Industrial electricity supervision and energy storage system and method based on Internet of Things

Abstract

The invention discloses an industrial power consumption supervision and energy storage system and method based on the Internet of Things, and particularly relates to the field of industrial power consumption supervision. According to the method, industrial power consumption equipment parameters are collected in real time through an Internet of Things sensor network deployed in an industrial site, and after classified storage in a cloud database, data cleaning, conversion and statistical analysis are carried out, so that the industrial power consumption supervision and energy storage system is obtained. And key indexes such as electrical load characteristics, equipment operation efficiency, energy storage state parameters and the like are obtained. Calculating an electricity utilization risk assessment index based on the electricity utilization load characteristics, the abnormal frequency and the fault probability, obtaining an energy storage efficiency assessment index in combination with the energy storage state parameters, the equipment efficiency and the power demand trend, and dividing the industrial electricity utilization safety risk and the energy storage system problem through the comprehensive optimization coefficient. And finally, displaying the operation state in real time by using a visual chart and triggering an early warning response. Intelligent supervision, risk prediction and optimal scheduling of the industrial power utilization and energy storage system are realized, and the energy utilization efficiency and the operation safety are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial power supervision, and more particularly to an industrial power supervision and energy storage system and method based on the Internet of Things. BACKGROUND

[0002] In the field of industrial power and energy storage, with the continuous development of industrial production and the improvement of intelligent level, the supervision of industrial power and the management of energy storage system become increasingly important. At present, there are related technologies for monitoring the operation data of industrial power equipment and energy storage equipment in industrial sites.

[0003] However, there are certain limitations in data collection, processing, analysis, risk assessment and optimization decision-making, such as the existing technology in processing and analyzing the collected power and energy storage data, which is difficult to fully and deeply mine the information in the data, and cannot accurately obtain key indicators such as power load characteristics, equipment operation efficiency, energy storage state parameters, etc., thereby affecting the accurate grasp of the operation status of industrial power and energy storage system, and the existing technology lacks a scientific and systematic risk assessment system, cannot comprehensively evaluate the power risk and energy storage efficiency based on multiple key parameters, cannot accurately calculate the power risk assessment index and energy storage efficiency evaluation index, making it difficult to predict the safety risks in industrial power process and the operation problems of energy storage system in advance, and the existing technology cannot intelligently generate optimization decisions and strategies according to the comprehensive evaluation results when facing industrial power safety risks and energy storage system operation problems, cannot realize efficient scheduling and optimized operation of industrial power equipment and energy storage system, and cannot meet the intelligent demand of industrial production for power supervision and energy storage management. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide an industrial power supervision and energy storage system and method based on the Internet of Things, which solves the problems in the above background technology through the following scheme.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: an industrial power supervision and energy storage method based on the Internet of Things, comprising: S1: data collection and storage: through the Internet of Things sensor network deployed in the industrial site, real-time monitoring and collecting the operation data of industrial power equipment and energy storage equipment, creating power equipment data table and energy storage equipment data table in the cloud database respectively, and storing the collected real-time data into the database;

[0006] S2: data processing and analysis: data processing is performed on the collected power and energy storage data to obtain power load characteristics, equipment operation efficiency, energy storage state parameters, power abnormal frequency, equipment failure probability and power demand change trend;

[0007] S3: Intelligent analysis and evaluation: based on the electrical load characteristics, equipment operation efficiency, energy storage state parameters, power consumption abnormal frequency, equipment failure probability and power demand trend, the power consumption risk evaluation index and energy storage efficiency evaluation index are calculated;

[0008] S4: Safety risk prediction and optimization decision: the comprehensive optimization coefficient is calculated by the power consumption risk evaluation index and the energy storage efficiency evaluation index;

[0009] S5: Risk level division and strategy generation: according to the comprehensive optimization coefficient, the industrial power safety risk and the energy storage system operation problem are detected, and the industrial power safety risk is divided into low, medium and high levels, and the energy storage system operation problem is divided into general, serious and urgent;

[0010] S6: Early warning display and execution control: the monitored power consumption data and the running state of the energy storage system in the industrial power consumption process are displayed in the form of visual charts, the changes of industrial power consumption behavior and energy storage system operation are displayed in real time, the monitored results are stored in the cloud database for managers to query, and the detected industrial power safety risk and energy storage system operation problem are responded in real time.

