Load characteristic acquisition and analysis system of electric energy metering device based on Internet of Things perception

Abstract

The invention discloses an electric energy metering device load characteristic acquisition and analysis system based on Internet of Things perception, and relates to the technical field of power utilization safety, the system comprises electric meter modules, a concentrator module and an Internet of Things platform, each electric meter module comprises a characteristic library, an acquisition unit, an identification unit, a screening unit and a transmission unit; the feature library comprises current features generated based on historical big data of the electrical equipment; the acquisition unit is used for acquiring circuit fluctuation to obtain current data, comparing the current data with the feature library and judging whether new electrical equipment is accessed or not; and the identification unit generates a group of new identification channels after confirming that new electrical equipment is accessed, and transmits the current data of the corresponding electrical equipment. According to the invention, by arranging the feature library in the ammeter module and combining the real-time acquisition and analysis of the current data, the load characteristics of the equipment can be accurately identified, and for the newly accessed electrical equipment, the system identifies the type of the newly accessed electrical equipment through comparison and matches the newly accessed electrical equipment with the feature library.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity safety, and in particular to a load characteristic collection and analysis system for an electric energy metering device based on Internet of Things perception. Background Art

[0002] With the continuous development of intelligent and Internet of Things technologies, intelligent monitoring and management of electrical equipment has gradually become an important means to improve the safety and operating efficiency of power systems. Traditional power metering and electrical equipment monitoring systems mainly rely on simple current and power detection, which makes it difficult to conduct in-depth analysis of the specific load characteristics of the equipment, especially in terms of safety monitoring and fault warning of dangerous electrical equipment. Existing technologies are difficult to achieve real-time, efficient and accurate identification and emergency response. Dangerous electrical equipment may have hidden dangers such as severe current fluctuations, overload or abnormal operation. If they cannot be discovered and handled in time, they may cause equipment damage, fire and other safety accidents, posing a serious threat to production and personnel safety.

[0003] After searching, a Chinese patent (publication number: CN114778992A) discloses an electrical appliance safety detection method, which includes the following steps: S1. Real-time collection of household electrical appliance power consumption data, including: total power consumption; S2. Real-time collection of household power consumption data to identify the current running load; S3. Monitoring the current waveform of the running load, matching the current waveform characteristics of the running load with the current waveform characteristics of the corresponding load category. If a fault arc occurs, the running load will be identified as faulty; S4. Based on the faulty load, an alarm is issued and a power-off operation is performed, and the operation data is saved.

[0004] In the prior art, although emerging technologies such as the Internet of Things and cloud computing have been introduced, many systems still have deficiencies, especially in the accurate identification, risk assessment and emergency response of dangerous electrical equipment. Therefore, how to achieve real-time monitoring and accurate identification of load characteristics of electrical equipment, especially efficient identification and rapid response of dangerous electrical equipment, is one of the important research and development directions. Therefore, the present invention proposes a load characteristic collection and analysis system for electric energy metering devices based on IoT perception. Summary of the invention

[0005] The purpose of the present invention is to provide a load characteristic collection and analysis system for an electric energy metering device based on Internet of Things perception to solve the problems mentioned in the above background technology.

