Electrical equipment state monitoring method based on Internet of Things

Abstract

The invention relates to the technical field of electrical equipment monitoring, in particular to an electrical equipment state monitoring method based on the Internet of Things, which comprises the following steps: S1, acquiring operation information, use information and power consumption of a power supply area of electrical equipment; s2, constructing a time node, and analyzing node output electric quantity and node electricity consumption; s3, analyzing the operation characteristics of the generator and the operation characteristics of the storage battery; s4, classifying and storing the operation characteristics of the generator according to the use information to obtain node types, and analyzing use fluctuation characteristics and maintenance influence parameters; s5, analyzing the equipment supply parameter and the electric energy backflow parameter according to the operation information of the electrical equipment, and analyzing the supply and storage stability parameter; step S6, analyzing node features; and S7, analyzing and outputting the node state of the equipment according to the node characteristics. According to the invention, accurate monitoring of the operation state of the electrical equipment is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical equipment monitoring, and particularly to a method for monitoring the status of electrical equipment based on the Internet of Things. Background Art

[0002] With the continuous progress of industrial automation and intelligence, traditional monitoring methods rely on manual inspections, which have limitations such as low efficiency and difficulty in real-time monitoring. Modern technologies use sensors, data processing, and the Internet of Things to achieve comprehensive and accurate monitoring, improving operation and maintenance efficiency and safety.

[0003] Chinese Patent Publication No.: CN116610482A discloses a method for intelligently monitoring the operating status of electrical equipment, including: obtaining temperature change data information of electrical equipment and constructing a time-series temperature change characteristic curve, obtaining abnormal temperature data in the time-series temperature change characteristic curve, obtaining the first non-mutation degree and the second non-mutation degree of the abnormal temperature data, obtaining the final non-mutation degree based on the first non-mutation degree and the second non-mutation degree, and evaluating the influence value of abnormal data points with a higher non-mutation degree. This invention realizes the analysis of abnormal temperature data during the operation of electrical equipment, but does not realize the comprehensive analysis by combining the data during the use of electrical equipment and the data characteristics during the operation process, resulting in low efficiency in data analysis of electrical equipment and inaccurate monitoring of the status of electrical equipment. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for monitoring the status of electrical equipment based on the Internet of Things to solve at least one of the problems existing in the prior art.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A method for monitoring the status of electrical equipment based on the Internet of Things, including:

[0007] Constructing time nodes, and analyzing the node output power and node power consumption according to the time nodes, the operating information of electrical equipment, and the power consumption of the power supply area;

[0008] Analyzing the operating characteristics of the generator and the operating characteristics of the battery according to the operating information of the electrical equipment;

[0009] Analyzing the equipment supply parameters and the power return parameters according to the node output power and the operating information of the electrical equipment, and analyzing the supply and storage stability parameters according to the equipment supply parameters and the power return parameters;

[0010] Analyzing the node characteristics according to the node power consumption, the operating characteristics of the generator and the battery, the usage fluctuation characteristics, the maintenance influence parameters, and the supply and storage stability parameters;

[0011] Analyze and output the status of the device node according to the node characteristics.

[0012] Furthermore, during the operation of the electrical device, set a time node every preset analysis period, calculate the average value of the generator power between the current time node and the previous time node, and analyze the node output power according to the average value of the generator power to obtain the node output power Q(i), and use the sum of the power consumption in the power supply area between the current time node and the previous time node as the node power consumption q(i).

[0013] Furthermore, analyze the operating characteristics of the generator according to the generator temperature W1(i), the generator speed n(i), and the generator output voltage U1(i) to obtain the generator operating characteristics A(i);

[0014] Analyze the operating characteristics of the battery according to the battery temperature W2(i), the battery power R(i), and the battery output voltage U2(i) to obtain the battery operating characteristics B(i).

