A real-time configuration modification and validation management system and method based on the Internet of Things

Abstract

The present invention discloses a real-time configuration modification and validation management system and method based on the Internet of Things, which relates to the technical field of configuration data management. The system comprises: a configuration information processing module, a configuration information presetting module, and a user operation scheduling module; the configuration information processing module is used to divide all configuration information in a distributed system into configuration item units and perform feature identification; the configuration information presetting module is used to predict user access data in a future time period and determine the preset configuration item units of each memory node in the future time period; the user operation scheduling module determines the configuration update order between each memory node when the user updates the configuration information, corrects the update identification of each configuration item unit in each memory node, and calculates the query results of each required configuration item unit in each memory node when the user queries the configuration information, and determines the memory node for all the user's required configuration item unit data queries.

Description

Technical Field

[0001] The present invention relates to the technical field of configuration data management, and in particular to a system and method for real-time configuration modification and validation management based on the Internet of Things. Background Art

[0002] In current complex large-scale distributed systems, the management of configuration information for user operations has the following technical defects: On the one hand, in existing distributed systems, in order to ensure the actual usage experience of users accessing different nodes, commonly used user-oriented configuration information is generally stored in all customer-oriented access nodes to ensure that users can perform data query and update operations in the first time. However, in actual use, due to the differences in configuration data modules involved in user demand functions, different nodes have different degrees of data redundancy and performance resource waste; On the other hand, when dynamically managing the query and update operations of user demand configuration data, the access nodes for the demand configuration data are usually determined based on the real-time performance status of each node. There is a lack of accurate analysis of the user demand configuration data, resulting in a lack of accurate and personalized scheduling of structured configuration data when actually allocating nodes, resulting in resource allocation deviations in node resource management and failure to further optimize;

[0003] Therefore, a real-time configuration modification and validation management system and method based on the Internet of Things is needed to solve the above technical defects. Summary of the Invention

[0004] The purpose of the present invention is to provide a system and method for real-time configuration modification and validation management based on the Internet of Things to solve the problems raised in the prior art.

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

[0006] A method for real-time configuration modification and effectiveness management based on the Internet of Things, the method comprising the following analysis steps:

[0007] Step S100: Divide all configuration information in the distributed system into configuration item units, and perform feature identification on each configuration item unit;

[0008] Step S200: Obtaining historical data on access to configuration information of different types of users in each memory node in the distributed system, summarizing and statistically analyzing it, and predicting the expected number of accesses to each configuration item unit by different users in each memory node in a future time period;

[0009] Step S300: Obtain feature identification data for each configuration item, calculate the access demand rate of each configuration item unit in each memory node in the future time period based on the user access prediction data of each configuration item unit in each memory node, determine the preset configuration item unit for each memory node in each time period, and update the preset configuration item unit data in the memory node at the time point where the time period changes;

[0010] Step S400: When the user updates the configuration information, the real-time operating status data of each memory node is obtained, the configuration update order among the memory nodes is determined, and the update identification of each configuration item unit in each memory node is corrected according to the update time;

[0011] Step S500: When a user queries configuration information, the configuration information queried by the user is divided into required configuration item units, the query results of each required configuration item unit in each memory node are calculated, and the maximum query result strategy is comprehensively analyzed to determine the memory node for all user required configuration item unit data queries.

[0012] In the above technical solution, step S100 includes the following steps:

[0013] Step S101: Acquire all configuration information stored in the distributed system and extract corresponding field information of the configuration information stored in the database;

[0014] Step S102: Divide the configuration information into configuration item units according to the fields to which they belong, and perform feature identification on each configuration item unit;

[0015] For any configuration item unit x, the characteristic identifier is recorded as: x[DB x ,F x ,S x ];

[0016] Among them, DB x The database node where the configuration item unit x is stored, F x is the field name of the configuration item unit x, S x The maximum space occupied when storing data in the field of configuration item unit x;

[0017] By dividing all configuration information in the system into units and identifying characteristics, the system configuration data is managed in a refined manner, which improves the manageability of the configuration information, facilitates the storage and update of the dynamic scheduling configuration data, and provides a data basis for the scheduling of subsequent user queries and update operations.

[0018] In the above technical solution, step S200 includes the following contents:

[0019] Obtaining historical data on access to configuration information of different types of users in each memory node in the distributed system, dividing the configuration information in the historical user access data into configuration item units according to step S100, and dividing and summarizing the historical user access data by time period, counting the number of accesses to different configuration item units by different types of users in each memory node in each time period, and then using a time series prediction model to analyze the expected number of accesses to each configuration item unit by different types of users in each memory node in each future time period;

[0020] By analyzing historical user access data and using a time series prediction model to predict user access data in future time periods, the access frequency of configuration item units can be estimated in advance, the accuracy of resource allocation can be improved, the configuration distribution of memory nodes can be optimized, the access load of memory nodes and databases can be balanced, and the load deviation between memory nodes and database nodes can be reduced, which can cause high concurrent access and lead to system redundancy delays.

