Micro-energy Harvesting Supervision and Regulation Method and System Based on Data Analysis

Abstract

Embodiments of the present invention relate to the technical field of micro-energy harvesting supervision and regulation, and specifically disclose a micro-energy harvesting supervision and regulation method and system based on data analysis. Embodiments of the present invention perform micro-energy monitoring and trend analysis; select multiple micro-energy supply nodes; perform direct status collection; perform indirect status collection; perform hierarchical regulation analysis by integrating the micro-energy supply trend, multiple direct collection data, and multiple indirect collection data, select multiple priority supply nodes, and perform priority supply regulation. It can analyze the micro-energy supply trend, perform direct status collection and indirect status collection on multiple micro-energy supply nodes, then perform hierarchical regulation analysis, select multiple priority supply nodes, and perform priority supply regulation, so as to achieve refined and intelligent micro-energy harvesting supervision and regulation, ensure that the collection and utilization of micro-energy match the actual demand, can avoid energy waste, and can ensure the stability and reliability of power supply.

Description

Technical Field

[0001] The present invention belongs to the technical field of micro energy harvesting supervision and regulation, and particularly relates to a method and system for micro energy harvesting supervision and regulation based on data analysis. Background Art

[0002] Micro energy harvesting supervision and regulation involves processes such as the operation, monitoring, regulation, and control of a micro energy harvesting system, converting the energy in the surrounding environment into electrical energy to supply power to MEMS devices in a wireless sensor network.

[0003] With the rapid development of the Internet of Things technology, wireless sensor networks have been widely used in various fields. As an important means of power supply for wireless sensor networks, the supervision and regulation of micro energy harvesting technology is particularly important.

[0004] In the prior art, the process of micro energy harvesting supervision and regulation is relatively simple, lacking sufficient refinement and intelligence, unable to analyze the states of different micro energy supply nodes and perform hierarchical regulation according to different states, resulting in the collection and utilization of micro energy not being able to match the actual demand, which is likely to cause energy waste and cannot guarantee the stability and reliability of power supply. Summary of the Invention

[0005] The purpose of the embodiments of the present invention is to provide a method and system for micro energy harvesting supervision and regulation based on data analysis, aiming to solve the problems raised in the background art.

[0006] To achieve the above purpose, the embodiments of the present invention provide the following technical solutions:

[0007] A method for micro energy harvesting supervision and regulation based on data analysis, the method specifically includes the following steps:

[0008] Perform micro energy monitoring, obtain micro energy monitoring data, and analyze the rules of the micro energy monitoring data to determine the micro energy supply trend;

[0009] Determine the wireless sensor network with micro energy supply requirements, and select multiple micro energy supply nodes;

[0010] Directly collect the states of multiple micro energy supply nodes, obtain multiple directly collected data, and perform identification and analysis to determine multiple sensor disconnection nodes;

[0011] Obtain the interconnection record information of multiple micro energy supply nodes, select multiple sensor interconnection nodes, and indirectly collect the states of multiple sensor disconnection nodes to obtain multiple indirectly collected data;

[0012] Based on the above-mentioned micro-energy supply trend, hierarchical regulation and analysis are performed on multiple pieces of the directly collected data and multiple pieces of the indirectly collected data, multiple priority power supply nodes are selected, and priority power supply regulation is carried out.

[0013] A micro-energy collection supervision and regulation system based on data analysis, the system includes a micro-energy monitoring and analysis unit, a power supply node selection unit, a direct status collection unit, an indirect status collection unit, and a priority power supply regulation unit, where:

[0014] The micro-energy monitoring and analysis unit is used to monitor micro-energy, obtain micro-energy monitoring data, and perform regular analysis on the micro-energy monitoring data to determine the micro-energy supply trend;

[0015] The power supply node selection unit is used to determine a wireless sensor network with micro-energy supply requirements and select multiple micro-energy power supply nodes;

[0016] The direct status collection unit is used to directly collect the status of multiple micro-energy power supply nodes, obtain multiple directly collected data, and perform identification and analysis to determine multiple sensor disconnection nodes;

[0017] The indirect status collection unit is used to obtain the interconnection record information of multiple micro-energy power supply nodes, select multiple sensor interconnection nodes, indirectly collect the status of multiple sensor disconnection nodes, and obtain multiple indirectly collected data;

[0018] The priority power supply regulation unit is used to perform hierarchical regulation and analysis by integrating the micro-energy supply trend, multiple directly collected data, and multiple indirectly collected data, select multiple priority power supply nodes, and perform priority power supply regulation.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] In the embodiment of the present invention, micro-energy monitoring and regular analysis are carried out; multiple micro-energy power supply nodes are selected; direct status collection is carried out; indirect status collection is carried out; hierarchical regulation and analysis are carried out by integrating the micro-energy supply trend, multiple directly collected data, and multiple indirectly collected data, multiple priority power supply nodes are selected, and priority power supply regulation is carried out. It can analyze the micro-energy supply trend, directly collect and indirectly collect the status of multiple micro-energy power supply nodes, then perform hierarchical regulation and analysis, select multiple priority power supply nodes, and perform priority power supply regulation, so as to realize refined and intelligent micro-energy collection supervision and regulation, ensure that the collection and utilization of micro-energy match the actual demand, avoid energy waste, and ensure the stability and reliability of power supply. Description of the Drawings

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0022] Figure 1 It shows the flowchart of the method provided by the embodiments of the present invention.

