Comprehensive energy monitoring management method, system and equipment based on Internet of Things, and medium

Abstract

The invention relates to the technical field of energy monitoring, in particular to a comprehensive energy monitoring management method, system and device based on the Internet of Things and a medium. According to the method, initial grading is carried out based on energy consumption parameters of equipment, corresponding data acquisition schemes are configured for different grades, the change trend of equipment operation characteristics is captured in time through intelligent comparative analysis of historical data and real-time data, and finally, the monitoring grade and the acquisition rule are dynamically adjusted based on an analysis result. Generating an energy management strategy according with the actual demand; equipment feature recognition, dynamic hierarchical management and self-adaptive data acquisition are organically combined, so that the monitoring precision is improved, and meanwhile, the system resource consumption is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of energy monitoring, and in particular to a comprehensive energy monitoring management method, system, equipment and medium based on the Internet of Things. Background Art

[0002] With the advancement of science and technology and social development, the number of electrical equipment in various places is increasing, and the power environment is becoming more and more complex. From university laboratories to industrial workshops, from commercial buildings to data centers, the demand for refined energy management is becoming more and more urgent. Establishing a scientific and reasonable power management system, improving energy efficiency, and ensuring power safety have become important issues faced by all industries.

[0003] At present, the mainstream power management system uses fixed monitoring equipment for data collection and analysis, and some systems are equipped with intelligent gateways and cloud platforms for data processing. These systems monitor power-consuming equipment in a unified manner through preset collection schemes, and make abnormal judgments and alarms based on fixed thresholds. Energy monitoring lacks pertinence and is difficult to meet the refined management needs in complex power consumption environments. This situation needs to be further improved. Summary of the invention

[0004] In order to solve the problem that the existing energy monitoring lacks pertinence and is difficult to meet the refined management needs in a complex power consumption environment, the present application provides a comprehensive energy monitoring management method, system, device and medium based on the Internet of Things, which adopts the following technical solutions: In a first aspect, the present application provides a comprehensive energy monitoring and management method based on the Internet of Things, comprising the following steps: Obtaining energy consumption parameters of electrical equipment, and determining a monitoring level according to the energy consumption parameters; Configure collection rules according to the monitoring level, and collect real-time data according to the collection rules; Acquire historical data of the electric device, compare the historical data with the real-time data, and update the monitoring level based on the comparison result; adjusting the collection rules based on the updated monitoring level; Re-execute data collection according to the adjusted collection rules and generate an energy management strategy.

[0005] By adopting the above technical solutions, due to the increasingly complex modern electricity consumption environment, there are great differences in the energy consumption characteristics and usage patterns of different equipment. For example, precision instruments in laboratories require continuous and stable power supply and relatively fixed energy consumption, while air conditioning, lighting and other equipment show obvious periodic fluctuation characteristics, which brings huge challenges to energy monitoring and management; existing technologies often adopt unified monitoring standards, which are difficult to adapt to such differentiated needs, resulting in unsatisfactory monitoring effects; this application first performs initial classification based on the energy consumption parameters of the equipment, configures corresponding data collection schemes for different levels, and timely captures the changing trends of equipment operation characteristics through intelligent comparative analysis of historical data and real-time data. Finally, based on the analysis results, the monitoring level and collection rules are dynamically adjusted, and energy management strategies that meet actual needs are generated accordingly; the device feature recognition, dynamic hierarchical management and adaptive data collection are organically combined to improve monitoring accuracy while reducing system resource consumption.

[0006] Optionally, determining the monitoring level according to the energy consumption parameter specifically includes the following steps: According to the energy consumption parameters, the peak-to-valley power difference value, the operation time fluctuation value and the load change rate of the electrical equipment are obtained; Obtaining a dynamic coefficient of the equipment according to the peak-to-valley power difference value, the operating time fluctuation value and the load change rate; Based on the numerical range of the equipment dynamic coefficient and the energy consumption parameter, the monitoring level of the electrical equipment is determined, and the monitoring level is positively correlated with the numerical range of the equipment dynamic coefficient and the energy consumption parameter.