[0021] In the above technical solution, step S300 is divided into the following steps:

[0022] Step S301: Acquire feature identification data of each configuration item and information on the estimated number of visits to each configuration item unit by different types of users in each memory node;

[0023] Step S302: Calculate the access demand rate of each configuration item unit in each memory node in the future time period;

[0024] For any configuration item unit x, the access demand rate in memory node m is calculated as follows:

[0025]

[0026] Among them, R A x,m is the access demand rate of configuration item unit x in memory node m, f(x,m) is the comprehensive access frequency of configuration item unit x in memory node m in the future time period, T is the time length of the time period, u is the user type number, N u is the number of user types, k u N is the user authority coefficient of type u, est (u,x,m) is the expected number of accesses to the configuration item unit x by the user of type u in the memory node m in the future time period, k x is the access real-time requirement coefficient of configuration item unit x, S x The maximum space occupied by the field data of the configuration item unit x when storing it, S m is the memory size of memory node m, and exp() is the exponential function of the natural logarithm e.

[0027] Step S303: Determine the preset configuration item units for each memory node in the future time period based on the access demand rate of each configuration item unit in each memory node in the future time period, and update the preset configuration item unit data in the memory node at the time point where the time period alternates;

[0028] For any memory node x, set the preset memory threshold Th x , sort all configuration item units in descending order according to the access demand rate in memory node x in the future time period, set each configuration item unit as the candidate configuration item unit of memory node x in the future time period, and calculate the maximum space occupied by the field data of all candidate configuration item units when storing, and construct the space occupation constraint equation as follows:

[0029]

[0030] Among them, n is the unit number of the configuration item to be selected, S n N is the maximum space occupied by the field data of the candidate configuration item unit n when storing data. x are the parameters to be determined for the space occupancy constraint equation;

[0031] The parameter N is obtained using the space occupancy constraint equation x , and select the first N x The candidate configuration item unit is used as the pre-configured configuration item unit for the memory node x in the future time period, and all pre-configured configuration item unit data are loaded into the memory node x in descending order according to the access demand rate at the start time of the future time period;

[0032] Based on access frequency, real-time requirements and memory occupancy, the preset configuration item units of the memory nodes are dynamically determined, and the space occupancy constraint equation is used to limit the preset loading of the configuration item units of each memory node, realizing prediction-based resource optimization allocation and avoiding memory resource waste and hot node overload problems.

[0033] In the above technical solution, step S400 is divided into the following steps:

[0034] Step S401: When a user updates configuration information, the configuration update order among the memory nodes is determined based on the real-time operating status data of each memory node;

[0035] Divide the configuration information updated by the user into configuration item units using the method in step S100, and record the divided configuration item units as user-updated configuration item units;

[0036] For any user-updated configuration item unit, all memory nodes storing the user-updated configuration item unit are screened out and sorted in ascending order based on the ratio of the memory node's real-time load rate to the load rate threshold. The data of the user-updated configuration item unit in each memory node is updated in turn, and the first memory node in the sorting order updates the data of the user-updated configuration item unit to the database;

[0037] Step S402: Obtain the update time of each configuration item unit in each memory node and modify the update identifier;

[0038] For any configuration item unit x in memory node n, if memory node n receives configuration item unit x data submitted by the user and updates the data, the data update time point is set to the update time of configuration item unit x in memory node n, and the update time is encrypted and corrected to the update identifier of configuration item unit x in memory node n. At the same time, the update identifier is corrected to the update identifier of configuration item unit x in the distributed system;

[0039] If memory node n receives configuration item unit x data sent by other memory nodes for data update, the update time of configuration item unit x in the memory node that sent the data is set to the update time of configuration item unit x in memory node n, and the update identifier of configuration item unit x in the memory node that sent the data is corrected to the update identifier of the configuration item unit in the memory node;

[0040] When updating the configuration item unit data in the memory node, the update identification correction data is decoded with the original update identification data and the update time is compared;

[0041] If the update time in the update identification correction data is earlier than the update time in the original update identification data, the data of the configuration item unit in the memory node is updated; if the update time in the update identification correction data is earlier than or the same as the update time in the original update identification data, the original data in the configuration item unit in the memory node is maintained and no data update is performed;

[0042] Dynamically adjust update priorities to reduce the pressure on high-load nodes and improve the system's concurrent processing capabilities. At the same time, through the correction and comparison analysis of update identifiers, ensure the consistency of configuration information in a distributed environment and avoid data conflicts between different nodes that cause abnormal system behavior.

