Low-voltage switch cabinet remote intelligent control system based on Internet technology

Abstract

The invention relates to the technical field of power switch cabinet control, in particular to a low-voltage switch cabinet remote intelligent control system based on the internet technology, which comprises a power internet of things management and control center, a power database, an internet of things state supervision unit, a thermal diffusion risk assessment unit, a regulation and control assessment feedback warning unit, a precision demand assessment unit and a remote management and control unit. According to the method, analysis is performed preliminarily from two points of Internet of Things management and control and the working state, on one hand, the Internet of Things state of the target low-voltage switch cabinet is known, and on the other hand, the working state of the target low-voltage switch cabinet can be known, so that the management and control effect of the target low-voltage switch cabinet is improved; analysis is carried out from two aspects of actual working condition deviation and deviation bearing performance, and reasonable and targeted regulation and control management is carried out according to different regulation and control precision deviation levels, so that the operation interference of regulation and control deviation on the target low-voltage switch cabinet is reduced, and the improvement of the operation safety and stability of the target low-voltage switch cabinet is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of power switchgear control, and in particular to a remote intelligent control system for low-voltage switchgear based on Internet technology. Background Art

[0002] Low-voltage switchgear refers to the use in power system generation, transmission, distribution, power conversion and consumption to play functions such as on-off, control or protection; the rated current of low-voltage switchgear cabinets is 50Hz alternating current, and the rated voltage is 380v. The power distribution system is used as power, lighting and power conversion and control of power distribution. This product has the characteristics of strong breaking capacity, good dynamic and thermal stability, flexible electrical schemes, convenient combination, series and novel structure;

[0003] Switchgear is an important electrical equipment widely used in the power system. However, in the prior art, it is impossible to conduct safety supervision on the Internet of Things control and working status of low-voltage switchgear, thereby reducing the remote control effect and operation safety of low-voltage switchgear, and it is impossible to analyze based on the two points of the actual heat dissipation deviation and the bearing deviation performance of low-voltage switchgear, and thus it is impossible to conduct reasonable and targeted precise control on low-voltage switchgear, reducing the operation safety of low-voltage switchgear;

[0004] In view of the above technical defects, a solution is proposed now. Summary of the Invention

[0005] The purpose of the present invention is to provide a remote intelligent control system for low-voltage switchgear based on Internet technology to solve the above-mentioned technical defects. The present invention initially analyzes from two aspects of Internet of Things control and working status. On the one hand, it understands the Internet of Things status of the target low-voltage switchgear, and on the other hand, it helps to understand the working status of the target low-voltage switchgear, thereby helping to conduct rational management on the target low-voltage switchgear to improve the control effect of the target low-voltage switchgear. Based on the premise of heat dissipation regulation, it analyzes from two angles of actual working condition deviation and bearing deviation performance, and conducts reasonable and targeted regulation management according to different regulation precision deviation levels to reduce the operation interference of the regulation deviation on the target low-voltage switchgear, thereby helping to improve the operation safety and stability of the target low-voltage switchgear, and at the same time helping to improve the regulation effect of the target low-voltage switchgear.

[0006] The purpose of the present invention can be achieved by the following technical solutions: A remote intelligent control system for low-voltage switchgear based on Internet technology includes a power Internet of Things control center, a power database, an Internet of Things status supervision unit, a heat diffusion risk assessment unit, a regulation evaluation feedback warning unit, a precision requirement assessment unit, and a remote control unit;

[0007] The power IoT control center retrieves the IoT risk information of the target low-voltage switchgear from the power database and sends the IoT risk information to the IoT status supervision unit; the IoT status supervision unit is used to perform IoT control and status classification supervision and analysis on the received IoT risk information, compare and analyze the obtained working status information, and obtain a normal signal or an overload signal;

