A multi-mode energy efficiency monitoring method and system

Abstract

The application discloses a kind of multi-mode energy efficiency monitoring method and system, several electric energy monitoring nodes are carried out energy efficiency monitoring to associated power load object, the method comprises: certain electric energy monitoring node obtains the target monitoring information of associated load object by electric energy signal acquisition and monitoring data processing with current monitoring mode;According to the hierarchical abnormal condition matched with the load object, the monitoring mode is balanced and oriented evaluation in security, energy saving and system data demand by state mode analysis, and the corresponding monitoring mode code is obtained;When the monitoring mode code jumps, the electric energy monitoring node executes energy efficiency monitoring to the load object according to the corresponding updated monitoring mode parameter, starts to perform.The system is composed of several electric energy monitoring nodes.The application can be adjusted to the target state of power load object, and the energy efficiency monitoring mode has better security, flexibility and energy efficiency monitoring data efficiency.

Description

Technical Field

[0001] This invention relates to the technical field of edge intelligence and measurement and control in the wireless Internet of Things, mainly to edge collaborative sensing networks and collaborative data management systems for energy efficiency monitoring, and particularly to a multi-mode energy efficiency monitoring method and system. 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] For monitoring and management of electrical equipment in industrial environments, an information service system is established that combines distributed, low-power, big data, continuous, edge and central intelligent management. This system provides information services such as monitoring, surveillance, aggregation, evaluation and guidance for energy utilization efficiency and safety levels of electrical nodes. It can not only provide real-time safety monitoring, but also provide decision-making basis for continuously improving energy use efficiency and safety management.

[0007] 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.

[0008] 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).

[0009] 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.

[0010] Energy efficiency monitoring based on existing IoT sensing and monitoring technologies has the following main drawbacks:

[0011] 1) The problem of coordination in energy efficiency monitoring: There is a lack of flexible collaborative service between on-site network service nodes. Distributed energy monitoring nodes are merely target monitoring nodes, uploading the collected data to the host computer. There is a lack of necessary collaborative monitoring capabilities between monitoring nodes, including collaborative monitoring data processing, collaborative protection, and dynamic adjustment of monitoring strategies and contingency plans for different target scenarios.

[0012] 2) The problem of scenario relevance in energy efficiency monitoring and assessment: There is a lack of necessary correlation between power monitoring and scenario status, and the energy efficiency monitoring mode lacks pertinence and flexibility, resulting in the lack of pertinence of energy efficiency assessment information for power consumption scenarios and management improvement links.

[0013] 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.

[0014] 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.

[0015] 5) Security of energy / electricity monitoring equipment: Energy / electricity 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.

[0016] 6) Balancing Energy Efficiency and Security: There is a lack of targeted and flexible selection and adaptation capabilities based on the current target scenario and load status when the load is 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 loads experience transient anomalies.

[0017] 7) The balance between real-time performance and stability: Existing technologies have not adequately addressed the balance between real-time performance and stability in transient protection. If the abnormal protection responds based on the effective value over a period of time, the lack of real-time performance leads to excessively long transient abnormal response times. Furthermore, when transient distortions occur in the power signal, the effective value cannot accurately reflect the transient impact. However, responding based on transient monitoring results in significant errors and instability, especially when transient pulse distortion is large.

[0018] 8) 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, it is necessary to adopt special arc protection technologies that are too complex and costly in order to prevent arcing.

[0019] Therefore, how power monitoring nodes can flexibly adjust the monitoring mode to monitor energy efficiency based on the current scenario status and target monitoring information, as well as the abnormal status of the current load object, has become an urgent technical problem to be solved. Summary of the Invention

[0020] The technical problem this invention aims to solve is that, based on the graded abnormal conditions matched with the load object, the power monitoring node performs a balanced assessment of the monitoring mode in terms of safety, energy efficiency, and system data requirements through state mode parsing; when the monitoring mode code changes, it initiates energy efficiency monitoring of the load object according to the corresponding updated monitoring mode parameters, so as to flexibly adjust the monitoring mode according to the target scenario and the state of the load object, thereby performing energy efficiency monitoring in a more flexible and effective manner, reducing data redundancy and unnecessary resource occupation while ensuring safety, and improving the system data efficiency for energy efficiency monitoring.

[0021] To address the above problems, this invention proposes a multi-mode energy efficiency monitoring method and system.

[0022] In a first aspect, the present invention discloses a multi-mode energy efficiency monitoring method, in which several power monitoring nodes perform energy efficiency monitoring on associated power load objects. The method includes the following steps: a power monitoring node obtains target monitoring information of associated load objects through power signal acquisition and monitoring data processing in the current monitoring mode; based on the graded abnormal conditions matched with the load objects, a balance orientation assessment is performed on safety, energy saving and system data requirements through state mode parsing to obtain the corresponding monitoring mode code; when the monitoring mode code changes, the power monitoring node starts to perform energy efficiency monitoring on the load objects according to the corresponding updated monitoring mode parameters.

[0023] Optionally, the monitoring mode includes at least an energy-saving monitoring mode and an anomaly monitoring mode; when the current load object is in normal operation, before an anomaly occurs, the monitoring mode defaults to the energy-saving monitoring mode, which is an energy-saving oriented monitoring mode; once the power monitoring node determines that the current load object has any anomaly, it immediately enters a non-energy-saving oriented anomaly monitoring mode, which includes a safety monitoring mode and a critical monitoring mode.

[0024] Optionally, the monitoring modes include energy-saving monitoring mode, safety monitoring mode, and critical monitoring mode; the power monitoring node adjusts the corresponding monitoring mode parameters according to the current monitoring mode code, specifically including: 1) when the load is in normal operation, adopting energy-saving monitoring mode; 2) when the power load is in a potentially abnormal state, adopting safety monitoring mode; 3) when the load is in a critical abnormal state, adopting critical monitoring mode.

[0025] Optionally, the power monitoring node and / or surrounding collaborative sensing nodes obtain the target state information by performing target state assessment on the target monitoring information.

