CN116742799A - A power distribution auxiliary monitoring and early warning system based on Internet of Things technology - Google Patents

Abstract

The invention discloses a power distribution auxiliary monitoring and early warning system based on Internet of Things technology, specifically related to the field of monitoring and early warning, including an enterprise area division module, an equipment data collection module, an equipment data preprocessing module, an equipment data processing module, and a gas concentration monitoring module. , gas concentration preprocessing module, gas concentration processing module, data early warning analysis module, power distribution auxiliary evaluation module, network communication module, enterprise Internet of Things security database module, the invention provides a power distribution auxiliary monitoring and early warning system based on Internet of Things technology , collect the parameter values of each monitoring sub-area, realize remote monitoring, processing and evaluation of the power distribution system through Internet of Things technology, further analyze and obtain the equipment performance monitoring coefficient, compare it with the preset equipment performance monitoring coefficient threshold, and accurately identify In areas with abnormal status, necessary measures can be taken to avoid the expansion of faults, reduce enterprise operating costs, and help enterprises formulate more scientific management strategies and decisions.

Description

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

Technical field

The present invention relates to the technical field of power distribution auxiliary monitoring and early warning. More specifically, the invention relates to an auxiliary power distribution monitoring and early warning system based on Internet of Things technology.

Background technique

With the continuous improvement of social informatization, the number of equipment in power distribution rooms and power distribution stations in public places is increasing day by day, and the number of equipment in their environment is also increasing day by day. It is necessary to build a power distribution auxiliary monitoring with informationization, automation and interactive characteristics. It is urgent for the early warning system to achieve a high degree of integration of power flow and information flow to ensure stable operation.

The application of the Internet of Things in power distribution auxiliary monitoring and early warning is an inevitable result of the development of information and communication technology to a certain stage. It will 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 improves the utilization efficiency of the existing power system infrastructure. At present, the application of Internet of Things technology in smart grids has involved all aspects of power generation, transmission, transformation, distribution, and power consumption, and provides centralized and integrated monitoring. , greatly improving operation and maintenance efficiency.

However, there are still some shortcomings in its actual use. For example, the implementation of power distribution auxiliary monitoring and early warning technology requires a large amount of funds to purchase equipment, and its use requires training of relevant technical personnel. It is highly complex and requires manual data collection and recording, which cannot be realized. Real-time, accurate and comprehensive monitoring and early warning cannot achieve large-scale automated control.

In recent years, with the large-scale transformation of the distribution network of the electric power sector, distribution lines and equipment, and power consumption have increased significantly. Various hidden faults cannot be discovered in time. Because traditional technology needs to wait for manual troubleshooting and detection, fault handling is difficult. It takes a long time to respond to faults in a timely manner, and many fault locations rely on the experience of personnel. There is a certain degree of subjectivity and probability of misjudgment, leading to economic losses for customers and enterprises.

Contents of the invention

In order to overcome the above-mentioned shortcomings of the prior art, embodiments of the present invention provide a power distribution auxiliary monitoring and early-warning system based on Internet of Things technology to solve the problems raised in the above-mentioned background technology.

In order to achieve the above objects, the present invention provides the following technical solutions:

Enterprise area division module: used to divide the target industrial enterprise area into monitoring sub-areas according to equal area division, and number each monitoring sub-area in the target industrial enterprise area as 1, 2,..., i,... .,n.

Equipment data acquisition module: used to set temperature sensors and current sensors in each monitoring sub-area of the target industrial enterprise area, collect temperature parameters and ambient temperature parameters of power distribution equipment through temperature sensors, and collect power consumption of power distribution equipment through current sensors. parameters and total power consumption parameters, and transmit the parameter values to the device 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, and calculate the power consumption parameters of the power distribution equipment. The device power consumption weight is obtained, and the two average values are calculated through the ambient temperature parameters and total power consumption parameters, and the data are transmitted to the device 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 equipment temperature weight and ambient temperature, calculate equipment power performance index through equipment power consumption weight and total power consumption, and Transfer the data to the data warning analysis module.