[0015] Furthermore, the method further includes:

[0016] Classify and store the operating characteristics of the generator according to the usage information to obtain the node type, and analyze the usage fluctuation characteristics and maintenance influence parameters according to the generator operating characteristics and the node type;

[0017] The storage analysis method of the generator operating characteristics includes:

[0018] Analyze the first node type of the generator operating characteristics according to the gasoline type;

[0019] Classify the generator operating characteristics according to the gasoline type used during its operation, and use the gasoline type as the first node type of the generator operating characteristics;

[0020] Analyze the second node type of the generator operating characteristics according to the maintenance time;

[0021] Use the time node corresponding to the maintenance time as the maintenance time node, set the second node type of the generator operating characteristics at the maintenance time node as one category, set the second node type of the generator operating characteristics at the M time nodes before the maintenance time node as the second category, and set the second node type of the generator operating characteristics at the M time nodes after the maintenance time node as the third category, where M represents the first maintenance extraction parameter.

[0022] Furthermore, the storage analysis method of the generator operating characteristics further includes:

[0023] Analyze the usage fluctuation characteristics according to the generator operating characteristics and the first node type;

[0024] Extract the node numbers corresponding to the generator operation characteristics with the same first node type as the first type number set U1(n), and analyze the usage fluctuation characteristics based on the first type number set U1(n) and the generator operation characteristic A(i) to obtain the usage fluctuation characteristic S1(n);

[0025] Analyze the maintenance influence parameters according to the generator operation characteristics and the second node type;

[0026] Extract the node numbers corresponding to the generator operation characteristics with the second node type of category one as the second type number set U2, extract the node numbers corresponding to the generator operation characteristics with the second node type of category two as the third type number set U3, extract the node numbers corresponding to the generator operation characteristics with the second node type of category three as the fourth type number set U4, and analyze the maintenance influence parameters based on the second type number set U2, the third type number set U3, the fourth type number set U4, and the generator operation characteristic A(i) to obtain the maintenance influence parameter S2.

[0027] Furthermore, the analysis method of the supply and storage stability parameters of electrical equipment includes:

[0028] Analyze the equipment supply parameters and the power energy return parameters according to the node output power, the generator operation information, and the battery operation information;

[0029] Analyze the equipment supply parameters and the power energy return parameters according to the node output power Q(i), the generator output voltage U1(i), the battery output voltage U2(i), the generator power P(i), and the battery power R(i) to obtain the equipment supply parameter L1(i) and the power energy return parameter L2(i);

[0030] Analyze the supply and storage stability parameters according to the equipment supply parameters and the power energy return parameters;

[0031] Analyze the supply and storage stability parameters according to the equipment supply parameter L1(i) and the power energy return parameter L2(i) to obtain the supply and storage stability parameter H(i).

[0032] Furthermore, the analysis method of the node characteristics of electrical equipment includes:

[0033] Analyze the node characteristics according to the node power consumption, the generator operation characteristics, the battery operation characteristics, and the supply and storage stability parameters;

[0034] Analyze the generator usage type according to the usage fluctuation characteristics and the gasoline type;

[0035] Analyze the maintenance effectiveness according to the maintenance interval and the maintenance influence parameters.

[0036] Further, the node characteristics F(i) are obtained by analyzing the node power consumption q(i), the generator operation characteristics A(i), the battery operation characteristics B(i), and the supply and storage stability parameter H(i).

[0037] Further, the generator usage type is determined according to the gasoline type and the usage fluctuation characteristics S1(n) at the current analysis time node. The generator usage type includes Type I and Type II. When the generator usage type is Type II, the analysis process of the node characteristics is improved, and the improved node characteristics are F1(i).

[0038] The maintenance effectiveness is judged according to the maintenance interval t(i) and the maintenance influence parameter S2. The maintenance effectiveness includes effective and ineffective. When the maintenance effectiveness is effective, the analysis process of the node characteristics is further improved, and the improved node characteristics are F2(i).

[0039] Further, the device node status is analyzed according to the node characteristics F(i) to determine the device node status, which includes abnormal battery status, normal electrical device status, and abnormal gasoline generator status.