[0023] Figure 2 It shows the flowchart of micro-energy monitoring and analysis in the method provided by the embodiments of the present invention.

[0024] Figure 3 It shows the flowchart of selecting micro-energy power supply nodes in the method provided by the embodiments of the present invention.

[0025] Figure 4 It shows the flowchart of direct status collection in the method provided by the embodiments of the present invention.

[0026] Figure 5 It shows the flowchart of indirect status collection in the method provided by the embodiments of the present invention.

[0027] Figure 6 It shows the flowchart of priority power supply regulation in the method provided by the embodiments of the present invention.

[0028] Figure 7 It shows the application architecture diagram of the system provided by the embodiments of the present invention.

[0029] Figure 8 It shows the structural block diagram of the micro-energy monitoring and analysis unit in the system provided by the embodiments of the present invention.

[0030] Figure 9 It shows the structural block diagram of the direct status collection unit in the system provided by the embodiments of the present invention.

[0031] Figure 10 It shows the structural block diagram of the indirect status collection unit in the system provided by the embodiments of the present invention. Detailed implementation manners

[0032] In order to make the purpose, technical solutions and advantages of the present invention clearer, the following further details the present invention in conjunction with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0033] It can be understood that in the prior art, the process of micro-energy acquisition supervision and regulation is relatively simple, lacking sufficient refinement and intelligence, unable to analyze the states of different micro-energy supply nodes, and then conduct hierarchical regulation according to different states, resulting in the mismatch between the acquisition and utilization of micro-energy and the actual demand, which is likely to cause energy waste and cannot guarantee the stability and reliability of power supply.

[0034] To solve the above problems, in the embodiments of the present invention, micro-energy monitoring is carried out to obtain micro-energy monitoring data, and the regular analysis of the micro-energy monitoring data is performed to determine the micro-energy supply trend; a wireless sensor network with micro-energy supply requirements is determined, and multiple micro-energy supply nodes are selected; direct state acquisition is performed on the multiple micro-energy supply nodes to obtain multiple directly acquired data, and identification and analysis are carried out to determine multiple sensor disconnection nodes; the interconnection record information of the multiple micro-energy supply nodes is obtained, multiple sensor interconnection nodes are selected, indirect state acquisition is performed on the multiple sensor disconnection nodes to obtain multiple indirectly acquired data; hierarchical regulation analysis is performed by integrating the micro-energy supply trend, multiple directly acquired data, and multiple indirectly acquired data, multiple priority power supply nodes are selected, and priority power supply regulation is carried out. It is possible to analyze the micro-energy supply trend, perform direct state acquisition and indirect state acquisition on multiple micro-energy supply nodes, then carry out hierarchical regulation analysis, select multiple priority power supply nodes, and perform priority power supply regulation, so as to realize refined and intelligent micro-energy acquisition supervision and regulation, ensure the match between the acquisition and utilization of micro-energy and the actual demand, and both avoid energy waste and guarantee the stability and reliability of power supply.

[0035] Figure 1 The flowchart of the method provided by the embodiments of the present invention is shown.

[0036] Specifically, for the micro-energy acquisition supervision and regulation method based on data analysis, the method specifically includes the following steps:

[0037] Step S101, perform micro-energy monitoring, obtain micro-energy monitoring data, and perform regular analysis on the micro-energy monitoring data to determine the micro-energy supply trend.

[0038] In the embodiments of the present invention, a target monitoring area that needs to perform micro-energy acquisition supervision and regulation is determined, and multiple monitoring types are determined. Then, in the target monitoring area, according to the multiple monitoring types, micro-energy monitoring is carried out to obtain micro-energy monitoring data. By performing time series processing on the micro-energy monitoring data, time series monitoring data sorted by time is generated, and then regular analysis is performed on the time series monitoring data to determine the micro-energy supply trend.

[0039] It can be understood that the multiple monitoring types may include solar energy, thermal energy, wind energy, kinetic energy, etc.

[0040] Specifically,Figure 2 The flowchart of micro - energy monitoring and analysis in the method provided by the embodiments of the present invention is shown.

[0041] Among them, in the preferred embodiment provided by the present invention, the micro - energy monitoring, obtaining micro - energy monitoring data, and analyzing the rules of the micro - energy monitoring data to determine the micro - energy supply trend specifically include the following steps:

[0042] Step S1011, determine the target monitoring area;

[0043] Step S1012, determine multiple monitoring types;

[0044] Step S1013, in the target monitoring area, according to the multiple monitoring types, conduct micro - energy monitoring to obtain micro - energy monitoring data;

[0045] Step S1014, perform time - series processing on the micro - energy monitoring data to generate time - series monitoring data;

[0046] Step S1015, analyze the rules of the time - series monitoring data to determine the micro - energy supply trend.