Gas concentration monitoring module: used to set up gas concentration sensors in each monitoring sub-area of the target industrial enterprise area, monitor the concentration parameter values of indoor gases 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 data transmitted by the gas concentration preprocessing module, calculate the spontaneous combustion gas concentration safety index based on the gas concentration weight, and transmit the data to the data early warning analysis module.

Data early warning analysis module: used to receive three sets of indices transmitted by the equipment data processing module and gas concentration processing module, and calculate the equipment performance monitoring coefficient through equipment temperature performance index, equipment power performance index, and self-ignition gas concentration safety index.

Power distribution auxiliary evaluation module: used to obtain the equipment performance monitoring coefficients of each monitoring sub-area in the target industrial enterprise area, compare it with the preset equipment performance monitoring coefficients, and send the results to the corresponding managers, who will respond 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 Internet of Things server to synchronize the received instructions and data information.

Enterprise Internet of Things security database module: used to store the equipment performance monitoring coefficients of each monitoring sub-area in the target industrial enterprise area in the Internet of Things server, extract the historical parameter values of the equipment performance monitoring coefficients, and analyze the changes and fluctuations of the coefficients, which will be abnormal Each monitoring sub-area of the status is numbered and displayed.

The target industrial enterprise area must be divided into monitoring sub-areas in more than three parts according to the equal area division method, and temperature sensors, current sensors and gas concentration sensors must be installed in each monitoring sub-area within the target industrial enterprise area.

The specific collection methods in the equipment data collection module are:

The temperature parameters of power distribution equipment and ambient temperature parameters collected by temperature sensors in each monitoring sub-area of the target industrial enterprise area are marked as a _i and q _i respectively, where i=1,2,...,n,i means Number for the i-th monitoring sub-area.

The power consumption parameters and total power consumption parameters of power distribution equipment collected through current sensors in each monitoring sub-area of the target industrial enterprise area are marked as l _i and _{Ltotal i} respectively, where i=1,2,... ,n,i represents the i-th monitoring sub-area number.

The specific processing methods in the device data preprocessing module are:

According to the temperature parameters of the power distribution equipment, the equipment temperature weight formula is calculated as:

According to the power consumption parameters of the power distribution equipment, the formula for calculating the power consumption weight of the equipment is:

The calculation formula of k ₁ , k ₂ ,..., k _n is:

where x _i can be replaced by a, l.

The formula based on the ambient temperature parameters is:

The formula based on the total power consumption parameters is:

The formula for calculating the temperature performance index of the equipment is:

Among them, α represents the equipment temperature performance index, A represents the equipment temperature weight, Q represents the ambient temperature, and λ represents other influencing factors of the equipment temperature performance.

The formula for calculating the power performance index of the equipment is:

Among them, β represents the equipment power performance index, L represents the _total power consumption, L represents the equipment power consumption weight, and λ represents other influencing factors of the equipment power performance index.

The specific method of the gas concentration monitoring module is:

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 j _i , where i=1,2,...,n, i represents the i-th monitoring sub-area serial number.

The specific processing method of the gas concentration pretreatment module is:

The calculation formula of k ₁ , k ₂ ,...,k _n is:

The calculation formula of the self-igniting gas concentration safety index is:

Among them, γ represents the self-igniting gas concentration safety index, u represents the self-igniting gas type, J represents the gas concentration, ΔJ represents the standard unit concentration of self-igniting gas, and λ represents other influencing factors of the self-igniting gas concentration safety index.

The formula of the equipment performance monitoring coefficient is:

Among them, 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-ignition gas concentration safety index, λ ₁ represents other influencing factors of the equipment power performance index, and λ ₂ represents are other influencing factors of the self-igniting gas concentration safety index, and λ ₃ represents other influencing factors of the equipment temperature performance index.

