A local data storage method for an interactive window management system

Abstract

The present invention relates to the field of data storage technology, and specifically to a local data storage method for an interactive window management system. This method obtains the initial correlation strength between each data based on the logical interval distance between any two data on the cloud server and the page position distance on the interactive page, and corrects the initial correlation strength based on the user's access data set to obtain the target correlation strength. The probability value of the user accessing the unaccessed data is obtained based on the access interactive page set and the target correlation strength between each target data of each user and the unaccessed data associated with the same interactive page; the corresponding unaccessed data is loaded locally in sequence according to the loading sequence determined by the probability value. Therefore, combined with the user's access data and the situation of accessing the interactive page, the data with a higher user access intention is predicted at a deeper level, thereby accurately storing the data with a higher user access intention, significantly increasing the efficiency of data storage.

Description

Technical Field

[0001] The present invention relates to the technical field of data storage, and in particular to a local data storage method of an interactive window management system. Background Art

[0002] An interactive window management system allows users to intuitively see various visual interactions through a user interface and conveniently implement various interactive functions through various interactive methods. The interactive functions of an interactive window management system require the support of various types of data. Typically, all data required by the interactive window management system is stored in a cloud server. Generally, when a user operates the interactive window management system, the user-specified data needs to be loaded from the cloud to the user's local device.

[0003] Currently, in the existing technology, in order to speed up data loading, not only the access data specified based on the user's interactive behavior is usually loaded, but also the data in the area adjacent to the location of the access data on the cloud server can be pre-loaded. However, this way of storing data does not take into account changes in user access needs. For example, when the user switches interactive pages or accesses data that is far away from the accessed data, it is easy to cause the storage of data with low user access intention, which makes it difficult to cope with changes in user access needs. In addition, nowadays, even a single user interaction may involve a large amount of data. Storing too much data at one time will lead to the consumption of unnecessary resources and storage space, thereby slowing down the response speed of the interactive window management system. Summary of the Invention

[0004] In order to solve the technical problem in the prior art of storing data locally without considering the changing access needs of users, resulting in the storage of data with low user access intention, the present invention aims to provide a local data storage method for an interactive window management system. The technical solution adopted is as follows:

[0005] The present invention proposes a local data storage method for an interactive window management system, the method comprising:

[0006] Acquire an access data set and an access interaction page set of at least one user who uses the interactive window management system;

[0007] Determining the initial association strength between each piece of data associated with each interactive page based on the logical interval distance and page position distance between any two pieces of data associated with each interactive page in the interactive window management system; wherein the logical interval distance is determined based on the logical address of the data on the cloud server, and the page position distance is determined based on the coordinate position of the data on the corresponding interactive page;

[0008] According to each target data in the access data set described by each user, the initial correlation strength is modified to obtain a target correlation strength between each data;

[0009] Obtaining a probability value of a user accessing unvisited data based on the frequency of accessing a target interactive page in the interactive page set and the target association strength between each target data of each user and the unvisited data associated with the same interactive page;

[0010] According to the loading sequence determined by the probability value, the corresponding unaccessed data are loaded locally in sequence.

[0011] Furthermore, the process of obtaining the accessed data set and the accessed interactive page set includes:

[0012] If a user who is using the interactive window management system is identified, a call is made to the cloud server;

[0013] Based on the identified user interaction behavior on the interactive window management system, the target interactive page visited by the user and the target duration of the visit to the target interactive page are obtained through the cloud server;

[0014] Determining the visited interactive page set based on the target interactive page and the corresponding target duration;

[0015] Based on the specified data loaded from the cloud server by the interactive behavior, the target data loaded in the data associated with the target interactive page is obtained, and the access data set of the user is determined based on the target data.

[0016] Furthermore, the local data storage method of the interactive window management system further includes:

[0017] If an initial user who uses the interactive window management system for the first time is identified, preset initialization data is loaded into the local storage of the initial user through the cloud server;

[0018] An initialization page pre-built based on the initialization data is displayed to the initial user.