[0047] Furthermore, in the process of performing time - series processing on the micro - energy monitoring data to generate time - series monitoring data, the following relational expressions exist:

[0048] ;

[0049] Among them, represents the time - series monitoring data, represents the time variable, represents the time t at the i th monitoring point of the energy data, represents the energy volatility of the t th monitoring point at time i , represents the fluctuation stability range of the th monitoring point, represents the monitoring point i and other monitoring points represents the correlation coefficient between the monitoring point i and other monitoring points in the time series, respectively represent different weights in the time - series processing process, represents the data smoothing function, represents the fluctuation regulation function, represents the correlation analysis function;

[0050] Among them, the following relational expressions exist in the calculation process of the data smoothing function:

[0051] ;

[0052] Among them, represents the smoothing window size, represents the time weight factor;

[0053] Among them, the following relational expressions exist in the calculation process of the fluctuation regulation function:

[0054] ;

[0055] Among them, the following relational expressions exist in the calculation process of the correlation analysis function:

[0056] ;

[0057] Among them, represents the total number of monitoring points, represents the time decay coefficient, represents the base of the natural logarithm;

[0058] Among them, for the monitoring point i and other monitoring points i ’ the following relational expressions exist in the calculation process of the correlation coefficient between them:

[0059] ;

[0060] Among them, respectively represent the energy data of the monitoring point i and other monitoring points , represents the covariance calculation operation, represents the variance calculation operation.

[0061] In the embodiments of the present invention, the data smoothing function can effectively remove random noise in the monitoring data through the weighted average method, extract the core trend information by fluctuations, improve the data quality, and avoid error propagation; while the fluctuation regulation function can quickly identify the anomalies in the monitoring points and correct them in a non-linear manner, avoiding the risk of data distortion and retaining the normal fluctuation characteristics of the data; and the correlation analysis function provides a flexible way to quantify the relationship between monitoring points and can consider the information in both time and space dimensions; by considering multiple factors, finally, the smoothing values, fluctuation regulation values, and correlation feature values of the micro-energy monitoring data at all times are accumulated through an integration operation to form the time-series monitoring data.

[0062] Furthermore, the micro-energy acquisition supervision and regulation method based on data analysis further includes the following steps:

[0063] Step S102: Determine a wireless sensor network with a micro - energy power supply requirement and select multiple micro - energy power supply nodes.

[0064] In the embodiment of the present invention, by determining a wireless sensor network with a micro - energy power supply requirement, multiple wireless sensor nodes in the wireless sensor network are then subjected to power supply identification. From the multiple wireless sensor nodes, multiple MEMS devices are determined and marked as micro - energy power supply nodes, thereby realizing the selection of multiple micro - energy power supply nodes.

[0065] Specifically, Figure 3 The flowchart of selecting micro - energy power supply nodes in the method provided by the embodiment of the present invention is shown.

[0066] Among them, in the preferred embodiment provided by the present invention, the step of determining a wireless sensor network with a micro - energy power supply requirement and selecting multiple micro - energy power supply nodes specifically includes the following steps:

[0067] Step S1021: Determine a wireless sensor network with a micro - energy power supply requirement;

[0068] Step S1022: Determine multiple wireless sensor nodes in the wireless sensor network;

[0069] Step S1023: Perform power supply identification on multiple wireless sensor nodes and select multiple micro - energy power supply nodes from the multiple wireless sensor nodes.

[0070] Further, the step of performing power supply identification on multiple wireless sensor nodes and selecting multiple micro - energy power supply nodes from the multiple wireless sensor nodes specifically includes the following steps:

[0071] Obtain all wireless sensor node information in the network. The wireless sensor node information includes energy status, transmission performance, and geographical location, where the energy status at least includes the dynamic energy consumption rate;

[0072] Conduct a function evaluation based on the dynamic energy consumption rate, energy demand threshold, and current remaining energy ratio of each wireless sensor node, and normalize the evaluation results to obtain the function score of the wireless sensor node. The corresponding process has the following relationship:

[0073] ;

[0074] Among them, represents the dynamic energy consumption rate of the j th wireless sensor node, represents the energy demand threshold of the j th wireless sensor node, represents the jThe current remaining energy ratio of a wireless sensor node represents the function score of the j th wireless sensor node;

[0075] Determine the importance coefficient corresponding to each wireless sensor node, and after summarizing the importance coefficients of all wireless sensor nodes, perform normalization processing to obtain the importance weight of the wireless sensor node. The corresponding process has the following relational expression

[0076] ;

[0077] where, represents the importance coefficient of the wireless sensor node, represents the total number of wireless sensor nodes, represents the j th importance weight of the wireless sensor node;

[0078] Calculate the performance score of the wireless sensor node according to the data transmission delay time, maximum allowable delay time, data transmission rate, and theoretical maximum transmission rate of each wireless sensor node. The corresponding process has the following relational expression:

[0079] ;

[0080] where, represents the j th performance score of the wireless sensor node, represents the j th data transmission delay time of the wireless sensor node, represents the maximum allowable delay time in the network, represents the j th data transmission rate of the wireless sensor node, represents the theoretical maximum transmission rate in the network, represents the sensitivity index;

[0081] Based on the function score of the wireless sensor node and the performance score of the wireless sensor node, and combined with the importance weight of the wireless sensor node for adjustment, obtain the j th comprehensive score of the wireless sensor node. The corresponding process has the following relational expression:

[0082] ;

[0083] where, represents the j th comprehensive score of the wireless sensor node;

[0084] Sort the comprehensive scores of all wireless sensor nodes in descending order to form a list, and select the first several wireless sensor nodes from the list as micro-energy supply nodes.