[0026] Optionally, the target monitoring node performs first monitoring data processing on the first monitoring information, then parses the state mode to export the corresponding monitoring mode code, and executes the corresponding monitoring mode according to the monitoring mode code.

[0027] Optionally, the monitoring mode is a code corresponding to the power consumption scenario state, reflecting the relative energy efficiency status and / or power consumption risk index of the load object in the current power consumption scenario; when the scenario state changes, the surrounding collaborative sensing nodes obtain the corresponding monitoring mode parameters by indexing the scenario state code Ns, and start the monitoring data processing of the corresponding monitoring mode according to the monitoring mode parameters.

[0028] Optionally, the power monitoring node can connect the load to different signal coupling circuits by switching its internal signal coupling circuits under different monitoring modes, so that the energy saving, safety and real-time response of different monitoring modes are available when the load is in different states.

[0029] Secondly, the present invention also discloses a multi-mode energy efficiency monitoring system, which is a system established using the multi-mode energy efficiency monitoring method described in the first aspect; the system consists of several power monitoring nodes, which perform energy efficiency monitoring on several load objects in a target scenario, wherein different power monitoring nodes serve as target monitoring nodes and / or collaborative sensing nodes.

[0030] Optionally, the system includes a collaborative data management system for collaboratively managing the data acquisition process of energy efficiency monitoring; the management system includes at least a data acquisition management module and an energy efficiency monitoring information service module; the management system obtains classified monitoring data through classified data acquisition and processing, and provides energy efficiency monitoring information services based on the classified monitoring data through sensitive comparison evaluation.

[0031] Optionally, the management system obtains various difference parameters through classification difference comparison calculation, and performs sensitive comparison evaluation based on the difference index of the corresponding category; the sensitive comparison evaluation 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 evaluation weights W(i) of energy efficiency and hidden dangers relative to different operating states or operating parameters, that is: energy efficiency index Ki = ∑W(i)*D(i), and weighted evaluation is performed for different operating states; hazard index Kj = ∑W(j)*D(j), and weighted evaluation is performed for different operating parameters.

[0032] As can be seen from the technical solution provided by the present invention, the power monitoring node of the present invention, based on the graded abnormal conditions matched with the load object, performs a balanced assessment of safety, energy efficiency, and system data requirements through state mode parsing, obtains the corresponding monitoring mode code, and initiates the execution of energy efficiency monitoring of the load object. This allows for flexible adjustment of the monitoring mode according to the target scenario and the state of the load object, thereby solving the problems of flexibility and process in energy efficiency monitoring modes.

[0033] Therefore, compared to existing technologies, this invention can adjust the energy efficiency monitoring mode based on the target state of the current power consumption scenario and load object, so as to achieve a dynamic and optimal balance between safety, energy saving, and system data requirements. This invention also provides collaborative management of the collaborative monitoring data processing process for energy monitoring data acquisition, resulting in better real-time data acquisition and overall efficiency for the energy monitoring scenario object state. It solves the problems of safety, real-time performance, energy saving, 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 response to anomalies, and high security. Specifically, this is reflected in the following aspects:

[0034] 1) The power monitoring node of the present invention evaluates the balance orientation of the monitoring mode in terms of safety, energy saving and system data requirements through state mode analysis based on the graded abnormal conditions matched with the load object. In order to flexibly adjust the monitoring mode according to the target scenario and the state of the load object, the energy efficiency monitoring is carried out in a more flexible and effective mode. Under the premise of ensuring safety, data redundancy and unnecessary resource occupation are reduced, and the system data efficiency for energy efficiency monitoring is improved.

[0035] 2) The power monitoring nodes of this invention (such as power meters, power metering sensors, power metering sockets, etc.) can support the acquisition of power monitoring data; based on the perception and identification of the load object, power signal monitoring and abnormal response processing are performed to provide more targeted and effective protection for the transient process of the load object being connected or disconnected (plugged in and out).

[0036] 3) In the connection and disconnection of load objects, the power monitoring node of the present invention identifies and senses the access of the power load; 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.

[0037] 4) The power monitoring node of this invention adopts a low-power energy-saving monitoring mode for power loads under normal conditions. When the load is not connected (no load) or is operating normally, the power monitoring node is in energy-saving monitoring mode, which helps to save power consumption in power 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.

[0038] 5) The power monitoring node of this invention is based on target monitoring information processing. Through state mode evaluation, for load objects in abnormal state, the real-time performance and security of monitoring data are improved by upgrading the monitoring mode level. When the load object is in a potentially abnormal state, the potential abnormal monitoring mode is activated. This facilitates rapid abnormal response and abnormal response processing, including recording the abnormal process, protecting data, and abnormal alarms.

[0039] 5) When the load object is in a critical abnormal state, the power monitoring node of the present invention starts the critical abnormal monitoring mode to obtain transient abnormal characteristic parameters through critical real-time tracking and processing, which is beneficial to improve 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, so that the collaborative sensing node can obtain the pre-trigger response quickly and reliably in a short time.

[0040] 7) In the critical anomaly monitoring mode, the power monitoring node of the present invention 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 by predicting transient impact and critical feedback monitoring; when transient distortion occurs in the power signal, it can respond quickly, thus solving the balance problem between real-time performance and stability.

[0041] 8) The power monitoring node of this invention is based on an edge collaborative sensing network and is oriented towards power consumption scenarios. All or part of the power monitoring nodes can be used as both target monitoring nodes and collaborative sensing nodes, which makes the power monitoring node equipment have good hardware reusability and wireless interoperability.

[0042] 9) This invention solves the balance and flexibility issues between data real-time performance and resource consumption, stability and response speed, energy efficiency and security by processing target scene state perception, front-end data sensitivity pre-selection, state mode evaluation, and mode parameter adjustment.

[0043] 10) The system of the present invention has the collaborative service capability of edge collaborative computing for application: the collaborative sensing node not only provides wireless network communication services, but also has the service capability of collaborative data processing as edge collaborative computing for sensing and monitoring applications (such as location tracking, energy monitoring, and lighting control), strong collaborative concurrent service capability, good network configuration convenience, self-healing capability, and high stability. Attached Figure Description

[0044] 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.

