Intelligent and safe electricity utilization safety monitoring system

Abstract

The invention discloses an intelligent and safe electricity utilization safety monitoring system which comprises an intelligent sensing layer, a network transmission layer, a cloud processing layer and a user interaction layer. The intelligent sensing layer comprises a plurality of intelligent sensor units, the intelligent sensor units are installed on power distribution equipment and used for collecting real-time parameters in real time, and the real-time parameters comprise current, voltage, electric leakage, temperature and fault arc parameters of a line; the network transmission layer is used for transmitting parameters and abnormal alarm information acquired by the intelligent sensor unit to the cloud processing layer; the cloud processing layer receives and stores the real-time parameters, and analyzes and processes the real-time parameters; and the user interaction layer is in communication connection with the cloud processing layer, and is used for a user to check parameters, receive an alarm notification and remotely control related equipment. According to the invention, more intelligent power utilization safety monitoring can be carried out.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power safety monitoring, in particular to a smart power safety monitoring system. BACKGROUND

[0002] In the process of power utilization, abnormal changes in parameters such as current, voltage, leakage, temperature and fault arc of the line may cause power hidden dangers, safety risks and even faults, which threaten the normal operation of power distribution equipment and the safety of personnel and property. Therefore, comprehensive and accurate monitoring of power utilization state is the key to ensuring power safety.

[0003] Therefore, a power safety monitoring system is needed to monitor power safety and ensure power safety.

[0004] The existing power safety monitoring system has a single monitoring type and low intelligence, which cannot achieve comprehensive safety monitoring and has a certain impact on the use of safety protection systems. Therefore, a smart power safety monitoring system is proposed. SUMMARY

[0005] The present application solves the problems of the prior art by the following technical solutions. The present application comprises an intelligent perception layer, a network transmission layer, a cloud processing layer and a user interaction layer. The intelligent perception layer comprises a plurality of intelligent sensor units, The intelligent sensor units are installed on power distribution equipment for real-time acquisition of real-time parameters, including current, voltage, leakage, temperature and fault arc parameters of the line. The network transmission layer is used to transmit the parameters collected by the intelligent sensor units and abnormal alarm information to the cloud processing layer. The cloud processing layer receives and stores the real-time parameters and analyzes and processes the real-time parameters. The user interaction layer is in communication connection with the cloud processing layer for users to view parameters, receive alarm notifications and remotely control related equipment.

[0006] Further, the intelligent sensor units comprise current sensors, voltage sensors, leakage sensors, temperature sensors and fault arc sensors. The current sensors, voltage sensors, leakage sensors, temperature sensors and fault arc sensors respectively collect current, voltage, leakage, temperature and fault arc parameters of the line.

[0007] Further, the network transmission layer uses NB-IoT or GPRS communication mode for data transmission.

[0008] Further, the cloud processing layer comprises a data storage module, a data analysis module and a control instruction generation module; the data storage module is used for storing the current, voltage, electric leakage, temperature and fault arc parameters collected by the intelligent sensing layer; the data analysis module is used for analyzing the stored parameters, identifying electricity hidden dangers, evaluating electricity risks and performing fault accurate positioning; the control instruction generation module is used for generating remote control instructions according to the analysis results.

[0009] Further, the data analysis module analyzes the parameters by using big data analysis and AI algorithms, and the AI algorithms include but are not limited to neural network algorithms and support vector machine algorithms.

[0010] Further, when performing electricity risk evaluation, the data analysis module first performs standardization processing on the collected current, voltage, electric leakage, temperature and fault arc parameters respectively to obtain standardized values of the parameters, then respectively assigns corresponding weights to the standardized values, and obtains a comprehensive risk value through weighted calculation, the calculation formula of the comprehensive risk value is: Comprehensive risk value = current weight x current standardized value + voltage weight x voltage standardized value + electric leakage weight x electric leakage standardized value + temperature weight x temperature standardized value + fault arc weight x fault arc standardized value, and finally determines the risk level according to the comprehensive risk value. The weights of the parameters are determined by training the AI algorithms through historical fault data, and the weight values can be dynamically adjusted according to different application scenarios.

[0011] Further, when identifying electricity hidden dangers, the data analysis module compares the real-time collected current, voltage, electric leakage, temperature and fault arc parameters with the preset normal parameter range, calculates the deviation degree of each parameter, and the calculation formula of the deviation degree is: Deviation degree = (real-time parameter value - normal parameter range middle value) / normal parameter range middle value, when the deviation degree of any parameter exceeds the preset deviation degree threshold, it is determined that there is an electricity hidden danger. The preset normal parameter range and the preset deviation degree threshold can be set and modified according to the type and use environment of the power distribution equipment.

