Low-voltage electrician operation safety intelligent early warning method and system based on multi-dimensional data

Abstract

The invention discloses a low-voltage electrician operation safety intelligent early warning method and system based on multi-dimensional data, and relates to the field of low-voltage electrician operation safety early warning, and the method comprises the steps: obtaining a three-dimensional layout diagram of a low-voltage electrician operation region in response to an initial instruction; determining the position of each type of equipment in the low-voltage electrician operation area based on the three-dimensional layout map, and acquiring equipment data of each type of equipment in real time through a multi-type sensor; determining whether an abnormal operation exists or not through the equipment data; under the condition that the abnormal operation exists, alarm information is sent out, and the work type equipment corresponding to the abnormal operation is positioned; and sending the alarm information to the work type equipment, so that the work type equipment visualizes the alarm information. The problem that potential safety hazards cannot be recognized and early warned in time in the low-voltage electrician operation process can be effectively solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of low-voltage electrical work safety warning, and in particular to a low-voltage electrical work safety intelligent warning method and system based on multi-dimensional data. Background Art

[0002] During the practical phase of low-voltage electrical work exams, monitoring candidates' operations for potential safety hazards and providing alerts for violations are crucial. However, current anomaly detection in exam rooms is often achieved through manual inspections. However, manual inspections have limitations. The experience of examiners or invigilators, a key factor in identifying anomaly detection, directly and significantly impacts their ability to identify violations or safety hazards. Experienced examiners are more sensitive to subtle anomalies in candidates' operations and are more likely to identify and address them promptly. However, less experienced examiners, lacking sufficient practical experience and experience, may miss key details due to unfamiliarity with operational procedures or insufficient ability to identify violations when faced with complex exam environments and the variability of candidate behavior. This can lead to potential safety hazards not being discovered and addressed promptly, resulting in inaccurate anomaly detection. This inaccurate anomaly detection poses safety risks to both examinees and examiners.

[0003] There is currently no better solution to the above problems. Summary of the Invention

[0004] The embodiments of the present application provide a low-voltage electrical work safety intelligent early warning method and system based on multi-dimensional data, which is used to solve the problem that safety hazards cannot be identified and warned in a timely manner during low-voltage electrical work, and improve the accuracy of abnormal identification in the examination room.

[0005] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0006] In a first aspect, a multi-dimensional data-based intelligent early warning method for low-voltage electrical work safety is provided, which is applied to a low-voltage electrical work safety test monitoring system. The low-voltage electrical work safety test monitoring system includes multiple types of sensors, cameras, and wearable devices. The method includes:

[0007] In response to a start instruction, obtaining a three-dimensional layout diagram of a low-voltage electrical work area;

[0008] Determine the location of each type of equipment in the low-voltage electrical work area based on the three-dimensional layout diagram, and acquire equipment data of each type of equipment in real time through the multi-type sensors;

[0009] determining whether there is abnormal operation based on the device data;

[0010] In the event of abnormal operation, an alarm message will be issued and the equipment corresponding to the abnormal operation will be located;

[0011] Sending the alarm information to the type of equipment so that the type of equipment can visualize the alarm information;

[0012] The abnormal operation includes abnormal equipment status of the equipment and abnormal electrical parameters of the equipment.

[0013] In a possible implementation of the first aspect, the multiple types of sensors include an electrical parameter sensor and a device status sensor, the device data includes electrical parameter data and device data, and determining whether abnormal operation exists based on the device data includes:

[0014] Acquire, through the multi-type sensors, a set of electrical parameters of the type of equipment and a set of equipment data of the type of equipment;

[0015] Extracting features of the electrical parameter set and the device data set respectively to obtain an electrical parameter feature set and a device data feature set;

[0016] Based on the electrical parameter feature set and the device data feature set, it is determined whether there is a device state abnormality or an electrical parameter abnormality.

[0017] In a possible implementation of the first aspect, the low-voltage electrical work safety examination monitoring system further includes a camera and a wearable device, the abnormal operation further includes an abnormal operation by an operator, and the method further includes:

[0018] Acquiring a set of biometric data of an operator through the camera and the wearable device;

[0019] Determine whether there is any abnormal operation by the personnel through the biometric data set.

[0020] In a possible implementation of the first aspect, the electrical parameter anomaly includes a ground monitoring anomaly, a three-phase loss anomaly, and a safety voltage anomaly, and extracting features of the electrical parameter set to obtain the electrical parameter feature set includes:

[0021] The electrical parameter sensor is used to obtain the three-phase voltage, ground current value and ground resistance value of the equipment in real time;

[0022] Determining the three-phase loss abnormality based on the three-phase voltage;

[0023] Calculating the average resistance value and the standard deviation of the resistance within a preset time period based on the ground resistance value;

[0024] Calculating the current average value and current standard deviation within a preset time period based on the ground current value;

[0025] Extracting characteristics of the resistance average value and the resistance standard deviation, and determining abnormal state characteristics and stable state characteristics of the grounding resistance of the equipment;

[0026] Determining grounding monitoring abnormality based on the grounding resistance abnormal state characteristics and the grounding resistance stable state characteristics;

[0027] Extracting characteristics of the current average value and the current standard deviation, and determining abnormal state characteristics and stable state characteristics of the grounding current of the equipment;

[0028] The safety voltage abnormality is determined based on the ground current abnormal state characteristics and the ground current stable state characteristics.

[0029] In a possible implementation of the first aspect, the three-phase voltage includes a first-phase voltage, a second-phase voltage, and a third-phase voltage, and determining the three-phase loss abnormality based on the three-phase voltage includes:

[0030] Using a preset phase shift circuit, the first phase voltage, the second phase voltage, and the third phase voltage are respectively delayed by a preset phase angle to obtain a first delayed phase voltage, a second delayed phase voltage, and a third delayed phase voltage;

[0031] Adding the first phase voltage to the second delayed phase voltage, adding the second phase voltage to the third delayed phase voltage, and adding the third phase voltage to the first delayed phase voltage to obtain a first voltage difference, a second voltage difference, and a third voltage difference, respectively;

[0032] rectifying the first voltage difference, the second voltage difference, and the third voltage difference to obtain a first rectified voltage, a second rectified voltage, and a third rectified voltage;

[0033] determining whether the first rectified voltage, the second rectified voltage, and the third rectified voltage are zero;

[0034] If at least one of the first rectified voltage, the second rectified voltage, and the third rectified voltage is zero, it is determined that the three-phase loss is abnormal.

[0035] In a possible implementation of the first aspect, issuing an alarm message when an abnormal operation occurs includes:

[0036] When the abnormal operation is any one of abnormal human operation, abnormal equipment status and abnormal electrical parameters, a first-level alarm message is issued;

[0037] In the event that the abnormal operation is any two of the abnormal operation by the personnel, the abnormal state of the equipment, and the abnormal electrical parameters, a secondary alarm message is issued;

[0038] In the case where the abnormal operation is the abnormal operation of the personnel, the abnormal state of the equipment and the abnormal electrical parameters, a third-level alarm message is issued.

[0039] In a possible implementation of the first aspect, locating the type of equipment corresponding to the abnormal operation includes:

[0040] Obtaining the time when the abnormal operation occurs;

[0041] Performing time matching on the time when the abnormal operation occurs, the device data, and the biometric data of the operator to obtain a time matching result;

[0042] spatially matching the device data with the biometric data of the operator to obtain a spatial matching result;

[0043] The type of equipment corresponding to the abnormal operation is determined by combining the time matching result and the space matching result.

[0044] In a possible implementation of the first aspect, time-matching the time at which the abnormal operation occurs, the device data, and the biometric data of the operator to obtain a time matching result includes:

[0045] Obtaining a timestamp of the biometric data set and the device data;

[0046] Using the timestamp of the abnormal operation as a master clock, and using the timestamps of the biometric data set and the device data as slave clocks;

[0047] Using a preset network time synchronization protocol, sending the timestamp of the master clock to the slave clock;

[0048] Get the timestamp of network delay;

[0049] The timestamp of the slave clock is adjusted according to the timestamp of the master clock and the timestamp of the network delay to obtain a time matching result.

[0050] In a possible implementation of the first aspect, spatially matching the device data with the biometric data of the operator to obtain a spatial matching result includes:

[0051] Taking any one of the above mentioned equipment as the origin, a coordinate system is constructed;

[0052] Mapping the multiple types of sensors, the camera, the wearable device, and the operator into the coordinate system;

[0053] Obtaining the preset coordinates of the QR code preset in the coordinate system;

[0054] Measuring the measurement coordinates of the QR code preset in the coordinate system using the camera, and correcting the preset coordinates using the measured coordinates to obtain a corrected coordinate system;

[0055] converting the device data and the operator's biometric data into the calibrated coordinate system;

[0056] In the corrected coordinate system, a preset spatial registration algorithm is used to perform spatial synchronization processing on the device data and the operator's biometric data to obtain a spatial matching result.

[0057] In a second aspect, the present application provides an electronic device, comprising:

[0058] a memory configured to store instructions; and

[0059] The processor is configured to call the instructions from the memory and implement the above-mentioned low-voltage electrical work safety intelligent early warning method based on multi-dimensional data when executing the instructions.