[0045] Figure 1 This is a flowchart of a multi-mode energy efficiency monitoring method disclosed in an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram illustrating the role relationship between power monitoring nodes and various sensing nodes in a multi-mode energy efficiency monitoring system disclosed in an embodiment of the present invention; wherein, G1 and G2 represent general wireless base stations (as cooperative sensing nodes), R1 to R4 represent multiplexed wireless base stations (as cooperative sensing nodes), E1 to E5 represent power monitoring nodes (target and / or cooperative 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;

[0047] Figure 3 This is an architecture diagram of a multi-mode energy efficiency monitoring system disclosed in an embodiment of the present invention;

[0048] Figure 4 This is a software module architecture diagram of an edge collaborative sensing network system in a multi-mode energy efficiency monitoring system disclosed in an embodiment of the present invention;

[0049] Figure 5 This is a software module architecture diagram of a collaborative data management system in a multi-mode energy efficiency monitoring system disclosed in an embodiment of the present invention. Detailed Implementation

[0050] 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.

[0051] Example 1, please refer to Figure 1 This is a flowchart of a multi-mode energy efficiency monitoring method disclosed in an embodiment of the present invention. In an IoT edge domain wireless collaborative sensing network system, several power monitoring nodes, associated with a target scenario, perform energy efficiency monitoring on the associated power load objects. The method includes the following steps:

[0052] Step S101: A certain power monitoring node, as a target monitoring node and / or a collaborative sensing node, obtains the target monitoring information of the associated load object through power signal acquisition and monitoring data processing in the current monitoring mode.

[0053] Step S102: Based on the hierarchical abnormal conditions matching the load object, the target state information obtained by the target monitoring information is evaluated based on the current target scenario state. The monitoring mode is evaluated by state mode parsing based on the energy efficiency assessment feedback in terms of safety, energy saving and system data requirements, and the corresponding monitoring mode code is obtained. The monitoring mode includes at least an energy-saving monitoring mode and an abnormal monitoring mode.

[0054] Step S103: When the monitoring mode code changes, the power monitoring node starts to perform energy efficiency monitoring on the load object according to the corresponding updated monitoring mode parameters, and obtains the target monitoring information of the load object through monitoring data processing.

[0055] The implementation of the above steps is further explained as follows:

[0056] When the power monitoring node acts as a collaborative sensing node, it provides wireless collaborative sensing services to other target devices / monitoring nodes / and their associated target objects based on limited sensitivity processing, including location tracking and monitoring data processing.

[0057] The power monitoring node, acting as a collaborative sensing node (base station equipment), provides collaborative sensing services to surrounding target devices.

[0058] An edge monitoring / surveillance network system, consisting of several energy / electricity monitoring nodes (as target sensing nodes and / or collaborative sensing nodes), serves as a subset of the wireless collaborative sensing network and provides information services for energy / energy efficiency monitoring and management to surrounding target scene objects.

[0059] The energy / energy efficiency monitoring includes monitoring and metering of energy-consuming / electrical equipment (i.e., load objects) to conduct related monitoring of energy efficiency, safety, and equipment utilization.

[0060] The power monitoring node derives the monitoring mode code based on the scenario status code Ns corresponding to the current target status information, so as to adjust the corresponding monitoring mode.

[0061] The monitoring mode code is a code (corresponding to the target status information / scene status code Ns) that reflects the potential risk range of the current (associated with the target scene) operating status of the target object.

[0062] 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 benchmark or historical year-on-year energy consumption state.

[0063] The state mode parsing is as follows: the power monitoring node evaluates the balance orientation of the monitoring mode in terms of safety, energy saving, real-time response capability and system data requirements based on the current target state information and energy efficiency assessment feedback (in a dynamic balance manner / strategy); the target state information is obtained by processing several state variables in the current target monitoring information through state evaluation.

[0064] 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.

[0065] 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.

[0066] The power monitoring node sends a verification trigger signal when it detects that a load object is connected. 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).

[0067] The verification trigger signal can be any one or a combination of the following: 1) a verification request signal actively sent by the power / 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 power 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).

[0068] 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.

[0069] The object identification tag is an electronic tag device used to identify and verify the load object;

[0070] The object identification signal is a response signal transmitted via a wireless beacon and / or AC carrier.

[0071] When the target monitoring node detects and identifies a load object accessing the system, the power monitoring node 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.

[0072] The power monitoring node obtains the first monitoring information (i.e., real-time monitoring data acquisition) of the current load object's (several) state variables Xi (in the time domain) through power signal acquisition and processing;

[0073] The power 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.

[0074] The power signal acquisition and processing includes obtaining the 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 the power signal input.

[0075] The data processing includes first monitoring data processing on the collected data (electrical energy signal), including: pre-processing digital filtering, feature variable extraction, and variable tracking processing.

[0076] The power 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.

[0077] The target monitoring node performs object matching verification by receiving the object identification signal sent by the load object and the corresponding object identification information, so as to configure and adjust the monitoring mode parameters that match the currently accessed load object. The monitoring mode parameters include at least the corresponding hierarchical abnormal conditions and / or abnormal handling plans.

[0078] The monitoring mode parameters include the mode parameter Pi for the target monitoring node to process the monitoring data of the load object, and the hierarchical abnormal conditions for parsing the state mode based on the current monitoring information.

[0079] The abnormal states include abnormal power supply from the power grid and / or abnormal power consumption by the load; the abnormal conditions refer to the judgment conditions corresponding to different levels of abnormal states.

[0080] The abnormal state levels include potential abnormal states (latent abnormal states), critical abnormal states, and explicit abnormal states.