[0012] Further, when performing fault accurate positioning, the data analysis module combines the collected fault arc parameters and the change of current and voltage to calculate the current change rate and the voltage change rate: Current change rate = (current time current value - previous time current value) / time interval; Voltage change rate = (current time voltage value - previous time voltage value) / time interval; By analyzing the position characteristics of the parameter abnormality and the distribution of the current rate of change and the voltage rate of change, the specific line or equipment where the fault occurs is determined.

[0013] Further, the user interaction layer includes a mobile phone APP and a computer terminal, both of which can display the parameters collected by the intelligent sensing layer in real time, receive the alarm notifications sent by the cloud processing layer, and send remote control instructions to the cloud processing layer.

[0014] Further, the intelligent sensing layer can also send the collected current, voltage, leakage, temperature, and fault arc parameters to the network transmission layer in real time, the network transmission layer transmits the parameters to the cloud processing layer in real time, the cloud processing layer dynamically monitors the real-time parameters, and when an abnormal parameter is detected, sends an alarm notification to the user interaction layer through the network transmission layer, while the control instruction generation module generates corresponding control instructions and sends them to the control unit of the power distribution equipment through the network transmission layer, realizing remote control of the equipment.

[0015] Compared with the prior art, the smart safe power utilization safety monitoring system has the following advantages: the system collects multi-dimensional parameters such as current, voltage, leakage, temperature, and fault arc through multiple sensors, realizes comprehensive monitoring of power utilization, and avoids the limitations of single parameter monitoring; the analysis and processing are intelligent, and the system uses big data analysis and AI algorithms (such as neural networks and support vector machines) to improve the accuracy of power utilization hazard identification, risk assessment, and fault location; the risk assessment is scientific and reasonable, the comprehensive risk value is obtained through standardized processing and weighted calculation, and the parameter weights are determined and dynamically adjusted through historical data training, which can adapt to different application scenarios and make the risk assessment more practical; the hazard identification is highly targeted, the hazards are identified by calculating the parameter deviation, and the normal parameter range and deviation threshold can be set and modified according to the equipment type and use environment, improving the flexibility and adaptability of hazard identification; the fault location is accurate and efficient, and combined with the fault arc parameters and the current and voltage rate of change, the specific line or equipment where the fault occurs can be accurately determined, facilitating rapid troubleshooting. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is the overall structure diagram of the present application. DETAILED DESCRIPTION

[0017] The embodiments of the present application will be described in detail below, and the embodiments are implemented on the premise of the technical scheme of the present application, and detailed implementation methods and specific operation processes are given, but the protection scope of the present application is not limited to the following embodiments.

[0018] As Figure 1As shown, the embodiment provides a technical scheme: a smart safe power use safety monitoring system, comprising a smart sensing layer, a network transmission layer, a cloud processing layer and a user interaction layer; The smart sensing layer comprises a plurality of smart sensor units, The smart sensor unit is installed on a power distribution device for real-time acquisition of real-time parameters, including current, voltage, leakage, temperature and fault arc parameters of the line; The network transmission layer is used for transmitting the parameters and abnormal alarm information collected by the smart sensor unit to the cloud processing layer; The cloud processing layer receives and stores the real-time parameters, and analyzes and processes the real-time parameters; The user interaction layer is in communication connection with the cloud processing layer, and is used for allowing the user to view parameters, receive alarm notifications and remotely control related devices.

[0019] The smart sensor unit comprises a current sensor, a voltage sensor, a leakage sensor, a temperature sensor and a fault arc sensor; The current sensor, voltage sensor, leakage sensor, temperature sensor and fault arc sensor correspondingly collect current, voltage, leakage, temperature and fault arc parameters of the line; The smart sensor unit comprises current, voltage, leakage, temperature and fault arc sensors, and each sensor correspondingly collects a specific parameter, which has the advantages that: different key power parameters are collected by special sensors, which can ensure the accuracy and professionalism of each parameter data; at the same time, the current, voltage, leakage, temperature and fault arc parameters are comprehensively covered, realizing omnibearing and multidimensional monitoring of the power use state, avoiding the limitation of single parameter monitoring, providing comprehensive and reliable basic data for subsequent power hidden danger identification, risk assessment and fault positioning, thereby improving the sensing ability of the system to the power use safety state.

