Intelligent building data management method and system based on data collection

Abstract

The invention provides an intelligent building data management method and system based on data collection, and relates to the technical field of intelligent buildings, and the intelligent building data management method based on data collection comprises the following steps: obtaining building data generated by an intelligent building, and carrying out the preprocessing of the building data; based on the characteristics of the building data, performing hierarchical processing on the preprocessed building data through importance analysis to obtain data sets of different hierarchies; for data sets of different hierarchies, building data are stored by adopting a hierarchical storage method; and dynamically evaluating the stored building data regularly, and performing optimization management on the stored building data based on a dynamic evaluation result. According to the invention, the stored building data is dynamically evaluated and optimized, and the data can be dynamically adjusted and optimized according to the actual data access mode and demand, so that the access efficiency and response speed of the data are improved, and better support is provided for the operation and management of the intelligent building.

Description

Technical Field

[0001] The present invention relates to the field of intelligent building technology, and in particular, to an intelligent building data management method and system based on data collection. Background Art

[0002] Smart buildings are advanced building systems that use modern science and information technology to achieve intelligent and automated management of building systems. They use the Internet and various sensor devices to enable various devices and systems within the building to communicate with each other and automatically respond and make decisions, thus playing an important role in improving energy efficiency, safety, service quality and management efficiency. Smart buildings can provide a variety of services such as smart security, smart energy management, smart parking, smart environmental protection and smart services.

[0003] In smart building systems, data collection usually relies on technologies such as sensors, monitoring equipment, and intelligent control devices. These data are processed and analyzed through edge computing or cloud computing to achieve more efficient data management and operational decisions. Edge computing has advantages in processing speed and response time. It can process data instantly at the location where it is generated, reducing the need to transmit data to the cloud, thereby improving processing efficiency and reducing latency. However, the data scale of smart buildings is usually very large, and the security and integrity of the data have become issues that cannot be ignored. Once the data is accidentally damaged or lost, it will not only have a serious impact on the daily management of the building, but may also limit the functionality of the entire building management system, thereby affecting the safety and operational efficiency within the building.

[0004] In order to meet these challenges, the design of smart building systems needs to include a strong data backup and recovery strategy to ensure data security and recoverability under any circumstances. Traditional data management methods often adopt static data processing strategies, ignoring the dynamic characteristics of data attributes changing over time. This method cannot accurately reflect the real-time status of data, such as changes in access frequency and data growth trends, thus affecting the effectiveness of data management and the optimal configuration of data resources.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In view of this, the present invention provides a smart building data management method and system based on data collection to solve the above-mentioned problem of being unable to accurately reflect the real-time status of data, affecting the effectiveness of data management and the optimal configuration of data resources.

[0007] In order to solve the above problems, the specific technical solutions adopted by the present invention are as follows: According to one aspect of the present invention, a smart building data management method based on data collection is provided, and the smart building data management method based on data collection comprises the following steps: S1. Obtain building data generated by smart buildings and pre-process them; S2. Based on the characteristics of building data, the pre-processed building data is layered through importance analysis to obtain data collections at different levels; S3. For data collections at different levels, a hierarchical storage method is used to store building data; S4. Regularly conduct dynamic evaluation on the stored building data, and optimize the management of the stored building data based on the dynamic evaluation results.

[0008] Preferably, based on the characteristics of the building data, the preprocessed building data is hierarchically processed through importance analysis to obtain data collections at different levels, including the following steps: S21. Based on the characteristics of the building data, identify the type of building data and construct a building data network model; S22, evaluating the importance of nodes in the building data network model by using an overlapping box covering algorithm; S23. Sort each node according to the importance evaluation result of each node, and divide the building data into different levels according to the sorting structure to obtain data collections of different levels.

[0009] Preferably, based on the characteristics of the building data, identifying the type of building data and constructing a building data network model comprises the following steps: S211, analyzing the sources of building data, determining the types of building data and establishing data sets for each type of data; S212. Analyze the association between each type of building data by using a correlation analysis method according to the type of building data; S213, in the building data network model, taking the data set of each type of data as a node; S214. According to the association between the data types, the corresponding nodes are connected in the building data network model to form edges, and the building data network model is established by combining the nodes and edges.

