A user power data collection and analysis terminal based on the Internet of Things

Abstract

The present invention belongs to the technical field of user power data collection and analysis, and relates to a user power data collection and analysis terminal based on the Internet of Things. The present invention helps to evaluate the user's load contribution and potential impact on the power grid by analyzing the energy balance and power grid influence of each designated user, and then take targeted energy-saving measures to improve power supply reliability and economy. By obtaining each user belonging to each user type, it is helpful to deeply understand the power consumption model of different user types, so as to formulate more accurate user energy-saving transformation and power consumption behavior adjustment, reduce energy consumption and carbon emissions, and by analyzing the power quality and fault conditions of each user belonging to each user type, it is conducive to quickly locating the fault point, improving the efficiency of fault detection and maintenance, reducing the power outage time and economic losses caused by faults, and taking targeted measures to improve power quality and protect power equipment and power grid safety.

Description

Technical Field

[0001] The invention belongs to the technical field of user electric energy data collection and analysis, and relates to a user electric energy data collection and analysis terminal based on the Internet of Things. Background Art

[0002] User power data refers to various information related to user power consumption behavior, which is collected by power meters, sensors and other monitoring equipment. With the continuous development of the power market and the diversification of user power demand, the requirements for power management are getting higher and higher. Users need to understand their own power consumption, including power consumption, power consumption time, power load, etc., in order to better manage power consumption and save energy. At the same time, power companies also need to collect user power data to settle electricity bills and plan power grids. Therefore, the collection and analysis system for user power data is of great significance and role.

[0003] In the prior art, there are also some related solutions involving the collection and analysis of user power data. For example, China Patent Publication No. CN115357642A is a method and system for collecting and analyzing power consumption based on deep learning. It provides an energy consumption collection and analysis method from bottom-level data collection to top-level data analysis. At the same time, when analyzing energy consumption, a classification model is first established, and the collected energy consumption sequence is classified according to the characteristics. A prediction model is established based on the classification. Different prediction models are used for energy consumption prediction for different types of users, which improves the accuracy, generalization and interpretability.

[0004] Another Chinese patent publication number is CN113722300A, which is a data cleaning system and method for calculating the operation error of an electric energy meter based on electricity consumption characteristics. The system can mark the abnormal electric energy meter data or the electricity consumption information of the power supply company collected during the operation error calculation of the electric energy meter, and then delete the abnormal data to avoid the abnormal data and normal data being mixed into the range of the electric energy meter operation error calculation, thereby ensuring the accuracy of the electric energy meter operation error calculation, providing qualified and high-quality data for the error data analysis of the user's electric energy meter, and providing an analysis basis for the operation error of the electric energy meter.

[0005] Although the above schemes propose some solutions for the collection and analysis of user power data, there are still certain limitations: on the one hand, the existing schemes mainly analyze user power data after classification or avoid analysis errors, but the existing schemes ignore the analysis of the user's influence on the power grid and classify and analyze it accordingly, so that it is impossible to evaluate the user's load contribution and potential impact on the power grid, and it is impossible to timely discover abnormalities and waste in energy use, so as to take targeted energy-saving measures and improve power grid dispatching decisions and optimization.

[0006] On the other hand, existing solutions ignore the detection and analysis of user power quality and the causes of abnormal power data, making it impossible to quickly locate the fault point, thereby failing to improve the efficiency of fault detection and maintenance, and failing to reduce power outage time and economic losses caused by faults. It is also not conducive to taking targeted measures to improve power quality and protect power equipment and power grid safety. Summary of the invention

[0007] In view of this, in order to solve the problems raised in the above background technology, a user power data collection and analysis terminal based on the Internet of Things is now proposed.

[0008] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a user power data collection and analysis terminal based on the Internet of Things, including: a user power data acquisition and analysis module, a user type acquisition module, a user data acquisition module, a user data analysis module and a cloud database.

[0009] The user power data acquisition and analysis module is used to record each user connected to the distributed power source as a designated user, obtain the power data of each designated user, analyze the energy balance and grid impact of each designated user, and provide feedback on the energy balance and grid impact of each designated user.

[0010] The user type acquisition module is used to analyze the user type of each specified user, and then obtain the users to which each user type belongs.