[0020] The network transmission layer adopts NB-IoT or GPRS communication mode for data transmission.

[0021] The cloud processing layer comprises a data storage module, a data analysis module and a control instruction generation module; the data storage module is used for storing the current, voltage, leakage, temperature and fault arc parameters collected by the smart sensing layer; the data analysis module is used for analyzing the stored parameters, identifying power hidden dangers, assessing power risks and accurately positioning faults; the control instruction generation module is used for generating remote control instructions according to the analysis results; The data storage module provides reliable storage for the collected various power parameters, ensures data traceability, and provides complete data basis for subsequent analysis and processing; The data analysis module can effectively identify power consumption hazards, assess power consumption risks and realize accurate fault positioning through analysis of parameters, thereby improving the system's perception and judgment of power consumption safety issues; The control instruction generation module generates remote control instructions according to the analysis results, realizes closed-loop management from data monitoring and analysis to active intervention, facilitates timely response to power consumption abnormalities and enhances the system's active protection capability; The clear functions of each module and the coordinated cooperation enable the cloud processing layer to form a complete processing chain, thereby improving the system's intelligent management level and response efficiency for power consumption safety.

[0022] The data analysis module analyzes parameters using big data analysis and AI algorithms, which include but are not limited to neural network algorithms and support vector machine algorithms.

[0023] When the data analysis module assesses power consumption risks, it first standardizes the collected current, voltage, leakage, temperature and fault arc parameters to obtain standardized values of each parameter, then assigns each standardized value with a corresponding weight, calculates a comprehensive risk value through weighted calculation, and finally determines a risk level according to the comprehensive risk value. Comprehensive risk value = current weight x current standardized value + voltage weight x voltage standardized value + leakage weight x leakage standardized value + temperature weight x temperature standardized value + fault arc weight x fault arc standardized value. The weights of each parameter are determined by training the AI algorithm based on historical fault data, and the weight values can be dynamically adjusted according to different application scenarios. Standardizing each parameter unifies the measurement scale of different types of parameters, making the comprehensive assessment of multiple parameters comparable. The weighted calculation of the comprehensive risk value takes into account the influence of multiple parameters such as current, voltage, leakage, temperature and fault arc, avoiding the one-sidedness of single parameter assessment and reflecting the overall power consumption risk comprehensively. The weights of each parameter are determined based on historical fault data training the AI algorithm, which is consistent with the actual fault law and improves the assessment accuracy. The weights can be dynamically adjusted according to different application scenarios, enhancing the system's adaptability to diversified power consumption environments. The risk level is determined based on the comprehensive risk value, making the assessment results intuitive and clear, and facilitating quick risk judgment. As in the household electricity scene, the system first standardizes the collected current, voltage, leakage, etc. Parameters (such as converting parameters in different units to standardized values between 0-1). Since the household is more sensitive to leakage risk, after historical data training, the leakage parameter weight is set to 0.3, and the temperature weight is set to 0.2 (other parameter weights are allocated). After calculating the comprehensive risk value by weighting, if the value is in the high risk interval, it is determined to be high risk, prompting the user to troubleshoot; If switched to an industrial scene, high temperature equipment is prone to failure, the temperature parameter weight may be adjusted to 0.4, making the risk assessment more in line with the characteristics of industrial power consumption.

[0024] The data analysis module compares the real-time collected current, voltage, leakage, temperature, and fault arc parameters with the pre-set normal parameter range when identifying electricity hazards, calculates the deviation degree of each parameter, and the calculation formula of the deviation degree is: Deviation degree = (real-time parameter value - normal parameter range middle value) / normal parameter range middle value, when the deviation degree of any parameter exceeds the pre-set deviation degree threshold, it is determined that there is an electricity hazard; The pre-set normal parameter range and the pre-set deviation degree threshold can be set and modified according to the type and use environment of the power distribution equipment; The electricity hazard identification logic is clear, by comparing the real-time parameters with the pre-set normal range and calculating the deviation degree, it can intuitively and quantitatively determine whether the parameters are abnormal, improving the objectivity and accuracy of hazard identification; It has good flexibility and adaptability, the pre-set normal parameter range and deviation degree threshold can be set and modified according to the type and use environment of the power distribution equipment, which can adapt to the electricity monitoring needs in different scenes and ensure accurate identification of hazards in various environments; The judgment standard is clear, based on the judgment method of whether the deviation degree exceeds the threshold, which avoids subjective factors and makes the hazard identification process more standardized and reliable.