[0019] Furthermore, the method for obtaining the initial correlation strength includes:

[0020] For each interactive page, the logical interval distance between each data associated with the interactive page is determined based on the difference between the logical addresses of any two data on the cloud server;

[0021] Determine the page position distance between each data item associated with the interactive page based on the distance between the positions of the interactive modes corresponding to any two data items on the interactive page;

[0022] The initial association strength between the data associated with the interactive page is obtained according to the logical interval distance and the page position distance, wherein the logical interval distance is negatively correlated with the initial association strength, and the page position distance is negatively correlated with the initial association strength.

[0023] Furthermore, the method for obtaining the target association strength includes:

[0024] Analyze any two first target data associated with the same interactive page obtained from the access data set of each user to obtain the total number of first users, the total number of second users, and the interval average value corresponding to the any two first target data;

[0025] determining a correction coefficient between any two first target data according to the first total number of users, the second total number of users, and the interval average value;

[0026] The initial correlation strength between the data identical to any two first target data is corrected according to the correction coefficient to obtain the target correlation strength.

[0027] Furthermore, a method for obtaining the first total number of users, the second total number of users, and the interval average value includes:

[0028] Based on the number of first access data sets in which any two first target data are respectively located, determined from all the access data sets, taking the maximum value of the number of the first access data sets as the total number of the first users;

[0029] Based on the number of second access data sets in which any two first target data are commonly present, determined from all the access data sets, the number of the second access data sets is used as the total number of the second users;

[0030] The interval average value is obtained based on an average value of the number of target data intervals between any two first target data in the corresponding second access data set.

[0031] Furthermore, the local data storage method of the interactive window management system further includes:

[0032] If there are two data that are different from any two first target data, the initial correlation strength between the two data that are different from any two first target data is used as the target correlation strength.

[0033] Furthermore, the method for obtaining the probability value includes:

[0034] Based on the total number of times and total duration of occurrence of the same target interaction page obtained from the visited interaction page set of each user;

[0035] Summing the target association strengths between each target data of the user and the unvisited data associated with the same interactive page to obtain a total association strength between the unvisited data determined based on the user's interactive behavior;

[0036] The probability value of the user accessing each unaccessed data is obtained according to the total number of times, the total duration and the total association strength, and the total number of times, the total duration and the total association strength are all positively correlated with the probability value.

[0037] Furthermore, the process of summing the target association strengths between each target data of the user and the unvisited data associated with the same interactive page includes:

[0038] performing a summation operation on the target association strengths between all target data and unvisited data associated with the target data on the same interactive page to determine a first association strength;

[0039] performing a summation operation on the target association strengths between all associated unvisited data and the target data from the same interactive page to determine a second association strength;

[0040] The total association strength of the user is determined based on the sum of the first association strength and the second association strength.

[0041] Furthermore, the locally loading the corresponding unaccessed data in sequence according to the loading sequence determined by the probability value includes:

[0042] Arrange the probability values ​​of users accessing unaccessed data in descending order to generate a loading sequence;

[0043] According to the order of the loading sequence, each unaccessed data is loaded into the user's local device in turn.

[0044] The present invention has the following beneficial effects:

[0045] The present invention first obtains a user's access data set and a set of visited interactive pages, so as to subsequently predict possible changes in the user's access needs based on the user's own access behavior. Secondly, based on the logical distance between any two data items associated with each interactive page on the cloud server and the page location distance on the interactive page, an initial correlation strength between each data item is obtained. This not only takes into account the simultaneous accessibility of data of the same type, but also the subsequent simultaneous accessibility of data on the same interactive page, and preliminarily predicts the likelihood of each data item being subsequently accessed. The initial correlation strength is then further modified based on the user's actual access behavior to obtain a target correlation strength. The target correlation strength is further calculated based on the user's access behavior to interactive pages, and the probability of the user accessing unaccessed data is obtained. Unaccessed data is data that has not yet been locally stored. Therefore, based on the user's access data and interactive page access behavior, a more in-depth prediction of data with a high user access intention is made from the user's perspective. This reduces the scope and quantity of data required for loading, and then locally stores the corresponding data in descending order of probability, accurately storing data with a high user access intention and significantly improving data storage efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] Figure 1 A flow chart of a local data storage method of an interactive window management system provided by one embodiment of the present invention;