[0043] In the above technical solution, step S500 is divided into the following steps:

[0044] Step S501: When a user queries configuration information, all the configuration information queried by the user is divided into configuration item units using the method in step S100, and the divided configuration item units are recorded as the required configuration item units of each user;

[0045] Step S502: Calculate the query results of each user's required configuration item unit in each memory node;

[0046] For any type v user, obtain the required configuration item unit data of type v user, select any required configuration item unit y, filter all memory nodes storing configuration item unit y at the current time point as candidate memory nodes, and for any candidate memory node m s , the query result E(v,y,m s ) is calculated as follows:

[0047]

[0048] E(v,y,m s )=k v ×M(v,y,m s )×f(y,m s );

[0049] Among them, M(v,y,m s ) is a user of type v in memory node m s The performance adaptability of the configuration item unit y is queried in , where α and β are the load evaluation coefficient and the delay evaluation coefficient respectively, L(y,m s ) is the memory node m s Query the resource load rate of configuration item unit y, is the memory node m s The resource load rate threshold, t delay (v,m s ) is a user of type v accessing memory node m s The access delay, k v is the user authority coefficient of type v; f(y,m s ) is the data consistency judgment function, when the memory node m s When the update flag of configuration item unit y in the distributed system is consistent with the update flag of configuration item unit x, the function output is 1. s When the update identifier of the configuration item unit y in the distributed system is inconsistent with the update identifier of the configuration item unit x, the function output is 0;

[0050] Step S503: Calculate the query results of each required configuration item unit included in the configuration information queried by each user when querying each candidate memory node, use dynamic programming to determine the maximum query result strategy for each user's configuration information query, and then determine the memory node for querying all the user's required configuration item unit data;

[0051] A query effectiveness calculation formula is introduced to comprehensively consider node load rate, access latency and data consistency, dynamically optimize query node selection, and dynamically adjust the query path of user demand data to ensure full utilization of system node resources while taking into account the optimization of user access query performance, thereby improving the practicality of the system in high concurrency scenarios.

[0052] A configuration real-time modification and effectiveness management system based on the Internet of Things that applies the configuration real-time modification and effectiveness management method based on the Internet of Things described in the above technical solution includes: a configuration information processing module, a configuration information presetting module, and a user operation scheduling module;

[0053] The configuration information processing module is used to divide all configuration information in the distributed system into configuration item units and perform feature identification; the configuration information presetting module is used to predict user access data in the future time period and determine the preset configuration item units of each memory node in the future time period; when the user updates the configuration information, the user operation scheduling module determines the configuration update order between the memory nodes, and corrects the update identification of each configuration item unit in each memory node. When the user queries the configuration information, the module calculates the query results of each required configuration item unit in each memory node, and determines the memory node for all user required configuration item unit data queries.

[0054] In the above technical solution, the configuration information processing module includes: a configuration information division unit and a feature identification unit;

[0055] The configuration information division unit divides all configuration information in the distributed system into configuration item units; the feature identification unit performs feature identification on each configuration item unit.

[0056] In the above technical solution, the configuration information presetting module includes: an access data prediction unit, an access demand rate analysis unit, and a configuration data presetting unit;

[0057] The access data prediction unit obtains the access history data of different types of user configuration information in each memory node in the distributed system, and uses a time series prediction model to predict the expected number of accesses to each configuration item unit by different users in each memory node in the future time period; the access demand rate analysis unit is used to calculate the access demand rate of each configuration item unit in each memory node in the future time period; the configuration data presetting unit is used to determine the preset configuration item unit of each memory node in each time period, and update the preset configuration item unit data in the memory node at the time point where the time period alternates.

[0058] In the above technical solution, the user operation scheduling module includes: a configuration data update scheduling unit and a configuration data query scheduling unit;

[0059] The configuration data update scheduling unit is used to determine the configuration update order between each memory node, and to modify the update identification of each configuration item unit in each memory node according to the update time; the configuration data query scheduling unit is used to calculate the query results of each required configuration item unit in each memory node, and comprehensively analyze the maximum query results strategy to determine the memory node for all user required configuration item unit data queries.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] In this invention, by analyzing user access history data and calculating the access demand rate, a dynamic distribution strategy for configuration data is determined. Factors such as configuration item access frequency, user real-time demand, and node storage space occupancy are comprehensively considered to dynamically adjust the distribution of configuration items in memory nodes. At the same time, by constructing a space occupancy constraint equation, memory node resource utilization is further optimized to avoid overloading of some memory nodes and degradation of local system performance.