[0008] The thermal diffusion risk assessment unit is used to respond to the overload signal, collect the internal environment information of the target low-voltage switchgear at the same time, perform heat treatment demand evaluation and feedback analysis on the internal environment information, perform discrimination processing on the obtained heat treatment demand coefficient, and obtain a stable signal or a regulation signal;

[0009] The regulation evaluation feedback warning unit is used to respond to the regulation signal, collect the heat dissipation information of the target low-voltage switchgear at the same time, perform regulation accuracy deviation feedback control processing on the heat dissipation information, perform magnitude classification processing on the obtained regulation management evaluation coefficient, and obtain the regulation accuracy deviation level TJ;

[0010] The accuracy demand evaluation unit is used to respond to the regulation signal, collect the heat dissipation demand information of the target low-voltage switchgear at the same time, perform accuracy demand magnitude evaluation and processing analysis on the heat dissipation demand information, and obtain the preset bearing performance coefficient CJn.

[0011] Preferably, the IoT control and status classification supervision and analysis process is as follows:

[0012] Collect the operation period of the target low-voltage switchgear and set it as the time threshold, obtain the IoT risk information between the target low-voltage switchgear and the remote supervision end within the time threshold. The IoT risk information includes the signal loss frequency and the loss duration. Obtain the product value obtained by multiplying the corresponding values of the IoT risk information, and set it as the remote control risk coefficient. Perform discrimination processing on the remote control risk coefficient and the preset remote control risk coefficient threshold to obtain a feedback instruction or a control signal.

[0013] Preferably, when a feedback instruction is generated, obtain the working status information of the target low-voltage switchgear within the time threshold. The working status information represents the load current, and perform comparison and analysis on the working status information to obtain a normal signal or an overload signal.

[0014] Preferably, the heat treatment demand evaluation and feedback analysis process is as follows:

[0015] Divide the time threshold into i sub - time periods, where i is a natural number greater than zero. Obtain the maximum value Wmax and the minimum value Wmin of the internal temperature value W of the target low - voltage switchgear in each sub - time period. Obtain the value obtained by subtracting Wmin from Wmax, and set it as the internal heat diffusion hindrance value. Then perform discrimination processing on the internal heat diffusion hindrance value. If the internal heat diffusion hindrance value is greater than or equal to the preset internal heat diffusion hindrance value threshold, determine that the corresponding sub - time period is an inefficient period. Obtain the ratio of the number of corresponding inefficient periods to the total number of sub - time periods, set it as the heat treatment demand coefficient, and perform discrimination processing on the heat treatment demand coefficient to obtain a stable signal or a regulation signal.

[0016] Preferably, the process of evaluating and analyzing the accuracy demand level is as follows: Obtain the heat dissipation demand information of the target low - voltage switchgear within the current time threshold. The heat dissipation demand information includes the working damage coefficient and the working depreciation coefficient. Compare and analyze the working damage coefficient and the working depreciation coefficient with the preset working damage coefficient threshold and the preset working depreciation coefficient threshold. Set the number of corresponding values in the working damage coefficient and the working depreciation coefficient that are greater than or equal to the preset working damage coefficient threshold and the preset working depreciation coefficient threshold as the interference tolerance index, and perform discrimination processing on the interference tolerance index to obtain high - level tolerance, medium - level tolerance, and low - level tolerance. Then obtain the preset tolerance performance coefficients CJn corresponding to high - level tolerance, medium - level tolerance, and low - level tolerance, where n = 1, 2, 3.

[0017] Preferably, obtain the historical working information of the target low - voltage switchgear. The historical working information includes the total number of historical overloads, the total duration of historical overloads, and the total number of historical over - heat operations. The total number of historical over - heat operations represents the number of times when the operating temperature of the target low - voltage switchgear exceeds the preset operating temperature threshold and the corresponding duration exceeds the preset duration. Then set the number of corresponding values in the historical working information that exceed the preset threshold as the working damage coefficient; the working depreciation coefficient represents the product value obtained by multiplying the input duration of the target low - voltage switchgear by the value obtained by normalizing the number of corresponding values in the management information that exceed the preset threshold. The input duration represents the duration between the production date and the current date. The management information includes the interval maintenance duration and the number of component replacements.

