An energy consumption anomaly monitoring device based on multi-source data collection
Patent Information
- Application Number
- CN202522584870.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Utility models(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-05
- Publication Date
- 2026-10-09
- Estimated Expiration
- 2035-12-05
AI Technical Summary
[0004]实用新型目的：本实用新型的目的是提供一种可以解决仅采集单一类型能耗数据、无法全面准确反映实际能耗状况及数据处理分析能力有限、能耗异常发现不及时等问题的基于多源数据采集的能耗异常监测装置
[0012]Beneficial Effects: Compared with existing technologies, this utility model has the following significant advantages: 1. Multi-source energy consumption data monitoring capability: By collecting various energy consumption data through the multi-source data acquisition module, it provides managers with comprehensive and accurate energy consumption data and remote monitoring capabilities, improving energy management efficiency; 2. Abnormal energy consumption judgment capability: The data processing and analysis module performs in-depth processing and analysis on the collected energy consumption data, extracts key feature parameters, and the abnormal judgment module performs real-time judgment based on preset rules, enabling rapid and accurate identification of abnormal energy consumption situations; 3. Abnormal early warning and alarm capability: The alarm module provides multiple alarm methods, and the appropriate alarm method can be selected according to actual needs, ensuring that managers can receive alarm information in a timely manner, promptly handle abnormal energy consumption problems, and reduce energy waste and risk losses.
Smart Images
Figure CN224839015U_ABST
Abstract
Description
Technical Field
[0001] This utility model relates to an energy consumption anomaly monitoring device based on multi-source data acquisition. Background Technology
[0002] In recent years, with social development and the increasing energy consumption, energy management has become increasingly important. However, traditional energy monitoring devices can mostly only collect single types of energy consumption data, such as electricity data or water flow data. This single-source data collection method cannot comprehensively monitor the use of multiple energy sources, making it difficult to fully and accurately reflect energy consumption data and anomaly information. When anomalies occur during the coordinated use of multiple energy sources, they are often unable to be effectively identified and judged. In addition, traditional devices have limited data processing and analysis capabilities, failing to conduct in-depth mining and analysis of the collected data in a timely manner, resulting in insufficient timeliness in the detection of energy consumption anomalies and failing to provide strong decision support for energy management.
[0003] Therefore, accurate energy consumption monitoring and timely detection of energy consumption anomalies are crucial for improving energy efficiency, reducing energy costs, minimizing energy waste, and achieving the goals of energy conservation and emission reduction. Summary of the Invention
[0004] Purpose of the utility model: The purpose of this utility model is to provide an energy consumption anomaly monitoring device based on multi-source data acquisition that can solve the problems of only collecting a single type of energy consumption data, failing to comprehensively and accurately reflect the actual energy consumption status, having limited data processing and analysis capabilities, and failing to detect energy consumption anomalies in a timely manner.
[0005] Technical solution: The present invention provides an energy consumption anomaly monitoring device based on multi-source data acquisition, comprising a main control chip, on which IIC bus, RS485 bus, IO output interface, 4G / 5G module, WIFI module, LAN port, RS232 debugging module and power supply module are respectively connected. The main control chip is an STM32F407 microcontroller with data processing and peripheral expansion capabilities.
[0006] Furthermore, the main control chip also houses interconnected multi-source data acquisition modules, data processing and analysis modules, anomaly detection modules, alarm modules, and communication modules.
[0007] Furthermore, the multi-source data acquisition module is equipped with an RS485 bus interface and a pulse acquisition interface that support the access of multiple different types of sensors; The different types of sensors include power sensors for collecting power consumption data, water flow sensors for collecting water flow data, gas sensors for collecting gas consumption data and concentration data, and thermal energy sensors for collecting thermal energy-related data. The sensor collects the data and then transmits it to the data processing and analysis module.
[0008] Furthermore, the data processing and analysis module receives digital signals from the multi-source data acquisition module and preprocesses them; Then, feature extraction is performed on the preprocessed data to extract key parameters that reflect energy consumption characteristics; at the same time, this module can store the collected raw data and processed data in local storage for subsequent querying and analysis. The digital signal includes data cleaning and filtering operations.