[0006] The present invention can be implemented through the following technical solutions: a load characteristic acquisition and analysis system for electric energy metering devices based on IoT perception, including multiple electric meter modules, a concentrator module, and an IoT platform; Each of the electric meter modules includes a feature library, a collection unit, an identification unit, a screening unit and a transmission unit; The feature library includes current features generated based on historical big data of electrical equipment; The acquisition unit acquires current data of the new electrical equipment after it is connected to the circuit to obtain the current data of the corresponding electrical equipment. The current data includes current waveform and current characteristics. The current characteristics include current peak value, waveform period, frequency distribution, and harmonic components, which are used to assist in identifying the load characteristics of different electrical equipment. When a new electrical device is connected, the acquisition unit compares the current data with the feature library to determine whether a new electrical device is connected; The identification unit is used to collect current data by the collection unit, and after confirming that a new electrical device is connected, generates a set of new identification channels for transmitting current data of the corresponding electrical device; The screening unit has a built-in screening network to identify and mark the current data in each identification channel, including "safe" and "suspicious" marks; The transmission unit transmits the current data marked as “safe” and the current data marked as “suspicious” to the concentrator module; The concentrator module is used to receive the current data transmitted by each meter module, including a monitoring channel and a connection channel. The monitoring channel preferentially allocates bandwidth and computing resources, and pre-processes each current data, including: The current data marked as "safe" is compressed to remove redundant data, reduce the transmission bandwidth and computing resource consumption, and then transmitted to the IoT platform through the connection channel; Compare and analyze each current data marked as "suspicious" to determine whether the corresponding current data is dangerous use data; If the current data marked as "suspicious" is judged to be dangerous usage data, it will be transmitted to the IoT platform through the monitoring channel; After receiving each current data, the IoT platform activates an emergency response mechanism for the current data determined to be dangerous usage data; The remaining current data is stored, optimized and monitored.

[0007] A further technical improvement of the present invention is that the method for the identification unit to establish an identification channel includes the following steps: Step 1: The acquisition unit extracts the current features in the current data and matches them with the device data features in the feature library; During matching, similarity calculation is performed to determine whether a new electrical device is connected. After the connected new electrical device successfully matches the device data feature in the feature library, the new electrical device is marked as "recognizable"; Step 2, assigning unique identifiers and timestamps to the current data generated by different electrical devices; The timestamp is the time when the current data is collected, ensuring the uniqueness and order of each current data in time; The unique identifier is a dynamically distributed identifier of each new electrical device by the acquisition unit after the new electrical device is identified; Step 3: The identification unit dynamically divides bandwidth and memory resources to establish a virtual channel based on the unique identifier and timestamp; The virtual channel is used to transmit current data corresponding to a unique identifier and a time stamp.

[0008] A further technical improvement of the present invention is that when a new electrical device is connected to the acquisition unit, if the corresponding device data feature is not matched in the feature library, the acquisition unit stores it in the feature library of the local meter module, and after authorization, transmits it to the Internet of Things platform through the concentrator module; After the IoT platform identifies the current characteristics of the new electrical equipment, it transmits them to the remaining meter modules and updates their feature libraries.

[0009] A further technical improvement of the present invention is that the method for establishing the screening network comprises the following steps: S1. Set up dangerous electrical equipment and extract the current characteristics of the dangerous electrical equipment in the characteristic library; S2. Integrate the current characteristics of hazardous electrical equipment; The various features extracted from the current data of each dangerous electrical device are stored in multi-dimensional vector form. These feature vectors will serve as the basic units in the screening network to help the screening algorithm identify different types of equipment; S3, generating a screening network based on screening rules and topological structures; The screening rule is the core algorithm that determines how to compare the current data with the data in the feature library and then determine whether the current data comes from dangerous electrical equipment; The topology of the screening network determines how the current data flows in the screening network and how it is classified based on feature judgments. For current data marked as "dangerous", its priority in the virtual channel is increased, and more bandwidth and computing resources are allocated; For current data marked as "safe", bandwidth and computing resources sufficient for regular monitoring are allocated.

[0010] A further technical improvement of the present invention is that the comparative analysis of each current data marked as "suspicious" by the concentrator module includes: Comparative analysis across electricity meters: The concentrator module compares the current data of the same type of electrical equipment in multiple meter modules to determine whether there is an abnormality; If the current data of the same type of electrical equipment has a large difference, it means that the equipment may be faulty or not being used normally. The difference value can be obtained based on the historical big data analysis of the same type of electrical equipment; Time Series Analysis: The concentrator module compares the current suspicious current data with the historical data of the electrical equipment. If there is a sharp fluctuation in a short period of time, the concentrator module compares the current fluctuation of the electrical equipment with that of other electrical equipment in the same time period to determine whether it is a normal working state or an abnormal phenomenon has occurred; Analysis of load characteristics classification: The concentrator module performs comparative analysis based on the typical load patterns of different electrical devices. If the current data of a certain electrical device does not match its normal load pattern, it indicates that the device is faulty or is being used abnormally; Simultaneous and differential analysis: The concentrator module monitors whether the current data of each electrical device changes synchronously. If the current waveforms of multiple electrical devices change synchronously, such as peaks and troughs appearing in the same time period, it indicates that external factors have caused the current abnormality of each electrical device.

