Micro-energy acquisition supervision and control method and system based on data analysis

Abstract

The embodiment of the invention relates to the technical field of micro-energy acquisition, supervision and regulation, and particularly discloses a micro-energy acquisition, supervision and regulation method and system based on data analysis. According to the embodiment of the invention, micro-energy monitoring and law analysis are carried out; selecting a plurality of micro-energy supply nodes; carrying out direct state acquisition; indirect state collection is carried out; and performing hierarchical regulation and control analysis by integrating the micro-energy energy supply trend, the plurality of direct acquisition data and the plurality of indirect acquisition data, and selecting a plurality of priority energy supply nodes to perform priority energy supply regulation and control. The micro energy supply trend can be analyzed, direct state collection and indirect state collection can be carried out on a plurality of micro energy supply nodes, then hierarchical regulation and control analysis is carried out, a plurality of priority energy supply nodes are selected, and priority energy supply regulation and control are carried out, so that refined and intelligent micro energy collection supervision and control are realized; it is ensured that collection and utilization of micro energy are matched with actual requirements, energy waste can be avoided, and the stability and reliability of energy supply can be guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of micro energy collection supervision and control, and in particular, relates to a micro energy collection supervision and control method and system based on data analysis. Background Art

[0002] Micro energy harvesting supervision and regulation involves the operation, monitoring, regulation and control of the micro energy harvesting system, which converts the energy in the surrounding environment into electrical energy to power the MEMS devices in the wireless sensor network.

[0003] With the rapid development of 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 existing technology, the micro-energy collection, supervision and control process is relatively simple, lacking sufficient refinement and intelligence. It is impossible to analyze the status of different micro-energy supply nodes and then perform hierarchical control according to different statuses. As a result, the collection and utilization of micro-energy cannot match actual needs, which easily causes energy waste and cannot guarantee the stability and reliability of energy supply. Summary of the invention

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

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: A micro-energy harvesting supervision and control method based on data analysis, 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; The micro-energy supply trend, the multiple directly collected data and the multiple indirectly collected data are comprehensively analyzed for hierarchical regulation, and multiple priority supply nodes are selected for priority supply regulation.

[0007] A micro-energy collection supervision and control system based on data analysis, 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.

[0008] Compared with the prior art, the present invention has the following beneficial effects: The embodiment of the present invention performs micro-energy monitoring and regularity analysis; selects multiple micro-energy supply nodes; performs direct status collection; performs indirect status collection; performs hierarchical regulation and analysis based on the micro-energy supply trend, multiple direct collection data, and multiple indirect collection data, and selects multiple priority supply nodes for priority supply regulation. It is capable of analyzing the micro-energy supply trend, performing direct status collection and indirect status collection on multiple micro-energy supply nodes, and then performing hierarchical regulation and analysis, selecting multiple priority supply nodes, and performing priority supply regulation, thereby realizing refined and intelligent micro-energy collection supervision and regulation, ensuring that the collection and utilization of micro-energy matches actual needs, avoiding energy waste, and ensuring the stability and reliability of energy supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0010] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0011] Figure 2 A flow chart of micro-energy monitoring and analysis in the method provided by an embodiment of the present invention is shown.

[0012] Figure 3 A flow chart of selecting a micro-energy supply node in the method provided by an embodiment of the present invention is shown.

[0013] Figure 4 A flow chart of performing direct state acquisition in the method provided by an embodiment of the present invention is shown.

[0014] Figure 5 A flow chart of indirect state collection in the method provided by an embodiment of the present invention is shown.

[0015] Figure 6 A flow chart of performing priority energy supply control in the method provided in an embodiment of the present invention is shown.

[0016] Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0017] Figure 8 The structure block diagram of the micro energy monitoring and analysis unit in the system provided by the embodiment of the present invention is shown.

[0018] Fig. 9 The structure block diagram of the direct state acquisition unit in the system provided by the embodiment of the present invention is shown.

[0019] Fig.10 The structure block diagram of the indirect state acquisition unit in the system provided by the embodiment of the present invention is shown. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] It is understandable that in the existing technology, the micro-energy collection supervision and control process is relatively simple, lacking sufficient refinement and intelligence. It is unable to perform status analysis on different micro-energy supply nodes and then perform hierarchical regulation according to different states. As a result, the collection and utilization of micro-energy cannot match actual needs, which easily causes energy waste and cannot guarantee the stability and reliability of energy supply.