[0011] Preferably, the industrial power supervision energy storage system based on Internet of Things comprises a data acquisition module, a data processing center, an intelligent analysis module, an optimization decision module and an early warning display module;

[0012] The data acquisition module comprises an electric data acquisition unit and an energy storage data acquisition unit, which are used to acquire real-time power consumption and energy storage data and transmit them to the data processing center through the industrial Internet of Things network;

[0013] The electric data acquisition unit uses high-precision power sensors to monitor the power consumption data of industrial power equipment in real time, and the energy storage data acquisition unit uses Internet of Things intelligent terminals to monitor the running state and energy storage parameters of energy storage equipment in real time;

[0014] The data processing center is used for data processing of the collected power consumption and energy storage data, to obtain the electrical load characteristics, equipment operation efficiency, energy storage state parameters, power consumption abnormal frequency, equipment failure probability and power demand trend;

[0015] The intelligent analysis module is used to calculate the power consumption risk evaluation index and energy storage efficiency evaluation index based on the electrical load characteristics, equipment operation efficiency, energy storage state parameters, power consumption abnormal frequency, equipment failure probability and power demand trend;

[0016] The optimization decision module is used to calculate a comprehensive optimization coefficient through the electricity risk assessment index and the energy storage efficiency assessment index, and to classify the risk level based on the industrial electricity safety risks and energy storage system operation problems detected by the comprehensive optimization coefficient. The industrial electricity safety risks are classified as low, medium and high, and the energy storage system operation problems are classified as general, serious and urgent.

[0017] The early warning display module is used to send early warning prompts to the management personnel terminal based on the detected industrial power safety risks and energy storage system operation problems according to the risk level classification. At the same time, it transmits the optimization strategy to the execution unit. The execution unit automatically adjusts the industrial power equipment and energy storage system or prompts manual intervention according to the optimization strategy, so as to realize intelligent supervision of industrial power consumption and optimized operation of energy storage system.

[0018] The technical effects and advantages of this invention are as follows:

[0019] 1. This invention performs data cleaning, transformation and statistical analysis on the collected data, which can accurately obtain key indicators such as power load characteristics and equipment operating efficiency, and deeply mine the information in the data, laying a solid foundation for intelligent analysis and evaluation.

[0020] 2. This invention predicts industrial electricity safety risks and optimizes energy storage system scheduling strategies by comprehensively optimizing coefficients. It can intelligently generate optimization decisions based on the evaluation results, realize intelligent management of industrial electricity and energy storage systems, and improve the system's operating efficiency and safety.

[0021] 3. This invention accurately classifies the risk levels of industrial power safety risks and energy storage system operation problems based on comprehensive optimization coefficients, and generates corresponding optimization strategies, such as adjusting equipment operating parameters and optimizing charging and discharging strategies, thereby achieving precise management of risks and problems. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the overall structure of the present invention.

[0023] Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] As attached Figure 1The IoT-based industrial electricity monitoring and energy storage method shown includes:

[0026] S1: Data Acquisition and Storage: Through an IoT sensor network deployed in the industrial site, real-time monitoring and collection of operating data of industrial electrical equipment and energy storage equipment are performed. Data tables for electrical equipment and energy storage equipment are created in the cloud database, and the collected real-time data is classified and stored in the database.

[0027] The operating data of the energy storage device includes, but is not limited to, voltage, current, power, electricity, and device operating status.

[0028] S2: Data Processing and Analysis: Data processing is performed on the collected electricity consumption and energy storage data to obtain electricity load characteristics, equipment operating efficiency, energy storage status parameters, frequency of electricity consumption anomalies, probability of equipment failure, and trends in electricity demand.

