CN110727690A - Data updating method - Google Patents
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Abstract
The invention relates to a data updating method, which comprises the following steps: step S1: receiving and buffering data to be updated, and storing the data identification to be updated in a queue to be processed and/or a target update queue; step S2: preprocessing a target update queue in sequence; step S3: determining a processing mode of the target update queue and performing corresponding processing; step S4: and executing the data update in the target update queue. The invention can effectively remove the duplicate of the data to be updated in a double-layer queue mode; these targets are activated or woken up only if an update is really needed; the learning speed and the classification accuracy of a classification mode are improved by performing multi-level organization of multi-dimensional target characteristics; and a diversified updating stage is provided by constructing an updating path diagram, so that the user experience is greatly improved.
Description
[ technical field ] A method for producing a semiconductor device
The invention belongs to the technical field of computers, and particularly relates to a data updating method.
[ background of the invention ]
With the continuous and rapid development of internet science and technology and big data technology, data has more and more importance in people's life, and the aspects of people's traffic, identity safety, property safety and the like are related to, and almost all trades are influenced by data more or less. Furthermore, as the informatization technology is mature, the frequency of data interaction is increased, and the management and the monitoring of data are also important. Data applications have penetrated almost every field, are widely present in our daily lives, and have become an essential element of every processing unit. However, because data is multi-level and multi-source, the data holding objects are diversified, the data updating frequency is higher and higher, and the data management platform has dynamics and complexity, so that a lot of difficulties are brought to data management and monitoring including data updating. In a conventional data updating environment, research is often performed on the data updating problem, such as: data updating between data and the outside, synchronous updating between databases and the like, and when the source database is updated in full, the target database can only be updated accordingly. The types of updates are common incremental updates as well as full-scale updates, and the like. The traditional data updating method is difficult to deal with the characteristics of terminals, users and data with obvious differentiation in the times of big data and internet. These traditional updating methods are very rigid, have poor adaptability and high resource consumption. The invention can effectively remove the duplicate of the data to be updated in a double-layer queue mode; these targets are activated or woken up only if an update is really needed; updating data analysis based on the types can be carried out on different types of updating targets by setting a minimum updating unit, so that the data to be updated are preprocessed based on the dependency relationship; the learning speed and the classification accuracy of a classification mode are improved by performing multi-level organization of multi-dimensional target characteristics; and a diversified updating stage is provided by constructing an updating path diagram, so that the user experience is greatly improved.
[ summary of the invention ]
In order to solve the above problems in the prior art, the present invention provides a data updating method, including:
step S1: receiving and buffering data to be updated, and storing the data identification to be updated in a queue to be processed and/or a target update queue;
step S2: preprocessing a target update queue in sequence;
step S3: determining a processing mode aiming at the target update queue and carrying out corresponding processing;
step S4: and executing the data update in the target update queue.
Further, in step S1, specifically, the step includes: storing data to be updated in a temporary buffer area, and storing a data identifier to be updated in a queue to be processed; and deleting the data to be updated or storing the data to be updated in the target update queue based on the processing result of the queue to be processed.
Further, the data identifier to be updated is stored in the queue to be processed according to the sequence of the receiving time.
Further, when a new data identifier to be updated is stored, matching is performed in the queue to be processed according to the identifier, if the same data identifier to be updated is matched, whether the data to be updated corresponding to the two matched data identifiers to be updated is the same is further judged, and if the same, the newly received data to be updated is deleted; otherwise, storing the data identification to be updated into a target update queue corresponding to the target for which the data to be updated is updated, and storing the data to be updated into a storage space corresponding to the target.
Further, the target is a database.
Further, the object update queue corresponds to an updated object.
Further, the number of the target update queues is one or more.
Further, the deletion is to empty the temporary buffer area.
Further, the determining whether the data to be updated corresponding to the two matched data to be updated identifiers is the same specifically includes: and judging whether the abstract values and/or the version numbers of the corresponding data to be updated are the same.
Furthermore, the same terminal corresponds to a plurality of different data targets to be updated.
The beneficial effects of the invention include: effective duplicate removal of data to be updated can be performed in a double-layer queue mode; these targets are activated or woken up only if an update is really needed; updating data analysis based on the types can be carried out on different types of updating targets by setting a minimum updating unit, so that the data to be updated are preprocessed based on the dependency relationship; the learning speed and the classification accuracy of a classification mode are improved by performing multi-level organization of multi-dimensional target characteristics; and a diversified updating stage is provided by constructing an updating path diagram, so that the user experience is greatly improved.
[ description of the drawings ]
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, and are not to be considered limiting of the invention, in which:
FIG. 1 is a diagram illustrating a data updating method according to the present invention.