[0060] This technical solution uses multiple sensors to capture real-time data from each type of equipment, comprehensively and accurately reflecting the equipment's operating status and electrical parameters. When abnormal operation is detected, an alert is immediately issued and the specific equipment is located, enabling managers or examiners to respond quickly. Furthermore, a three-dimensional layout is used to determine the location of equipment, providing a visual display of the work area and facilitating understanding of the work environment. Visualized alerts clearly indicate abnormal equipment status, alerting candidates and operators to safety concerns. This helps reduce accidents caused by negligence, improves safety management in examination halls, and effectively addresses the lack of timely identification and early warning of safety hazards during low-voltage electrical work, thereby improving the accuracy of abnormality detection in examination halls. By assessing the candidate's operational process, this system provides objective and accurate data support for exam assessment. It also assesses the candidate's operational skills and safety awareness, providing targeted guidance for training and education.

[0061] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flowchart of a low-voltage electrical work safety intelligent early warning method based on multi-dimensional data provided in an embodiment of the present application;

[0063] Figure 2 A waveform diagram of a first phase voltage and a second phase voltage before and after phase shifting provided in an embodiment of the present application;

[0064] Figure 3 This is a waveform diagram of the first phase voltage and the third phase voltage before and after phase shifting provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0066] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0067] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0068] Figure 1 The following schematically shows a flow chart of a low-voltage electrical work safety intelligent early warning method based on multi-dimensional data according to an embodiment of the present application. Figure 1As shown, an embodiment of the present application provides a low-voltage electrical work safety intelligent early warning method based on multi-dimensional data, which is applied to a low-voltage electrical work safety examination monitoring system. The low-voltage electrical work safety examination monitoring system includes multiple types of sensors, cameras and wearable devices. The method may include the following steps.

[0069] S110, in response to a start instruction, obtaining a three-dimensional layout diagram of a low-voltage electrical work area;

[0070] S120, determining the location of each type of equipment in the low-voltage electrical work area based on the three-dimensional layout diagram, and acquiring equipment data of each type of equipment in real time through multiple types of sensors;

[0071] S130, determining whether there is abnormal operation based on device data;

[0072] S140. In the event of abnormal operation, an alarm message is issued and the equipment corresponding to the abnormal operation is located;

[0073] S150, sending the alarm information to the work equipment so that the work equipment can visualize the alarm information;

[0074] S160, wherein the abnormal operation includes abnormal equipment status of the type of equipment and abnormal electrical parameters of the type of equipment.

[0075] First, in response to a start command, a three-dimensional layout diagram of the low-voltage electrical work area is obtained. That is, when the examiner triggers the start command, the system enters a monitoring and early warning state. After responding to the start command, a three-dimensional layout diagram of the low-voltage electrical work area is obtained. This can be retrieved from a pre-set database. The three-dimensional layout diagram may exist in the form of a digital model, containing the location information of all types of equipment, facilities, obstacles, and other items in the work area.

[0076] Subsequently, the location of each type of equipment in the low-voltage electrical work area is determined based on the three-dimensional layout diagram, and equipment data for each type of equipment is acquired in real time using multiple types of sensors. In this embodiment, the three-dimensional layout diagram provides spatial information about the low-voltage electrical work area, including the precise locations of various types of equipment, facilities, obstacles, and so on. Specifically, a pre-built or real-time acquired three-dimensional layout diagram is loaded into the system, and the layout diagram is parsed using a spatial parsing algorithm to extract the location information of the equipment. In this embodiment, the spatial parsing algorithm can be a computer vision algorithm. In other words, the location of each type of equipment in the three-dimensional layout diagram is determined using a computer vision algorithm. After determining the location of each type of equipment, equipment data for each type of equipment is acquired in real time using multiple types of sensors. Once the location of each type of equipment is determined, the multiple types of sensors can be used to acquire the equipment's status data and electrical parameters in real time. These multiple types of sensors can include current sensors, voltage sensors, temperature sensors, vibration sensors, and other sensors, used to monitor key indicators such as current, voltage, temperature, and vibration. Specifically, first, corresponding sensors are deployed near and on each type of equipment in the work area. Sensor parameters such as sampling frequency and accuracy are configured based on the equipment's characteristics and monitoring requirements. Subsequently, the status data and electrical parameters of the equipment are collected in real time through sensors and transmitted to the system. The system will process and analyze the collected data, extract key indicators and patterns for subsequent abnormal monitoring and early warning.

[0077] Next, the equipment data is used to determine whether there are any abnormal operations. In this embodiment, abnormal operations include abnormal equipment status, abnormal electrical parameters of the equipment, and abnormal human operation. In other words, in low-voltage electrical work safety test monitoring, various types of data collected from the equipment (such as operating parameters, status indicators, operation records, etc.) are used to analyze and determine whether the equipment is operating normally and as expected, or whether the operator has engaged in abnormal operating behavior that is inconsistent with the normal operating mode and may indicate a failure or safety hazard. Specifically, first, it is necessary to use various sensors, monitoring systems, or data recording devices to collect equipment operating data in real time or regularly. This data may include physical parameters such as temperature, pressure, speed, current, voltage, and vibration frequency, as well as logical information such as the device's on / off status and operating instructions. The collected data is then sent to a data analysis system for processing and analysis to identify abnormal patterns in the data or values that deviate from the normal range. These abnormalities may indicate potential problems or abnormal operation of the equipment. Based on the results of the data analysis, the system determines whether the equipment is operating abnormally. Once abnormal operation is detected, the system triggers an alarm mechanism and notifies relevant personnel through sound, light, text message, email, etc. Operators or maintenance personnel will take appropriate measures to investigate, diagnose and solve the problem based on the alarm information.

[0078] In the event of an abnormal operation, an alarm is issued and the equipment associated with the abnormal operation is located. Specifically, the system first utilizes various sensors, cameras, and wearable devices to collect real-time equipment operating data, such as temperature, pressure, current, and vibration frequency. This collected data is then analyzed to identify abnormal patterns. Once an abnormal operation is detected, the system immediately triggers an alarm. After issuing the alarm, the abnormal data is analyzed to determine the equipment location where the abnormal operation occurred. Layout and structural diagrams of the equipment can be used to further locate the specific location of the abnormal operation. The system then identifies the type of work associated with the abnormal operation based on the type and purpose of the equipment.

[0079] Next, the alarm information is sent to the work equipment to visualize the alarm information. First, it is necessary to identify the work equipment that needs to receive the alarm information. The alarm information can be sent to the equipment via wired communication (such as Ethernet, serial communication, etc.). If the work equipment supports wireless communication (such as Wi-Fi, Bluetooth, LoRa, etc.), the alarm information can be sent wirelessly. After receiving the alarm information, the work equipment needs to parse and process it, extract key information, and display it on a visual interface. To attract the operator's attention, the work equipment can emit a warning signal such as sound and light when receiving the alarm information. At the same time, a confirmation mechanism is provided, allowing the operator to close the warning signal after confirming the alarm information.

[0080] In this embodiment, abnormal operations include abnormal equipment status and abnormal electrical parameters of equipment. Abnormal equipment status refers to a significant difference between the physical or operating state of a piece of equipment during operation and its normal state. Such abnormalities may manifest as decreased equipment performance, component damage, unstable operation, or malfunction. For example, decreased equipment performance may manifest as indicators such as production capacity, processing accuracy, or operating efficiency falling below normal levels; component damage may manifest as wear, breakage, deformation, or other damage to one or more components of the equipment; unstable operation may manifest as vibration, noise, temperature rise, and other instability during operation; and malfunction may manifest as a complete halt or inability to perform its intended task. Abnormal electrical parameters refer to significant differences between the electrical parameters (such as voltage, current, and frequency) of a piece of equipment during operation and their normal set values or standard ranges. Such abnormalities may indicate a fault or potential risk in the equipment's electrical system. For example, abnormal voltage may manifest as input or output voltage that is too high or too low, exceeding the normal range; abnormal current may manifest as current that is too high or too low, inconsistent with normal operating conditions; and abnormal frequency may manifest as a mismatch between the operating frequency of the equipment and the power supply frequency, resulting in malfunction.

[0081] Real-time data from each type of equipment is acquired through multiple sensors, comprehensively and accurately reflecting the equipment's operating status and electrical parameters. When abnormal operation is detected, an alarm is immediately issued and the specific type of equipment is located, enabling managers or examiners to respond quickly. A three-dimensional layout diagram is used to determine the location of equipment, providing a visual display of the work area and facilitating understanding of the working environment for managers or examiners. Visualized alarm information allows equipment to intuitively display abnormal conditions, alerting candidates and operators to safety concerns. This helps reduce safety incidents caused by human negligence and improves safety management in the examination room. By assessing the candidate's operating process, objective and accurate data is provided to support examination assessments. It can also assess the candidate's operational skills and safety awareness, providing targeted guidance for training and education.

[0082] In one implementation of this embodiment, the multiple types of sensors include an electrical parameter sensor and an equipment status sensor. The multiple types of sensors are used to obtain an electrical parameter set and an equipment data set of the type of equipment, respectively. The method determines whether abnormal operation exists based on the equipment data. The method includes:

[0083] S210, using multiple types of sensors, respectively acquiring an electrical parameter set and an equipment data set of the type of equipment;

[0084] S220, extracting features of the electrical parameter set and the device data set respectively to obtain an electrical parameter feature set and a device data feature set;

[0085] S230: Determine whether there is an abnormal device state or an abnormal electrical parameter based on the electrical parameter feature set and the device data feature set.