[0011] A further technical improvement of the present invention is that: each meter module is provided with a public key, and the Internet of Things platform is provided with a private key corresponding to the private key of each meter module; When each meter module transmits current data to the concentrator module, it uses the public key to encrypt the current data marked as "safe" and expose the current data marked as "suspicious", so that the concentrator module can pre-process the current data marked as "suspicious"; After each current data is transmitted to the IoT platform, the IoT platform uses the corresponding private key to decrypt the encrypted current data.

[0012] A further technical improvement of the present invention is that: each electric meter module encrypts the private information in the current data marked as "suspicious" with a public key at the same time, and associates an identification token between the private information and the corresponding current data, and the encrypted private information is transmitted along with the current data marked as "safe"; The concentrator module directly reads the current waveform and current characteristic data in the current data marked as "suspicious" to pre-process the suspicious data; After receiving each current data, the IoT platform uses the private key to decrypt it, and matches the "suspicious" current data with its privacy information through the identification token.

[0013] A further technical improvement of the present invention is that the emergency response mechanism includes establishing a direct communication channel; After the direct communication channel is established on the IoT platform, the corresponding electric meter module converts the identification channel of the current data judged as dangerous usage data into an emergency channel, and stops the screening unit from screening the transmitted current data, and the concentrator module stops the preprocessing of the corresponding current data; The current data is directly uploaded to the IoT platform through the identification channel and monitoring channel; At the same time, the meter module and the concentrator module give priority to allocating bandwidth to the identification channel and the monitoring channel in the direct communication channel on the basis of ensuring the normal transmission of the remaining current data.

[0014] A further technical improvement of the present invention is that the emergency response mechanism performs corresponding warning operations based on the duration, fluctuation amplitude, and occurrence time of the current data determined as dangerous usage data; Warning operations are divided into three risk levels, from low to high: Risk Level I: The meter module and concentrator module first perform internal self-tests, including verifying whether the hardware or software is faulty, checking the calibration status of the sensor, and verifying the current data processing algorithm; The purpose is to eliminate the fault of the system itself through self-test and ensure that there is no problem in the collection and transmission of current data. If the self-test passes, it means that the problem may not be in the device itself; Risk Level II: User notification and confirmation; If the self-check does not find any problems, the IoT platform sends an alarm message to the user of the corresponding meter module. The alarm content includes abnormal current data and possible risk information; The user needs to confirm whether he is aware of the abnormal situation; If the user does not confirm, or the current data is still abnormal after the user confirms, the risk level will be increased to Level III; Risk Level III: Emergency Response; The IoT platform continues to send emergency alerts to users, reminding them that the abnormal behavior may lead to safety issues; In addition, the IoT platform sends danger information to the linked fire-fighting units, including the location of the equipment and the type of anomaly.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention can accurately identify the load characteristics of equipment by building a feature library into the meter module and combining it with real-time collection and analysis of current data. For newly connected electrical equipment, the system identifies its type and matches it with the feature library by comparing features such as current waveform, frequency distribution, and harmonic components. This precise identification mechanism can effectively distinguish dangerous electrical equipment from normal equipment, especially abnormal load characteristics caused by equipment aging, failure, or improper use; And through the built-in screening network, the system can screen the current data in real time and mark "safe" and "suspicious" current data. When "suspicious" data is detected, the system will further determine whether the data comes from dangerous electrical equipment through cross-meter comparison analysis, time series analysis, and load characteristic classification analysis. If the current characteristics of the equipment are significantly different from historical data, or the current waveform does not match the normal load mode of the equipment, the system can determine that the equipment may be faulty or abnormally used, and mark it as "dangerous" equipment, quickly triggering the corresponding early warning mechanism; At the same time, when dangerous electrical equipment is identified, the system can dynamically adjust resource allocation to ensure that "dangerous" current data is processed first. Through the monitoring channel and the identification channel, the dangerous current data is directly uploaded to the IoT platform, and bandwidth and computing resources are prioritized for further analysis and emergency response. At the same time, other "safe" data is transmitted through regular connection channels, ensuring the efficiency and flexibility of system processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0017] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. Example 1

