A power substation mobile inspection equipment data acquisition method, system and device based on Zigbee and UWB technology and a storage medium

Abstract

The application discloses a kind of based on Zigbee and UWB technology's substation mobile inspection equipment data acquisition method, system, equipment and storage medium, the method includes: by mobile acquisition terminal according to preset path executes cruise, and based on UWB technology real-time obtains the relative position information between the mobile acquisition terminal and equipment data acquisition node;Relative position information is based on cooperative positioning and wake-up processing to be executed, and wake-up determination result is obtained;In the case where wake-up determination result is to satisfy wake-up condition, equipment node wake-up and two-way security authentication are executed by Zigbee communication link;After authentication passes, equipment state data collected by equipment data acquisition node is obtained by Zigbee communication link, and equipment state data is uploaded to background monitoring center.The technical scheme can improve the automation level and data reliability of substation state monitoring.

Description

Technical Field

[0001] This invention relates to the field of power equipment condition monitoring technology, and in particular to a method, system, equipment and storage medium for collecting mobile inspection data of substations. Background Technology

[0002] Substations are critical nodes in the power grid system. The operational status of their key equipment, such as metal oxide surge arresters (MOAs), SF6 gas-insulated equipment, and oil-filled equipment, directly affects the safe and stable operation of the entire power grid. Among these, the full-wave leakage current and resistive component of the surge arrester, as well as the number of trips, are important indicators for judging the aging and moisture condition of its valve plates and its ability to absorb overvoltage energy. Meanwhile, parameters such as gas pressure (or density) of equipment like SF6 circuit breakers and the oil temperature and level of oil-filled equipment are core indicators for ensuring the insulation strength and arc-extinguishing performance of the equipment. Currently, the collection and management of these key operational data mainly rely on the following methods, but each has significant drawbacks:

[0003] The first method is the traditional manual inspection. In this model, maintenance personnel need to regularly visit the substation to conduct on-site inspections of various equipment: visually reading the leakage current value and operation count counter on the surge arrester monitor; observing the readings of the mechanical pressure gauges (or density gauges) on the SF6 equipment; and checking the oil level gauges and oil temperature gauges of the oil-filled equipment. The entire process is highly dependent on manual labor, which is not only inefficient and labor-intensive, but also prone to inaccurate or incomplete data recording due to human error. More importantly, maintenance personnel must be in close contact with live high-voltage equipment, posing significant personal safety risks, especially during inclement weather or when equipment malfunctions require increased inspections; the limitations and dangers of this method become even more pronounced during these times.

[0004] The second type is the fixed online monitoring system. This solution achieves automatic acquisition of key parameters by deploying intelligent monitoring units integrating sensors and communication modules on critical equipment. Data is transmitted to the back-end system in the main control room in near real-time via wired (e.g., cable, fiber optic) or wireless public network (e.g., 4G / 5G). Although this method effectively improves the timeliness and continuity of data acquisition and avoids the safety risks of manual inspection, its drawbacks are also significant: firstly, the overall cost of equipment procurement, installation, and cabling is very high; secondly, complex cabling projects are difficult to implement in the renovation of old substations. Therefore, this solution is usually only used for key monitoring of a few core devices and is difficult to achieve full coverage of the massive number of devices in the entire substation from both economic and technical perspectives.

[0005] The third approach involves using automated methods such as inspection robots or drones. As an emerging method for intelligent operation and maintenance of substations, these devices are equipped with high-definition visible light cameras, infrared thermal imagers, and other sensors, enabling non-contact inspections of equipment along preset paths, effectively covering areas difficult for humans to access. However, this technology has a fundamental limitation: its data acquisition heavily relies on indirect reading based on image recognition. Specifically, it requires photographing equipment dials and counters before using algorithms to identify the readings. This process is easily affected by multiple factors such as ambient light, shooting angle, lens cleanliness, and glass reflection, making it difficult to guarantee accuracy and reliability. Performance significantly degrades at night or in inclement weather, failing to meet the stringent requirements for data accuracy and reliability in power equipment condition monitoring.

[0006] In summary, existing technologies have significant gaps in the automated and highly reliable acquisition of internal operating data for key substation equipment (surge arresters, SF6 equipment). They either rely on high-risk, low-efficiency manual methods or on costly, difficult-to-widely-access fixed online monitoring, while advanced mobile inspection platforms are limited by the technological bottlenecks of image recognition. Therefore, there is an urgent need for an innovative solution that integrates the automation advantages of mobile inspection with the precision advantages of data transmission. Summary of the Invention

[0007] This invention provides a data acquisition method, system, equipment, and storage medium for substation mobile inspection equipment based on Zigbee and UWB technologies, in order to solve the problems of low accuracy, poor reliability, and susceptibility to environmental interference in meter data acquisition caused by existing mobile inspection platforms such as robots and drones, which rely on image recognition technology.

[0008] This invention innovatively embeds a low-power Zigbee communication module into the meters of equipment under test in substations, making them intelligent data sources. When a mobile inspection platform equipped with a UWB positioning module and a Zigbee communication module approaches, it can automatically wake up and trigger to complete close-range, direct, and digital acquisition of internal status parameters of the equipment (such as leakage current, number of actions, gas pressure, oil temperature and oil level), thereby fundamentally improving the automation level and data reliability of substation status monitoring.

[0009] According to one aspect of the present invention, a data acquisition method for substation mobile inspection equipment based on Zigbee and UWB technologies is provided, comprising:

[0010] The mobile acquisition terminal performs a cruise along a preset path and acquires the relative position information between the mobile acquisition terminal and the device data acquisition node in real time based on UWB technology.

[0011] Based on the relative position information, perform cooperative positioning and wake-up processing to obtain a wake-up determination result;

[0012] If the wake-up determination result meets the wake-up conditions, device node wake-up and two-way security authentication are performed through the Zigbee communication link.