[0011] The user data acquisition module is used to obtain the electric energy monitoring data and electric meter operation fault data of each user of each user type.

[0012] The user data analysis module is used to analyze the power quality of each user of each user type and provide feedback, analyze the fault conditions of each user of each user type, and provide early warning and feedback.

[0013] The cloud database is used to store the user type evaluation coefficient range corresponding to each user type, store the current stability threshold, voltage stability threshold and grid frequency synchronization stability threshold, store the power factor threshold and power factor stability threshold.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention obtains the electric energy data of each designated user, analyzes the energy balance and grid influence of each designated user, and provides feedback, which is helpful to evaluate the user's load contribution and potential impact on the grid, and is conducive to timely discovering anomalies and waste in energy use, so as to take targeted energy-saving measures, and is helpful to design a more scientific and reasonable grid layout, improve power supply reliability and economy, and ensure stable operation of the grid.

[0015] 2. The present invention analyzes the user type of each designated user and then obtains the users to which each user type belongs, which helps to gain a deeper understanding of the electricity consumption models of different user types, thereby formulating more accurate energy-saving transformation and electricity consumption behavior adjustments for users, thereby reducing energy consumption and carbon emissions.

[0016] 3. The present invention analyzes the power quality of each user of each user type and provides feedback, analyzes the fault conditions of each user of each user type and provides early warning and feedback, detects and analyzes the user's power quality and the causes of abnormal power data, which is conducive to quickly locating the fault point, thereby improving the efficiency of fault detection and maintenance, shortening the power outage time, increasing the speed of power supply restoration, reducing the power outage time and economic losses caused by faults, and is conducive to taking targeted measures to improve power quality and protect electrical equipment and power grid safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0019] Figure 2 It is a schematic diagram of the system business flow of the present invention.

[0020] Figure 3 This is a schematic diagram of fault condition analysis for users belonging to the user type of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1As shown, the present invention provides a user power data acquisition and analysis terminal based on the Internet of Things, and the specific modules are distributed as follows: user power data acquisition and analysis module, user type acquisition module, user data acquisition module, user data analysis module and cloud database. Among them, the connection mode between the modules is: the user power data acquisition and analysis module is connected to the user type acquisition module, the user type acquisition module is connected to the user data acquisition module, the user data acquisition module is connected to the user data analysis module, and the cloud database is connected to the user type acquisition module and the user data analysis module respectively.

[0023] It needs to be further explained that the system business flow diagram is as follows Figure 2 shown.

[0024] The user power data acquisition and analysis module is used to record each user connected to the distributed power source as a designated user, obtain the power data of each designated user, analyze the energy balance and grid impact of each designated user, and provide feedback on the energy balance and grid impact of each designated user.

[0025] As a preferred feasibility example, the electric energy data of each designated user includes the forward active power, reverse active power, forward reactive power and reverse reactive power in each year, month, day and time period.

[0026] It needs to be further explained that the specific method of obtaining the forward active power, reverse active power, forward reactive power and reverse reactive power of each designated user in each year, month, day and time period is: directly extracting the forward active power, reverse active power, forward reactive power and reverse reactive power of each designated user in each year, month, day and time period from the bidirectional smart meter deployed by each designated user.

[0027] As a preferred feasibility example, the energy balance of each designated user is specifically analyzed by extracting the forward active power and reverse active power of each designated user in each year, month, day and time period, respectively recorded as ,in , is the ID of each specified user. is the number of specified users, , is the number of each year, is the number of years, , is the number of each month, , is the number of each day, is the number of days, , The number of each time period.

[0028] According to the calculation formula Get the net active power of each specified user in each year, month, day and time period , and further add them up to get the total net active power of each designated user .

[0029] It should be further explained that the specific method for obtaining the total net active power of each designated user is: according to the cumulative calculation formula Get the total net active power of each specified user .

[0030] like , then the The energy balance of a specified user is recorded as a positive balance if , then the The energy balance of a specified user is recorded as energy balance. , then the The energy balance of a specified user is recorded as a negative balance.

[0031] As a preferred feasibility example, the specific analysis method of the grid influence of each designated user includes: extracting the forward reactive power and reverse reactive power of each designated user in each year, month, day and time period, and recording them as .

[0032] According to the calculation formula Get the net reactive power of each specified user in each year, month, day and time period , and further add them up to get the total net reactive power of each designated user .

