Configuration real-time modification and effective management system and method based on Internet of Things

Abstract

The invention discloses a real-time configuration modification and effective management system and method based on the Internet of Things, and 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 for performing configuration item unit division and feature identification on all configuration information in the distributed system; the configuration information presetting module is used for predicting user access data in a future time period and determining a preset configuration item unit of each memory node in the future time period; the user operation scheduling module is used for determining a configuration updating sequence among the memory nodes when a user updates the configuration information, performing updating identification correction on the configuration item units in the memory nodes, and calculating query effects of the required configuration item units in the memory nodes when the user queries the configuration information; and determining memory nodes for data query of all the demand configuration item units of the user.

Description

Technical Field

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

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

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

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

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

[0006] A configuration real-time modification and effectiveness management method based on the Internet of Things, the method includes 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: Obtain and summarize the access historical data of different types of user configuration information in each memory node in the distributed system, and predict the expected access times of different users to each configuration item unit in each memory node in the future time period;

[0009] Step S300: Obtain the feature identification data of each configuration item, calculate the access demand rate of each configuration item unit in each memory node in the future time period according to the user access prediction data of each configuration item unit in each memory node, determine the preset configuration item units of each memory node in each time period, and update and store the preset configuration item unit data in the memory node at the time point of time period alternation;

[0010] Step S400: When the user updates the configuration information, obtain the real-time operation status data of each memory node, determine the configuration update order among the memory nodes, and correct the update identification of each configuration item unit in each memory node according to the update time;

[0011] Step S500: When the user queries the configuration information, split the configuration information queried by the user into required configuration item units, calculate the query effectiveness of each required configuration item unit in each memory node query, and comprehensively analyze the maximum query effectiveness strategy to determine the memory node for querying the data of all required configuration item units of the user.

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

[0013] Step S101: Obtain all the configuration information stored in the distributed system, and extract the corresponding field 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 feature identification is denoted as: x[DB x ,F x ,S x ;

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

[0017] By dividing all the configuration information in the system into units and performing feature identification, the system configuration data is managed in a refined manner, the manageability of the configuration information is improved, and it is convenient to dynamically schedule the storage and update of the configuration data, providing a data basis for the subsequent scheduling of user query and update operations.

[0018] In the above technical solution, the following contents are included in the step S200:

[0019] Obtain the access historical data of different types of user configuration information in each memory node of the distributed system, divide the configuration information in the user access historical data into configuration item units according to the step S100, divide and summarize the user access historical data according to time periods, count the access times of different types of users to different configuration item units in each memory node within each time period, and then use a time series prediction model to analyze the expected access times of different types of users to different configuration item units in each memory node in future time periods;

[0020] By analyzing the user access historical data, using a time series prediction model to predict the user access data in future time periods, estimating the access frequency of configuration item units in advance, improving the accuracy of resource allocation, optimizing the configuration distribution of memory nodes, balancing the memory node and database access loads, and reducing the high concurrency access caused by the load deviation between the memory node and the database node, resulting in system redundancy delay.

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

[0022] Step S301: Obtain the feature identification data of each configuration item and the expected access times information of different types of users to each configuration item unit 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 formula for calculating the access demand rate in memory node m is as follows:

[0025]

[0026] Where, 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 time period division, u is the user type number, N u is the number of user types, k u is the user permission coefficient of type u, N est (u,x,m) is the expected access times of type u users to configuration item unit x in memory node m in the future time period, k x is the access real-time demand coefficient of configuration item unit x, S x is the maximum occupied space when storing the data of the field to which configuration item unit x belongs, 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 of each memory node within the future time period according to the access demand rates of each configuration item unit in each memory node within the future time period, and update and store the data of the preset configuration item units in the memory nodes at the time point of time period alternation;

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

[0029]

[0030] where n is the candidate configuration item unit number, and S n is the sum of the maximum occupied spaces when storing the data of the field to which the candidate configuration item unit n belongs, and N x is the parameter to be solved in the space occupancy constraint equation;

[0031] Use the space occupancy constraint equation to obtain the parameter N x , and select the top N x candidate configuration item units as the preset configuration item units of the memory node x in the future time period, and successively load all the data of the preset configuration item units into the memory node x in descending order of the access demand rate at the start time point of the future time period;

[0032] Dynamically determine the preset configuration item units of the memory nodes based on the access frequency, real-time requirements, and memory occupancy, and use the space occupancy constraint equation to limit the preset loading of the configuration item units of each memory node, achieving prediction-based resource optimization allocation and avoiding memory resource waste and hot node overload problems.

