The data parsing speed is calculated by combining the data volume and the corresponding timestamp. After the data resource is allocated, according to the system data parsing strategy, the obtained mobile application data of the data resource is parsed using the calculated data parsing speed and data volume threshold;

[0049] Further, the data acquisition and storage module includes a mobile application data acquisition unit and a mobile application data storage unit;

[0050] The mobile application data acquisition unit acquires mobile application data at the time interval set by the timer. When the data resource listener detects a data resource application request, it triggers the acquisition action and calls the system mobile application data acquisition interface to obtain the mobile application data that is currently running;

[0051] The mobile application data storage unit stores the acquired mobile application data into the corresponding data structure and performs backup processing;

[0052] Further, the resource allocation module includes a priority identification unit and a data resource allocation unit;

[0053] The priority identification unit calculates the priority score of each mobile application by analyzing the usage logs in the mobile application data and constructs a priority mapping table;

[0054] The data resource allocation unit uses the data resource listener to monitor the data resource application request. When only one mobile application applies for the data resource, it allocates the first data resource to the mobile application. When multiple mobile applications apply for the data resource at the same time, it ranks the mobile applications according to the priority mapping table, allocates the first data resource to the mobile application with the highest priority, allocates the second data resource to the mobile application with the second highest priority, and the mobile applications that do not obtain the data resource enter the waiting state and record the waiting order. When the data resource is released, the data resource is reallocated according to the waiting order;

[0055] Further, the data parsing adjustment module includes a data volume threshold calculation unit and a data parsing speed calculation unit;

[0056] The data volume threshold calculation unit counts the number of occurrences of each data volume according to the data interaction information in the mobile application data, calculates the total duration of each data volume occurrence, and sets the data volume corresponding to the longest total duration as the data volume threshold;

[0057] A data parsing speed calculation unit calculates the data parsing speed by combining the data volume and timestamp in the mobile application data.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention can effectively solve the problem of parsing mobile application data. By optimizing data resource analysis, calculating priority scores based on mobile application usage logs, and reasonably allocating data resources with different priorities, it avoids data parsing conflicts and improves resource utilization. In terms of adjusting data parsing strategies, by means of the calculated data parsing speed and data volume threshold, it ensures stable and complete data parsing, prevents data chaos and loss. In addition, it can flexibly adjust the data collection time interval to adapt to different system performances and requirements, reduce the system burden, optimize the data storage structure, facilitate data management and use, and comprehensively improve the system operation efficiency and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic flowchart structure diagram of an intelligent adjustment system and method based on mobile application data parsing according to the present invention;

[0060] Figure 2 It is a schematic system structure diagram of an intelligent adjustment system and method based on mobile application data parsing according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] 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.

[0062] Embodiment: As Figure 1 - Figure 2 shown, the present invention provides a technical solution, an intelligent adjustment method based on mobile application data parsing. The method includes:

[0063] Step S100: The system collects mobile application data according to a preset time interval and stores the mobile application data;

[0064] Among them, step S100 includes:

[0065] Step S101: Set a timer, which is used to control the time interval of data collection. The time interval of the timer is set according to the performance and requirements of the system;

[0066] Step S102: When the data resource listener detects a data resource application request, trigger the timer and call the data collection interface of the system. The collection interface collects the application data of the mobile applications currently running.

[0067] Step S103: Store the collected application data into the corresponding data structure, and perform backup processing on the application data;

[0068] Step S200: Analyze the mobile application data to obtain the priority scores of each mobile application;

[0069] Among them, Step S200 includes:

[0070] Step S201: Read the application data from the stored data structure, where the application data includes all usage logs of each mobile application, and the usage logs include the unique identifier, usage date, usage times, usage duration, and data interaction information of the mobile application;

[0071] Step S202: Obtain the number of days difference between the current usage date and the last usage date of the mobile application according to the usage date;

[0072] Step S203: Accumulate the usage times recorded in each usage log of the mobile application to obtain the total usage times of the mobile application;

[0073] Step S204: Accumulate the usage durations recorded in each usage log of the mobile application to obtain the total usage duration of the mobile application, and obtain the average usage duration of the mobile application through the total usage times and the total usage duration;