[0033] It should be further explained that the specific method of obtaining the total net reactive power of each designated user is: according to the cumulative calculation formula Get the total net active power of each specified user .

[0034] Analyze the impact of each designated user on the power grid .

[0035] The present invention obtains the electric energy data of each designated user, analyzes the energy balance and grid influence of each designated user, and feeds back the data, which is helpful to evaluate the user's load contribution and potential impact on the grid, and is conducive to timely discovering abnormalities and waste in energy use, so as to take targeted energy-saving measures, and is conducive to designing a more scientific and reasonable grid layout, improving power supply reliability and economy, and ensuring stable operation of the grid.

[0036] The user type acquisition module is used to analyze the user type of each specified user, and then obtain the users to which each user type belongs.

[0037] As a preferred feasibility example, in the user type analysis process of each designated user, it is necessary to obtain each net production period, each energy balance period and each net consumption period of each designated user in each year, month and day. The specific acquisition method includes: extracting the net active power of each designated user in each year, month, day and time period , and compare them with 0 respectively. If the net active power of a specified user in a certain period of a certain year, month, day, and time is greater than 0, then the specified user in that year, month, day, and time is recorded as the net production period of the specified user in that year, month, and day. If the net active power of a specified user in that year, month, day, and time is equal to 0, then the specified user in that year, month, day, and time is recorded as the energy balance period of the specified user in that year, month, and day. If the net active power of a specified user in that year, month, day, and time is less than 0, then the specified user in that year, month, day, and time is recorded as the net consumption period of the specified user in that year, month, and day. Then, the net production period, energy balance period, and net consumption period of each specified user in each year, month, and day are obtained, and they are fed back.

[0038] As a preferred feasibility example, the user type of each designated user is specifically analyzed in the following manner: extracting each net production period, each energy balance period and each net consumption period of each designated user in each year, month and day, further counting the number of net production periods, energy balance periods and net consumption periods of each designated user in each year, month and day, and then accumulating them to obtain the total number of net production periods, energy balance periods and net consumption periods of each designated user, which are recorded as .

[0039] It should be further explained that the specific statistical method for the total number of net production time periods, total number of energy balance time periods and total number of net consumption time periods of each designated user is: extract the net production time periods, energy balance time periods and net consumption time periods of each designated user in each year, month and day, and record them as , further according to the cumulative formula Get the total number of net production periods for each specified user , according to the cumulative formula Get the total number of energy balance periods for each specified user , according to the cumulative formula Get the total number of net consumption periods for each specified user .

[0040] Analyze the user type evaluation coefficient of each specified user ,in is the set ratio threshold.

[0041] It should be further explained that the set ratio threshold can be set to 0.25.

[0042] The user type evaluation coefficient of each designated user is matched with the user type evaluation coefficient range corresponding to each user type stored in the cloud database to obtain the user type of each designated user, and further classified according to the user type to obtain the users belonging to each user type.

[0043] It should be further explained that the user types include but are not limited to net producers, net production-biased users, energy balancers, net consumption-biased users and net consumers.

[0044] The present invention analyzes the user type of each designated user and then obtains the users to which each user type belongs, which helps to gain a deeper understanding of the electricity consumption models of different user types, thereby formulating more accurate energy-saving transformation and electricity consumption behavior adjustments for users, and reducing energy consumption and carbon emissions.

[0045] The user data acquisition module is used to obtain the electric energy monitoring data and electric meter operation fault data of each user of each user type.

[0046] As a preferred feasibility example, the electric energy monitoring data of the electric meter of each user belonging to each user type includes the current, voltage, grid frequency synchronization and power factor of each year, month, day and time period.

[0047] It needs to be further explained that the specific method of obtaining the current, voltage, grid frequency synchronization and power factor of each user of each user type in each year, month, day and time period is: directly extracting the current, voltage, grid frequency synchronization and power factor of each user of each user type in each year, month, day and time period from the bidirectional smart meters deployed for each user of each user type.

[0048] The electric meter operation fault data of each user belonging to each user type includes the number of power outages, the number of voltage surges, the number of voltage dips and the number of voltage overloads.