[0029] Specifically, the processing includes data cleaning, data conversion, and data statistical analysis. By statistically analyzing the power consumption patterns and energy storage status, the power safety risks and the changing trends of the operating status parameters of the energy storage system during industrial power consumption are monitored in real time.

[0030] The electrical load characteristics are used to analyze the impact of load variation patterns of industrial electrical equipment on production electricity consumption. The formula for calculating the electrical load characteristics based on the operating time T of the electrical equipment is: L=α×P max +β×P avg +γ×P min , where P max P represents the maximum power consumption. avg P represents the average power consumption. min This represents the minimum power consumption, with α, β, and γ being weighting coefficients set according to the power consumption characteristics of different industrial scenarios.

[0031] The equipment operating efficiency is used to evaluate the energy utilization efficiency of industrial electrical equipment. The formula for calculating the equipment operating efficiency is: Among them W 有效 W represents the electrical energy consumed by the equipment to perform its work effectively. 总 This indicates the total electrical energy consumed by the equipment;

[0032] The energy storage state parameters are used to describe the energy storage level and health status of the energy storage device. The formula for calculating the energy storage state parameters based on parameters such as the number of charge-discharge cycles and battery temperature of the energy storage device is as follows:

[0033] Where C 剩余 C represents the remaining capacity of the energy storage device. 额定 The rated capacity is represented by δ, the health coefficient of the energy storage device is represented by ΔT, and the temperature difference between the battery and the standard temperature is represented by T.标准 Indicates standard temperature;

[0034] The power consumption anomaly frequency is used to monitor the frequency of abnormal situations occurring during industrial power consumption. The formula for calculating the power consumption anomaly frequency is: Where n 异常 n represents the number of abnormal electricity consumption events. 总 This represents the total number of electricity consumption events, where f is the frequency of electricity consumption data collection.

[0035] Equipment failure probability is used to predict the likelihood of failure of industrial electrical equipment. The formula for calculating equipment failure probability based on historical failure data and equipment operating parameters is as follows:

[0036] Where P represents the probability of equipment failure, with a value ranging from [0,1]. A larger value indicates a higher probability of failure. X1, X2, ..., X n The equipment operating parameters need to be standardized, α1, α2, ..., α n The weighting coefficients are obtained through training on historical fault data and reflect the degree of influence of each parameter on the fault. e refers to the natural constant.

[0037] The electricity demand change trend is used to predict changes in electricity demand during industrial production. The formula for calculating the electricity demand change trend based on collected historical electricity consumption data and production plans is as follows:

[0038] D = a × D 历史 +b×P 计划 +c×T, where D 历史 P represents historical electricity demand data. 计划 Let T represent the planned production output, and a, b, and c represent the time period.

[0039] S3: Intelligent Analysis and Evaluation: Based on electrical load characteristics, equipment operating efficiency, energy storage status parameters, frequency of abnormal electricity consumption, equipment failure probability, and electricity demand change trends, the electricity consumption risk assessment index and energy storage efficiency assessment index are calculated.

[0040] The electrical risk assessment index is based on the analysis of electrical load characteristics L, abnormal electrical frequency F, and equipment failure probability P. The formula for calculating the electrical risk assessment index is: u=λ1×L+λ2×F+λ3×P, where λ1, λ2, and λ3 are weighting coefficients, which are determined according to industrial electrical safety standards and historical safety events.

[0041] The energy storage efficiency evaluation index is based on the analysis of energy storage state parameters S, equipment operating efficiency η, and electricity demand change trend D. The formula for calculating the energy storage efficiency evaluation index is: v=μ1×S+μ2×η+μ3×ln(D+1), where μ1, μ2, and μ3 are weighting coefficients, which are set according to the design requirements and operating objectives of the energy storage system.