[0019] See also Figure 1 As shown, the present invention provides a load characteristic collection and analysis system for electric energy metering devices based on IoT perception, including multiple electric meter modules, a concentrator module, and an IoT platform; Each of the electric meter modules includes a feature library, a collection unit, an identification unit, a screening unit and a transmission unit; The feature library includes current features generated based on historical big data of electrical equipment; The acquisition unit acquires current data of the new electrical equipment after it is connected to the circuit to obtain the current data of the corresponding electrical equipment. The current data includes current waveform and current characteristics. The current characteristics include current peak value, waveform period, frequency distribution, and harmonic components, which are used to assist in identifying the load characteristics of different electrical equipment. When a new electrical device is connected, the acquisition unit compares the current data with the feature library to determine whether a new electrical device is connected; When a new electrical device is connected to the acquisition unit, if the corresponding device data feature is not matched in the feature library, the acquisition unit stores it in the feature library of the local meter module, and after authorization, transmits it to the Internet of Things platform through the concentrator module; After the IoT platform identifies the current characteristics of the new electrical equipment, it transmits them to the remaining meter modules and updates their characteristic libraries; The identification unit is used to collect current data by the collection unit, and after confirming that a new electrical device is connected, generates a set of new identification channels for transmitting current data of the corresponding electrical device; The method for the identification unit to establish an identification channel includes the following steps: Step 1: The acquisition unit extracts the current features in the current data and matches them with the device data features in the feature library; During matching, similarity calculation is performed to determine whether a new electrical device is connected. After the connected new electrical device successfully matches the device data feature in the feature library, the new electrical device is marked as "recognizable"; Step 2, assigning unique identifiers and timestamps to the current data generated by different electrical devices; The timestamp is the time when the current data is collected, ensuring the uniqueness and order of each current data in time; The unique identifier is a dynamically distributed identifier of each new electrical device by the acquisition unit after the new electrical device is identified; Step 3: The identification unit dynamically divides bandwidth and memory resources to establish a virtual channel based on the unique identifier and timestamp; The virtual channel is used to transmit current data corresponding to a unique identifier and a time stamp; The screening unit has a built-in screening network to identify and mark the current data in each identification channel, including "safe" and "suspicious" marks; The method for establishing the screening network comprises the following steps: S1. Set up dangerous electrical equipment and extract the current characteristics of the dangerous electrical equipment in the feature library, including: Time domain characteristics: such as current waveform, amplitude, period, pulse characteristics, and mutation; Frequency domain characteristics: such as harmonic components and frequency distribution; Current peak: reflects the power demand of electrical equipment; Instantaneous rate of change: current fluctuations and mutations of the equipment; Similarity of waveforms: Calculate the similarity of waveforms based on statistical methods and similarity algorithms; S2. Integrate the current characteristics of hazardous electrical equipment; The various features extracted from the current data of each dangerous electrical device are stored in multi-dimensional vector form. These feature vectors will serve as the basic units in the screening network to help the screening algorithm identify different types of equipment; S3, generating a screening network based on screening rules and topological structures; The screening rules are the core algorithms that determine how to compare the current data with the data in the feature library and then determine whether the current data comes from dangerous electrical equipment, including; Feature extraction: extract time domain features and frequency domain features from current data; Matching rules: preset matching thresholds. When the similarity between the characteristics of current data and the characteristics of dangerous equipment in the feature library exceeds the matching threshold, the data will be marked as "suspicious"; For specific dangerous electrical equipment, the screening network defines a corresponding feature module. In this embodiment, for a battery charger, a high-frequency pulse feature is set in the feature module to facilitate the screening network to quickly identify the battery charger; The topology of the screening network determines how the current data flows in the screening network and how it is classified based on feature judgment. It mainly includes the following parts: Data receiving layer: responsible for receiving data from the electric meter module, including current data of various electrical equipment; Screening layer: Match the current data according to the screening rules and determine whether the current data belongs to the current characteristics of dangerous equipment; Decision-making layer: Mark the current data as "safe" or "suspicious" based on the screening results; Output layer: adjust the resource allocation of the corresponding recognition channel according to the classification results; For