[0049] It needs to be further explained that the specific method of obtaining the number of power outages, voltage swells, voltage dips and voltage overloads for each user of each user type is: directly extracting the number of power outages, voltage swells, voltage dips and voltage overloads for each user of each user type from the electric meter maintenance record sheet of each user of each user type.

[0050] The user data analysis module is used to analyze the power quality of each user of each user type and provide feedback, analyze the fault conditions of each user of each user type, and provide early warning and feedback.

[0051] As a preferred feasibility example, the specific analysis method of the power quality of each user belonging to each user type includes: extracting the current, voltage and grid frequency synchronization of each user belonging to each user type in each year, month, day and time period, and recording them as ,in , is the number of each user type, is the number of user types, , is the ID of each user, is the number of users.

[0052] According to the analytical formula Get the current stability of each user belonging to each user type ,in is the set current allowable difference. Similarly, the voltage stability of each user belonging to each user type can be obtained. Frequency synchronization and stability of the power grid .

[0053] It should be further explained that the set current allowable difference can be set to 0.1.

[0054] It should be explained that the analysis of current stability is to control the fluctuation of the user's current within a certain range, so as to ensure the stability of the user's electricity consumption. The current allowable difference is set to 0.1, which is based on the precise calculation and evaluation of the user's current stability to ensure that the user's electricity consumption can maintain normal operation within the difference range.

[0055] It should be further explained that the specific method for obtaining the voltage stability and grid frequency synchronization stability of each user belonging to each user type is: according to the analytical formula Get the voltage stability of each user belonging to each user type ,in is the set voltage allowable difference.

[0056] It should be further explained that the set current allowable difference can be set to 0.1.

[0057] It should be explained that the voltage stability analysis is to control the voltage fluctuation of users within a certain range, so as to ensure the stability of users' electricity consumption. The voltage tolerance is set to 0.1, which is based on the accurate calculation and evaluation of users' voltage stability, so as to ensure that users' electricity consumption can maintain normal operation within the tolerance range.

[0058] According to the analytical formula Get the grid frequency synchronization stability of each user belonging to each user type ,in It is the allowable difference of the set grid frequency synchronization.

[0059] It should be further explained that the set grid frequency synchronization allowable difference can be set to 0.1.

[0060] It should be explained that the analysis of the stability of the grid frequency synchronization is to control the fluctuation of the user's grid frequency synchronization within a certain range, so as to ensure the stability of the user's electricity consumption. The grid frequency synchronization tolerance is set to 0.1, which is based on the precise calculation and evaluation of the user's grid frequency synchronization stability, to ensure that within the tolerance range, the user's electricity consumption can maintain normal operation.

[0061] Analyze the power quality evaluation coefficient of each user belonging to each user type ,in They are the current stability threshold, voltage stability threshold and grid frequency synchronization stability threshold extracted from the cloud database respectively.

[0062] The power quality evaluation coefficient of each user belonging to each user type is compared with the set power quality evaluation coefficient threshold. If the power quality evaluation coefficient of a user belonging to a certain user type is greater than the power quality evaluation coefficient threshold, the power quality situation of the user belonging to this user type is recorded as qualified; otherwise, the power quality situation of the user belonging to this user type is recorded as unqualified, thereby obtaining the power quality situation of each user belonging to each user type.

[0063] It needs to be further explained that the significance and role of obtaining the power quality conditions of each user belonging to each user type and providing feedback thereon are as follows: each user belonging to each user type whose power quality conditions are unqualified is recorded as a marked user of each user type, and their corresponding numbers are fed back, and further detailed analysis is performed on them to determine the existence and causes of power quality problems and to take solutions.

[0064] It should be explained that the solutions include but are not limited to the following: (1) Optimizing the power electronic equipment for connecting distributed energy to the power grid, such as inverters, rectifiers, etc., to reduce the generation of harmonics and electromagnetic interference.

[0065] (2) In terms of voltage fluctuation, voltage stabilizers, voltage regulators and other equipment can be used to adjust and stabilize the voltage in the power grid. In terms of frequency deviation, frequency stabilizers, frequency regulators and other equipment can be used to adjust and stabilize the frequency in the power grid.

[0066] (3) Optimize the topology structure by rationally adjusting the layout of the power grid’s transmission lines and substations to reduce losses and voltage drops during power transmission and improve the transmission efficiency and stability of power quality.