[ detailed description ] embodiments
The present invention will now be described in detail with reference to the drawings and specific embodiments, wherein the exemplary embodiments and descriptions are provided only for the purpose of illustrating the present invention and are not to be construed as limiting the present invention.
The updating data is not executed immediately when arriving, and whether the updating data is executed depends on the terminal state and the user characteristics;
a data update method applied by the present invention is described in detail, as shown in fig. 1, the method includes:
step S1: receiving and buffering data to be updated, and storing the data to be updated identifier in a queue to be processed and/or a target update queue, specifically: storing data to be updated in a temporary buffer area, and storing a data identifier to be updated in a queue to be processed; deleting the data to be updated or storing the data to be updated in a target updating queue based on the processing result of the queue to be processed;
storing the data identifier to be updated in the queue to be processed according to the sequence of the receiving time; when a new data identifier to be updated is stored, matching is carried out in the queue to be processed according to the identifier, if the same data identifier to be updated is matched, whether the data to be updated corresponding to the two matched data identifiers to be updated is the same is further judged, and if the data to be updated is the same, the newly received data to be updated is deleted; otherwise, storing the data identification to be updated into a target update queue corresponding to the target for which the data to be updated is updated, and storing the data to be updated into a storage space corresponding to the target; wherein a size of a storage space corresponding to the target is limited; the target updating queue corresponds to an updated target; the number of the target updating queues is one or more;
wherein the deleting is emptying the temporary buffer area;
preferably: the judging whether the data to be updated corresponding to the two matched data to be updated identifiers is the same specifically includes: judging whether the abstract values and/or the version numbers of the corresponding data to be updated are the same;
the same terminal corresponds to a plurality of different data objects to be updated, for example: when receiving the update data, the update data are subjected to unified primary processing, so that the corresponding update programs of a plurality of different targets do not need to be activated or awakened for simple repeated operation and the like, and the targets are activated or awakened only when the update is really needed, thereby greatly reducing the possibility of generating large resource overhead. Meanwhile, the updating mode can better accord with the user habit and the terminal characteristic; even when the data packet of a pseudo update type is attacked, the target to be updated can still use the allocated resources to carry out execution and processing in order;
preferably: modifying the queue to be processed based on the target update queue;
step S2: preprocessing a target update queue in sequence; the method specifically comprises the following steps: determining a minimum updating unit type based on the target type and the type of the data to be updated, further determining useless data to be updated, and deleting the marks of the useless data to be updated and the corresponding data to be updated in the target updating queue;
the determining the minimum update unit type based on the target type and the data type to be updated specifically includes: inquiring the corresponding minimum updating unit type according to the target type and the type of the data to be updated; providing query by pre-storing corresponding relation; for example: the target type is an application program, the updating data type of the application program is a binary type, and the corresponding minimum updating unit is inquired to be a minimum structural unit and the like; the target type is a database, the updating data type is text data, and the minimum updating unit is a form and the like after being inquired;
the determining of the useless data to be updated specifically includes: traversing the data to be updated corresponding to each data to be updated identifier in the target updating queue, and determining useless data to be updated in the target updating queue based on the updating relation between the minimum updating unit of the target to be updated and the data to be updated;
preferably: when a minimum updating unit for which the previous data to be updated is exactly the same as or completely covered by the next data to be updated, determining whether data to be updated between the previous data to be updated and the next data to be updated has a data dependency relationship with respect to the minimum updating unit, and if not, determining that the previous data to be updated is useless data to be updated; the minimum updating unit is the minimum data unit (data block) to be updated in the target, the minimum problem unit (such as the minimum bug unit), the minimum structure unit (such as dll unit) and the like;
the data dependency relationship refers to a data dependency relationship existing between the execution of the update operation of the subsequent data to be updated and the minimum unit for which the update operation of the previous update data is directed, and the data dependency relationship is a structural dependency relationship (for example, data call, etc.) and a data value (for example, the value needs to be judged in the call process, etc.); adjustment of the update sequence without regard to dependencies can cause updates to be erroneous; preferably: the updating relation among the updating data is the updating relation aiming at the minimum updating unit; the updating condition can be determined according to the version relationship, and the corresponding updating condition is recorded in the version information; in a common case, Version1 is optimized for Bug1, Version2 is also optimized for Bug1, and Version is optimized in a better way, wherein Version1 is determined to be useless updating data; after useless updating data preprocessing, all the data to be updated in the target updating queue are the updating data needing to be subjected to updating operation;
step S3: determining a processing mode aiming at the target update queue and carrying out corresponding processing; specifically, the method comprises the following steps: classifying the processing mode of the target updating queue based on the multi-dimensional target characteristics, and processing the target updating queue according to the classified processing mode;