[0085] In the embodiments of the present invention, by comprehensively evaluating the energy supply requirements and performance status of nodes, the deviation that may be brought by a single evaluation index is avoided, the node selection is made more scientific and reasonable, and the wireless sensor nodes that truly need energy supply can be accurately located, thereby avoiding the waste of micro energy resources. Moreover, the setting of the energy supply priority enables the key wireless sensor nodes to always operate stably and ensures the overall performance of the network.

[0086] Furthermore, the micro energy harvesting supervision and control method based on data analysis further includes the following steps:

[0087] Step S103: Directly collect the status of multiple micro energy supply nodes, obtain multiple directly collected data, and perform identification and analysis to determine multiple sensor disconnection nodes.

[0088] In the embodiments of the present invention, a direct collection instruction is generated and sent to multiple micro energy supply nodes, and then multiple directly collected data fed back are received. By performing identity recognition on the multiple directly collected data, multiple directly feedback identities are obtained. Furthermore, based on the multiple directly feedback identities, multiple micro energy supply nodes are matched, multiple sensor feedback nodes are selected, and then multiple sensor feedback nodes are excluded from the multiple micro energy supply nodes, and the remaining multiple micro energy supply nodes are marked as sensor disconnection nodes, so as to realize the identification and determination of multiple sensor disconnection nodes.

[0089] It can be understood that the multiple directly collected data correspond to the multiple sensor feedback nodes.

[0090] Specifically, Figure 4 The flowchart of directly collecting the status in the method provided by the embodiments of the present invention is shown.

[0091] Among them, in the preferred embodiment provided by the present invention, the step of directly collecting the status of multiple micro energy supply nodes, obtaining multiple directly collected data, and performing identification and analysis to determine multiple sensor disconnection nodes specifically includes the following steps:

[0092] Step S1031: Generate and send a direct collection instruction to multiple micro energy supply nodes;

[0093] Step S1032: Receive multiple directly collected data fed back;

[0094] Step S1033: Perform identity recognition on the multiple directly collected data to obtain multiple directly feedback identities;

[0095] Step S1034: Based on the multiple directly feedback identities, match multiple micro energy supply nodes and select multiple sensor feedback nodes;

[0096] Step S1035, based on multiple said sensing feedback nodes, determine multiple sensing disconnection nodes from multiple said micro energy supply nodes.

[0097] Further, the identity recognition of multiple said directly collected data to obtain multiple direct feedback identities specifically includes the following steps:

[0098] Extract the spatio-temporal features of the micro energy supply nodes from the directly collected data;

[0099] Extract the spatio-temporal feature templates of each identity from the identity template library;

[0100] Calculate the similarity between the spatio-temporal features and the spatio-temporal feature templates in terms of eigenvalue and time node, and perform weighted summation on the corresponding similarity results to obtain the static similarity score;

[0101] Extract the dynamic feature change rate from the directly collected data;

[0102] Extract the corresponding dynamic feature change rate template from the identity template library as a reference;

[0103] Calculate the change rate difference between the feature change rate and the dynamic feature change rate template to obtain the first matching degree score;

[0104] Obtain the global state variables in the network, where the global state variables include the network energy supply trend and the interconnection relationship of micro energy supply nodes;

[0105] Match the dynamic feature change rate with the global state variables to obtain the second matching score; for example, during the matching process, if the overall network energy supply is low and the energy consumption rate of the current micro energy supply node is very high, there may be an anomaly, and the score will be low;

[0106] Perform weighted summation on the first matching degree score and the second matching score to obtain the dynamic feature score;

[0107] Construct a graph structure with micro energy supply nodes as vertices and data interactions between micro energy supply nodes as edges according to the directly collected data;

[0108] Extract the historical graph structure of each identity from the identity template library;

[0109] Compare the coincidence degree between the graph structure and the historical graph structure to obtain the global state regulation score;

[0110] Accumulate the static similarity score, the dynamic feature score, and the global state regulation score to obtain the total score of each identity template, and select the identity with the highest total score as the direct feedback identity.

[0111] In the embodiments of the present invention, by analyzing the temporal and spatial dynamic characteristics of nodes, the regularity and characteristics of node behaviors can be captured, thereby achieving accurate identity recognition; and the introduction of dynamic features enables the system to not be limited to static data and be more capable of adapting to complex scenarios where node characteristics change over time. By combining local node characteristics with the global network state, the rationality and overall consistency of the recognition results are ensured. Identity recognition is achieved through a multi-modal fusion method that combines the spatio-temporal characteristics, dynamic change rates, and global graph structure of nodes, ensuring the accuracy of the recognition.

[0112] Further, the micro energy harvesting supervision and regulation method based on data analysis further includes the following steps:

[0113] Step S104, obtain the interconnection record information of multiple micro energy supply nodes, select multiple sensing interconnection nodes, perform indirect state acquisition on multiple sensing disconnection nodes, and obtain multiple indirect acquisition data.

