IoT-based energy monitoring data acquisition methods and systems

Abstract

This invention discloses an energy monitoring data acquisition method and system based on the Internet of Things (IoT). Several energy monitoring nodes collaboratively collect energy monitoring data from a target object. The method includes: a target monitoring node processing monitoring data in its current monitoring mode to obtain target monitoring information; surrounding collaborative sensing nodes sending scene status information or monitoring mode information back to the target monitoring node based on the received target monitoring information; the target monitoring node adjusting and setting monitoring mode parameters to send the obtained target monitoring information to the surrounding collaborative sensing nodes, transmitting the categorized monitoring data to a collaborative data management system via flexible data upload; and the management system collaboratively managing the energy monitoring data acquisition process. This invention allows for flexible adjustment of the monitoring mode in response to changes in the energy monitoring scene status, resulting in better collaboration and reusability of the monitoring node devices.

Description

Technical Field

[0001] This invention relates to the technical field of edge intelligence and measurement and control in the Internet of Things (IoT), mainly to edge collaborative sensing networks and collaborative data management systems for energy monitoring, and particularly to an IoT-based method and system for energy monitoring data acquisition. Background Technology

[0002] The overall efficiency of energy utilization is mainly reflected in safety, energy saving, and economy. With the development of IoT smart technology, energy monitoring and safety management not only focus on energy production, storage, transmission, and distribution, but also need to more broadly and deeply cover the entire process of energy use and consumption of distributed energy nodes, based on monitoring and surveillance of energy load objects and related terminal equipment in different target scenarios within the user's scope.

[0003] The challenge that IoT edge intelligence technology for target scenarios needs to address is scene-aware-based decision-making and services. The state of a target scenario is determined by several target objects associated with it and their associated state variables. Most of these state variables often originate from wireless sensors or other sensing and monitoring devices that are the target object devices. These sensing and monitoring devices, as target sensing nodes, are also the target object devices served by the edge sensing network, and they have directly established association and binding relationships with the mobile objects or location environment of the target scenario they serve.

[0004] Considering the wireless coverage issue of intelligent services in IoT scenarios, as the number of target devices in the surrounding environment increases, if the edge domain's sensing service capabilities for low-power target devices rely entirely or excessively on dedicated service nodes or base station equipment (such as IoT hosts, routers, gateways / relays, positioning base stations, etc.), it will lead to insufficient wireless coverage and computing power for sensing service capabilities or higher resource costs.

[0005] Target sensing nodes have sensing and monitoring capabilities for specific physical objects. However, considering issues such as power consumption, resources, computing power, number of installations, or technical compatibility, they are usually not required to be reused as network service nodes. But when necessary and when power consumption and resources allow, they can also fulfill some of the responsibilities of network service nodes to improve the reusability and cost-effectiveness of edge network system hardware devices.

[0006] Establishing a distributed, continuous, and large-scale equipment status and energy monitoring system can provide information services such as monitoring, summarizing, evaluating, and guiding the energy utilization efficiency and safety level of nodes, providing a basis for decision-making to continuously improve energy efficiency and safety management.

[0007] For monitoring and management of electrical equipment in industrial environments, based on key technologies such as wireless multi-mode management and collaborative positioning and tracking, an information service system is established that can provide distributed, low-power, big data, continuous, edge and central intelligent management.

[0008] The power monitoring node is a type of energy monitoring node that monitors the power consumption periods and power status of distributed devices, and provides safety warnings and protection based on contingency plans. The system can perform online statistics based on real-time monitoring data and segmented recorded data, providing users with online visual monitoring and information services.

[0009] The basic information for electricity consumption monitoring includes: 1) Equipment status: equipment matching and binding, usage time period, location area and related information; 2) Real-time power monitoring data: real-time collection and timed reporting of data; 3) Historical data records: including normal segmented records and abnormal log records; 4) Statistical information such as electricity consumption, electricity status and electricity anomalies (early warning and protection logs).

[0010] Application management reports are generated based on user needs: 1) Energy conservation, environmental protection and energy utilization efficiency management of equipment; 2) Equipment usage efficiency and safety hazard reports; 3) Power management improvement guidance information; 4) Safety monitoring and dispatch management, etc.

[0011] Existing IoT edge sensing and monitoring technologies have the following main shortcomings:

[0012] 1) Collaboration issues: Edge service node devices lack a complete wireless sensing capability model, and there is a lack of flexible collaborative service cooperation among field network service nodes. The distributed energy monitoring nodes in the field environment merely act as target monitoring nodes, uploading the collected data to the host computer; there is a lack of necessary collaborative monitoring capabilities among the monitoring nodes, including collaborative monitoring data processing, collaborative protection, and dynamic adjustment of monitoring strategies and contingency plans for different target scenario states.

[0013] 2) Edge computing issues: From a physical perspective, including edge cloud computing, cloud-edge collaborative computing, on-site network computing, smart terminal computing, and target object computing, existing edge computing technologies, especially the data processing and intelligent decision-making undertaken by edge domain smart hardware devices, still lack overall hierarchy and rely too much on individual core smart devices (IoT hosts, smart gateways, routers).

[0014] 3) Reusability of edge devices: In terms of device utilization efficiency, edge service nodes have low reusability, relying too much on dedicated smart devices (IoT hosts, smart gateways, routers, positioning base stations) and making less use of some low-cost reusable nodes that also have wireless sensing and computing capabilities.

[0015] 4) Issues with Low-Power Devices: Existing edge wireless network communication technologies mainly include two types: wireless connections (point-to-point or point-to-multipoint) and mesh networks. Wireless interoperability for low-power target devices still lacks a fast and efficient mechanism. Specifically, wireless connections require prior exchange of wireless communication parameters based on a handshake protocol; and mesh network nodes have not yet effectively solved the problem of rapid scenario-triggered response and reply mechanisms when responding to peripheral low-power devices.

[0016] 5) Security of energy / electricity monitoring equipment: Energy monitoring node equipment (such as water, electricity and gas meters, metering sensors, electricity metering sockets, etc.) can support the acquisition of energy / electricity monitoring data and realize many intelligent management capabilities through status monitoring, location sensing, remote control and anomaly handling. However, its security still needs to be further improved in terms of load equipment matching, transient anomaly response and protection.

[0017] 6) Balancing Energy Efficiency and Anomaly Handling Capabilities: There is a lack of flexible and targeted selection and adaptation capabilities based on the current target scenario and load state when load objects are in different operating states (e.g., not connected or operating normally after connection, potential anomalies, or critical anomalies). Indiscriminate real-time monitoring data processing not only leads to unnecessary loss of sensitive resources (such as power consumption, computing power, and bandwidth) and a large amount of data redundancy, but also results in a lack of more real-time and effective anomaly handling capabilities when key target load objects experience real transient anomalies.

[0018] 7) Lack of safety protection for transient connection and disconnection: Existing safety protection technologies mainly target the operation of the load, but lack more targeted and effective protection for transient processes during connection and disconnection (such as plugging and unplugging). For example, for hot-plugging of electrical loads in special industrial environments, special arc protection technologies with overly complex structures and extremely high costs must be adopted to prevent arcing.

[0019] Therefore, in the process of collecting energy monitoring data for target objects, IoT-based energy monitoring nodes need to have better reusability, collaboration, and flexibility. How to select a reasonable energy monitoring mode for different object states in different scenarios to achieve a dynamic balance in terms of security, energy saving, real-time response capability, and system data requirements; and how to collaboratively manage the collaborative monitoring data processing process of energy monitoring data collection in the IoT edge domain to achieve better real-time data collection and comprehensive efficiency for the object states in the energy monitoring scenario have become urgent technical problems to be solved. Summary of the Invention

[0020] The technical problem this invention aims to solve is that, through collaborative monitoring, the target monitoring node and surrounding collaborative sensing nodes obtain monitoring mode information based on scene state information through state pattern parsing. This enables the target monitoring node to flexibly adjust monitoring mode parameters and acquire target monitoring information through monitoring data processing, thereby solving the problem of the coordination and flexibility of energy monitoring modes. Based on collaborative monitoring, the surrounding collaborative sensing nodes obtain categorized monitoring data by classifying the target monitoring information and transmit this categorized monitoring data to the collaborative data management system via flexible data upload, further solving the problems of edge collaborative data processing and collaborative management systems for energy monitoring data acquisition.