the multi-dimensional target features comprise a first feature, a second feature, a third feature and a summary feature; wherein the first feature, the second feature and the third feature respectively describe attribute features of different participants related to the target update; the summary and feature representation is characterized in that specific parameter representation of the target is represented under the summary and effect of updating related participants;
preferably: the first characteristic is a target characteristic, and the second characteristic is a resource and/or an available resource of a terminal to which the target belongs; wherein: the resource refers to inherent resource, and the available resource is the current available resource; the third characteristic is a user characteristic; summarizing the characteristics of the target, wherein the comprehensive characteristics comprise the use frequency of the target, the average use time of the target and the like; for example: when the target is an application program, the target characteristics are the type of the program and the like;
preferably: classifying by adopting a neural network; wherein the neural network has a hierarchy of 3 layers; specifically, the method comprises the following steps: inputting the first, second and third characteristics into the first, second and third models respectively to obtain first, second and third outputs respectively; inputting the first feature and the summary and summary feature, the second feature and the summary and summary feature, and the third feature and the summary and summary feature into a fourth model, a fifth model, and a sixth model respectively to obtain a fourth output, a fifth output, and a sixth output; inputting the fourth, fifth and sixth outputs as inputs to the top model to obtain a classification result; thus, the bottom layer corresponds to the own characteristics of each independent participant, and the middle layer correspondingly represents the information interaction condition between each participant and the common representation parameters; finally, the top layer can represent the relation between the multi-dimensional parameters and the final classification result; although the current deep neural network with more than 3 layers is very popular in research, the network computation cost with excessive layers is greatly increased, the processing of the target queue is trained and classified based on the characteristics of participants, and a balance between the computation cost and the accuracy is achieved by adopting a 3-layer model;
preferably: training the model by adopting training data; the adopted training data come from the historical data of the terminal or from the use condition of the current target on each type of terminal;
the processing mode comprises an emptying recording mode, a receiving rejection mode and an opportunity selection updating mode; the emptying recording mode specifically comprises the following steps: sequentially forming identification sequences by all the identifications of the data to be updated in the target update queue, storing the identification sequences, and deleting the current target update queue and the corresponding update data; when the subsequent updating needs to be executed, corresponding data to be updated is obtained according to the identification sequence and is updated, and partial terminal resources can be released after the target updating queue is deleted in the mode; this way corresponds to that no data update is needed for a while, but the update data and its identification sequence are saved to be updated at the next election; the receiving refusal mode is as follows: deleting the current target update queue and the corresponding update data thereof, and rejecting the subsequent reception of the update data aiming at the target; the machine selection updating mode is as follows: data to be updated in the target updating queue needs to be executed opportunistically; wherein the prioritizing comprises performing an update immediately and after a delay of a certain length of time; optionally, the setting of the immediate execution updating mode is classified and independent of the computer selection updating mode, and the setting can be specifically set by a user;
step S4: executing data update in the target update queue; specifically; determining an updating time and a reminding type simultaneously according to the multi-dimensional parameters; when the reminding type is not reminding, directly executing all data updating in the target updating queue when the updating time arrives; otherwise, when the reminding type is reminding, when the updating time arrives, reminding the user to update the data at the updating time, and updating the data based on the user feedback; the multidimensional parameters comprise target attributes (such as types of targets), target updating frequency, user characteristics (updating habits of users and the like), target updating queue length and/or corresponding updating data size, terminal resources and/or available resources; the user feedback may be a common deferred update, immediate update, or the like;
preferably: determining an updating time and a reminding type by adopting a neural network model; the updating time is a multi-classification result, and different classification results are identified for the multi-classification result through a numerical interval; the reminding type is a dichotomous classification result; the model containing two types of output labels, namely the opportunity and the reminding type, is set at the same time for prediction, so that part of data update is invisible to a user, and the user experience is greatly improved; in fact, many update modes in the prior art are absolutely based on user feedback or absolutely based on non-user feedback, the former repeatedly reminds to cause the user experience to be obviously reduced, and how to process many updates is unknown for the user; the latter obviously increases a large amount of update which is not intended by the user, and causes obvious resource waste;
preferably: determining an updating opportunity and a reminding type by adopting a machine learning model; the method comprises the steps that a first machine learning model is adopted to determine updating opportunity, and a second machine learning model is adopted to determine reminding types; setting weights to quickly obtain available machine learning models, thereby reducing machine learning time; the input parameters of the first machine learning model and the second machine learning model are the same, but the weight setting of the input parameters is different when different machine learning models are input;