[0040] The beneficial effects of the present invention are as follows: By collecting and analyzing the operation information, usage information, and power consumption of the power supply area of the electrical equipment, and monitoring the operation status of the electrical equipment by integrating the operation data characteristics, maintenance data, and power consumption data of the power supply area of the electrical equipment, the monitoring of the equipment status is realized from multiple angles of data content, data changes before and after maintenance, and electrical equipment usage data, thereby improving the analysis efficiency of the operation data of the electrical equipment and the accuracy of the status monitoring of the electrical equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0042] Figure 1 It is a flowchart of the method for monitoring the status of electrical equipment based on the Internet of Things in this embodiment.

[0043] Figure 2 It is a flowchart of the method for storing and analyzing the operation characteristics of the generator in this embodiment.

[0044] Figure 3 It is a flowchart of the method for analyzing the supply and storage stability parameter of the electrical equipment in this embodiment.

[0045] Figure 4It is a flowchart of the analysis method for node features of the electrical equipment in this embodiment. Detailed implementation manners

[0046] To more clearly illustrate the present invention, the present invention will be further described below in conjunction with preferred embodiments and the accompanying drawings. Similar components in the drawings are denoted by the same reference numerals. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the protection scope of the present invention.

[0047] It should be noted that although terms such as first, second, and third may be used in the embodiments of the present application for description, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0048] Please refer to Figure 1 as shown, which is a method for monitoring the state of electrical equipment based on the Internet of Things in this embodiment, including:

[0049] Step S1, collecting the operation information, usage information, and power consumption of the power supply area of the electrical equipment. The electrical equipment includes a gasoline generator and a storage battery. The operation information includes the generator operation information and the storage battery operation information. The generator operation information includes the generator temperature, generator speed, generator output voltage, and generator power. The storage battery operation information includes the storage battery temperature, storage battery power, and storage battery output voltage. In this embodiment, the temperature unit is °C, the speed unit is rP1 s, the voltage unit is V, the power unit is kW, the power unit is kW·h, the usage information is the usage information of the gasoline generator, including the gasoline type, maintenance time, and maintenance interval. The gasoline type is the type of gasoline used, and the maintenance interval is the time interval between the current time and the previous maintenance time. The operation information of the electrical equipment is collected by sensors installed on the electrical equipment, the usage information of the electrical equipment is collected by user interaction input, and the power consumption of the power supply area is collected by importing the background data of the power consumption management platform of the power supply area.

[0050] Specifically, this embodiment is applied to the power outage in a small-scale power supply area, and a gasoline generator is used to provide power for the area. By monitoring the operation data and usage data of the generator and the storage battery in the area, the monitoring of the state of the electrical equipment is realized.

[0051] Please continue to refer to Figure 1 as shown, the method for monitoring the state of electrical equipment based on the Internet of Things further includes:

[0052] Step S2: Construct time nodes, and analyze the node output power and node power consumption based on the time nodes, the operating information of the electrical equipment, and the power consumption of the power supply area.

[0053] Specifically, in step S2 of this embodiment, during the operation of the electrical equipment, a time node is set every preset analysis period, and the average value of the generator power between the current time node and the previous time node is calculated. Then, the node output power is analyzed based on the average value of the generator power. Let the node output power be Q(i), and it is set that Q(i)=AvgP1(i)×T / 60. The total power consumption of the power supply area between the current time node and the previous time node is used as the node power consumption. Here, AvgP1(i) represents the average value of the generator power between the current time node and the previous time node, i represents the node number, i∈N + , P1(i) represents the generator power, and T represents the duration of the preset analysis period. In this embodiment, the preset analysis period is set to 10 minutes. In this embodiment, the setting of the preset analysis period is not specifically limited, and those skilled in the art can freely set it, such as it can also be set to 5 minutes, 15 minutes, 30 minutes, etc.

[0054] Specifically, through the construction of time nodes in step S2 of this embodiment, the collected data is corresponding to each time node, ensuring the integrity and unity of data analysis, so as to analyze the node output power and node power consumption. The node output power represents the power output of the generator between adjacent time nodes, and the node power consumption represents the total regional power consumption between adjacent time nodes, thereby improving the analysis efficiency of the operating data of the electrical equipment and the accuracy of the status monitoring of the electrical equipment.