[0114] In the embodiments of the present invention, by obtaining the interconnection record information of multiple micro energy supply nodes, extracting target interconnection information related to multiple sensing disconnection nodes from the interconnection record information, analyzing the target interconnection information, selecting sensing interconnection nodes that have a communication interconnection relationship with multiple sensing disconnection nodes from multiple sensing feedback nodes, generating an indirect acquisition instruction, and sending the indirect acquisition instruction to multiple sensing interconnection nodes, triggering the interconnection communication channels between multiple sensing interconnection nodes and the corresponding sensing disconnection nodes, and performing indirect state acquisition on multiple sensing disconnection nodes through multiple sensing interconnection nodes to obtain multiple indirect acquisition data corresponding to the multiple sensing disconnection nodes.

[0115] Specifically, Figure 5 shows the flowchart of performing indirect state acquisition in the method provided by the embodiments of the present invention.

[0116] Among them, in the preferred embodiment provided by the present invention, the steps of obtaining the interconnection record information of multiple micro energy supply nodes, selecting multiple sensing interconnection nodes, performing indirect state acquisition on multiple sensing disconnection nodes, and obtaining multiple indirect acquisition data specifically include the following steps:

[0117] Step S1041, obtain the interconnection record information of multiple micro energy supply nodes;

[0118] Step S1042, extract target interconnection information related to multiple sensing disconnection nodes from the interconnection record information;

[0119] Step S1043, analyze the target interconnection information, and select the sensing interconnection nodes corresponding to multiple sensing disconnection nodes from multiple sensing feedback nodes;

[0120] Step S1044: Generate and send indirect acquisition instructions to multiple said sensing and interconnected nodes;

[0121] Step S1045: Through multiple said sensing and interconnected nodes, indirectly acquire the states of multiple said sensing and disconnected nodes to obtain multiple indirect acquisition data.

[0122] Furthermore, the micro energy acquisition supervision and regulation method based on data analysis further includes the following steps:

[0123] Step S105: Perform hierarchical regulation analysis by integrating the micro energy supply trend, multiple said direct acquisition data, and multiple said indirect acquisition data, select multiple priority energy supply nodes, and perform priority energy supply regulation.

[0124] In an embodiment of the present invention, according to the micro energy supply trend, determine stage energy supply data, and based on the stage energy supply data, perform hierarchical regulation analysis on multiple direct acquisition data and multiple indirect acquisition data, select multiple priority energy supply nodes from multiple micro energy supply nodes, and then perform priority energy supply regulation for the multiple priority energy supply nodes.

[0125] It can be understood that in the stage energy supply data, the expected power supply amounts in different time stages are recorded.

[0126] Specifically, Figure 6 shows a flowchart of performing priority energy supply regulation in the method provided by an embodiment of the present invention.

[0127] Among them, in a preferred embodiment provided by the present invention, the step of performing hierarchical regulation analysis by integrating the micro energy supply trend, multiple said direct acquisition data, and multiple said indirect acquisition data, selecting multiple priority energy supply nodes, and performing priority energy supply regulation specifically includes the following steps:

[0128] Step S1051: Determine stage energy supply data according to the micro energy supply trend;

[0129] Step S1052: Based on the stage energy supply data, perform hierarchical regulation analysis on multiple said direct acquisition data and multiple said indirect acquisition data, and select multiple priority energy supply nodes;

[0130] Step S1053: Perform priority energy supply regulation for multiple said priority energy supply nodes.

[0131] Furthermore, Figure 7 shows an application architecture diagram of the system provided by an embodiment of the present invention.

[0132] Among them, in another preferred embodiment provided by the present invention, the micro energy acquisition supervision and regulation system based on data analysis includes:

[0133] The micro-energy monitoring and analysis unit 101 is configured to perform micro-energy monitoring, obtain micro-energy monitoring data, and analyze the pattern of the micro-energy monitoring data to determine the micro-energy supply trend.

[0134] In an embodiment of the present invention, the micro-energy monitoring and analysis unit 101 determines a target monitoring area that requires micro-energy collection, supervision, and regulation, determines multiple monitoring types, and then performs micro-energy monitoring in the target monitoring area according to the multiple monitoring types to obtain micro-energy monitoring data. By performing time-series processing on the micro-energy monitoring data, time-series monitoring data sorted by time is generated, and then the pattern of the time-series monitoring data is analyzed to determine the micro-energy supply trend.

[0135] Specifically, Figure 8 FIG. shows a structural block diagram of the micro-energy monitoring and analysis unit 101 in the system provided by the embodiment of the present invention.

[0136] Among them, in a preferred embodiment provided by the present invention, the micro-energy monitoring and analysis unit 101 specifically includes:

[0137] The area determination module 1011 is configured to determine the target monitoring area;

[0138] The type determination module 1012 is configured to determine multiple monitoring types;

[0139] The micro-energy monitoring module 1013 is configured to perform micro-energy monitoring in the target monitoring area according to the multiple monitoring types to obtain micro-energy monitoring data;

[0140] The time-series processing module 1014 is configured to perform time-series processing on the micro-energy monitoring data to generate time-series monitoring data;

[0141] The pattern analysis module 1015 is configured to analyze the pattern of the time-series monitoring data to determine the micro-energy supply trend.

[0142] Furthermore, the micro-energy collection, supervision, and regulation system based on data analysis further includes:

[0143] The energy supply node selection unit 102 is configured to determine a wireless sensor network with a micro-energy supply requirement and select multiple micro-energy supply nodes.