[0013] After successful authentication, the device status data collected by the device data acquisition node is obtained through the Zigbee communication link, and the device status data is uploaded to the background monitoring center. According to another aspect of the present invention, a substation mobile inspection data acquisition system is provided, comprising:

[0014] Mobile data acquisition terminals, equipment data acquisition nodes, and back-end monitoring center;

[0015] The mobile data acquisition terminal is equipped with a UWB positioning module and a Zigbee communication module.

[0016] The device's data acquisition node is equipped with a Zigbee communication module and a sensor module;

[0017] The substation mobile inspection data acquisition system is used to execute the substation mobile inspection equipment data acquisition method based on Zigbee and UWB technology as described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0019] At least one processor;

[0020] and a memory communicatively connected to the at least one processor;

[0021] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the data acquisition method for substation mobile inspection equipment based on Zigbee and UWB technologies according to any embodiment of the present invention.

[0022] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the data acquisition method for substation mobile inspection equipment based on Zigbee and UWB technologies as described in any embodiment of the present invention.

[0023] The technical solution of this invention involves a mobile data acquisition terminal performing a patrol along a preset path and acquiring the relative position information between the mobile data acquisition terminal and the device data acquisition node in real time based on UWB technology. Based on the relative position information, collaborative positioning and wake-up processing are performed to obtain a wake-up determination result. If the wake-up determination result meets the wake-up conditions, device node wake-up and two-way security authentication are performed via a Zigbee communication link. After successful authentication, the mobile data acquisition terminal acquires the device status data collected by the device data acquisition node via the Zigbee communication link and uploads the device status data to the background monitoring center. This technical solution solves the problems of low meter data acquisition accuracy, poor reliability, and susceptibility to environmental interference caused by existing mobile inspection platforms such as robots and drones relying on image recognition technology. This invention embeds a low-power Zigbee communication module into the meter of the device under test in a substation, making it an intelligent data source. When a mobile inspection platform equipped with a UWB positioning module approaches, it can automatically wake up and trigger to complete close-range, direct, and digital acquisition of internal equipment status parameters (such as leakage current, number of actions, gas pressure, oil temperature and oil level), thereby fundamentally improving the automation level and data reliability of substation status monitoring.

[0024] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart illustrating a data acquisition method for a substation mobile inspection device based on Zigbee and UWB technologies, provided as an embodiment of the present invention;

[0027] Figure 2 This invention provides an overall architecture diagram of a mobile inspection data acquisition system for substations.

[0028] Figure 3 This is a hardware circuit framework diagram of a device data acquisition node provided in an embodiment of the present invention;

[0029] Figure 4 This is a hardware circuit framework diagram of a mobile data acquisition terminal provided in an embodiment of the present invention;

[0030] Figure 5A flowchart illustrating another data acquisition method for substation mobile inspection equipment based on Zigbee and UWB technologies provided in this embodiment of the invention;

[0031] Figure 6 This is a schematic diagram of another substation mobile inspection data acquisition system provided in an embodiment of the present invention;

[0032] Figure 7 A flowchart illustrating another data acquisition method for substation mobile inspection equipment based on Zigbee and UWB technologies provided in this embodiment of the invention;

[0033] Figure 8 The decision flowchart of the cooperative positioning and wake-up algorithm based on Kalman filtering and RSSI adaptive calibration provided in the embodiments of the present invention is shown below.

[0034] Figure 9 A schematic diagram of the electronic device for implementing the data acquisition method of substation mobile inspection equipment based on Zigbee and UWB technologies in this embodiment of the invention. Detailed Implementation

[0035] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0036] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0037] Before introducing the technical solutions of the embodiments of the present invention, the related technologies will be described:

[0038] Zigbee communication technology is a low-power, low-data-rate, short-range wireless communication technology based on the IEEE 802.15.4 standard. Its characteristics include strong self-organizing capabilities, extremely low node power consumption, low cost, and low latency, making it ideal for short-range, intermittent data communication between devices. It has been widely used in smart homes, industrial control, and other fields.

[0039] Ultra-wideband (UWB) positioning technology is a high-precision wireless ranging technology based on nanosecond-level narrow pulse signals. It can achieve centimeter-level real-time positioning through advanced algorithms. It possesses extremely strong multipath resolution and anti-interference capabilities, supports refresh rates up to hundreds of hertz, accurately captures instantaneous position changes of high-speed moving targets, and combines penetration with low power consumption. It can maintain sub-decimeter-level positioning accuracy even in complex industrial environments and stably output continuous high-precision spatial coordinates.

[0040] Mobile inspection robots and unmanned aerial vehicles (UAVs) are mobile platforms that integrate technologies such as environmental perception, path planning, autonomous navigation, and multi-sensor fusion. Inside substations, they are mainly used to perform repetitive, tedious, and dangerous inspection tasks, and are one of the core pieces of equipment for building "unmanned substations."

[0041] Figure 1 This is a flowchart illustrating a data acquisition method for substation mobile inspection equipment based on Zigbee and UWB technologies, provided as an embodiment of the present invention. This embodiment is applicable to the acquisition of data from key substation equipment. The method can be executed by a substation mobile inspection data acquisition system, which can be implemented in hardware and / or software. Figure 1 As shown, the method specifically includes the following steps:

[0042] S110. The mobile acquisition terminal performs a cruise according to a preset path and obtains the relative position information between the mobile acquisition terminal and the device data acquisition node in real time based on UWB technology.

[0043] In this context, "mobile data acquisition terminal" refers to a terminal device mounted on an inspection robot or drone. Data acquisition nodes can be low-power acquisition devices installed on power equipment meters. Relative position information can be understood as the distance and orientation information between the mobile data acquisition terminal and the corresponding acquisition node.