[0086] In this embodiment, the multiple types of sensors include electrical parameter sensors and equipment status sensors, wherein the electrical parameter sensors may be voltage sensors, current transformers, etc. The voltage sensor is used to monitor the input voltage of the electrical equipment; the current transformer is used to measure the current of large current electrical equipment. The equipment status sensor may be a vibration sensor and an acoustic emission sensor; a vibration sensor is a sensor that can measure the vibration parameters of an object, and it can convert the vibration signal into an electrical signal or other form of signal for subsequent analysis and processing. In equipment status monitoring, the vibration sensor is mainly used to monitor the vibration of the equipment and evaluate the operating status and health of the equipment; the acoustic emission sensor is a sensor that can receive acoustic emission signals and convert them into electrical signals. Acoustic emission signals are transient elastic wave signals generated by changes in the internal microstructure of the material during stress (such as crack extension, plastic deformation, etc.); by capturing these signals, the acoustic emission sensor can monitor internal damage and potential failures of the equipment.

[0087] Using multiple sensor types, electrical parameter sets and device data sets for each type of equipment are acquired. Specifically, electrical parameter sensors, such as voltage and current sensors, acquire voltage and current parameter sets for each type of equipment. Equipment status sensors, such as vibration and acoustic emission sensors, acquire acoustic emission and vibration signal data sets for each type of equipment. Furthermore, this system can be integrated with PLC or microcontroller programming to provide status indication and feedback. The motor's running direction can be displayed on the user interface and monitored via LED indicators (green for forward rotation and red for reverse rotation). A direction sensor or current direction detection module can be integrated into the audible and visual alarms for abnormal direction (such as buzzers and flashing warning lights). This allows for real-time monitoring of the motor's direction. If the direction deviates from the set direction, an alarm is triggered and the power supply is automatically cut off, providing intuitive status feedback. Furthermore, intelligent alarms can be used to monitor the grounding status of low-voltage electrical equipment and lines. When a grounding failure is detected, operators are immediately alerted through audible and visual alarms, ensuring safe equipment operation and personnel safety, and preventing electrical fires and other safety incidents caused by grounding failures.

[0088] After obtaining the electrical parameter set and the equipment data set for the work equipment, features of the electrical parameter set and the equipment data set are extracted, respectively, to obtain an electrical parameter feature set and an equipment data feature set. In this embodiment, the electrical parameter feature set is a data set extracted from the original electrical parameter set that reflects key characteristics of the equipment's electrical performance. The equipment data feature set is a data set extracted from the original equipment data set that reflects the equipment's operating status and mechanical health characteristics. Specifically, based on the equipment's characteristics and testing requirements, key parameters that reflect the equipment's electrical performance, such as voltage, current, and frequency, are selected. Subsequently, representative features, such as mean, variance, peak value, and harmonic content, are extracted from the selected parameters. The electrical parameter feature set can contain statistical features or frequency domain features. Statistical features can include the mean and variance of voltage, and the peak value and effective value of current. Frequency domain features can include power spectral density and harmonic content, which are used to analyze the frequency components of electrical signals. Next, based on the equipment type and monitoring requirements, key parameters that reflect the equipment's operating status, such as vibration and acoustic emission, are selected. Representative features, such as the spectral characteristics of the vibration signal and the energy distribution of the acoustic emission signal, are extracted from the selected parameters. Among them, vibration characteristics can be vibration frequency, amplitude, phase, etc., which are used to evaluate the mechanical health status of the equipment; acoustic emission characteristics can be the energy, frequency, duration, etc. of the acoustic emission signal, which are used to detect internal damage of the equipment.

[0089] Based on the electrical parameter feature set and the device data feature set, determine whether there is an abnormal device status or electrical parameter abnormality. Specifically, collect the electrical parameter feature set and the device data feature set, such as key parameters such as voltage, current, power, frequency, vibration, and temperature. Extract representative features from the preprocessed data, such as mean, variance, peak, spectral characteristics, etc., and select key features for analysis based on the characteristics of the device and monitoring requirements. Compare the preset reasonable threshold range with the electrical parameter feature set and the device data feature set respectively to identify abnormal fluctuations or deviations from the normal range. Identifying abnormal fluctuations or deviations from the normal range can be done through cluster analysis, anomaly detection algorithms, etc., to identify abnormal patterns in the data, determine whether there is an abnormal device status or electrical parameter abnormality, and evaluate the degree of abnormality of the device status or electrical parameters based on the results of anomaly detection.

[0090] By using multiple types of sensors, we can obtain the electrical parameter sets and equipment data sets of the work equipment, and determine whether there are abnormal equipment status or electrical parameter abnormalities. This can more comprehensively reflect the electrical performance of the equipment, intuitively reflect the operating status and mechanical health characteristics of the equipment, promptly discover potential abnormalities or faults, take measures to eliminate safety hazards, and ensure the safe operation of the equipment.

[0091] In one implementation of this embodiment, the low-voltage electrical work safety test monitoring system further includes a camera and a wearable device, the abnormal operation further includes an abnormal operation of an operator, and the method further includes:

[0092] S310, obtaining a set of biometric data of the operator through a camera and a wearable device;

[0093] S320. Determine whether there is any abnormal operation by the person through the biometric data collection.

[0094] In this embodiment, the low-voltage electrical work safety test monitoring system also includes a camera and a wearable device. Specifically, the camera can capture real-time footage of the test site, and the wearable device can be a smart bracelet or other device. The camera and wearable device are used to obtain a set of biometric data from the operator. In this embodiment, the biometric data set includes electrocardiogram (ECG) signal characteristics, eye tracking characteristics, and motion trajectory characteristics. ECG signal characteristics refer to the waveform of electrical conduction generated by cardiac activity. In biometric recognition, ECG signals can also reflect heart health and are used for health monitoring and disease prevention to prevent health problems and potential accidents in operators. Eye tracking technology tracks eye movements by measuring the location of the eye's gaze point or the movement of the eyeball relative to the head. Eye tracking characteristics can reflect cognitive processes such as the operator's attention allocation and visual search strategy. In safety assessments, eye tracking characteristics can be used to determine whether the operator is paying attention and following correct operating procedures. Motion trajectory refers to the path of movement of the body or a part of the body during an action, characterized by form, direction, and amplitude. Motion trajectory characteristics can reflect the operator's movement habits, skill level, and other aspects. In safety assessment, by analyzing the operator's motion trajectory characteristics, we can determine whether his operation is standardized and whether there are potential safety hazards.

[0095] Subsequently, the system uses biometric data sets to determine whether any operator operational anomalies have occurred. Specifically, biometric data sets contain various biometric information about the operator, reflecting their physiological state, attention distribution, and motor habits. Key information reflecting the operator's behavioral characteristics is then extracted, such as the frequency of the ECG signal, the gaze trajectory of eye tracking, and the speed and amplitude of the movement trajectory. Machine learning and deep learning algorithms are then used to perform anomaly detection on these extracted features, identifying abnormal data that deviates significantly from normal patterns. Based on the output of the anomaly detection algorithm, the system determines whether the operator has engaged in abnormal behavior or improper operation.

[0096] In this embodiment, another implementation method for determining whether abnormal operation occurs by using an electrical parameter feature set, a device data feature set, and a biometric feature data set includes the following steps:

[0097] Construct a multi-source feature set by combining the electrical parameter feature set, the device data feature set, and the biometric data set;

[0098] Extract multidimensional features from multi-source feature sets;

[0099] Mapping multidimensional features into a three-order tensor, where the three-order tensor includes time dimension, space dimension and channel dimension;

[0100] Based on the third-order tensor, a three-dimensional security situation matrix is constructed, where the time dimension corresponds to the rows of the three-dimensional security situation matrix, the space dimension corresponds to the columns of the three-dimensional security situation matrix, and the channel dimension corresponds to the third dimension of the three-dimensional security situation matrix.

[0101] First, a multi-source feature set is constructed by combining the electrical parameter feature set, the device data feature set, and the biometric data set. In other words, the three feature sets are combined into a single multi-source feature set, and the feature vectors are concatenated. Because features from different data sources may have different dimensions and distributions, the multi-source feature set needs to be normalized to ensure that all features are on the same scale. This can be achieved through methods such as Z-score normalization and Min-Max normalization.

[0102] Secondly, the multidimensional features of the multi-source feature set are extracted, and the multidimensional features can be automatically extracted by a deep neural network (such as CNN). After obtaining the multidimensional features, the multidimensional features are mapped to a third-order tensor. In this embodiment, the third-order tensor includes a time dimension, a space dimension, and a channel dimension. The third-order tensor is a multidimensional array with three dimensions, which are defined as the time dimension, the space dimension, and the channel dimension. The time dimension represents the change of data over time; the space dimension represents the distribution of data in space; and the channel dimension represents the different attributes or channels of the data. Specifically, the multidimensional features are divided into the time dimension, the space dimension, and the channel dimension. This needs to be determined according to the specific application scenario and data characteristics. According to the result of the dimensional division, the multi-source feature set is reshaped into a third-order tensor, which can be implemented using some data processing tools or libraries, such as NumPy, Pandas, etc.