current data marked as "dangerous", its priority in the virtual channel is increased, and more bandwidth and computing resources are allocated; By setting up a screening network, the calculation pressure of the meter module is reduced; For current data marked as "safe", bandwidth and computing resources that meet regular monitoring requirements are allocated; The transmission unit transmits the current data marked as “safe” and the current data marked as “suspicious” to the concentrator module; The concentrator module is used to receive the current data transmitted by each meter module, including a monitoring channel and a connection channel. The monitoring channel preferentially allocates bandwidth and computing resources, and pre-processes each current data, including: The current data marked as "safe" is compressed to remove redundant data, reduce the transmission bandwidth and computing resource consumption, and then transmitted to the IoT platform through the connection channel; Compare and analyze each current data marked as "suspicious" to determine whether the corresponding current data is dangerous use data; If the current data marked as "suspicious" is judged to be dangerous usage data, it will be transmitted to the IoT platform through the monitoring channel; The comparative analysis of each current data marked as "suspicious" by the concentrator module includes: Comparative analysis across electricity meters: The concentrator module compares the current data of the same type of electrical equipment in multiple meter modules to determine whether there is an abnormality; If the current data of the same type of electrical equipment has a large difference, it means that the equipment may be faulty or not being used normally. The difference value can be obtained based on the historical big data analysis of the same type of electrical equipment; Time Series Analysis: The concentrator module compares the current suspicious current data with the historical data of the electrical equipment. If there is a sharp fluctuation in a short period of time, the concentrator module compares the current fluctuation of the electrical equipment with that of other electrical equipment in the same time period to determine whether it is a normal working state or an abnormal phenomenon has occurred; Analysis of load characteristics classification: The concentrator module performs comparative analysis based on the typical load patterns of different electrical devices. If the current data of a certain electrical device does not match its normal load pattern, it indicates that the device is faulty or is being used abnormally; Simultaneous and differential analysis: The concentrator module monitors whether the current data of each electrical device changes synchronously. If the current waveforms of multiple electrical devices change synchronously, such as the peaks and troughs appear in the same time period, it indicates that external factors have caused the current abnormality of each electrical device; The concentrator module includes a monitoring channel and a connection channel. The monitoring channel is allocated bandwidth and computing resources first; And the concentrator module transmits the current data judged as dangerous usage data to the IoT platform through the monitoring channel; And the rest of the current data, the concentrator module transmits to the IoT platform through the connection channel; After receiving each current data, the IoT platform activates an emergency response mechanism for the current data determined to be dangerous usage data; Emergency response mechanisms include establishing direct communication channels; After the direct communication channel is established on the IoT platform, the corresponding electric meter module converts the identification channel of the current data judged as dangerous usage data into an emergency channel, and stops the screening unit from screening the transmitted current data, and the concentrator module stops the preprocessing of the corresponding current data; The current data is directly uploaded to the IoT platform through the identification channel and monitoring channel; At the same time, the meter module and the concentrator module give priority to allocating bandwidth to the identification channel and the monitoring channel in the direct communication channel on the basis of ensuring the normal transmission of the remaining current data; The emergency response mechanism performs corresponding warning operations based on the duration, fluctuation amplitude, and occurrence time of the current data judged as dangerous usage data; Warning operations are divided into three risk levels, from low to high: Risk Level I: The meter module and concentrator module first perform internal self-tests, including verifying whether the hardware or software is faulty, checking the calibration status of the sensor, and verifying the current data processing algorithm; Its purpose is to eliminate the fault of the system itself through self-test and ensure that there is no problem in the collection and transmission of current data. If the self-test passes, it means that the problem may not be in the device itself; Risk Level II: User notification and confirmation; If the self-check does not find any problems, the IoT platform sends an alarm message to the user of the corresponding meter module. The alarm content includes abnormal current data and possible risk information; The user needs to confirm whether he is aware of the abnormal situation; If the user does not confirm, or the current data is still abnormal after the user confirms, the risk level will be increased to Level III; Risk Level III: Emergency Response; The IoT platform continues to send emergency alerts to users, reminding them that the abnormal behavior may lead to safety issues; And the IoT platform sends danger information to the linked fire units, including the location of the equipment and the type of anomaly; The remaining current data is stored, optimized and monitored.