[0044] Specifically, the mobile data acquisition terminal travels within the substation area along a pre-planned route. During this process, the mobile data acquisition terminal continuously collects location-related data between itself and each equipment acquisition node using a UWB module, forming real-time relative position information.

[0045] In some possible implementations, the real-time acquisition of relative position information between the mobile acquisition terminal and the device data acquisition node based on UWB technology includes: acquiring distance information between the mobile acquisition terminal and the device data acquisition node in real time through the UWB module mounted on the mobile acquisition terminal; determining the relative distance and relative radial velocity between the mobile acquisition terminal and the device data acquisition node based on the distance information, and using the relative distance and the relative radial velocity as the relative position information.

[0046] The UWB module refers to the hardware module integrated into the mobile data acquisition terminal for high-precision ranging. Relative distance can be understood as the straight-line distance between the mobile data acquisition terminal and the device's data acquisition node. Relative radial velocity refers to the rate at which the distance between the two changes over time.

[0047] Specifically, the mobile data acquisition terminal uses its onboard UWB module to collect distance information between itself and the device's data acquisition nodes in real time. Then, based on this distance information, it calculates the relative distance and relative radial velocity, using both types of information as relative position information.

[0048] S120. Perform cooperative positioning and wake-up processing based on the relative position information to obtain a wake-up determination result.

[0049] Cooperative localization and wake-up processing refers to a comprehensive judgment process that combines location information with wake-up strategies. The wake-up determination result can be whether the wake-up conditions are met or not.

[0050] Specifically, the acquired relative position information can be input into a preset processing flow to analyze and judge the position information, and finally obtain a judgment result on whether the device acquisition node can be woken up.

[0051] S130. If the wake-up determination result meets the wake-up conditions, the device node wake-up and two-way security authentication are performed through the Zigbee communication link.

[0052] The wake-up condition can be understood as the preset criteria for allowing the wake-up operation to be performed. Two-way security authentication refers to the process of mutual verification of legitimacy between the mobile data acquisition terminal and the device acquisition node.

[0053] Specifically, when the wake-up determination result meets the preset conditions, the mobile acquisition terminal can send a wake-up signal to the target device acquisition node via the Zigbee link. Both parties complete mutual authentication according to preset rules to ensure the legitimacy of the communication object.

[0054] S140. After successful authentication, the device status data collected by the device data acquisition node is obtained through the Zigbee communication link, and the device status data is uploaded to the background monitoring center.

[0055] Among these, equipment status data refers to the power equipment operation-related parameters collected by the equipment acquisition nodes. The back-end monitoring center can be understood as the system used by the substation to receive, store, and manage inspection data.

[0056] Specifically, after two-way security authentication is successful, the mobile data acquisition terminal can obtain the status data uploaded by the device acquisition node through the established Zigbee link, and then upload the collected data to the background monitoring center for management.

[0057] In some possible implementations, obtaining the device status data collected by the device data acquisition node through the Zigbee communication link includes: sending a data request instruction to the device data acquisition node; receiving the timestamped device status data fed back by the device data acquisition node; and performing credibility preprocessing on the device status data.

[0058] Among these, the data request command refers to the control command used to request the device's data acquisition node to upload data. The timestamp can be understood as information identifying when the data was generated. Credibility preprocessing refers to the initial verification and filtering of the collected data.

[0059] Specifically, after successful security authentication, the mobile data acquisition terminal sends a data request command to the device acquisition node. Upon receiving the command, the device acquisition node sends back timestamped device status data to the mobile data acquisition terminal, which then performs credibility preprocessing on the received data.

[0060] In some possible implementations, uploading the device status data to the background monitoring center includes: packaging the preprocessed device status data, location information, timestamp, and communication quality information into a data package; and uploading the packaged data to the background monitoring center via wide area communication when the mobile acquisition terminal returns to the preset location.

[0061] Data packaging refers to integrating various related information into a unified data format. Wide-area communication methods can be understood as communication methods such as 4G, 5G, and Wi-Fi that enable long-distance transmission.

[0062] Specifically, the pre-processed device status data, location information, timestamps, and communication quality information are packaged together. When the mobile data acquisition terminal returns to its preset location, the packaged data is uploaded to the backend monitoring center via wide area communication.

[0063] The technical solution of this invention involves a mobile data acquisition terminal performing a patrol along a preset path and acquiring the relative position information between the mobile data acquisition terminal and the device data acquisition node in real time based on UWB technology. Based on the relative position information, collaborative positioning and wake-up processing are performed to obtain a wake-up determination result. If the wake-up determination result meets the wake-up conditions, device node wake-up and two-way security authentication are performed via a Zigbee communication link. After successful authentication, device status data collected by the device data acquisition node is acquired via the Zigbee communication link and uploaded to the background monitoring center. This technical solution solves the problems of low meter data acquisition accuracy, poor reliability, and susceptibility to environmental interference caused by existing mobile inspection platforms such as robots and drones relying on image recognition technology. This invention embeds a low-power Zigbee communication module into the meter of the device under test in a substation, making it an intelligent data source. When a mobile inspection platform equipped with a UWB positioning module and a Zigbee communication module approaches, it can automatically wake up and trigger to complete close-range, direct, and digital acquisition of internal equipment status parameters (such as leakage current, number of actions, gas pressure, oil temperature and oil level), thereby fundamentally improving the automation level and data reliability of substation status monitoring.

[0064] Figure 2 This is a general architecture diagram of a substation mobile inspection data acquisition system provided in an embodiment of the present invention. Figure 2 As shown, the core concept of this invention lies in constructing a collaborative data acquisition system of "mobile platform + device terminal micronet". This system consists of mobile acquisition terminals deployed on inspection robots or drones, and device data acquisition nodes installed on each meter of the equipment to be monitored (such as surge arrester monitoring meters, SF6 gas pressure gauges, oil temperature and level gauges, etc.). The device data acquisition nodes directly collect data through built-in sensors and are normally in an ultra-low power sleep state. When the mobile platform patrols near the equipment, it establishes a secure Zigbee wireless connection with the device data acquisition nodes through a specific wake-up and communication mechanism, triggering data upload. Finally, the mobile acquisition terminal forwards the collected data to the backend monitoring center.