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

[0034] Step S401: When the user updates the configuration information, determine the configuration update order among the memory nodes according to the real-time operation status data of each memory node;

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

[0036] For any user to update a configuration item unit, filter out all memory nodes storing the user's updated configuration item unit, sort them in ascending order according to the ratio of the real-time load rate of the memory node to the load rate threshold, and sequentially update the data of the user's updated configuration item unit in each memory node. Then, the first memory node in the sorting updates the data of the user's updated configuration item unit to the database;

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

[0038] For the configuration item unit x in any memory node n, if the memory node n receives the data of the configuration item unit x submitted by the user for data update, set the data update time point as the update time of the configuration item unit x in the memory node n, encrypt the update time, and correct it as the update identifier of the configuration item unit x in the memory node n. At the same time, correct this update identifier as the update identifier of the configuration item unit x in the distributed system;

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

[0040] When performing data update on the configuration item unit in the memory node, compare the update time after decoding the update identifier correction data and the original update identifier data;

[0041] If the update time in the update identifier correction data is earlier than the update time in the original update identifier data, perform data update on the configuration item unit in the memory node; 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, maintain the original data in the configuration item unit in the memory node and do not perform data update;

[0042] Dynamically adjust the update priority, reduce the pressure on high-load nodes, improve the concurrent processing ability of the system. At the same time, through the correction and comparison analysis of the update identifier, ensure the consistency of the configuration information in the distributed environment and avoid system behavior anomalies caused by data conflicts in different nodes.

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

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

[0045] Step S502: Calculate the query effectiveness of each requirement configuration item unit of the user in each memory node query;

[0046] For any type v user, obtain the requirement configuration item unit data of type v user, select any one requirement configuration item unit y among them, screen all memory nodes storing the configuration item unit y at the current time point as candidate memory nodes, and for any candidate memory node m s , the query effectiveness E(v, y, m s ) of the requirement configuration item unit y of type v user 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 the performance adaptation degree of type v user querying the configuration item unit y in the memory node m s , α and β are the load evaluation coefficient and delay evaluation coefficient respectively, L(y, m s ) is the resource load rate of the memory node m s querying the configuration item unit y, is the resource load rate threshold of the memory node m s , t delay (v, m s ) is the access delay of type v user accessing the memory node m s , k v is the permission coefficient of type v user; f(y, m s ) is the data consistency judgment function. When the update identifier of the configuration item unit y in the memory node m s is consistent with the update identifier of the configuration item unit x in the distributed system, the function output is 1. When the update identifier of the configuration item unit y in the memory node m s is inconsistent with the update identifier of the configuration item unit x in the distributed system, the function output is 0;

[0050] Step S503: Calculate the query effectiveness of each requirement configuration item unit included in the queried configuration information of each user when querying in each candidate memory node, use the dynamic programming method to determine the maximum query effectiveness strategy when each user queries the configuration information, and then determine the memory nodes for querying all the requirement configuration item unit data of the user;

[0051] The query effectiveness calculation formula is introduced. Considering the node load rate, access latency, and data consistency comprehensively, the query node selection is dynamically optimized. By dynamically adjusting the query path of user demand data, while ensuring the full utilization of system node resources, the optimization of user access query performance is taken into account, improving the practicality of the system in high-concurrency scenarios.

[0052] An Internet of Things-based configuration real-time modification and effectiveness management system applying a method for Internet of Things-based configuration real-time modification and effectiveness management described in the above technical solution. The system includes: a configuration information processing module, a configuration information preset module, and a user operation scheduling module;

[0053] The configuration information processing module is used to divide configuration items in all configuration information in the distributed system into units and perform feature identification; the configuration information preset 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 among memory nodes, corrects the update identification of each configuration item unit in each memory node, and when the user queries the configuration information, calculates the query effectiveness of each required configuration item unit query in each memory node and determines the memory nodes for querying all required configuration item unit data of the user.

[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 preset module includes: an access data prediction unit, an access demand rate analysis unit, and a configuration data preset unit;

[0057] The access data prediction unit predicts the expected access times of different users to each configuration item unit in each memory node in the future time period by obtaining the access historical data of different types of user configuration information in each memory node in the distributed system and using a time series prediction model; 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 preset unit is used to determine the preset configuration item units of each memory node in each time period and update and store the preset configuration item unit data in the memory node at the time point of time period alternation.