[0067] (4) Improve grid parameters and adjust grid parameters such as resistance, inductance and capacitance to adapt to changes after the access of distributed power sources and reduce the occurrence of power quality problems.

[0068] As a preferred feasibility example, in the process of fault condition analysis of each user belonging to each user type, it is necessary to construct the equipment abnormality evaluation index and the power grid abnormality evaluation index of each user belonging to each user type. The specific analysis method includes: extracting the power factor of each user belonging to each user type in each year, month, day and time period , the power factor stability of each user of each user type can be obtained by analyzing the current stability of each user of each user type. , analyze the device abnormality evaluation index of each user belonging to each user type ,in They are the power factor threshold and power factor stability threshold extracted from the cloud database respectively.

[0069] It should be further explained that the specific method for obtaining the power factor stability of each user belonging to each user type is: according to the calculation formula Get the power factor stability of each user belonging to each user type ,in It is the allowable difference of the set power factor.

[0070] It should be further explained that the set power factor allowable difference value can be set to 0.01.

[0071] It should be explained that power factor is an important concept in electrical engineering, which represents the ratio of useful work to total electrical power in a circuit. Setting the power factor tolerance to 0.01 allows the equipment to fluctuate within a certain range, which in turn limits the power factor fluctuation range, reduces reactive power loss, improves economic benefits and energy efficiency, and ensures that the performance and efficiency of the equipment will not be seriously affected. A low power factor will increase reactive power loss, thereby reducing energy efficiency.

[0072] Extract the number of power outages, voltage surges, voltage sags, and voltage overloads for each user of each user type and record them as , analyze the power grid abnormality evaluation index of each user belonging to each user type .

[0073] As a preferred feasibility example, the specific analysis method of the fault warning situation of each user belonging to each user type includes: extracting the equipment abnormality evaluation index and the power grid abnormality evaluation index of each user belonging to each user type, and comparing them with the set equipment abnormality evaluation index threshold and the power grid abnormality evaluation index threshold respectively.

[0074] If the equipment abnormality evaluation index of a user of a certain user type is greater than or equal to the equipment abnormality evaluation index threshold, and the power grid abnormality evaluation index of the user of the user type is less than the power grid abnormality evaluation index threshold, then the fault condition of the user of the user type is recorded as equipment abnormality.

[0075] If the equipment abnormality evaluation index of a user of a certain user type is less than the equipment abnormality evaluation index threshold, and the power grid abnormality evaluation index of the user of the user type is greater than or equal to the power grid abnormality evaluation index threshold, then the fault condition of the user of the user type is recorded as a power grid abnormality.

[0076] If the equipment abnormality evaluation index of a user of a certain user type is greater than or equal to the equipment abnormality evaluation index threshold, and the power grid abnormality evaluation index of the user of the user type is greater than or equal to the power grid abnormality evaluation index threshold, then the fault condition of the user of the user type is recorded as equipment and power grid abnormality.

[0077] Then the fault conditions of each user belonging to each user type are obtained.

[0078] It should be further explained that the analysis diagram of the failure conditions of each user belonging to each user type is as follows: Figure 3 shown.

[0079] As a preferred feasibility example, the specific operation of issuing an early warning for the fault conditions of each user belonging to each user type is: extracting the fault conditions of each user belonging to each user type, and if it is a device abnormality, further determining the abnormal device, and issuing an early warning and feedback.

[0080] It should be further explained that the specific method of determining abnormal devices is to obtain the power factor of each device of each user of each user type in each year, month, day and time period. ,in , is the number of each device, is the number of devices, according to the calculation formula Get the power factor stability of each device of each user belonging to each user type , analyze the abnormal evaluation index of each device of each user belonging to each user type .

[0081] The abnormal evaluation index of each device of each user of each user type is compared with the set device abnormal evaluation index threshold. If the abnormal evaluation index of a device of a user of a certain user type is greater than or equal to the device abnormal evaluation index threshold, the device of the user of this user type is recorded as the abnormal device of the user of this user type, and then the abnormal devices of the user of this user type are obtained, and their corresponding numbers are fed back.

[0082] If it is a power grid abnormality, it will be warned and fed back.

[0083] If it is an abnormality of equipment and power grid, the abnormal equipment will be further identified, and early warning and feedback will be issued.