The overall analysis formula is

The specific evaluation method of the power distribution auxiliary evaluation module is:

Compare the equipment performance monitoring coefficient θ of each monitoring sub-area in the target industrial enterprise area with the preset equipment performance monitoring coefficient threshold Δθ. If θ>Δθ, it indicates that there are potential safety hazards in the power system of the target industrial enterprise. Further analyze the influencing factors, and pass The equipment temperature performance index α is compared with the preset equipment temperature performance standard status index. If α>Δα in a monitoring sub-area, it indicates that the equipment temperature in the monitoring sub-area is abnormal. The handling measures will be sent to the corresponding manager through the equipment. The power performance index β is compared with the preset equipment power standard status performance index. If β>Δβ in a monitoring sub-area, it indicates that there is an abnormality in the working status of the equipment in the monitoring sub-area. The handling measures will be sent to the corresponding management personnel through spontaneous combustion. The gas concentration safety index γ is compared with the preset standard concentration safety index Δγ of spontaneous combustion gas concentration. If γ > Δγ in a monitoring sub-area, it indicates that there is a risk of spontaneous combustion in the monitoring sub-area, and an early warning gas spontaneous combustion risk instruction is sent to the corresponding manager. .

Technical effects and advantages of the present invention:

1. The present invention provides a power distribution auxiliary monitoring and early warning system based on Internet of Things technology. By collecting equipment data and gas concentration parameter values of each monitoring sub-area in the target industrial enterprise area, the parameter values are preprocessed to obtain processed data. , further analyze to obtain the equipment performance monitoring coefficient, and compare it with the preset equipment performance monitoring coefficient threshold, so as to comprehensively understand the operation of the power distribution equipment, accurately identify abnormal status areas, discover problems in a timely manner, and take necessary measures to avoid fault expansion. , effectively improve equipment operating efficiency;

2. The present invention uses Internet of Things technology to realize remote monitoring, processing and evaluation of the power distribution system without requiring excessive manpower and material investment. It can effectively reduce enterprise operating costs, improve efficiency, and help enterprises formulate more scientific management strategies. and decision-making.

Description of drawings

Figure 1 is a schematic diagram of the system module connection of the present invention.

Figure 2 is a schematic diagram of the power distribution auxiliary evaluation module of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

Please refer to Figure 1. The present invention provides a power distribution auxiliary monitoring and early warning system based on Internet of Things technology, including an enterprise area division module, an equipment data collection module, an equipment data preprocessing module, an equipment data processing module, and a gas concentration monitoring module. , gas concentration preprocessing module, gas concentration processing module, data early warning analysis module, power distribution auxiliary evaluation module, network communication module, and enterprise Internet of Things security database module.

The enterprise area division module is connected to the network communication module, the network communication module is connected to the equipment data collection module and the gas concentration monitoring module, the equipment data collection module is connected to the equipment data preprocessing module, and 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 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 assessment Module connection, the power distribution auxiliary evaluation module is connected to the enterprise IoT security database module.

The enterprise area division module is used to divide the target industrial enterprise area into monitoring sub-areas according to equal area division, and number each monitoring sub-area in the target industrial enterprise area as 1, 2,...,i,. ..,n.

In one possible design, the target industrial enterprise area is divided into monitoring sub-areas in more than three equal-area areas, and temperature sensors and current sensors are set in each monitoring sub-area in the target industrial enterprise area. sensors and gas concentration sensors.

The equipment data collection 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 power distribution equipment through the temperature sensor, and collect power consumption of the power distribution equipment through the current sensor. parameters and total power consumption parameters, and transmit the parameter values to the device data preprocessing module.

In a possible design, the specific collection method in the equipment data collection module is:

The temperature parameters of power distribution equipment and ambient temperature parameters collected through temperature sensors in each monitoring sub-area of the target industrial enterprise area are marked as a _i and q _i respectively, where i=1,2,...,n,i means Number for the i-th monitoring sub-area.

The power consumption parameters and total power consumption parameters of power distribution equipment collected through current sensors in each monitoring sub-area of the target industrial enterprise area are marked as l _i and _{Ltotal i} respectively, where i=1,2,... ,n,i represents the i-th monitoring sub-area number.

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

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 power distribution equipment, and calculate the equipment temperature weight through the power consumption parameters of the power distribution equipment. Calculate the weight of the equipment's power consumption, calculate the two average values of the two groups through the ambient temperature parameters and the total power consumption parameters, and transmit the data to the equipment data processing module.

In one possible design, the specific processing method in the device data preprocessing module is:

According to the temperature parameters of the power distribution equipment, the equipment temperature weight formula is calculated as:

According to the power consumption parameters of the power distribution equipment, the formula for calculating the power consumption weight of the equipment is:

The calculation formula of k ₁ , k ₂ ,...,k _n is:

where x _i can be replaced by a, l.