[0103] Subsequently, a three-dimensional security situation matrix is constructed based on the third-order tensor. The time dimension corresponds to the rows of the three-dimensional security situation matrix, the spatial dimension corresponds to the columns of the three-dimensional security situation matrix, and the channel dimension corresponds to the third dimension of the three-dimensional security situation matrix. In this embodiment, the time dimension represents the changes in security events or states over time. In a three-dimensional security situation matrix, the time dimension corresponds to the rows of the matrix, with each row representing the security situation at a specific time point or time period. The spatial dimension represents the distribution of security events or states in space. In the matrix, the spatial dimension corresponds to the columns of the matrix. Each column represents the security situation of a specific spatial location or region. The channel dimension represents the different attributes or types of security data. In a three-dimensional matrix, the channel dimension corresponds to the third dimension (depth or layer) of the matrix. Each layer represents a specific security attribute or data type, such as an intrusion detection system alert, network traffic statistics, or user behavior patterns. Next, the data is aggregated or interpolated as needed to match the dimensional requirements of the three-dimensional matrix. Specifically, a three-dimensional matrix is populated using a set of electrical parameter features, a set of device data features, and a set of biometric data. Each data point is placed in the corresponding position in the matrix based on the mapping relationship between time, space, and channels. If there is no data at a certain location (for example, no security incident occurred at a certain spatial location at a certain point in time), it can be marked with zero or a specific missing value.

[0104] Determining whether there is abnormal operation based on the electrical parameter feature set, the device data feature set, and the biometric feature data set also includes the following steps:

[0105] Based on the time dimension, the matrix elements in the three-dimensional security situation matrix are updated to provide real-time feedback on the matrix elements in the three-dimensional security situation matrix;

[0106] Using a preset tensor decomposition method, the updated three-dimensional security situation matrix is subjected to tensor decomposition to obtain a decomposed three-dimensional security situation matrix;

[0107] Using the preset filter, the data of the decomposed three-dimensional security situation matrix is detected for abnormal feature data, and the detected abnormal feature data is weighted and highlighted;

[0108] Determine the dynamic risk index through abnormal characteristic data;

[0109] Use the dynamic risk index to determine whether there are abnormal operations.

[0110] First, based on the time dimension, the matrix elements in the three-dimensional security situation matrix are updated to provide real-time feedback of the matrix elements in the three-dimensional security situation matrix. In this embodiment, the three-dimensional security situation matrix is a high-level data structure used to represent the network security situation. Specifically, the latest security data is collected from various security monitoring devices, systems, and logs, and the collected data is ensured to cover the three dimensions of time, space, and channels. A fixed time interval is set (such as every second, every minute, every hour, etc.), and the matrix elements are regularly updated according to this time interval. If the data before a certain point in time is outdated or invalid, it can be marked as expired or deleted.

[0111] Next, a preset tensor decomposition method is used to perform tensor decomposition on the updated three-dimensional security situation matrix, obtaining a decomposed three-dimensional security situation matrix. Tensor decomposition is a high-order data processing technique. In this embodiment, the preset tensor decomposition method may be CP decomposition, Tucker decomposition, or the like. Subsequently, the updated three-dimensional security situation matrix is used as input and the selected tensor decomposition method is applied. Through the decomposition process, the high-order security situation matrix is decomposed into multiple lower-order tensors or matrices, obtaining a decomposed three-dimensional security situation matrix. The decomposition result is typically a set of lower-order tensors or matrices that represent the information of the original high-order security situation matrix.

[0112] Using a preset filter, the decomposed three-dimensional security situation matrix data is subjected to anomaly feature detection and weighted emphasis on the detected anomaly features. Specifically, before performing anomaly feature detection, an appropriate filter must be selected based on the characteristics of the data and the needs of anomaly detection. For example, a median-based Hampel filter, a median filter, or other filters suitable for time series or multidimensional data can be used. Subsequently, the decomposed three-dimensional security situation matrix is used as input data for the filter. This data contains security information in multiple dimensions, such as time, space (e.g., network nodes), and security event types. The filter is applied to the input data to detect anomaly features. The filter identifies outliers by comparing the differences or statistical characteristics of a data point with those of surrounding data points. Next, the detected anomaly features are weighted to highlight their importance within the entire dataset. This weighting can be reflected in the visualization by increasing the weight of the outlier or changing its color or size.

[0113] A dynamic risk index is determined based on abnormal feature data. Specifically, in this embodiment, abnormal feature data refers to data points discovered during security monitoring or data analysis that significantly deviate from normal patterns or expected behaviors. The dynamic risk index is an indicator used to quantitatively assess the current security situation. It is calculated based on the analysis results of abnormal feature data and combined with a specific algorithm or model. The value of the dynamic risk index fluctuates with changes in the security situation, thereby reflecting the current security risk level. Machine learning algorithms, such as cluster analysis and classification algorithms, can be used to process abnormal feature data. A risk index model is constructed based on the processing results. The abnormal feature data is then input into the risk index model to obtain the dynamic risk index.

[0114] The Dynamic Risk Index is used to determine whether there are any abnormal operations. Specifically, a higher Dynamic Risk Index value indicates a greater current security risk and the possibility of abnormal operations or threats. Conversely, a lower Dynamic Risk Index value indicates a relatively stable security situation and a lower likelihood of abnormal operations.

[0115] Monitoring abnormal human operations through cameras and wearable devices, combined with biometric data sets, can not only improve the comprehensiveness and accuracy of monitoring, but also achieve real-time early warning and intervention, improve the security and fairness of examinations, and promote the development of intelligence and automation.

[0116] In one implementation of this embodiment, the electrical parameter anomaly includes a ground monitoring anomaly, a three-phase loss anomaly, and a safety voltage anomaly. The features of the electrical parameter set are extracted to obtain an electrical parameter feature set, including:

[0117] S410, using electrical parameter sensors, obtaining three-phase voltage, ground current value, and ground resistance value of the equipment in real time;

[0118] S420: Determine a three-phase loss abnormality based on the three-phase voltage;

[0119] S430, calculating the average resistance value and the standard deviation of the resistance within a preset time period based on the ground resistance value;

[0120] S440, calculating the current average value and current standard deviation within a preset time period based on the ground current value;

[0121] S450, extracting characteristics of the average resistance value and the standard deviation of the resistance, and determining characteristics of abnormal state and stable state of grounding resistance of the equipment;

[0122] S460, determining grounding monitoring abnormality based on the grounding resistance abnormal state characteristics and the grounding resistance stable state characteristics;

[0123] S470, extracting characteristics of the current average value and the current standard deviation, and determining characteristics of abnormal ground current state and stable ground current state of the equipment;

[0124] S480. Determine safety voltage abnormality based on the ground current abnormal state characteristics and the ground current stable state characteristics.

[0125] In this embodiment, electrical parameter anomalies include ground monitoring anomalies, three-phase loss anomalies, and safety voltage anomalies. A ground monitoring anomaly refers to an abnormal change in the grounding state in the electrical system, which may involve conditions such as excessive ground resistance, a broken ground wire, or poor grounding. A three-phase loss anomaly refers to a phenomenon in which one or more phases of a three-phase AC power system cannot supply power normally due to a fault. A safety voltage anomaly refers to a phenomenon in which the voltage value in the electrical system exceeds the safe range. A safety voltage is a voltage that will not directly cause death or disability. The "safety extra-low voltage" allowed for continuous contact under normal environmental conditions is 36V.

[0126] Extract the characteristics of the electrical parameter set to obtain the electrical parameter feature set. Specifically, first, the three-phase voltage, ground current value and ground resistance value of the equipment are obtained in real time through the electrical parameter sensor. In this embodiment, the three-phase voltage refers to the voltage value of each of the three phases A, B and C in the three-phase AC system; the ground current refers to the current flowing into the earth through the ground wire when the electrical equipment is working normally or malfunctioning; the ground resistance refers to the resistance value between the ground electrode of the electrical equipment and the earth. Specifically, through the voltage sensor, the sensor is connected to the three-phase power line of the equipment. The sensor can capture and convert the voltage signal in real time and output it as a readable numerical value or signal, thereby obtaining the three-phase voltage value in real time. Use a current sensor and connect it in series with the ground wire of the equipment. The sensor can measure the current in the ground wire in real time and output the corresponding current value or signal. The ground resistance value can be obtained by measuring the ground resistance value in real time or periodically.

[0127] After obtaining the three-phase voltage, ground current value, and ground resistance value, the three-phase phase loss anomaly is determined based on the three-phase voltage. By monitoring the voltage values of phases A, B, and C in the three-phase AC system, it is determined whether there is a voltage loss in one or more phases. Specifically, a three-phase phase loss anomaly refers to a phenomenon in which one or more phases in a three-phase AC system lose voltage due to some reason (such as line breakage, poor contact, power failure, etc.), resulting in a three-phase voltage imbalance. By using a voltage sensor or voltage transmitter, the voltage values of phases A, B, and C in the three-phase AC system can be monitored in real time. The monitored voltage values can be compared with the normal voltage values to observe whether there are significant differences, thereby determining whether there is a three-phase phase loss anomaly.