[0048] Figure 2 A schematic diagram of a process for obtaining an access data set and an access interaction page set provided by one embodiment of the present invention;

[0049] Figure 3 An example diagram of the duration of a user's visit to each interactive page provided by one embodiment of the present invention;

[0050] Figure 4 A schematic diagram of a process for obtaining initial correlation strength provided by one embodiment of the present invention;

[0051] Figure 5 A schematic diagram of a process for obtaining target association strength according to an embodiment of the present invention;

[0052] Figure 6A schematic diagram of a process for obtaining a probability value provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0053] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of a local data storage method for an interactive window management system proposed by the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0054] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0055] The following describes in detail a specific solution of a local data storage method for an interactive window management system provided by the present invention with reference to the accompanying drawings.

[0056] See also Figure 1 , which shows a flow chart of a local data storage method of an interactive window management system provided by one embodiment of the present invention, the method comprising:

[0057] S101: Acquire an access data set and an access interactive page set of at least one user who uses an interactive window management system.

[0058] It should be understood that the interactive window management system includes multiple interactive pages, which provide a variety of visual interaction methods to realize the interactive functions that the interactive window management system can provide, and the realization of these visual interaction methods requires the support of various types of data. All data contained in the interactive window management system are pre-stored on the cloud server. As the user starts the interactive window management system and performs various interactive behaviors on the intended interactive page, the specified data can be loaded from the cloud server based on the user's interactive behavior to provide corresponding interactive functions. Therefore, the cloud server can be used to understand the user's actual access to data and interactive pages in this operation of the interactive window management system.

[0059] It should be noted that the various visual interaction methods provided on the interactive page can be set according to actual needs. For example, it can be a visual interactive function button, or an interaction method between an input box and a form. This embodiment does not make specific limitations.

[0060] The process of accessing data collection and accessing interactive page collection is as follows Figure 2 Shown, including:

[0061] S101-1: If a user who is using the interactive window management system is identified, a cloud server is called.

[0062] S101-2: Based on the identified interactive behavior of the user on the interactive window management system, the target interactive page visited by the user and the target duration of the visit to the target interactive page are obtained through the cloud server.

[0063] S101 - 3 : Determine a set of visited interactive pages based on the target interactive page and the corresponding target duration.

[0064] S101 - 4 : Based on the designated data loaded from the cloud server by the interactive behavior, target data loaded in the data associated with the target interactive page is obtained, and the user's access data set is determined based on the target data.

[0065] It is understandable that, for different users, the determined access interaction page sets are also different.

[0066] What needs to be understood is that even for the same user, different interactive pages can reflect different user needs and intentions. Some interactive pages can reflect the user's strong demand intentions for them. Therefore, the longer the interactive page stay time, the more likely the user's identity and needs are to be predicted. When the user uses the interactive window management system, the user may also have different stay times on different interactive pages. The longer the stay time on the page, the more likely the data on that page will be accessed later.

[0067] For example, the example diagram of the time users spend visiting each interactive page is as follows: Figure 3 As shown, interactive page A, interactive page B and interactive page C are different interactive pages. The length of the graphic where the interactive page is located in the example figure reflects the duration of the interactive page. Figure 3 It can be seen that when users visit interactive page B for the first time, they stay the longest.

[0068] Among them, it should be noted that Figure 3 The order of arrangement of each interactive page is determined according to the order of user access.

[0069] It should be understood that due to the different identities of users, the usage and usage habits of the interactive window management system are also different. There may be users who use the interactive window management system for the first time. Among them, when the user terminal and the cloud server of the interactive window management system have not established a historical interaction data cache, or when there is a user identity identifier that has not been assigned the authority to operate the interactive window management system, it can be determined that the user identified is using the interactive window management system for the first time. When the user uses the interactive window management system for the first time, an initialization interface can be provided to the user. At this time, the user has not performed various interactive operations. The data contained in the initialization interface is usually some of the most basic data. In order to ensure that various functions on the initialization interface are available for normal use by users, the data contained in the initialization interface can be downloaded from the cloud server.