[0074] Step S205: Calculate the corresponding priority score for the mobile application according to the number of days difference, the total usage times, and the average usage duration, and the priority score calculation method is as follows:

[0075]

[0076] Among them, Q is the priority score of the mobile application, A is the number of days difference between the current usage date and the last usage date of the mobile application, ω1 is the weight factor corresponding to the number of days difference, B is the average usage duration of the mobile application, ω2 is the weight factor corresponding to the average usage duration, C is the total usage times of the mobile application, and ω3 is the weight factor corresponding to the total usage times;

[0077] In the embodiments of the present invention, three mobile applications a, b, and c are selected, and their relevant data are as follows:

[0078] Application a: The number of days difference between the current usage date and the last usage date is 5 days, the average usage duration is 10 minutes, and the total usage times is 20 times. Let the weight factors ω1 = 0.2, ω2 = 0.3, and ω3 = 0.5, then the priority score of application a is Q a= 0.2 ÷ 5 + 0.3 × 10 + 0.5 × 20 = 13.04;

[0079] Application b: The number of days between the current usage date and the last usage date is 3 days, the average usage duration is 15 minutes, and the total number of usage times is 15 times. Then the priority score of application b is Q b = 0.2 ÷ 3 + 0.3 × 15 + 0.5 × 15 = 12.1;

[0080] Application c: The number of days between the current usage date and the last usage date is 8 days, the average usage duration is 8 minutes, and the total number of usage times is 25 times. Then the priority score of application b is Q c = 0.2 ÷ 8 + 0.3 × 8 + 0.5 × 25 = 14.9;

[0081] Step S206: Associate the calculated priority score with the unique identifier of the mobile application to construct a priority mapping table;

[0082] Step S300: Analyze the mobile application data to obtain the data parsing speed and data volume threshold of each mobile application;

[0083] Among them, step S300 includes:

[0084] Step S301: Calculate the current data parsing volume size of the mobile application according to the data interaction information in the mobile application data. The data interaction information includes the data volume and the time stamp corresponding to each data volume;

[0085] Step S302: Count the number of occurrences of each data volume according to the data volume and the time stamp corresponding to each data volume, and calculate the total parsing duration corresponding to each data volume according to the number of times and the time stamp. Set the data volume with the longest total parsing duration as the data volume threshold;

[0086] Step S303: Calculate the data parsing speed according to the data volume and the time stamp corresponding to each data volume. The data parsing speed calculation method is as follows:

[0087]

[0088] Among them, V represents the data parsing speed, S i represents the i-th data volume, S i+1 represents the (i + 1)-th data volume, t i represents the time stamp corresponding to the i-th data volume, t i+1 represents the time stamp corresponding to the (i + 1)-th data volume, and N represents the total number of data volumes;

[0089] In an embodiment of the present invention, three data volumes of 5, 8, and 6 are selected. These three data volumes are at the time nodes of 0s, 3s, and 5s respectively. According to the above formula, the following results are obtained:

[0090]

[0091] Therefore, the data parsing speed is 1;

[0092] Step S400: Allocate data resources to the mobile applications applying for data resources according to the obtained priority scores;

[0093] Among them, step S400 includes:

[0094] Step S401: Monitor the data resource application requests of mobile applications through a data resource listener. The data resource listener is provided with a first data resource and a second data resource, and the priority of the first data resource is higher than that of the second data resource;

[0095] Step S402: When the data resource listener monitors that there is only one data resource application request from a mobile application, allocate the first data resource to the mobile application;

[0096] Step S403: When the data resource listener monitors application requests for data resources from multiple mobile applications at the same time, retrieve in the priority mapping table according to the unique identifier of the mobile application, and sort the mobile applications in descending order of priority scores;

[0097] Step S404: Allocate the first data resource to the mobile application with the highest priority score, and allocate the second data resource to the mobile application with the priority score second only to the highest priority score;

[0098] Step S405: Set the mobile applications that do not obtain data resources to the waiting state, and record the waiting order of each mobile application according to the priority score sorting. When the first data resource or the second data resource is released, re-allocate the data resources according to the waiting order;