[0018] Preferably, the process of feedback control and management of the regulation accuracy deviation is as follows:

[0019] Obtain the heat dissipation information of the target low - voltage switchgear within the current time threshold. The heat dissipation information includes the ventilation speed and the operating power. Then obtain the value obtained by multiplying the corresponding values in the heat dissipation information, and set it as the actual diffusion evaluation coefficient. Obtain the value obtained by subtracting the preset diffusion evaluation coefficient threshold from the actual diffusion evaluation coefficient, and set it as the actual working condition coefficient. Then perform discrimination processing on the actual working condition coefficient to obtain a standard signal or an inefficient heat dissipation signal.

[0020] Preferably, when generating an inefficient heat dissipation signal, obtain the difference between the actual operating condition coefficient and zero, set the difference between the actual operating condition coefficient and zero as the actual operating condition deviation value, and compare and analyze the actual operating condition deviation value with the preset actual operating condition deviation value range to obtain the preset deviation level PC corresponding to the actual operating condition deviation value being within the preset actual operating condition deviation value range;

[0021] Multiply the numerical value corresponding to the preset deviation level PC by the preset tolerance performance coefficient CJn, set it as the regulation management evaluation coefficient, and perform a magnitude division process on the regulation management evaluation coefficient to obtain first-level precision, second-level precision, and third-level precision, and set the first-level precision, second-level precision, and third-level precision as the regulation precision deviation level TJ, TJ = 1, 2, 3.

[0022] The beneficial effects of the present invention are as follows:

[0023] (1) The present invention initially analyzes from two aspects of Internet of Things control and working state. On the one hand, it understands the Internet of Things state of the target low-voltage switchgear, and on the other hand, it helps to understand the working state of the target low-voltage switchgear, thereby contributing to the rational management of the target low-voltage switchgear to improve the control effect of the target low-voltage switchgear. And through an information progressive method, a heat treatment requirement evaluation feedback analysis is carried out on the internal environment information to understand whether the ventilation effect of the current target low-voltage switchgear is qualified, so as to timely adjust the ventilation parameters of the target low-voltage switchgear to accelerate the heat dissipation of the target low-voltage switchgear and reduce the impact of temperature on the operation of the target low-voltage switchgear;

[0024] (2) Based on the premise of heat dissipation regulation, the present invention analyzes from two angles of actual operating condition deviation and tolerance deviation performance, and conducts reasonable and targeted regulation management according to different regulation precision deviation levels to reduce the interference of regulation deviation on the operation of the target low-voltage switchgear, thereby contributing to improving the operation safety and stability of the target low-voltage switchgear, and at the same time contributing to improving the regulation effect of the target low-voltage switchgear. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The following further describes the present invention with reference to the drawings;

[0026] Figure 1 is the system flow block diagram of the present invention;

[0027] Figure 2 is the partial reference analysis diagram of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0028] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0029] Embodiment 1:

[0030] Please refer to Figures 1 to 2 As shown, the present invention is a remote intelligent control system for low-voltage switchgear based on Internet technology, including a power Internet of Things management and control center, a power database, an Internet of Things status supervision unit, a heat diffusion risk assessment unit, a regulation and evaluation feedback warning unit, an accuracy requirement assessment unit, and a remote management and control unit. The power database is in one-way communication connection with the power Internet of Things management and control center. The power Internet of Things management and control center is in one-way communication connection with the Internet of Things status supervision unit. The Internet of Things status supervision unit is in one-way communication connection with both the heat diffusion risk assessment unit and the remote management and control unit. The heat diffusion risk assessment unit is in one-way communication connection with the regulation and evaluation feedback warning unit, the accuracy requirement assessment unit, and the remote management and control unit. The accuracy requirement assessment unit is in one-way communication connection with the regulation and evaluation feedback warning unit. The regulation and evaluation feedback warning unit is in one-way communication connection with the remote management and control unit;