[0042] S4: Safety Risk Prediction and Optimization Decision-Making: Calculate the comprehensive optimization coefficient through the electricity risk assessment index and the energy storage efficiency assessment index;

[0043] The comprehensive optimization coefficient is used to predict safety risks in industrial electricity consumption and optimize the scheduling strategy of energy storage system. The comprehensive optimization coefficient is based on the electricity risk assessment index u and the energy storage efficiency assessment index v. The comprehensive optimization coefficient is: ω=(u-u0)×(v-v0), where u0 represents the electricity safety threshold and v0 represents the energy storage efficiency threshold.

[0044] It should be further explained that when the comprehensive optimization coefficient ω is less than the safety optimization threshold ω0, it indicates that the industrial power consumption and energy storage system are operating normally and real-time monitoring continues. When the comprehensive optimization coefficient ω is greater than or equal to the safety optimization threshold ω0, it indicates that there is a safety risk in the industrial power consumption process or that the energy storage system is operating inefficiently and needs to be optimized and adjusted. The values ​​of the power consumption safety threshold u0, the energy storage efficiency threshold v0, and the safety optimization threshold ω0 are set and adjusted by industrial power experts based on historical data and industrial production safety standards, and the optimization decision results are transmitted to the execution unit.

[0045] S5: Risk Level Classification and Strategy Generation: Based on the industrial electricity safety risks and energy storage system operation problems detected by the comprehensive optimization coefficient, risk levels are classified, and industrial electricity safety risks are classified into low, medium and high levels, and energy storage system operation problems are classified into general, serious and urgent levels.

[0046] The criteria for classifying risk levels are as follows:

[0047] When the safety optimization threshold ω0 > the comprehensive optimization coefficient ω≥0, that is, the electricity risk assessment index u < the electricity safety threshold u0, the energy storage efficiency assessment index v≥ the energy storage efficiency threshold v0, or the electricity risk assessment index u≥ the electricity safety threshold u0, the energy storage efficiency index v< the energy storage efficiency threshold v0, the industrial electricity safety risk is classified as low level.

[0048] When the comprehensive optimization coefficient ω = 0, that is, the electricity risk assessment index u = the electricity safety threshold u0 and the energy storage efficiency assessment index v = the energy storage efficiency threshold v0, the industrial electricity safety risk is classified as medium level.

[0049] When the comprehensive optimization coefficient ω>0, that is, the electricity risk assessment index u≥ the electricity safety threshold u0 and the energy storage efficiency assessment index v< the energy storage efficiency threshold v0, the industrial electricity safety risk is classified as high level;

[0050] When the comprehensive optimization coefficient ω is in the interval [ω1, ω0), where ω1 < ω0, it is determined to be a general problem.

[0051] When the comprehensive optimization coefficient ω is in the interval [ω2, ω1), where ω2 < ω1, it is judged as a serious problem.

[0052] When the comprehensive optimization coefficient ω≥ω0, it is determined to be an urgent problem.

[0053] At the same time, corresponding optimization strategies are generated based on the risk level, such as adjusting the operating parameters of electrical equipment, optimizing the charging and discharging strategies of energy storage systems, and issuing equipment maintenance reminders. The risk level classification results and optimization strategies are then transmitted to the early warning display unit.

[0054] S6: Early Warning Display and Execution Control: By displaying the monitored industrial electricity consumption data and the operating status of the energy storage system in a visual chart format, the system can display the changes in industrial electricity consumption behavior and energy storage system operation in real time. The displayed monitoring results are stored in a cloud database for managers to query, and real-time early warning responses are provided for detected industrial electricity safety risks and energy storage system operation problems.

[0055] As attached Figure 2 The IoT-based industrial power consumption monitoring and energy storage system shown includes: a data acquisition module, a data processing center, an intelligent analysis module, an optimization decision-making module, and an early warning display module;

[0056] The data acquisition module includes an electricity data acquisition unit and an energy storage data acquisition unit, which are used to collect real-time electricity and energy storage data and transmit them to the data processing center through the industrial Internet of Things network.