[0062] In the present invention, priority management is performed on the user's configuration data update operation based on node load perception. In the scenario of high-frequency user queries, the user data update operation is synchronized to ensure load balancing of each memory node when the configuration data is updated. At the same time, an update identifier is introduced to compare and monitor the data consistency of each memory node in the system to avoid data conflict failures in the system.

[0063] In the present invention, personalized analysis is also performed on the query results of different users querying configuration item units in each memory node, and comprehensive analysis is performed to determine the data source of each user's configuration item unit to ensure optimal overall system operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a flow chart of a method for real-time configuration modification and effectiveness management based on the Internet of Things of the present invention;

[0065] Figure 2 This is an organizational chart of a real-time configuration modification and validation management system based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0067] Example: See Figure 1-Figure 2 , the present invention provides the following technical solutions:

[0068] like Figure 1 As shown, the present invention provides a method for real-time configuration modification and effectiveness management based on the Internet of Things, which includes the following analysis steps:

[0069] Step S100: Divide all configuration information in the distributed system into configuration item units, and perform feature identification on each configuration item unit;

[0070] Step S200: Obtaining historical data on access to configuration information of different types of users in each memory node in the distributed system, summarizing and statistically analyzing it, and predicting the expected number of accesses to each configuration item unit by different users in each memory node in a future time period;

[0071] Step S300: Obtain feature identification data for each configuration item, calculate the access demand rate of each configuration item unit in each memory node in the future time period based on the user access prediction data of each configuration item unit in each memory node, determine the preset configuration item unit for each memory node in each time period, and update the preset configuration item unit data in the memory node at the time point where the time period changes;

[0072] Step S400: When the user updates the configuration information, the real-time operating status data of each memory node is obtained, the configuration update order among the memory nodes is determined, and the update identification of each configuration item unit in each memory node is corrected according to the update time;

[0073] Step S500: When a user queries configuration information, the configuration information queried by the user is divided into required configuration item units, the query results of each required configuration item unit in each memory node are calculated, and the maximum query result strategy is comprehensively analyzed to determine the memory node for all user required configuration item unit data queries.

[0074] The step S100 includes the following steps:

[0075] Step S101: Acquire all configuration information stored in the distributed system and extract corresponding field information of the configuration information stored in the database;

[0076] Step S102: Divide the configuration information into configuration item units according to the fields to which they belong, and perform feature identification on each configuration item unit;

[0077] For any configuration item unit x, the characteristic identifier is recorded as: x[DB x ,F x ,S x ];

[0078] Among them, DB x The database node where the configuration item unit x is stored, F x is the field name of the configuration item unit x, S xThe maximum space occupied when storing data in the field of configuration item unit x;

[0079] In specific implementation, in order to ensure the performance reliability of multi-user and high-concurrency scenarios in distributed systems, a memory-distributed structure will be used for access diversion, and to ensure data security, a database-distributed structure will be used to achieve data redundancy backup and multi-terminal storage. In today's usage scenarios, the above two distributed structures are usually used in combination to take into account both system performance and data security. Therefore, when analyzing system configuration data, all configuration data must be synchronously processed and divided to achieve overall system optimization management and ensure balanced management of performance resource allocation and user experience among all nodes.

[0080] The step S200 includes the following contents:

[0081] Obtain access history data of configuration information of different types of users in each memory node in the distributed system, divide the configuration information in the user access history data into configuration item units according to the step S100, and divide and summarize the user access history data according to time periods, count the number of visits to different configuration item units by different types of users in each memory node in each time period, and then use the time series prediction model to analyze the expected number of visits to each configuration item unit by different types of users in each memory node in each future time period.

[0082] The step S300 is divided into the following steps:

[0083] Step S301: Acquire feature identification data of each configuration item and information on the estimated number of visits to each configuration item unit by different types of users in each memory node;

[0084] Step S302: Calculate the access demand rate of each configuration item unit in each memory node in the future time period;

[0085] For any configuration item unit x, the access demand rate in memory node m is calculated as follows:

[0086]

[0087] Among them, R A x,m is the access demand rate of configuration item unit x in memory node m, f(x,m) is the comprehensive access frequency of configuration item unit x in memory node m in the future time period, T is the time length of the time period, u is the user type number, N u is the number of user types, k u N is the user authority coefficient of type u, est (u,x,m) is the expected number of accesses to the configuration item unit x by the user of type u in the memory node m in the future time period, k xis the access real-time requirement coefficient of configuration item unit x, S x The maximum space occupied by the field data of the configuration item unit x when storing it, S m is the memory size of memory node m, exp() is the exponential function of the natural logarithm e;

[0088] During specific implementation, taking into account the differences in different types of users and access configuration data of each user on each memory node at future time points, the expected access frequency of each configuration item unit is introduced, and combined with the space occupied by each configuration item unit in each memory node for analysis, a comprehensive assessment is made of the access needs of different configuration item units in each memory node in the future time period, ensuring that high-demand configuration data can be stored in the memory first, ensuring the smoothness of the user access experience.