[0065] A substation mobile inspection data acquisition system includes: equipment data acquisition nodes, mobile acquisition terminals, and a background monitoring center.

[0066] Equipment data acquisition nodes: fixedly installed on the meters of the power equipment to be monitored, such as surge arrester leakage current meters, circuit breaker gas pressure meters, transformer oil temperature and level meters, etc.

[0067] Figure 3 The hardware circuit framework diagram of the device data acquisition node provided in the embodiment of the present invention is as follows: Figure 3 As shown, the hardware circuit of the device data acquisition node mainly includes:

[0068] Sensor module: Used to directly collect the physical status parameters of the equipment, such as leakage current of surge arresters, number of actuations, pressure of SF6 equipment, and oil temperature and level of transformers, and transmit these data to the microcontroller unit (MCU).

[0069] Microcontroller Unit (MCU): Used to control the working logic, data processing and storage of the device's data acquisition node, as well as to control and transmit data to other modules of the device's data acquisition node.

[0070] Zigbee communication module: As a slave node, it is responsible for receiving data to be transmitted from the microcontroller unit (MCU) and transmitting this data to the mobile data acquisition terminal (robot / drone) via wireless communication.

[0071] Wake-up circuit module: A low-power detection circuit used to detect wireless wake-up signals from the mobile acquisition terminal to wake up the microcontroller unit (MCU) to transmit data to the mobile acquisition terminal via the Zigbee communication module.

[0072] Power management module: responsible for powering all modules of the node and using efficient power management strategies to keep the node in an ultra-low power sleep state most of the time.

[0073] Figure 4 This is a hardware circuit diagram of a mobile data acquisition terminal provided in an embodiment of the present invention. The mobile data acquisition terminal is mounted on an inspection robot or drone and cruises along a predetermined path with the mobile platform. Figure 4 As shown, the hardware circuit framework of the mobile data acquisition terminal mainly includes:

[0074] Main control unit: As the host computer of the mobile platform, it is responsible for the coordinated control, data storage and task scheduling of other modules of the mobile acquisition terminal.

[0075] Zigbee communication module: As the master node / coordinator in the region, it receives control commands from the master control unit, sends directional wake-up signals to the device data acquisition nodes, and receives data from the corresponding data acquisition nodes through wireless communication. Then, it transmits the received data to the master control unit for storage.

[0076] Precise positioning and ranging module: Receives control commands from the main control unit and uses UWB technology to achieve precise positioning of the mobile acquisition terminal relative to the device's data acquisition node (accuracy up to centimeter level). It transmits the acquired distance and position data to the main control unit for calculation and processing of relevant wake-up algorithms, ensuring the effectiveness of wake-up and communication.

[0077] Wide Area Communication Module: Receives control commands and collected data from the main control unit. After the mobile data collection terminal has collected all the equipment data and flies back to the inspection robot or drone base, it packages and transmits all the collected equipment data to the background monitoring center through 4G / 5G or Wi-Fi technology.

[0078] Path planning and navigation module: Receives control commands and data communication from the main control unit, is used to plan inspection routes, and ensures that the mobile data acquisition terminal can enter the effective communication range of each device node.

[0079] Power management module: responsible for powering all modules of the mobile data acquisition terminal.

[0080] Back-end monitoring center: Located in the substation's main control room or remote centralized control station, it includes servers, databases, and monitoring software. It is responsible for receiving, parsing, storing, displaying, and analyzing data uploaded by mobile data acquisition terminals, and for fault diagnosis and early warning.

[0081] Figure 5 A flowchart illustrating another data acquisition method for substation mobile inspection equipment based on Zigbee and UWB technologies provided in this embodiment of the invention is shown below. Figure 5 As shown, the method includes the following steps:

[0082] S510, cruise and positioning.

[0083] The mobile data acquisition terminal cruises along a preset path and uses UWB positioning technology to obtain the precise coordinates and relative distances of the data acquisition nodes of the inspection drone, inspection robot, and related equipment in real time.

[0084] S520, dynamic judgment and targeted wake-up.

[0085] When a mobile terminal enters the "wake-up zone" of a device node, its cooperative positioning and wake-up controller generates and transmits a directional wake-up signal containing the device node's ID. At this time, other nodes remain dormant due to ID mismatch.

[0086] S530, Security Authentication and Data Requests.

[0087] The awakened device node starts the Zigbee module and performs two-way security authentication with the mobile terminal (such as a challenge-response mechanism based on a pre-set key). After successful authentication, the mobile terminal sends a data request command.

[0088] S540, data upload and caching.

[0089] The device node sends the cached recent data (including timestamps) to the mobile terminal via the Zigbee link, and the mobile terminal's data credibility preprocessing module performs preliminary verification.

[0090] S550, data aggregation and remote transmission.

[0091] Once the mobile data acquisition terminal has collected all the device data and returned to the inspection robot or drone's base, it packages all the device data, its own location, timestamp, communication quality, and other information, and sends it to the backend monitoring center via 4G / 5G / WIFI network.

[0092] S560, In-depth Analysis and Evaluation.

[0093] The data credibility assessment model in the back-end monitoring center performs in-depth data processing, identifies and alerts on potentially problematic data, stores high-credibility data in the database, and provides visualization.

[0094] Figure 6 This is a schematic diagram of another substation mobile inspection data acquisition system provided in an embodiment of the present invention, as shown below. Figure 6 As shown, after collecting all equipment data via Zigbee communication, the inspection drone or robot returns to the drone nest or robot room, then packages all the data, and transmits the packaged equipment data to the background monitoring center in the main control room via communication methods such as 4G / 5G / Wi-Fi.