[0128] Next, the ground resistance value is used to calculate the average resistance value and the standard deviation of the resistance within the preset time period. That is, the arithmetic mean of all ground resistance measurements within the preset time period and the square root of the average of the squares of the differences between the ground resistance measurements and the average resistance value. To calculate the average resistance value, it is assumed that n ground resistance measurements are performed within the preset time period. The calculation formula for the average resistance value is:

[0129]

[0130] in, Represents the average resistance, R1, R2, ..., Rn are the resistance values of each measurement, and n is the number of measurements;

[0131] The formula for calculating the standard deviation of resistance is as follows:

[0132]

[0133] Among them, R i Indicates the resistance value measured each time. represents the average resistance, σ R represents the standard deviation of resistance;

[0134] Subsequently, the current average value and current standard deviation within the preset time period are calculated based on the ground current value. The current average value is the arithmetic mean of all ground current measurement values within the preset time period, reflecting the overall level or trend of the ground current within the time period; the current standard deviation measures the degree of dispersion between the ground current value and the average value, reflecting the fluctuation of the ground current within the preset time period.

[0135] Assuming that there are n ground current measurement values within a preset time period, recorded as I1, I2, ..., In, the calculation formula for the current average value is:

[0136]

[0137] in, Indicates the average current value, n indicates the number of times the current value is measured, I i represents n ground current measurement values, which are recorded as the sum of I1, I2, ..., In;

[0138] Next, we extract characteristics of the average resistance and standard deviation of resistance to determine the abnormal and stable ground resistance characteristics of the equipment. Specifically, under normal circumstances, the average ground resistance value should remain within a relatively stable range. This range is typically determined by factors such as the equipment design, installation environment, and surface conditions. If the average ground resistance value remains within this range for a long time, the grounding system can be considered stable. If the average ground resistance value suddenly deviates from the normal range, it may indicate a problem with the grounding system. For example, a significant increase in the average ground resistance value may indicate increased contact resistance between the ground electrode and the ground surface, or poor contact with the ground wire. Similarly, under stable conditions, the standard deviation of ground resistance should be small, indicating that the measured ground resistance values are relatively concentrated with minimal fluctuation, indicating stable grounding system performance. However, a significant increase in the standard deviation of ground resistance indicates that the measured ground resistance values are relatively dispersed with significant fluctuation, indicating that the grounding system is subject to external interference, such as changes in surface humidity or corrosion of the ground electrode, leading to unstable ground resistance. In other words, if the average resistance value deviates from the normal range and the standard deviation increases significantly, the grounding system is considered abnormal and requires further inspection and repair. If the average resistance value is within the normal range and the standard deviation is small, the grounding system is considered stable. Feature extraction can be performed using machine learning, deep learning, and other algorithms to extract abnormal and stable grounding resistance characteristics of equipment.

[0139] Ground monitoring anomalies are identified by analyzing the abnormal and stable ground resistance state characteristics. Specifically, when the ground resistance exceeds the normal range, abnormal state characteristics will appear, such as a significant increase in ground resistance or electrical equipment abnormalities. A significant increase in ground resistance may be due to damage to the grounding device, insufficient surface moisture, or a fault in the grounding line. Electrical equipment abnormalities may be caused by equipment overload or abnormal power-on / off operation, which may be related to increased ground resistance. When the ground resistance is stable and within the normal range, the equipment meets the power system's ground resistance requirements and can effectively conduct fault currents. Furthermore, the electrical equipment is operating normally, without any issues such as overload or abnormal power-on / off operation. In other words, by comparing and analyzing the abnormal ground resistance state characteristics with the stable ground resistance state characteristics, it is possible to determine whether ground monitoring anomalies exist.

[0140] After determining ground monitoring anomalies, the characteristics of the current average and current standard deviation are extracted to determine the abnormal and stable ground current characteristics of the equipment. In this embodiment, the current average refers to the sum of the current measurements over a period of time divided by the number of measurements, reflecting the average level or central trend of the current. The current standard deviation refers to the square root of the average of the sum of the squares of the deviations of the current measurements from the average, reflecting the dispersion or fluctuation range of the current measurements. When the average ground current value deviates significantly from the normal range, it may indicate an abnormality in the grounding system. For example, a sudden increase in the average ground current value may indicate a decrease in ground resistance, a short circuit in the grounding line, or equipment leakage. If the standard deviation of the ground current increases, the fluctuation range of the current value increases, which may indicate unstable factors in the grounding system. For example, ground resistance fluctuations caused by poor grounding line contact, aging grounding equipment, or environmental factors (such as changes in surface humidity). When the current average value is within the normal range, it indicates that the average ground current value remains within the normal range, indicating that the grounding system is stable. When the current standard deviation is small, it indicates that the standard deviation of the ground current is small, indicating that the fluctuation range of the current value is small and the grounding system is relatively stable. Extracting the characteristics of the current average value and current standard deviation can be achieved through machine learning, deep learning and other algorithms, thereby determining the abnormal state characteristics and stable state characteristics of the grounding current of the equipment.

[0141] Subsequently, safety voltage anomalies are determined based on the abnormal ground current state characteristics and the stable ground current state characteristics. In this embodiment, the abnormal ground current state characteristic refers to the ground current of the electrical equipment deviating from the normal range, which may manifest as excessively high or low current values or large current fluctuations. The stable ground current state characteristic refers to the ground current of the electrical equipment fluctuating within the normal range, with a small fluctuation range and remaining relatively stable. The safety voltage anomaly refers to the operating voltage of the electrical equipment exceeding the safe voltage range. Specifically, when a ground fault occurs in electrical equipment, the ground current will increase significantly, and may also cause the operating voltage of the equipment to change. If this change exceeds the safe voltage range, it may pose a danger to humans. For example, in a low-voltage power distribution system, if a single-phase ground fault occurs, the voltage of the non-fault phase may increase, exceeding the safe voltage limit. In other words, by comparing and analyzing the abnormal ground current state characteristics with the stable ground current state characteristics, it is possible to determine whether the voltage is within the safe range.

[0142] By acquiring and analyzing electrical parameters in real time, abnormal conditions in the electrical system can be promptly detected and addressed, ensuring safe, stable, and efficient operation of the electrical system. This is of great significance for improving equipment reliability, reducing maintenance costs, and protecting personnel safety.

[0143] In one implementation of this embodiment, the three-phase voltage includes a first phase voltage, a second phase voltage, and a third phase voltage. Determining a three-phase loss abnormality based on the three-phase voltage includes:

[0144] S510, using a preset phase shift circuit, respectively delaying the first phase voltage, the second phase voltage, and the third phase voltage by a preset phase angle to obtain a first delayed phase voltage, a second delayed phase voltage, and a third delayed phase voltage;

[0145] S520, adding the first phase voltage to the second delayed phase voltage, adding the second phase voltage to the third delayed phase voltage, and adding the third phase voltage to the first delayed phase voltage to obtain a first voltage difference, a second voltage difference, and a third voltage difference, respectively;

[0146] S530, rectifying the first voltage difference, the second voltage difference, and the third voltage difference to obtain a first rectified voltage, a second rectified voltage, and a third rectified voltage;

[0147] S540, determining whether the first rectified voltage, the second rectified voltage, and the third rectified voltage are zero;

[0148] S550: If at least one of the first rectified voltage, the second rectified voltage, and the third rectified voltage is zero, determine that a three-phase loss abnormality occurs.

[0149] Figure 2 The waveform diagram of the first phase voltage and the second phase voltage before and after phase shifting provided in the embodiment of the present application is as follows: Figure 2 As shown, the first phase voltage and the second phase voltage are divided into phases that are delayed by 60° using a phase shift circuit; Figure 3 The waveform diagram of the first phase voltage and the third phase voltage before and after phase shifting provided in the embodiment of the present application is as follows: Figure 3 As shown, the first phase voltage and the third phase voltage are divided into phases with a phase angle of 60° delayed by a phase shift circuit.

[0150] In this embodiment, the three-phase voltage includes a first-phase voltage, a second-phase voltage, and a third-phase voltage, wherein the first-phase voltage corresponds to phase A in the three-phase electrical system; the second-phase voltage corresponds to phase B in the three-phase electrical system; and the third-phase voltage corresponds to phase C in the three-phase electrical system.

[0151] A preset phase-shifting circuit is used to delay the first phase voltage, the second phase voltage, and the third phase voltage by a preset phase angle, respectively, to obtain a first delayed phase voltage, a second delayed phase voltage, and a third delayed phase voltage. In this embodiment, the preset phase-shifting circuit is used to change the phase angle of alternating current. Specifically, the first phase voltage is input into the preset phase-shifting circuit, which configures the first phase voltage according to the desired phase delay angle. The phase delay angle can be determined based on actual conditions. In this embodiment, the phase delay angle can be 60°. That is, by adjusting the inductance or capacitance value in the circuit, the output voltage (i.e., the first delayed phase voltage) lags the input voltage by a phase angle of 60°. Similarly, the second phase voltage is input into another preset phase-shifting circuit, and according to the same principle, the circuit parameters are adjusted to obtain the desired phase delay, outputting a second delayed phase voltage. The third phase voltage is also processed in the same way, and a third preset phase-shifting circuit is used to output a third delayed phase voltage. This voltage lags the specified phase angle relative to the original third phase voltage. In this embodiment, the phase angle delayed by the first, second, and third phase voltages is 60°.