[0022] To solve the above problems, the embodiment of the present invention performs micro-energy monitoring, obtains micro-energy monitoring data, and performs regular analysis on the micro-energy monitoring data to determine the trend of micro-energy supply; determines a wireless sensor network with micro-energy supply demand, selects multiple micro-energy supply nodes; directly collects the status of multiple micro-energy supply nodes, obtains multiple direct collection data, and performs identification analysis to determine multiple sensor disconnection nodes; obtains the interconnection record information of multiple micro-energy supply nodes, selects multiple sensor interconnection nodes, and performs indirect status collection on multiple sensor disconnection nodes to obtain multiple indirect collection data; comprehensively analyzes the trend of micro-energy supply, multiple direct collection data, and multiple indirect collection data, and selects multiple priority supply nodes for priority supply regulation. It is possible to analyze the trend of micro-energy supply, perform direct status collection and indirect status collection on multiple micro-energy supply nodes, and then perform hierarchical regulation analysis, select multiple priority supply nodes, and perform priority supply regulation, thereby realizing refined and intelligent micro-energy collection supervision and regulation, ensuring that the collection and utilization of micro-energy matches the actual demand, which can avoid energy waste and ensure the stability and reliability of energy supply.

[0023] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0024] Specifically, the micro-energy harvesting supervision and control method based on data analysis includes the following steps: Step S101 , performing micro-energy monitoring, acquiring micro-energy monitoring data, and performing regularity analysis on the micro-energy monitoring data to determine a micro-energy supply trend.

[0025] In an embodiment of the present invention, a target monitoring area that requires micro-energy collection supervision and regulation is determined, and multiple monitoring types are determined. Then, in the target monitoring area, micro-energy monitoring is performed according to the multiple monitoring types to obtain micro-energy monitoring data. The micro-energy monitoring data is processed in a time series manner to generate time-series monitoring data sorted by time. The time series monitoring data is then analyzed for regularities to determine the micro-energy supply trend.

[0026] It is understandable that multiple monitoring types may include solar energy, thermal energy, wind energy, kinetic energy, etc.

[0027] Specifically, Figure 2 A flow chart of micro-energy monitoring and analysis in the method provided by an embodiment of the present invention is shown.

[0028] Among them, in the preferred embodiment provided by the present invention, the micro-energy monitoring, obtaining the micro-energy monitoring data, and analyzing the regularity of the micro-energy monitoring data to determine the micro-energy supply trend specifically include the following steps: Step S1011, determining a target monitoring area; Step S1012, determining multiple monitoring types; Step S1013, performing micro-energy monitoring in the target monitoring area according to the plurality of monitoring types to obtain micro-energy monitoring data; Step S1014, performing time series processing on the micro-energy monitoring data to generate time series monitoring data; Step S1015, performing regularity analysis on the time series monitoring data to determine the trend of micro-energy supply.

[0029] Furthermore, 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 The correlation coefficient between Indicates monitoring point i With other monitoring points The weight distribution in the time series, They represent the unused weights in the time series processing process, represents the data smoothing function, represents the fluctuation control function, represents the association analysis function; Among them, the calculation process of the 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.

[0030] In an embodiment of the present invention, the data smoothing function can effectively remove random noise in the monitoring data through the weighted average method, extract core trend information from the fluctuation, improve data quality and avoid error propagation; the fluctuation control function can quickly identify anomalies in the monitoring points and correct them in a nonlinear way, avoiding the risk of data distortion and retaining the normal fluctuation characteristics of the data; the correlation analysis function provides a flexible way to quantify the relationship between monitoring points and can simultaneously consider information in time and space dimensions; by considering multiple factors, the smoothing value, fluctuation control value and correlation characteristic value of the micro-energy monitoring data at all times are finally accumulated through an integral operation to form time series monitoring data.

[0031] Furthermore, the micro-energy harvesting supervision and control method based on data analysis also includes the following steps: Step S102: determine a wireless sensor network with micro-energy supply requirements, and select multiple micro-energy supply nodes.

[0032] In an embodiment of the present invention, a wireless sensor network with micro-energy supply requirements is determined, multiple wireless sensor nodes in the wireless sensor network are identified, and power supply is identified for the multiple wireless sensor nodes. Multiple MEMS devices are determined from the multiple wireless sensor nodes and marked as micro-energy supply nodes, thereby achieving the selection of multiple micro-energy supply nodes.

[0033] Specifically, Figure 3 A flow chart of selecting a micro-energy supply node in the method provided by an embodiment of the present invention is shown.

