A distribution auxiliary monitoring and early warning system based on Internet of Things technology

Abstract

The present invention discloses a power distribution auxiliary monitoring and early warning system based on the Internet of Things technology, which specifically relates to the field of monitoring and early warning, including an enterprise area division module, an equipment data acquisition module, an equipment data preprocessing module, an equipment data processing module, a gas concentration monitoring module, a gas concentration preprocessing module, a gas concentration processing module, a data early warning analysis module, a power distribution auxiliary evaluation module, a network communication module, and an enterprise Internet of Things security database module. The present invention provides a power distribution auxiliary monitoring and early warning system based on the Internet of Things technology, which collects parameter values ​​of each monitoring sub-area, realizes remote monitoring, processing and evaluation of the power distribution system through the Internet of Things technology, further analyzes to obtain the equipment performance monitoring coefficient, compares it with the preset equipment performance monitoring coefficient threshold, accurately identifies the abnormal state area, takes necessary measures to avoid the expansion of faults, reduces the enterprise operation cost, and helps the enterprise to formulate more scientific management strategies and decisions.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution auxiliary monitoring and early warning, and more specifically, to a power distribution auxiliary monitoring and early warning system based on Internet of Things technology. Background Art

[0002] With the continuous improvement of the degree of social informatization, the number of equipment in distribution rooms and distribution stations in public places is increasing day by day, and the number of equipment in their environment is also increasing. It is urgent to build a distribution auxiliary monitoring and early warning system with information, automation and interactive characteristics, realize the highly integrated fusion of power flow and information flow, and ensure stable operation.

[0003] The application of Internet of Things in distribution auxiliary monitoring and early warning is the inevitable result of the development of information and communication technology to a certain stage. It will be able to effectively integrate communication infrastructure resources and power system infrastructure resources, so that information and communication infrastructure resources can serve the operation of the power system, thereby improving the informatization level of the power system and improving the utilization efficiency of the existing power system infrastructure. At present, the application of Internet of Things technology in smart grids has involved each link of power generation, transmission, transformation, distribution and consumption, and carried out centralized and integrated monitoring, which greatly improved the operation and maintenance efficiency.

[0004] However, there are still some shortcomings in its actual use. For example, the implementation of distribution auxiliary monitoring and early warning technology requires a large amount of funds to purchase equipment, the use requires training of relevant technical personnel, it is highly complex, and manual data collection and recording are required. It cannot achieve real-time, accurate and comprehensive monitoring and early warning, and there is no way to achieve large-scale automated control.

[0005] In recent years, with the large-scale transformation of the power distribution network in the power sector, the distribution lines, equipment and electricity consumption have increased significantly, and various potential faults cannot be discovered in time. Since traditional technologies need to wait for manual investigation and detection, the fault handling time is relatively long and the fault cannot be responded to in time. In addition, many fault locations rely on the experience of the personnel, which has a certain degree of subjectivity and probability of misjudgment, resulting in economic losses to customers and enterprises. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a power distribution auxiliary monitoring and early warning system based on Internet of Things technology, which is used to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] Enterprise area division module: used to divide the target industrial enterprise area into various monitoring sub-areas according to equal area division method, and number the monitoring sub-areas in the target industrial enterprise area as 1, 2, ..., n in sequence.

[0009] Equipment data acquisition module: used to set temperature sensors and current sensors in each monitoring sub-area within the target industrial enterprise area, collect temperature parameters and ambient temperature parameters of the distribution equipment through temperature sensors, collect power consumption parameters and total power consumption parameters of the distribution equipment through current sensors, and transmit parameter values ​​to the equipment data preprocessing module.

[0010] Equipment data preprocessing module: It is used to receive the parameter values ​​of each monitoring sub-area in the target industrial enterprise area transmitted by the equipment data acquisition module, calculate the equipment temperature weight through the temperature parameters of the distribution equipment, calculate the equipment power consumption weight through the power consumption parameters of the distribution equipment, calculate two sets of average values ​​through the ambient temperature parameters and the total power consumption parameters, and transmit the data to the equipment data processing module.