[0095] Figure 7 This is a flowchart illustrating another data acquisition method for substation mobile inspection equipment based on Zigbee and UWB technologies, provided as an embodiment of the present invention. Based on the above embodiments, this embodiment further optimizes the collaborative positioning and wake-up processing. Technical terms that are the same as or corresponding to those in the above embodiments will not be repeated here. Figure 7 As shown, the method specifically includes the following steps:

[0096] S700: The mobile acquisition terminal performs a cruise according to a preset path and acquires the relative position information between the mobile acquisition terminal and the device data acquisition node in real time based on UWB technology.

[0097] S710. Construct a state estimation model, and perform Kalman filtering prediction processing based on the state estimation model to obtain the predicted state and prediction error covariance.

[0098] In this context, the state estimation model refers to the mathematical model used to describe the relative motion state. The predicted state can be understood as the estimated value of the current state calculated based on the data from the previous moment.

[0099] Specifically, a state estimation model can be constructed first, and Kalman filtering prediction can be performed based on the model to obtain the predicted state and the covariance of the prediction error.

[0100] In some possible implementations, the construction of the state estimation model includes: using the relative distance and the relative radial velocity as state vectors, constructing a state transition relationship using a uniform motion model, and performing state prediction based on the state transition relationship to obtain the predicted state and the prediction error covariance.

[0101] In this context, the state vector refers to the set of variables used to represent the system's state. A uniform motion model can be understood as a motion model that assumes the velocity remains constant over a short period. The state transition relationship refers to the correspondence between states as they change over time.

[0102] Specifically, the relative distance and relative radial velocity can be combined into a state vector. A uniform motion model is used to establish the state transition relationship, and state prediction is performed according to this relationship to obtain the predicted state and the covariance of the prediction error.

[0103] S720. After acquiring UWB observation data, perform adaptive calibration of path loss parameters to obtain effective path loss parameters.

[0104] Among them, the effective path loss parameter refers to the path loss related parameters after calibration and smoothing.

[0105] Specifically, when acquiring UWB data, the system adaptively calibrates the path loss parameters to obtain effective path loss parameters.

[0106] In some possible implementations, the adaptive calibration of the path loss parameters to obtain effective path loss parameters includes: using the UWB observation data as a true distance benchmark to back-calculate the path loss index corresponding to the current environment; and smoothing the path loss index through low-pass filtering to obtain the effective path loss parameters.

[0107] Among these, the true distance benchmark can be understood as using UWB observation data as a more accurate distance reference. The path loss index refers to a parameter characterizing the degree of signal loss during propagation in the environment. Low-pass filtering refers to filtering methods used to smooth data and suppress fluctuations.

[0108] Specifically, UWB observation data is used as the true distance benchmark. Then, the path loss index under the current environment is calculated based on this distance and the corresponding RSSI data. The calculated index is then smoothed using a low-pass filter to obtain stable and effective path loss parameters.

[0109] S730. Based on the UWB observation data, perform high-weight update processing on the predicted state and the prediction error covariance.

[0110] S740. Then, based on the RSSI observation data and the effective path loss parameters, a low-weight update process is performed on the updated predicted state and prediction error covariance to obtain the optimal state estimate and optimal error covariance.

[0111] S750. Based on the optimal state estimation and the optimal error covariance, perform wake-up condition determination to obtain the wake-up determination result.

[0112] S760. If the wake-up determination result is that the wake-up condition is not met, repeat the steps of Kalman filter prediction processing, update processing and wake-up condition determination until a wake-up determination result that meets the wake-up condition is obtained.

[0113] Among them, the optimal state estimate refers to the state result with higher accuracy obtained after multiple updates.

[0114] Specifically, in the above steps, a high-weight update process is first performed on the predicted state and prediction error covariance based on UWB observation data. Then, a low-weight update process is performed on the state and covariance after the high-weight update based on RSSI observation data and effective path loss parameters to obtain the optimal state estimate and optimal error covariance. Further, a wake-up condition determination is performed based on the optimal state estimate and optimal error covariance to obtain the wake-up determination result. If the wake-up determination result does not meet the wake-up condition, the Kalman filter prediction process, update process, and wake-up condition determination steps can be repeated until a wake-up determination result that meets the wake-up condition is obtained. By adopting a process of prediction, calibration, two-level update, and repeated update determination, the accuracy of state estimation can be improved in complex environments, making the wake-up determination more stable.

[0115] In some possible implementations, the step of performing wake-up condition determination based on the optimal state estimation and the optimal error covariance to obtain the wake-up determination result includes: extracting the estimated distance and estimated speed from the optimal state estimation; calculating the trace of the optimal error covariance as a confidence index; determining whether the estimated distance falls within a preset communication range, whether the estimated speed meets the stable motion requirements, and whether the confidence index meets the preset confidence requirements, to obtain the wake-up determination result.

[0116] Here, estimated distance refers to the distance value extracted from the optimal state estimate. Estimated velocity can be understood as the velocity value extracted from the optimal state estimate. Confidence index refers to an indicator used to characterize the reliability of the state estimation results.

[0117] Specifically, the estimated distance and estimated velocity are extracted from the optimal state estimate. The trace of the optimal error covariance is calculated and used as a confidence index. Furthermore, it is determined whether the estimated distance, estimated velocity, and confidence index meet preset requirements, thereby obtaining the wake-up determination result.

[0118] S770. If the wake-up determination result meets the wake-up conditions, the transmit power is adaptively adjusted based on the effective path loss parameter and the estimated distance in the optimal state estimation.

[0119] S780 sends a directional wake-up signal carrying the target node identifier and performs two-way security authentication with the woken device data acquisition node through a preset key.