[0020] In this embodiment, when the battery car charger in the home is connected to the power grid, the collection unit of the meter module immediately detects the device connection and collects the corresponding current data. The collected current data includes current waveform, current peak value, waveform period, frequency distribution, harmonic components and other characteristics, which are used to help identify the electrical device; The screening unit compares the obtained current data with the current data after the screening network to determine whether it is a battery vehicle charger; If it is recognized that the battery vehicle charger is connected, the screening unit transmits the current data to the concentrator module through the transmission unit; The concentrator module receives the current data from the meter module. If the current data is "suspicious", the concentrator module pre-processes the current data, compares the current data of similar devices in different meters, performs synchronization and differentiation analysis, and further confirms whether it is a dangerous behavior; If the current data is determined to be a typical current characteristic of a battery vehicle charger, the behavior is marked as "dangerous"; If the current data is marked as "dangerous", the concentrator module will transmit the data to the IoT platform first. For other normal data, the concentrator module will compress the data to reduce bandwidth consumption; When the IoT platform receives current data marked as "dangerous", it immediately activates the emergency response mechanism. Based on the characteristics of the current data, the IoT platform determines the risk level and starts the corresponding processing procedure. Example 2

[0021] The load characteristics collection and analysis system of electric energy metering devices based on IoT perception includes multiple electric meter modules, concentrator modules, and IoT platform; Each of the electric meter modules includes a feature library, a collection unit, an identification unit, a screening unit and a transmission unit; The feature library includes current features generated based on historical big data of electrical equipment; The acquisition unit acquires current data of the new electrical equipment after it is connected to the circuit to obtain the current data of the corresponding electrical equipment. The current data includes current waveform and current characteristics. The current characteristics include current peak value, waveform period, frequency distribution, and harmonic components, which are used to assist in identifying the load characteristics of different electrical equipment. When a new electrical device is connected, the acquisition unit compares the current data with the feature library to determine whether a new electrical device is connected; The identification unit is used to collect current data by the collection unit, and after confirming that a new electrical device is connected, generates a set of new identification channels for transmitting current data of the corresponding electrical device; The screening unit has a built-in screening network to identify and mark the current data in each identification channel, including "safe" and "suspicious" marks; The transmission unit transmits the current data marked as “safe” and the current data marked as “suspicious” to the concentrator module; The concentrator module is used to receive the current data transmitted by each meter module, including a monitoring channel and a connection channel. The monitoring channel preferentially allocates bandwidth and computing resources, and pre-processes each current data, including: The current data marked as "safe" is compressed to remove redundant data, reduce the transmission bandwidth and computing resource consumption, and then transmitted to the IoT platform through the connection channel; Compare and analyze each current data marked as "suspicious" to determine whether the corresponding current data is dangerous use data; If the current data marked as "suspicious" is judged to be dangerous usage data, it will be transmitted to the IoT platform through the monitoring channel; After receiving each current data, the IoT platform activates an emergency response mechanism for the current data determined to be dangerous usage data; The remaining current data is stored, optimized and monitored.