[0034] Among them, in the preferred embodiment provided by the present invention, the determining of the wireless sensor network with micro-energy supply demand and selecting multiple micro-energy supply nodes specifically include the following steps: Step S1021, determining a wireless sensor network with micro-energy supply requirements; Step S1022, determining a plurality of wireless sensor nodes in the wireless sensor network; Step S1023, performing energy supply identification on the plurality of wireless sensor nodes, and selecting a plurality of micro-energy supply nodes from the plurality of wireless sensor nodes.

[0035] Furthermore, 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 jThe 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 corresponds to 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.

[0036] In the embodiment of the present invention, by comprehensively evaluating the energy supply demand and performance status of the nodes, the deviation that may be caused by a single evaluation indicator is avoided, making the node selection more scientific and reasonable, and being able to accurately locate the wireless sensor nodes that really need energy, thereby avoiding the waste of micro-energy resources. The setting of energy supply priority enables key wireless sensor nodes to always maintain stable operation and ensure the overall performance of the network.

[0037] Furthermore, the micro-energy harvesting supervision and control method based on data analysis also includes the following steps: Step S103, directly collecting status of the plurality of micro-energy supply nodes, acquiring a plurality of directly collected data, and performing identification and analysis to determine a plurality of sensor disconnection nodes.

[0038] In an embodiment of the present invention, a direct collection instruction is generated and sent to multiple micro-energy supply nodes, and then multiple feedback direct collection data are received, and multiple direct feedback identities are obtained by performing identity recognition on the multiple direct collection data. Then, based on the multiple direct feedback identities, multiple micro-energy supply nodes are matched, and multiple sensor feedback nodes are selected. Then, multiple sensor feedback nodes are eliminated from the multiple micro-energy supply nodes, and the remaining multiple micro-energy supply nodes are marked as sensor disconnection nodes, thereby realizing the identification and determination of multiple sensor disconnection nodes.

[0039] It can be understood that a plurality of directly collected data corresponds to a plurality of sensor feedback nodes.

[0040] Specifically, Figure 4A flow chart of performing direct state acquisition in the method provided by an embodiment of the present invention is shown.

[0041] Among them, in the preferred embodiment provided by the present invention, the direct state acquisition of the plurality of micro-energy supply nodes, the acquisition of a plurality of directly acquired data, and the identification and analysis, and the determination of a plurality of sensor disconnection nodes specifically include the following steps: Step S1031, generating and sending a direct collection instruction to a plurality of micro-energy supply nodes; Step S1032, receiving multiple directly collected data from feedback; Step S1033, performing identity recognition on the multiple directly collected data to obtain multiple direct feedback identities; Step S1034, matching the plurality of micro-energy supply nodes based on the plurality of direct feedback identities, and selecting a plurality of sensor feedback nodes; Step S1035: Based on the plurality of sensor feedback nodes, determine a plurality of sensor disconnection nodes from the plurality of micro-energy supply nodes.

[0042] Furthermore, the performing of identity recognition on the multiple directly collected data to obtain multiple direct feedback identities specifically includes the following 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; The dynamic feature change rate is matched with the global state variable to obtain a second matching score; for example, during the matching process, if the energy supply of the entire network is low, and the energy consumption rate of the current micro-energy supply node is very high, there may be an abnormality, and the score is low; 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.

[0043] In the embodiment of the present invention, by analyzing the temporal and spatial dynamic characteristics of the node, the regularity and characteristics of the node behavior can be captured, thereby achieving accurate identity recognition; and the introduction of dynamic features makes the system not limited to static data, but more adaptable to complex scenarios where node characteristics change over time, and by combining local node characteristics with the global network state, the rationality and overall consistency of the recognition results are guaranteed. Identity recognition is achieved by combining the temporal and spatial characteristics of the node, the dynamic change rate, and the global graph structure to form a multimodal fusion method, which ensures the accuracy of recognition.

[0044] Furthermore, the micro-energy harvesting supervision and control method based on data analysis also includes the following steps: Step S104, obtaining interconnection record information of the plurality of micro-energy supply nodes, selecting a plurality of sensor interconnection nodes, performing indirect status collection on the plurality of sensor disconnection nodes, and obtaining a plurality of indirect collection data.