[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 among memory nodes and correct the update identifiers for 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 effectiveness of each required configuration item unit queried in each memory node, and comprehensively analyze the maximum query effectiveness strategy to determine the memory nodes for querying the data of all required configuration item units of the user.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0061] In the present invention, by analyzing the user access historical data, making a decision on the dynamic distribution strategy of configuration data based on the calculation of access demand rate, comprehensively considering factors such as the access frequency of configuration items, the real-time needs of users, and the storage space occupation of nodes, the distribution of configuration items in memory nodes is dynamically adjusted. At the same time, by constructing a space occupation constraint equation, the utilization of memory node resources is further optimized, avoiding the deterioration of the local performance of the system caused by overloading of some memory nodes.

[0062] In the present invention, priority management is performed on the configuration data update operations of users based on node load perception. In the scenario of high-frequency user queries, the synchronous progress of user data update operations is taken into account to ensure the load balance 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, avoiding data conflict faults in the system.

[0063] In the present invention, the query effectiveness of different users querying configuration item units in each memory node is also analyzed through personalization, and the data sources of configuration item units of each user are comprehensively analyzed and decided to ensure the optimal overall operation efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 is a flowchart of a method for real-time modification and effective management of configuration based on the Internet of Things according to the present invention;

[0065] Figure 2 is an organizational structure diagram of a system for real-time modification and effective management of configuration based on the Internet of Things according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0066] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0067] Embodiment: Please refer to Figure 1 - Figure 2 The present invention provides the following technical solutions:

[0068] As Figure 1 shown, the present invention provides a method for real-time modification and effective management of configurations based on the Internet of Things. The method 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: Obtain and statistically analyze the access history data of different types of user configuration information in each memory node of the distributed system, and predict the expected access times of different users to each configuration item unit in each memory node in the future time period;

[0071] Step S300: Obtain the feature identification data of each configuration item, calculate the access demand rate of each configuration item unit in each memory node in the future time period according to the user access prediction data of each configuration item unit in each memory node, determine the preset configuration item units in each memory node in each time period, and update and store the preset configuration item unit data in the memory node at the time point of time period alternation;

[0072] Step S400: When the user updates the configuration information, obtain the real-time running state data of each memory node, determine the configuration update order among the memory nodes, and correct the update identification of each configuration item unit in each memory node according to the update time;

[0073] Step S500: When the user queries the configuration information, divide the configuration information queried by the user into required configuration item units, calculate the query effectiveness of each required configuration item unit in each memory node query, and comprehensively analyze the maximum query effectiveness strategy to determine the memory node for querying all the required configuration item unit data of the user.

[0074] The said Step S100 includes the following steps:

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

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

[0077] For any configuration item unit x, the feature identification is denoted as: x[DB x ,F x ,S x ;

[0078] Among them, DB x is the database node where the configuration item unit x is stored, F x is the field name to which the configuration item unit x belongs, and S xIt is the maximum occupied space when storing the data of the field to which the configuration item unit x belongs;

[0079] In specific implementation, in a distributed system, to ensure the performance reliability in a multi-user high-concurrency scenario, a memory-distributed structure is adopted for access shunting, and to ensure data security, a database-distributed structure is adopted to implement data redundancy backup and multi-terminal storage; in today's usage scenarios, usually the above two distributed structures are used comprehensively, taking into account both system performance and data security. Therefore, when analyzing the system configuration data, all configuration data synchronization processing needs to be divided to achieve overall optimization management of the system and ensure balanced management of performance resource allocation and user experience among all nodes.

[0080] The step S200 includes the following content:

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

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

[0083] Step S301: Obtain the feature identification data of each configuration item and the information of the expected access times of different types of users to each configuration item unit 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 calculation formula of the access demand rate in the memory node m is as follows:

[0086]

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

[0088] In specific implementation, considering the differences in different types of users of each memory node and the access to configuration data by each user at future time points, the predicted access frequency of each configuration item unit is introduced, and an analysis is carried out in combination with the space occupation of each configuration item unit in each memory node, so as to comprehensively evaluate the access requirements of different configuration item units in each memory node in the future time period, ensure that the configuration data with high requirements can be preferentially stored in the memory, and ensure the fluency of the user access experience.

[0089] Step S303: Determine the preset configuration item units of each memory node in the future time period according to the access demand rate of each configuration item unit in each memory node in the future time period, and update and store the data of the preset configuration item units in the memory node at the time point of time period alternation;

[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 the memory node x in the future time period, and sequentially set each configuration item unit as the candidate configuration item unit of the memory node x in the future time period, and calculate the sum of the maximum occupied spaces when the data of the fields to which all candidate configuration item units belong are stored, and construct the following space occupation constraint equation:

[0091]

[0092] where n is the candidate configuration item unit number, S n is the sum of the maximum occupied spaces when the data of the field to which the candidate configuration item unit n belongs is stored, N x is the parameter to be solved in the space occupation constraint equation;

[0093] Use the space occupation constraint equation to obtain the parameter N x , and select the top N x Candidate configuration item units as the preset configuration item units of the memory node x in the future time period, and sequentially load all the data of the preset configuration item units into the memory node x in descending order according to the access demand rate at the starting time point of the future time period;

[0094] In 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 occupation constraint equation is constructed by setting a preset memory threshold to limit the amount of configuration data preset in the memory and avoid the decline of the access efficiency of each memory node caused by excessive memory occupation.