[0021] To address the aforementioned problems, this invention proposes an energy monitoring data acquisition method and system based on the Internet of Things.

[0022] In a first aspect, this invention discloses an energy monitoring data acquisition method based on the Internet of Things (IoT). Several energy monitoring nodes collaboratively collect energy monitoring data from a target object, wherein different energy monitoring nodes serve as target monitoring nodes and / or collaborative sensing nodes. The method includes the following steps: a target monitoring node, in its current monitoring mode, processes the collected signal data to obtain target monitoring information and sends it to surrounding collaborative sensing nodes; the collaborative sensing nodes, based on the received target monitoring information, perform scene state parsing and feed back corresponding scene state information to the target monitoring node when the scene state changes; the target monitoring node, based on the received scene state information, derives monitoring mode parameters for the current target monitoring information through state mode parsing and activates the corresponding monitoring mode.

[0023] Secondly, this invention also discloses another method for energy monitoring data acquisition based on the Internet of Things (IoT). Several energy monitoring nodes collaboratively collect energy monitoring data from a target object, where different energy monitoring nodes serve as target monitoring nodes and / or collaborative sensing nodes. The method includes the following steps: a collaborative sensing node obtains monitoring mode information through state mode parsing based on the acquired target monitoring information and sends the monitoring mode information back to the associated target monitoring node; the target monitoring node adjusts and sets monitoring mode parameters according to the received monitoring mode information, obtains target monitoring information through monitoring data processing, and sends it to surrounding collaborative sensing nodes; the collaborative sensing nodes obtain classified monitoring data by classifying the target monitoring information and transmit the classified monitoring data to a collaborative data management system.

[0024] Optionally, the target monitoring node performs time-slot isolation protection on the transient process of coupled acquisition of the preceding signal input to avoid time-domain overlap between the signal acquisition time slot and the power pulse time slot, so that the signal acquisition time slot is in a time slot with relatively low interference.

[0025] Optionally, the collaborative sensing node is a device role that provides wireless collaborative sensing services, which include at least status monitoring mode management and edge monitoring data management; the energy monitoring node, as a target monitoring node, is reused as a collaborative sensing node when it receives a pre-triggered response; when the energy monitoring node acts as a collaborative sensing node, it provides wireless collaborative sensing services to other surrounding target monitoring nodes and their associated target objects based on limited sensitivity processing.

[0026] Optionally, the state mode parsing is that the energy monitoring node performs a balanced assessment of the monitoring mode in terms of security, energy saving, real-time response capability, and system data requirements based on the current target state information.

[0027] Optionally, the collaborative sensing node is based on edge monitoring data management and forms classified monitoring data through classified monitoring data processing. The classified monitoring data includes real-time monitoring data, historical monitoring data, logs, and statistical data records.

[0028] Optionally, the target monitoring node sends a verification trigger signal before object matching verification is required. The object identification tag sends the object identification signal in response to the verification trigger signal. The target monitoring node performs object matching verification on the object identification signal.

[0029] Thirdly, the present invention also discloses an energy monitoring data acquisition system based on the Internet of Things (IoT). The system is established using the IoT-based energy monitoring data acquisition method described in the first and second aspects. The system includes an edge collaborative sensing network system and a collaborative data management system. The management system performs collaborative management of the energy monitoring data acquisition process. The network system includes several energy monitoring nodes that collect energy monitoring data from target objects, wherein different energy monitoring nodes serve as target monitoring nodes and / or collaborative sensing nodes.

[0030] Optionally, the management system includes at least a data acquisition management module and a monitoring information service module to provide energy efficiency / safety monitoring information services; the data acquisition management module includes classified data processing and target data management; the monitoring information service module is used to obtain service information related to target energy efficiency monitoring, such as real-time display, safety emergency handling, and sensitive comparison assessment, through monitoring and processing of real-time monitoring data and referencing and analyzing historical monitoring data.

[0031] Optionally, the management system obtains classified monitoring data through classified data collection and processing, and provides energy efficiency monitoring information based on the classified monitoring data through sensitive comparison evaluation; the management system obtains various difference parameters through classified difference comparison calculation, and performs sensitive comparison evaluation according to the difference index of the corresponding category.

[0032] As can be seen from the technical solution provided by the present invention, the power monitoring node of the present invention obtains the object identification signal sent by the current power load object in the response mode by sending a verification trigger signal, so as to identify and sense the access of the power load; it performs object matching verification on the received object identification signal to configure and adjust the monitoring mode parameters that match the current load object, thereby solving the problems of matching security and monitoring mode flexibility for the current load object; when the load object is in a critical abnormal state, it starts the critical monitoring mode to obtain transient abnormal characteristic parameters, thereby improving the response speed to transient abnormalities and solving the balance between energy saving and safety; it sends an abnormal trigger status beacon so that it can be received and abnormally processed by the surrounding cooperative sensing nodes; thereby solving the problem of coordination in sensing and triggering the target abnormal state.

[0033] Therefore, compared with existing technologies, this invention is based on the identification and perception of electrical loads for sensing monitoring and anomaly response processing. Through flexible adjustment of monitoring modes and collaborative monitoring processing, and collaborative management of the collaborative monitoring data processing process for energy monitoring data acquisition, it achieves better real-time data acquisition and overall efficiency for the state of objects in energy monitoring scenarios. This solves the problems of security, real-time performance, energy efficiency, and flexibility in the energy monitoring process, and has beneficial effects such as high efficiency in monitoring data parsing and processing, good interoperability and collaboration among nodes, fast anomaly trigger response, and high security. Specifically, this is reflected in the following aspects:

[0034] 1) In the load object access and outgoing process, the energy monitoring node of the present invention identifies and senses the access of the load object; performs object matching verification on the received object identification signal, so as to configure and adjust the monitoring mode parameters that match the current load object, thereby solving the problems of matching security and monitoring mode flexibility for the current load object.

[0035] 3) The energy monitoring node adopts a low-power energy-saving monitoring mode for the load object under normal conditions. When the load object is not connected (no load) or is operating normally, the energy monitoring node is in energy-saving monitoring mode, which helps to save power consumption in energy monitoring and reduce data redundancy; especially in order to reduce installation costs in wireless narrowband wireless communication, flexible data uploading reduces wireless interference and data resource competition.

[0036] 4) Based on target monitoring information processing, the energy monitoring node evaluates the status mode and improves the real-time performance and security of monitoring data by upgrading the monitoring mode level for load objects in abnormal states. When a load object is in a potentially abnormal state, the potential abnormal monitoring mode is activated. This facilitates rapid abnormal response and abnormal response handling, including recording the abnormal process, protecting data, and abnormal alarms.

[0037] 5) When the load object is in a critical abnormal state, the energy monitoring node starts the critical abnormal monitoring mode to obtain transient abnormal characteristic parameters through critical real-time tracking and processing, which is conducive to improving the real-time performance and consistency of the abnormal response; by sending abnormal trigger status beacons with higher activity, the trigger response is fast and the priority is high, enabling the collaborative sensing node to obtain the pre-trigger response quickly and reliably in a short time.

[0038] 6) In the critical anomaly monitoring mode, the energy monitoring node obtains transient anomaly characteristic parameters by performing critical real-time tracking processing on the state variable Xi, and solves the stability and consistency problem of transient anomaly response through transient impact quantity prediction and critical feedback monitoring; when transient distortion occurs in the power signal, it can respond quickly, solving the balance problem between real-time performance and stability.