[0031] The power Internet of Things management and control center retrieves the Internet of Things risk information of the target low-voltage switchgear from the power database and sends the Internet of Things risk information to the Internet of Things status supervision unit;

[0032] The Internet of Things status supervision unit is used to conduct Internet of Things management and control and status classification supervision and analysis on the received Internet of Things risk information. On the one hand, it understands the Internet of Things status of the target low-voltage switchgear, and on the other hand, it helps to understand the working status of the target low-voltage switchgear, thereby contributing to the rational management of the target low-voltage switchgear to improve the management and control effect of the target low-voltage switchgear. The specific process of Internet of Things management and control and status classification supervision and analysis is as follows:

[0033] Collect the operation period of the target low-voltage switchgear and set it as the time threshold. Obtain the Internet of Things risk information between the target low-voltage switchgear and the remote supervision end within the time threshold. The Internet of Things risk information includes the signal loss frequency, loss duration, etc. Obtain the product value obtained by multiplying the corresponding values of the Internet of Things risk information, and set the product value obtained by multiplying the corresponding values of the Internet of Things risk information as the remote control risk coefficient. Compare the remote control risk coefficient with the preset remote control risk coefficient threshold for discrimination processing:

[0034] If the remote control risk coefficient is less than the preset remote control risk coefficient threshold, a feedback instruction is generated;

[0035] If the remote control risk coefficient is greater than or equal to the preset remote control risk coefficient threshold, a control signal is generated and sent to the remote control unit. After receiving the control signal, the remote control unit immediately performs the preset warning operation corresponding to the control signal to manage the target low-voltage switchgear IoT, improve the IoT stability of the target low-voltage switchgear, and help improve the control stability of the target low-voltage switchgear;

[0036] When a feedback instruction is generated, the working state information of the target low-voltage switchgear within the time threshold is obtained. The working state information represents the load current, and the working state information is compared and analyzed:

[0037] If the working state information is less than the preset working state information, a normal signal is generated;

[0038] If the working state information is greater than or equal to the preset working state information, an overload signal is generated. The normal signal or overload signal is sent to the remote control unit. After receiving the normal signal or overload signal, the remote control unit immediately performs the preset warning operation corresponding to the normal signal or overload signal to intuitively understand the working state of the target low-voltage switchgear and perform targeted processing operations;

[0039] When an overload signal is generated, the heat diffusion risk assessment unit is used to respond to the overload signal, and at the same time collect the internal environment information of the target low-voltage switchgear, and perform heat treatment demand evaluation and feedback analysis on the internal environment information to understand whether the ventilation effect of the current target low-voltage switchgear is qualified, so as to timely adjust the ventilation parameters of the target low-voltage switchgear to accelerate the heat dissipation of the target low-voltage switchgear and reduce the impact of temperature on the operation of the target low-voltage switchgear. The specific heat treatment demand evaluation and feedback analysis process is as follows:

[0040] The time threshold is divided into i sub-time periods, where i is a natural number greater than zero. The maximum value Wmax and minimum value Wmin of the internal temperature value W of the target low-voltage switchgear in each sub-time period are obtained. The value obtained by subtracting Wmin from Wmax is obtained, and the value obtained by subtracting Wmin from Wmax is set as the internal heat diffusion obstruction value, and the internal heat diffusion obstruction value is judged. If the internal heat diffusion obstruction value is greater than or equal to the preset internal heat diffusion obstruction value threshold, the corresponding sub-time period is determined as an inefficient period. The ratio of the number of corresponding inefficient periods to the total number of sub-time periods is obtained, and the ratio of the number of corresponding inefficient periods to the total number of sub-time periods is set as the heat treatment demand coefficient, and the heat treatment demand coefficient is judged:

[0041] If the ratio between the heat treatment demand coefficient and the preset heat treatment demand coefficient threshold is less than 1, a relatively stable signal is generated;

[0042] If the ratio between the heat treatment demand coefficient and the preset heat treatment demand coefficient threshold is greater than or equal to 1, a control signal is generated, and the stable signal or the control signal is sent to the remote control unit. After receiving the stable signal or the control signal, the remote control unit immediately performs the preset warning operation corresponding to the stable signal or the control signal, so as to timely adjust the ventilation parameters of the target low-voltage switchgear, accelerate the heat dissipation of the target low-voltage switchgear, and reduce the impact of temperature on the operation of the target low-voltage switchgear.

[0043] Embodiment 2:

[0044] When a control signal is generated, the control evaluation feedback warning unit is used to respond to the control signal, collect the heat dissipation information of the target low-voltage switchgear, and perform feedback control processing on the heat dissipation information for the control accuracy deviation. According to different control accuracy deviation levels, reasonable and targeted control management is carried out to reduce the operation interference of the control deviation on the target low-voltage switchgear, and then help to improve the operation safety and stability of the target low-voltage switchgear. The specific control accuracy deviation feedback control processing process is as follows:

[0045] Obtain the heat dissipation information of the target low-voltage switchgear within the current time threshold. The heat dissipation information includes ventilation speed, operating power, etc. Then, obtain the value obtained by multiplying the corresponding values in the heat dissipation information, and set the value obtained by multiplying the corresponding values in the heat dissipation information as the actual diffusion evaluation coefficient. Obtain the value obtained by subtracting the preset diffusion evaluation coefficient threshold from the actual diffusion evaluation coefficient, and set it as the actual working condition coefficient, and perform discrimination processing on the actual working condition coefficient:

[0046] If the actual working condition coefficient is greater than or equal to zero, it is determined as a standard signal;

[0047] If the actual working condition coefficient is less than zero, it is determined as an inefficient heat dissipation signal;

[0048] When an inefficient heat dissipation signal is generated:

[0049] Obtain the difference between the actual working condition coefficient and zero, and set the difference between the actual working condition coefficient and zero as the actual working condition deviation value. Compare and analyze the actual working condition deviation value with the preset actual working condition deviation value interval, and obtain the preset deviation level PC corresponding to the actual working condition deviation value located in the preset actual working condition deviation value interval. It should be noted that the larger the value of the preset deviation level PC, the higher the control quantity demand during the control process. Each preset actual working condition deviation value interval is set with a corresponding preset deviation level PC, and the intervals are in ascending order;

[0050] Multiply the value corresponding to the preset deviation level PC by the value corresponding to the preset tolerance performance coefficient CJn, set the product value obtained by multiplying the value corresponding to the preset deviation level PC by the value corresponding to the preset tolerance performance coefficient CJn as the regulation management evaluation coefficient, and perform a magnitude division process on the regulation management evaluation coefficient:

[0051] If the regulation management evaluation coefficient is less than the minimum value in the preset regulation management evaluation coefficient range, it is determined as the first-level precision;

[0052] If the regulation management evaluation coefficient belongs to the preset regulation management evaluation coefficient range, it is determined as the second-level precision;

[0053] If the regulation management evaluation coefficient is greater than the maximum value in the preset regulation management evaluation coefficient range, it is determined as the third-level precision. Among them, the greater the deviation risk of the target low-voltage switchgear corresponding to the first-level precision, the second-level precision, and the third-level precision. Set the first-level precision, the second-level precision, and the third-level precision as the regulation precision deviation level TJ, TJ = 1, 2, 3. That is, when the regulation precision deviation level TJ = 1, it represents the first-level precision; when the regulation precision deviation level TJ = 2, it represents the second-level precision; when the regulation precision deviation level TJ = 3, it represents the third-level precision. It should be noted that the larger the value of the regulation precision deviation level TJ, the higher the regulation precision requirement for remote management. Send the current regulation precision deviation level TJ to the remote control unit. After receiving the current regulation precision deviation level TJ, the remote control unit immediately performs the preset warning operation corresponding to the current regulation precision deviation level TJ, and performs reasonable and targeted regulation management according to different regulation precision deviation levels to reduce the operation interference of the regulation deviation on the target low-voltage switchgear, thereby helping to improve the operation safety and stability of the target low-voltage switchgear;