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

[0096] Step S401: When the user updates the configuration information, determine the configuration update order among the memory nodes according to the real-time running status data of each memory node;

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

[0098] For any user-updated configuration item unit, screen out all memory nodes storing the user-updated configuration item unit, sort them in ascending order according to the ratio of the real-time load rate of the memory node to the load rate threshold, and sequentially update the data of the user-updated configuration item unit in each memory node, and perform data update of the user-updated configuration item unit in the database from the first memory node in the sorting;

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

[0100] For the configuration item unit x in any memory node n, if the memory node n receives the data of the configuration item unit x submitted by the user for data update, set the data update time point as the update time of the configuration item unit x in the memory node n, encrypt the update time and correct it as the update flag of the configuration item unit x in the memory node n, and at the same time correct the update flag as the update flag of the configuration item unit x in the distributed system;

[0101] If the memory node n receives the data of the configuration item unit x sent by other memory nodes for data update, set the update time of the configuration item unit x in the sending data memory node as the update time of the configuration item unit x in the memory node n, and correct the update flag of the configuration item unit x in the sending data memory node as the update flag of the configuration item unit x in the memory node n;

[0102] When performing data update of the configuration item unit x in the memory node n, compare the update time after decoding the update flag correction data with the original update flag data;

[0103] If the update time in the update flag correction data is earlier than the update time in the original update flag data, perform data update on the configuration item unit x in the memory node; if the update time in the update flag correction data is earlier than or the same as the update time in the original update flag data, maintain the original data in the configuration item unit x in the memory node and do not perform data update;

[0104] In specific implementation, considering that when each memory node updates data, it is inevitable that multiple sources of data will synchronize data to the same memory node simultaneously. Therefore, the update identifier of the memory node that performs data update synchronization is corrected according to the update time corresponding to the update identifier, further ensuring the timeliness and consistency of data between distributed memory nodes and avoiding 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 by using the method in the 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 effectiveness of each required configuration item unit of the user in each memory node query;

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

[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 the performance adaptation degree of type v user querying the configuration item unit y in the memory node m s , α and β are the load evaluation coefficient and delay evaluation coefficient respectively, L(y, m s ) is the resource load rate of the memory node m s querying the configuration item unit y, is the resource load rate threshold of the memory node m s , t delay (v, m s ) is the access delay of type v user accessing the memory node m s , k v is the permission coefficient of type v user; 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 middle 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 effectiveness of each required configuration item unit included in the configuration information queried by each user when querying in each candidate memory node, use the dynamic programming method to determine the maximum query effectiveness strategy when each user queries the configuration information, and then determine the memory node for querying all the required configuration item unit data of the user;

[0113] In specific implementation, since there are often multiple users performing query operations on configuration data during high-concurrency access, and the configuration information queried by users is different, the configuration data queried by users is analyzed unit by unit, and the query effectiveness of each configuration item unit when querying in different memory nodes is calculated; among them, in order to balance the reliability of system performance and the actual experience of user access, when calculating the query effectiveness, the node load occupancy rate and access delay parameters are introduced for analysis, and a consistency judgment function is added to ensure the rationality and scientificity of user access operation scheduling, and further ensure the reliability of system resource scheduling and allocation;

[0114] When allocating resources and scheduling data for the users performing query operations in the system, the dynamic programming method is used to obtain a strategy that maximizes the comprehensive query effectiveness. In actual use, since there is an operation time for the query operation, it is necessary to determine the start time point and the jump-out time point of the algorithm to avoid repeated operations of the algorithm when the same batch of users perform resource allocation. Therefore, it is possible to select to perform a new round of resource scheduling strategy analysis when the system receives a new user query operation request, and use the analysis result as the memory node allocation result for the required configuration data of all the users currently performing data query operations.

[0115] Such as Figure 2 shown, the present invention also provides a real-time configuration modification and effectiveness management system based on the Internet of Things, and the system includes: 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 the configuration item units and feature identification for all the configuration information in the distributed system; the configuration information presetting module is used to predict the user access data in the 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 among the memory nodes when the user updates the configuration information, corrects the update identifier of each configuration item unit in each memory node, and also calculates the query effectiveness of each required configuration item unit when querying in each memory node when the user queries the configuration information, and determines the memory node for querying all the required configuration item unit data of the user.