[0055] Please continue to refer to Figure 1 As shown, the method for monitoring the status of electrical equipment based on the Internet of Things further includes:

[0056] Step S3: Analyze the operating characteristics of the generator and the operating characteristics of the battery according to the operating information of the electrical equipment.

[0057] Specifically, in step S3 of this embodiment, the operating characteristics of the generator are analyzed based on the generator temperature, generator speed, and generator output voltage. The operating characteristics of the generator are set as A(i), and it is set that A(i) = AvgU1(i) × [σn(i) + 1] × lgAvgW1(i) / Avgn(i); where AvgU1(i) represents the average value of the generator output voltage between the current time node and the previous time node, U1(i) represents the generator output voltage, σn(i) represents the standard deviation of the generator speed between the current time node and the previous time node, Avgn(i) represents the average value of the generator speed between the current time node and the previous time node, n(i) represents the generator speed, and AvgW1(i) represents the average value of the generator temperature between the current time node and the previous time node, and W1(i) represents the generator temperature.

[0058] Specifically, in step S3 of this embodiment, the operating characteristics of the battery are analyzed based on the battery temperature, battery power, and battery output voltage. The operating characteristics of the battery are set as B(i), and it is set that B(i) = lgAvgW2(i) × [AvgR(i) - AvgR(i - 1)] × AvgU2(i); where AvgW2(i) represents the average value of the battery temperature between the current time node and the previous time node, W2(i) represents the battery temperature, AvgR(i) represents the average value of the battery power between the current time node and the previous time node, R(i) represents the battery power, AvgU2(i) represents the average value of the battery output voltage between the current time node and the previous time node, and U2(i) represents the battery output voltage.

[0059] Specifically, in this embodiment, through the analysis of the operating information of the electrical equipment in step S3, the operating characteristics of the engine and the battery are analyzed, so that when analyzing the data of different electrical equipment, the data content is at the same order of magnitude, realizing the analysis of the operating characteristics of the electrical equipment, thereby improving the analysis efficiency of the operating data of the electrical equipment and improving the accuracy of the status monitoring of the electrical equipment.

[0060] Please continue to refer to Figure 1 as shown, the electrical equipment status monitoring method based on the Internet of Things further includes:

[0061] Step S4, classifying and storing the operating characteristics of the generator according to the usage information to obtain the node type, and analyzing the usage fluctuation characteristics and maintenance influence parameters according to the operating characteristics of the generator and the node type.

[0062] Please refer to Figure 2 as shown, which is a storage analysis method for the operating characteristics of the generator, including:

[0063] Step S41, analyze the first node type of the generator operation characteristics according to the gasoline type.

[0064] Specifically, in step S41 of this embodiment, the generator operation characteristics are classified according to the gasoline type used during its operation, and the gasoline type is used as the first node type of the generator operation characteristics.

[0065] Please continue to refer to Figure 2 As shown, the storage analysis method of the generator operation characteristics further includes:

[0066] Step S42, analyze the second node type of the generator operation characteristics according to the maintenance time.

[0067] Specifically, in step S42 of this embodiment, the time node corresponding to the maintenance time is used as the maintenance time node. The second node type of the generator operation characteristics at the maintenance time node is set as one category, the second node type of the generator operation characteristics at M time nodes before the maintenance time node is set as two categories, and the second node type of the generator operation characteristics at M time nodes after the maintenance time node is set as three categories, where M represents the first maintenance extraction parameter, and 50 ≤ M ≤ 100. It can be understood that the value of the first maintenance extraction parameter is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as it satisfies the analysis of the second node type. The optimal value of the first maintenance extraction parameter is: M = 75.

[0068] Specifically, in this embodiment, through the analysis of the maintenance time in step S42, the engine operation characteristics corresponding to the time nodes before and after maintenance are extracted, increasing the diversity of data analysis, thereby improving the analysis efficiency of the operation data of electrical equipment and the accuracy of the status monitoring of electrical equipment.

[0069] Please continue to refer to Figure 2 As shown, the storage analysis method of the generator operation characteristics further includes:

[0070] Step S43, analyze the usage fluctuation characteristics according to the generator operation characteristics and the first node type.