[0057] The power consumption data acquisition unit uses a high-precision power sensor to monitor the power consumption data of industrial electrical equipment in real time, and the energy storage data acquisition unit uses an Internet of Things smart terminal to monitor the operating status and energy storage parameters of the energy storage equipment in real time.

[0058] The data processing center is used to process the collected electricity consumption and energy storage data to obtain electricity load characteristics, equipment operating efficiency, energy storage status parameters, electricity consumption anomaly frequency, equipment failure probability, and electricity demand change trend.

[0059] The processing includes data cleaning, data conversion, and data statistical analysis. By statistically analyzing the power consumption patterns and energy storage status, the power safety risks and the changing trends of the operating status parameters of the energy storage system during industrial power consumption are monitored in real time.

[0060] The electrical load characteristics are used to analyze the impact of load variation patterns of industrial electrical equipment on production electricity consumption. The formula for calculating the electrical load characteristics based on the operating time T of the electrical equipment is: L=α×P max +β×P avg +γ×P min , where P max P represents the maximum power consumption. avg P represents the average power consumption. min This represents the minimum power consumption, with α, β, and γ being weighting coefficients set according to the power consumption characteristics of different industrial scenarios.

[0061] The equipment operating efficiency is used to evaluate the energy utilization efficiency of industrial electrical equipment. The formula for calculating the equipment operating efficiency is: Among them W 有效 W represents the electrical energy consumed by the equipment to perform its work effectively. 总 This indicates the total electrical energy consumed by the equipment;

[0062] The energy storage state parameters are used to describe the energy storage level and health status of the energy storage device. The formula for calculating the energy storage state parameters based on parameters such as the number of charge-discharge cycles and battery temperature of the energy storage device is as follows:

[0063] Where C 剩余 C represents the remaining capacity of the energy storage device. 额定 The rated capacity is represented by δ, the health coefficient of the energy storage device is represented by ΔT, and the temperature difference between the battery and the standard temperature is represented by T. 标准 Indicates standard temperature;

[0064] The power consumption anomaly frequency is used to monitor the frequency of abnormal situations occurring during industrial power consumption. The formula for calculating the power consumption anomaly frequency is: Where n 异常 n represents the number of abnormal electricity consumption events. 总 This represents the total number of electricity consumption events, where f is the frequency of electricity consumption data collection.

[0065] Equipment failure probability is used to predict the likelihood of failure of industrial electrical equipment. The formula for calculating equipment failure probability based on historical failure data and equipment operating parameters is as follows:

[0066] Where P represents the probability of equipment failure, with a value ranging from [0,1]. A larger value indicates a higher probability of failure. X1, X2, ..., X n The equipment operating parameters need to be standardized, α1, α2, ..., α n The weighting coefficients are obtained through training on historical fault data and reflect the degree of influence of each parameter on the fault. e refers to the natural constant.

[0067] The electricity demand change trend is used to predict changes in electricity demand during industrial production. The formula for calculating the electricity demand change trend based on collected historical electricity consumption data and production plans is as follows:

[0068] D = a × D 历史 +b×P 计划 +c×T, where D 历史 P represents historical electricity demand data. 计划 Let T represent the planned production output, and a, b, and c represent the time period.

[0069] The intelligent analysis module is used to calculate the electricity risk assessment index and the energy storage efficiency assessment index based on the electrical load characteristics, equipment operating efficiency, energy storage status parameters, frequency of abnormal electricity consumption, probability of equipment failure, and trend of electricity demand changes.

[0070] The electrical risk assessment index is based on the analysis of electrical load characteristics L, abnormal electrical frequency F, and equipment failure probability P. The formula for calculating the electrical risk assessment index is: u=λ1×L+λ2×F+λ3×P, where λ1, λ2, and λ3 are weighting coefficients, which are determined according to industrial electrical safety standards and historical safety events.