[0011] Equipment data processing module: used to receive data transmitted by the equipment data preprocessing module, calculate the equipment temperature performance index through the equipment temperature weight and ambient temperature, calculate the equipment power performance index through the equipment power consumption weight and total power consumption, and transmit the data to the data early warning analysis module.

[0012] Gas concentration monitoring module: used to set up gas concentration sensors in each monitoring sub-area within the target industrial enterprise area, monitor the concentration parameter values ​​of indoor gas through the gas concentration sensors, and transmit the parameter values ​​to the gas concentration preprocessing module.

[0013] Gas concentration preprocessing module: used to receive the parameter values ​​of each monitoring sub-area in the target industrial enterprise area transmitted by the gas concentration monitoring module, calculate the gas concentration weight, and transmit the data to the gas concentration processing module.

[0014] Gas concentration processing module: used to receive data transmitted by the gas concentration preprocessing module, calculate the spontaneous combustion gas concentration safety index according to the gas concentration weight, and transmit the data to the data early warning analysis module.

[0015] Data early warning analysis module: used to receive three sets of indexes transmitted by the equipment data processing module and the gas concentration processing module, and calculate the equipment performance monitoring coefficient through the equipment temperature performance index, equipment power performance index, and spontaneous combustion gas concentration safety index.

[0016] Power distribution auxiliary evaluation module: used to obtain the equipment performance monitoring coefficient of each monitoring sub-area in the target industrial enterprise area, compare it with the preset equipment performance monitoring coefficient, and send the results to the corresponding management personnel, who will take corresponding treatment measures.

[0017] Network communication module: used to extract data collected by sensors in each monitoring sub-area within the target industrial enterprise area, and connect with the Internet of Things server to synchronize the received instructions and data information.

[0018] Enterprise IoT security database module: used to store the equipment performance monitoring coefficients of each monitoring sub-area within the target industrial enterprise area in the IoT server, extract the historical parameter values ​​of the equipment performance monitoring coefficients, analyze the fluctuations of the coefficients, and number and display each monitoring sub-area in an abnormal state.

[0019] The sub-areas of dividing the target industrial enterprise area into each monitoring sub-area in an equal area division manner need to be greater than three, and a temperature sensor, a current sensor and a gas concentration sensor are set in each monitoring sub-area in the target industrial enterprise area.

[0020] The specific collection method in the equipment data collection module is:

[0021] The temperature parameters of the power distribution equipment a collected by the temperature sensors in each monitoring sub-area within the target industrial enterprise area are i and ambient temperature parameter q i , where i = 1, 2, ..., n, i represents the number of the i-th monitoring sub-area.

[0022] The power consumption parameters of the distribution equipment collected by the current sensors in each monitoring sub-area within the target industrial enterprise area are i and total power consumption parameter L 总 i, where i = 1, 2, ..., n, and i represents the number of the i-th monitoring sub-area.

[0023] The specific processing method in the device data preprocessing module is:

[0024] The equipment temperature weight formula is calculated based on the temperature parameters of the power distribution equipment:

[0025]

[0026] The formula for calculating the equipment power consumption weight based on the power consumption parameters of the power distribution equipment is:

[0027]

[0028] k1, k2, ..., k n The calculation formula is:

[0029] where x i Can be replaced by a i , l i .

[0030] The formula based on the ambient temperature parameter is:

[0031]

[0032] The formula based on the total power consumption parameter is:

[0033]

[0034] The equipment temperature performance index calculation formula is:

[0035] Where α represents the device temperature performance index, A represents the device temperature weight, Q represents the ambient temperature, and λ represents other influencing factors of the device temperature performance.

[0036] The equipment power performance index calculation formula is:

[0037] Where β represents the equipment power performance index, L 总 It is represented by the total power consumption, L is represented by the power consumption weight of the equipment, and λ is represented by other influencing factors of the equipment power performance index.