[0039] 7) Energy monitoring nodes are based on edge collaborative sensing networks and are oriented towards electricity consumption scenarios. All or some of the energy monitoring nodes can serve as both target monitoring nodes and collaborative sensing nodes, which enables energy monitoring node equipment to have good hardware reusability and wireless interoperability.

[0040] 8) By processing target scene state perception, front-end data sensitivity pre-selection, state mode evaluation, and mode parameter adjustment, the balance and flexibility issues between data real-time performance and resource consumption, stability and response speed, energy saving and security are resolved.

[0041] 9) Prioritize sensitivity for high data processing efficiency: Prioritize processing and uploading data based on sensitive status changes; reduce (or non-priority) unnecessary data redundancy (data that has already been uploaded but has no valid status change), resulting in higher collaborative data processing efficiency for real-time location and status change monitoring and data uploading of target objects.

[0042] 10) Strong reusability of network equipment resources: The collaborative sensing node can be a service role, and sensing nodes of different topology types (such as target, relay or center) in the edge domain can be dynamically reused (based on time-sharing or configuration); not only dedicated wireless network service nodes (gateway, base station), but also other application nodes (smart socket, smart light node, power monitoring node) can be used as collaborative sensing nodes.

[0043] 11) Good collaboration and strong coverage: The collaborative perception node provides collaborative services to the surrounding target perception nodes based on the pre-sensing trigger and task mechanism; according to the scene state parsing algorithm, it provides variable tracking calculation with different priorities and effective durations for different pre-sensing triggers.

[0044] 12) It has network self-healing ability and high stability: the multi-node collaborative service data transmission is an elastic data path with dynamic balance, selectivity and redundancy, and better network self-healing ability, thus having higher stability, reliability and offline (network disconnection) processing ability. Attached Figure Description

[0045] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 This is a first flowchart of an energy monitoring data acquisition method based on the Internet of Things disclosed in an embodiment of the present invention;

[0047] Figure 2 This is a second flowchart of the energy monitoring data acquisition method based on the Internet of Things disclosed in an embodiment of the present invention;

[0048] Figure 3 This is a schematic diagram illustrating the role relationship between energy monitoring nodes and various sensing nodes in an IoT-based energy monitoring data acquisition system disclosed in an embodiment of the present invention. In this diagram, G1 and G2 represent general wireless base stations (as collaborative sensing nodes), R1 to R4 represent multiplexed wireless base stations (as collaborative sensing nodes), E1 to E5 represent energy monitoring nodes (target and / or collaborative sensing nodes), and S1 to S9 represent target objects (target sensing nodes): including power monitoring nodes (as target monitoring nodes) and other various target positioning / monitoring devices.

[0049] Figure 4 This is a system architecture diagram of an Internet of Things-based energy monitoring data acquisition system disclosed in an embodiment of the present invention;

[0050] Figure 5 This is a software module architecture diagram of an edge collaborative sensing network system in an energy monitoring data acquisition system disclosed in an embodiment of the present invention;

[0051] Figure 6 This is a software module architecture diagram of the collaborative data management system in the energy monitoring data acquisition system disclosed in an embodiment of the present invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the described embodiments are part of, but not all, of this invention; the embodiments are only used to explain the invention and do not limit the invention.

[0053] Example 1, please refer to Figure 1 This is a flowchart of an energy monitoring data acquisition method based on the Internet of Things (IoT) disclosed in an embodiment of the present invention. Several energy monitoring nodes (such as power monitoring nodes) in a wireless IoT edge domain (or a wireless cooperative sensing network system or a subset thereof, including) collaboratively collect energy monitoring data from a target object in a target scenario. Different energy monitoring nodes serve as target monitoring nodes and / or cooperative sensing nodes. The method includes the following steps:

[0054] Step S101: A target monitoring node, in the current monitoring mode, obtains target monitoring information (associated with the target object) by processing the signal acquisition data, and sends it to the surrounding (at least one) collaborative sensing node.

[0055] In step S102, the collaborative sensing node obtains target state information through target state evaluation based on the received target monitoring information, and obtains corresponding scene state information through scene state parsing (obtaining the corresponding scene state information). When the scene state changes, the corresponding scene state information is fed back to the target monitoring node.

[0056] In step S103, the target monitoring node obtains target status information by evaluating the target status for the current target monitoring information based on the received scene status information, derives the (corresponding) monitoring mode parameters by parsing the status mode, and starts the corresponding monitoring mode (based on the monitoring mode parameters).

[0057] Furthermore, the energy monitoring node, as the target monitoring node itself and / or a collaborative sensing node, obtains classified monitoring data by classifying the target monitoring information (based on edge monitoring data management), and transmits the classified monitoring data to the collaborative data management system (in a flexible data upload manner).

[0058] Wireless collaborative sensing network (hereinafter referred to as sensing network) is a wireless network in the edge domain of the Internet of Things (IoT) that consists of collaborative sensing nodes (as network service nodes) and provides collaborative sensing services (including object identification, location tracking, status monitoring, control monitoring and information push) to surrounding target devices.

[0059] The target object device includes object recognition or sensing monitoring devices (such as passive positioning devices, wearable devices, distributed sensors, monitoring and surveillance devices, and peripheral execution devices) that are associated with or bound to the target scene or its target object.

[0060] Sensing and monitoring equipment refers to devices with wireless sensing and monitoring capabilities, including target sensing nodes (as target object devices or scene sensors) that directly perform sensing and monitoring of target scene objects, or collaborative sensing nodes that perform sensing and monitoring of front-end sensing nodes.

[0061] A local subset consisting of several collaborative sensing nodes performs wireless collaborative sensing of the state information of (several) target scene objects.

[0062] The collaborative sensing node is a network node role with collaborative sensing service capabilities, that is, a wireless network node in the collaborative sensing network that has the ability to provide collaborative sensing services to surrounding target devices.

[0063] The collaborative sensing node is a collaborative service node that faces surrounding sensing nodes, and can be a wireless base station device or a general sensing node; the sensing node is a network node that can perform sensing and monitoring of target objects.

[0064] The collaborative sensing refers to the process by which multiple sensing nodes in a wireless network perform sensing monitoring and related services through collaborative sensing processing, targeting a common target scene or a subset thereof (including the target object).

[0065] The collaborative perception includes collaborative positioning, collaborative tracking, collaborative monitoring, collaborative surveillance, and related collaborative services.

[0066] Target perception node / target monitoring node is a network node role that directly perceives and monitors target objects (using built-in sensors);

[0067] Target sensing nodes are the target objects served by the collaborative sensing network and its collaborative sensing nodes, including target positioning / tracking / monitoring nodes, and sensing and monitoring devices that have established association or binding relationships with the target objects they serve.

[0068] The sensing node / target monitoring node can enter a monitoring mode with different value orientation strategies (such as monitoring accuracy / real-time performance, data upload continuity / real-time performance, and self-power consumption) by adjusting the following multiple modes and their combinations (such as high-speed / full-speed acquisition, real-time / timed upload, low-power energy saving, etc.).

[0069] The scene state transition refers to a transition that occurs in the target scene by judging the specified associated target object and target state variable or their combination, and the transition meets the predetermined degree of change.

[0070] The degree of change includes one or a combination of the following: 1) the spatiotemporal range of the current target object; 2) the range of values ​​of the current target state variables; 3) the rate of change and / or the settling time of the scene state.

[0071] Typically, real-time transitions or stable transitions are determined by combining the range of change of the target state variable with / or the settling time.

[0072] For example: The target scenario is determining whether there are people or no people in the room:

[0073] 1) When any sensing node (such as a human body sensor) detects a person in an unmanned state, it can immediately cause a real-time change in the scene state (unmanned → manned).

[0074] 2) When a sensing node does not detect a person in a person-occupied state, the scene state will not change. Only after a period of stable time (cooldown time, relaxation time) when all sensing nodes within the target scene range no longer detect a person will a stable scene state change (occupied → unoccupied) occur.