[0070] In some implementations of the embodiments of the present invention, if an initial user who uses the interactive window management system for the first time is identified, preset initialization data is loaded into the local memory of the initial user through the cloud server; an initialization page pre-built based on the initialization data is displayed to the initial user.

[0071] It should be noted that the specific data of the initialization data can be preset according to actual conditions, and this embodiment does not limit it.

[0072] S102: Determine the initial correlation strength between each data associated with each interactive page according to the logical interval distance and the page position distance between any two data associated with each interactive page in the interactive window management system.

[0073] It should be noted that the logical spacing distance is determined based on the logical address of the data on the cloud server, and the page position distance is determined based on the coordinate position of the data on the corresponding interactive page.

[0074] What needs to be understood is that in the interactive window management system, there are some connections between the shared access situations of various data. For the same type of data, the logical addresses of these data stored on the cloud server are similar. For example, after the data of interactive page A is accessed, the other data contained in interactive page A is more likely to be accessed by the user subsequently. After a certain data is accessed, the data on the same interactive page is also more likely to be accessed together. Moreover, the same type of data is reflected on the interactive page and is also used to implement the same type of interactive function. In actual scenarios, for the sake of beauty and convenience, the position of the same type of data on the interactive page is also close. Therefore, by analyzing the logical interval distance and page position distance between data, we can further understand the possibility of shared access between various data.

[0075] The process of obtaining the initial correlation strength is as follows Figure 4Shown, including:

[0076] S102-1: For each interactive page, determine the logical interval distance between each data associated with the interactive page according to the difference between the logical addresses of any two data on the cloud server.

[0077] It is understandable that the interactive window management system has multiple interactive pages, and the interactive pages are associated with multiple pieces of data.

[0078] Logical address, used to indicate the address of a data through machine language instructions.

[0079] For example, assuming that the logical address is represented by a hexadecimal number, taking a 32-bit operating system as an example, the logical address range of the 32-bit operating system is 0x00000000 to 0xFFFFFFFF. Assuming that data 1 is stored at logical address A (0x1000) and data 2 is stored at logical address B (0x1020), the logical interval distance between data 1 and data 2 = the difference between logical address A and logical address B = 0x1020-0x1000 = 0x20 (hexadecimal difference).

[0080] S102-2: Based on the distance between the positions of the interaction modes corresponding to any two data on the interaction page, determine the page position distance between each data associated with the interaction page.

[0081] It should be noted that since the representation of the interaction method is different and the interactive functions that can be realized are also different, the location of the interaction method will change according to actual needs. The means of obtaining the location of the interaction method is a technical means well known to technical personnel in this field and will not be repeated in this embodiment.

[0082] S102-3: Obtaining initial correlation strengths between data associated with interactive pages based on the logical interval distance and the page position distance. The logical interval distance is negatively correlated with the initial correlation strength, and the page position distance is negatively correlated with the initial correlation strength.

[0083] Since the closer the logical addresses stored on the cloud server are, the smaller the logical interval is, and the more likely the adjacent data will be accessed by the user later, the logical interval distance is negatively correlated with the initial association strength. The closer the corresponding interaction methods of two data are on the interactive page, the greater the possibility that the other data will be accessed after one data is accessed. Therefore, the page position distance is negatively correlated with the initial association strength. Therefore, the initial association strength can be expressed by the following formula:

[0084]

[0085] Among them, it is assumed that the interactive window management system coexists pages, each containing pieces of data; They are used to represent the interactive window management system The interactive page under With the data, ; Used to indicate the The interactive page under With the The logical interval distance between data; Used to indicate the The interactive page under With the The page position distance between the data; Used to indicate the The interactive page under With the The initial correlation strength between the data.

[0086] S103: According to each target data in the access data set of each user, the initial correlation strength is modified to obtain the target correlation strength between each data.

[0087] It is important to understand that the initial association strength only analyzes the association between data accesses by comparing the logical spacing distance and page position distance between data on the interactive page. However, different users have different usage habits, and their needs are constantly changing. Therefore, the initial association strength can also be modified based on the user's access to the interactive window management system, so as to more deeply predict the data that users have a higher access intention from the user's perspective.