[0009] Furthermore, the anomaly detection module performs real-time detection of energy consumption data based on the feature parameters extracted by the data processing and analysis module and in conjunction with preset energy consumption anomaly detection rules. The judgment rules are set according to different application scenarios and energy types. For example, a reasonable fluctuation range of energy consumption is set, and when the actual energy consumption exceeds this range, it is judged as abnormal energy consumption; or by comparing with historical data of the same period, when there is a significant difference between the energy consumption and the historical data, it is judged as abnormal.
[0010] Furthermore, the alarm module is connected to the anomaly detection module. When the anomaly detection module determines that an energy consumption anomaly has occurred, the alarm module issues an alarm signal. The alarm signals include audible alarms, visual alarms, and alarm information sent to management personnel via network.
[0011] Furthermore, the communication module is responsible for transmitting the data collected by the device and abnormal alarm information to the remote monitoring center or the mobile terminal of the management personnel. The communication module employs both wired and wireless communication methods.
[0012] Beneficial Effects: Compared with existing technologies, this utility model has the following significant advantages: 1. Multi-source energy consumption data monitoring capability: By collecting various energy consumption data through the multi-source data acquisition module, it provides managers with comprehensive and accurate energy consumption data and remote monitoring capabilities, improving energy management efficiency; 2. Abnormal energy consumption judgment capability: The data processing and analysis module performs in-depth processing and analysis on the collected energy consumption data, extracts key feature parameters, and the abnormal judgment module performs real-time judgment based on preset rules, enabling rapid and accurate identification of abnormal energy consumption situations; 3. Abnormal early warning and alarm capability: The alarm module provides multiple alarm methods, and the appropriate alarm method can be selected according to actual needs, ensuring that managers can receive alarm information in a timely manner, promptly handle abnormal energy consumption problems, and reduce energy waste and risk losses. Attached Figure Description
[0013] Figure 1 This is a schematic diagram of the overall structure and connection of this utility model; Figure 2 This is a schematic diagram showing the connections of each module in the main control chip of this utility model; Figure 3 This is a flowchart of the real-time data acquisition process in the working principle of this utility model; Figure 4 This is a flowchart of the data cleaning and filtering process in the working principle of this utility model; Figure 5 This is a flowchart of the energy consumption anomaly detection and judgment process in the working principle of this utility model; In the diagram: 1 is the main control chip, 11 is the multi-source data acquisition module, 12 is the data processing and analysis module, 13 is the anomaly detection module, 14 is the alarm module, and 15 is the communication module. 2 is the IIC bus, 3 is the RS485 bus, 4 is the IO output interface, 5 is the 4G / 5G module, 6 is the WIFI module, 7 is the LAN port, 8 is the RS232 debugging module, and 9 is the power supply module. Detailed Implementation
[0014] The specific technical solution of this utility model will be further described in detail below with reference to specific examples.
[0015] As shown in the figure, the energy consumption anomaly monitoring device based on multi-source data acquisition described in this utility model includes a main control chip 1, an IIC bus 2, an RS485 bus 3, an IO output interface 4, a 4G / 5G module 5, a WIFI module 6, a LAN port 7, an RS232 debugging module 8, and a power supply module 9, which are interconnected (wired or signal connected). Specifically, as shown... Figure 1 As shown; Its core control (i.e., main control chip 1) adopts the STM32F407 microcontroller, which has data processing capabilities and peripheral expansion capabilities, and can meet the needs of multi-source data acquisition and analysis control. The main control chip 1 also houses functional modules, which mainly include a multi-source data acquisition module 11, a data processing and analysis module 12, an anomaly detection module 13, an alarm module 14, and a communication module 15, all interconnected (via wired or signal connections). A schematic diagram of the module connections is shown below. Figure 2 As shown.