[0152] Next, the first phase voltage is added to the second delayed phase voltage, the second phase voltage is added to the third delayed phase voltage, and the third phase voltage is added to the first delayed phase voltage, respectively, to obtain a first voltage difference, a second voltage difference, and a third voltage difference. Specifically, the addition of the first phase voltage and the second delayed phase voltage can be implemented using an adder circuit. An adder circuit refers to an electronic circuit that performs the addition operation of two or more input signals. In other words, the delayed voltage is superimposed on the previous phase voltage using an adder circuit. For example, Ua and Ub' are superimposed to obtain Uab, where Ua represents the first phase voltage; Ub' represents the voltage of the second phase voltage after being processed by the delay circuit; and Uab represents the first voltage difference. Ua and Ub' are added using the adder circuit, and the resulting sum is recorded as Uab. In this embodiment, the first voltage difference refers to the voltage sum obtained by adding the first phase voltage Ua to the second phase voltage Ub' after being processed by the delay circuit using the adder circuit. Similarly, an adder circuit is used to add the second phase voltage and the third delayed phase voltage to obtain a second voltage difference. In this embodiment, the second voltage difference refers to the voltage sum obtained by adding the second phase voltage and the third delayed phase voltage processed by the delay circuit using the adder circuit. For example, Ub and Uc' are superimposed to obtain Ubc, where Ub represents the second phase voltage and Uc' represents the voltage after the third phase voltage is processed by the delay circuit. The resulting sum is denoted as Ubc. In this embodiment, the second voltage difference refers to the voltage sum obtained by adding the second phase voltage Ub and the third delayed phase voltage Uc' using the adder circuit. Similarly, adding the third phase voltage and the first delayed phase voltage can be implemented using an adder circuit. That is, the adder circuit is used to superimpose the delayed voltage with the previous phase voltage. For example, Uc and Ua' are superimposed to obtain Uca, where Uc represents the third phase voltage and Ua' represents the voltage after the first phase voltage is processed by the delay circuit. Uc and Ua' are added using the adder circuit, and the resulting sum is denoted as Uca. In this embodiment, the third phase voltage is obtained by adding the third phase voltage Uc and the first delayed phase voltage Ua′ through an adder circuit.

[0153] Subsequently, the first voltage difference, the second voltage difference, and the third voltage difference are rectified to obtain a first rectified voltage, a second rectified voltage, and a third rectified voltage. In this embodiment, the first rectified voltage refers to a DC voltage obtained by rectifying the first voltage difference; the second voltage difference refers to a DC voltage obtained by rectifying the second voltage difference; and the third rectified voltage refers to a DC voltage obtained by rectifying the third voltage difference. Specifically, the first voltage difference, the second voltage difference, and the third voltage difference are rectified using a preset rectifier circuit. The rectifier circuit is a circuit that converts alternating current (AC) into direct current (DC), using nonlinear elements such as diodes to achieve unidirectional conductivity, thereby converting the AC voltage into a unidirectional pulsating DC voltage. The rectifier circuit in this embodiment can be a full-wave rectifier bridge, which can convert AC voltage into a full-wave DC voltage. It is composed of four diodes and can convert AC voltage into DC voltage with higher efficiency, and the output waveform is smoother. The first voltage difference Uab, the second voltage difference Ubc and the third voltage difference Uca are used as input voltages. Specifically, Uab is input to the input end of the full-wave rectifier bridge respectively, and the output end of the rectifier bridge will output the first rectified voltage Vab; similarly, Ubc and Uca are input to the input ends of the other two full-wave rectifier bridges respectively to obtain the second rectified voltage Vbc and the third rectified voltage Vca.

[0154] After obtaining the first, second, and third rectified voltages, a comparator circuit can be used to determine whether the first, second, and third rectified voltages are zero. A comparator circuit is an electronic circuit whose core function is to compare two input voltages and output a logic level signal representing "high" or "low" based on the comparison result. Specifically, a comparator circuit is set for each rectified voltage (Vab, Vbc, and Vca). The comparator's reference voltage is set to a threshold voltage very close to 0. To account for non-ideal factors in the rectifier circuit and power supply, the threshold voltage can be slightly adjusted as needed to ensure accuracy. If the rectified voltage is less than the threshold voltage, the comparator outputs a high level; if the rectified voltage is greater than the threshold voltage, the comparator outputs a low level. Subsequently, the outputs of the three comparators are combined using a logic gate circuit. If the outputs of all comparators indicate that the rectified voltage is close to 0 (i.e., all output high levels or all output low levels, depending on the configuration of the comparators), the logic gate circuit outputs a signal indicating that the phase sequence is correct; if the output of any comparator indicates that the rectified voltage is not 0, the logic gate circuit outputs a signal indicating that the phase sequence is wrong or there is a phase loss, so that it can be determined whether the voltages of the first rectified voltage, the second rectified voltage, and the third rectified voltage are zero.

[0155] If at least one of the first rectified voltage, the second rectified voltage and the third rectified voltage is zero, a three-phase phase loss abnormality is determined. That is to say, under normal circumstances, if the three-phase system is balanced and the phase sequence is correct, each rectified voltage should have a non-zero voltage value. If one of the rectified voltages is monitored to be zero or close to zero, it may indicate that there is a problem with the corresponding power supply or line.

[0156] By utilizing a preset phase-shift circuit, calculating the voltage difference, rectifying, and determining whether the rectified voltage is zero, a three-phase loss anomaly is determined. This not only improves the accuracy of phase loss detection and the robustness of the system, but also achieves a quick response and reduces maintenance costs.

[0157] In one implementation of this embodiment, when an abnormal operation occurs, issuing an alarm message includes:

[0158] S610: If the abnormal operation is any one of abnormal human operation, abnormal equipment status, and abnormal electrical parameters, a level 1 alarm message is issued;

[0159] S620: If the abnormal operation is any two of abnormal human operation, abnormal equipment status, and abnormal electrical parameters, a level 2 alarm message is issued;

[0160] S630: When the abnormal operation is caused by abnormal human operation, abnormal equipment status, and abnormal electrical parameters, a third-level alarm message is issued.

[0161] In the event of abnormal operation, an alarm message is issued. Specifically, first, when the abnormal operation is any one of abnormal human operation, abnormal equipment status, and abnormal electrical parameters, a level one alarm message is issued. In this embodiment, abnormal human operation refers to abnormal conditions caused by the operator's erroneous behavior or improper operation, such as incorrect operation of a switch, failure to follow operating procedures, etc.; abnormal equipment status refers to abnormal conditions caused by failure or performance degradation of the equipment itself, such as motor overheating, transformer oil leakage, circuit breaker jitter, etc.; abnormal electrical parameters refer to abnormal conditions caused by deviation of the electrical parameters of the power system from the normal range, such as voltage fluctuation, current imbalance, frequency offset, etc. In other words, when any of the abnormal conditions of abnormal human operation, abnormal equipment status, and abnormal electrical parameters occurs, a level one alarm message will be issued. The level one alarm message indicates that an abnormal condition that requires immediate attention has occurred, and the abnormal device can be marked by displaying a semi-transparent color block in the visual layer of the electronic device.

[0162] A Level 2 alarm is issued when any two of the following abnormal operations are detected: human operator error, abnormal equipment status, or abnormal electrical parameters. This indicates a more serious abnormality. When a Level 2 alarm is issued, a flashing red frame will be displayed in the electronic device visualization layer, indicating the abnormal device. Voice prompts will also be added, such as "Stop operation!" or "Left cable overload."

[0163] When abnormal operations occur, including abnormal human operation, abnormal device status, and abnormal electrical parameters, a level 3 warning message is issued. That is, when all three abnormal conditions occur, a level 3 warning message is issued. In this embodiment, a level 3 warning message indicates an extremely serious abnormality that may cause significant damage to the entire system or device. When a level 3 warning message appears, a full-screen mask can be applied to the visual layer of the electronic device. Full-screen masking refers to overlaying a semi-transparent or opaque background on the user interface to block other content, focusing the user's attention on the current task or prompt. The abnormal device is marked with a striking color (such as red or orange). A semi-transparent mask (such as black or gray) can retain some background visibility, while an opaque mask completely blocks the background. A pulsing navigation arrow can also be added to alert staff members. A pulsing navigation arrow is a dynamic visual element that attracts user attention through periodic changes (such as size, color, or transparency) and guides users to complete specific operations. For example, it can clearly indicate the operation a staff member needs to perform (such as clicking a button to indicate that the staff member has confirmed the abnormal device).

[0164] By implementing a hierarchical alarm mechanism, abnormal operations can be handled more efficiently, response speed and accuracy can be improved, resource allocation can be optimized, system stability can be enhanced, security management level can be improved, and continuous improvement can be promoted.

[0165] In one implementation of this embodiment, locating the type of equipment corresponding to the abnormal operation includes:

[0166] S710. Obtain the time when the abnormal operation occurs;

[0167] S720: Time-match the time when the abnormal operation occurred, the device data, and the operator's biometric data to obtain a time matching result;

[0168] S730, spatially matching the device data and the operator's biometric data to obtain a spatial matching result;

[0169] S740: Determine the type of work equipment corresponding to the abnormal operation by combining the time matching result and the space matching result.

[0170] First, the time when the abnormal operation occurred can be obtained by checking the log of the abnormal operation recorded by the low-voltage electrical work safety examination monitoring system. The log of the abnormal operation includes the timestamp of the occurrence. Secondly, the time when the abnormal operation occurred, the device data and the biometric data of the operator are time-matched to obtain the time matching result. Specifically, first, it is necessary to ensure that the abnormal operation record, device data record and biometric data record all contain accurate timestamps. Then, by comparing these timestamps, find the device data and biometric data closest to the time when the abnormal operation occurred. Due to the influence of various factors (such as data transmission delay, device response time, etc.), there may be slight differences between the timestamps. Therefore, a reasonable time window can be set, and the device data and biometric data occurring within this time window are regarded as related to the abnormal operation. Through the time window, the time when the abnormal operation occurred within the time window, the device data and the biometric data of the operator are time-matched. Among them, these data contain timestamp information for subsequent time matching, thereby obtaining the time matching result.