[0081] It should be noted that the graded anomaly conditions include the judgment conditions for the current variable characteristic parameter range and its time-domain change characteristic parameter range; typically, the judgment conditions for entering or exiting different levels of anomaly states have asymmetry in the time domain and / or value domain; for example, from normal to anomaly state (or from low-level anomaly to high-level anomaly state), it can take effect immediately when the current characteristic parameter conditions are met; conversely, from anomaly to normal state (or from high-level anomaly to low-level anomaly state), after the current characteristic parameter conditions are met, a certain observation period is still required as a cooling-off time for the anomaly to be resolved.

[0082] Hierarchical anomaly conditions include potential anomaly conditions, critical anomaly conditions, and transient anomaly conditions:

[0083] The power 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.

[0084] Based on the aforementioned graded anomaly conditions, determine the classification / level of the anomaly state:

[0085] The graded abnormal conditions also include the judgment of the operating status of the power 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.

[0086] The critical anomaly condition is the condition used to determine whether the load object has entered a critical anomaly state based on the current state variable and its transient expected value. The state variable is included in the first monitoring information.

[0087] The potential anomaly conditions are the conditions used to determine whether a load object is in a potential anomaly state when performing state pattern parsing.

[0088] When the load object is in a potential abnormal state (before entering a critical abnormal state), the power 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).

[0089] 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.

[0090] According to the anomaly handling plan, the power monitoring node obtains the mode parameter Pi through the index (of the current scenario status code) and executes the critical anomaly response processing corresponding to the mode parameter.

[0091] The critical anomaly response processing involves real-time monitoring of transient anomaly pulses that are about to occur, using critical feedback monitoring to obtain the transient anomaly trigger response.

[0092] The aforementioned anomaly handling plan refers to the scenario response plan for a critical anomaly state (a critical triggering state);

[0093] Critical anomaly response handling is a type of critical response handling (anomaly handling based on critical response); while setting critical feedback monitoring, data protection, anomaly alarms, and anomaly protection can be performed in parallel, either or in combination.

[0094] The critical feedback monitoring refers to the current collaborative sensing node or its preceding sensing node, under critical triggering conditions, (based on the current sensing and monitoring mode) adjusting the signal front end of its own node or preceding node based on the monitoring and collection information of the target state variable in the time domain and the judgment (including calculation or query) of the approach degree of the transient triggering response, so as to compare and monitor the current front-end input signal in real time, and obtain the transient triggering response when the preceding triggering conditions are met.

[0095] The front-end input signal is a coupling signal before data acquisition of the target state variable;

[0096] The feedback adjustment includes signal coupling adjustment (such as adjusting the coupling loop, attenuating the gain) and / or adjusting the reference value or rated value of the rated comparison signal;

[0097] The feedback adjustment methods include one or a combination of the following: 1) the collaborative sensing node performs feedback control on the target monitoring node as a front-end node (such as sending active control information); 2) the back-end processing unit of the target monitoring node sets the feedback of its own signal front-end (processing module).

[0098] The target sensing / monitoring node, based on the critical signal feedback unit (included in the signal front-end processing module), performs real-time comparison between the front-end input signal and the current rated comparison signal to obtain a transient trigger response when the pre-triggered conditions are met.

[0099] The power monitoring node serves as the target monitoring node. Based on the target state information, it performs state mode analysis according to the dynamic balancing strategy and makes elastic feedback adjustments to the current monitoring mode parameters. The target state information is obtained by evaluating the target state variables contained in the target monitoring information.

[0100] The dynamic balancing strategy refers to a weighting strategy that balances factors such as resource power consumption, response speed, and data processing capability based on the necessity of dynamic requirements when selecting the current monitoring mode or processing mode.

[0101] 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.

[0102] 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.

[0103] A local subset consisting of several collaborative sensing nodes performs wireless collaborative sensing of the state information of several target scene objects.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] The collaborative perception includes collaborative positioning, collaborative tracking, collaborative monitoring, collaborative surveillance, and related collaborative services.

[0108] 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.

[0109] The perception and monitoring includes processes such as positioning, monitoring, communication, modulation, tracking, surveillance, and control for target scenes / objects.

[0110] The target scene object is the target object associated with the target scene;

[0111] 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.

[0112] 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.

[0113] 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 composed of several subsets or object states.

[0114] Scene status: such as the people in a specified area / room (occupied / unoccupied).

[0115] Target state information refers to information describing the state of the target scene and its changes;

[0116] The collaborative sensing node obtains target status information by sensing and monitoring target objects within the wireless coverage area associated with the target scene (using wireless scanning detection).

[0117] 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.

[0118] The power monitoring node is a collaborative service node with multiple device roles, including a target monitoring / monitoring / tracking node, a wireless linkage node / beacon base station, and a collaborative sensing node / positioning base station.

[0119] The lighting control sensing node / power monitoring node can be reused as a target / cooperative sensing node, providing cooperative sensing services to surrounding target objects based on wireless data reception response.

[0120] 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;

[0121] 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.

[0122] 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.

[0123] Target perception node / target monitoring node is a network node role that directly perceives and monitors target objects (using built-in sensors);

[0124] 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.

[0125] 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.

[0126] Target state variables include direct variables or indirect indices that are associated with predetermined scenarios such as environmental state, target object, and event triggering.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The aforementioned forward sensing node refers to the sensing and monitoring device that acquires and sends state variables to the current collaborative sensing node.

[0131] 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.

[0132] 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.

[0133] The scenario response plan is a data structure that associates different scenario status codes with one or a set of mode parameters / mode processing procedures and mode processing.

[0134] The collaborative sensing node (preceding or current sensing node) determines whether the scene state meets the scene triggering conditions by parsing the scene state and obtains the corresponding scene triggering response;

[0135] That is, once the relevant sensing node determines that a scene state change has occurred, it can obtain a scene trigger response, unless it is blocked by the scene trigger condition.

[0136] 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 - monitoring data processing associated with the mode parameters - mode processing flow.

[0137] The mode parameter Pi includes index / call parameters for the mode processing flow;

[0138] Execute the corresponding mode processing flow according to the operation mode parameters included in the mode parameters;

[0139] The mode processing flow includes scene linkage processing such as scene linkage control, scene linkage configuration, and scene linkage communication.

[0140] 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.