[0120] Here, transmit power refers to the output power of the mobile acquisition terminal when sending signals through the Zigbee module. Directed wake-up signal refers to a wake-up signal carrying the target node identifier and directed only to the target node. Pre-set key can be understood as authentication evidence pre-stored between the terminal and the node.

[0121] Specifically, the transmit power can be adaptively adjusted based on the effective path loss parameter and the estimated distance in the optimal state estimation. The mobile acquisition terminal sends a directional wake-up signal carrying the target node identifier and completes two-way security authentication with the woken-up node through a preset key.

[0122] Understandably, adaptively adjusting the transmit power can reduce overall power consumption, and key authentication can improve the security of wake-up.

[0123] S790. After successful authentication, the device status data collected by the device data acquisition node is obtained through the Zigbee communication link, and the device status data is uploaded to the background monitoring center.

[0124] In some possible implementations, the method further includes: skipping the steps of adaptive calibration of path loss parameters and high-weight update processing when UWB observation data is not obtained; and directly using the predicted state and the prediction error covariance for low-weight update processing of the RSSI observation data to obtain the optimal state estimate and the optimal error covariance.

[0125] Understandably, when UWB observation data is unavailable, the adaptive calibration of path loss parameters and the high-weight update process can be skipped. Instead, the predicted state and prediction error covariance obtained from Kalman filtering can be directly used for the low-weight update process corresponding to the RSSI observation data to obtain the optimal state estimate and optimal error covariance.

[0126] In this embodiment of the invention, mobile inspection and data acquisition terminals (robots and drones) wake up the device data acquisition nodes by reaching their designated "wake-up areas," thereby collecting data from these nodes. Traditional wake-up methods are mostly based on Received Signal Strength Indication (RSSI). However, RSSI is extremely unstable in environments with multipath fading and electromagnetic interference, leading to large ranging errors based on fixed path loss models, resulting in wake-up failures or excessive power consumption. While Ultra-Wideband (UWB) technology offers high positioning accuracy, its high module cost and power consumption make it unsuitable for continuous operation of terminal devices. Furthermore, the mobile inspection and data acquisition terminals (robots and drones) experience slight vibrations during movement, causing the relative distance and angle between them and the device data acquisition nodes to constantly change, affecting information transmission quality. Therefore, there is an urgent need for an intelligent wake-up algorithm that combines the high accuracy of UWB with the low power consumption of RSSI and can adapt to environmental changes.

[0127] To overcome the aforementioned problems, when the mobile inspection and data acquisition terminal (robot or drone) arrives near the device data acquisition node, it wakes up the target device node with extremely high success rate and minimal power consumption, and establishes a reliable data communication link. This invention further proposes a cooperative positioning and wake-up algorithm based on Kalman filtering and RSSI adaptive calibration.

[0128] The state-space model of the cooperative localization wake-up algorithm based on Kalman filtering and RSSI adaptive calibration is established as follows:

[0129] Define the state vector as follows:

[0130] (1)

[0131] in, This represents the relative distance between the mobile inspection equipment (drone and robot) and the equipment data acquisition node at time k; Let be the rate of change of the relative distance at time k, i.e., the relative radial velocity.

[0132] The state transition equation is expressed as:

[0133] (2)

[0134] This equation describes how the state evolves from the previous time k-1 to the current time k.

[0135] F is the state transition equation, assuming a short time interval. If the motion between the mobile inspection equipment (drones and robots) and the equipment data acquisition nodes is a uniform velocity model, then the state transition equation F can be expressed as:

[0136] (3)

[0137] It refers to process noise, which represents factors not considered in the model (such as acceleration disturbances, environmental disturbances). The statement is as follows:

[0138] (4)

[0139] Assuming process noise White noise with a mean of 0 and a covariance matrix of Q. and denoted by and representing the intensity of noise in the distance and velocity processes, respectively, Q represents the uncertainty of the model (such as acceleration perturbations).

[0140] The observation model of the cooperative localization wake-up algorithm based on Kalman filtering and RSSI adaptive calibration includes the UWB distance observation equation and the RSSI (signal strength) observation equation.

[0141] The UWB distance observation equation provides high-precision direct distance measurement, and its expression is:

[0142] (5)

[0143] Observation matrix Only the distance d is observed, and the velocity is not directly observed. It is UWB observation noise. Assuming it's Gaussian white noise, due to the high accuracy of UWB, therefore... The value is very small (e.g., 0.01 m). 2 This reflects the reliability of this observation method.

[0144] The RSSI observation equation converts the signal strength RSSI into a distance observation value, and its expression is as follows:

[0145] (6)

[0146] In the formula, It is the distance observation value based on the signal strength RSSI at time k. This refers to the signal strength received at time k. The instantaneous path loss exponent at time k, The transmit power of the Zigbee module on the drone / robot. This is a reference distance (usually taken as 1m). At reference distance Path loss at this location. This distance observation. Ignoring random variables Meanwhile, this model relies on an uncertain path loss exponent n, therefore The noise level is extremely high.

[0147] The RSSI observation equation can be formally written as:

[0148] (7)

[0149] The observation matrix is ​​also , It is RSSI ranging noise. Its variance Very large (e.g., 1m) 2 (or larger), reflecting the unreliability of this observation method.

[0150] One of the main innovations of this algorithm lies in the parameter calibration of the online path loss model. Traditionally, a preset, fixed path loss exponent n is used. This embodiment of the invention employs a real-time calibrated path loss exponent. This allows the model to adapt to specific environments and locations. The specific calculation method is as follows:

[0151] When the UAV obtains a high-precision distance observation value via UWB at time k... At that time, we consider it as the "real distance" at the current moment. At the same time, record the RSSI (signal strength) value measured at this moment. .