[0089] Step S303: Determine the preset configuration item units for each memory node in the future time period based on the access demand rate of each configuration item unit in each memory node in the future time period, and update the preset configuration item unit data in the memory node at the time point where the time period alternates;

[0090] For any memory node x, set the preset memory threshold Th x , sort all configuration item units in descending order according to the access demand rate in memory node x in the future time period, set each configuration item unit as the candidate configuration item unit of memory node x in the future time period, and calculate the maximum space occupied by the field data of all candidate configuration item units when storing, and construct the space occupation constraint equation as follows:

[0091]

[0092] Among them, n is the unit number of the configuration item to be selected, S n N is the maximum space occupied by the field data of the candidate configuration item unit n when storing data. x are the parameters to be determined for the space occupancy constraint equation;

[0093] The parameter N is obtained using the space occupancy constraint equation x , and select the first N x The candidate configuration item unit is used as the pre-configured configuration item unit for the memory node x in the future time period, and all pre-configured configuration item unit data are loaded into the memory node x in descending order according to the access demand rate at the start time of the future time period;

[0094] In the specific implementation, since the increase in the amount of data stored in the memory will lead to an increase in the delay of memory data processing, a space occupancy constraint equation is constructed by setting a preset memory threshold to limit the amount of preset configuration data in the memory, avoiding excessive memory occupation and resulting in a decrease in the access efficiency of each memory node.

[0095] The step S400 is divided into the following steps:

[0096] Step S401: When a user updates configuration information, the configuration update order among the memory nodes is determined based on the real-time operating status data of each memory node;

[0097] Divide the configuration information updated by the user into configuration item units using the method in step S100, and record the divided configuration item units as user-updated configuration item units;

[0098] For any user-updated configuration item unit, all memory nodes storing the user-updated configuration item unit are screened out and sorted in ascending order based on the ratio of the memory node's real-time load rate to the load rate threshold. The data of the user-updated configuration item unit in each memory node is updated in turn, and the first memory node in the sorting order updates the data of the user-updated configuration item unit to the database;

[0099] Step S402: Obtain the update time of each configuration item unit in each memory node and modify the update identifier;

[0100] For any configuration item unit x in memory node n, if memory node n receives configuration item unit x data submitted by the user and updates the data, the data update time point is set to the update time of configuration item unit x in memory node n, and the update time is encrypted and corrected to the update identifier of configuration item unit x in memory node n. At the same time, the update identifier is corrected to the update identifier of configuration item unit x in the distributed system;

[0101] If memory node n receives configuration item unit x data sent by other memory nodes for data update, the update time of configuration item unit x in the memory node that sent the data is set to the update time of configuration item unit x in memory node n, and the update identifier of configuration item unit x in the memory node that sent the data is corrected to the update identifier of configuration item unit x in memory node n;

[0102] When updating the data of configuration item unit x in memory node n, the update identification correction data is decoded with the original update identification data and then the update time is compared;

[0103] If the update time in the update identification correction data is earlier than the update time in the original update identification data, the data of the configuration item unit x in the memory node is updated; if the update time in the update identification correction data is earlier than or the same as the update time in the original update identification data, the original data in the configuration item unit x in the memory node is maintained and the data is not updated;

[0104] During the specific implementation, it is considered that when each memory node is updating data, it is inevitable that multiple sources of data will synchronize data on the same memory node at the same time. Therefore, the update identifier of the memory node that is performing data update synchronization is corrected according to the update time corresponding to the update identifier, so as to further ensure the timeliness and consistency of data between distributed memory nodes and avoid data conflict failures in the system.