[0054] When a regulation signal is generated, the precision demand evaluation unit is used to respond to the regulation signal, and at the same time collect the heat dissipation demand information of the target low-voltage switchgear, and perform a precision demand magnitude evaluation and processing analysis on the heat dissipation demand information to understand the current regulation precision requirement of the target low-voltage switchgear. The specific precision demand magnitude evaluation and processing analysis process is as follows:

[0055] Obtain the heat dissipation demand information of the target low-voltage switchgear within the current time threshold. The heat dissipation demand information includes the working damage coefficient and the working loss coefficient. Compare and analyze the working damage coefficient and the working loss coefficient with the preset working damage coefficient threshold and the preset working loss coefficient threshold. Set the number of the working damage coefficient and the working loss coefficient that are greater than or equal to the corresponding preset working damage coefficient threshold and the preset working loss coefficient threshold as the interference tolerance index, and perform a discrimination process on the interference tolerance index:

[0056] If the interference tolerance index = 0, it is determined as high-level tolerance;

[0057] If the interference tolerance index = 1, it is determined as medium-level tolerance;

[0058] If the interference tolerance index = 2, it is determined as low-level tolerance. Among them, the interference tolerance performance of the target low-voltage switchgear corresponding to high-level tolerance, medium-level tolerance, and low-level tolerance is abnormally reduced. Then, the preset tolerance performance coefficients CJn corresponding to high-level tolerance, medium-level tolerance, and low-level tolerance are obtained, where n = 1, 2, 3. That is, when n = 1, it represents high-level tolerance, and the preset tolerance performance coefficient is CJ1; when n = 2, it represents medium-level tolerance, and the preset tolerance performance coefficient is CJ2; when n = 3, it represents low-level tolerance, and the preset tolerance performance coefficient is CJ3. It should be noted that CJ1 > CJ2 > CJ3 > 0;

[0059] In the embodiment of the present invention, the historical working information of the target low-voltage switchgear is obtained. The historical working information includes the total number of historical overloads, the total duration of historical overloads, the total number of historical overheat operations, etc. The total number of historical overheat operations represents the number of times when the operating temperature of the target low-voltage switchgear exceeds the preset operating temperature threshold and the corresponding duration exceeds the preset duration. Then, the number of values of the historical working information that exceed the preset threshold is set as the working damage coefficient. It should be noted that the larger the value of the working damage coefficient, the higher the subsequent requirements for the operation management of the target low-voltage switchgear, and at the same time, the higher the requirement for the regulation accuracy. The potential risk of the operation of the target low-voltage switchgear is higher, and the interference tolerance performance is reduced;

[0060] In the embodiment of the present invention, the working depreciation coefficient represents the product value obtained by multiplying the input duration of the target low-voltage switchgear by the value obtained by normalizing the number of values of the management information that exceed the preset threshold. The input duration represents the duration between the production date and the current date. The management information includes the interval maintenance duration, the number of component replacements, etc. It should be noted that the working depreciation coefficient is an influence parameter reflecting the interference tolerance performance of the target low-voltage switchgear. The larger the value of the working depreciation coefficient, the higher the requirements for the management and regulation of the target low-voltage switchgear;