[0016] Specifically: (I) Multi-source data acquisition module 11 The multi-source data acquisition module 11 is equipped with an RS485 bus interface and a pulse acquisition interface, which supports the access of multiple different types of sensors for collecting energy consumption data of various energy sources; the sensors include power sensors for collecting power consumption data, such as voltage, current, power and other parameters. A water flow sensor is used to collect water flow data; Gas sensors are used to collect gas consumption data and concentration data; Thermal sensors are used to collect thermal-related data, such as heat and temperature. The sensor collects and processes the data, then transmits it to the data processing and analysis module 12. The multi-source data acquisition module 11 can comprehensively cover different types of energy consumption, providing a rich data foundation for subsequent energy consumption analysis. (II) Data Processing and Analysis Module 12 The data processing and analysis module 12 receives digital signals from the multi-source data acquisition module 11 and preprocesses them, including data cleaning, filtering and other operations, to remove noise and outliers and improve data quality. Then, feature extraction is performed on the preprocessed data to extract key parameters that can reflect energy consumption characteristics, such as peak, valley, average, and rate of change of energy consumption. In addition, the module also has a data storage function, storing the collected raw data and processed data in local storage for subsequent querying and analysis; (III) Anomaly Detection Module 13 The anomaly judgment module 13 judges the energy consumption data in real time based on the feature parameters extracted by the data processing and analysis module and in combination with the preset energy consumption anomaly judgment rules. The judgment rules can be flexibly set according to different application scenarios and energy types. For example, a reasonable fluctuation range for energy consumption can be set, and when the actual energy consumption exceeds this range, it is judged as an abnormal energy consumption; or by comparing with historical data for the same period, when the energy consumption differs significantly from historical data, it is judged as abnormal. The anomaly judgment module can quickly and accurately identify abnormal energy consumption situations; (iv) Alarm Module 14 The alarm module 14 is connected to the anomaly judgment module 13. When the anomaly judgment module 13 determines that an energy consumption anomaly has occurred, the alarm module 14 immediately issues an alarm signal. The alarm signals include audible alarms, visual alarms, and alarm information sent to management personnel via the network, so that management personnel can take timely measures to deal with abnormal energy consumption problems. (V) Communication Module 15 The communication module 15 is responsible for transmitting the data collected by the device and abnormal alarm information to the remote monitoring center or the mobile terminal of the management personnel. The communication module 15 supports wired and wireless communication methods, such as Ethernet, Wi-Fi, 4G / 5G, to ensure real-time and stable data transmission, facilitating remote monitoring and management by administrators.
[0017] The specific steps of the energy consumption anomaly monitoring process for this device are as follows: The first step involves accessing the data to be monitored via the multi-source data acquisition module 11. The RS485 interface of the multi-source data acquisition module 11 connects to electricity meters, water meters, gas meters, etc., to collect real-time electricity data, water consumption data, and gas consumption data. The collected data is then integrated into specific parameters. Electricity equipment data parameters include timestamp, equipment number, meter number, kilowatt-hours, voltage value, current value, and power, forming an "electricity" data group. Water equipment data parameters include timestamp, equipment number, meter number, and tonnage, forming a "water" data group. Gas equipment data parameters include timestamp, equipment number, meter number, and gas quantity, forming a "gas" data group. The collected multi-source energy consumption data is then transmitted to the data processing and analysis module 12. The real-time data acquisition process is as follows: Figure 3 As shown; The second step is that the data processing and analysis module 12 receives multi-source energy consumption data from the front end, including electricity data group, water data group and gas data group. Through the built-in data cleaning and filtering mechanism, small missing and blank data in the data are deleted, and obviously abnormal deviation data are deleted. Finally, the missing data is filled by the calculated mean and median, and the high-quality data after processing is retained. Based on the feature value extraction mechanism, the processed data is used to extract the peak, valley, and average values of electricity (voltage, current, power), water volume (tons), and gas volume (tons) by minute. The extracted feature value data are packaged and stored into Feature_electricity, Feature_water, and Feature_gas data groups to facilitate subsequent anomaly detection module analysis and identification of abnormal energy consumption data; data cleaning and filtering mechanisms are as follows: Figure 4 As shown; The third step involves the anomaly detection module 13 identifying whether there is abnormal energy consumption data in the feature parameter data group extracted by the data processing and analysis module 12 through its built-in energy consumption anomaly detection rule program. The energy consumption anomaly detection rule includes a fluctuation range anomaly threshold S parameter, the specific value of which can be manually configured. The fluctuation range anomaly threshold S is a fluctuation range given outside the actual normal linear change law value of energy consumption; exceeding this range indicates an anomaly. For electricity, water, and gas, it is represented by S_e, S_w, and S_g. Anomaly severity levels are set to indicate the severity of the anomaly, referring to the anomaly level classified when the S parameter has exceeded the set range. Different range values are used for electricity, water, and gas, with default values shown in Table 1 below.