[0171] After obtaining the temporal matching results, the device data and the operator's biometric data are spatially matched to obtain a spatial matching result. Spatial matching refers to the alignment and association of data with spatial attributes (such as the device's location data and the location information in the operator's biometric data) to reveal the spatial relationship between them. Specifically, the device data and the operator's biometric data are collected and ensured to contain spatial attributes (such as coordinate information, address descriptions, etc.). Based on the spatial attributes of the data, the device data and the operator's biometric data are spatially aligned. An appropriate spatial matching algorithm, such as coordinate-based matching or description-based matching, can be selected to achieve this. The selected matching algorithm is then applied to spatially match the device data and the operator's biometric data. In this embodiment, a data association algorithm can also be used to further determine which device data and biometric data are most relevant to abnormal operations, thereby associating the data. Subsequently, the correlation between the device data and biometric data and the abnormal operations is analyzed. For example, the device data can be checked to see if it indicates an abnormal state (such as excessive temperature or excessive current), and the biometric data can be checked to see if the operator exhibits abnormal behavior (such as abnormal operating patterns or abnormal physiological reactions). Based on the results of time matching and data correlation analysis, the possible causes of abnormal operations can be inferred. For example, if the device data shows an abnormal state and the biometric data shows abnormal behavior such as operator fatigue or distraction, it can be inferred that the abnormal operation is caused by device failure or operator inattention.

[0172] Combining the time and space matching results, determine the type of work and equipment corresponding to the abnormal operation. Specifically, conduct a comprehensive analysis of the time and space matching results to identify the equipment data and biometric data that meet both the time and space conditions. This data will serve as the key basis for determining the type of work and equipment corresponding to the abnormal operation. Based on this comprehensive data, identify the type, model, and location of the equipment associated with the abnormal operation. This can be achieved by consulting the equipment database and comparing equipment identification information. Subsequently, based on the type, model, and purpose of the equipment, determine the type of work associated with the abnormal operation. Finally, confirm and verify the determined type of work and equipment to ensure the accuracy and reliability of the results. This can be achieved by communicating with on-site operators, equipment management personnel, or production process experts, and comparing with actual on-site conditions.

[0173] By locating the equipment corresponding to abnormal operations, we can significantly improve positioning accuracy, enhance anomaly detection capabilities, optimize safety management and decision-making, and improve data analysis and processing capabilities, which will help improve the efficiency and level of safety management.

[0174] In one implementation of this embodiment, the time when the abnormal operation occurred, the device data, and the operator's biometric data are time-matched to obtain a time matching result, including:

[0175] S810, obtaining a timestamp of a biometric data set and device data;

[0176] S820: Use the timestamp of the abnormal operation as the master clock, and the timestamps of the biometric data set and the device data as the slave clocks.

[0177] S830. Send the timestamp of the master clock to the slave clock using a preset network time synchronization protocol.

[0178] S840. Obtain a timestamp of network delay;

[0179] S850 : Adjust the timestamp of the slave clock using the timestamp of the master clock and the timestamp of the network delay to obtain a time matching result.

[0180] First, obtain the timestamps of the biometric data set and the device data. This can be done by searching for records containing the biometric data and the device data in the low-voltage electrical work safety test monitoring system. These records should contain a timestamp associated with each data point, indicating the time when the data was recorded. The biometric data set, as the name implies, is a collection of biometric data. In this embodiment, the biometric data can be electrocardiogram (ECG) signal feature data, eye tracking feature data, and motion trajectory feature data. ECG signal refers to the electrical signal generated by the heart during beating; eye tracking feature data refers to tracking and recording the eye movement trajectory through a specific device (such as an eye tracker); motion trajectory feature data refers to the path formed when a human body or object moves in space.

[0181] Secondly, the timestamp of the abnormal operation is used as the master clock, and the timestamps of the biometric data set and device data are used as slave clocks. In other words, the timestamp of the abnormal operation is set as the master clock, indicating that the time of the abnormal operation is regarded as the reference time, and the timestamps of other data will be synchronized or compared with it. The role of the master clock is to provide a unified time reference point for the entire system or data set, facilitating subsequent data analysis and troubleshooting. At the same time, the timestamps of the biometric data set and device data are set as slave clocks. The timestamps of these data will be used to synchronize or compare with the master clock (the timestamp of the abnormal operation) to ensure data consistency and accuracy. The role of the slave clock is to record the time when the biometric data and device data were generated, making it easier to associate this data with the abnormal operation in subsequent analysis.

[0182] Subsequently, the master clock's timestamp is sent to the slave clock using a preset network time synchronization protocol. The network time synchronization protocol is a protocol used to synchronize computer system clocks within a network. It enables computers within the network to calibrate their system clocks through network communication, ensuring that all system clocks remain highly consistent. In this embodiment, the preset network time synchronization protocol can be the NTP protocol, a protocol used to synchronize computer clocks. Specifically, first, the master clock is configured with the relevant parameters of the network time synchronization protocol, including the server address, port number, and synchronization interval, to ensure that the master clock can send timestamp information. Second, the slave clock is configured with the client parameters of the network time synchronization protocol, including the server address, port number, and synchronization policy, to enable it to receive and apply timestamp information from the master clock. Once the configuration is complete, the slave clock will begin attempting to communicate with the master clock and synchronize time. This typically involves sending a message requesting a timestamp to the master clock and receiving the timestamp information returned by the master clock.

[0183] To obtain network latency timestamps, first select an appropriate measurement method based on the application scenario and requirements, such as the Ping command. Record the send timestamp when sending a data packet and the receive timestamp when receiving a data packet. Ensure the accuracy and synchronization of the timestamps. Calculate network latency based on the recorded timestamps. For round-trip time (RTT), subtract the send timestamp from the receive timestamp. For one-way time, more complex measurement and calculation methods may be required. Analyze the calculated network latency data to evaluate network performance and stability. Generate reports or charts to visualize the latency data as needed.

[0184] After obtaining the network delay timestamp, the slave clock's timestamp is adjusted using the master clock's timestamp and the network delay timestamp to achieve a time matching result. Specifically, the network delay period is measured by sending and receiving data packets, recording the send and receive timestamps of the data packets and calculating the difference between them. The calculated adjustment amount is then added to the slave clock's timestamp to obtain the adjusted timestamp. This adjusted timestamp should be closer to the master clock's timestamp. The slave clock's timestamp is then adjusted based on this adjusted timestamp, achieving time synchronization. The time matching result refers to the degree of match between the slave clock's timestamp and the master clock's timestamp after the adjustment process.

[0185] By time-matching the time when abnormal operations occurred, device data, and the operator's biometric data, a high-precision association is achieved between the time when abnormal operations occurred, device data, and the operator's biometric data. This not only improves the accuracy and relevance of data analysis, but also provides stronger security compliance and decision-making support capabilities.

[0186] In one implementation of this embodiment, spatial matching is performed on the device data and the operator's biometric data to obtain a spatial matching result, including:

[0187] S910. Use any type of equipment as the origin to construct a coordinate system;

[0188] S920, mapping multiple types of sensors, cameras, wearable devices, and operators into a coordinate system;

[0189] S930, obtaining the preset coordinates of the QR code preset in the coordinate system;

[0190] S940: Using a camera to measure the coordinates of the QR code in the preset coordinate system, and correcting the preset coordinates using the measured coordinates to obtain a corrected coordinate system;

[0191] S950, converting the device data and the operator's biometric data into a calibrated coordinate system;

[0192] S960. In the calibrated coordinate system, use a preset spatial registration algorithm to perform spatial synchronization processing on the device data and the operator's biometric data to obtain a spatial matching result.

[0193] Spatial matching is performed between the equipment data and the operator's biometric data to obtain a spatial matching result. Specifically, a coordinate system is constructed, using any type of equipment as the origin. First, a specific point on the selected equipment is selected as the origin of the coordinate system. Subsequently, a mathematical model is established to describe the coordinate system based on the equipment's geometry and operational requirements. This model can include information such as the equipment's size, shape, and positional relationships. The mathematical model and measurement tools (such as a coordinate measuring machine) are then used to determine the coordinate values of each point within the work area within the coordinate system.

[0194] Subsequently, multiple types of sensors, cameras, wearable devices, and operators are mapped into coordinate systems. For multiple types of sensors, their data is converted into a unified coordinate system based on their installation location and orientation. The spatial position of the camera in the 3D layout is mapped into the coordinate system, and the wearable device is mapped into the unified coordinate system.

[0195] Obtain the preset coordinates of the preset QR code in the coordinate system. In this embodiment, the preset QR code usually determines its position in a specific coordinate system. If the QR code has been clearly marked with its coordinates in the coordinate system when it was set, then this information can be directly read.