[0084] The cloud database is used to store the user type evaluation coefficient range corresponding to each user type, store the current stability threshold, the voltage stability threshold and the grid frequency synchronization stability threshold, store the power factor threshold and the power factor stability threshold.

[0085] The present invention analyzes the power quality of each user of each user type and feeds back the same, analyzes the fault conditions of each user of each user type and gives early warning and feedback, detects and analyzes the user's power quality and the cause of abnormal power data, thereby facilitating the rapid location of the fault point, thereby improving the efficiency of fault detection and maintenance, shortening the power outage time, increasing the speed of power supply restoration, reducing the power outage time and economic losses caused by the fault, and facilitating the adoption of targeted measures to improve the power quality and protect the safety of electrical equipment and power grids.

[0086] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A user power data collection and analysis terminal based on the Internet of Things, characterized by: include: The user power data acquisition and analysis module is used to record each user connected to the distributed power source as a designated user, obtain the power data of each designated user, analyze the energy balance and grid impact of each designated user, and provide feedback on the energy balance and grid impact of each designated user; A user type acquisition module is used to analyze the user type of each specified user and then obtain the users to which each user type belongs; A user data acquisition module is used to obtain the meter power monitoring data and meter operation fault data of each user of each user type; The user data analysis module is used to analyze the power quality of each user of each user type and provide feedback, analyze the fault conditions of each user of each user type, and provide early warning and feedback; A cloud database, used to store the user type evaluation coefficient range corresponding to each user type, store the current stability threshold, voltage stability threshold and grid frequency synchronization stability threshold, store the power factor threshold and power factor stability threshold; The specific analysis methods of the energy balance of each designated user include: Extract the forward active power and reverse active power of each specified user in each year, month, day and time period, and record them as ,in , is the ID of each specified user. is the number of specified users, , is the number of each year, is the number of years, , is the number of each month, , is the number of each day, is the number of days, , is the number of each time period; According to the calculation formula Get the net active power of each specified user in each year, month, day and time period , and further add them up to get the total net active power of each designated user ; like , then the The energy balance of a specified user is recorded as a positive balance if , then the The energy balance of a specified user is recorded as energy balance. , then the The energy balance of a specified user is recorded as a negative balance; The specific analysis method of the power grid influence of each designated user includes: Extract the forward reactive power and reverse reactive power of each specified user in each year, month, day and time period, and record them as ; According to the calculation formula Get the net reactive power of each specified user in each year, month, day and time period , and further add them up to get the total net reactive power of each designated user ; Analyze the impact of each designated user on the power grid .

2. According to the user power data collection and analysis terminal based on the Internet of Things according to claim 1, it is characterized by: The electric energy data of each designated user includes the forward active power, reverse active power, forward reactive power and reverse reactive power in each year, month, day and time period; The electric energy monitoring data of the electric meter of each user belonging to each user type includes the current, voltage, grid frequency synchronization and power factor of each year, month, day and time period; The electric meter operation fault data of each user belonging to each user type includes the number of power outages, the number of voltage surges, the number of voltage dips and the number of voltage overloads.

3. The user power data collection and analysis terminal based on the Internet of Things according to claim 1 is characterized by: In the user type analysis process of each designated user, it is necessary to obtain each net production period, each energy balance period and each net consumption period of each designated user in each year, month and day, and the specific acquisition method includes: Extract the net active power of each specified user in each year, month, day and time period , and compare them with 0 respectively. If the net active power of a specified user in a certain period of a certain year, month, day, and time is greater than 0, then the specified user in that year, month, day, and time is recorded as the net production period of the specified user in that year, month, and day. If the net active power of a specified user in that year, month, day, and time is equal to 0, then the specified user in that year, month, day, and time is recorded as the energy balance period of the specified user in that year, month, and day. If the net active power of a specified user in that year, month, day, and time is less than 0, then the specified user in that year, month, day, and time is recorded as the net consumption period of the specified user in that year, month, and day. Then, the net production period, energy balance period, and net consumption period of each specified user in each year, month, and day are obtained, and they are fed back.