The formula based on the ambient temperature parameters is:

The formula based on the total power consumption parameters is:

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

In a possible design, the equipment temperature performance index calculation formula is:

Among them, α represents the equipment temperature performance index, A represents the equipment temperature weight, Q represents the ambient temperature, and λ represents other influencing factors of the equipment temperature performance.

The formula for calculating the power performance index of the equipment is:

Among them, β represents the equipment power performance index, L represents the _total power consumption, L represents the equipment power consumption weight, and λ represents other influencing factors of the equipment power performance index.

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 gases through the gas concentration sensors, and transmit the parameter values to the gas concentration preprocessing module.

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

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 j _i , where i=1,2,...,n, i represents the i-th monitoring sub-area serial number.

The gas concentration sensor brand is Firstrate and the model is BRW100-G102.

The gas concentration preprocessing module is 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.

In one possible design, the specific processing method of the gas concentration pretreatment module is:

The calculation formula of k ₁ , k ₂ ,...,k _n is:

The gas concentration processing module is used to receive the data transmitted by the gas concentration preprocessing module, calculate the self-ignition gas concentration safety index according to the gas concentration weight, and transmit the data to the data early warning analysis module.

In one possible design, the calculation formula of the self-igniting gas concentration safety index is:

Among them, γ represents the self-igniting gas concentration safety index, u represents the self-igniting gas type, J represents the gas concentration, ΔJ represents the standard unit concentration of self-igniting gas, and λ represents other influencing factors of the self-igniting gas concentration safety index.

Please refer to Figure 2. The power distribution auxiliary evaluation module includes equipment performance monitoring coefficient, equipment temperature performance index, equipment power performance index, and self-ignition gas concentration safety index.

The data early warning analysis module is used to receive three sets of indices 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 self-ignition gas concentration safety index.

In one possible design, the formula of the equipment performance monitoring coefficient is:

Among them, 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-ignition gas concentration safety index, λ ₁ represents other influencing factors of the equipment power performance index, and λ ₂ represents are other influencing factors of the self-igniting gas concentration safety index, and λ ₃ represents other influencing factors of the equipment temperature performance index.

The overall analysis formula is

The power distribution auxiliary evaluation module is used to obtain the equipment performance monitoring coefficients of each monitoring sub-area in the target industrial enterprise area, compare it with the preset equipment performance monitoring coefficients, and send the results to the corresponding managers, who will conduct Corresponding handling measures.

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

Compare the equipment performance monitoring coefficient θ of each monitoring sub-area in the target industrial enterprise area with the preset equipment performance monitoring coefficient threshold Δθ. If θ>Δθ, it indicates that there are potential safety hazards in the power system of the target industrial enterprise. Further analyze the influencing factors, and pass The equipment temperature performance index α is compared with the preset equipment temperature performance standard status index. If α>Δα in a monitoring sub-area, it indicates that the equipment temperature in the monitoring sub-area is abnormal. The handling measures will be sent to the corresponding manager through the equipment. The power performance index β is compared with the preset equipment power standard status performance index. If β>Δβ in a monitoring sub-area, it indicates that there is an abnormality in the working status of the equipment in the monitoring sub-area. The handling measures will be sent to the corresponding management personnel through spontaneous combustion. The gas concentration safety index γ is compared with the preset standard concentration safety index Δγ of spontaneous combustion gas concentration. If γ > Δγ in a monitoring sub-area, it indicates that there is a risk of spontaneous combustion in the monitoring sub-area, and an early warning gas spontaneous combustion risk instruction is sent to the corresponding manager. .

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

Among them, the brands and models of sensors in each monitoring sub-area within the target industrial enterprise area are designated.

Enterprise Internet of Things security database module: used to store the equipment performance monitoring coefficients of each monitoring sub-area in the target industrial enterprise area in the Internet of Things server, extract the historical parameter values of the equipment performance monitoring coefficients, and analyze the changes and fluctuations of the coefficients, which will be abnormal Each monitoring sub-area of the status is numbered and displayed.