[0105] The step S500 is divided into the following steps:

[0106] Step S501: When a user queries configuration information, all the configuration information queried by the user is divided into configuration item units using the method in step S100, and the divided configuration item units are recorded as the required configuration item units of each user;

[0107] Step S502: Calculate the query results of each user's required configuration item unit in each memory node;

[0108] For any type v user, obtain the required configuration item unit data of type v user, select any required configuration item unit y, filter all memory nodes storing configuration item unit y at the current time point as candidate memory nodes, and for any candidate memory node m s , the query result E(v,y,m s ) is calculated as follows:

[0109]

[0110] E(v,y,m s )=k v ×M(v,y,m s )×f(y,m s );

[0111] Among them, M(v,y,m s ) is a user of type v in memory node m s The performance adaptability of the configuration item unit y is queried in , where α and β are the load evaluation coefficient and the delay evaluation coefficient respectively, L(y,m s ) is the memory node m s Query the resource load rate of configuration item unit y, is the memory node m s The resource load rate threshold, t delay (v,m s ) is a type v user accessing memory node m s The access delay, k v is the user authority coefficient of type v; f(y,m s ) is the data consistency judgment function, when the memory node m sWhen the update identifier of the configuration item unit in the memory node is consistent with the update identifier of the configuration item unit in the distributed system, the function output is. When the update identifier of the configuration item unit in the memory node is inconsistent with the update identifier of the configuration item unit in the distributed system, the function output is;

[0112] Step S503: Calculate the query results of each required configuration item unit included in the configuration information queried by each user when querying each candidate memory node, use dynamic programming to determine the maximum query result strategy for each user's configuration information query, and then determine the memory node for querying all the user's required configuration item unit data;

[0113] In specific implementation, because during high-concurrency access, there are often multiple users querying configuration data, and the configuration information queried by users is different, the configuration data queried by users is analyzed in a unitized manner, and the query results of each configuration item unit when querying in different memory nodes are calculated. In particular, to balance the reliability of system performance and the actual user access experience, the node load occupancy rate and access delay parameters are introduced for analysis when calculating the query results. At the same time, a consistency judgment function is added to ensure the rationality and scientific nature of user access operation scheduling, and further ensure the reliability of system resource scheduling and allocation.

[0114] When allocating resources and scheduling data for users who perform query operations in the system, dynamic programming is used to obtain a strategy that maximizes the overall query effectiveness. In actual use, since query operations have operation time, it is necessary to determine the start time and exit time of the algorithm to avoid repeated calculations of the algorithm when the same group of users perform resource allocation. Therefore, when the system receives a new user query operation request, a new round of resource scheduling strategy analysis can be performed, and the analysis results can be used as the memory node allocation result for the configuration data of all current users performing data query operations.

[0115] like Figure 2 As shown, the present invention also provides a real-time configuration modification and validation management system based on the Internet of Things, the system comprising: a configuration information processing module, a configuration information presetting module, and a user operation scheduling module;

[0116] The configuration information processing module is used to divide all configuration information in the distributed system into configuration item units and perform feature identification; the configuration information presetting module is used to predict user access data in the future time period and determine the preset configuration item units of each memory node in the future time period; when the user updates the configuration information, the user operation scheduling module determines the configuration update order between the memory nodes, and corrects the update identification of each configuration item unit in each memory node. When the user queries the configuration information, the module calculates the query results of each required configuration item unit in each memory node, and determines the memory node for all user required configuration item unit data queries.

[0117] The configuration information processing module includes: a configuration information division unit and a feature identification unit;

[0118] The configuration information division unit divides all configuration information in the distributed system into configuration item units; the feature identification unit performs feature identification on each configuration item unit.

[0119] The configuration information presetting module includes: an access data prediction unit, an access demand rate analysis unit, and a configuration data presetting unit;

[0120] The access data prediction unit obtains the access history data of different types of user configuration information in each memory node in the distributed system, and uses a time series prediction model to predict the expected number of accesses to each configuration item unit by different users in each memory node in the future time period; the access demand rate analysis unit is used to calculate the access demand rate of each configuration item unit in each memory node in the future time period; the configuration data presetting unit is used to determine the preset configuration item unit of each memory node in each time period, and update the preset configuration item unit data in the memory node at the time point where the time period alternates.

[0121] The user operation scheduling module includes: a configuration data update scheduling unit and a configuration data query scheduling unit;

[0122] The configuration data update scheduling unit is used to determine the configuration update order between each memory node, and to modify the update identification of each configuration item unit in each memory node according to the update time; the configuration data query scheduling unit is used to calculate the query results of each required configuration item unit in each memory node, and comprehensively analyze the maximum query results strategy to determine the memory node for all user required configuration item unit data queries.