[0141] In actual implementation, the data structure for scenario-triggered responses is as follows:

[0142] 1) Sensor (Unknown type): [Search] Device name / Device ID or MAC --> Device type code;

[0143] 2) Sensor (known class), [index] Device type code --> Scene status code, [Monitoring variable 1,...Monitoring variable n];

[0144] The sensor refers to the target sensing node.

[0145] When the collaborative sensing node determines that a scene state change has occurred in the current target scene, it will send a scene state beacon containing a scene state code identifier (as a trigger state identifier) ​​(by broadcasting a wireless beacon).

[0146] The scene state beacon is created by the current sensing node and can be used as an object state beacon received by subsequent collaborative sensing nodes;

[0147] The scene status code identifier is used as a trigger status identifier for subsequent collaborative sensing nodes to identify and determine triggering and linkage response; the trigger status identifier is the same as or associated with the corresponding scene status code.

[0148] When the mode is processed as a limited sensitivity processing for a resource-sensitive conflict, the collaborative sensing node uses the sensitivity deviation △S of the target state variables associated with several target object devices in the current evaluation cycle as (specifying or influencing) the priority order of the limited sensitivity processing to be carried out in this time.

[0149] By comparing the sensitivity deviation △S between different target devices and / or different state variables Xi, the priority order of the limited sensitive processing to be performed (for the current target scenario state) is determined; thus, when resource sensitivity conflicts cause the backlog of pre-buffered data, it is allowed to discard variable data with relatively low priority.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] The monitoring modes include:

[0154] 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;

[0155] 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.;

[0156] 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.);

[0157] 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.

[0158] Based on the wireless connection request initiated by the collaborative sensing node, the necessity or urgency of uploading the current data is determined by the object status information and its sensitivity deviation ΔS, and the predetermined data upload time interval parameter and / or wireless communication mode are flexibly balanced and adjusted.

[0159] Example 2, for the aforementioned Figure 1 The implementation of the flowchart steps is further explained below:

[0160] The monitoring modes include at least an energy-saving monitoring mode and an anomaly monitoring mode. When the current load object is in normal operation and no anomaly occurs, the monitoring mode defaults to the energy-saving monitoring mode (first monitoring mode), which is an energy-saving oriented monitoring mode.

[0161] Once the power 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.

[0162] Methods for adjusting mode parameters in energy-saving monitoring mode:

[0163] 1) Set the data acquisition / processing mode parameters corresponding to lower power consumption (such as reducing the data acquisition cycle and processing cycle);

[0164] 2) Reduce the amount of data collected or calculated: such as time-sliding statistics, reduce the tracking and calculation of state variables;

[0165] 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.

[0166] 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).

[0167] The monitoring mode includes a safety monitoring mode, which is executed when the power monitoring node determines that the current load object is in a potentially abnormal state.

[0168] The security monitoring mode is between the energy-saving and critical monitoring modes. It requires more resources (such as computing power, power consumption, data storage and uploading) than the energy-saving monitoring mode to solve problems such as real-time sensitive response, monitoring records and anomaly handling.

[0169] Compared to the energy-saving monitoring mode, the purpose and main problems to be solved by the safety monitoring mode include:

[0170] 1) Sensitive Response: By performing security tracking and monitoring / processing of abnormal state variables, it has a more sensitive response speed and stronger resource processing capabilities for more advanced critical or transient anomaly triggers.

[0171] 2) Monitoring records: Save more complete and continuous anomaly monitoring information, such as anomaly sensitive point variable tracking (first monitoring information), anomaly segment statistics (second monitoring information), and anomaly handling logs;

[0172] 3) Anomaly handling: Perform pre-planned anomaly handling (or preprocessing) based on the current anomaly level (before higher-level anomalies): such as data protection (for high-priority data), anomaly indication, and alarms.

[0173] The monitoring mode includes a critical monitoring mode, which is executed when the power monitoring node determines that the current load object is in a critical abnormal state.

[0174] Compared to the security monitoring mode, the critical monitoring mode requires concentrated computing power to perform real-time tracking and processing of abnormal state variables. By predicting transient abnormal characteristics in real time, the pre-trigger conditions are adjusted in response to the transient abnormality, thereby achieving a faster (lower latency) sensitive response to the triggering of transient abnormalities.

[0175] Critical monitoring mode refers to the process of real-time tracking of abnormal state variables. Based on the degree of convergence between the current transient abnormality characteristic parameters and the transient abnormality conditions, the pre-trigger conditions are dynamically adjusted by feedback to the front-level (including the front-level node and / or the front end of this node). This allows for more sensitive acquisition of the corresponding transient abnormality trigger response when the transient abnormality conditions are met.

[0176] The safety protection circuit refers to the circuit that is in a state of safety detection signal when the electrical load is connected to the power supply port, including one or a combination of the following: 1) safety voltage, 3) high impedance weak signal, 4) transient overload protection.

[0177] Safety detection signals can be stepped-down signals, high-impedance signals, weak pulse signals; carrier signals, mutual inductance coupling signals, voltage divider signals, DC signals, etc.

[0178] The safety circuit / safety voltage is used for transient detection and protection when the power supply port is in an open circuit state, from the time the electrical load is open to the time the load is connected.

[0179] In specific implementation, the safety voltage can be a step-down signal output, a voltage divider detection circuit, or a DC detection circuit; the high-impedance weak signal refers to a protection device (such as a simple protection resistor, which can prevent transient poor contact, transient arcing, etc.) connected in series in the safety protection circuit; the overload protection includes short-circuit protection, power protection, overcurrent protection, and leakage protection.

[0180] 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 sends the object identification signal in response to the verification trigger signal. The target monitoring node then performs object matching verification on the object identification signal.

[0181] The object identification signal is sent by an object identification tag that is built into or externally bound to the load object;

[0182] The object matching verification includes determining the spatial range of the object's distance / location based on the wireless signal strength (RSSI) of the object identification signal.