[0144] In an embodiment of the present invention, the energy supply node selection unit 102 determines a wireless sensor network with a micro-energy supply requirement, multiple wireless sensor nodes in the wireless sensor network, and then performs energy supply identification on the multiple wireless sensor nodes. Multiple MEMS devices are determined from the multiple wireless sensor nodes and marked as micro-energy supply nodes, thereby realizing the selection of multiple micro-energy supply nodes.

[0145] The direct status acquisition unit 103 is configured to directly acquire the status of multiple micro - energy supply nodes, obtain multiple direct acquisition data, and perform identification and analysis to determine multiple sensing disconnection nodes.

[0146] In an embodiment of the present invention, the direct status acquisition unit 103 generates a direct acquisition instruction, sends the direct acquisition instruction to multiple micro - energy supply nodes, then receives multiple directly - feedback acquisition data, performs identity recognition on the multiple directly - feedback acquisition data to obtain multiple directly - feedback identities, and then based on the multiple directly - feedback identities, matches multiple micro - energy supply nodes, selects multiple sensing feedback nodes, and then excludes the multiple sensing feedback nodes from the multiple micro - energy supply nodes, marks the remaining multiple micro - energy supply nodes as sensing disconnection nodes, and realizes the identification and determination of multiple sensing disconnection nodes.

[0147] Specifically, Figure 9 FIG. shows the structural block diagram of the direct status acquisition unit 103 in the system provided by the embodiment of the present invention.

[0148] Among them, in a preferred embodiment provided by the present invention, the direct status acquisition unit 103 specifically includes:

[0149] The first instruction sending module 1031 is configured to generate and send a direct acquisition instruction to multiple micro - energy supply nodes;

[0150] The direct data receiving module 1032 is configured to receive multiple directly - feedback acquisition data;

[0151] The identity recognition module 1033 is configured to perform identity recognition on multiple directly - feedback acquisition data to obtain multiple directly - feedback identities;

[0152] The matching selection module 1034 is configured to match multiple micro - energy supply nodes based on multiple directly - feedback identities and select multiple sensing feedback nodes;

[0153] The disconnection selection module 1035 is configured to determine multiple sensing disconnection nodes from multiple micro - energy supply nodes based on multiple sensing feedback nodes.

[0154] Furthermore, the micro - energy acquisition supervision and regulation system based on data analysis further includes:

[0155] The indirect status acquisition unit 104 is configured to obtain the interconnection record information of multiple micro - energy supply nodes, select multiple sensing interconnection nodes, indirectly acquire the status of multiple sensing disconnection nodes, and obtain multiple indirectly - feedback acquisition data.

[0156] In an embodiment of the present invention, the indirect status acquisition unit 104 obtains the interconnection record information of multiple micro-energy supply nodes, extracts target interconnection information related to multiple sensor disconnection nodes from the interconnection record information, then analyzes the target interconnection information, selects sensor interconnection nodes having a communication interconnection relationship with the multiple sensor disconnection nodes from multiple sensor feedback nodes, generates an indirect acquisition instruction, and sends the indirect acquisition instruction to the multiple sensor interconnection nodes, triggers the interconnection communication channels between the multiple sensor interconnection nodes and the corresponding sensor disconnection nodes, and indirectly acquires the status of the multiple sensor disconnection nodes through the multiple sensor interconnection nodes to obtain indirect acquisition data corresponding to the multiple sensor disconnection nodes.

[0157] Specifically, Figure 10 FIG. shows the structural block diagram of the indirect status acquisition unit 104 in the system provided by the embodiment of the present invention.

[0158] Among them, in the preferred embodiment provided by the present invention, the indirect status acquisition unit 104 specifically includes:

[0159] An information acquisition module 1041, configured to acquire the interconnection record information of the multiple micro-energy supply nodes;

[0160] An information extraction module 1042, configured to extract target interconnection information related to the multiple sensor disconnection nodes from the interconnection record information;

[0161] An interconnection selection module 1043, configured to analyze the target interconnection information and select sensor interconnection nodes corresponding to the multiple sensor disconnection nodes from the multiple sensor feedback nodes;

[0162] A second instruction sending module 1044, configured to generate and send an indirect acquisition instruction to the multiple sensor interconnection nodes;

[0163] An indirect status acquisition module 1045, configured to indirectly acquire the status of the multiple sensor disconnection nodes through the multiple sensor interconnection nodes to obtain multiple pieces of indirect acquisition data.

[0164] Further, the micro-energy acquisition supervision and regulation system based on data analysis further includes:

[0165] A priority energy supply regulation unit 105, configured to perform hierarchical regulation analysis by integrating the micro-energy supply trend, the multiple pieces of direct acquisition data, and the multiple pieces of indirect acquisition data, select multiple priority energy supply nodes, and perform priority energy supply regulation.

[0166] In an embodiment of the present invention, the priority energy supply control unit 105 determines stage energy supply data according to the micro energy supply trend. Based on the stage energy supply data, it performs hierarchical control analysis on multiple directly collected data and multiple indirectly collected data, selects multiple priority energy supply nodes from multiple micro energy supply nodes, and then performs priority energy supply control for micro energy supply to the multiple priority energy supply nodes.