[0071] Specifically, in step S43 of this embodiment, the node numbers corresponding to the generator operation characteristics with the same first node type are extracted as the first type number set, and the usage fluctuation characteristics are analyzed according to the first type number set and the generator operation characteristics. The usage fluctuation characteristics are set as S1(n), and it is set that where U1(n) represents the first type number set, n represents the first node type number, and NU1(n) represents the number of node numbers in the first type number set. The first node type number is defined as the number for distinguishing different first node types.

[0072] Specifically, in this embodiment, through the analysis of the first node type in step S43, a first type number set is analyzed to achieve the comprehensive extraction of data of the same type, thereby analyzing the usage fluctuation characteristics. The usage fluctuation characteristics are used to represent the fluctuation characteristics of the engine operation data when using the same gasoline type, so as to improve the analysis efficiency of the operation data of electrical equipment and the accuracy of the state monitoring of electrical equipment.

[0073] Please continue to refer to Figure 2 As shown, the storage analysis method of the generator operation characteristics further includes:

[0074] Step S44, analyzing the maintenance influence parameters according to the generator operation characteristics and the second node type.

[0075] Specifically, in step S44 of this embodiment, the node numbers corresponding to the generator operation characteristics of which the second node type is one category are extracted as the second type number set, the node numbers corresponding to the generator operation characteristics of which the second node type is two categories are extracted as the third type number set, and the node numbers corresponding to the generator operation characteristics of which the second node type is three categories are extracted as the fourth type number set. And the maintenance influence parameters are analyzed according to the second type number set, the third type number set, the fourth type number set U4 and the generator operation characteristics. The maintenance influence parameter is set as S2, and it is set that where m represents the second maintenance extraction parameter, m ∈ N + and m ≤ M, u2 represents the node number corresponding to the generator operation characteristics of which the second node type is one category, U2 represents the second type number set, NU2 represents the number of node numbers in the second type number set, NU3 represents the number of node numbers in the third type number set, and NU4 represents the number of node numbers in the fourth type number set.

[0076] Specifically, in this embodiment, through the analysis of the generator operation characteristics and the second node type in step S44, the generator operation characteristics within a period of time before and after the maintenance time are extracted, so as to analyze the maintenance influence parameters. The maintenance influence parameters are used to represent the difference characteristics of the engine operation data before and after the maintenance, thereby further improving the analysis efficiency of the operation data of electrical equipment and the accuracy of the state monitoring of electrical equipment.

[0077] Please continue to refer to Figure 1 As shown, the electrical equipment state monitoring method based on the Internet of Things further includes:

[0078] Step S5, analyzing the equipment supply parameters and the power energy return parameters according to the node output power and the operation information of the electrical equipment, and analyzing the supply storage stability parameters according to the equipment supply parameters and the power energy return parameters.

[0079] Please refer to Figure 3 as shown, which is an analysis method for the supply and storage stability parameters of electrical equipment, including:

[0080] Step S51: Analyze the equipment supply parameters and power return parameters based on the node output power, generator operation information, and battery operation information.

[0081] Specifically, in this embodiment, in step S51, the equipment supply parameters and power return parameters are analyzed based on the node output power, generator output voltage, battery output voltage, generator power, and battery power. If U2(i)>0, set the equipment supply parameter as L1(i) and the power return parameter as L2(i), and set L1(i)=[U1(i)+U2(i)] / [2×U1(i)], L2 = 0; if U2(i)=0, set the equipment supply parameter as L1(i) and the power return parameter as L2(i), and set L1 = 0.5, L2(i)=[R(i)-R(i - 1)] / Q(i).

[0082] Please continue to refer to Figure 3 as shown, the analysis method for the supply and storage stability parameters of the electrical equipment further includes:

[0083] Step S52: Analyze the supply and storage stability parameters based on the equipment supply parameters and power return parameters.

[0084] Specifically, in this embodiment, in step S52, the supply and storage stability parameters are analyzed based on the equipment supply parameters and power return parameters, and set the supply and storage stability parameter as H(i), and set H(i)=e L1(i)+L2(i) .