[0196] The camera measures the coordinates of the QR code in a preset coordinate system. The preset coordinates are then corrected using the measured coordinates to obtain a corrected coordinate system. Specifically, a camera captures an image of a scene containing a QR code, and image processing algorithms (such as edge detection and contour extraction) are used to identify the QR code's position. The QR code's unique features (such as position detection patterns and correction patterns) are then used for precise positioning. Based on the parameters obtained from camera calibration and the QR code's position in the image, a 3D reconstruction algorithm is used to convert the QR code's image coordinates into measured coordinates in 3D space. The measured QR code coordinates are then compared with the preset coordinates. The preset coordinates are typically determined based on actual needs during QR code deployment. The error between the measured and preset coordinates is calculated. Based on the error analysis results, the preset coordinates are corrected using an appropriate correction algorithm (such as the least squares method or iterative closest point algorithm). The goal of the correction is to make the preset coordinates as consistent as possible with the measured coordinates, thereby obtaining a corrected coordinate system.

[0197] Subsequently, the device data and the operator's biometric data are converted to a calibrated coordinate system. Specifically, the device data and the operator's biometric data undergo spatial information conversion. First, the spatial location information (e.g., coordinates) of the device corresponding to the electrical parameter data is determined. Then, based on the device's spatial location information, the electrical parameter data is converted to a unified spatial reference. This can be achieved using a geographic information system (GIS) or related spatial data processing software. The operator's biometric data is converted to the calibrated coordinate system. For three-dimensional biometric data such as facial recognition, three-dimensional reconstruction technology can be used to convert data from different perspectives to the same perspective or coordinate system. For two-dimensional biometric data such as fingerprints and irises, image registration, affine transformation, and other methods can be used to perform spatial information conversion. Next, the spatial location information (e.g., coordinates) of the device corresponding to the device status data is determined. Then, based on the device's spatial location information, the spatial information in the device status data is converted to a unified spatial reference. This can be achieved using a geographic information system (GIS) or related spatial data processing software.

[0198] Finally, in the calibrated coordinate system, a preset spatial registration algorithm is used to perform spatial synchronization processing on the device data and the operator's biometric data to obtain a spatial matching result. In this embodiment, the preset spatial registration algorithm can be selected based on the specific application scenario and data characteristics. The preset spatial registration algorithm can be SIFT (Scale Invariant Feature Transform), which is a classic image feature extraction algorithm. Specifically, the preset spatial registration algorithm is used to convert the device data and biometric data into the calibrated coordinate system. Based on the spatial registration, the device data and biometric data are matched, including calculating the spatial relationship between the two, such as distance and angle, to determine whether they are in the same spatial location or nearby.

[0199] By monitoring and matching device data with operator biometric data in real time and performing spatial matching, devices can be more accurately identified and operated, improving work efficiency and response speed. Potential safety hazards and abnormal behavior can also be discovered in a timely manner, allowing appropriate measures to be taken to prevent and address them.

[0200] The present application also provides an electronic device, comprising:

[0201] a memory configured to store instructions; and

[0202] The processor is configured to call instructions from the memory and implement the above-mentioned low-voltage electrical work safety intelligent early warning method based on multi-dimensional data when executing the instructions.

[0203] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0204] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0205] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0206] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0207] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0208] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0209] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0210] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0211] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included within the scope of the claims of the present application.

Claims

1. A low-voltage electrical work safety intelligent early warning method based on multi-dimensional data, characterized in that: Applied to a low-voltage electrical work safety test monitoring system, the low-voltage electrical work safety test monitoring system includes multiple types of sensors, cameras, and wearable devices. The method includes: In response to a start instruction, obtaining a three-dimensional layout diagram of a low-voltage electrical work area; Determine the location of each type of equipment in the low-voltage electrical work area based on the three-dimensional layout diagram, and acquire equipment data of each type of equipment in real time through the multi-type sensors; determining whether there is abnormal operation based on the device data; In the event of abnormal operation, an alarm message will be issued and the equipment corresponding to the abnormal operation will be located; Sending the alarm information to the type of equipment so that the type of equipment can visualize the alarm information; The abnormal operation includes abnormal equipment status of the equipment and abnormal electrical parameters of the equipment.

2. The method according to claim 1, characterized in that The multi-type sensors include an electrical parameter sensor and a device status sensor, the device data include electrical parameter data and device data, and determining whether there is an abnormal operation based on the device data includes: Acquire, through the multi-type sensors, a set of electrical parameters of the type of equipment and a set of equipment data of the type of equipment; Extracting features of the electrical parameter set and the device data set respectively to obtain an electrical parameter feature set and a device data feature set; Based on the electrical parameter feature set and the device data feature set, it is determined whether there is a device state abnormality or an electrical parameter abnormality.

3. The method according to claim 2, characterized in that The low-voltage electrical work safety examination monitoring system further includes a camera and a wearable device, the abnormal operation further includes an abnormal operation of an operator, and the method further includes: Acquiring a set of biometric data of an operator through the camera and the wearable device; Determine whether there is any abnormal operation by the personnel through the biometric data set.

4. The method according to claim 2, characterized in that The electrical parameter anomaly includes ground monitoring anomaly, three-phase loss anomaly and safety voltage anomaly. The extracting the features of the electrical parameter set to obtain the electrical parameter feature set includes: The electrical parameter sensor is used to obtain the three-phase voltage, ground current value and ground resistance value of the equipment in real time; Determining the three-phase loss abnormality based on the three-phase voltage; Calculating the average resistance value and the standard deviation of the resistance within a preset time period based on the ground resistance value; Calculating the current average value and current standard deviation within a preset time period based on the ground current value; Extracting characteristics of the resistance average value and the resistance standard deviation, and determining abnormal state characteristics and stable state characteristics of the grounding resistance of the equipment; Determining grounding monitoring abnormality based on the grounding resistance abnormal state characteristics and the grounding resistance stable state characteristics; Extracting characteristics of the current average value and the current standard deviation, and determining abnormal state characteristics and stable state characteristics of the grounding current of the equipment; The safety voltage abnormality is determined based on the ground current abnormal state characteristics and the ground current stable state characteristics.

5. The method according to claim 4, characterized in that The three-phase voltage includes a first phase voltage, a second phase voltage, and a third phase voltage. The determining the three-phase loss abnormality based on the three-phase voltage includes: Using a preset phase shift circuit, the first phase voltage, the second phase voltage, and the third phase voltage are respectively delayed by a preset phase angle to obtain a first delayed phase voltage, a second delayed phase voltage, and a third delayed phase voltage; Adding the first phase voltage to the second delayed phase voltage, adding the second phase voltage to the third delayed phase voltage, and adding the third phase voltage to the first delayed phase voltage to obtain a first voltage difference, a second voltage difference, and a third voltage difference, respectively; rectifying the first voltage difference, the second voltage difference, and the third voltage difference to obtain a first rectified voltage, a second rectified voltage, and a third rectified voltage; determining whether the first rectified voltage, the second rectified voltage, and the third rectified voltage are zero; If at least one of the first rectified voltage, the second rectified voltage, and the third rectified voltage is zero, it is determined that the three-phase loss is abnormal.

6. The method according to claim 1, characterized in that In the event of abnormal operation, the alarm information is issued, including: When the abnormal operation is any one of abnormal human operation, abnormal equipment status and abnormal electrical parameters, a first-level alarm message is issued; In the event that the abnormal operation is any two of the abnormal operation by the personnel, the abnormal state of the equipment, and the abnormal electrical parameters, a secondary alarm message is issued; In the case where the abnormal operation is the abnormal operation of the personnel, the abnormal state of the equipment and the abnormal electrical parameters, a third-level alarm message is issued.

7. The method according to claim 1, characterized in that The types of equipment corresponding to the positioning and abnormal operation include: Obtaining the time when the abnormal operation occurs; Performing time matching on the time when the abnormal operation occurs, the device data, and the biometric data of the operator to obtain a time matching result; spatially matching the device data with the biometric data of the operator to obtain a spatial matching result; The type of equipment corresponding to the abnormal operation is determined by combining the time matching result and the space matching result.

8. The method according to claim 7, characterized in that The time matching of the abnormal operation occurrence time, the device data, and the operator's biometric data to obtain a time matching result includes: Obtaining a timestamp of the biometric data set and the device data; Using the timestamp of the abnormal operation as a master clock, and using the timestamps of the biometric data set and the device data as slave clocks; Using a preset network time synchronization protocol, sending the timestamp of the master clock to the slave clock; Get the timestamp of network delay; The timestamp of the slave clock is adjusted according to the timestamp of the master clock and the timestamp of the network delay to obtain a time matching result.

9. The method according to claim 7, characterized in that The spatial matching of the device data and the biometric data of the operator to obtain a spatial matching result includes: Taking any one of the above mentioned equipment as the origin, a coordinate system is constructed; Mapping the multiple types of sensors, the camera, the wearable device, and the operator into the coordinate system; Obtaining the preset coordinates of the QR code preset in the coordinate system; Measuring the measurement coordinates of the QR code preset in the coordinate system using the camera, and correcting the preset coordinates using the measured coordinates to obtain a corrected coordinate system; converting the device data and the operator's biometric data into the calibrated coordinate system; In the corrected coordinate system, a preset spatial registration algorithm is used to perform spatial synchronization processing on the device data and the operator's biometric data to obtain a spatial matching result.

10. An electronic device, characterized in that: include: a memory configured to store instructions; as well as The processor is configured to call the instruction from the memory and implement the low-voltage electrical work safety intelligent early warning method based on multi-dimensional data according to any one of claims 1 to 9 when executing the instruction.

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