[0071] The energy storage efficiency evaluation index is based on the analysis of energy storage state parameters S, equipment operating efficiency η, and electricity demand change trend D. The formula for calculating the energy storage efficiency evaluation index is: v=μ1×S+μ2×η+μ3×ln(D+1), where μ1, μ2, and μ3 are weighting coefficients, which are set according to the design requirements and operating objectives of the energy storage system.

[0072] The optimization decision module is used to calculate a comprehensive optimization coefficient through the electricity risk assessment index and the energy storage efficiency assessment index, and to classify the risk level based on the industrial electricity safety risks and energy storage system operation problems detected by the comprehensive optimization coefficient. The industrial electricity safety risks are classified as low, medium and high, and the energy storage system operation problems are classified as general, serious and urgent.

[0073] The comprehensive optimization coefficient is used to predict safety risks in industrial electricity consumption and optimize the scheduling strategy of energy storage system. The comprehensive optimization coefficient is based on the electricity risk assessment index u and the energy storage efficiency assessment index v. The comprehensive optimization coefficient is: ω=(u-u0)×(v-v0), where u0 represents the electricity safety threshold and v0 represents the energy storage efficiency threshold.

[0074] The criteria for classifying risk levels are as follows:

[0075] When the safety optimization threshold ω0 > the comprehensive optimization coefficient ω≥0, that is, the electricity risk assessment index u < the electricity safety threshold u0, the energy storage efficiency assessment index v≥ the energy storage efficiency threshold v0, or the electricity risk assessment index u≥ the electricity safety threshold u0, the energy storage efficiency index v< the energy storage efficiency threshold v0, the industrial electricity safety risk is classified as low level.

[0076] When the comprehensive optimization coefficient ω = 0, that is, the electricity risk assessment index u = the electricity safety threshold u0 and the energy storage efficiency assessment index v = the energy storage efficiency threshold v0, the industrial electricity safety risk is classified as medium level.

[0077] When the comprehensive optimization coefficient ω>0, that is, the electricity risk assessment index u≥ the electricity safety threshold u0 and the energy storage efficiency assessment index v< the energy storage efficiency threshold v0, the industrial electricity safety risk is classified as high level;

[0078] When the comprehensive optimization coefficient ω is in the interval [ω1, ω0), where ω1 < ω0, it is determined to be a general problem.

[0079] When the comprehensive optimization coefficient ω is in the interval [ω2, ω1), where ω2 < ω1, it is judged as a serious problem.

[0080] When the comprehensive optimization coefficient ω≥ω0, it is determined to be an urgent problem.

[0081] The early warning display module is used to send early warning prompts to the management personnel terminal based on the detected industrial power safety risks and energy storage system operation problems according to the risk level classification. At the same time, it transmits the optimization strategy to the execution unit. The execution unit automatically adjusts the industrial power equipment and energy storage system or prompts manual intervention according to the optimization strategy, so as to realize intelligent supervision of industrial power consumption and optimized operation of energy storage system.

[0082] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0083] In conclusion, 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 within the protection scope of the present invention.

Claims

1. An industrial electricity consumption monitoring and energy storage method based on the Internet of Things, characterized in that, include: S1: Data Acquisition and Storage: Through an IoT sensor network deployed in the industrial site, real-time monitoring and collection of operating data of industrial electrical equipment and energy storage equipment are performed. Data tables for electrical equipment and energy storage equipment are created in the cloud database, and the collected real-time data is classified and stored in the database. S2: Data Processing and Analysis: Data processing is performed on the collected electricity consumption and energy storage data to obtain electricity load characteristics, equipment operating efficiency, energy storage status parameters, frequency of electricity consumption anomalies, probability of equipment failure, and trends in electricity demand. S3: Intelligent Analysis and Evaluation: Based on electrical load characteristics, equipment operating efficiency, energy storage status parameters, frequency of abnormal electricity consumption, equipment failure probability, and electricity demand change trends, the electricity consumption risk assessment index and energy storage efficiency assessment index are calculated. S4: Safety Risk Prediction and Optimization Decision-Making: Calculate the comprehensive optimization coefficient through the electricity risk assessment index and the energy storage efficiency assessment index; S5: Risk Level Classification and Strategy Generation: Based on the industrial electricity safety risks and energy storage system operation problems detected by the comprehensive optimization coefficient, risk levels are classified, and industrial electricity safety risks are classified into low, medium and high levels, and energy storage system operation problems are classified into general, serious and urgent levels. S6: Early Warning Display and Execution Control: By displaying the monitored industrial electricity consumption data and the operating status of the energy storage system in a visual chart format, the system can display the changes in industrial electricity consumption behavior and energy storage system operation in real time. The displayed monitoring results are stored in a cloud database for managers to query, and real-time early warning responses are provided for detected industrial electricity safety risks and energy storage system operation problems.