[0085] Please continue to refer to Figure 1 as shown, the method for monitoring the state of electrical equipment based on the Internet of Things further includes:

[0086] Step S6: Analyze the node characteristics based on the node power consumption, generator operation characteristics, battery operation characteristics, usage fluctuation characteristics, maintenance influence parameters, and supply and storage stability parameters.

[0087] Please refer to Figure 4 as shown, which is an analysis method for the node characteristics of electrical equipment, including:

[0088] Step S61: Analyze the node characteristics based on the node power consumption, generator operation characteristics, battery operation characteristics, and supply and storage stability parameters.

[0089] Specifically, in step S61 of this embodiment, the node characteristics are analyzed according to the node power consumption, generator operation characteristics, battery operation characteristics, and power supply and storage stability parameters, and the node characteristics are set as F(i), and it is set that F(i) = Q(i) × e CA(i)+CB(i) / [q(i) × H(i)]; where CA(i) represents the short-term generator characteristics, CB(i) represents the short-term battery characteristics, where v represents the first short-term extraction parameter, v ∈ N and v ≤ V, and V represents the second short-term extraction parameter, 5 ≤ V ≤ 10. It can be understood that in this embodiment, the value of the second short-term extraction parameter is not specifically limited, and those skilled in the art can freely set it as long as it satisfies the analysis of the node characteristics. The optimal value of the second short-term extraction parameter is: V = 5.

[0090] Specifically, in step S61 of this embodiment, through the analysis of the node power consumption, generator operation characteristics, battery operation characteristics, and power supply and storage stability parameters, the node characteristics are analyzed, and the node characteristics are used to represent the characteristic relationship between the operation data and power consumption data of electrical equipment in a short period of time, so as to realize the comprehensive analysis of the power supply situation of electrical equipment, thereby improving the analysis efficiency of the operation data of electrical equipment and improving the accuracy of the status monitoring of electrical equipment.

[0091] Please continue to refer to Figure 4 as shown, the method for analyzing the node characteristics of the electrical equipment further includes:

[0092] Step S62, analyzing the generator usage type according to the usage fluctuation characteristics and gasoline type.

[0093] Specifically, in step S62 of this embodiment, the generator usage type is analyzed according to the gasoline type and usage fluctuation characteristics of the current analysis time node. If S1(n1) ≤ s1, it is determined that the generator usage type is type one; otherwise, it is determined that the generator usage type is type two, and the analysis process of the node characteristics is improved. The improved node characteristics are F1(i), and it is set that F1(i) = F(i) / e S1(n1) ; where n1 represents the first node type number corresponding to the gasoline type of the current analysis time node, and s1 represents the usage type threshold, 0.05 ≤ s1 ≤ 0.1. It can be understood that in this embodiment, the value of the usage type threshold is not specifically limited, and those skilled in the art can freely set it as long as it satisfies the judgment of the generator usage type. The optimal value of the usage type threshold is: s1 = 0.08.

[0094] Specifically, in this embodiment, through the analysis of the usage fluctuation characteristics and gasoline types in step S62, the engine usage type is analyzed. The engine usage type is divided into two categories according to the magnitude of the usage fluctuation characteristics corresponding to the currently used gasoline type. When the usage fluctuation characteristics corresponding to the currently used gasoline type are relatively large, the analysis process of the node characteristics is improved, so that the improved node characteristics are related to the operation data of the generator when using the same gasoline type, thereby improving the analysis efficiency of the operation data of the electrical equipment and enhancing the accuracy of the status monitoring of the electrical equipment.

[0095] Please continue to refer to Figure 4 as shown, the method for analyzing the node characteristics of the electrical equipment further includes:

[0096] Step S63, analyze the maintenance effectiveness based on the maintenance interval and maintenance influence parameters.

[0097] Specifically, in step S63 of this embodiment, when analyzing the maintenance effectiveness based on the maintenance interval and maintenance influence parameters, if t(i) / T ≤ M and S2 ≤ s2, it is determined that the maintenance effectiveness is effective, and the analysis process of the node characteristics is further improved. The improved node characteristics are F2(i), and it is set that F2(i) = F1(i) × e S2^[t(i) / T] ; otherwise, it is determined that the maintenance effectiveness is ineffective, where t(i) represents the maintenance interval, s2 represents the maintenance threshold, and 0.8 ≤ s2 ≤ 0.9. It can be understood that in this embodiment, no specific limitation is made on the value of the maintenance threshold, and those skilled in the art can freely set it as long as it meets the analysis of the maintenance effectiveness. The optimal value of the maintenance threshold is: s2 = 0.9.