[0061] In summary, the present invention initially analyzes from two aspects of Internet of Things control and working status. On the one hand, it understands the Internet of Things status of the target low-voltage switchgear, and on the other hand, it helps to understand the working status of the target low-voltage switchgear, thereby helping to carry out reasonable management of the target low-voltage switchgear to improve the control effect of the target low-voltage switchgear. And through the information progressive method, the heat treatment demand evaluation and feedback analysis of the internal environment information are carried out to understand whether the ventilation effect of the current target low-voltage switchgear is qualified, so as to timely adjust the ventilation parameters of the target low-voltage switchgear, so as to accelerate the heat dissipation of the target low-voltage switchgear and reduce the influence of temperature on the operation of the target low-voltage switchgear;

[0062] Based on the premise of heat dissipation regulation, analyze from two perspectives: the deviation of the actual working conditions and the performance of bearing the deviation. Carry out reasonable and targeted regulation management according to different regulation precision deviation levels to reduce the operation interference of the regulation deviation on the target low-voltage switchgear, thereby helping to improve the operation safety and stability of the target low-voltage switchgear, and at the same time helping to improve the regulation effect of the target low-voltage switchgear.

[0063] The setting of the threshold value is for the convenience of comparison. Regarding the size of the threshold value, it depends on the number of sample data and the base number set by those skilled in the art for each group of sample data; as long as the proportional relationship between the parameter and the quantified value is not affected.

[0064] The above is only the preferred specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A remote intelligent control system for low-voltage switchgear based on Internet technology, characterized in that: It includes power IoT control center, power database, IoT status supervision unit, thermal diffusion risk assessment unit, regulation and evaluation feedback warning unit, accuracy demand assessment unit and remote control unit; The power IoT control center retrieves the IoT risk information of the target low-voltage switchgear from the power database, and sends the IoT risk information to the IoT state supervision unit; the IoT state supervision unit is used to perform IoT control and state division supervision analysis on the received IoT risk information, compare and analyze the obtained working state information, and obtain a normal signal or an overload signal; The heat diffusion risk assessment unit is used to respond to the overload signal and collect the internal environment information of the target low-voltage switchgear at the same time, conduct feedback analysis on the heat treatment demand evaluation of the internal environment information, perform discrimination processing on the obtained heat treatment demand coefficient, and obtain a stable signal or a control signal; The control evaluation feedback warning unit is used to respond to the control signal, collect the heat dissipation information of the target low-voltage switchgear, and perform control and management of the control accuracy deviation feedback on the heat dissipation information, divide the obtained control management evaluation coefficient into magnitudes, and obtain the control accuracy deviation level TJ; The accuracy requirement evaluation unit is used to respond to the control signal and collect the heat dissipation requirement information of the target low-voltage switchgear at the same time, perform accuracy requirement magnitude evaluation and analysis on the heat dissipation requirement information, and obtain the preset withstand performance coefficient CJn.

2. The low-voltage switchgear remote intelligent control system based on Internet technology according to claim 1 is characterized in that: The IoT control and state division supervision analysis process is as follows: The operating time period of the target low-voltage switchgear is collected and set as the time threshold, and the IoT risk information of the target low-voltage switchgear and the remote supervision end within the time threshold is obtained. The IoT risk information includes the signal loss frequency and loss duration. The product value obtained by multiplying the corresponding numerical values ​​of the IoT risk information is obtained and set as the remote control risk coefficient. The remote control risk coefficient is distinguished and processed with the preset remote control risk coefficient threshold to obtain feedback instructions or control signals.

3. The low-voltage switchgear remote intelligent control system based on Internet technology according to claim 2 is characterized in that: When a feedback instruction is generated, the working status information of the target low-voltage switchgear within the time threshold is obtained, the working status information represents the load current, and the working status information is compared and analyzed to obtain a normal signal or an overload signal.