[0167] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily have to be completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages does not necessarily have to be sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0168] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0169] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0170] The above-described embodiments merely represent several implementation manners of the present invention. Their descriptions are relatively specific and detailed, but should not be construed as limiting the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

[0171] The above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A micro-energy harvesting supervision and control method based on data analysis, characterized in that: The method specifically comprises the following steps: Performing micro-energy monitoring, acquiring micro-energy monitoring data, and performing regularity analysis on the micro-energy monitoring data to determine the micro-energy supply trend; Determine a wireless sensor network with micro-energy supply requirements and select multiple micro-energy supply nodes; Directly collect status of multiple micro-energy supply nodes, obtain multiple directly collected data, and perform identification and analysis to determine multiple sensor disconnection nodes; Obtaining interconnection record information of a plurality of the micro-energy supply nodes, selecting a plurality of sensor interconnection nodes, performing indirect state collection on a plurality of the sensor disconnection nodes, and obtaining a plurality of indirect collection data; Comprehensively analyzing the micro-energy supply trend, the multiple directly collected data, and the multiple indirectly collected data, and selecting multiple priority energy supply nodes to perform priority energy supply regulation; The micro-energy monitoring, obtaining the micro-energy monitoring data, and analyzing the micro-energy monitoring data to determine the micro-energy supply trend specifically include the following steps: Determine the target monitoring area; Identify multiple monitoring types; In the target monitoring area, performing micro-energy monitoring according to the plurality of monitoring types, and acquiring micro-energy monitoring data; Performing time series processing on the micro-energy monitoring data to generate time series monitoring data; Conduct regular analysis on the time series monitoring data to determine the trend of micro-energy supply.

2. The micro-energy collection supervision and control method based on data analysis according to claim 1 is characterized in that: The process of performing time series processing on the micro energy monitoring data to generate time series monitoring data corresponds to the following relationship: ； in, Represents time series monitoring data, represents the time variable, Indicates time t The moment i Energy data of each monitoring point, Indicates time t Moment i The energy fluctuation rate of each monitoring point is represents the fluctuation stability range of the monitoring point, Indicates monitoring point i With other monitoring points i ’ The correlation coefficient between Indicates monitoring point i With other monitoring points i ’ The weight distribution in the time series, They represent different weights in the time series processing process. represents the data smoothing function, represents the fluctuation control function, Represents the association analysis function.

3. The micro-energy collection supervision and control method based on data analysis according to claim 2 is characterized in that: The calculation process of data smoothing function has the following relationship: ； in, represents the smoothing window size, represents the time weight factor; Among them, the calculation process of the fluctuation control function has the following relationship: ； Among them, the calculation process of the correlation analysis function has the following relationship: ； in, represents the total number of monitoring points, represents the time attenuation coefficient, represents the base of natural logarithms; Among them, the monitoring points i With other monitoring points i ’ The calculation process of the correlation coefficient between them has the following relationship: ； in, Respectively indicate monitoring points i With other monitoring points Energy data, represents the covariance calculation operation, Represents a variance calculation operation.

4. The micro-energy collection supervision and control method based on data analysis according to claim 3 is characterized in that: The step of determining a wireless sensor network with micro-energy supply requirements and selecting a plurality of micro-energy supply nodes specifically comprises the following steps: Identify wireless sensor networks with micro-energy supply requirements; Determining a plurality of wireless sensor nodes in the wireless sensor network; Energy supply identification is performed on the plurality of wireless sensor nodes, and a plurality of micro-energy supply nodes are selected from the plurality of wireless sensor nodes.

5. The micro-energy collection monitoring and control method based on data analysis according to claim 4 is characterized in that: The step of identifying the energy supply of the plurality of wireless sensor nodes and selecting a plurality of micro-energy supply nodes from the plurality of wireless sensor nodes specifically comprises the following steps: Acquire information of all wireless sensor nodes in the network, the wireless sensor node information including energy status, transmission performance and geographic location, wherein the energy status at least includes a dynamic energy consumption rate; The function evaluation is performed according to the dynamic energy consumption rate, energy demand threshold and current remaining energy ratio of each wireless sensor node, and the evaluation results are normalized to obtain the function score of the wireless sensor node. The corresponding process has the following relationship: ； in, Indicates j The dynamic energy consumption rate of wireless sensor nodes, Indicates j The energy requirement threshold of a wireless sensor node, Indicates j The current remaining energy ratio of each wireless sensor node, Indicates j Functionality score of wireless sensor nodes; Determine the importance coefficient corresponding to each wireless sensor node, and summarize and normalize the importance coefficients of all wireless sensor nodes to obtain the importance weight of the wireless sensor node. The corresponding process has the following relationship: ； in, represents the importance coefficient of wireless sensor nodes, represents the total number of wireless sensor nodes, Indicates j Importance weight of each wireless sensor node; The performance score of the wireless sensor node is calculated based on the data transmission delay time, maximum allowable delay time, data transmission rate and theoretical maximum transmission rate of each wireless sensor node. The corresponding process has the following relationship: ； in, Indicates j The performance score of each wireless sensor node is Indicates j The data transmission delay of each wireless sensor node is Indicates the maximum allowed delay time in the network. Indicates j The data transmission rate of each wireless sensor node is Indicates the theoretical maximum transmission rate in the network. represents the sensitivity index; Based on the function score and performance score of the wireless sensor nodes, the importance weight of the wireless sensor nodes is adjusted to obtain the first j The comprehensive score of each wireless sensor node has the following relationship: ； in, Indicates j Comprehensive score of wireless sensor nodes; The comprehensive scores of all wireless sensor nodes are sorted in descending order to form a list, and the first several wireless sensor nodes are selected from the list as micro-energy supply nodes.