[0010] Preferably, evaluating the importance of nodes in the building data network model by using an overlapping box cover algorithm comprises the following steps: S221, determining the exclusion quality of each node by performing dependency analysis on the nodes in the building data network model; S222, using the centrality theory analysis method to calculate the centrality of each node in the building data network model, and based on the exclusion quality and centrality of the node, using the overlapping box covering algorithm to divide the building data network model into nodes; S223. Determine the number of overlaps of each node, and evaluate the importance of the nodes in the building data network model according to the number of overlaps.

[0011] Preferably, determining the exclusion quality of each node by performing dependency analysis on the nodes in the building data network model comprises the following steps: S2211. Calculate the connectivity factor between any two nodes according to the connection relationship between nodes in the building data network model; S2212, using the connectivity factor as a weight, and establishing a weighted adjacency matrix according to the adjacency relationship between nodes; S2213. For each node, based on the weighted adjacency matrix, calculate the sum of weights on all paths from the node to other nodes to obtain the dependency of each node on other nodes; S2214. The dependence of each node on other nodes is taken as the exclusion quality of each node.

[0012] Preferably, the centrality of each node in the building data network model is calculated by using the centrality theory analysis method, and based on the exclusion quality and centrality of the node, the building data network model is divided into nodes by using the overlapping box covering algorithm, which includes the following steps: S2221. For each pair of nodes in the building data network model, calculate the shortest distance between them; S2222. Calculate the node influence value of each node using a node influence value calculation formula according to the shortest distance between each pair of nodes and the exclusion mass of each node; S2223. For each node, select the node with the smallest node influence value as the central node; S2224, taking the selected central node as the starting point, dividing all nodes whose distance from the central node is less than or equal to the preset radius into the same box; S2225. Mark the nodes that have been divided into boxes as covered, and reselect the node with the smallest ratio of mass to centrality from the uncovered nodes as the central node of the next box, and repeat step S2224 until all nodes are divided into at least one box.

[0013] Preferably, the node influence value calculation formula is: ; In the formula, F i Representation Node i Node influence value of m i Representation Node i The exclusion quality value of d ij Representation Node i To Node j The shortest distance between.

[0014] Preferably, regularly dynamically evaluating the stored building data, and optimizing the management of the stored building data based on the dynamic evaluation results includes the following steps: S41. Regularly collect dynamic parameters of building data after storage, the dynamic parameters including access frequency and data growth trend; S42, calculating the evaluation index of the dynamic parameters according to the collected dynamic parameters; S43, establishing a dynamic evaluation matrix using entropy weight method according to the obtained evaluation indicators; S44, dynamically evaluating the stored building data by calculating relative closeness according to the established dynamic evaluation matrix; S45. Based on the dynamic evaluation results, the stored building data is optimized and managed through a pre-established optimization strategy.

[0015] Preferably, according to the obtained evaluation index, establishing a dynamic evaluation matrix using the entropy weight method includes the following steps: S431, constructing an initial evaluation matrix based on the obtained evaluation indicators and performing standardization processing to obtain a standardized evaluation matrix; S432. Calculate the information entropy of each evaluation index according to the standardized evaluation matrix; S433, calculating the weight of each evaluation indicator according to the information entropy of each evaluation indicator; S434, multiplying the weight of each indicator by the standard data in the standardized evaluation matrix to obtain a dynamic evaluation matrix.

[0016] According to another aspect of the present invention, there is provided an intelligent building data management system based on data collection, the intelligent building data management system based on data collection comprising: a data acquisition module, a data processing module, a data storage module and a data optimization module, and the data acquisition module, the data processing module, the data storage module and the data optimization module are connected in sequence; A data acquisition module is used to acquire building data generated by intelligent buildings and perform preprocessing; The data processing module is used to perform hierarchical processing on the pre-processed building data based on the characteristics of the building data through importance analysis to obtain data collections at different levels; A data storage module is used to store building data using a hierarchical storage method for data collections at different levels; The data optimization module is used to regularly perform dynamic evaluation on the stored building data and optimize the management of the stored building data based on the dynamic evaluation results.

[0017] The beneficial effects of the present invention are: 1. The present invention can classify and manage the data according to its importance and characteristics by preprocessing and hierarchical processing on the data generated by the smart building, thereby improving the data management efficiency. The hierarchical storage method is used to store and manage data at different levels, which can make more effective use of storage resources. By dynamically evaluating and optimizing the management of stored building data, the data can be dynamically adjusted and optimized according to the actual data access mode and demand, thereby improving the data access efficiency and response speed, while reducing costs, and providing better support for the operation and management of smart buildings.