4. The low-voltage switchgear remote intelligent control system based on Internet technology according to claim 1 is characterized in that: The heat treatment demand evaluation feedback analysis process is as follows: The time threshold is divided into i sub-time periods, where i is a natural number greater than zero, and Wmax and Wmin of the internal temperature value W of the target low-voltage switchgear in each sub-time period are obtained, and the value obtained by subtracting Wmin from Wmax is obtained, and it is set as the internal heat diffusion barrier value, and the internal heat diffusion barrier value is discriminated and processed. If the internal heat diffusion barrier value is greater than or equal to the preset internal heat diffusion barrier value threshold, the corresponding sub-time period is determined to be an inefficient time period, and the ratio of the corresponding number of inefficient time periods to the total number of sub-time periods is obtained, which is set as the heat treatment demand coefficient, and the heat treatment demand coefficient is discriminated and processed to obtain a stable signal or a control signal.

5. The low-voltage switchgear remote intelligent control system based on Internet technology according to claim 1 is characterized in that: The precision requirement magnitude evaluation processing and analysis process is as follows: obtain the heat dissipation requirement information of the target low-voltage switchgear within the current time threshold, the heat dissipation requirement information includes the working damage coefficient and the working depreciation coefficient, compare and analyze the working damage coefficient and the working depreciation coefficient with the preset working damage coefficient threshold and the preset working depreciation coefficient threshold, set the number of working damage coefficients and working depreciation coefficients that are greater than or equal to the preset working damage coefficient threshold and the preset working depreciation coefficient threshold as the interference tolerance index, and perform discrimination processing on the interference tolerance index to obtain high-level tolerance, intermediate tolerance and low-level tolerance, and then obtain the preset tolerance performance coefficients CJn corresponding to the high-level tolerance, intermediate tolerance and low-level tolerance, n=1, 2, 3.

6. The low-voltage switchgear remote intelligent control system based on Internet technology according to claim 5 is characterized in that: The historical working information of the target low-voltage switchgear is obtained, and the historical working information includes the total number of historical overloads, the total duration of historical overloads, and the total number of historical overheating operations. The total number of historical overheating operations indicates the number of times that the operating temperature of the target low-voltage switchgear exceeds the corresponding duration of the preset operating temperature threshold, and then the number of times that the corresponding value of the historical working information exceeds the preset threshold is set as the working damage coefficient; The working depreciation coefficient represents the product of the input time of the target low-voltage switchgear and the number of management information values ​​exceeding the preset threshold after data normalization. The input time represents the time between the production date and the current date, and the management information includes the interval maintenance time and the number of parts replacement times.

7. The low-voltage switchgear remote intelligent control system based on Internet technology according to claim 1 is characterized in that: The control accuracy deviation feedback control process is as follows: The heat dissipation information of the target low-voltage switchgear within the current time threshold is obtained, and the heat dissipation information includes ventilation speed and operating power. Then, the value obtained by multiplying the corresponding numerical values ​​in the heat dissipation information is obtained, and it is set as the actual diffusion evaluation coefficient. The value obtained by subtracting the preset diffusion evaluation coefficient threshold from the actual diffusion evaluation coefficient is obtained, and it is set as the actual operating condition coefficient. The actual operating condition coefficient is discriminated and processed to obtain a standard signal or an inefficient heat dissipation signal.

8. The low-voltage switchgear remote intelligent control system based on Internet technology according to claim 7 is characterized in that: When an inefficient heat dissipation signal is generated, the difference between the actual operating condition coefficient and zero is obtained, and the difference between the actual operating condition coefficient and zero is set as the actual operating condition deviation value, and the actual operating condition deviation value is compared and analyzed with a preset actual operating condition deviation value interval to obtain a preset deviation level PC corresponding to the actual operating condition deviation value being within the preset actual operating condition deviation value interval; Multiply the preset deviation level PC by the numerical value corresponding to the preset bearing performance coefficient CJn, set it as the regulation and management evaluation coefficient, and divide the regulation and management evaluation coefficient into levels to obtain first-level accuracy, second-level accuracy and third-level accuracy. Set the first-level accuracy, second-level accuracy and third-level accuracy as the regulation and control accuracy deviation level TJ, TJ=1, 2, 3.

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