[0038] The specific method of the gas concentration monitoring module is as follows:

[0039] The indoor gas concentration parameter values ​​collected by the gas concentration sensor in each monitoring sub-area in the target industrial enterprise area are marked as j i , where i = 1, 2, ..., n, i represents the number of the i-th monitoring sub-area.

[0040] The specific processing method of the gas concentration preprocessing module is:

[0041]

[0042] k1, k2, ..., k n The calculation formula is:

[0043]

[0044] The calculation formula of the safety index of the spontaneous combustion gas concentration is:

[0045] Among them, γ represents the safety index of spontaneous combustion gas concentration, u represents the type of spontaneous combustion gas, J represents the gas concentration, ΔJ represents the standard unit concentration of spontaneous combustion gas, and λ represents other influencing factors of the safety index of spontaneous combustion gas concentration.

[0046] The formula for the equipment performance monitoring coefficient is:

[0047] Wherein θ represents the equipment performance monitoring coefficient, α represents the equipment temperature performance index, β represents the equipment power performance index, γ represents the spontaneous combustion gas concentration safety index, λ1 represents other influencing factors of the equipment power performance index, λ2 represents other influencing factors of the spontaneous combustion gas concentration safety index, and λ3 represents other influencing factors of the equipment temperature performance index.

[0048] The formula for the equipment performance monitoring coefficient is:

[0049] The specific evaluation method of the power distribution auxiliary evaluation module is as follows:

[0050] The equipment performance monitoring coefficient θ of each monitoring sub-area in the target industrial enterprise area is compared with the preset equipment performance monitoring coefficient threshold Δθ. If θ>Δθ, it indicates that there are safety hazards in the power system of the target industrial enterprise. The influencing factors are further analyzed. The equipment temperature performance index α is compared with the preset equipment temperature performance standard status index. If α>Δα of a certain monitoring sub-area, it indicates that the equipment temperature of the monitoring sub-area is abnormal. The processing measures are sent to the corresponding management personnel. The equipment power performance index β is compared with the preset equipment power standard status performance index. If β>Δβ of a certain monitoring sub-area, it indicates that the equipment working state of the monitoring sub-area is abnormal. The processing measures are sent to the corresponding management personnel. The self-ignition gas concentration safety index γ is compared with the preset self-ignition gas concentration standard concentration safety index Δγ. If γ>Δγ of a certain monitoring sub-area, it indicates that there is a risk of spontaneous combustion in the monitoring sub-area. The warning gas spontaneous combustion risk instruction is sent to the corresponding management personnel.

[0051] Technical effects and advantages of the present invention:

[0052] 1. The present invention provides a power distribution auxiliary monitoring and early warning system based on the Internet of Things technology. By collecting the equipment data and gas concentration parameter values ​​of each monitoring sub-area in the target industrial enterprise area, the parameter values ​​are pre-processed to obtain the processed data, and the equipment performance monitoring coefficient is further analyzed to be compared with the preset equipment performance monitoring coefficient threshold. In this way, the operation of the power distribution equipment can be fully understood, the abnormal state area can be accurately identified, the problem can be discovered in time, and necessary measures can be taken to avoid the expansion of the fault, and the equipment operation efficiency can be effectively improved;

[0053] 2. The present invention realizes remote monitoring, processing and evaluation of the power distribution system through the Internet of Things technology, which does not require excessive manpower and material resources. It can effectively reduce the operating costs of enterprises, improve efficiency, and help enterprises formulate more scientific management strategies and decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0055] Figure 2 Schematic diagram of the power distribution auxiliary evaluation module of the present invention. DETAILED DESCRIPTION

[0056] 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.

[0057] See also Figure 1 As shown, the present invention provides a distribution auxiliary monitoring and early warning system based on Internet of Things technology, including an enterprise area division module, an equipment data acquisition module, an equipment data preprocessing module, an equipment data processing module, a gas concentration monitoring module, a gas concentration preprocessing module, a gas concentration processing module, a data early warning analysis module, a distribution auxiliary evaluation module, a network communication module, and an enterprise Internet of Things security database module.