[0075] The monitoring modes include:

[0076] 1) Signal coupling acquisition mode: signal coupling parameters, AD acquisition mode parameters (such as acquisition period), acquisition preprocessing mode (such as filtering mode parameters), etc.; types and default modes of various state variables;

[0077] 2) Monitoring data processing mode: data processing parameters (processing cycle, sensitive processing parameters, data selection / removal / statistical parameters), process variable types and algorithm accuracy, data storage area management parameters and data storage mode, etc.;

[0078] 3) Wireless data transmission mode: On / off of wireless mode (such as BLE, WiFi, Ethernet, 4G / 5G), wireless communication mode (such as power level and modulation method, scanning / broadcast time period, interval / phase, time slot width, duty cycle, etc.);

[0079] 4) Data upload mode: The target monitoring node (as an edge node) uploads data to the host or management system in real time, on a timed basis, or by active or passive request.

[0080] During implementation, the processing of monitoring data for electricity metering includes:

[0081] 1) Acquire signal timing and perform statistical calculations on the acquired data.

[0082] 2) Low-power flexible acquisition and processing,

[0083] 3) Anomaly detection and handling (saving, alarm, protection), etc.

[0084] 4) Other modules include: Bluetooth connectivity and remote configuration data processing, zero-crossing control (to be determined), etc.

[0085] Regarding data storage and record keeping, a data storage / retrieval interface—FIFO buffer (real-time buffered data, historical data) management—was designed.

[0086] Example 2

[0087] Please refer to Figure 2 This is a flowchart of another IoT-based energy monitoring data acquisition method disclosed in an embodiment of the present invention. Several energy monitoring nodes (such as electricity monitoring nodes) collaboratively collect energy monitoring data from a target object (in a target scenario). Different energy monitoring nodes serve as target monitoring nodes and / or collaborative sensing nodes. The method includes the following steps:

[0088] Step S201: A certain collaborative sensing node obtains target state information through target state assessment based on the target monitoring information (associated with at least one target object), obtains (corresponding) monitoring mode information through state mode parsing, and (when the monitoring mode code changes) sends the monitoring mode information back to (at least one) associated target monitoring node.

[0089] In step S202, the target monitoring node adjusts and sets the monitoring mode parameters according to the received monitoring mode information, obtains target monitoring information (associated with the target object) through monitoring data processing, and sends it to the surrounding (at least one) collaborative sensing node.

[0090] In step S203, the collaborative sensing node obtains classified monitoring data by performing classified monitoring data processing on the target monitoring information (based on edge monitoring data management), and transmits the classified monitoring data to the collaborative data management system (in a flexible data upload manner).

[0091] Regarding the aforementioned Figure 1 , Figure 2 The implementation of the flowchart steps is further explained below:

[0092] The target objects include those involved in the production (conversion), storage, and consumption of energy such as electricity, water, gas, coal, wind, and solar energy. These target objects include: energy production equipment (power generation equipment), energy storage equipment (energy storage equipment), and energy consumption equipment (i.e., load objects).

[0093] The target monitoring information includes first monitoring information and second monitoring information.

[0094] The monitoring mode information includes monitoring mode codes and / or monitoring mode parameters. The monitoring mode code is a code (corresponding to the target state information / scene state code Ns) that reflects the potential risk range of the current (associated with the target scene) operating state of the target object.

[0095] The monitoring modes include energy-saving monitoring mode, safety monitoring mode, and critical monitoring mode;

[0096] The energy monitoring node adjusts the corresponding monitoring mode parameters according to the current monitoring mode code, specifically including: 1) when the load object is in normal operation, adopting the energy-saving monitoring mode (first monitoring mode); 2) when the load object is in a potentially abnormal state, adopting the safety monitoring mode (second monitoring mode); 3) when the load object enters a critical abnormal state, adopting the critical monitoring mode (third monitoring mode).

[0097] The energy-saving and critical monitoring modes are defined as monitoring modes from low to high; if necessary, the above three basic monitoring modes can be further subdivided into different sub-modes.

[0098] It should be noted that, for any relatively low-level monitoring mode, when the judgment meets any higher-level abnormal conditions (including directly obtaining hardware trigger response), it can directly enter any corresponding higher-level monitoring mode.

[0099] For example, in energy-saving or safety monitoring mode, when it is determined that the transient anomaly conditions are met, there is no need to enter the critical monitoring mode first; the transient anomaly trigger response can be obtained directly.

[0100] Abnormal power usage conditions include potential abnormal conditions, critical abnormal conditions, and obvious abnormal conditions:

[0101] 1) Potential abnormal state: that is, a potential or latent abnormal operating state, but which has not yet reached the critical or obvious abnormal state;

[0102] 2) Critical abnormal state: refers to a critical state in which a manifest abnormal state may be about to appear in the transient process;

[0103] 3) Overt abnormal state: An abnormal state that has occurred and has not yet been resolved.

[0104] The normal operating status includes the current load object being in a state of normal maintenance, shutdown (load open circuit), or a specified operating parameter range;

[0105] The potential abnormal states may include: unsafe hazards, proximity anomalies, trend anomalies, and other states that require safety monitoring / tracking.

[0106] The critical abnormal state is an unstable state that may enter an explicit abnormal state within a transient period, or it may revert to a potential abnormal state or a normal operating state.

[0107] The energy monitoring node derives the monitoring mode code based on the scenario status code Ns corresponding to the current target status information, in order to adjust the corresponding monitoring mode. The monitoring mode code is a code (corresponding to the target status information / scenario status code Ns) that reflects the potential risk range of the current (associated with the target scenario) operating status of the target object.

[0108] For example, if the target object is an electrical load object, the operating state can refer to the relative energy consumption state, which is the ratio of the current energy consumption state to the baseline or historical year-on-year energy consumption state.

[0109] When the current load object is in normal operating condition, and before any abnormal state occurs, the monitoring mode defaults to the energy-saving monitoring mode (first monitoring mode), which is an energy-saving oriented monitoring mode.

[0110] Once the energy monitoring node determines that the current load object has any (level) of abnormal state, it immediately enters the (corresponding) non-energy-saving abnormal monitoring mode, which includes a safety monitoring mode and a critical monitoring mode.

[0111] Methods for adjusting mode parameters in energy-saving monitoring mode:

[0112] 1) Set the data acquisition / processing mode parameters corresponding to lower power consumption (such as reducing the data acquisition cycle and processing cycle);

[0113] 2) Reduce the amount of data collected or calculated: such as time-sliding statistics, reduce the tracking and calculation of state variables;

[0114] 3) Set the low-power wireless mode parameter Pi (such as reducing wireless scanning activity, broadcast frequency and power), and periodically turn on other wireless modes (such as WiFi, Ethernet, 4G / 5G) or reduce their duty cycle.

[0115] 4) Reduce the data upload frequency (e.g., extend the scheduled upload period) or reduce the data upload volume (e.g., increase the time interval for variable data, remove variable data with small changes).

[0116] The second monitoring data processing includes location tracking calculation. The collaborative sensing node, as a collaborative positioning base station, performs the location tracking calculation based on the location signal variable Xi (included in the first monitoring information) sent by the target device through limited sensitivity processing.

[0117] The collaborative data management system runs on a host computer or collaborative server and performs collaborative management of the monitoring data collection process.

[0118] The collaborative sensing node performs flexible data upload based on limited sensitivity processing. As an edge node, it uploads the flexible data using narrowband wireless communication according to the current data upload mode, uploading the target monitoring information (including real-time monitoring data and / or historical monitoring data) to the system host (host / server). The edge node is defined relative to the host or management system.

[0119] The target scene object is the target object associated with the target scene;

[0120] The target scene (hereinafter referred to as scene) is a combination of several target objects and their location environment within a given physical space-time; the target scene may contain several subsets of target scenes.