[0088] The process of obtaining target association strength is as follows: Figure 5 Shown, including:

[0089] S103-1: Analyze any two first target data associated with the same interactive page obtained from the access data set of each user to obtain the total number of first users, the total number of second users, and the interval average value corresponding to the any two first target data.

[0090] It is understandable that the access data sets obtained are different for different users.

[0091] In order to accurately obtain the total number of first users, as an example, based on the number of first access data sets in which any two first target data are respectively located, the maximum value of the number of first access data sets is taken as the total number of first users.

[0092] To accurately obtain the total number of second users, as an example, based on the number of second access data sets in which any two first target data are commonly found from all access data sets, the number of second access data sets is used as the total number of second users.

[0093] In order to accurately obtain the interval average value, as an example, the interval average value is obtained based on the average value of the number of target data between any two first target data in the corresponding second access data set.

[0094] For example, suppose there are users A, B and C, and user A's access data set A is { , , }, user B's access data set B is { , }, user C's access data set C is { , , }, then and As the first target data, in access data set A, access data set B and access data set C, respectively, The first access data set includes access data set A and access data set B, a total of two sets, The first access data set includes access data set A and access data set C, a total of two sets; then and The number of first access data sets in each is 2, and the total number of first users is 2; in access data set A, access data set B, and access data set C, and The second access data sets that appear together are access data set A and access data set C, two sets in total, so the number of second users is 2; and When access data set A and access data set C exist together, the interval between the two data and For these two data, the interval average is 1.

[0095] S103 - 2 : Determine a correction coefficient between any two first target data according to the total number of first users, the total number of second users, and the interval average value.

[0096] According to user access patterns, if two data items are accessed together, there must be a further connection between the two items. Furthermore, for all users using the interactive window management system, the access patterns of all users can be used as analytical data to further explore user access intentions. The larger the number of first users of two items, the higher the probability that one of the two items appears in other accessed data sets, further indicating a higher likelihood that the user accesses both items. Similarly, the larger the number of second users of two items, the higher the probability that the two items appear together in other accessed data sets, further demonstrating a higher likelihood that the two items are accessed together. Therefore, the larger the ratio of the second number of users to the first number of users, the higher the likelihood that the two items are accessed together, which is positively correlated with the correction coefficient. The larger the interval between two items, the longer the interval between accessing one item and accessing the other, resulting in temporal discontinuity. This, to some extent, reflects a lower user enthusiasm for accessing both items, i.e., a lower likelihood of accessing both items. Therefore, the interval average is negatively correlated with the correction coefficient.

[0097] S103 - 3 : Correcting the initial correlation strength between the data identical to any two first target data according to the correction coefficient to obtain the target correlation strength.

[0098] The target association strength can be expressed by the following formula:

[0099]

[0100] in, Used to indicate the The interactive page under With the The number of first users of the data; Used to indicate the The interactive page under With the The number of second users of the data; For all simultaneous accesses to data The interactive page under With the The average value of the number of interval data between the data; Used to indicate the The interactive page under With the Correction coefficient between data; Used to indicate the The interactive page under With the The target correlation strength between data.

[0101] It is important to understand that multiple data may exist on the same interactive page. Even if users access data on the same interactive page, two data may exist and be accessed together. Therefore, these data that have not been accessed together reflect the user's low intention for these data and do not need to be corrected.

[0102] In this embodiment, if there are two data that are different from any two first target data, the initial correlation strength between the two data that are different from any two first target data is used as the target correlation strength.

[0103] S104: Obtain a probability value of a user accessing unvisited data based on the frequency of accessing the target interactive page in the interactive page set and the target association strength between each target data of each user and the unvisited data associated with the same interactive page.

[0104] It should be understood that when users use the interactive window management system, they may stay on different interactive pages for different lengths of time, and they can switch between different interactive pages. Interactive pages with more stay times can show the user's strong demand intention, and thus the interactive pages with more stay times can be used to further predict the data that the user has a higher intention to visit in the future, so as to further narrow the scope and quantity of the data required for loading.