[0018] 2. The present invention can stratify building data according to importance through importance analysis and hierarchical processing, so as to better manage data at different levels, help optimize data storage and access efficiency, and improve data processing efficiency and performance. By evaluating and dividing the importance of nodes through the overlapping box coverage algorithm, key nodes and data sets in the data network model can be better identified, which helps to optimize data management strategies, improve the efficiency and quality of data processing, and provide important support for the safe, reliable and efficient management of building data.

[0019] 3. The present invention can timely understand the changes in data by regularly collecting dynamic parameters of building data, such as access frequency and data growth trend, and calculate evaluation indicators based on the collected dynamic parameters. By establishing a dynamic evaluation matrix through the entropy weight method, the value of stored building data can be objectively evaluated. By calculating the information entropy and weight of each evaluation indicator, the weights of different evaluation indicators can be reasonably allocated to more accurately reflect the importance and contribution of the data. According to the established dynamic evaluation matrix, the stored building data is dynamically evaluated by calculating the relative proximity, which can better understand the actual situation and needs of the data, help formulate optimization management strategies, optimize the management of stored building data in a targeted manner, and improve the efficiency and effectiveness of data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 is a flow chart of a smart building data management method based on data collection according to an embodiment of the present invention; Figure 2 is a principle block diagram of an intelligent building data management system based on data collection according to an embodiment of the present invention; Figure 3 It is a structural schematic diagram of a computer device according to an embodiment of the present invention.

[0021] In the figure: 1. Data acquisition module; 2. Data processing module; 3. Data storage module; 4. Data optimization module. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0023] According to an embodiment of the present invention, a smart building data management method and system based on data collection are provided.

[0024] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to an embodiment of the present invention, a smart building data management method based on data collection is provided, and the smart building data management method based on data collection includes the following steps: S1. Obtain building data generated by smart buildings and pre-process them; Specifically, the building data generated by smart buildings are collected from smart buildings, including data collected by various smart devices and sensors (such as temperature sensors, energy consumption monitors, security systems, etc.), operation logs and event logs in the building management system, and feedback and data submitted by users through various interfaces (such as mobile applications or web pages); Among them, preprocessing includes data cleaning, data denoising and other processing; data cleaning mainly solves errors and inconsistencies in the data to improve the quality and availability of the data. Data cleaning includes: removing duplicate records, processing missing values ​​and correcting errors; data denoising refers to removing noise in the data. Noise usually refers to any outliers or errors that do not belong to the normal distribution of the data.

[0025] S2. Based on the characteristics of building data, the pre-processed building data is layered through importance analysis to obtain data collections at different levels; As a preferred implementation, based on the characteristics of building data, the pre-processed building data is layered through importance analysis to obtain data collections at different levels, including the following steps: S21. Based on the characteristics of the building data, identify the type of building data and construct a building data network model; As a preferred implementation, based on the characteristics of building data, identifying the type of building data and constructing a building data network model includes the following steps: S211, analyzing the sources of building data, determining the types of building data and establishing data sets for each type of data; It should be noted that the sources of building data are diverse, including equipment data, sensor data, user usage data, etc., and the type of building data is determined according to the source of the building data. The building data types include basic data, monitoring data, usage data, etc. Basic data include building structural parameters, equipment information, floor plans, etc. Monitoring data include temperature, humidity, energy consumption, personnel flow, etc. Usage data include air conditioning and lighting control operations recorded by the building management system.

[0026] S212. Analyze the association between each type of building data by using a correlation analysis method according to the type of building data; Specifically, according to the type of building data, analyzing the association between each type of data through the correlation analysis method includes the following steps: By calculating the Pearson correlation coefficient between different data types, the strength and direction of the linear relationship between them can be quantified; Causal analysis methods (such as Granger causality test, causal diagram, etc.) can be used to explore the causal relationship between data types; Through clustering algorithms (such as K-means, hierarchical clustering, etc.), data types with similar characteristics are grouped together to reveal the potential associations between them.