[0025] As in the household lighting circuit, the pre-set normal temperature range is 25-45℃ (middle value 35℃), and the deviation degree threshold is set to 0.2. When the real-time monitoring line temperature is 50℃, the deviation degree = (50-35) / 35 ≈ 0.43, which exceeds the threshold 0.2, and it is determined that there is an electricity hazard; In the industrial large motor circuit, because the temperature itself is high when the motor is running, the pre-set normal temperature range is 60-100℃ (middle value 80℃), and the deviation degree threshold is set to 0.3. If the real-time temperature is 105℃, the deviation degree = (105-80) / 80 = 0.31, which exceeds the threshold, and it is determined that there is a hazard; If the temperature is 95℃, the deviation degree = (95-80) / 80 = 0.1875, which does not exceed the threshold, and it is determined that there is no hazard. By adjusting the parameter range and threshold for different equipment types, the accuracy of hazard identification is ensured.

[0026] Further, the data analysis module, when performing precise fault positioning, combines the collected fault arc parameters and the changes in current and voltage to calculate the current change rate and the voltage change rate: Current change rate = (current time current value - previous time current value) / time interval; Voltage change rate = (current time voltage value - previous time voltage value) / time interval; By analyzing the position characteristics of parameter anomalies and the distribution of current change rate and voltage change rate, the specific line or equipment where the fault occurs is determined; The fault positioning is more accurate. By combining fault arc parameters and the dynamic changes of current and voltage (calculating the change rate), the dynamic characteristics at the time of fault occurrence can be captured, avoiding positioning deviation caused by relying only on static parameters. The positioning is more targeted. By combining the position characteristics of parameter anomalies and the distribution of change rate, the fault can be specified to a specific line or equipment, reducing the scope of investigation and improving fault handling efficiency. The analysis dimension is more comprehensive, combining the key fault signal of fault arc and the change trend of current and voltage, making the positioning logic more rigorous and reducing the probability of misjudgment.

[0027] For example, in a multi-story power distribution system of a certain shopping mall, the system detects that the 5th floor has abnormal fault arc parameters, and calculates that the current change rate in this area is -0.8 A / s (current rapidly decreases) and the voltage change rate is -0.05 kV / s (voltage rapidly decreases). By combining the position characteristics of parameter anomalies (5th floor east line) and the concentrated distribution area of current and voltage change rate, it is determined that the fault occurs in a lighting line on the 5th floor east side. Maintenance personnel can directly go to the line for investigation without checking each floor and line, greatly shortening the investigation time.

[0028] The user interaction layer includes a mobile phone APP and a computer terminal, both of which can display the parameters collected by the intelligent sensing layer in real time, receive the alarm notifications sent by the cloud processing layer, and send remote control instructions to the cloud processing layer.

[0029] The intelligent sensing layer can also send the collected current, voltage, leakage, temperature, and fault arc parameters to the network transmission layer in real time. The network transmission layer transmits the parameters to the cloud processing layer in real time. The cloud processing layer dynamically monitors the real-time parameters. When an abnormal parameter is detected, an alarm notification is sent to the user interaction layer through the network transmission layer, and the control instruction generation module generates a corresponding control instruction, which is sent to the control unit of the power distribution equipment through the network transmission layer, realizing remote control of the equipment; Real-time dynamic monitoring of power utilization parameters is realized, so that the cloud processing layer can obtain the latest power utilization state in time, laying a foundation for quickly discovering abnormalities; when parameter abnormalities are monitored, the user interaction layer can be quickly sent an alarm notification, so that the user knows the power utilization abnormality in the first time, facilitating timely response; meanwhile, remote control instructions are generated and sent to the power distribution equipment control unit, so that remote intervention on the equipment is realized, measures can be taken proactively before the fault expands, and safety risks are reduced; a complete closed loop from parameter collection, transmission, monitoring, alarm to remote control is formed, the quick response capability and proactive protection level of the system to power utilization abnormalities are improved, and the power utilization safety and management efficiency are enhanced.

[0030] In addition, the terms "first", "second", "third", etc. are used herein only to describe various circumstances, and cannot be interpreted or deduced to indicate or imply relative importance or imply the number of the technical features indicated. Therefore, the features defined with "first", "second", etc. can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0031] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, different embodiments or examples described in the present application and the features of different embodiments or examples can be combined and modified by those skilled in the art without contradiction.