[0183] The monitoring modes include energy-saving monitoring mode, safety monitoring mode, and critical monitoring mode;

[0184] The power monitoring node adjusts the corresponding monitoring mode parameters according to the current monitoring mode code, specifically including:

[0185] 1) When the load object is in normal operating condition, adopt the energy-saving monitoring mode (first monitoring mode);

[0186] 2) When the electrical load is in a potentially abnormal state, a safety monitoring mode (second monitoring mode) is activated;

[0187] 3) When the load object is in a critical abnormal state, the critical monitoring mode (third monitoring mode) is adopted.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Abnormal power usage conditions include potential abnormal conditions, critical abnormal conditions, and obvious abnormal conditions:

[0192] 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;

[0193] 2) Critical abnormal state: refers to a critical state in which a manifest abnormal state may be about to appear in the transient process;

[0194] 3) Overt abnormal state: An abnormal state that has occurred and has not yet been resolved.

[0195] 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;

[0196] The potential abnormal states may include: unsafe hazards, proximity anomalies, trend anomalies, and other states that require safety monitoring / tracking.

[0197] 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.

[0198] The power monitoring node and / or surrounding collaborative sensing nodes respond to the scene state and, facing the target scene object, obtain the target state information by evaluating the target state of the monitoring information.

[0199] The target monitoring information includes first monitoring information and second monitoring information.

[0200] The target state assessment is a limited sensitivity process, which includes the screening, citation, and assessment of the first and second monitoring information.

[0201] At least one power monitoring node (as an energy measurement node) in the power consumption scenario obtains (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 data processing the power signal input of the load object (in a specified signal coupling acquisition mode) based on the current monitoring mode.

[0202] The power monitoring node, acting as a target monitoring node, monitors the power consumption of one or more electrical load objects that are the target objects. When a portable power outlet is bound to a load object, the power outlet, acting as a target tracking / monitoring node, has the following node role characteristics:

[0203] 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);

[0204] 2) Mobile location upload: Location information is uploaded only when the user determines that its own environmental location status (a scene status) has changed;

[0205] 3) Potential anomaly trigger (a scenario trigger): At least one target state variable comes from the preceding perception node.

[0206] The power monitoring node, as the target monitoring node, receives the front-end signal input of the target object, acquires the collected data through signal coupling (based on the front-end processing of the monitoring signal), and performs the first monitoring data processing on the collected data to obtain the first monitoring information.

[0207] 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 (e.g., electricity consumption).

[0208] Electrical loads include the loads constituted by electrical equipment / appliances within a specified range;

[0209] The electrical load object (hereinafter referred to as the load object or electrical load) is the target object that is monitored as the electrical load.

[0210] The load object corresponds to a physical object or range of physical objects (such as electrical equipment / components, power nodes / branches), and is composed of one or more electrical equipment and / or power nodes;

[0211] The load object is the target object in the specified power consumption scenario and corresponds to the power / target monitoring node.

[0212] An electrical load object can correspond to one or more electrical devices or electrical nodes; typically, an electrical load object or electrical device corresponds to an equipment asset code.

[0213] Multiple electrical devices can collectively form a power consumption node (intermediate node), and a single electrical device can also contain multiple power consumption nodes (branch nodes), such as power consumption nodes that monitor several branches within a computer and monitor, or a refrigerator compressor.

[0214] The aforementioned power monitoring refers to the status monitoring of power consumption, efficiency, and safety for power load objects.

[0215] The power signal state variable is a state variable for power monitoring, which is a target state variable that reflects the power consumption scenario and its power load.

[0216] The power consumption scenario status refers to the various physical states associated with the power consumption scenario and the load objects it contains, including current power supply parameters, energy consumption, safety and surrounding environment.

[0217] The status of the power consumption scenario includes the status of the power signal and / or the status of the load object; it may also include the environmental status related to power safety, including the internal environment of the power equipment and the surrounding environment (such as temperature, humidity, smoke, gas concentration, etc.).

[0218] Electrical energy signal status: refers to the physical state of electrical energy (AC, DC, air coupling) input / output and its coupled signals; for AC power supply, electrical energy signal status refers to AC signal status.

[0219] Load object status: refers to the transient and cumulative consumption of electrical energy by the electrical load, as well as other physical states related to the electrical load and the electrical environment.

[0220] The target state assessment is a limited sensitivity process, which includes the screening, citation, and assessment of the first and second monitoring information.

[0221] The power monitoring node (based on signal acquisition data) obtains first monitoring information 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).

[0222] The second monitoring data processing includes location tracking calculation. The power monitoring node acts as a cooperative positioning base station and 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.

[0223] The power monitoring node, acting as a collaborative sensing node, performs flexible data upload based on limited sensitivity processing. As an edge node, the collaborative sensing node, according to the current data upload mode (using narrowband wireless communication data transmission), performs the flexible data upload, 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.

[0224] The collaborative sensing node (as the target monitoring node) obtains the current (included in the target monitoring information) state variable through (first / second) monitoring data processing based on the current (scene) monitoring mode, derives the scene state code Ns (and the corresponding monitoring mode code) through scene state parsing, and performs elastic feedback adjustment on the monitoring mode according to the mode parameter {Pi} obtained by indexing the scene state code (or the corresponding monitoring mode code).

[0225] The sensing node selects a monitoring mode (such as signal acquisition mode, data processing mode, wireless communication mode, and data upload mode) that matches the current target scene state based on the pre-configured plan (from the system host) and / or real-time request.

[0226] The collaborative sensing node / target monitoring node (according to the current monitoring mode) performs monitoring data processing (a limited sensitivity processing) on ​​the first monitoring information to obtain the monitoring data of the second monitoring information.

[0227] The second monitoring information includes real-time monitoring data obtained by processing the first monitoring information in real-time or by limited sensitivity processing.

[0228] The second monitoring information may also include historical monitoring data (recorded offline or online) formed by directly saving the real-time monitoring data or by processing the data with a third monitoring data.

[0229] The third monitoring data processing refers to data processing performed to improve data efficiency or security, including: reducing the amount of monitoring data (such as selection and statistics) and improving data correlation (such as classification and citation relationships).