[0027] S213, in the building data network model, taking the data set of each type of data as a node; S214. According to the association between the data types, the corresponding nodes are connected in the building data network model to form edges, and the building data network model is established by combining the nodes and edges.

[0028] S22, evaluating the importance of nodes in the building data network model by using an overlapping box covering algorithm; As a preferred implementation, evaluating the importance of nodes in the building data network model by using an overlapping box coverage algorithm includes the following steps: S221, determining the exclusion quality of each node by performing dependency analysis on the nodes in the building data network model; As a preferred embodiment, by performing dependency analysis on the nodes in the building data network model, determining the exclusion quality of each node includes the following steps: S2211. Calculate the connectivity factor between any two nodes according to the connection relationship between nodes in the building data network model; Specifically, according to the connection relationship between nodes in the building data network model, calculating the connectivity factor between any two nodes includes the following steps: For any two nodes A and B, find all paths from A to B, including directly connected paths and indirectly connected paths through other nodes; The number of paths connecting two nodes is taken as the connectivity factor.

[0029] S2212, using the connectivity factor as a weight, and establishing a weighted adjacency matrix according to the adjacency relationship between nodes; Specifically, the connectivity factor is used as a weight, and a weighted adjacency matrix is ​​established according to the adjacency relationship between nodes, including the following steps: According to the building data network model, the connection relationship between each node is determined, which can be expressed as an adjacency matrix, where the value of the matrix element indicates whether the nodes are connected, usually 1 for connected and 0 for unconnected; For each pair of nodes, the connectivity factor is calculated based on the number or weight of the connected paths. If the weight of the path is already given, the path weight can be used directly as the connectivity factor; if the number of paths is more important, the number of each path can be used as the connectivity factor; The calculated connectivity factor is used as the weight of the connection between nodes, and then a weighted adjacency matrix is ​​established according to the adjacency relationship between nodes; in the weighted adjacency matrix, each element of the matrix represents the connection weight between the corresponding nodes.

[0030] For example, if the connectivity factor between node A and node B is 3, and the connectivity factor between node A and node C is 2, then the corresponding elements in the weighted adjacency matrix are the weight between node A and node B is 3, the weight between node A and node C is 2, and the weights of other unconnected nodes are 0.

[0031] S2213. For each node, based on the weighted adjacency matrix, calculate the sum of weights on all paths from the node to other nodes to obtain the dependency of each node on other nodes; S2214. The dependence of each node on other nodes is taken as the exclusion quality of each node.

[0032] It should be noted that exclusion quality usually refers to the importance or weight of a node in the network. This concept can be used to indicate the relative value or function of a node in the entire network, and usually affects the results of network analysis, such as path calculation, centrality measurement, etc.

[0033] S222, using the centrality theory analysis method to calculate the centrality of each node in the building data network model, and based on the exclusion quality and centrality of the node, using the overlapping box covering algorithm to divide the building data network model into nodes; As a preferred implementation, the centrality of each node in the building data network model is calculated using the centrality theory analysis method, and based on the exclusion quality and centrality of the node, the building data network model is divided into nodes using the overlapping box covering algorithm, including the following steps: S2221. For each pair of nodes in the building data network model, calculate the shortest distance between them; It should be noted that the shortest distance refers to the path with the least number of edges or the smallest sum of weights from one node to another; Specifically, the Dijkstra algorithm is an algorithm for solving the single-source shortest path problem. The algorithm takes a node (source point) as the center and gradually expands to all reachable nodes in the graph until the shortest path from the source point to the specified point is found. The calculation process is: Starting from the source point, the shortest distance from the source point to itself is initialized to 0, and the shortest distance to all other nodes is infinite; Use a priority queue to store all nodes and sort them according to the distance from the node to the source; Take out the nodes in the queue one by one and update the distances of their adjacent nodes; if the path from the current node to the adjacent node is shorter than the known shortest path, update the shortest path and priority queue; Repeat the above process until all nodes have been visited or the queue is empty.

[0034] S2222. Calculate the node influence value of each node using a node influence value calculation formula according to the shortest distance between each pair of nodes and the exclusion mass of each node; Specifically, the node influence value calculation formula is: ; In the formula, F i Representation Node i Node influence value of m i Representation Node i The exclusion quality value of d ij Representation Node i To Node j The shortest distance between.