4. The user power data collection and analysis terminal based on the Internet of Things according to claim 3 is characterized by: The specific analysis method of the user type of each designated user includes: Extract the net production time periods, energy balance time periods and net consumption time periods of each designated user in each year, month and day, further count the number of net production time periods, energy balance time periods and net consumption time periods of each designated user in each year, month and day, and then add them up to get the total number of net production time periods, energy balance time periods and net consumption time periods of each designated user, which are recorded as ; Analyze the user type evaluation coefficient of each specified user ,in is the set ratio threshold; The user type evaluation coefficient of each designated user is matched with the user type evaluation coefficient range corresponding to each user type stored in the cloud database to obtain the user type of each designated user, and further classified according to the user type to obtain the users belonging to each user type.

5. The user power data collection and analysis terminal based on the Internet of Things according to claim 1 is characterized by: The specific analysis method of the power quality of each user belonging to each user type includes: Extract the current, voltage and grid frequency synchronization of each user of each user type in each year, month, day and time period, and record them as ,in , is the number of each user type, is the number of user types, , is the ID of each user, is the number of users; According to the analytical formula Get the current stability of each user belonging to each user type ,in is the set current allowable difference. Similarly, the voltage stability of each user belonging to each user type can be obtained. Frequency synchronization and stability of the power grid ; Analyze the power quality evaluation coefficient of each user belonging to each user type ,in They are the current stability threshold, voltage stability threshold and grid frequency synchronization stability threshold extracted from the cloud database respectively; The power quality evaluation coefficient of each user belonging to each user type is compared with the set power quality evaluation coefficient threshold. If the power quality evaluation coefficient of a user belonging to a certain user type is greater than the power quality evaluation coefficient threshold, the power quality situation of the user belonging to this user type is recorded as qualified; otherwise, the power quality situation of the user belonging to this user type is recorded as unqualified, thereby obtaining the power quality situation of each user belonging to each user type.

6. The user power data collection and analysis terminal based on the Internet of Things according to claim 5 is characterized by: In the process of analyzing the fault conditions of each user belonging to each user type, it is necessary to construct the equipment abnormality evaluation index and the power grid abnormality evaluation index of each user belonging to each user type, and the specific analysis method includes: Extract the power factor of each user of each user type for each year, month, day and time period , the power factor stability of each user of each user type can be obtained by analyzing the current stability of each user of each user type. , analyze the device abnormality evaluation index of each user belonging to each user type ,in They are the power factor threshold and power factor stability threshold extracted from the cloud database respectively; Extract the number of power outages, voltage surges, voltage sags, and voltage overloads for each user of each user type and record them as , analyze the power grid abnormality evaluation index of each user belonging to each user type .

7. The user power data collection and analysis terminal based on the Internet of Things according to claim 6 is characterized by: The specific analysis method of the failure conditions of each user belonging to each user type includes: Extract the device anomaly evaluation index and the power grid anomaly evaluation index of each user belonging to each user type, and compare them with the set device anomaly evaluation index threshold and the power grid anomaly evaluation index threshold respectively; If the device abnormality evaluation index of a user of a certain user type is greater than or equal to the device abnormality evaluation index threshold, and the power grid abnormality evaluation index of the user of the user type is less than the power grid abnormality evaluation index threshold, then the fault condition of the user of the user type is recorded as device abnormality; If the device anomaly evaluation index of a user of a certain user type is less than the device anomaly evaluation index threshold, and the power grid anomaly evaluation index of the user of the user type is greater than or equal to the power grid anomaly evaluation index threshold, then the fault condition of the user of the user type is recorded as a power grid anomaly; If the device anomaly evaluation index of a user of a certain user type is greater than or equal to the device anomaly evaluation index threshold, and the power grid anomaly evaluation index of the user of the user type is greater than or equal to the power grid anomaly evaluation index threshold, then the fault condition of the user of the user type is recorded as device and power grid anomaly; Then the fault conditions of each user belonging to each user type are obtained.

8. The user power data collection and analysis terminal based on the Internet of Things according to claim 7 is characterized by: The specific operation of early warning of the fault situation of each user belonging to each user type is: Extract the fault conditions of each user belonging to each user type. If it is an equipment abnormality, further determine the abnormal device and issue an early warning and feedback; If it is a power grid abnormality, it will be warned and fed back; If it is an abnormality of equipment and power grid, the abnormal equipment will be further identified, and early warning and feedback will be issued.

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