[0032] Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and cannot be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A smart and safe electricity monitoring system, characterized in that, It includes an intelligent sensing layer, a network transmission layer, a cloud processing layer, and a user interaction layer; The intelligent sensing layer includes several intelligent sensor units. The intelligent sensor unit is installed on the power distribution equipment to collect real-time parameters, including line current, voltage, leakage current, temperature and fault arc parameters. The network transmission layer is used to transmit the parameters and abnormal alarm information collected by the intelligent sensor unit to the cloud processing layer; The cloud processing layer receives and stores the real-time parameters, and analyzes and processes the real-time parameters. The user interaction layer is connected to the cloud processing layer and is used to allow users to view parameters, receive alarm notifications, and remotely control related equipment.

2. The intelligent safe electricity monitoring system according to claim 1, characterized in that: The intelligent sensor unit includes a current sensor, a voltage sensor, a leakage current sensor, a temperature sensor, and a fault arc sensor. The current sensor, voltage sensor, leakage current sensor, temperature sensor, and fault arc sensor are respectively used to collect the current, voltage, leakage current, temperature, and fault arc parameters of the line.

3. The intelligent safe electricity monitoring system according to claim 1, characterized in that: The cloud processing layer includes a data storage module, a data analysis module, and a control command generation module. The data storage module stores current, voltage, leakage current, temperature, and fault arc parameters collected by the intelligent sensing layer. The data analysis module analyzes the stored parameters to identify potential electrical hazards, assess electrical risks, and accurately locate faults. The control command generation module generates remote control commands based on the analysis results.

4. The intelligent safe electricity monitoring system according to claim 3, characterized in that: The data analysis module uses big data analysis and AI algorithms to analyze the parameters. The AI ​​algorithms include, but are not limited to, neural network algorithms and support vector machine algorithms.

5. The intelligent safe electricity monitoring system according to claim 3, characterized in that: When conducting electricity risk assessment, the data analysis module first standardizes the collected current, voltage, leakage current, temperature, and fault arc parameters to obtain standardized values ​​for each parameter. Then, it assigns corresponding weights to each standardized value and calculates the comprehensive risk value through weighted average. The formula for calculating the comprehensive risk value is as follows: The comprehensive risk value is calculated as follows: current weight × current standardized value + voltage weight × voltage standardized value + leakage current weight × leakage current standardized value + temperature weight × temperature standardized value + fault arc weight × fault arc standardized value. Finally, the risk level is determined based on the comprehensive risk value. The weights of each parameter are determined by training the AI ​​algorithm with historical fault data, and the weight values ​​can be dynamically adjusted according to different application scenarios.

6. The intelligent safe electricity monitoring system according to claim 3, characterized in that: When identifying potential electrical hazards, the data analysis module compares the real-time collected parameters of current, voltage, leakage current, temperature, and fault arc with preset normal parameter ranges, and calculates the deviation of each parameter. The formula for calculating the deviation is as follows: Deviation = (real-time parameter value - midpoint of normal parameter range) / midpoint of normal parameter range. When the deviation of any parameter exceeds the preset deviation threshold, it is determined that there is a potential electrical hazard. The preset normal parameter range and preset deviation threshold can be set and modified according to the type of power distribution equipment and the usage environment.

7. The intelligent safe electricity monitoring system according to claim 3, characterized in that: When performing precise fault location, the data analysis module combines the collected fault arc parameters with changes in current and voltage to calculate the rate of change of current and voltage. Rate of change of current = (current value at current moment - current value at previous moment) / time interval; Voltage change rate = (current voltage value - previous voltage value) / time interval; By analyzing the location characteristics of abnormal parameters and the distribution of current and voltage change rates, the specific line or equipment where the fault occurred can be identified.

8. The intelligent safe electricity monitoring system according to claim 1, characterized in that: The intelligent sensing layer can also send the collected current, voltage, leakage current, temperature, and fault arc parameters to the network transmission layer in real time. The network transmission layer then transmits the parameters to the cloud processing layer in real time. The cloud processing layer dynamically monitors the real-time parameters. When an abnormal parameter is detected, it sends an alarm notification to the user interaction layer through the network transmission layer. At the same time, the control command generation module generates corresponding control commands and sends them to the control unit of the power distribution equipment through the network transmission layer, thereby realizing remote control of the equipment.

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