[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 preset module includes: an access data prediction unit, an access demand rate analysis unit, and a configuration data preset unit;

[0120] The access data prediction unit predicts the expected access times of different users to each configuration item unit in each memory node in a future time period by obtaining the access historical data of different types of user configuration information in each memory node of the distributed system and using a time series prediction model; the access demand rate analysis unit is used to calculate the access demand rate of each configuration item unit in each memory node in a future time period; the configuration data preset unit is used to determine the preset configuration item units of each memory node in each time period, and update and store the data of the preset configuration item units in the memory node at the time point of time period alternation.

[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 correct 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 effectiveness of each required configuration item unit in each memory node query, and comprehensively analyze the maximum query effectiveness strategy to determine the memory node for querying the data of all required configuration item units of the user.

[0123] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present invention. Any reference signs in the claims should not be regarded as limiting the claimed claims.

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: Obtain the historical data of different types of user configuration information access in each memory node in the distributed system for summary statistics, and predict the expected number of accesses to each configuration item unit by different users in each memory node in the future time period; Step S300: acquiring feature identification data of each configuration item, calculating the access demand rate of each configuration item unit in each memory node in the future time period according to the user access prediction data of each configuration item unit in each memory node, determining the preset configuration item unit of each memory node in each time period, and updating the preset configuration item unit data in the memory node at the time point of the alternation of the time period; Step S400: when the user updates the configuration information, the real-time operation status data of each memory node is obtained, the configuration update order between 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 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 querying all the user's required configuration item unit data.

2. According to the method of real-time configuration modification and effectiveness management based on the Internet of Things according to claim 1, it is 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 x, the feature identifier is recorded as: x[DB x ,F x ,S x ]; Among them, DB x is the database node where the configuration item unit x is stored, F x is the name of the field to which the configuration item unit x belongs, S x The maximum space occupied when storing data of the field belonging to the configuration item unit x.

3. According to the method of real-time configuration modification and effectiveness management based on the Internet of Things in claim 1, it 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 x, the access demand rate in memory node m is calculated as follows: 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 length of time divided into time periods, u is the user type number, N u is the number of user types, k u is the user authority coefficient of type u, N est (u,x,m) is the estimated number of accesses to configuration item unit x by users of type u in 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. Step S303: determining the preset configuration item units of each memory node in the future time period according to the access demand rate of each configuration item unit in each memory node in the future time period, and updating the preset configuration item unit data in the memory node at the time point of the alternating time period; 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: Where 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 The parameters to be determined for the space occupancy constraint equation; Use the space occupancy constraint equation to find the parameter N x , and select the first N x The candidate configuration item unit is used as the preset configuration item unit of the memory node x in the future time period, and all preset configuration item unit data are loaded into the memory node x in descending order according to the access demand rate at the start time point of the future time period.

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 the user updates the configuration information, the configuration update order among the memory nodes is determined according to the real-time operation 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 the memory nodes are sorted in descending order according to the ratio of the real-time load rate of the memory node 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 updates the data of the user-updated configuration item unit to the database; Step S402: obtaining the update time of each configuration item unit in each memory node and performing update identification correction; For any configuration item unit x in any memory node n, if the memory node n receives the configuration item unit x data submitted by the user for data update, the data update time point is set to the update time of the configuration item unit x in the memory node n, and the update time is encrypted and corrected to the update identifier of the configuration item unit x in the memory node n, and the update identifier is corrected to the update identifier of the configuration item unit x in the distributed system; 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 sending 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 sending the data is corrected to the update identifier of configuration item unit x in memory node n; 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; 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.

6. 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 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 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 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: E(v,y,ms)=kv×M(v,y,ms)×f(y,ms); 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 in the distributed system, the function output is 0; 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 querying all user's required configuration item unit data.

7. A configuration real-time modification and effectiveness management system based on the Internet of Things using a configuration real-time modification and effectiveness management method based on the Internet of Things as described in any one of claims 1 to 6, 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 identify features; the configuration information presetting module is used to predict user access data in future time periods 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 query results of each required configuration item unit in each memory node are calculated, and the memory nodes for all user required configuration item unit data queries are determined.

8. The configuration real-time modification and validation management system based on the Internet of Things according to claim 7 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; and the feature identification unit performs feature identification on each configuration item unit.

9. The configuration real-time modification and validation management system based on the Internet of Things according to claim 7 is 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 the 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.

10. The configuration real-time modification and validation management system based on the Internet of Things according to claim 7, 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.

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