[0152] Will and Substitute the path loss model into the equation to calculate the effective path loss exponent at time k under the current environment. Using ignore random variables Model form:

[0153] (8)

[0154] Solve :

[0155] (9)

[0156] In the formula, The instantaneous path loss exponent is obtained by inverse calculation using UWB at time k. The transmit power of the Zigbee module on the drone / robot. This is a reference distance (usually taken as 1m). At reference distance Path loss at that location.

[0157] Because of single-point calculation It will still be affected by random variables Fluctuations due to measurement noise are addressed by using a first-order low-pass filter to achieve a smooth and stable output. :

[0158] (10)

[0159] Let the instantaneous path loss exponent after the first-order low-pass filter be the instantaneous path loss exponent at this moment, which is: .

[0160] It is the effective path loss exponent of the previous moment. It is the forgetting factor (0 < α < 1). The closer α is to 1, the better the system learns new information. The less sensitive the system is to changes, the better its stability but the slower its adaptability; the smaller the α, the faster the system responds to new data, but the poorer the smoothing effect. α is typically chosen to be between 0.8 and 0.9.

[0161] Figure 8 The decision flowchart of the cooperative localization wake-up algorithm based on Kalman filtering and RSSI adaptive calibration provided in the embodiments of the present invention is as follows: Figure 8 As shown, the main steps include:

[0162] S810, System Initialization:

[0163] Initially, the drone flies to the approximate location of the target device and initializes the Kalman filter:

[0164] The state vector is:

[0165] (11)

[0166] Based on the UWB ranging information, the velocity is initialized to 0.

[0167] Error covariance matrix initialization:

[0168] (12)

[0169] and The initial value is uncertain and can be relatively large.

[0170] initialization This is an empirical value, generally set between 1 and 5, and can be adjusted based on the actual debugging results.

[0171] S820, Kalman filter prediction step prior estimation calculation:

[0172] Based on the optimal estimate from the previous time step, predict the current state and error covariance:

[0173] (13)

[0174] S830. Determine if there is a UWB data update. If there is a UWB data update, execute the following:

[0175] S8301. Perform online path loss model calibration to obtain the calibrated path loss index. The calibration calculation method is shown in equations (8), (9), and (10) above.

[0176] S8302 and UWB observation updates (high-weighted updates) are performed as follows:

[0177] UWB Kalman update, calculating Kalman gain :

[0178] (14)

[0179] Obtain the UWB distance observation at time k. Update the state estimate using UWB distance observations:

[0180] (15)

[0181] Update error covariance:

[0182]

[0183] Will and The "optimal estimate" for the current moment is denoted as... and .

[0184] S8303, RSSI observation update (low-weighted update), the update process is as follows:

[0185] Substitute the corrected path loss index Calculate RSSI distance observations The calculation formula is shown in equation (6) above:

[0186] RSSI Kalman Update, Calculating Kalman Gain :

[0187] (16)

[0188] Due to variance ,so It will be much smaller This means that the filter has very low confidence in RSSI observations and only makes minor adjustments.

[0189] Fine-tuning the state estimate using RSSI observations:

[0190] (17)

[0191] Update error covariance

[0192] (18)

[0193] Similarly and The "optimal estimate" for the current moment is denoted as... and .

[0194] If it is determined in step S830 that there is no UWB data update, then skip the two steps of online path loss model calibration in step S8301 and UWB observation update (high-weight update) in step S8302, and directly use the predicted value in step S820. and The RSSI observations are directly used for the update (low-weight update) in step S8303.

[0195] S840, Intelligent Wake-up Decision

[0196] From the final "optimal estimate" Extract information:

[0197] Optimal distance estimation:

[0198] (The first element of the state vector)

[0199] Optimal speed estimation:

[0200] (The second element of the state vector)

[0201] Confidence level estimate: , which is represented as the trace of the error covariance matrix, and represents the total error variance.

[0202] The wake-up criteria are:

[0203] Condition 1: (This indicates that the robot / drone has entered effective communication range and the communication quality is good.)

[0204] Condition 2: (This indicates that the robot / drone is moving relatively slowly, making it suitable for establishing a stable connection.)

[0205] Condition 3: (This indicates that the robot / drone state estimate is sufficiently confident and the filter has converged.)

[0206] This represents the distance threshold between the mobile data acquisition terminal and the device's data acquisition node. This is represented as the speed threshold of the mobile data acquisition terminal. These three values ​​are set according to the actual situation on site, representing the confidence threshold.

[0207] If all the above wake-up criteria are met, the mobile acquisition terminal sends a targeted wake-up command to the target device's data acquisition node to achieve data acquisition.

[0208] If all the above wake-up discrimination conditions are not met, the algorithm returns to the S820 Kalman filter prediction step prior estimation calculation and repeats the algorithm loop until all the above wake-up discrimination conditions are met.

[0209] Figure 9 This is a schematic diagram of the electronic device used to implement the data acquisition method for substation mobile inspection equipment based on Zigbee and UWB technologies according to embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptops, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0210] like Figure 9 As shown, the electronic device 90 includes at least one processor 91 and a memory, such as a read-only memory (ROM) 92 and a random access memory (RAM) 93, communicatively connected to the at least one processor 91. The memory stores computer programs executable by the at least one processor. The processor 91 can perform various appropriate actions and processes based on the computer program stored in the ROM 92 or loaded into the RAM 93 from storage unit 98. The RAM 93 can also store various programs and data required for the operation of the electronic device 90. The processor 91, ROM 92, and RAM 93 are interconnected via a bus 94. An input / output (I / O) interface 95 is also connected to the bus 94.

[0211] Multiple components in electronic device 90 are connected to I / O interface 95, including: input unit 96, such as keyboard, mouse, etc.; output unit 97, such as various types of displays, speakers, etc.; storage unit 98, such as disk, optical disk, etc.; and communication unit 99, such as network card, modem, wireless transceiver, etc. Communication unit 99 allows electronic device 90 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0212] Processor 91 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 91 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 91 performs the various methods and processes described above, such as the data acquisition method for substation mobile inspection equipment based on Zigbee and UWB technologies.