[0105] The process of obtaining the probability value is as follows Figure 6 Shown, including:

[0106] S104-1: Based on the total number of times and total duration of appearance of the same target interactive page obtained from the visited interactive page set of each user.

[0107] S104-2: Sum the target association strengths between each target data of the user and the unvisited data associated with the same interactive page to obtain the total association strength determined by the user between the unvisited data based on the user's interactive behavior.

[0108] It can be understood that the unaccessed data is used to represent the data associated with the interactive page that has not been locally loaded into the user's local device.

[0109] In order to fully obtain the total association strength, as an example, a sum operation is performed on the target association strengths between all target data and the unvisited data associated with them on the same interactive page to determine the first association strength; a sum operation is performed on the target association strengths between all associated unvisited data and the target data from the same interactive page to determine the second association strength; based on the sum of the first association strength and the second association strength, the total association strength of the user is determined.

[0110] Assume that interactive window management system coexists interactive pages, each containing The access data set of a user k is , , the total association strength can be expressed by the following formula:

[0111]

[0112] in, Used to return access data collection The number of data included; Subscript data Must be included in the access data collection ; or To represent data Unaccessed data The strength of the target association between It is used to indicate the number of the user k who has visited the data set. Data of interactive pages and data of non-visited pages The total strength of the association between Used to indicate the first association strength; Used to indicate the second association strength.

[0113] S104-3: Obtain a probability value of the user accessing each unaccessed data according to the total number of times, total duration, and total association strength. The total number of times, total duration, and total association strength are all positively correlated with the probability value.

[0114] When using the interactive window management system, the more times a user switches to a certain interactive page, the more likely the user is to use this interactive page multiple times. This means that the probability that subsequent users will access the data on this interactive page is higher. The total number of times is positively correlated with the probability value. The longer a user stays on a certain interactive page, the higher the probability that subsequent users will access the data on this interactive page. The total duration is positively correlated with the probability value. Therefore, the probability value can be expressed by the following formula:

[0115]

[0116] in, Used to indicate the user k's use of the interactive window management system for data The The total duration of stay on each interactive page; Used to represent the user k's interaction with the data The The total number of times an interactive page is visited.

[0117] S105: According to the loading sequence determined by the probability value, the corresponding unaccessed data are loaded locally in sequence.

[0118] It is important to understand that the probability value is used to indicate the likelihood of unaccessed data being accessed later. Different unaccessed data will have different probability values, representing different levels of user access intention. Since the interaction process requires synchronization with cloud server data, and data loading also takes some time, in order to respond to user interaction needs in a timely manner, data can be loaded locally one by one according to the probability value.

[0119] In order to synchronize the data predicted to have a high user access intention with the cloud server in a timely manner, as a possible implementation method, the corresponding unaccessed data is loaded into the local device in descending order of the probability value of the unaccessed data.

[0120] As an example, the probability values ​​of users accessing unaccessed data are arranged in descending order to generate a loading sequence; and each unaccessed data is loaded into the user's local device in sequence according to the order of the loading sequence.

[0121] It can be understood that local devices include user terminals, edge computing nodes and other devices that perform local storage and processing.

[0122] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0123] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A local data storage method for an interactive window management system, characterized in that: The method comprises: Acquire an access data set and an access interaction page set of at least one user who uses the interactive window management system; Determining the initial association strength between each piece of data associated with each interactive page based on the logical interval distance and page position distance between any two pieces of data associated with each interactive page in the interactive window management system; wherein the logical interval distance is determined based on the logical address of the data on the cloud server, and the page position distance is determined based on the coordinate position of the data on the corresponding interactive page; According to each target data in the access data set described by each user, the initial correlation strength is modified to obtain a target correlation strength between each data; Obtaining a probability value of a user accessing unvisited data based on the frequency of accessing a target interactive page in the interactive page set and the target association strength between each target data of each user and the unvisited data associated with the same interactive page; According to the loading sequence determined by the probability value, the corresponding unaccessed data are loaded locally in sequence.