[0045] In an embodiment of the present invention, interconnection record information of multiple micro-energy supply nodes is obtained, target interconnection information related to multiple sensor disconnection nodes is extracted from the interconnection record information, and then the target interconnection information is analyzed, and sensor interconnection nodes that have a communication interconnection relationship with multiple sensor disconnection nodes are selected from multiple sensor feedback nodes. By generating indirect collection instructions and sending the indirect collection instructions to multiple sensor interconnection nodes, the interconnection communication channels between the multiple sensor interconnection nodes and the corresponding sensor disconnection nodes are triggered, and the indirect status collection of the multiple sensor disconnection nodes is performed through the multiple sensor interconnection nodes, and the indirect collection data corresponding to the multiple sensor disconnection nodes are obtained.

[0046] Specifically, Figure 5 A flow chart of indirect state collection in the method provided by an embodiment of the present invention is shown.

[0047] Among them, in the preferred embodiment provided by the present invention, 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 include the following steps: Step S1041, obtaining interconnection record information of a plurality of micro-energy supply nodes; Step S1042, extracting target interconnection information related to the plurality of disconnected sensor nodes from the interconnection record information; Step S1043, 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; Step S1044, generating and sending an indirect collection instruction to the plurality of sensor interconnection nodes; Step S1045, performing indirect status collection on the plurality of disconnected sensor nodes through the plurality of interconnected sensor nodes to obtain a plurality of indirect collection data.

[0048] Furthermore, the micro-energy harvesting supervision and control method based on data analysis also includes the following steps: Step S105, comprehensively analyzing the micro-energy supply trend, the multiple directly collected data, and the multiple indirectly collected data, selecting multiple priority energy supply nodes, and performing priority energy supply regulation.

[0049] In an embodiment of the present invention, according to the micro-energy supply trend, the stage energy supply data is determined, and based on the stage energy supply data, a plurality of directly collected data and a plurality of indirectly collected data are subjected to hierarchical regulation and analysis, and a plurality of priority energy supply nodes are selected from a plurality of micro-energy supply nodes, and then priority energy supply regulation of micro-energy supply is performed to the plurality of priority energy supply nodes.

[0050] It is understandable that the stage energy supply data records the estimated power supply in different time stages.

[0051] Specifically, Figure 6 A flow chart of performing priority energy supply control in the method provided in an embodiment of the present invention is shown.

[0052] Among them, in the preferred embodiment provided by the present invention, the comprehensive micro-energy supply trend, multiple directly collected data and multiple indirectly collected data are analyzed for hierarchical regulation, multiple priority supply nodes are selected, and the priority supply regulation specifically includes the following steps: Step S1051, determining stage energy supply data according to the micro-energy supply trend; Step S1052, based on the stage energy supply data, performing hierarchical control analysis on the plurality of directly collected data and the plurality of indirectly collected data, and selecting a plurality of priority energy supply nodes; Step S1053, performing priority energy supply regulation of micro-energy supply to the plurality of priority energy supply nodes.

[0053] Furthermore, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0054] Among them, in another preferred embodiment provided by the present invention, the micro-energy collection monitoring and control system based on data analysis includes: The micro energy monitoring and analysis unit 101 is 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.

[0055] 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, and determines a plurality of monitoring types, and then performs micro-energy monitoring in the target monitoring area according to the plurality of monitoring types to obtain micro-energy monitoring data, and generates time series monitoring data sorted by time by performing time series processing on the micro-energy monitoring data, and then performs regularity analysis on the time series monitoring data to determine the micro-energy supply trend.

[0056] Specifically, Figure 8 It shows a structural block diagram of the micro energy monitoring and analysis unit 101 in the system provided by an embodiment of the present invention.

[0057] Among them, in the preferred embodiment provided by the present invention, the micro energy monitoring and analysis unit 101 specifically includes: The area determination module 1011 is used to determine the target monitoring area; A type determination module 1012, used to determine multiple monitoring types; The micro energy monitoring module 1013 is used to perform micro energy monitoring in the target monitoring area according to the plurality of monitoring types and obtain micro energy monitoring data; A time series processing module 1014 is used to perform time series processing on the micro energy monitoring data to generate time series monitoring data; The rule analysis module 1015 is used to perform rule analysis on the time series monitoring data to determine the trend of micro-energy supply.

[0058] Furthermore, the micro-energy collection monitoring and control system based on data analysis also includes: The energy supply node selection unit 102 is used to determine the wireless sensor network with micro energy supply demand and select multiple micro energy supply nodes.

[0059] In an embodiment of the present invention, the energy supply node selection unit 102 determines a wireless sensor network with micro-energy supply requirements, multiple wireless sensor nodes in the wireless sensor network, and then identifies the multiple wireless sensor nodes for energy supply, determines multiple MEMS devices from the multiple wireless sensor nodes, and marks them as micro-energy supply nodes, thereby realizing the selection of multiple micro-energy supply nodes.