[0035] S2223. For each node, select the node with the smallest node influence value as the central node; S2224, taking the selected central node as the starting point, dividing all nodes whose distance from the central node is less than or equal to the preset radius into the same box; S2225. Mark the nodes that have been divided into boxes as covered, and reselect the node with the smallest ratio of mass to centrality from the uncovered nodes as the central node of the next box, and repeat step S2224 until all nodes are divided into at least one box.

[0036] S223. Determine the number of overlaps of each node, and evaluate the importance of the nodes in the building data network model according to the number of overlaps.

[0037] Specifically, for example, assume that the building data network model contains 5 nodes, and their exclusion mass and centrality are as follows: Node A has an exclusion mass of 10 and a centrality of 0.2; Node B has an exclusion mass of 15 and a centrality of 0.3; The exclusion mass of node C is 8 and the centrality is 0.5; The exclusion mass of node D is 20 and the centrality is 0.4; The exclusion mass of node E is 12 and the centrality is 0.25; Assuming the preset radius is 3, first select the central node, calculate the node influence value, and then select the node with the smallest influence value as the central node. In this example, assume that node C has the smallest influence value, so node C is selected as the central node. Then, with node C as the center, divide the nodes with a distance less than or equal to 3 into the same box until all nodes are divided into at least one box. Finally, determine the number of overlaps for each node, and evaluate the importance of the node based on the number of overlaps.

[0038] S23. Sort each node according to the importance evaluation result of each node, and divide the building data into different levels according to the sorting structure to obtain data collections of different levels.

[0039] It should be noted that all nodes are sorted according to their importance values ​​in descending order; Set thresholds and determine tier division criteria based on the distribution of importance values ​​and business requirements; According to the determined hierarchical division criteria, the sorted nodes are divided into different hierarchies. Each hierarchical level will contain a group of nodes with similar importance. Then, based on the hierarchical division results, the building data is divided into different data collections.

[0040] Specifically, for example, suppose there is a building data network model with 10 nodes. After importance evaluation, each node gets an importance value. These values ​​are as follows: The importance value of node A is 90, the importance value of node B is 85, the importance value of node C is 80, the importance value of node D is 75, the importance value of node E is 70, the importance value of node F is 65, the importance value of node G is 60, the importance value of node H is 55, the importance value of node I is 50, and the importance value of node J is 45.

[0041] Set thresholds based on the distribution of importance values ​​and business requirements. For example, nodes with an importance greater than or equal to 80 belong to the first level (high importance level); nodes with an importance between 70 and 79 belong to the second level (medium importance level); and nodes with an importance less than 70 belong to the third level (low importance level). According to the set threshold, the nodes are divided into different levels: Among them, the first level (high importance level) includes node A, node B and node C; The second level (medium importance level) includes nodes D and E; The third level (low importance level) includes node F, node G, node H, node I and node J; According to the hierarchical division results, the building data is divided into three different data collections: Among them, the first-level data collection includes node A, node B, node C and their related building data; The second-level data collection includes node D, node E and their related building data; The third-level data collection includes nodes F to J and their related building data.

[0042] S3. For data collections at different levels, a hierarchical storage method is used to store building data; Specifically, for data collections at different levels, using a hierarchical storage method to store building data includes the following steps: According to the previously set hierarchical division, there are three data collections, corresponding to the three importance levels of high, medium and low; Among them, the first level (high importance level) is suitable for high-performance storage, such as SSD or high-performance local disk array, the second level (medium importance level) uses storage with slightly lower performance but lower cost, and the third level (low importance level) uses the lowest-cost storage solution, such as large-capacity HDD, archive storage or cold storage service.

[0043] Then, according to the determined storage tier characteristics, data migration and storage allocation are performed: First-tier data collection: Migrate all first-tier data (i.e., nodes A, B, C and their related data) to a high-performance storage layer, such as an SSD array or a high-performance cloud storage service.

[0044] Second-tier data collection: Migrate the second-tier data (i.e. nodes D and E and their related data) to a medium-performance storage tier, such as an enterprise-class HDD or a medium-performance cloud storage bucket.

[0045] Third-tier data collection: Migrate the third-tier data (i.e., nodes F to J and their related data) to a low-cost storage tier, such as a large-capacity HDD, archive storage, or cold storage area.