[0058] The enterprise area division module is connected to the network communication module, the network communication module is connected to the equipment data acquisition module and the gas concentration monitoring module, the equipment data acquisition module is connected to the equipment data preprocessing module, the equipment data preprocessing module is connected to the equipment data processing module, the gas concentration monitoring module is connected to the gas concentration preprocessing module, the gas concentration preprocessing module is connected to the gas concentration processing module, the equipment data processing module and the gas concentration processing module are connected to the data early warning analysis module, the data early warning analysis module is connected to the power distribution auxiliary evaluation module, and the power distribution auxiliary evaluation module is connected to the enterprise Internet of Things security database module.

[0059] The enterprise area division module is used to divide the target industrial enterprise area into monitoring sub-areas in an equal area division manner, and number the monitoring sub-areas in the target industrial enterprise area as 1, 2, ..., n in sequence.

[0060] In a possible design, the sub-areas into which the target industrial enterprise area is divided into each monitoring sub-area in an equal-area manner need to be greater than three, and a temperature sensor, a current sensor and a gas concentration sensor are set in each monitoring sub-area in the target industrial enterprise area.

[0061] The equipment data acquisition module is used to set temperature sensors and current sensors in each monitoring sub-area within the target industrial enterprise area, collect temperature parameters and ambient temperature parameters of the distribution equipment through the temperature sensors, collect power consumption parameters and total power consumption parameters of the distribution equipment through the current sensors, and transmit the parameter values ​​to the equipment data preprocessing module.

[0062] In a possible design, the specific collection method in the device data collection module is:

[0063] The temperature parameters of the power distribution equipment a collected by the temperature sensors in each monitoring sub-area within the target industrial enterprise area are i and ambient temperature parameter q i , where i = 1, 2, ..., n, i represents the number of the i-th monitoring sub-area.

[0064] The power consumption parameters of the distribution equipment collected by the current sensors in each monitoring sub-area within the target industrial enterprise area are i and total power consumption parameter L 总 i, where i = 1, 2, ..., n, and i represents the number of the i-th monitoring sub-area.

[0065] The temperature sensor brand is sinomeasure, model is SIN-WZP-PT100, and the current sensor brand is sinomeasure, model is SIN-DJI-100A.

[0066] The equipment data preprocessing module is used to receive the parameter values ​​of each monitoring sub-area in the target industrial enterprise area transmitted by the equipment data acquisition module, calculate the equipment temperature weight through the temperature parameters of the distribution equipment, calculate the equipment power consumption weight through the power consumption parameters of the distribution equipment, calculate two sets of average values ​​through the ambient temperature parameters and the total power consumption parameters, and transmit the data to the equipment data processing module.

[0067] In a possible design, the specific processing method in the device data preprocessing module is:

[0068] The equipment temperature weight formula is calculated based on the temperature parameters of the power distribution equipment:

[0069]

[0070] The formula for calculating the equipment power consumption weight based on the power consumption parameters of the power distribution equipment is:

[0071]

[0072] k1, k2, ..., k n The calculation formula is:

[0073] where x i Can be replaced by a, l.

[0074] The formula based on the ambient temperature parameter is:

[0075]

[0076] The formula based on the total power consumption parameter is:

[0077]

[0078] The device data processing module is used to receive data transmitted by the device data preprocessing module, calculate the device temperature performance index through the device temperature weight and the ambient temperature, calculate the device power performance index through the device power consumption weight and the total power consumption, and transmit the data to the data early warning analysis module.

[0079] In a possible design, the device temperature performance index calculation formula is:

[0080] Where α represents the device temperature performance index, A represents the device temperature weight, Q represents the ambient temperature, and λ represents other influencing factors of the device temperature performance.

[0081] The equipment power performance index calculation formula is:

[0082] Where β represents the equipment power performance index, L 总 It is represented as the total power consumption parameter, L is represented as the equipment power consumption weight, and λ is represented as other influencing factors of the equipment power performance index.