[0022] Compared with Example 1, the comparative analysis of each current data marked as "suspicious" by the concentrator module in Example 2 includes: Time Series Analysis: The concentrator module compares the current suspicious current data with the historical data of the electrical equipment. If there is a sharp fluctuation in a short period of time, the concentrator module compares the current fluctuation of the electrical equipment with that of other electrical equipment in the same time period to determine whether it is a normal working state or an abnormal phenomenon has occurred; Analysis of load characteristics classification: The concentrator module performs comparative analysis based on the typical load patterns of different electrical devices. If the current data of a certain electrical device does not match its normal load pattern, it indicates that the device is faulty or is being used abnormally; Simultaneous and differential analysis: The concentrator module monitors whether the current data of each electrical device marked as "suspicious" changes synchronously. If the current waveforms of multiple electrical devices change synchronously, such as peaks and troughs appearing in the same time period, it indicates that external factors have caused the current abnormality of each electrical device.

[0023] And each meter module is provided with a public key, and the IoT platform is provided with a private key corresponding to the private key of each meter module; Public and private keys are updated periodically to prevent potential security vulnerabilities caused by long-term use of the same key; When each meter module transmits current data to the concentrator module, it uses the public key to encrypt the current data marked as "safe" and expose the current data marked as "suspicious", so that the concentrator module can pre-process the current data marked as "suspicious"; After each current data is transmitted to the IoT platform, the IoT platform uses the corresponding private key to decrypt the encrypted current data; At the same time, each meter module encrypts the private information in the current data marked as "suspicious" with a public key, and associates an identification token between the private information and the corresponding current data. The encrypted private information is transmitted along with the current data marked as "safe"; The identification token is generated by a hash algorithm and a timestamp is added. At the same time, the identification token is set with an effective identification period, so that the identification token is only valid within the identification period and automatically becomes invalid after the expiration; The concentrator module directly reads the current waveform and current characteristic data in the current data marked as "suspicious" to pre-process the suspicious data; After receiving each current data, the IoT platform uses the private key to decrypt it, and matches the "suspicious" current data with its privacy information through the identification token.

[0024] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. The load characteristics collection and analysis system of electric energy metering device based on IoT perception includes an electric meter module, a concentrator module, and an IoT platform, and is characterized in that: Each of the electric meter modules includes a feature library, a collection unit, an identification unit, a screening unit and a transmission unit; The feature library includes current features generated based on historical big data of electrical equipment; The acquisition unit acquires circuit fluctuations to obtain current data, and compares the current data with the feature library to determine whether new electrical equipment is connected; After confirming that a new electrical device is connected, the identification unit generates a new set of identification channels to transmit current data of the corresponding electrical device; The screening unit has a built-in screening network to identify and mark the current data in each identification channel, including "safe" and "suspicious" marks; The transmission unit transmits each current data to the concentrator module; The concentrator module receives the current data transmitted by each electric meter module and pre-processes each current data; The current data marked as "safe" is transmitted to the IoT platform through the connection channel after data compression processing; The current data marked as "suspicious" is transmitted to the IoT platform through the monitoring channel after being judged as dangerous usage data; After receiving each current data, the IoT platform activates an emergency response mechanism for the current data determined to be dangerous usage data; The remaining current data is stored, optimized and monitored.

2. According to claim 1, the load characteristics acquisition and analysis system of electric energy metering device based on IoT perception is characterized in that: The method for the identification unit to establish an identification channel includes the following steps: Step 1: The acquisition unit extracts the current features in the current data and matches them with the device data features in the feature library; Step 2, assigning unique identifiers and timestamps to the current data generated by different electrical devices; Step 3: The identification unit dynamically divides bandwidth and memory resources to establish a virtual channel based on the unique identifier and timestamp; The virtual channel is used to transmit current data corresponding to a unique identifier and a time stamp.