2. The industrial electricity monitoring and energy storage method based on the Internet of Things according to claim 1, characterized in that: The electrical load characteristics are used to analyze the impact of load variation patterns of industrial electrical equipment on production electricity consumption. The formula for calculating the electrical load characteristics based on the operating time T of the electrical equipment is: L=α×P max +β×P avg +γ×P min , where P max P represents the maximum power consumption. avg P represents the average power consumption. min This represents the minimum power consumption, with α, β, and γ being weighting coefficients set according to the power consumption characteristics of different industrial scenarios. The equipment operating efficiency is used to evaluate the energy utilization efficiency of industrial electrical equipment. The formula for calculating the equipment operating efficiency is: Among them W 有效 W represents the electrical energy consumed by the equipment to perform its work effectively. 总 This indicates the total electrical energy consumed by the equipment; The energy storage state parameters are used to describe the energy storage level and health status of the energy storage device. The formula for calculating the energy storage state parameters based on parameters such as the number of charge-discharge cycles and battery temperature of the energy storage device is as follows: Where C 剩余 C represents the remaining capacity of the energy storage device. 额定 The rated capacity is represented by δ, the health coefficient of the energy storage device is represented by ΔT, and the temperature difference between the battery and the standard temperature is represented by T. 标准 Indicates standard temperature; The power consumption anomaly frequency is used to monitor the frequency of abnormal situations occurring during industrial power consumption. The formula for calculating the power consumption anomaly frequency is: Where n 异常 n represents the number of abnormal electricity consumption events. 总 This represents the total number of electricity consumption events, where f is the frequency of electricity consumption data collection. Equipment failure probability is used to predict the likelihood of failure of industrial electrical equipment. The formula for calculating equipment failure probability based on historical failure data and equipment operating parameters is as follows: Where P represents the probability of equipment failure, with a value ranging from [0,1]. The larger the value, the higher the probability of failure. X1, X2, ..., X n The equipment operating parameters need to be standardized, α1, α2, ..., α n The weighting coefficients are obtained through training on historical fault data and reflect the degree of influence of each parameter on the fault. e refers to the natural constant. The electricity demand change trend is used to predict changes in electricity demand during industrial production. The formula for calculating the electricity demand change trend based on collected historical electricity consumption data and production plans is as follows: D = a × D 历史 +b×P 计划 +c×T, where D 历史 P represents historical electricity demand data. 计划 Let T represent the planned production output, and a, b, and c represent the time period.

3. The industrial electricity monitoring and energy storage method based on the Internet of Things according to claim 1, characterized in that: The electricity risk assessment index is based on the analysis of electricity load characteristics L, electricity abnormality frequency F, and equipment failure probability P. The formula for calculating the electricity risk assessment index is: u=λ1×L+λ2×F+λ3×P, where λ1, λ2, and λ3 are weighting coefficients, which are determined according to industrial electricity safety standards and historical safety events. The energy storage efficiency evaluation index is based on the analysis of energy storage state parameters S, equipment operating efficiency η, and electricity demand change trend D. The formula for calculating the energy storage efficiency evaluation index is: v=μ1×S+μ2×η+μ3×ln(D+1), where μ1, μ2, and μ3 are weighting coefficients, which are set according to the design requirements and operating objectives of the energy storage system.