[0121] The mode parameters are associated with the scene state and include data information such as code, index, process, and parameters corresponding to the given mode;

[0122] The pattern processing, or pattern data processing, includes the process of data processing and information services such as data calculation, operation / control / monitoring, data storage / transmission / upload / push for a given pattern.

[0123] The scene status code (hereinafter referred to as the scene code) refers to the identification code that is associated with the target scene and is preset to reflect the change of scene status.

[0124] If the target object belongs to a subset of the target scene, the collaborative sensing node performs scene state analysis for the target scene based on the scene state analysis performed by itself and / or the preceding sensing node (obtaining the object state code and / or the scene state code of the local / subset).

[0125] The scene state parsing is oriented towards the target scene object and is completed by the collaborative sensing node itself or by collaborative sensing with other collaborative sensing nodes through collaborative sensing processing; when the target scene consists of multiple target objects, the scene state parsing is completed based on object state parsing.

[0126] The sensing node performs state monitoring and parsing on the variable values ​​and variable type information of one or more state monitoring variables by indexing the category of the state beacon sent by the target object device, thereby obtaining the state identification information of the target object. Scene state parsing.

[0127] Scene state analysis includes a collaborative sensing node or its host computer analyzing the scene state information (as a local or subset) of a target scene based on the scene state information provided by several forward sensing nodes. The analysis is performed using a superposition or aggregation algorithm.

[0128] The scene state information is obtained by multiple forward sensing nodes through scene state parsing, including one or a combination of the following methods:

[0129] 1) Perform collaborative perception processing (such as collaborative localization calculation) on the same target scene or object;

[0130] 2) For several subsets or objects contained in the same target scene, different front-end perception nodes perform scene state parsing respectively.

[0131] Location signal variables: refer to the physical variables of the location signals that are detected and received by the cooperative positioning base station and reflect the location movement status (coordinates, trajectory, motion) of the target object device.

[0132] The edge node receives the synchronization data packet via wireless time slot synchronization, activates the Mesh linkage node by recognizing the group control code, and establishes a wireless matching connection with the designated wireless router node according to the network configuration information (based on the SSID information therein).

[0133] A typical example is that the edge node obtains the wireless network configuration information by wireless scanning and detection via Bluetooth, and then establishes a wireless pairing connection with the designated wireless routing node (wireless router) via WiFi.

[0134] The collaborative sensing node, based on the target state information it has obtained, broadcasts a scene service beacon containing scene information and location information associated with the current mode parameters to the surrounding area via wireless broadcast.

[0135] Target status information includes one or a combination of the following information associated with the target scene and / or target object: environmental monitoring information, active positioning information, linkage alarm information, and advertising service information.

[0136] When the network performance of data upload is abnormal (such as network outage or failure to meet predetermined requirements), the real-time monitoring data in the upload buffer is filtered, extracted and saved (according to the current data saving mode) as historical monitoring data.

[0137] After network performance is restored, the historical monitoring data is uploaded to the upper host / cooperative service or management system in a first-in-first-out manner as an edge node, based on the data upload mode and managed by data tags or data pointers.

[0138] The collaborative sensing node obtains the current communication data status by monitoring data processing based on the current data upload mode; and adjusts the data upload mode flexibly based on the communication data status.

[0139] - The collaborative sensing node, as an edge node, adjusts the processing priority of target status information according to the current target scene status; the communication data status refers to the network data transmission quality (such as data transmission error / packet error rate, cached data retention / delay).

[0140] Example 3

[0141] Regarding the aforementioned Figure 1 or Figure 2 The implementation of the flowchart steps is further explained below:

[0142] The target monitoring node performs time slot isolation protection on the transient process of coupled acquisition of the front signal input to avoid time domain overlap between the signal acquisition time slot and the power pulse time slot, so that the signal acquisition time slot is in a time slot with relatively low interference.

[0143] The energy monitoring node obtains first monitoring information based on signal acquisition data through first monitoring data processing; then (by the node itself or surrounding collaborative sensing nodes) obtains second monitoring information based on the first monitoring information through second monitoring data processing (a limited sensitivity processing).

[0144] The energy monitoring node and / or surrounding collaborative sensing nodes respond to the scene status and, facing the target scene object, obtain the target status information by evaluating the target status of the target monitoring information; the target monitoring information includes first monitoring information and second monitoring information.

[0145] The target state assessment is a limited sensitivity process, which includes the screening, citation, and assessment of the first and second monitoring information.

[0146] The energy monitoring node obtains the first monitoring information (i.e., real-time monitoring data acquisition) of several state variables Xi (in the time domain) of the current load object through energy signal acquisition and processing;

[0147] The energy monitoring node (as a target monitoring node) makes a real-time judgment on the first monitoring information based on the critical anomaly conditions: whether the load object is in a critical anomaly state.

[0148] Energy signal acquisition and processing includes obtaining first monitoring information (i.e., real-time acquisition monitoring data) of several state variables Xi (in the time domain) through coupled acquisition and data processing of energy signal input.

[0149] The data processing includes first monitoring data processing of the energy signal acquisition data, including: pre-processing digital filtering, feature variable extraction, and variable tracking processing.

[0150] The coupling acquisition refers to signal coupling and AD acquisition of DC or AC (single-phase or multi-phase) electrical energy (transmission or supply); the signal coupling includes one or a combination of the following methods:

[0151] 1) Current coupling: direct sampling coupling (such as alloy resistors), current transformer coupling; 2) Voltage coupling: detection transformer / transformer, step-down / voltage divider unit; 3) pre-signal isolation coupling; 4) signal amplification and filtering unit.

[0152] When the energy monitoring node is used as the target monitoring node, it contains a signal coupling circuit that allows multiple selection of the input signal. By controlling the multiple selection switch / multi-throw linkage, the monitored load object is connected to the signal coupling circuit in different states. The signal coupling circuit includes different load protection resistors Rp and / or signal sampling resistors Rs in different states.

[0153] When the load protection resistor Rp is high impedance, transient protection is provided to the load object.

[0154] The signal coupling circuit includes different load protection resistors Rp and / or signal sampling resistors Rs under different states.

[0155] The collaborative sensing node is a device role that provides wireless collaborative sensing services, which include at least state monitoring mode management and edge monitoring data management.

[0156] The energy monitoring node, as the target monitoring node, is reused by the collaborative sensing node when a pre-triggered response is received, based on the device role configuration and / or time slot switching.

[0157] When the target monitoring node contains a multi-throw relay switch, different signal coupling circuits are connected to the load object when the multi-throw relay switch is in the "on" or "off" state.

[0158] When the multi-throw relay switch is in the "off state", an ultra-high impedance load protection resistor Rp is connected in series at the connection end of the load object; conversely, when the relay switch is in the "on state", the load protection resistor Rp is a low impedance close to zero.

[0159] Accordingly, the signal coupling loop includes different signal sampling resistors Ri and correspondingly different pre-attenuation gains under different states; for example:

[0160] Load protection resistor Rp, off-state: 500MΩ (megohms), on-state: 0;

[0161] Signal sampling resistor Rs: 500Ω (ohms) in off state, 5mΩ (milliohms) in on state.

[0162] In an electricity consumption scenario, at least one power monitoring node (as a type of energy measurement node) acquires first monitoring information (i.e., real-time monitoring data) of several target state variables of the load object in the time domain by coupling and processing the power signal input of the load object in a specified signal coupling acquisition mode based on the current monitoring mode.

[0163] The power monitoring node, as an energy monitoring node, monitors the power consumption of one or more electrical load objects as targets. When a portable power outlet is bound to a load object, the power outlet acts as a target tracking / monitoring node and has the following node role characteristics:

[0164] 1) Active / Passive Positioning: Actively discover surrounding collaborative positioning base stations (location trust levels: positioning nodes such as routers, gateways, light controls, and Bluetooth beacons);

[0165] 2) Mobile location upload: Location information is uploaded only when the user determines that its own environmental location status (a scene status) has changed;

[0166] 3) Potential anomaly trigger (a scenario trigger): At least one target state variable comes from the preceding perception node.