[0018] Table 1 S_e 10%~50% 50%~100% 100%~300% ＞300% S_w 20%~50% 50%~100% 100%~300% ＞300% S_g 20%~50% 50%~150% 150%~200% ＞200% Based on historical energy consumption data obtained over the past year, the data is integrated into an energy consumption line chart with time as the axis. The changes in energy consumption data are calculated using values at fixed time points. Data for the next day's midnight is obtained by selecting midnight as the node of each day, and the daily anomaly threshold S and real-time change value X are calculated according to the formulas S=Y and X=Y1-Y, where Y1 is the real-time value and Y is the historical value. Using each day as the baseline, historical records are obtained sequentially each day to advance S, ensuring the accuracy of data changes. Once the maximum S value is determined at the daily midnight node calculation, i.e., the range values of S_e, S_w, and S_g are determined, the daily real-time energy consumption value is calculated. The energy consumption range is monitored in real time, and the daily real-time change value X is compared with the range value. If an energy consumption anomaly occurs before the next calculation node, the time and specific data of the occurrence are recorded. In anomaly level, only the specific energy consumption data of the two time nodes that first appear within the range and last exceed the range are recorded. If the current monitored anomaly range is not exceeded before the next calculation node, a record is made 10 minutes before the node calculation. This completes the daily energy consumption anomaly monitoring. After reaching the node, the anomaly range S value is recalculated, and the energy consumption data for the next day is monitored in real time again. The power data includes voltage and power parameters, which are used to monitor the status of the equipment. The daily voltage deviation value V is calculated according to the formula V=V1-220 to determine the range of V change. V1 is the current value. The voltage change is monitored in real time every day to see if it exceeds the set value range, to determine whether there is an abnormality and the severity of the abnormality. The abnormal voltage data is shown in Table 2 below.
[0019] Table 2 V ±（7%~8%） ±（8%~10%） Exceeding ±10% The daily real-time power difference W is calculated using the formula W=W1-3000. The range of W variation is determined, and the daily real-time power variation is monitored to determine whether it exceeds the set value range. This determines whether there is an anomaly and the severity of the anomaly. This data is used to monitor power anomaly data, as shown in Table 3 below.
[0020] Table 3 W ±（10%~20%） ±（20%~50%） Exceeding ±50% The gas data includes the concentration parameter LEL. The obtained parameter is compared with the safe value "0% LEL", i.e., C=C1-0, to obtain the difference that exceeds the range, and to observe in real time whether there is any leakage abnormality. By the change of C, it is determined whether it exceeds the range of the set value and to determine the severity of the abnormality, as shown in Table 4 below.
[0021] Table 4 C 1%~3% 3%~4% ≥5% Once the data analysis and judgment are completed, the abnormal data will be integrated and associated with its related risk points. For example, if the associated risk point for water is electricity, and the water anomaly reaches a medium level or higher, considering the potential danger of water leakage to electricity, electricity data can be sent synchronously when water volume data is abnormal. This provides real-time data to assist in monitoring the abnormal status. If the electricity anomaly level reaches a high level or higher, the use of electricity, water, and gas can be directly cut off, and restored after the anomaly is handled. Similarly, if the associated risk point for gas is electricity, and the gas anomaly reaches a medium level or higher, considering the potential danger of gas leakage to electricity, electricity data can be sent synchronously when gas volume data is abnormal. This provides real-time data to assist in monitoring the abnormal status. If the gas anomaly level reaches a high level or higher, it is recommended to immediately activate the emergency response, immediately cut off gas and electricity, and arrange personnel to take protective measures and urgently handle the anomaly.