2. The local data storage method of the interactive window management system according to claim 1, characterized in that: The process of obtaining the accessed data set and the accessed interactive page set includes: If a user who is using the interactive window management system is identified, a call is made to the cloud server; Based on the identified user interaction behavior on the interactive window management system, the target interactive page visited by the user and the target duration of the visit to the target interactive page are obtained through the cloud server; Determining the visited interactive page set based on the target interactive page and the corresponding target duration; Based on the specified data loaded from the cloud server by the interactive behavior, the target data loaded in the data associated with the target interactive page is obtained, and the access data set of the user is determined based on the target data.

3. The local data storage method of the interactive window management system according to claim 2, characterized in that: The method comprises: If an initial user who uses the interactive window management system for the first time is identified, preset initialization data is loaded into the local storage of the initial user through the cloud server; An initialization page pre-built based on the initialization data is displayed to the initial user.

4. The local data storage method of the interactive window management system according to claim 1, characterized in that: The method for obtaining the initial correlation strength includes: For each interactive page, the logical interval distance between each data associated with the interactive page is determined based on the difference between the logical addresses of any two data on the cloud server; Determine the page position distance between each data item associated with the interactive page based on the distance between the positions of the interactive modes corresponding to any two data items on the interactive page; The initial association strength between the data associated with the interactive page is obtained according to the logical interval distance and the page position distance, wherein the logical interval distance is negatively correlated with the initial association strength, and the page position distance is negatively correlated with the initial association strength.

5. The local data storage method of the interactive window management system according to claim 1, characterized in that: The method for obtaining the target association strength includes: Analyze any two first target data associated with the same interactive page obtained from the access data set of each user to obtain the total number of first users, the total number of second users, and the interval average value corresponding to the any two first target data; determining a correction coefficient between any two first target data according to the first total number of users, the second total number of users, and the interval average value; The initial correlation strength between the data identical to any two first target data is corrected according to the correction coefficient to obtain the target correlation strength.

6. The local data storage method of the interactive window management system according to claim 5, characterized in that: A method for obtaining the first total number of users, the second total number of users, and the interval average value includes: Based on the number of first access data sets in which any two first target data are respectively located, determined from all the access data sets, taking the maximum value of the number of the first access data sets as the total number of the first users; Based on the number of second access data sets in which any two first target data are commonly present, determined from all the access data sets, the number of the second access data sets is used as the total number of the second users; The interval average value is obtained based on an average value of the number of target data intervals between any two first target data in the corresponding second access data set.

7. The local data storage method of the interactive window management system according to claim 5 or 6, characterized in that: The method comprises: If there are two data that are different from any two first target data, the initial correlation strength between the two data that are different from any two first target data is used as the target correlation strength.

8. The local data storage method of the interactive window management system according to claim 1, characterized in that: The method for obtaining the probability value includes: Based on the total number of times and total duration of occurrence of the same target interaction page obtained from the visited interaction page set of each user; Summing the target association strengths between each target data of the user and the unvisited data associated with the same interactive page to obtain a total association strength between the unvisited data determined based on the user's interactive behavior; The probability value of the user accessing each unaccessed data is obtained according to the total number of times, the total duration and the total association strength, and the total number of times, the total duration and the total association strength are all positively correlated with the probability value.

9. The local data storage method of the interactive window management system according to claim 8, characterized in that: The process of summing the target association strengths between each target data of the user and the unvisited data associated with the same interactive page includes: performing a summation operation on the target association strengths between all target data and unvisited data associated with the target data on the same interactive page to determine a first association strength; performing a summation operation on the target association strengths between all associated unvisited data and the target data from the same interactive page to determine a second association strength; The total association strength of the user is determined based on the sum of the first association strength and the second association strength.

10. The local data storage method of the interactive window management system according to claim 1, characterized in that: The locally loading the corresponding unaccessed data in sequence according to the loading sequence determined by the probability value includes: Arrange the probability values ​​of users accessing unaccessed data in descending order to generate a loading sequence; According to the order of the loading sequence, each unaccessed data is loaded into the user's local device in turn.

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