[0230] When a network outage causes data to accumulate in the real-time data acquisition buffer, the historical monitoring data is generated through second / third monitoring data processing.

[0231] Following the offline data storage method, abnormal characteristic data and segmented statistical data are filtered and saved as historical monitoring data in a first-in-first-out manner.

[0232] The abnormal feature data refers to the maximum abnormal point, the abnormal start point, and the abnormal end point of different state variables extracted from the real-time collected data and recorded and saved.

[0233] The segmented statistical data includes statistics on the average / fluctuation values, cumulative time, and number of abnormalities of different state variables within normal / abnormal time periods.

[0234] 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.

[0235] 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.

[0236] The target device (or simply the object device) is the wireless device associated with and bound to the target object.

[0237] Wireless devices that perceive (including location, tracking, monitoring, surveillance, and control) target objects.

[0238] 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).

[0239] A cooperative positioning base station is a wireless network node (base station equipment) with wireless cooperative positioning service capabilities;

[0240] 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.

[0241] 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).

[0242] 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.

[0243] The limited sensitive processing (hereinafter referred to as sensitive processing) is the mode processing when service resources for multiple target devices have sensitive conflicts;

[0244] The aforementioned limited sensitive processing refers to the processing of data with sensitive conflicts involving 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.

[0245] 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.

[0246] After network performance is restored, (depending on the data upload mode) the edge node uploads the historical monitoring data to the upper host / cooperative service or management system in a first-in-first-out manner through data tags or data pointers.

[0247] 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.

[0248] - 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).

[0249] During implementation, the processing of monitoring data for electricity metering includes:

[0250] 1) Acquire signal timing and perform statistical calculations on the acquired data.

[0251] 2) Low-power flexible acquisition and processing,

[0252] 3) Anomaly detection and handling (saving, alarm, protection), etc.

[0253] 4) Other modules include: Bluetooth connectivity and remote configuration data processing, zero-crossing control (to be determined), etc.

[0254] Regarding data storage and record keeping, a data storage / retrieval interface—FIFO buffer (real-time buffered data, historical data) management—was designed.

[0255] Example 3, for the aforementioned Figure 1 The implementation of the flowchart steps is further explained below:

[0256] The target monitoring node (according to the current monitoring mode) performs first monitoring data processing on the first monitoring information (to obtain second monitoring information), and then derives the monitoring mode code and mode parameters (corresponding to the electricity consumption scenario status code) through state mode parsing (based on scenario state parsing), and executes the monitoring mode (and mode processing) corresponding to the monitoring mode code Ns according to the monitoring mode code (and its associated mode parameters).

[0257] The monitoring mode is a code that corresponds to the power consumption scenario and reflects the range of relative energy efficiency status and / or power consumption risk index of the load object in the current power consumption scenario.

[0258] When the scene state changes, the surrounding collaborative sensing nodes obtain the corresponding monitoring mode parameters by indexing the scene state code Ns, and start the monitoring data processing of the corresponding monitoring mode (one mode processing) according to the monitoring mode parameters.

[0259] The power monitoring node connects the load to different signal coupling circuits by switching its internal signal coupling circuits under different monitoring modes, so that the energy saving, safety and real-time response of the monitoring mode are different when the load is in different states.

[0260] When the energy / electricity monitoring node (as the target monitoring node) contains a signal coupling loop that allows multiple selection of the input signal, the monitored load object can be connected to the signal coupling loop in different states by controlling the multiple selection switch / multi-throw linkage.

[0261] The signal coupling circuit includes different load protection resistors Rp and / or signal sampling resistors Rs under different conditions.

[0262] When the load protection resistor Rp is high impedance, transient protection is provided for the load object.

[0263] The power monitoring node contains a multi-throw relay switch. When the multi-throw relay switch is in the "on" or "off" state, different signal coupling circuits are connected to the load object respectively.

[0264] 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.

[0265] Accordingly, the signal coupling loop includes different signal sampling resistors Ri and correspondingly different pre-attenuation gains under different states; for example:

[0266] Load protection resistor Rp, off-state: 500MΩ (megohms), on-state: 0;

[0267] Signal sampling resistor Rs: 500Ω (ohms) in off state, 5mΩ (milliohms) in on state.

[0268] By connecting an ultra-high load protection resistor Rp through a signal coupling loop, even when the multi-throw relay switch is in the "off state", it can still monitor the power-on / power-off or load status changes of the load object.

[0269] The multi-throw relay automatically disconnects when the load is turned off or disconnected; and automatically reconnects when the load is turned on or connected.

[0270] Its advantages lie in avoiding the arcing problem caused by hot-plugging, improving safety, and extending the life of electrical switch contacts.

[0271] The power monitoring node performs time slot isolation protection on the transient process of coupled acquisition of the front-end 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.

[0272] The power pulse time slot refers to any transient time slot with relatively high power other than the coupling acquisition of the pre-amplifier signal input, such as: wireless transceiver, driving GPIO peripherals (such as switches or LED blinking), data communication / network uploading and other transient operation time slots;

[0273] Time slot isolation protection can reduce common-mode noise in the transient acquisition of AD, significantly reduce transient cross-interference, and improve the transient sampling accuracy and stability of signal coupling acquisition.

[0274] Each data packet of the real-time / historical monitoring data contains a data segment of several target state variables guided by a relative timestamp; the state variables consist of a state type code and a state variable value;

[0275] The host computer / system can restore the relative timestamps in the real-time / historical monitoring data to the corrected absolute timestamps based on the clock correction log (for each consecutive time period sequence number TSSN).

[0276] 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.

[0277] Example 4: This embodiment of the invention also discloses a multi-mode energy efficiency monitoring system, please refer to... Figure 2 The system is a system established using the aforementioned multi-mode energy efficiency monitoring method. As an edge collaborative sensing network system, the system consists of several power monitoring nodes and performs energy efficiency monitoring on several load objects in the target scenario. Different power monitoring nodes serve as target monitoring nodes and / or collaborative sensing nodes.