[0022] The anomaly detection module 13 sends the detected anomaly data to the alarm module 14 in real time during monitoring for anomaly warning; the energy consumption anomaly detection and judgment flowchart is as follows. Figure 4 As shown; Fourth, the alarm module 14 receives the alarm data transmitted from the anomaly judgment module 13, assembles the alarm data into alarm information, and sends it to the administrator through the communication method provided by the communication module 15. It also connects to the buzzer and indicator light through the local output interface to provide sound and light alarms, realizing real-time remote alarm and local alarm, which is used for the administrator's rapid response and anomaly location identification.
[0023] When the alarm module 14 transmits alarm data to the management personnel, it also transmits data on the associated risk points. The management personnel can respond and handle the abnormal points and their risk levels quickly. For example, if the abnormal gas concentration level reaches high or above, it will prompt and suggest cutting off the gas and power supply. If the abnormal power level reaches high or above, it will prompt and suggest cutting off the power supply, water supply and gas supply. The management personnel can immediately perform the cutting-off operation remotely.
[0024] In summary, this utility model generally contains the following main working parts: 1. Data Acquisition Section: Provides RS485 bus interface and IIC bus2 interface, and supports the connection of various energy sensors to collect energy consumption data in real time; 2. Monitoring section: Through data processing and anomaly monitoring mechanisms, the system accurately extracts data and determines whether there are any anomalies in energy consumption data; 3. Early warning section: Supports local and remote early warning modes, supports local audio and visual early warning, and supports remote early warning notification.
Claims
1. An energy consumption anomaly monitoring device based on multi-source data acquisition, characterized in that, It includes a main control chip (1), on which are connected an IIC bus (2), an RS485 bus (3), an IO output interface (4), a 4G / 5G module (5), a WIFI module (6), a LAN port (7), an RS232 debugging module (8), and a power module (9). The main control chip (1) is an STM32F407 microcontroller with data processing and peripheral expansion capabilities.
2. The energy consumption anomaly monitoring device based on multi-source data acquisition according to claim 1, characterized in that, The main control chip (1) also contains an interconnected multi-source data acquisition module (11), a data processing and analysis module (12), an anomaly judgment module (13), an alarm module (14), and a communication module (15).
3. The energy consumption anomaly monitoring device based on multi-source data acquisition according to claim 2, characterized in that, The multi-source data acquisition module (11) is equipped with an RS485 bus interface and a pulse acquisition interface that support the access of multiple different types of sensors; The different types of sensors include a power sensor for collecting power consumption data, a water flow sensor for collecting water flow data, a gas sensor for collecting gas consumption data and concentration data, and a thermal energy sensor for collecting thermal energy-related data. The sensor organizes the collected data and transmits it to the data processing and analysis module (12).
4. The energy consumption anomaly monitoring device based on multi-source data acquisition according to claim 2, characterized in that, The data processing and analysis module (12) receives digital signals from the multi-source data acquisition module (11) and preprocesses them; Then, feature extraction is performed on the preprocessed data to extract key parameters that reflect energy consumption characteristics; at the same time, this module can store the collected raw data and processed data in local storage for subsequent querying and analysis. The digital signal includes data cleaning and filtering operations.
5. The energy consumption anomaly monitoring device based on multi-source data acquisition according to claim 2, characterized in that, The anomaly judgment module (13) performs real-time judgment on energy consumption data based on the feature parameters extracted by the data processing and analysis module (12) and the preset energy consumption anomaly judgment rules. The judgment rules are set according to different application scenarios and energy types. For example, a reasonable fluctuation range of energy consumption is set. When the actual energy consumption exceeds this range, it is judged as abnormal energy consumption. Alternatively, by comparing data from the same historical period, an anomaly can be identified when there is a significant difference between energy consumption and historical data.
6. The energy consumption anomaly monitoring device based on multi-source data acquisition according to claim 2, characterized in that, The alarm module (14) is connected to the anomaly judgment module (13). When the anomaly judgment module (13) determines that an energy consumption anomaly has occurred, the alarm module (14) issues an alarm signal. The alarm signals include audible alarms, visual alarms, and alarm information sent to management personnel via network.
7. The energy consumption anomaly monitoring device based on multi-source data acquisition according to claim 2, characterized in that, The communication module (15) is responsible for transmitting the data collected by the device and abnormal alarm information to the remote monitoring center or the mobile terminal of the management personnel. The communication module (15) uses both wired and wireless communication methods.