[0123] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A method for real-time configuration modification and effectiveness management based on the Internet of Things, characterized in that , the method comprises the following analysis steps: Step S100: Divide all configuration information in the distributed system into configuration item units, and perform feature identification on each configuration item unit; Step S200: Obtaining historical data on access to configuration information of different types of users in each memory node in the distributed system, summarizing and statistically analyzing it, and predicting the expected number of accesses to each configuration item unit by different users in each memory node in a future time period; Step S300: Obtain feature identification data for each configuration item, calculate the access demand rate of each configuration item unit in each memory node in the future time period based on the user access prediction data of each configuration item unit in each memory node, determine the preset configuration item unit for each memory node in each time period, and update the preset configuration item unit data in the memory node at the time point where the time period changes; Step S400: When the user updates the configuration information, the real-time operating status data of each memory node is obtained, the configuration update order among the memory nodes is determined, and the update identification of each configuration item unit in each memory node is corrected according to the update time; Step S500: When a user queries configuration information, the configuration information is divided into required configuration item units, the query results of each required configuration item unit in each memory node are calculated, and the maximum query result strategy is comprehensively analyzed to determine the memory node for querying all the required configuration item unit data of the user; The step S500 is divided into the following steps: Step S501: When a user queries configuration information, all the configuration information queried by the user is divided into configuration item units using the method in step S100, and the divided configuration item units are recorded as the required configuration item units of each user; Step S502: Calculate the query results of each user's required configuration item unit in each memory node; For any type User, get type User's requirement configuration item unit data, select any requirement configuration item unit , filter the storage configuration item unit at the current time point All memory nodes are selected as candidate memory nodes. For any candidate memory node ,type User requirement configuration item unit Query results The calculation formula is as follows: ； ； in, For type User in memory node Query Configuration Item Unit The performance adaptability of and are the load assessment coefficient and the delay assessment coefficient respectively, Memory node Query Configuration Item Unit The resource load ratio, Memory node The resource load rate threshold, For type User access memory node The access delay, For type User authority coefficient; It is the data consistency judgment function. When the memory node Configuration item unit Update identification and configuration item unit in distributed system When the update identifiers are consistent, the function output is , when the memory node Configuration item unit Update identification and configuration item unit in distributed system When the update identifiers are inconsistent, the function output is ; Step S503: Calculate the query results of each required configuration item unit contained in the configuration information queried by each user when querying each candidate memory node, use dynamic programming method to determine the maximum query result strategy when each user queries the configuration information, and then determine the memory node for all user required configuration item unit data queries.

2. The method for real-time configuration modification and effectiveness management based on the Internet of Things according to claim 1, characterized in that: The step S100 includes the following steps: Step S101: Acquire all configuration information stored in the distributed system and extract corresponding field information of the configuration information stored in the database; Step S102: Divide the configuration information into configuration item units according to the fields to which they belong, and perform feature identification on each configuration item unit; For any configuration item unit , the feature identifier is recorded as: ; in, Configuration item unit The stored database nodes, Configuration item unit The name of the field to which it belongs, Configuration item unit The maximum space occupied by the field when storing data.

3. The method for real-time configuration modification and effectiveness management based on the Internet of Things according to claim 1 is characterized in that: The step S200 includes the following contents: Obtain access history data of configuration information of different types of users in each memory node in the distributed system, divide the configuration information in the user access history data into configuration item units according to the step S100, and divide and summarize the user access history data according to time periods, count the number of visits to different configuration item units by different types of users in each memory node in each time period, and then use the time series prediction model to analyze the expected number of visits to each configuration item unit by different types of users in each memory node in each future time period.

4. The method for real-time configuration modification and effectiveness management based on the Internet of Things according to claim 3 is characterized in that: The step S300 is divided into the following steps: Step S301: Acquire feature identification data of each configuration item and information on the estimated number of visits to each configuration item unit by different types of users in each memory node; Step S302: Calculate the access demand rate of each configuration item unit in each memory node in the future time period; For any configuration item unit , in the memory node The calculation formula for the access demand rate is as follows: ； ； in, Configuration item unit In memory node The access demand rate in Configure item units for future time periods In memory node The comprehensive access frequency in The length of time divided into time periods, is the user type number, is the number of user types, For type User authority coefficient, For memory nodes in the future time period Medium type User-defined configuration item unit The expected number of visits, Configuration item unit The access real-time demand coefficient, Configuration item unit The maximum space occupied by the data of the field when it is stored. Memory node The memory size, is the natural logarithm exponential function of ; Step S303: Determine the preset configuration item units for each memory node in the future time period based on the access demand rate of each configuration item unit in each memory node in the future time period, and update the preset configuration item unit data in the memory node at the time point where the time period alternates; For any memory node , set the preset memory threshold , all configuration item units are stored in memory nodes according to the future time period Sort the access demand rates in descending order, and set each configuration item unit as a memory node in the future time period. The candidate configuration item units are calculated, and the maximum space occupied by the field data of all candidate configuration item units is calculated when storing data, and the space occupation constraint equation is constructed as follows: ； in, is the unit number of the configuration item to be selected, Configuration item unit to be selected The maximum space occupied by the field data when storing, are the parameters to be determined for the space occupancy constraint equation; Use the space occupancy constraint equation to find the parameters , and select the front To the candidate configuration item unit as a memory node Preset configuration item unit in the future time period and send it to the memory node at the start time of the future time period. All preset configuration item unit data are loaded in descending order according to the access demand rate.