In this embodiment, what needs to be specifically explained is that the present invention provides a power distribution auxiliary monitoring and early warning system based on Internet of Things technology, which includes the following steps:

Step S01: Enterprise area division: Specifically, the target industrial enterprise area is divided into monitoring sub-areas according to equal area division, and each monitoring sub-area in the target industrial enterprise area is numbered 1, 2,..., i, ...,n.

Step S02: Equipment data collection: Specifically, temperature sensors and current sensors are set up in each monitoring sub-area of the target industrial enterprise area, the temperature parameters and ambient temperature parameters of the power distribution equipment are collected through the temperature sensors, and the usage of the power distribution equipment is collected through the current sensors. Electric power parameters and total power consumption parameters, and transmit the parameter values to the device data preprocessing module.

Step S03: Equipment data preprocessing: Specifically, it is 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, and calculate the equipment temperature weight through the power consumption of the power distribution equipment. Parameters are used to calculate the weight of equipment power consumption, and two sets of average values are calculated through ambient temperature parameters and total power consumption parameters, and the data are transmitted to the equipment data processing module.

Step S04: Equipment data processing: Specifically, receive the data transmitted by the equipment data preprocessing module, calculate the equipment temperature performance index through equipment temperature weight and ambient temperature, and calculate the equipment power performance index through equipment power consumption weight and total power consumption. , and transmit the data to the data early warning analysis module.

Step S05: Gas concentration monitoring: Specifically, gas concentration sensors are set up in each monitoring sub-area of the target industrial enterprise area, the concentration parameter values of the indoor gas are monitored through the gas concentration sensors, and the parameter values are transmitted to the gas concentration preprocessing module.

Step S06: Gas concentration preprocessing: specifically, 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.

Step S07: Gas concentration processing: Specifically, it receives the data transmitted by the gas concentration preprocessing module, calculates the spontaneous combustion gas concentration safety index according to the gas concentration weight, and transmits the data to the data early warning analysis module.

Step S08: Data early warning analysis: Specifically, the three sets of indices transmitted by the receiving equipment data processing module and the gas concentration processing module are calculated to obtain the equipment performance monitoring coefficient through the equipment temperature performance index, equipment power performance index, and self-ignition gas concentration safety index.

Step S09: Power distribution auxiliary assessment: specifically, obtain the equipment performance monitoring coefficients of each monitoring sub-area in the target industrial enterprise area, compare them with the preset equipment performance monitoring coefficients, and send the results to the corresponding managers, who will Take corresponding measures.

Step S10: Network communication: Specifically, it is to extract the data collected by sensors in each monitoring sub-area in the target industrial enterprise area, and connect it with the Internet of Things server to synchronize the received instructions and data information.

Step S11: Enterprise Internet of Things security database: Specifically, the equipment performance monitoring coefficients of each monitoring sub-area in the target industrial enterprise area are stored in the Internet of Things server, the historical parameter values of the equipment performance monitoring coefficients are extracted, the changes and fluctuations of the coefficients are analyzed, and the Each monitoring sub-area in an abnormal state is numbered and displayed.

In this embodiment, what needs to be specifically explained is that the present invention can comprehensively understand the operation of power distribution equipment, accurately identify abnormal status areas, discover problems in a timely manner, take necessary measures to avoid fault expansion, effectively improve equipment operation efficiency, and The remote monitoring, processing and evaluation of power distribution systems can be realized through Internet of Things technology without excessive manpower and material investment. It can effectively reduce enterprise operating costs, improve efficiency, and help enterprises formulate more scientific management strategies and decisions.

Finally: the above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included in this present invention. within the scope of protection of the invention.

Claims (10)

1. Power distribution auxiliary monitoring and early warning system based on internet of things technology, which is characterized by comprising:

an enterprise area dividing module: the method comprises the steps of dividing a target industrial enterprise area into monitoring subareas according to an equal area dividing mode, and numbering the monitoring subareas in the target industrial enterprise area as 1,2, i, n;

and the equipment data acquisition module is used for: the power distribution system comprises a power distribution equipment data preprocessing module, a power consumption parameter acquisition module and a power consumption parameter acquisition module, wherein the power distribution equipment data preprocessing module is used for acquiring power consumption parameters of the power distribution equipment;