[0099] Step S500: Parse the data of the mobile applications that obtain data resources through the data parsing speed and the data volume threshold;

[0100] Among them, step S500 includes:

[0101] Step S501: For the mobile applications that obtain data resources, process the data according to the data parsing speed and the data volume threshold;

[0102] Step S502: When the data resource is transferred, according to the system's data parsing strategy, process the mobile application data of the newly obtained data resource at a data parsing speed not exceeding the data volume threshold;

[0103] Step S503: For the mobile application that releases the data resource, stop parsing the data of the mobile application according to the system's data parsing strategy; if the data of the mobile application is not completely parsed, set the mobile application to the waiting state, and temporarily store the unparsed data in the corresponding data structure;

[0104] Among them, in order to better implement the above method, an intelligent adjustment system based on mobile application data parsing is also provided. The system includes a data collection and storage module, a resource allocation module, and a data parsing adjustment module;

[0105] The data collection and storage module is responsible for collecting mobile application data at preset time intervals. The time interval is controlled by a timer and set according to system performance and requirements. When the data resource listener detects a data resource application request, it triggers a data collection action. The collection interface obtains the mobile application data currently running and stores the mobile application data in the corresponding data structure, and performs backup processing;

[0106] The resource allocation module monitors data resource application requests through a data resource listener. Different priority first data resources and second data resources are set in the data resource listener. After the data resource listener detects a data resource application request, if only one mobile application applies, allocate the first data resource to the mobile application. If multiple mobile applications apply simultaneously, sort the mobile applications according to the priority mapping table, allocate the first data resource to the mobile application with the highest priority, and allocate the second data resource to the mobile application with the priority second only to the highest priority. The mobile applications that do not obtain the data resource enter the waiting state and record the waiting order. When the first data resource or the second data resource is released, reallocate the data resource according to the waiting order;

[0107] The data parsing adjustment module analyzes the mobile application data, calculates the data parsing speed and data volume threshold of each mobile application. The data volume threshold calculates the corresponding total duration by counting the number of occurrences of the data volume, and selects the data volume corresponding to the maximum value of the total duration as the data volume threshold. The data parsing speed is calculated by combining the data volume and the corresponding timestamp. After the data resource is allocated, according to the system data parsing strategy, use the calculated data parsing speed and data volume threshold to parse the mobile application data that obtains the data resource;

[0108] Among them, the data acquisition and storage module includes a mobile application data acquisition unit and a mobile application data storage unit;

[0109] The mobile application data acquisition unit acquires mobile application data at the time interval set by the timer. When the data resource listener detects a data resource application request, it triggers the acquisition action and calls the system mobile application data acquisition interface to obtain the mobile application data that is currently running;

[0110] The mobile application data storage unit stores the acquired mobile application data into the corresponding data structure and performs backup processing;

[0111] Among them, the resource allocation module includes a priority identification unit and a data resource allocation unit;

[0112] The priority identification unit calculates the priority scores of each mobile application by analyzing the usage logs in the mobile application data and constructs a priority mapping table;

[0113] The data resource allocation unit uses the data resource listener to monitor data resource application requests. When only one mobile application applies for data resources, it allocates the first data resource to the mobile application. When multiple mobile applications apply for data resources at the same time, it ranks the mobile applications according to the priority mapping table, allocates the first data resource to the mobile application with the highest priority, allocates the second data resource to the mobile application with the priority second only to the highest priority, and the mobile applications that do not obtain data resources enter the waiting state and record the waiting order. When the data resources are released, the data resources are reallocated according to the waiting order;

[0114] Among them, the data parsing and adjustment module includes a data volume threshold calculation unit and a data parsing speed calculation unit;

[0115] The data volume threshold calculation unit counts the number of occurrences of each data volume according to the data interaction information in the mobile application data, calculates the total duration of each data volume occurrence, and sets the data volume corresponding to the longest total duration as the data volume threshold;

[0116] The data parsing speed calculation unit calculates the data parsing speed by combining the data volume and timestamp in the mobile application data.