[0083] The gas concentration monitoring module is used to set up gas concentration sensors in each monitoring sub-area within the target industrial enterprise area, monitor the concentration parameter values ​​of indoor gas through the gas concentration sensors, and transmit the parameter values ​​to the gas concentration preprocessing module.

[0084] In a possible design, the specific method of the gas concentration monitoring module is:

[0085] The indoor gas concentration parameter values ​​collected by the gas concentration sensor in each monitoring sub-area in the target industrial enterprise area are marked as j i , where i = 1, 2, ..., n, i represents the number of the i-th monitoring sub-area.

[0086] The brand of the gas concentration sensor is Firstrate and the model is BRW100-G102.

[0087] The gas concentration preprocessing module is used to receive parameter values ​​of each monitoring sub-area in the target industrial enterprise area transmitted by the gas concentration monitoring module, calculate the gas concentration weight, and transmit the data to the gas concentration processing module.

[0088] In a possible design, the specific processing method of the gas concentration preprocessing module is:

[0089]

[0090] k1, k2, ..., k n The calculation formula is:

[0091]

[0092] The gas concentration processing module is used to receive data transmitted by the gas concentration preprocessing module, calculate the spontaneous combustion gas concentration safety index according to the gas concentration weight, and transmit the data to the data early warning analysis module.

[0093] In a possible design, the calculation formula of the spontaneous combustion gas concentration safety index is:

[0094] Among them, γ represents the safety index of spontaneous combustion gas concentration, u represents the type of spontaneous combustion gas, J represents the gas concentration, ΔJ represents the standard unit concentration of spontaneous combustion gas, and λ represents other influencing factors of the safety index of spontaneous combustion gas concentration.

[0095] See also Figure 2 As shown, the power distribution auxiliary evaluation module includes equipment performance monitoring coefficient, equipment temperature performance index, equipment power performance index, and spontaneous combustion gas concentration safety index.

[0096] The data early warning analysis module is used to receive three groups of indexes transmitted by the equipment data processing module and the gas concentration processing module, and calculate the equipment performance monitoring coefficient through the equipment temperature performance index, the equipment power performance index, and the spontaneous combustion gas concentration safety index.

[0097] In one possible design, the formula for the equipment performance monitoring coefficient is:

[0098] Wherein θ represents the equipment performance monitoring coefficient, α represents the equipment temperature performance index, β represents the equipment power performance index, γ represents the spontaneous combustion gas concentration safety index, λ1 represents other influencing factors of the equipment power performance index, λ2 represents other influencing factors of the spontaneous combustion gas concentration safety index, and λ3 represents other influencing factors of the equipment temperature performance index.

[0099] The formula for the equipment performance monitoring coefficient is:

[0100] The power distribution auxiliary evaluation module is used to obtain the equipment performance monitoring coefficient of each monitoring sub-area in the target industrial enterprise area, compare it with the preset equipment performance monitoring coefficient, and send the result to the corresponding management personnel, who will take corresponding processing measures.

[0101] In a possible design, the specific evaluation method of the power distribution auxiliary evaluation module is:

[0102] The equipment performance monitoring coefficient θ of each monitoring sub-area in the target industrial enterprise area is compared with the preset equipment performance monitoring coefficient threshold Δθ. If θ>Δθ, it indicates that there are safety hazards in the power system of the target industrial enterprise. The influencing factors are further analyzed. The equipment temperature performance index α is compared with the preset equipment temperature performance standard status index. If α>Δα of a certain monitoring sub-area, it indicates that the equipment temperature of the monitoring sub-area is abnormal. The processing measures are sent to the corresponding management personnel. The equipment power performance index β is compared with the preset equipment power standard status performance index. If β>Δβ of a certain monitoring sub-area, it indicates that the equipment working state of the monitoring sub-area is abnormal. The processing measures are sent to the corresponding management personnel. The self-ignition gas concentration safety index γ is compared with the preset self-ignition gas concentration standard concentration safety index Δγ. If γ>Δγ of a certain monitoring sub-area, it indicates that there is a risk of spontaneous combustion in the monitoring sub-area. The warning gas spontaneous combustion risk instruction is sent to the corresponding management personnel.