4. The IoT-based industrial electricity monitoring and energy storage method according to claim 1, characterized in that: The comprehensive optimization coefficient is used to predict safety risks in industrial electricity consumption and optimize the scheduling strategy of energy storage system. The comprehensive optimization coefficient is based on the electricity risk assessment index u and the energy storage efficiency assessment index v, and the comprehensive optimization coefficient is: ω=(u-u0)×(v-v0), where u0 represents the electricity safety threshold and v0 represents the energy storage efficiency threshold.

5. The IoT-based industrial electricity monitoring and energy storage method according to claim 1, characterized in that: The criteria for classifying risk levels are as follows: When the safety optimization threshold ω0 > the comprehensive optimization coefficient ω≥0, that is, the electricity risk assessment index u < the electricity safety threshold u0, the energy storage efficiency assessment index v≥ the energy storage efficiency threshold v0, or the electricity risk assessment index u≥ the electricity safety threshold u0, the energy storage efficiency index v< the energy storage efficiency threshold v0, the industrial electricity safety risk is classified as low level. When the comprehensive optimization coefficient ω = 0, that is, the electricity risk assessment index u = the electricity safety threshold u0 and the energy storage efficiency assessment index v = the energy storage efficiency threshold v0, the industrial electricity safety risk is classified as medium level. When the comprehensive optimization coefficient ω>0, that is, the electricity risk assessment index u≥ the electricity safety threshold u0 and the energy storage efficiency assessment index v< the energy storage efficiency threshold v0, the industrial electricity safety risk is classified as high level; When the comprehensive optimization coefficient ω is in the interval [ω1, ω0), where ω1 < ω0, it is determined to be a general problem; When the comprehensive optimization coefficient ω is in the interval [ω2, ω1), where ω2 < ω1, it is judged as a serious problem; When the comprehensive optimization coefficient ω≥ω0, it is determined to be an urgent problem.

6. An IoT-based industrial electricity monitoring and energy storage system, used to implement the IoT-based industrial electricity monitoring and energy storage method according to any one of claims 1-5, characterized in that, include: The system includes a data acquisition module, a data processing center, an intelligent analysis module, an optimization decision-making module, and an early warning display module. The data acquisition module includes an electricity data acquisition unit and an energy storage data acquisition unit, which are used to collect real-time electricity and energy storage data and transmit them to the data processing center through the industrial Internet of Things network. The power consumption data acquisition unit uses a high-precision power sensor to monitor the power consumption data of industrial electrical equipment in real time, and the energy storage data acquisition unit uses an Internet of Things smart terminal to monitor the operating status and energy storage parameters of the energy storage equipment in real time. The data processing center is used to process the collected electricity consumption and energy storage data to obtain electricity load characteristics, equipment operating efficiency, energy storage status parameters, electricity consumption anomaly frequency, equipment failure probability, and electricity demand change trend. The intelligent analysis module is used to calculate the electricity risk assessment index and the energy storage efficiency assessment index based on the electrical load characteristics, equipment operating efficiency, energy storage status parameters, frequency of abnormal electricity consumption, probability of equipment failure, and trend of electricity demand changes. The optimization decision module is used to calculate a comprehensive optimization coefficient through the electricity risk assessment index and the energy storage efficiency assessment index, and to classify the risk level based on the industrial electricity safety risks and energy storage system operation problems detected by the comprehensive optimization coefficient. The industrial electricity safety risks are classified as low, medium and high, and the energy storage system operation problems are classified as general, serious and urgent. The early warning display module is used to send early warning prompts to the management personnel terminal based on the detected industrial power safety risks and energy storage system operation problems according to the risk level classification. At the same time, it transmits the optimization strategy to the execution unit. The execution unit automatically adjusts the industrial power equipment and energy storage system or prompts manual intervention according to the optimization strategy, so as to realize intelligent supervision of industrial power consumption and optimized operation of energy storage system.

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