preferably: when a user is reminded, providing a plurality of updating stages aiming at the target updating queue and corresponding updating time thereof; the plurality of updating stages respectively correspond to different data to be updated, and the updating operation among the stages is independent; for example: the target updating queue comprises 10 data to be updated, the data to be updated respectively correspond to 3 stages, the data to be updated corresponding to the three stages can be independently executed, and respectively correspond to one updating time, namely three updating times; for a separate update phase, the user may choose to perform a partial update phase and defer performing other updates; or a combination of various update time overlays;
the providing of the plurality of update stages and the corresponding update times for the target update queue specifically includes: traversing a target updating queue, and determining a plurality of updating path graphs based on all the minimum updating units and the dependency relationship among the minimum updating units for which each updating data is aimed; the nodes in the multiple updating path graphs are updating data, and the two updating data which are executed successively are corresponding to each other; data dependency does not exist among the multiple update path graphs, that is, the updates among the multiple update path graphs can be completed synchronously, and the dependency on the data update does not exist among the corresponding different update data; the data updates in the update path graph have a dependency relationship, and each update path graph in the plurality of update path graphs can be executed in sequence; when the nodes in the graph do not have the order relationship, the data updating corresponding to the nodes without the order relationship can be synchronously executed; calculating the shortest execution time of the updated path graph according to the sequence relation among the nodes in the updated path graph and the execution time of each piece of updated data; each updating path graph corresponds to one updating stage, and the shortest execution time corresponds to the updating time corresponding to the updating path graph; an updating path diagram corresponds to an updating stage, so that a user can select between relatively independent data combinations to be updated, updating time is flexibly organized, and the staged updating to be executed is selected according to the time schedule; providing various combinations of the corresponding update time of the plurality of update stage machines based on the allowable degree of synchronous execution; the updating stages can be executed synchronously or sequentially based on the selection of a user, so that the updating time can be overlapped or spliced in parallel;
preferably: after the update corresponding to the update data is executed, deleting the corresponding data identification to be updated and the corresponding data to be updated in the target update queue; selectively modifying the queue to be processed;
the selected modification queue is used for deleting the identification of the data to be updated in the queue to be processed and the corresponding data to be updated in real time synchronization or delayed synchronization; when the delay synchronization is selected, although the data to be updated is deleted, the corresponding data to be updated identifier is still kept in the queue to be processed, so that the possible deduplication can be conveniently carried out; the real-time synchronization or the delay synchronization can be selected according to the type of the target; therefore, the problem of experience reduction such as repeated pushing of updated data for a specific target is avoided; when real-time synchronization is selected, deleting the data identification to be updated in the queue to be processed while deleting the data identification to be updated in the target update queue;
the above description is only a preferred embodiment of the present invention, and all equivalent changes or modifications of the structure, characteristics and principles described in the present invention are included in the scope of the present invention.
Claims (10)
1. A method for updating data, the method comprising:
step S1: receiving and buffering data to be updated, and storing the data identification to be updated in a queue to be processed and/or a target update queue;
step S2: preprocessing a target update queue in sequence;
step S3: determining a processing mode aiming at the target update queue and carrying out corresponding processing;
step S4: and executing the data update in the target update queue.
2. The data updating method according to claim 1, wherein the step S1 specifically includes: storing data to be updated in a temporary buffer area, and storing a data identifier to be updated in a queue to be processed; and deleting the data to be updated or storing the data to be updated in the target update queue based on the processing result of the queue to be processed.
3. The data updating method of claim 2, wherein the data identifier to be updated is stored in the queue to be processed according to the sequence of the receiving time.
4. The data updating method according to claim 3, wherein when a new data identifier to be updated is stored, matching is performed in the queue to be processed according to the identifier, if the same data identifier to be updated is matched, whether the data to be updated corresponding to the two matched data identifiers to be updated is the same is further judged, and if the same, the newly received data to be updated is deleted; otherwise, storing the data identification to be updated into a target update queue corresponding to the target for which the data to be updated is updated, and storing the data to be updated into a storage space corresponding to the target.
5. The data update method of claim 4, wherein the target is a database.
6. The data updating method of claim 5, wherein the target update queue corresponds to a target being updated.
7. The data updating method of claim 6, wherein the target updating queue is one or more.
8. The data updating method of claim 7, wherein the deleting is emptying the temporary buffer.
9. The data updating method according to claim 8, wherein the determining whether the data to be updated corresponding to the two matched data to be updated identifiers is the same specifically comprises: and judging whether the abstract values and/or the version numbers of the corresponding data to be updated are the same.
10. The data updating method of claim 9, wherein the same terminal corresponds to a plurality of different data objects to be updated.
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| CN114124846B (en) * | 2021-11-15 | 2023-08-11 | 聚好看科技股份有限公司 | Service queue consumption method and server |
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