[0213] In some embodiments, the substation mobile inspection equipment data acquisition method based on Zigbee and UWB technologies can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 98. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 90 via ROM 92 and / or communication unit 99. When the computer program is loaded into RAM 93 and executed by processor 91, one or more steps of the substation mobile inspection equipment data acquisition method based on Zigbee and UWB technologies described above can be performed. Alternatively, in other embodiments, processor 91 can be configured to perform the substation mobile inspection equipment data acquisition method based on Zigbee and UWB technologies by any other suitable means (e.g., by means of firmware).

[0214] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0215] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0216] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0217] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0218] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0219] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0220] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0221] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A data acquisition method for mobile inspection equipment in substations based on Zigbee and UWB technologies, characterized in that, include: The mobile acquisition terminal performs a cruise along a preset path and acquires the relative position information between the mobile acquisition terminal and the device data acquisition node in real time based on UWB technology. Based on the relative position information, perform cooperative positioning and wake-up processing to obtain a wake-up determination result; If the wake-up determination result meets the wake-up conditions, device node wake-up and two-way security authentication are performed through the Zigbee communication link. After successful authentication, the device status data collected by the device data acquisition node is obtained through the Zigbee communication link, and the device status data is uploaded to the background monitoring center.

2. The method according to claim 1, characterized in that, The real-time acquisition of the relative position information between the mobile acquisition terminal and the device data acquisition node based on UWB technology includes: The distance information between the mobile acquisition terminal and the device data acquisition node is collected in real time through the UWB module mounted on the mobile acquisition terminal. Based on the distance information, the relative distance and relative radial velocity between the mobile acquisition terminal and the device data acquisition node are determined, and the relative distance and relative radial velocity are used as the relative position information.

3. The method according to claim 1, characterized in that, The step of performing cooperative localization and wake-up processing based on the relative position information to obtain a wake-up determination result includes: A state estimation model is constructed, and Kalman filtering prediction processing is performed based on the state estimation model to obtain the predicted state and the prediction error covariance. With the UWB observation data acquired, adaptive calibration of the path loss parameters is performed to obtain effective path loss parameters. Based on the UWB observation data, the predicted state and the prediction error covariance are first updated with high weights. Then, based on the RSSI observation data and the effective path loss parameters, a low-weight update process is performed on the updated predicted state and prediction error covariance to obtain the optimal state estimate and optimal error covariance. Based on the optimal state estimation and the optimal error covariance, the wake-up condition determination is performed to obtain the wake-up determination result; If the wake-up determination result is that the wake-up condition is not met, the steps of Kalman filter prediction processing, update processing and wake-up condition determination are repeated until a wake-up determination result that meets the wake-up condition is obtained.

4. The method according to claim 3, characterized in that, The construction of the state estimation model includes: Using the relative distance and the relative radial velocity as state vectors, a uniform motion model is used to construct a state transition relationship, and state prediction is performed based on the state transition relationship to obtain the predicted state and the prediction error covariance.

5. The method according to claim 3, characterized in that, The adaptive calibration process for the path loss parameters yields effective path loss parameters, including: Using the UWB observation data as the true distance benchmark, the path loss index corresponding to the current environment is calculated in reverse. The effective path loss parameter is obtained by smoothing the path loss index using a low-pass filter.

6. The method according to claim 3, characterized in that, The wake-up condition determination based on the optimal state estimation and the optimal error covariance, to obtain the wake-up determination result, includes: Extract the estimated distance and estimated velocity from the optimal state estimate; The trace of the optimal error covariance is calculated as a confidence index; The wake-up determination result is obtained by determining whether the estimated distance falls within the preset communication range, whether the estimated speed meets the stable motion requirements, and whether the confidence index meets the preset confidence requirements.

7. The method according to claim 3, characterized in that, The process of waking up device nodes and performing two-way security authentication via the Zigbee communication link includes: Based on the effective path loss parameters and the estimated distance in the optimal state estimation, the transmit power is adaptively adjusted. Send a directional wake-up signal carrying the target node identifier, and perform two-way security authentication with the woken device data acquisition node through a preset key.

8. The method according to claim 3, characterized in that, The method further includes: In the absence of UWB observation data, skip the steps of adaptive calibration of path loss parameters and high-weight update. The predicted state and the prediction error covariance are directly used for low-weight update processing of the RSSI observation data to obtain the optimal state estimate and the optimal error covariance.

9. The method according to claim 1, characterized in that, The step of obtaining the device status data collected by the device data acquisition node through the Zigbee communication link includes: Send a data request command to the data acquisition node of the device; Receive the timestamped device status data fed back by the device data acquisition node, and perform credibility preprocessing on the device status data.

10. The method according to claim 1, characterized in that, Uploading the device status data to the backend monitoring center includes: The preprocessed device status data, location information, timestamps, and communication quality information are packaged together. When the mobile data acquisition terminal returns to its preset location, it uploads the packaged data to the background monitoring center via wide area communication.

11. A mobile inspection data acquisition system for substations, characterized in that, include: Mobile data acquisition terminals, equipment data acquisition nodes, and back-end monitoring center; The mobile data acquisition terminal is equipped with a UWB positioning module and a Zigbee communication module. The device's data acquisition node is equipped with a Zigbee communication module and a sensor module; The substation mobile inspection data acquisition system is used to execute the substation mobile inspection equipment data acquisition method based on Zigbee and UWB technology as described in any one of claims 1-10.

12. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the data acquisition method for substation mobile inspection equipment based on Zigbee and UWB technology as described in any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the data acquisition method for substation mobile inspection equipment based on Zigbee and UWB technologies as described in any one of claims 1-10.

Images