[0103] The network communication module is used to extract data collected by sensors in each monitoring sub-area within the target industrial enterprise area, and connect with the Internet of Things server to synchronize the received instructions and data information.

[0104] The sensors in each monitoring sub-area within the target industrial enterprise area have specified brands and models.

[0105] Enterprise IoT security database module: used to store the equipment performance monitoring coefficients of each monitoring sub-area within the target industrial enterprise area in the IoT server, extract the historical parameter values ​​of the equipment performance monitoring coefficients, analyze the fluctuations of the coefficients, and number and display each monitoring sub-area in an abnormal state.

[0106] In this embodiment, it should be specifically explained that the present invention provides a power distribution auxiliary monitoring and early warning system based on the Internet of Things technology, including the following steps:

[0107] Step S01: Enterprise area division: specifically, the target industrial enterprise area is divided into various monitoring sub-areas according to an equal area division method, and the monitoring sub-areas in the target industrial enterprise area are numbered 1, 2, ..., i, ..., n in sequence.

[0108] Step S02: Equipment data collection: specifically, temperature sensors and current sensors are set in each monitoring sub-area within the target industrial enterprise area, the temperature parameters and ambient temperature parameters of the distribution equipment are collected through the temperature sensors, the power consumption parameters and total power consumption parameters of the distribution equipment are collected through the current sensors, and the parameter values ​​are transmitted to the equipment data preprocessing module.

[0109] Step S03: Equipment data preprocessing: specifically, receiving the parameter values ​​of each monitoring sub-area in the target industrial enterprise area transmitted by the equipment data acquisition module, calculating the equipment temperature weight through the temperature parameters of the power distribution equipment, calculating the equipment power consumption weight through the power consumption parameters of the power distribution equipment, calculating two sets of average values ​​through the ambient temperature parameters and the total power consumption parameters, and transmitting the data to the equipment data processing module.

[0110] Step S04: Equipment data processing: specifically, receiving data transmitted by the equipment data preprocessing module, calculating the equipment temperature performance index through the equipment temperature weight and the ambient temperature, calculating the equipment power performance index through the equipment power consumption weight and the total power consumption, and transmitting the data to the data early warning analysis module.

[0111] Step S05: Gas concentration monitoring: specifically, a gas concentration sensor is set in each monitoring sub-area within the target industrial enterprise area, the concentration parameter value of the indoor gas is monitored by the gas concentration sensor, and the parameter value is transmitted to the gas concentration preprocessing module.

[0112] Step S06: Gas concentration preprocessing: specifically, receiving parameter values ​​of each monitoring sub-area in the target industrial enterprise area transmitted by the gas concentration monitoring module, calculating the gas concentration weight, and transmitting the data to the gas concentration processing module.

[0113] Step S07: gas concentration processing: specifically, receiving data transmitted by the gas concentration preprocessing module, calculating the spontaneous combustion gas concentration safety index according to the gas concentration weight, and transmitting the data to the data early warning analysis module.

[0114] Step S08: Data early warning analysis: specifically, receiving three groups of indexes transmitted by the equipment data processing module and the gas concentration processing module, and calculating the equipment performance monitoring coefficient through the equipment temperature performance index, the equipment power performance index, and the spontaneous combustion gas concentration safety index.

[0115] Step S09: Power distribution auxiliary evaluation: specifically, obtaining the equipment performance monitoring coefficient of each monitoring sub-area in the target industrial enterprise area, comparing it with the preset equipment performance monitoring coefficient, and sending the result to the corresponding management personnel, who will take corresponding processing measures.

[0116] Step S10: Network communication: specifically, extracting the data collected by sensors in each monitoring sub-area within the target industrial enterprise area, and connecting with the Internet of Things server to synchronize the received instructions and data information.