the device data preprocessing module: the power distribution device monitoring system comprises a power distribution device, a power consumption parameter calculation module, an environment temperature parameter calculation module, a total power consumption parameter calculation module, a power consumption parameter calculation module and a data processing module, wherein the power distribution device is used for receiving the parameter values of all monitoring subareas in a target industrial enterprise area transmitted by the power distribution device data acquisition module, calculating the temperature weight of the power distribution device through the temperature parameter of the power distribution device, calculating the power consumption weight of the power distribution device through the power consumption parameter of the power distribution device, calculating two groups of average values through the environment temperature parameter and the total power consumption parameter, and transmitting data to the power distribution device data processing module;

the device data processing module: the device data preprocessing module is used for receiving the data transmitted by the device data preprocessing module, calculating the device temperature performance index through the device temperature weight and the environment temperature, calculating the device power performance index through the device power consumption weight and the total power consumption, and transmitting the data to the data early warning analysis module;

a gas concentration monitoring module: the system comprises a gas concentration sensor, a gas concentration preprocessing module, a gas concentration sensor, a gas concentration control module and a gas concentration preprocessing module, wherein the gas concentration sensor is used for setting each monitoring subarea in a target industrial enterprise area, monitoring the concentration parameter value of indoor gas through the gas concentration sensor, and transmitting the parameter value to the gas concentration preprocessing module;

a gas concentration pretreatment module: the system comprises a gas concentration monitoring module, a gas concentration processing module, a gas concentration weighting calculation module and a gas concentration processing module, wherein the gas concentration monitoring module is used for receiving parameter values of all monitoring subareas in a target industrial enterprise area transmitted by the gas concentration monitoring module, calculating the gas concentration weighting and transmitting data to the gas concentration processing module;

a gas concentration processing module: the gas concentration pre-processing module is used for receiving the data transmitted by the gas concentration pre-processing 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;

the data early warning analysis module: the system comprises a data processing module, a gas concentration processing module, a device temperature performance index, a device power performance index and an spontaneous combustion gas concentration safety index, wherein the data processing module is used for receiving three groups of indexes transmitted by the device data processing module and the gas concentration processing module, and a device performance monitoring coefficient is calculated through the device temperature performance index, the device power performance index and the spontaneous combustion gas concentration safety index;

and the power distribution auxiliary evaluation module is used for: the method comprises the steps of acquiring equipment performance monitoring coefficients of all monitoring subareas in a target industrial enterprise area, comparing the equipment performance monitoring coefficients with preset equipment performance monitoring coefficients, sending the results to corresponding management personnel, and carrying out corresponding treatment measures by the management personnel;

and a network communication module: the system is used for extracting data collected by each monitoring subarea sensor in the target industrial enterprise area, interfacing with the Internet of things server and synchronizing received instructions and data information;

the enterprise internet of things security database module: the method comprises the steps of storing the equipment performance monitoring coefficients of all monitoring subareas in a target industrial enterprise area in an Internet of things server, extracting historical parameter values of the equipment performance monitoring coefficients, analyzing variation fluctuation of the coefficients, and numbering and displaying all monitoring subareas in an abnormal state.

2. The power distribution auxiliary monitoring and early warning system based on the technology of the internet of things according to claim 1, wherein: the target industrial enterprise area is divided into subareas of all monitoring subareas according to an equal area dividing mode, more than three parts are needed, and a temperature sensor, a current sensor and a gas concentration sensor are arranged in each monitoring subarea of the target industrial enterprise area.

3. The power distribution auxiliary monitoring and early warning system based on the technology of the internet of things according to claim 1, wherein: the specific acquisition mode in the equipment data acquisition module is as follows:

the temperature parameters of the distribution equipment and the environmental temperature parameters collected by the temperature sensors in each monitoring subarea in the target industrial enterprise area are respectively marked as a _i 、q _i Where i=1, 2,..n, i denotes the i-th monitoring subregion number;

the power consumption parameter and the total power consumption parameter of the power distribution equipment collected by the current sensor from each monitoring subarea in the target industrial enterprise area are respectively marked as l _i 、L _{Total i} Where i=1, 2,..n, i denotes the i-th monitoring sub-region number.