6. The micro-energy collection monitoring and control method based on data analysis according to claim 5 is characterized in that: The direct state collection of the plurality of micro-energy supply nodes, obtaining a plurality of directly collected data, and performing identification and analysis to determine a plurality of sensor disconnection nodes specifically comprises the following steps: Generate and send direct collection instructions to multiple micro-energy supply nodes; Receive direct data collection from multiple feedbacks; Performing identity recognition on the multiple directly collected data to obtain multiple direct feedback identities; Based on the multiple direct feedback identities, matching the multiple micro-energy supply nodes, and selecting multiple sensor feedback nodes; Based on the plurality of sensor feedback nodes, a plurality of sensor disconnection nodes are determined from the plurality of micro-energy supply nodes.

7. The micro-energy harvesting supervision and control method based on data analysis according to claim 6 is characterized in that: The step of performing identity recognition on the multiple directly collected data to obtain multiple direct feedback identities includes the following sub-steps: Extracting spatiotemporal characteristics of micro-energy supply nodes from directly collected data; Extracting a spatiotemporal feature template for each identity from an identity template library; Calculate the similarity between the spatiotemporal features and the spatiotemporal feature templates in terms of feature values ​​and time nodes, and perform weighted summation on the corresponding similarity results to obtain a static similarity score; Extract dynamic feature change rates from directly acquired data; Extract the corresponding dynamic feature change rate template from the identity template library as a reference; Calculate the difference between the feature change rate and the dynamic feature change rate template to obtain a first matching score; Obtaining global state variables in the network, wherein the global state variables include network energy supply trends and interconnection relationships between micro-energy supply nodes; Matching the dynamic feature change rate with the global state variable to obtain a second matching score; Performing a weighted summation of the first matching score and the second matching score to obtain a dynamic feature score; Based on the directly collected data, a graph structure is constructed with micro-energy supply nodes as vertices and data interactions between micro-energy supply nodes as edges; Extract the history graph structure of each identity from the identity template library; Compare the overlap between the graph structure and the historical graph structure to obtain the global state control score; The static similarity score, dynamic feature score and global state control score are accumulated to obtain the total score of each identity template, and the identity with the highest total score is selected as the direct feedback identity.

8. The micro-energy harvesting supervision and control method based on data analysis according to claim 7 is characterized in that: The obtaining of interconnection record information of the plurality of micro-energy supply nodes, selecting a plurality of sensor interconnection nodes, performing indirect state collection on the plurality of sensor disconnection nodes, and obtaining a plurality of indirect collection data specifically comprises the following steps: Obtaining interconnection record information of a plurality of micro-energy supply nodes; Extracting target interconnection information related to a plurality of the sensor disconnection nodes from the interconnection record information; Analyzing the target interconnection information, and selecting a plurality of sensor interconnection nodes corresponding to the sensor disconnection nodes from the plurality of sensor feedback nodes; Generate and send indirect collection instructions to a plurality of said sensor interconnection nodes; Through the plurality of interconnected sensor nodes, indirect status collection is performed on the plurality of disconnected sensor nodes to obtain a plurality of indirect collection data.

9. The micro-energy harvesting supervision and control method based on data analysis according to claim 8 is characterized in that: The step of comprehensively analyzing the micro-energy supply trend, the multiple directly collected data, and the multiple indirectly collected data to perform hierarchical regulation and control, selecting multiple priority energy supply nodes, and performing priority energy supply regulation specifically includes the following steps: Determining stage energy supply data according to the micro-energy supply trend; Based on the stage energy supply data, performing hierarchical control and analysis on the plurality of directly collected data and the plurality of indirectly collected data, and selecting a plurality of priority energy supply nodes; Prioritize the micro-energy supply to multiple priority energy supply nodes.

10. A micro-energy collection monitoring and control system based on data analysis, characterized in that: The system applies the micro-energy collection supervision and control method based on data analysis as described in any one of claims 1 to 9 above, and the system includes a micro-energy monitoring and analysis unit, an energy supply node selection unit, a direct state collection unit, an indirect state collection unit and a priority energy supply control unit, wherein: A micro-energy monitoring and analysis unit, used to perform micro-energy monitoring, obtain micro-energy monitoring data, and perform regularity analysis on the micro-energy monitoring data to determine the micro-energy supply trend; An energy supply node selection unit, used to determine a wireless sensor network with micro-energy supply requirements and select a plurality of micro-energy supply nodes; A direct state acquisition unit, used to perform direct state acquisition on a plurality of micro-energy supply nodes, obtain a plurality of directly acquired data, and perform identification and analysis to determine a plurality of sensor disconnection nodes; An indirect state acquisition unit, used to acquire interconnection record information of a plurality of the micro-energy supply nodes, select a plurality of sensor interconnection nodes, perform indirect state acquisition on a plurality of the sensor disconnection nodes, and acquire a plurality of indirect acquisition data; The priority energy supply control unit is used to comprehensively analyze the micro-energy supply trend, multiple directly collected data and multiple indirectly collected data, select multiple priority energy supply nodes, and perform priority energy supply control.

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