[0046] S4. Regularly conduct dynamic evaluation on the stored building data, and optimize the management of the stored building data based on the dynamic evaluation results.

[0047] As a preferred implementation, regularly dynamically evaluating the stored building data, and optimizing the stored building data based on the dynamic evaluation results includes the following steps: S41. Regularly collect dynamic parameters of building data after storage, the dynamic parameters including access frequency and data growth trend; It should be noted that access frequency refers to the number of times data is accessed or requested within a certain period of time. This parameter can help understand the usage pattern and activity of data; data growth trend refers to the change of data volume over time, which reflects the speed of data generation and the growth rate of storage demand.

[0048] The access frequency can be obtained by analyzing the storage access logs regularly (such as daily, weekly or monthly), recording the number of accesses and time of each data set, and the size of each data set can be counted regularly (such as daily, weekly or monthly), and its growth rate can be calculated to obtain the data growth trend.

[0049] S42, calculating the evaluation index of the dynamic parameters according to the collected dynamic parameters; It should be noted that the access frequency evaluation indicators include average access frequency and access frequency standard deviation, etc.; the data growth trend evaluation indicators include average growth rate and compound growth rate, etc.; S43, establishing a dynamic evaluation matrix using entropy weight method according to the obtained evaluation indicators; As a preferred implementation, according to the obtained evaluation index, establishing a dynamic evaluation matrix using the entropy weight method includes the following steps: S431, constructing an initial evaluation matrix based on the obtained evaluation indicators and performing standardization processing to obtain a standardized evaluation matrix; S432. Calculate the information entropy of each evaluation index according to the standardized evaluation matrix; It should be noted that, for each evaluation indicator, the probability distribution of each evaluation indicator on all evaluation objects is obtained by dividing the standardized value of each indicator by the sum of all values ​​of the indicator, and then the information entropy of each evaluation indicator is calculated by the information entropy calculation formula; wherein, the value of information entropy is between 0 and 1; close to 0 means that the data is very concentrated on this indicator and the amount of information provided is small; close to 1 means that the data is very dispersed on this indicator and the amount of information provided is large.

[0050] S433, calculating the weight of each evaluation indicator according to the information entropy of each evaluation indicator; It should be noted that the difference coefficient of each evaluation indicator is obtained by subtracting the information entropy of each evaluation indicator from 1, and then the difference coefficient is normalized to obtain the weight of each evaluation indicator.

[0051] S434, multiplying the weight of each indicator by the standard data in the standardized evaluation matrix to obtain a dynamic evaluation matrix.

[0052] S44, dynamically evaluating the stored building data by calculating relative closeness according to the established dynamic evaluation matrix; Specifically, according to the established dynamic evaluation matrix, the stored building data is dynamically evaluated by calculating the relative closeness, including the following steps: Determine the ideal solution and the negative ideal solution. The ideal solution is a solution composed of values ​​in which all evaluation indicators reach the optimal state. For benefit-type indicators (the larger the value, the better), the ideal solution is the maximum value of the indicator; for cost-type indicators (the smaller the value, the better), the ideal solution is the minimum value of the indicator; the negative ideal solution is the opposite of the ideal solution, and is a solution composed of values ​​in which all evaluation indicators reach the worst state.

[0053] For each building data, the distance from the value in its evaluation matrix to the ideal solution and the negative ideal solution is calculated by Euclidean distance; The relative closeness is obtained by the ratio of the distance from the building data to the ideal solution to the sum of the distances to the ideal solution and the negative ideal solution. The closer the value of the relative closeness is to 1, the closer the building data is to the ideal state; the closer the value is to 0, the closer it is to the negative ideal state.

[0054] S45. Based on the dynamic evaluation results, the stored building data is optimized and managed through a pre-established optimization strategy.

[0055] Specifically, pre-established optimization strategies include: access optimization, backup and recovery, and data migration; Among them, access optimization: For frequently accessed data sets, caching strategies or optimized data layout can be adopted to improve access speed.

[0056] Backup and recovery: Develop appropriate backup and recovery strategies based on the importance and access frequency of data to ensure data security and reliability.

[0057] Data migration: For data sets with significant growth trends, you can consider migrating to a more efficient storage system or adopting a distributed storage solution.