[0167] When the energy monitoring node acts as a collaborative sensing node, it provides wireless collaborative sensing services (including location tracking and monitoring data processing) to other surrounding target monitoring nodes and their associated target objects based on limited sensitivity processing.

[0168] The energy monitoring node, acting as a collaborative sensing node (base station equipment), provides collaborative sensing services to surrounding target devices.

[0169] The energy monitoring includes the monitoring and metering of electricity / energy equipment (i.e. load objects) to conduct related monitoring of energy efficiency, safety and equipment utilization.

[0170] The power / target monitoring node receives a pre-signal input from the target scene object, obtains the collected data through signal coupling based on signal front-end processing, and performs the first monitoring data processing on the collected data to obtain the first monitoring information.

[0171] The electrical energy / energy monitoring includes real-time or cumulative monitoring of any or a combination of the following electrical energy physical quantities related to electrical energy / energy supply and consumption: 1) location / area (range), 2) load node status (e.g., on / off, power-on time), 3) real-time monitored physical quantities: such as power, 4) cumulative monitored physical quantities: such as electricity consumption.

[0172] The collaborative sensing node is an edge data acquisition node with the capability of a wireless base station; the collaborative sensing node receives target status information sent by surrounding target monitoring nodes and other target objects (in the form of status beacons) via wireless scanning detection.

[0173] The collaborative sensing node performs the positioning and tracking calculation based on the positioning signal variable Xi (first monitoring information in the time domain) of the target object obtained by wireless scanning detection. This includes positioning correction calculation based on modulation state identifier, digital filtering of positioning variables, multi-point collaborative positioning, and trajectory tracking calculation.

[0174] The energy monitoring node performs second monitoring data processing on the first monitoring information according to the current monitoring mode to obtain second monitoring information. Then, it derives the monitoring mode code and its mode parameters (corresponding to the electricity consumption scenario status code) through state mode parsing (based on scenario state parsing). Based on the monitoring mode code and its associated mode parameter Pi, it executes the monitoring mode and mode processing corresponding to the monitoring mode code.

[0175] The energy monitoring node performs state mode analysis based on the graded abnormal conditions matched with the load object. When the load object is assessed to be in an abnormal state, the corresponding abnormal monitoring mode is immediately activated, including: 1) when the load object is in a potential abnormal state, the safety monitoring mode is activated; 2) when the load object is in a critical abnormal state, the critical monitoring mode is activated.

[0176] The judgment conditions for entering or exiting different levels of abnormal states are asymmetric (in the time domain and / or value domain): from normal to abnormal state, it takes effect immediately when the current characteristic parameter conditions are met; conversely, from abnormal to normal state (or from high-level abnormal to low-level abnormal state), after the current characteristic parameter conditions are met, a certain observation period is required as the judgment condition for the abnormality to be resolved.

[0177] The state mode parsing is as follows: the power monitoring node, based on the current target state information and energy efficiency assessment feedback, performs a dynamic balancing assessment of the monitoring mode in terms of safety, energy saving, real-time response capability, and system data requirements using a dynamic balancing method / strategy; the target state information is obtained by processing several state variables in the current target monitoring information through state assessment.

[0178] The monitoring mode code (a scenario status code) is obtained by dynamically parsing the target state information, and the corresponding monitoring mode parameters are obtained according to the index of the monitoring mode code.

[0179] The aforementioned balance orientation assessment refers to a weighting strategy that balances factors such as resource power consumption, response speed, and data processing capability based on balance orientation parameters when selecting the current monitoring mode.

[0180] The third monitoring data processing includes: when there is a judgment that meets the abnormal conditions, marking the abnormal monitoring data contained in the second monitoring information or directly saving it to the monitoring record data area.

[0181] Hierarchical anomaly conditions include potential anomaly conditions, critical anomaly conditions, and transient anomaly conditions:

[0182] The energy monitoring node performs state mode parsing based on the graded anomaly conditions (for the current target monitoring information), including: judging the target scene state associated with the load object based on the graded anomaly conditions: 1) when the potential anomaly conditions are met, enter the potential anomaly state; 2) when the critical anomaly conditions are met, enter the critical anomaly state; 3) when the transient anomaly conditions are met, immediately trigger transient anomaly protection processing.

[0183] Based on the aforementioned graded anomaly conditions, determine the classification / level of the anomaly state:

[0184] The graded abnormal conditions also include the judgment of the operating status of the energy monitoring node (or system) itself, such as the abnormal conditions for judging the status of network outage, power-on restart, communication timeout, self-test abnormality, etc.

[0185] When the load object is in a potential abnormal state (before entering a critical abnormal state), the energy monitoring node performs safety tracking and monitoring of the abnormal state variables of the load object in a safety monitoring mode (i.e., the second monitoring mode); according to the critical abnormal condition, when the load object is in the critical abnormal state, the critical monitoring mode is immediately activated (to perform critical abnormal response processing).

[0186] The safety tracking and monitoring includes one or a combination of the following methods: 1) Based on real-time collection and tracking of the abnormal state variables, determine whether a critical abnormal state has been entered; 2) Set a pre-trigger condition according to the critical trigger condition in the form of critical feedback monitoring so as to obtain a critical abnormal trigger response when the critical trigger condition is met.

[0187] The target monitoring node performs state pattern analysis based on target monitoring information according to a dynamic balancing strategy, and makes elastic feedback adjustments to the current monitoring mode parameters; the target monitoring information is obtained by evaluating the target state of the state variables contained in the target monitoring information.

[0188] The collaborative sensing node is based on edge monitoring data management. It forms classified monitoring data by (processing the second monitoring information) classified monitoring data. The classified monitoring data includes real-time monitoring data, historical monitoring data, logs and statistical data records.

[0189] (In the event of a network outage or abnormal handling) the edge data management includes offline data protection processing, and after the network is restored, the classified monitoring information is uploaded to the system host (host / cooperative server).

[0190] When the target monitoring node detects and identifies a load object accessing the system, it performs object matching verification based on the association information between the load object and the power timing signal, and determines whether the access status of the load object meets the security matching conditions.

[0191] The power timing refers to the characteristic timing of the combination of power-on and / or coupled signals, such as the zero-crossing point, signal peak, or specific phase point after a certain power-on time (or a certain AC counting cycle).

[0192] When the target monitoring node detects and identifies a load object accessing the system, or before other object matching verification is required (such as timed or abnormally triggered verification), it sends a verification trigger signal. The object identification tag bound to the load object responds to the verification trigger signal by sending the object identification signal in a response manner. The target monitoring node then performs object matching verification on the object identification signal.

[0193] The object identification tag generates the object identification signal according to the verification trigger signal and ID identification information using a given identification verification algorithm.

[0194] The load object includes an object identification tag bound in an embedded or external manner, and transmits an object identification signal associated with a timing signal based on energy signal coupling, the object identification signal being transmitted by the object identification tag;

[0195] The energy monitoring node performs object matching verification based on the association information between the object identification signal and the time series signal contained in the object identification signal.

[0196] The load object (or the associated object identification tag) is coupled by an electrical signal and sends an object identification signal in response to an electrical timing signal (as a verification trigger signal).

[0197] The load object or object identification tag sends an object identification signal containing object identification information in an active or responsive manner based on the associated trigger of the timing signal.

[0198] The target monitoring node performs object matching and verification based on the object identification information, establishes an association and binding relationship with the load object, and obtains the monitoring mode parameter Pi through the association index of the object identification information.

[0199] When the energy monitoring node detects that a load object has been connected, it sends a verification trigger signal. The load object (or the object identification tag associated with it) responds to the verification trigger signal by sending an object identification signal (containing object identification information).