[0060] The direct status acquisition unit 103 is used to perform direct status acquisition on the 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.

[0061] In an embodiment of the present invention, the direct state acquisition unit 103 generates a direct acquisition instruction and sends the direct acquisition instruction to multiple micro-energy supply nodes, and then receives multiple feedback direct acquisition data, obtains multiple direct feedback identities by performing identity identification on the multiple direct acquisition data, and then matches the multiple micro-energy supply nodes based on the multiple direct feedback identities, selects multiple sensor feedback nodes, and then eliminates multiple sensor feedback nodes from the multiple micro-energy supply nodes, and marks the remaining multiple micro-energy supply nodes as sensor disconnection nodes, thereby realizing the identification and determination of multiple sensor disconnection nodes.

[0062] Specifically, Fig. 9 The structure block diagram of the direct state acquisition unit 103 in the system provided by the embodiment of the present invention is shown.

[0063] Among them, in the preferred implementation manner provided by the present invention, the direct state acquisition unit 103 specifically includes: A first instruction sending module 1031 is used to generate and send a direct collection instruction to a plurality of micro-energy supply nodes; A direct data receiving module 1032 is used to receive multiple directly collected data from feedback; An identity recognition module 1033 is used to perform identity recognition on the multiple directly collected data to obtain multiple direct feedback identities; A matching selection module 1034, configured to match the plurality of micro-energy supply nodes based on the plurality of direct feedback identities, and select a plurality of sensor feedback nodes; The disconnection selection module 1035 is used to determine a plurality of sensor disconnection nodes from a plurality of the micro-energy supply nodes based on the plurality of the sensor feedback nodes.

[0064] Furthermore, the micro-energy collection monitoring and control system based on data analysis also includes: The indirect state acquisition unit 104 is used to acquire the interconnection record information of the plurality of micro-energy supply nodes, select a plurality of sensor interconnection nodes, perform indirect state acquisition on the plurality of sensor disconnection nodes, and acquire a plurality of indirect acquisition data.

[0065] 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 the target interconnection information related to the multiple sensor disconnection nodes from the interconnection record information, and then analyzes the target interconnection information. From the multiple sensor feedback nodes, a sensor interconnection node that has a communication interconnection relationship with the multiple sensor disconnection nodes is selected, and an indirect acquisition instruction is generated and sent to the multiple sensor interconnection nodes, thereby triggering the interconnection communication channel between the multiple sensor interconnection nodes and the corresponding sensor disconnection nodes. Through the multiple sensor interconnection nodes, the multiple sensor disconnection nodes are indirectly state-collected, and the indirect acquisition data corresponding to the multiple sensor disconnection nodes are obtained.

[0066] Specifically, Fig.10 It shows a structural block diagram of the indirect state acquisition unit 104 in the system provided by the embodiment of the present invention.

[0067] In a preferred embodiment of the present invention, the indirect state acquisition unit 104 specifically includes: The information acquisition module 1041 is used to obtain the interconnection record information of the plurality of micro energy supply nodes; An information extraction module 1042, configured to extract target interconnection information related to the plurality of disconnected sensor nodes from the interconnection record information; The interconnection selection module 1043 is used to analyze the target interconnection information and select a plurality of sensor interconnection nodes corresponding to the sensor disconnection nodes from the plurality of sensor feedback nodes; A second instruction sending module 1044 is used to generate and send indirect collection instructions to the plurality of sensor interconnection nodes; The indirect status collection module 1045 is used to perform indirect status collection on the plurality of disconnected sensor nodes through the plurality of interconnected sensor nodes to obtain a plurality of indirect collection data.

[0068] Furthermore, the micro-energy collection monitoring and control system based on data analysis also includes: The priority energy supply control unit 105 is used to comprehensively analyze the micro-energy supply trend, the multiple directly collected data and the multiple indirectly collected data, select multiple priority energy supply nodes, and perform priority energy supply control.

[0069] In an embodiment of the present invention, the priority energy supply regulation unit 105 determines the stage energy supply data according to the micro-energy energy supply trend, and based on the stage energy supply data, performs hierarchical regulation and 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 regulation of micro-energy supply to multiple priority energy supply nodes.

[0070] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0071] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an 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 (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0072] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0073] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in 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 the unused 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 wireless sensor nodes 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 corresponds to 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 harvesting supervision 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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