[0058] like Figure 2 As shown, according to another embodiment of the present invention, there is provided an intelligent building data management system based on data collection, the intelligent building data management system based on data collection comprises: a data acquisition module 1, a data processing module 2, a data storage module 3 and a data optimization module 4, and the data acquisition module 1, the data processing module 2, the data storage module 3 and the data optimization module 4 are connected in sequence; Data acquisition module 1, used to acquire building data generated by the intelligent building and perform preprocessing; Data processing module 2, used for hierarchical processing of pre-processed building data through importance analysis based on the characteristics of building data to obtain data collections at different levels; The data storage module 3 is used to store building data using a hierarchical storage method for data collections at different levels; The data optimization module 4 is used to regularly perform dynamic evaluation on the stored building data, and optimize the stored building data based on the dynamic evaluation results.

[0059] Figure 3 An embodiment of a computer device of the present invention is shown. The computer device may be a server, and the computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0060] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0061] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0062] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0063] In summary, with the aid of the above-mentioned technical scheme of the present invention, the present invention can classify and manage the data according to its importance and characteristics by pre-processing and hierarchical processing on the data generated by the intelligent building, thereby improving the data management efficiency. The hierarchical storage method is used to store and manage data at different levels, which can more effectively utilize storage resources. By dynamically evaluating and optimizing the management of stored building data, the data can be dynamically adjusted and optimized according to the actual data access mode and needs, thereby improving the data access efficiency and response speed, while reducing costs, and providing better support for the operation and management of intelligent buildings. The present invention can hierarchically analyze and hierarchically process the building data according to its importance, thereby better managing data at different levels, helping to optimize data storage and access efficiency, and improving data processing efficiency and performance. The importance of nodes is evaluated and divided by the overlapping box coverage algorithm, which can better identify Identifying key nodes and data sets in the data network model helps to optimize data management strategies, improve the efficiency and quality of data processing, and provide important support for the safe, reliable and efficient management of building data; the present invention can timely understand the changes in data by regularly collecting dynamic parameters of building data, such as access frequency and data growth trend, and calculate evaluation indicators based on the collected dynamic parameters. By establishing a dynamic evaluation matrix through the entropy weight method, the value of stored building data can be objectively evaluated. By calculating the information entropy and weight of each evaluation indicator, the weights of different evaluation indicators can be reasonably allocated to more accurately reflect the importance and contribution of the data. According to the established dynamic evaluation matrix, the stored building data is dynamically evaluated by calculating the relative proximity, which can better understand the actual situation and needs of the data, help formulate optimization management strategies, optimize the management of stored building data in a targeted manner, and improve the efficiency and effectiveness of data management.

[0064] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0065] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A smart building data management method based on data collection, characterized in that: The intelligent building data management method based on data collection includes the following steps: S1. Obtain building data generated by smart buildings and pre-process them; S2. Based on the characteristics of building data, the pre-processed building data is layered through importance analysis to obtain data collections at different levels; S3. For data collections at different levels, a hierarchical storage method is used to store building data; S4. Regularly conduct dynamic evaluation on the stored building data, and optimize the management of the stored building data based on the dynamic evaluation results.

2. The intelligent building data management method based on data collection according to claim 1 is characterized in that: Based on the characteristics of the building data, the pre-processed building data is layered through importance analysis to obtain data collections at different levels, including the following steps: S21. Based on the characteristics of the building data, identify the type of building data and construct a building data network model; S22, evaluating the importance of nodes in the building data network model by using an overlapping box covering algorithm; S23. Sort each node according to the importance evaluation result of each node, and divide the building data into different levels according to the sorting structure to obtain data collections of different levels.

3. The intelligent building data management method based on data collection according to claim 2 is characterized in that: The method of identifying the type of building data and constructing a building data network model based on the characteristics of the building data includes the following steps: S211, analyzing the sources of building data, determining the types of building data and establishing data sets for each type of data; S212. Analyze the association between each type of building data by using a correlation analysis method according to the type of building data; S213, in the building data network model, taking the data set of each type of data as a node; S214. According to the association between the data types, the corresponding nodes are connected in the building data network model to form edges, and the building data network model is established by combining the nodes and edges.