[0278] The implementation of the above system is further explained as follows:

[0279] When the power monitoring node acts as a collaborative sensing node, it obtains classified monitoring data by classifying the target monitoring information (based on edge monitoring data management), and transmits the classified monitoring data to the management system in a flexible data upload manner.

[0280] The system includes a collaborative data management system (running on a collaborative server) for collaborative management of the data acquisition process for energy efficiency monitoring; the management system includes at least a data acquisition management module and an energy efficiency monitoring information service module.

[0281] The management system obtains classified monitoring data through classified data collection and processing, and provides energy efficiency monitoring information services (such as sensitivity assessment reports) based on the classified monitoring data through sensitivity comparison assessment, including energy efficiency assessment and hazard assessment.

[0282] Based on the sensitivity comparison evaluation information, the management system adjusts or provides adjustment suggestions for the hierarchical abnormal conditions and balance orientation parameters of the associated load object devices.

[0283] 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.

[0284] 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.

[0285] 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.:

[0286] 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.

[0287] 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.

[0288] It should be noted that,

[0289] Energy efficiency index refers to the energy efficiency or energy consumption level of a target device per unit time under different operating conditions;

[0290] The hazard index refers to the level of safety hazard corresponding to different operating parameters of the target equipment;

[0291] The sensitivity comparison assessment can generate sensitivity assessment information (reports) periodically or irregularly for target devices in different categories and regions.

[0292] The difference index Di includes a difference comparison of any or a combination of the following:

[0293] 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;

[0294] 2) Differences in the same target equipment at different operating periods or in different regions / environments;

[0295] 3) Differences between comparable target equipment during the same operating period and / or the same operating time period;

[0296] 4) Differences in comparable target equipment at different operating periods or in different regions / environments.

[0297] 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).

[0298] The operating time period refers to the time period divided by energy-consuming equipment based on startup and measured by operating time;

[0299] 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.

[0300] 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 multi-mode energy efficiency monitoring method, characterized in that, A number of power monitoring nodes perform energy efficiency monitoring on associated power load objects. The method includes the following steps: A certain power monitoring node obtains the target monitoring information of the associated load object through power signal acquisition and monitoring data processing in the current monitoring mode; Based on the graded abnormal conditions matching the load object, the corresponding monitoring mode code is obtained by performing a balance assessment on safety, energy saving and system data requirements based on energy efficiency assessment feedback through state mode parsing. The power monitoring node derives the monitoring mode code based on the scenario status code corresponding to the current target status information, so as to adjust the corresponding monitoring mode; When the monitoring mode code changes, the power monitoring node starts to perform energy efficiency monitoring on the load object according to the updated monitoring mode parameters.

2. The multi-mode energy efficiency monitoring method as described in claim 1, characterized in that, The monitoring modes include at least an energy-saving monitoring mode and an anomaly monitoring mode; When the current load object is in normal operating condition and no abnormal state occurs, the monitoring mode defaults to the energy-saving monitoring mode, which is an energy-saving oriented monitoring mode. Once the power monitoring node determines that the current load object has any abnormal state, it immediately enters a non-energy-saving abnormal monitoring mode, which includes a safety monitoring mode and a critical monitoring mode.

3. The multi-mode energy efficiency monitoring method as described in claim 1, characterized in that, The monitoring modes include energy-saving monitoring mode, safety monitoring mode, and critical monitoring mode; The power 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 operating condition, adopt the energy-saving monitoring mode; 2) When the electrical load is in a potentially abnormal state, a safety monitoring mode should be activated; 3) When the load object is in a critical abnormal state, the critical monitoring mode is adopted.

4. The multi-mode energy efficiency monitoring method as described in claim 1, characterized in that, The power monitoring node and / or surrounding collaborative sensing nodes obtain the target status information by evaluating the target status of the monitoring information.

5. A multi-mode energy efficiency monitoring method as described in any one of claims 1 to 4, characterized in that, The target monitoring node performs first monitoring data processing on the first monitoring information, then parses the status mode to export the corresponding monitoring mode code, and executes the corresponding monitoring mode according to the monitoring mode code.

6. A multi-mode energy efficiency monitoring method as described in any one of claims 1 to 4, characterized in that, The monitoring mode is a code that corresponds to the state of the electricity consumption scenario and reflects the range of relative energy efficiency status and / or electricity risk index of the load object in the current electricity consumption scenario; When the scene state changes, the surrounding collaborative sensing nodes obtain the corresponding monitoring mode parameters by indexing the scene state code Ns, and start the monitoring data processing of the corresponding monitoring mode according to the monitoring mode parameters.

7. A multi-mode energy efficiency monitoring method as described in any one of claims 1 to 4, characterized in that, The power monitoring node connects the load to different signal coupling circuits by switching its internal signal coupling circuits under different monitoring modes, so that the energy saving, safety and real-time response of the monitoring mode are different when the load is in different states.

8. A multi-mode energy efficiency monitoring system, characterized in that, The system is a system established using the multi-mode energy efficiency monitoring method according to any one of claims 1 to 7; The system consists of several power monitoring nodes, which perform energy efficiency monitoring on several load objects in the target scenario. Different power monitoring nodes serve as target monitoring nodes and / or collaborative sensing nodes.

9. The multi-mode energy efficiency monitoring system as described in claim 8, characterized in that, The system includes a collaborative data management system for collaboratively managing the data acquisition process of energy efficiency monitoring; the management system includes at least a data acquisition management module and an energy efficiency monitoring information service module. The management system obtains classified monitoring data through classified data collection and processing, and provides energy efficiency monitoring information services based on the classified monitoring data through sensitive comparison evaluation.

10. A multi-mode energy efficiency monitoring system as described in claim 8 or 9, characterized in that, The management system obtains various difference parameters by comparing and contrasting categories, and conducts sensitivity comparison assessments based on the difference indices of the corresponding categories. 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.: The energy efficiency index Ki = ∑W(i)*D(i) is used to evaluate different operating conditions. The hazard index Kj = ∑W(j)*D(j) is used to evaluate different operating parameters.

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