[0098] Specifically, in this embodiment, through the analysis of the maintenance interval and maintenance influence parameters in step S63, the maintenance effectiveness is analyzed. The maintenance effectiveness is divided into two categories according to the difference before and after maintenance and the duration since the previous maintenance. When the maintenance is effective, the analysis process of the node characteristics is further improved, so that the improved node characteristics are related to the maintenance data of the generator, thereby improving the analysis efficiency of the operation data of the electrical equipment and enhancing the accuracy of the status monitoring of the electrical equipment.

[0099] Please continue to refer to Figure 1 as shown, the method for monitoring the status of electrical equipment based on the Internet of Things further includes:

[0100] Step S7, analyze and output the status of the equipment node based on the node characteristics.

[0101] Specifically, in step S7 of this embodiment, the state of the device node is analyzed according to the node characteristics. If F(i) ≤ f1, it is determined that the state of the device node is abnormal for the battery; if f1 < F(i) < f2, it is determined that the state of the device node is normal for the electrical equipment; if F(i) > f2, it is determined that the state of the device node is abnormal for the gasoline generator. Among them, f1 represents the first state threshold, 0.6 ≤ f1 ≤ 0.8, and f2 represents the second state threshold, 1.2 ≤ f2 ≤ 1.4. It can be understood that in this embodiment, the specific values of the state thresholds are not specifically limited, and those skilled in the art can freely set them as long as the analysis of the device node state is satisfied. The optimal values of the state thresholds are: f1 = 0.7, f2 = 1.3.

[0102] Obviously, the above embodiments of the present invention are merely examples for clearly explaining the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or variations can be made based on the above description. It is impossible to enumerate all the implementation manners here. Any obvious changes or variations derived from the technical solutions of the present invention still fall within the protection scope of the present invention.

Claims

1. An electrical equipment status monitoring method based on the Internet of Things, characterized in that, Including: Construct time nodes, and analyze the node output power and node power consumption according to the time nodes, the operation information of electrical equipment, and the power consumption of the power supply area; Analyze the operation characteristics of the generator and the operation characteristics of the storage battery according to the operation information of the electrical equipment; Analyze the equipment supply parameters and the power return parameters according to the node output power and the operation information of the electrical equipment, and analyze the supply and storage stability parameters according to the equipment supply parameters and the power return parameters; Analyze the node characteristics according to the node power consumption, the operation characteristics of the generator and the storage battery, the usage fluctuation characteristics, the maintenance influence parameters, and the supply and storage stability parameters; Analyze and output the equipment node status according to the node characteristics.

2. The method for monitoring the state of an electrical device based on the Internet of Things according to claim 1, wherein During the operation of the electrical equipment, set a time node every preset analysis period, calculate the average value of the generator power between the current time node and the previous time node, and analyze the node output power according to the average value of the generator power to obtain the node output power Q(i), and use the total power consumption of the power supply area between the current time node and the previous time node as the node power consumption q(i).

3. The method for monitoring the state of an electrical device based on the Internet of Things according to claim 2, wherein, Analyze the operation characteristics of the generator according to the generator temperature W1(i), the generator speed n(i), and the generator output voltage U1(i) to obtain the generator operation characteristics A(i); Analyze the operation characteristics of the storage battery according to the storage battery temperature W2(i), the storage battery power R(i), and the storage battery output voltage U2(i) to obtain the storage battery operation characteristics B(i).