4. The intelligent building data management method based on data collection according to claim 2 is characterized in that: The importance evaluation of nodes in the building data network model by using the overlapping box coverage algorithm comprises the following steps: S221, determining the exclusion quality of each node by performing dependency analysis on the nodes in the building data network model; S222, using the centrality theory analysis method to calculate the centrality of each node in the building data network model, and based on the exclusion quality and centrality of the node, using the overlapping box covering algorithm to divide the building data network model into nodes; S223. Determine the number of overlaps of each node, and evaluate the importance of the nodes in the building data network model according to the number of overlaps.

5. The intelligent building data management method based on data collection according to claim 4 is characterized in that: The method of determining the exclusion quality of each node by performing dependency analysis on the nodes in the building data network model comprises the following steps: S2211. Calculate the connectivity factor between any two nodes according to the connection relationship between nodes in the building data network model; S2212, using the connectivity factor as a weight, and establishing a weighted adjacency matrix according to the adjacency relationship between nodes; S2213. For each node, based on the weighted adjacency matrix, calculate the sum of weights on all paths from the node to other nodes to obtain the dependency of each node on other nodes; S2214. The dependence of each node on other nodes is taken as the exclusion quality of each node.

6. The intelligent building data management method based on data collection according to claim 5 is characterized in that: The method of calculating the centrality of each node in the building data network model by using the centrality theory analysis method and dividing the building data network model into nodes by using the overlapping box covering algorithm based on the exclusion quality and centrality of the node includes the following steps: S2221. For each pair of nodes in the building data network model, calculate the shortest distance between them; S2222. Calculate the node influence value of each node using a node influence value calculation formula according to the shortest distance between each pair of nodes and the exclusion mass of each node; S2223. For each node, select the node with the smallest node influence value as the central node; S2224, taking the selected central node as the starting point, dividing all nodes whose distance from the central node is less than or equal to the preset radius into the same box; S2225. Mark the nodes that have been divided into boxes as covered, and reselect the node with the smallest ratio of mass to centrality from the uncovered nodes as the central node of the next box, and repeat step S2224 until all nodes are divided into at least one box.

7. The intelligent building data management method based on data collection according to claim 6 is characterized in that: The node influence value calculation formula is: ； In the formula, F i Representation Node i Node influence value of m i Representation Node i The exclusion quality value of d ij Representation Node i To Node j The shortest distance between.

8. The intelligent building data management method based on data collection according to claim 1 is characterized in that: The periodic dynamic evaluation of the stored building data and the optimization management of the stored building data based on the dynamic evaluation results include the following steps: S41. Regularly collect dynamic parameters of building data after storage, wherein the dynamic parameters include access frequency and data growth trend; S42, calculating the evaluation index of the dynamic parameters according to the collected dynamic parameters; S43, establishing a dynamic evaluation matrix using entropy weight method according to the obtained evaluation indicators; S44, dynamically evaluating the stored building data by calculating relative closeness according to the established dynamic evaluation matrix; S45. Based on the dynamic evaluation results, the stored building data is optimized and managed through a pre-established optimization strategy.

9. The intelligent building data management method based on data collection according to claim 8 is characterized in that: The method of establishing a dynamic evaluation matrix using the entropy weight method according to the obtained evaluation index comprises the following steps: S431, constructing an initial evaluation matrix based on the obtained evaluation indicators and performing standardization processing to obtain a standardized evaluation matrix; S432. Calculate the information entropy of each evaluation index according to the standardized evaluation matrix; S433, calculating the weight of each evaluation indicator according to the information entropy of each evaluation indicator; S434, multiplying the weight of each indicator by the standard data in the standardized evaluation matrix to obtain a dynamic evaluation matrix.

10. An intelligent building data management system based on data collection, used to implement the intelligent building data management method based on data collection according to any one of claims 1 to 9, characterized in that: The intelligent building data management system based on data collection includes: a data acquisition module, a data processing module, a data storage module and a data optimization module, and the data acquisition module, the data processing module, the data storage module and the data optimization module are connected in sequence; The data acquisition module is used to acquire the building data generated by the intelligent building and perform preprocessing; The data processing module is used to perform hierarchical processing on the pre-processed building data through importance analysis based on the characteristics of the building data to obtain data collections at different levels; The data storage module is used to store building data using a hierarchical storage method for data collections at different levels; The data optimization module is used to regularly perform dynamic evaluation on the stored building data, and optimize the management of the stored building data based on the dynamic evaluation results.

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