5. The method for real-time configuration modification and effectiveness management based on the Internet of Things according to claim 1 is characterized in that: The step S400 is divided into the following steps: Step S401: When a user updates configuration information, the configuration update order among the memory nodes is determined based on the real-time operating status data of each memory node; Divide the configuration information updated by the user into configuration item units using the method in step S100, and record the divided configuration item units as user-updated configuration item units; For any user-updated configuration item unit, all memory nodes storing the user-updated configuration item unit are screened out and sorted in ascending order based on the ratio of the memory node's real-time load rate to the load rate threshold. The data of the user-updated configuration item unit in each memory node is updated in turn, and the first memory node in the sorting order updates the data of the user-updated configuration item unit to the database; Step S402: Obtain the update time of each configuration item unit in each memory node and modify the update identifier; For any memory node Configuration item unit in , if the memory node Receive configuration item units submitted by users If the data is updated, the data update time point is set to the memory node Configuration item unit The update time is encrypted and corrected to the memory node Configuration item unit The update identifier is modified to the configuration item unit in the distributed system Update ID of If the memory node Receive configuration item units sent by other memory nodes If data is updated, the configuration item unit in the memory node of the data will be sent The update time is set to the memory node Configuration item unit The update time and the configuration item unit in the memory node that sends the data The update identifier is corrected to the memory node Configuration item unit Update ID of In memory node Configuration item unit When data is updated, the update identification correction data is decoded with the original update identification data and the update time is compared; If the update time in the update identifier correction data is earlier than the update time in the original update identifier data, the configuration item unit in the memory node Update the data; if the update time in the update identifier correction data is earlier than or the same as the update time in the original update identifier data, the configuration item unit in the memory node is maintained The original data is not updated.

6. A configuration real-time modification and effectiveness management system based on the Internet of Things using the configuration real-time modification and effectiveness management method based on the Internet of Things according to any one of claims 1 to 5, characterized in that: The system includes: a configuration information processing module, a configuration information presetting module, and a user operation scheduling module; The configuration information processing module is used to divide all configuration information in the distributed system into configuration item units and perform feature identification; the configuration information presetting module is used to predict user access data in the future time period and determine the preset configuration item units of each memory node in the future time period; when the user updates the configuration information, the user operation scheduling module determines the configuration update order between the memory nodes, and corrects the update identification of each configuration item unit in each memory node. When the user queries the configuration information, the module calculates the query results of each required configuration item unit in each memory node, and determines the memory node for all user required configuration item unit data queries.

7. The configuration real-time modification and validation management system based on the Internet of Things according to claim 6 is characterized in that: The configuration information processing module includes: a configuration information division unit and a feature identification unit; The configuration information division unit divides all configuration information in the distributed system into configuration item units; the feature identification unit performs feature identification on each configuration item unit.

8. The system for real-time configuration modification and validation management based on the Internet of Things according to claim 6, characterized in that: The configuration information presetting module includes: an access data prediction unit, an access demand rate analysis unit, and a configuration data presetting unit; The access data prediction unit obtains the access history data of different types of user configuration information in each memory node in the distributed system, and uses a time series prediction model to predict the expected number of accesses to each configuration item unit by different users in each memory node in the future time period; the access demand rate analysis unit is used to calculate the access demand rate of each configuration item unit in each memory node in the future time period; the configuration data presetting unit is used to determine the preset configuration item unit of each memory node in each time period, and update the preset configuration item unit data in the memory node at the time point where the time period alternates.

9. The configuration real-time modification and validation management system based on the Internet of Things according to claim 6 is characterized in that: The user operation scheduling module includes: a configuration data update scheduling unit and a configuration data query scheduling unit; The configuration data update scheduling unit is used to determine the configuration update order between each memory node, and to modify the update identification of each configuration item unit in each memory node according to the update time; the configuration data query scheduling unit is used to calculate the query results of each required configuration item unit in each memory node, and comprehensively analyze the maximum query results strategy to determine the memory node for all user required configuration item unit data queries.

Images