[0200] The verification trigger signal can be any one or a combination of the following: 1) a verification request signal actively sent by the target monitoring node in the form of a wireless beacon or carrier pulse; 2) a (AC) carrier pulse signal or associated wireless signal associated with a timing signal (such as a power-on signal or a specific phase); 3) a trigger signal generated based on the timing algorithm constraints of the pre-triggered trigger (such as a predetermined count or timing).

[0201] The target monitoring node verifies whether the object matching condition is met by performing object matching on the object identification signal sent by the object identification tag bound to the load object.

[0202] The object identification tag is an electronic tag device used to identify and verify the load object;

[0203] The object identification signal is a response signal transmitted via a wireless beacon and / or AC carrier.

[0204] Those skilled in the art will understand that all or part of the processes or modules in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium.

[0205] Collaborative sensing services: These are collaborative services provided to surrounding sensing nodes, including wireless network communication and collaborative sensing processing for sensing and monitoring and their associated processes.

[0206] The perception and monitoring includes processes such as positioning, monitoring, communication, modulation, tracking, surveillance, and control for target scenes / objects.

[0207] The target device (hereinafter referred to as the target device) refers to the wireless device that serves as the service object of the surrounding wireless network node (base station equipment) and provides information interaction services.

[0208] The target device (or simply the object device) is the wireless device associated with and bound to the target object.

[0209] Wireless devices that perceive (including location, tracking, monitoring, surveillance, and control) target objects.

[0210] The target object is the target service object: it refers to the object (such as people, goods, assets and equipment, location and environment) that is being served (located, controlled, monitored and supervised).

[0211] The target scene state, or scene state for short, is a physical state of a specified target scene that is associated with the target scene (and can be a combination of several subsets or object states).

[0212] Target state information refers to information describing the state of the target scene and its changes;

[0213] The collaborative sensing node obtains target status information by sensing and monitoring target objects (within the wireless coverage range) associated with the target scene (using wireless scanning detection).

[0214] A cooperative positioning base station is a wireless network node (base station equipment) with wireless cooperative positioning service capabilities;

[0215] The collaborative positioning base station is a device role that constitutes the collaborative sensing network, and its physical form includes, but is not limited to, light control sensing nodes.

[0216] As a type of energy monitoring node, the power monitoring node is a target monitoring node used to monitor the power consumption of electrical load objects. Its node role can be either a target sensing node or a collaborative sensing node.

[0217] Energy monitoring nodes are collaborative service nodes with multiple device roles, including target monitoring / tracking nodes, wireless linkage nodes / beacon base stations, and collaborative sensing nodes / positioning base stations.

[0218] The energy monitoring node can be reused as a target / cooperative sensing node, and based on wireless data reception response, it provides cooperative sensing services to surrounding target objects.

[0219] The energy monitoring node has the capability to provide multi-role, multi-mode services for target scenarios and their target objects (lighting loads / electrical loads); for example, it includes: collaborative sensing / monitoring, location tracking, dimming control and / or power monitoring.

[0220] Target state variables (referred to as state variables) are physical state variables that are contained in the target state information and are associated with the target scene object, reflecting the target object and its associated environment.

[0221] Target state variables include direct variables or indirect indices that are associated with predetermined scenarios such as environmental state, target object, and event triggering.

[0222] The target state variable is a physical quantity or intermediate control state variable that constitutes the elements for judging the state of the target scene and its changes.

[0223] When a scenario needs to be described by multiple target state variables, different state variables can be contained in the same or multiple state beacons; that is, not all target state variables must be contained in the same state beacon.

[0224] A forward sensing node refers to the preceding collaborative sensing node from which the current wireless reception response of the collaborative sensing node originates. It can be the most forward target sensing node or an intermediate sensing node.

[0225] The aforementioned forward sensing node refers to the sensing and monitoring device that acquires and sends state variables to the current collaborative sensing node.

[0226] The preceding sensing nodes include target sensing nodes that obtain target state variables Xi through direct or indirect sensing or intermediate sensing nodes that receive and process data.

[0227] The aforementioned front-end sensing node, relative to the current collaborative sensing node, can have multiple device roles. It can refer to a dynamic front-end collaborative sensing node as a network service node, or it can refer to a target monitoring node (scene sensor or target object device) that is served on the periphery of the wireless network.

[0228] The scene state function obtains the scene state information corresponding to the current target scene (typically the scene state code and its associated information); the scene state function establishes a data structure or function relationship between the scene state information and one or a group of target state variables corresponding to the target scene and the scene trigger.

[0229] A status beacon is a wireless beacon or carrier beacon sent by a target device that reflects the device's own status and the status of the associated target device.

[0230] The collaborative sensing node detects and receives status beacons broadcast wirelessly from surrounding target devices via wireless scanning.

[0231] The limited sensitive processing (hereinafter referred to as sensitive processing) is the mode processing when service resources for multiple target devices have sensitive conflicts;

[0232] The aforementioned limited sensitive processing refers to the processing of data that involves sensitive conflicts with valuable resources (such as power consumption, memory, computational load, communication data volume, time consumption, etc.) – such as data monitoring, data storage, anomaly monitoring, data uploading, etc.

[0233] The current sensing node obtains the scene trigger response by recognizing scene state transitions.

[0234] Whether the collaborative sensing node changes or not upon receiving the scene state code Ns sent by the forward sensing node is a necessary condition for obtaining the scene trigger response and parsing the associated target state variable Xi.

[0235] The forward sensing node places the scene state code as a special state variable in its own state beacon.

[0236] The collaborative sensing node obtains the mode parameters through the mode index according to the scene response plan associated with the scene trigger response and starts the mode processing (such as monitoring data processing) associated with the mode parameter Pi.

[0237] For example, the data structure of the pattern index is: [Index] Scene Status Code --> Pattern Code, Priority, Validity Period; or, [Index] Pattern Code --> Pattern Parameters, Reference Pointer.

[0238] The mode parameter Pi includes index / call parameters for the mode processing flow; the corresponding mode processing flow is executed according to the operation mode parameters contained in the mode parameter; the mode processing flow includes scene linkage processing such as scene linkage control, scene linkage configuration, and scene linkage communication.

[0239] The mode processing flow includes data calculation and communication processes based on local or multi-machine collaboration, such as mode adjustment, data configuration, linkage processing, data saving and uploading, etc.

[0240] The mode parameters include the operation target parameters and / or operation mode parameters. Adjustments to the mode parameters include parameter assignment, parameter increment, parameter function calculation, and other adjustment operations.

[0241] In actual implementation, the data structure for scenario-triggered responses is as follows:

[0242] 1) Sensor (Unknown type): [Search] Device name / Device ID or MAC --> Device type code;

[0243] 2) Sensor (known class), [index] Device type code --> Scene status code, [Monitoring variable 1,...Monitoring variable n];

[0244] The sensor refers to the target sensing node.

[0245] Example 4

[0246] This invention also discloses an Internet of Things (IoT)-based energy monitoring data acquisition system. Please refer to [link / reference]. Figure 3The system is a system established using the IoT-based energy monitoring data acquisition method. The system includes an edge collaborative sensing network system and a collaborative data management system. The management system (running on the host / collaborative server) performs collaborative management of the energy monitoring data acquisition process.

[0247] The network system includes several energy monitoring nodes (including power monitoring nodes) that collect energy monitoring data from target objects (in the target scenario), wherein different energy monitoring nodes serve as target monitoring nodes and / or collaborative sensing nodes.

[0248] The implementation of the aforementioned management system is further explained as follows:

[0249] The management system includes at least a data acquisition and management module and a monitoring information service module, used to provide energy efficiency / safety monitoring information services;

[0250] The data acquisition and management module includes classified data processing and target data management.

[0251] The monitoring information service module is used to obtain service information related to target energy efficiency monitoring, such as real-time display, safety emergency handling, and sensitive comparison assessment, through monitoring and processing of real-time monitoring data and referencing and analyzing historical monitoring data.

[0252] The management system obtains classified monitoring data through classified data collection and processing, and provides energy efficiency monitoring information (and sensitivity assessment report) based on the classified monitoring data through sensitivity comparison assessment, including energy efficiency assessment and hazard assessment.