[0117] Step S11: Enterprise IoT security database: specifically, storing the equipment performance monitoring coefficients of each monitoring sub-area within the target industrial enterprise area in the IoT server, extracting the historical parameter values ​​of the equipment performance monitoring coefficients, analyzing the fluctuations of the coefficients, and numbering and displaying each monitoring sub-area in an abnormal state.

[0118] In this embodiment, it should be specifically explained that the present invention can fully understand the operation status of the power distribution equipment, accurately identify the abnormal status areas, discover problems in time, take necessary measures to avoid the expansion of faults, effectively improve the equipment operation efficiency, and realize remote monitoring, processing and evaluation of the power distribution system through the Internet of Things technology. It does not require excessive manpower and material resources, can effectively reduce the operating costs of the enterprise, improve efficiency, and help the enterprise to formulate more scientific management strategies and decisions.

[0119] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A power distribution auxiliary monitoring and early warning system based on Internet of Things technology, characterized in that: include: Enterprise area division module: used to divide the target industrial enterprise area into various monitoring sub-areas according to the equal area division method, and number the monitoring sub-areas in the target industrial enterprise area as 1, 2, ..., n in sequence; Equipment data acquisition module: used to set temperature sensors and current sensors in each monitoring sub-area within the target industrial enterprise area, collect temperature parameters and ambient temperature parameters of the power distribution equipment through the temperature sensors, collect power consumption parameters and total power consumption parameters of the power distribution equipment through the current sensors, and transmit the parameter values ​​to the equipment data preprocessing module; Equipment data preprocessing module: used to receive the parameter values ​​of each monitoring sub-area in the target industrial enterprise area transmitted by the equipment data acquisition module, calculate the equipment temperature weight through the temperature parameters of the power distribution equipment, calculate the equipment power consumption weight through the power consumption parameters of the power distribution equipment, calculate two sets of average values ​​through the ambient temperature parameters and the total power consumption parameters, and transmit the data to the equipment data processing module; Equipment data processing module: used to receive data transmitted by the equipment data preprocessing module, calculate the equipment temperature performance index through the equipment temperature weight and the ambient temperature, calculate the equipment power performance index through the equipment power consumption weight and the total power consumption, and transmit the data to the data early warning analysis module; Gas concentration monitoring module: used to set gas concentration sensors in each monitoring sub-area within the target industrial enterprise area, monitor the concentration parameter values ​​of indoor gas through the gas concentration sensors, and transmit the parameter values ​​to the gas concentration preprocessing module; Gas concentration preprocessing module: used to receive the parameter values ​​of each monitoring sub-area in the target industrial enterprise area transmitted by the gas concentration monitoring module, calculate the gas concentration weight, and transmit the data to the gas concentration processing module; Gas concentration processing module: used to receive the data transmitted by the gas concentration preprocessing module, calculate the spontaneous combustion gas concentration safety index according to the gas concentration weight, and transmit the data to the data early warning analysis module; Data early warning analysis module: used to receive three groups of indexes transmitted by the equipment data processing module and the gas concentration processing module, and calculate the equipment performance monitoring coefficient through the equipment temperature performance index, equipment power performance index, and spontaneous combustion gas concentration safety index; Power distribution auxiliary evaluation module: used to obtain the equipment performance monitoring coefficient of each monitoring sub-area in the target industrial enterprise area, compare it with the preset equipment performance monitoring coefficient, and send the results to the corresponding management personnel, who will take corresponding treatment measures; Network communication module: used to extract data collected by sensors in each monitoring sub-area within the target industrial enterprise area, and connect with the IoT server to synchronize the received instructions and data information; Enterprise IoT security database module: used to store the equipment performance monitoring coefficients of each monitoring sub-area in the target industrial enterprise area in the IoT server, extract the historical parameter values ​​of the equipment performance monitoring coefficients, analyze the fluctuations of the coefficients, and number and display