3. According to claim 1, the load characteristics acquisition and analysis system of electric energy metering device based on IoT perception is characterized in that: When a new electrical device is connected to the acquisition unit, if the corresponding device data feature is not matched in the feature library, the acquisition unit stores it in the feature library of the local meter module, and after authorization, transmits it to the Internet of Things platform through the concentrator module; After the IoT platform identifies the current characteristics of the new electrical equipment, it transmits them to the remaining meter modules and updates their feature libraries.

4. According to claim 1, the load characteristics acquisition and analysis system of electric energy metering device based on IoT perception is characterized in that: The method for establishing the screening network comprises the following steps: S1. Set up dangerous electrical equipment and extract the current characteristics of the dangerous electrical equipment in the characteristic library; S2. Integrate the current characteristics of hazardous electrical equipment; Various features extracted from the current data of each dangerous electrical equipment are stored in multi-dimensional vector form; S3. Generate a screening network based on screening rules and topological structure.

5. According to claim 1, the load characteristics acquisition and analysis system of electric energy metering device based on IoT perception is characterized in that: Emergency response mechanisms include establishing direct communication channels; After the direct communication channel is established on the IoT platform, the corresponding electric meter module converts the identification channel of the current data judged as dangerous usage data into an emergency channel, and stops the screening unit from screening the transmitted current data, and the concentrator module stops the preprocessing of the corresponding current data; The corresponding current data is directly uploaded to the IoT platform through the identification channel and monitoring channel; At the same time, the meter module and the concentrator module give priority to allocating bandwidth to the identification channel and the monitoring channel in the direct communication channel on the basis of ensuring the normal transmission of the remaining current data.

6. The load characteristics acquisition and analysis system of electric energy metering device based on IoT perception according to claim 5 is characterized in that: The emergency response mechanism performs corresponding warning operations based on the duration, fluctuation amplitude, and occurrence time of the current data judged as dangerous usage data.

7. The load characteristic acquisition and analysis system of electric energy metering device based on IoT perception according to claim 6 is characterized in that: Warning operations are divided into three risk levels, from low to high: Risk Level I: The meter module and concentrator module first perform internal self-tests, including verifying whether the hardware or software is faulty, checking the calibration status of the sensor, and verifying the current data processing algorithm; Risk Level II: User notification and confirmation; If the self-check does not find any problems, the IoT platform sends an alarm message to the user of the corresponding meter module; If the user does not confirm, or the current data is still abnormal after the user confirms, the risk level will be increased to Level III; Risk Level III: Emergency Response; The IoT platform continues to send emergency alerts to users, reminding them that the abnormal behavior may lead to safety issues; And the IoT platform sends danger information to the linked fire units.

8. The load characteristics acquisition and analysis system of electric energy metering device based on IoT perception according to claim 1 is characterized in that: Each meter module is provided with a public key, and the IoT platform is provided with a private key corresponding to the private key of each meter module; When each meter module transmits current data to the concentrator module, it uses the public key to encrypt the current data marked as "safe" and expose the current data marked as "suspicious"; After each current data is transmitted to the IoT platform, the IoT platform uses the corresponding private key to decrypt the encrypted current data.

9. The load characteristic acquisition and analysis system of electric energy metering device based on IoT perception according to claim 8 is characterized in that: Each meter module encrypts the private information in the current data marked as "suspicious" with a public key, and associates an identification token between the private information and the corresponding current data. The encrypted private information is transmitted along with the current data marked as "safe". The concentrator module directly reads the current waveform and current characteristic data in the current data marked as "suspicious" for preprocessing; After receiving each current data, the IoT platform uses the private key to decrypt it, and matches the "suspicious" current data with its privacy information through the identification token.

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