4. The method for monitoring the state of an electrical device based on the Internet of Things according to claim 3, wherein The method further includes: Classify and store the operation characteristics of the generator according to the usage information to obtain the node type, and analyze the usage fluctuation characteristics and the maintenance influence parameters according to the operation characteristics of the generator and the node type; The storage analysis method of the generator operation characteristics includes: Analyze the first node type of the generator operation characteristics according to the gasoline type; Classify the operation characteristics of the generator according to the gasoline type used during its operation, and use the gasoline type as the first node type of the generator operation characteristics; Analyze the second node type of the generator operation characteristics according to the maintenance time; Use the time node corresponding to the maintenance time as the maintenance time node, set the second node type of the generator operation characteristics at the maintenance time node as one category, set the second node type of the generator operation characteristics at the M time nodes before the maintenance time node as two categories, and set the second node type of the generator operation characteristics at the M time nodes after the maintenance time node as three categories, where M represents the first maintenance extraction parameter.

5. The method for monitoring the state of an electrical device based on the Internet of Things according to claim 4, characterized in that The storage analysis method of the generator operation characteristics further includes: Analyze the usage fluctuation characteristics according to the generator operation characteristics and the first node type; Extract the node numbers corresponding to the generator operation characteristics with the same first node type as the first type number set U1(n), and analyze the usage fluctuation characteristics according to the first type number set U1(n) and the generator operation characteristics A(i) to obtain the usage fluctuation characteristics S1(n); Analyze the maintenance influence parameters according to the generator operation characteristics and the second node type; Extract the node numbers corresponding to the generator operation characteristics with the second node type being one category as the second type number set U2, extract the node numbers corresponding to the generator operation characteristics with the second node type being two categories as the third type number set U3, extract the node numbers corresponding to the generator operation characteristics with the second node type being three categories as the fourth type number set U4, and analyze the maintenance influence parameters based on the second type number set U2, the third type number set U3, the fourth type number set U4, and the generator operation characteristic A(i) to obtain the maintenance influence parameter S2.

6. The method for monitoring the state of an electrical device based on the Internet of Things according to claim 5, characterized in that, The analysis method for the supply and storage stability parameters of electrical equipment includes: Analyze the equipment supply parameters and the power energy return parameters based on the node output power, the generator operation information, and the battery operation information; Analyze the equipment supply parameters and the power energy return parameters based on the node output power Q(i), the generator output voltage U1(i), the battery output voltage U2(i), the generator power P(i), and the battery power R(i) to obtain the equipment supply parameter L1(i) and the power energy return parameter L2(i); Analyze the supply and storage stability parameters based on the equipment supply parameters and the power energy return parameters; Analyze the supply and storage stability parameters based on the equipment supply parameter L1(i) and the power energy return parameter L2(i) to obtain the supply and storage stability parameter H(i).

7. The method for monitoring the state of an electrical device based on the Internet of Things according to claim 6, wherein The analysis method for the node characteristics of electrical equipment includes: Analyze the node characteristics based on the node power consumption, the generator operation characteristics, the battery operation characteristics, and the supply and storage stability parameters; Analyze the generator usage type based on the usage fluctuation characteristics and the gasoline type; Analyze the maintenance effectiveness based on the maintenance interval and the maintenance influence parameter.

8. The method for monitoring the state of an electrical device based on the Internet of Things according to claim 7, characterized in that, Analyze the node characteristics based on the node power consumption q(i), the generator operation characteristic A(i), the battery operation characteristic B(i), and the supply and storage stability parameter H(i) to obtain the node characteristic F(i).

9. The method for monitoring the state of an electrical device based on the Internet of Things according to claim 8, wherein Judge the generator usage type according to the gasoline type and the usage fluctuation characteristic S1(n) at the current analysis time node. The generator usage type includes one category and two categories. When the generator usage type is two categories, improve the analysis process of the node characteristics, and the improved node characteristic is F1(i); Judge the maintenance effectiveness according to the maintenance interval t(i) and the maintenance influence parameter S2. The maintenance effectiveness includes effective and ineffective. When the maintenance effectiveness is effective, further improve the analysis process of the node characteristics, and the improved node characteristic is F2(i).

10. The method for monitoring the state of an electrical device based on the Internet of Things according to claim 9, characterized in that, Analyze the equipment node status based on the node characteristic F(i) to determine the equipment node status. The equipment node status includes abnormal battery status, normal electrical equipment status, and abnormal gasoline generator status.

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