each monitoring sub-area in an abnormal state; The specific collection method in the equipment data collection module is: The temperature parameters of the power distribution equipment a collected by the temperature sensors in each monitoring sub-area within the target industrial enterprise area are i and ambient temperature parameter q i , where i = 1, 2, ..., n, i represents the number of the i-th monitoring sub-area; The power consumption parameters of the distribution equipment collected by the current sensors in each monitoring sub-area within the target industrial enterprise area are i and total power consumption parameter L 总i , where i = 1, 2, ..., n, i represents the number of the i-th monitoring sub-area; The specific processing method in the device data preprocessing module is: The equipment temperature weight formula is calculated based on the temperature parameters of the power distribution equipment: The formula for calculating the equipment power consumption weight based on the power consumption parameters of the power distribution equipment is: k1, k2, ..., k n The calculation formula is: where x i Can be replaced by a i , l i ; The formula based on the ambient temperature parameter is: The formula based on the total power consumption parameter is: The specific method of the gas concentration monitoring module is as follows: The indoor gas concentration parameter values ​​collected by the gas concentration sensor in each monitoring sub-area in the target industrial enterprise area are marked as j i , where i = 1, 2, ..., n, i represents the number of the i-th monitoring sub-area; The specific processing method of the gas concentration preprocessing module is: k1, k2, ..., k n The calculation formula is: The equipment temperature performance index calculation formula is: Where α represents the device temperature performance index, A represents the device temperature weight, Q represents the ambient temperature, and λ represents other factors affecting the device temperature performance; The equipment power performance index calculation formula is: Where β represents the equipment power performance index, L 总 It is represented as the total power consumption parameter, L is represented as the equipment power consumption weight, and λ is represented as other influencing factors of the equipment power performance index; The calculation formula of the safety index of the spontaneous combustion gas concentration is: Where γ represents the safety index of spontaneous combustion gas concentration, u represents the type of spontaneous combustion gas, J represents the gas concentration, ΔJ represents the standard unit concentration of spontaneous combustion gas, and λ represents other influencing factors of the safety index of spontaneous combustion gas concentration; The formula for the equipment performance monitoring coefficient is: Wherein θ represents the equipment performance monitoring coefficient, α represents the equipment temperature performance index, β represents the equipment power performance index, γ represents the self-igniting gas concentration safety index, λ1 represents other influencing factors of the equipment power performance index, λ2 represents other influencing factors of the self-igniting gas concentration safety index, and λ3 represents other influencing factors of the equipment temperature performance index; The formula for the equipment performance monitoring coefficient is: The specific evaluation method of the power distribution auxiliary evaluation module is as follows: The equipment performance monitoring coefficient θ of each monitoring sub-area in the target industrial enterprise area is compared with the preset equipment performance monitoring coefficient threshold Δθ. If θ>Δθ, it indicates that there are safety hazards in the power system of the target industrial enterprise. The influencing factors are further analyzed. The equipment temperature performance index α is compared with the preset equipment temperature performance standard status index. If α>Δα of a certain monitoring sub-area, it indicates that the equipment temperature of the monitoring sub-area is abnormal. The processing measures are sent to the corresponding management personnel. The equipment power performance index β is compared with the preset equipment power standard status performance index. If β>Δβ of a certain monitoring sub-area, it indicates that the equipment working state of the monitoring sub-area is abnormal. The processing measures are sent to the corresponding management personnel. The self-ignition gas concentration safety index γ is compared with the preset self-ignition gas concentration standard concentration safety index Δγ. If γ>Δγ of a certain monitoring sub-area, it indicates that there is a risk of spontaneous combustion in the monitoring sub-area. The warning gas spontaneous combustion risk instruction is sent to the corresponding management personnel.

2. According to claim 1, a power distribution auxiliary monitoring and early warning system based on Internet of Things technology is characterized by: The sub-areas of dividing the target industrial enterprise area into each monitoring sub-area in an equal area division manner need to be greater than three, and a temperature sensor, a current sensor and a gas concentration sensor are set in each monitoring sub-area in the target industrial enterprise area.

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