[0117] It will be apparent to those skilled in the art that the present invention is not limited to the details of the above-described exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, in all respects, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Accordingly, all changes that fall within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. An intelligent adjustment method based on mobile application data parsing, characterized in that: The method includes: Step S100: The system collects mobile application data according to a preset time interval and stores the mobile application data. Step S200: Analyze the mobile application data to obtain the priority scores of each mobile application. Step S300: Analyze the mobile application data to obtain the data parsing speed and data volume threshold of each mobile application. Step S400: Allocate data resources to the mobile applications applying for data resources according to the obtained priority scores. Step S500: Parse the mobile application data of the mobile applications obtaining data resources through the data parsing speed and data volume threshold.

2. The intelligent adjustment method based on mobile application data parsing according to claim 1, characterized in that: The step S100 includes: Step S101: Set a timer, which is used to control the time interval of data collection. The time interval of the timer is set according to the performance and requirements of the system. Step S102: When the data resource listener detects a data resource application request, trigger the timer and call the data collection interface of the system. The collection interface collects the application data of the currently running mobile applications. Step S103: Store the collected application data into the corresponding data structure and perform backup processing on the application data.

3. The intelligent adjustment method based on mobile application data parsing according to claim 1 is characterized in that: The step S200 includes: Step S201: Read the application data from the stored data structure. The application data includes all usage logs of each mobile application. The usage logs include the unique identifier, usage date, usage times, usage duration, and data interaction information of the mobile application. Step S202: Obtain the number of days between the current usage date and the last usage date of the mobile application according to the usage date. Step S203: Accumulate the usage times recorded in each usage log of the mobile application to obtain the total usage times of the mobile application. Step S204: Accumulate the usage durations recorded in each usage log of the mobile application to obtain the total usage duration of the mobile application. Obtain the average usage duration of the mobile application through the total usage times and the total usage duration. Step S205: Calculate the corresponding priority score for the mobile application according to the number of days difference, the total usage times, and the average usage duration. The priority score calculation method is as follows: Where Q is the priority score of the mobile application, A is the number of days between the current usage date and the last usage date of the mobile application, ω1 is the weight factor corresponding to the number of days difference, B is the average usage duration of the mobile application, ω2 is the weight factor corresponding to the average usage duration, C is the total usage times of the mobile application, and ω3 is the weight factor corresponding to the total usage times. Step S206: Associate the calculated priority score with the unique identifier of the mobile application to construct a priority mapping table.

4. An intelligent adjustment method based on mobile application data parsing according to claim 1, characterized in that: The step S300 includes: Step S301: Calculate the current data parsing volume size of the mobile application according to the data interaction information in the mobile application data. The data interaction information includes the data volume and the time stamp corresponding to each data volume. Step S302: Count the occurrences of each data volume based on the data volume and the timestamp corresponding to each data volume, calculate the total parsing duration corresponding to each data volume according to the number of occurrences and the timestamp, and set the data volume with the longest total parsing duration as the data volume threshold; Step S303: Calculate the data parsing speed according to the data volume and the timestamp corresponding to each data volume. The calculation method of the data parsing speed is as follows: Among them, V represents the data parsing speed, S i represents the i-th data volume, S i+1 represents the (i + 1)-th data volume, t i represents the timestamp corresponding to the i-th data volume, t i+1 represents the timestamp corresponding to the (i + 1)-th data volume, and N represents the total number of data volumes.

5. The intelligent adjustment method based on mobile application data parsing according to claim 1, wherein: The step S400 includes: Step S401: Monitor the data resource application requests of mobile applications through a data resource listener. The data resource listener has a first data resource and a second data resource, and the priority of the first data resource is higher than that of the second data resource; Step S402: When the data resource listener monitors only one data resource application request of a mobile application, allocate the first data resource to the mobile application; Step S403: When the data resource listener monitors application requests for data resources from multiple mobile applications at the same time, retrieve them in the priority mapping table according to the unique identifier of the mobile application, and sort the mobile applications in descending order of priority score; Step S404: Allocate the first data resource to the mobile application with the highest priority score, and allocate the second data resource to the mobile application with the priority score second only to the highest priority score; Step S405: Set the mobile applications that have not obtained data resources to the waiting state, and record the waiting order of each mobile application according to the priority score sorting. When the first data resource or the second data resource is released, re-allocate the data resources according to the waiting order.