4. The power distribution auxiliary monitoring and early warning system based on the technology of the internet of things according to claim 1, wherein: the specific processing mode in the equipment data preprocessing module is as follows:

the equipment temperature weight formula is calculated according to the temperature parameters of the power distribution equipment and is as follows:

and calculating an equipment electricity consumption weight formula according to the electricity consumption parameters of the power distribution equipment, wherein the electricity consumption weight formula is as follows:

the k is ₁ ，k ₂ ，...,k _n The calculation formula of (2) is as follows:

wherein x is _i Can be replaced by a and l;

the formula according to the ambient temperature parameter is:

the formula according to the total electricity consumption parameter is:

5. the power distribution auxiliary monitoring and early warning system based on the technology of the internet of things according to claim 1, wherein: the calculation formula of the temperature performance index of the equipment is as follows:

wherein alpha is denoted as deviceA temperature performance index, a being denoted as a device temperature weight, Q being denoted as ambient temperature, λ being denoted as other influencing factors of the device temperature performance;

the calculation formula of the electric performance index of the equipment is as follows:

where β is expressed as the device electrical performance index, L _{Total (S)} Expressed as total power usage, L as device power usage weight, and λ as other influencing factors of the device power performance index.

6. The power distribution auxiliary monitoring and early warning system based on the technology of the internet of things according to claim 1, wherein: the specific mode of the gas concentration monitoring module is as follows:

marking the indoor gas concentration parameter value collected by each monitoring subarea in the target industrial enterprise area through a gas concentration sensor as j _i Where i=1, 2,..n, i denotes the i-th monitoring sub-region number.

7. The power distribution auxiliary monitoring and early warning system based on the technology of the internet of things according to claim 1, wherein: the specific treatment mode of the gas concentration pretreatment module is as follows:

the k is ₁ ，k ₂ ，...,k _n The calculation formula of (2) is as follows:

8. the power distribution auxiliary monitoring and early warning system based on the technology of the internet of things according to claim 1, wherein: the calculation formula of the spontaneous combustion gas concentration safety index is as follows:

where γ is expressed as an spontaneous combustion gas concentration safety index, u is expressed as an spontaneous combustion gas species, J is expressed as a gas concentration, Δj is expressed as a standard unit concentration of spontaneous combustion gas, and λ is expressed as another influencing factor of the spontaneous combustion gas concentration safety index.

9. The power distribution auxiliary monitoring and early warning system based on the technology of the internet of things according to claim 1, wherein: the formula of the equipment performance monitoring coefficient is as follows:

where θ is expressed as a device performance monitoring coefficient, α is expressed as a device temperature performance index, β is expressed as a device power performance index, γ is expressed as an spontaneous combustion gas concentration safety index, λ ₁ Other influencing factors, lambda, expressed as the device power performance index ₂ Other influencing factors, lambda, expressed as the safety index of the concentration of the pyrophoric gas ₃ Other influencing factors expressed as device temperature performance index;

the integral analysis formula is

10. The power distribution auxiliary monitoring and early warning system based on the technology of the internet of things according to claim 1, wherein: the specific evaluation mode of the power distribution auxiliary evaluation module is as follows:

comparing the equipment performance monitoring coefficient theta of each monitoring subarea in the target industrial enterprise area with a preset equipment performance monitoring coefficient threshold value delta theta, if theta is larger than delta theta, indicating that potential safety hazards exist in the target industrial enterprise power system, further analyzing influence factors, comparing the equipment temperature performance index alpha with a preset equipment temperature performance standard state index, if alpha is larger than delta alpha of a certain monitoring subarea, indicating that abnormality exists in the equipment temperature of the monitoring subarea, sending a treatment measure to a corresponding manager, comparing the equipment power performance index beta with the preset equipment power standard state index, if beta is larger than delta beta, indicating that abnormality exists in the equipment working state of the monitoring subarea, sending the treatment measure to the corresponding manager, comparing the spontaneous combustion gas concentration safety index gamma with the preset spontaneous combustion gas concentration standard concentration safety index delta gamma, if gamma is larger than delta gamma, indicating that spontaneous combustion risk exists in the monitoring subarea, and sending an early warning gas spontaneous combustion risk instruction to the corresponding manager.