[0253] Based on the sensitive comparison evaluation information, the management system adjusts or provides adjustment suggestions for the hierarchical abnormal conditions and / or balance orientation parameters of the associated load object devices.

[0254] The energy efficiency refers to the actual energy consumption relative to different rated energy demands associated with different target scenarios; the energy efficiency assessment and hazard assessment are conducted based on the correlation of the target scenario status.

[0255] The sensitive comparison assessment includes: calculating the energy efficiency index and / or hazard index of a certain energy consumption target object based on the difference index D(i) and the set classification assessment weights W(i) for energy efficiency and potential hazards relative to different operating states or operating parameters, i.e.:

[0256] The energy efficiency index Ki = ∑W(i)*D(i) is used to evaluate different operating states, where W(i) and D(i) represent the weight of the i-th evaluation item and the difference index of the corresponding operating state, respectively.

[0257] The hidden danger index Kj = ∑W(j)*D(j) is used to evaluate different operating parameters, where W(j) and D(j) represent the evaluation weight of the i-th item and the difference index of the corresponding operating parameter, respectively.

[0258] It should be noted that the energy efficiency index refers to the energy efficiency or energy consumption level of the target equipment per unit time under different operating conditions;

[0259] The hazard index refers to the level of safety hazard corresponding to different operating parameters of the target equipment;

[0260] The sensitivity comparison assessment can generate sensitivity assessment information (reports) periodically or irregularly for target devices in different categories and regions.

[0261] The difference index Di includes a difference comparison of any or a combination of the following:

[0262] 1) The difference between the statistical values ​​of variables and the rated or expected values ​​of the same target equipment at different operating time periods;

[0263] 2) Differences in the same target equipment at different operating periods or in different regions / environments;

[0264] 3) Differences between comparable target equipment during the same operating period and / or the same operating time period;

[0265] 4) Differences in comparable target equipment at different operating periods or in different regions / environments.

[0266] The operating period refers to the period defined by the energy-consuming equipment based on its installation and commissioning, and measured by natural and cumulative operating time cycles (days).

[0267] The operating time period refers to the time period divided by energy-consuming equipment based on startup and measured by operating time;

[0268] The comparable target equipment refers to energy consumption monitoring target equipment that is comparable in terms of power consumption or power safety, and that is of the same type, model, and parameters.

[0269] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art can make other variations or modifications based on the above description. These should also be considered within the scope of protection of this invention, and will not affect the effectiveness of the invention or the practicality of the patent. It is neither necessary nor possible to exhaustively list all embodiments here. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims. Obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for acquiring energy monitoring data based on the Internet of Things, characterized in that, Several energy monitoring nodes collaboratively collect energy monitoring data from a target object, wherein different energy monitoring nodes serve as target monitoring nodes and / or collaborative sensing nodes. The method includes the following steps: A target monitoring node, in its current monitoring mode, processes the signal acquisition data to obtain target monitoring information associated with the target object and sends it to surrounding collaborative sensing nodes. The collaborative sensing node, based on the received target monitoring information, performs scene state analysis and feeds back the corresponding scene state information to the target monitoring node when the scene state changes. The scene state change is determined by judging the specified associated target object and target state variable or their combination, and a change that meets the predetermined degree of change occurs. The scene state analysis includes a collaborative sensing node or its host computer performing scene state analysis on the target scene based on scene state information provided by several front-end sensing nodes using a superposition or aggregation algorithm. The scene state information is obtained by multiple front-end sensing nodes through scene state analysis. Based on the received scene status information, the target monitoring node parses the current target monitoring information to derive the corresponding monitoring mode parameters and starts the corresponding monitoring mode based on the monitoring mode parameters.

2. A method for acquiring energy monitoring data based on the Internet of Things, characterized in that, Several energy monitoring nodes collaboratively collect energy monitoring data from a target object, wherein different energy monitoring nodes serve as target monitoring nodes and / or collaborative sensing nodes. The method includes the following steps: A certain collaborative sensing node obtains the corresponding monitoring mode information by parsing the state mode based on the target monitoring information associated with the target object, and sends the monitoring mode information back to the associated target monitoring node; wherein, the collaborative sensing node derives the monitoring mode code based on the scene state code corresponding to the current target state information, and sends the monitoring mode code as monitoring mode information back to the associated target monitoring node. The target monitoring node adjusts and sets the monitoring mode parameters according to the received monitoring mode information, obtains the target monitoring information associated with the target object through monitoring data processing, and sends it to the surrounding collaborative sensing nodes. The collaborative sensing node obtains classified monitoring data by performing classified monitoring data processing on the target monitoring information based on edge monitoring data management, and transmits the classified monitoring data to the collaborative data management system.

3. The energy monitoring data acquisition method based on the Internet of Things as described in claim 1 or 2, characterized in that, The target monitoring node performs time slot isolation protection on the transient process of coupled acquisition of the front signal input to avoid time domain overlap between the signal acquisition time slot and the power pulse time slot, so that the signal acquisition time slot is in a time slot with relatively low interference.

4. The energy monitoring data acquisition method based on the Internet of Things as described in claim 1 or 2, characterized in that, The collaborative sensing node is a device role that provides wireless collaborative sensing services, which include at least state monitoring mode management and edge monitoring data management. The energy monitoring node serves as the target monitoring node and is reused by the collaborative sensing node when it receives a pre-triggered response. When the energy monitoring node acts as a collaborative sensing node, it provides wireless collaborative sensing services to other surrounding target monitoring nodes and their associated target objects based on limited sensitivity processing.

5. The energy monitoring data acquisition method based on the Internet of Things as described in claim 1 or 2, characterized in that, The state mode is interpreted as follows: the energy monitoring node, based on the current target state information and energy efficiency assessment feedback, performs a dynamic balance assessment of the monitoring mode in terms of safety, energy saving, real-time response capability, and system data requirements.

6. The energy monitoring data acquisition method based on the Internet of Things as described in claim 1 or 2, characterized in that, The collaborative sensing nodes are managed based on edge monitoring data. They generate classified monitoring data through classified monitoring data processing. The classified monitoring data includes real-time monitoring data, historical monitoring data, logs, and statistical data records.

7. The energy monitoring data acquisition method based on the Internet of Things as described in claim 1 or 2, characterized in that, Before object matching verification is required, the target monitoring node sends a verification trigger signal. The object identification tag responds to the verification trigger signal by sending the object identification signal in a response manner. The target monitoring node then performs object matching verification on the object identification signal.

8. An energy monitoring data acquisition system based on the Internet of Things, characterized in that, The system is a system established using the Internet of Things-based energy monitoring data acquisition method according to any one of claims 1 to 7. The system includes an edge collaborative sensing network system and a collaborative data management system, wherein the management system performs collaborative management of the energy monitoring data acquisition process. The network system includes several energy monitoring nodes that collect energy monitoring data from target objects, with different energy monitoring nodes serving as target monitoring nodes and / or collaborative sensing nodes.

9. The IoT-based energy monitoring data acquisition system as described in claim 8, characterized in that, The management system includes at least a data acquisition and management module and a monitoring information service module, used to provide energy efficiency / safety monitoring information services; The data acquisition and management module includes classified data processing and target data management. The monitoring information service module is used to obtain service information related to target energy efficiency monitoring, such as real-time display, safety emergency handling, and sensitive comparison assessment, through monitoring and processing of real-time monitoring data and referencing and analyzing historical monitoring data.

10. The IoT-based energy monitoring data acquisition system as described in claim 8 or 9, characterized in that, The management system obtains classified monitoring data through classified data collection and processing, and provides energy efficiency monitoring information based on the classified monitoring data through sensitive comparison evaluation; The management system obtains various difference parameters by classifying difference comparison calculations and performs sensitivity comparison evaluation based on the difference index of the corresponding category.

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