6. The intelligent adjustment method based on mobile application data parsing according to claim 1, wherein: The step S500 includes: Step S501: For the mobile applications that have obtained data resources, parse the data according to the data parsing speed and the data volume threshold; Step S502: When the data resources are transferred, according to the data parsing strategy of the system, process the data of the mobile applications that newly obtain data resources at a speed not exceeding the data volume threshold according to the data parsing speed; Step S503: For the mobile applications that release data resources, stop parsing the data of the mobile applications according to the data parsing strategy of the system; if the data of the mobile applications is not parsed completely, set the mobile applications to the waiting state, and temporarily store the unparsed data in the corresponding data structure.

7. An intelligent adjustment system based on mobile application data parsing, which is used to execute the intelligent adjustment method based on mobile application data parsing according to any one of claims 1-6, characterized in that: The system includes a data acquisition and storage module, a resource allocation module, and a data parsing adjustment module; The data acquisition and storage module is responsible for collecting mobile application data at a preset time interval. The time interval is controlled by a timer and set according to system performance and requirements. When the data resource listener detects a data resource application request, it triggers a data acquisition action. The acquisition interface obtains the data of the currently running mobile applications, stores the mobile application data in the corresponding data structure, and performs backup processing; The resource allocation module monitors data resource application requests through a data resource listener. The data resource listener has a first data resource and a second data resource with different priorities. After the data resource listener detects a data resource application request, if only one mobile application applies, the first data resource is allocated to the mobile application. If multiple mobile applications apply simultaneously, the mobile applications are sorted by priority according to the priority mapping table. The first data resource is allocated to the mobile application with the highest priority, and the second data resource is allocated to the mobile application with the second-highest priority. The mobile applications that do not obtain the data resource enter the waiting state and record the waiting order. When the first data resource or the second data resource is released, the data resource is reallocated according to the waiting order. The data parsing and adjustment module analyzes the mobile application data, calculates the data parsing speed and data volume threshold of each mobile application. The data volume threshold is calculated by counting the total duration corresponding to the number of times the data volume appears, and the data volume corresponding to the maximum value of the total duration is selected as the data volume threshold. The data parsing speed is calculated by combining the data volume and the corresponding timestamp. After the data resource is allocated, according to the system data parsing strategy, the obtained data parsing speed and data volume threshold are used to parse the mobile application data of the mobile application that obtains the data resource.

8. An intelligent adjustment system based on mobile application data parsing according to claim 7, characterized in that: The data collection and storage module includes a mobile application data collection unit and a mobile application data storage unit; The mobile application data collection unit collects mobile application data at the time interval set by the timer. When the data resource listener detects a data resource application request, it triggers the collection action and calls the system mobile application data collection interface to obtain the mobile application data that is currently running. The mobile application data storage unit stores the collected mobile application data in the corresponding data structure and performs backup processing.

9. An intelligent adjustment system based on mobile application data parsing according to claim 7, characterized in that: The resource allocation module includes a priority identification unit and a data resource allocation unit; The priority identification unit calculates the priority score of each mobile application by analyzing the usage logs in the mobile application data and constructs a priority mapping table; The data resource allocation unit uses the data resource listener to monitor data resource application requests. When only one mobile application applies for the data resource, the first data resource is allocated to the mobile application. When multiple mobile applications apply for the data resource simultaneously, the mobile applications are sorted by priority according to the priority mapping table. The first data resource is allocated to the mobile application with the highest priority, and the second data resource is allocated to the mobile application with the second-highest priority. The mobile applications that do not obtain the data resource enter the waiting state and record the waiting order. When the data resource is released, the data resource is reallocated according to the waiting order.

10. An intelligent adjustment system based on mobile application data parsing according to claim 7, characterized in that: The data parsing and adjustment module includes a data volume threshold calculation unit and a data parsing speed calculation unit; The data volume threshold calculation unit counts the number of occurrences of each data volume according to the data interaction information in the mobile application data, calculates the total duration of each data volume occurrence, and sets the data volume corresponding to the longest total duration as the data volume threshold; The data parsing speed calculation unit calculates the data parsing speed in combination with the data volume and timestamp in the mobile application data.

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