[0007] By adopting the above technical scheme, it is difficult to accurately reflect the actual monitoring needs of the equipment by relying on a single energy consumption parameter. For example, although some equipment has a low average energy consumption, it fluctuates violently, or although some equipment has a large power, it operates stably; traditional methods often rely too much on static thresholds and cannot effectively capture the dynamic characteristics of equipment operation; this application first collects and calculates the peak-to-valley power difference value, operating time fluctuation value and load change rate of indicators reflecting the dynamic performance of the equipment, and then integrates these indicators into an equipment dynamic coefficient that can characterize the overall dynamic characteristics of the equipment through a specific algorithm. Finally, combined with the numerical range of the energy consumption parameter, a mapping relationship between equipment characteristics and monitoring levels is established. The rationality of the monitoring level is ensured through multi-dimensional quantitative analysis, which effectively solves the problem that the traditional single indicator evaluation method cannot accurately reflect the actual monitoring needs of the equipment.

[0008] Optionally, obtaining historical data of the electric device, comparing the historical data with the real-time data, and updating the monitoring level based on the comparison result specifically includes the following steps: Obtaining historical data of the electrical equipment in the same period and under similar load conditions, and calculating an energy consumption deviation value between the historical data and the real-time data; The monitoring weight coefficient is adjusted based on the energy consumption deviation value, and the monitoring level is updated according to the monitoring weight coefficient.

[0009] By adopting the above technical solution, since the energy consumption characteristics of electrical equipment under different working conditions will change over time, it is difficult to accurately determine whether the operating status of the equipment is abnormal by relying solely on real-time data. For example, a sudden increase in energy consumption of an air conditioner under the same temperature setting and usage period may indicate a decline in equipment performance, and an accurate judgment cannot be made based on current data alone. This application first filters out historical operating data that matches the current working conditions from the historical database, calculates the energy consumption deviation value between the real-time data and the historical benchmark, and then dynamically adjusts the monitoring weight coefficient based on the deviation value, thereby updating the monitoring level more scientifically.

[0010] Optionally, adjusting the collection rule based on the updated monitoring level specifically includes the following steps: Based on the updated monitoring level, obtain the historical collection rules and historical monitoring data of the power-consuming equipment; Comparing the monitoring level of the electric equipment with the historical monitoring level, and marking the electric equipment with the same comparison result as a stable equipment; According to the historical monitoring data of the stable device, adjusting the collection frequency and collection parameters corresponding to the stable device; According to the adjusted acquisition frequency and acquisition parameters, the acquisition rules corresponding to the stable device are updated.

[0011] By adopting the above technical solution, since the operating status and monitoring requirements of electrical equipment will change dynamically over time, simply adjusting the monitoring level without considering the stability characteristics of the equipment will easily lead to excessive consumption of collection resources or the generation of monitoring blind spots; for example, some equipment that has maintained a stable operating state for a long time is still collected data at a high frequency by the system, while some equipment that has just recovered stability but has potential risks has its collection frequency reduced too quickly. This situation is particularly prominent in the monitoring of laboratory precision instruments and industrial equipment; this application first obtains the historical collection rules and monitoring data of the equipment, identifies the stably operating equipment by comparing the current and historical monitoring levels, and then adjusts the collection frequency and parameters based on the historical monitoring data of these stable equipment, and finally forms updated collection rules; introduces equipment stability evaluation into the collection rule optimization process, and improves the accuracy of collection rule adjustment and resource allocation efficiency by establishing a dynamically balanced resource allocation mechanism.

[0012] Optionally, generating an energy management strategy includes the following steps: Based on the adjusted monitoring level and the preset energy consumption association rule, a number of different electrical devices are associated to obtain an associated device group; Generate energy consumption control parameters corresponding to the electrical equipment according to a preset energy consumption threshold of the electrical equipment; According to the preset energy consumption threshold of the associated device group, adjusting the energy consumption control parameter corresponding to the associated device group; An energy management strategy is generated based on the energy consumption control parameters.

[0013] By adopting the above-mentioned technical scheme, it is found that there are generally complex correlations and energy consumption interaction effects between devices in the power consumption environment. The energy management strategy of a single device is difficult to achieve the optimization of overall energy efficiency. For example, in a laboratory environment, the heat dissipation of precision instruments will affect the air-conditioning load, and the operation of the lighting system will affect the cooling demand of the air-conditioning. If this correlation effect between devices is not properly controlled, it is easy to cause energy waste and low equipment operation efficiency. The present application first identifies and constructs an associated device group based on the monitoring level and the preset energy consumption association rules, and then generates initial control parameters based on the energy consumption threshold of a single device. The control parameters are then optimized and adjusted by analyzing the overall energy consumption characteristics of the associated device group. Finally, an energy management strategy that takes into account both the needs of the single unit and the group is formed, and the collaborative management of the device group is realized. By establishing a multi-level energy consumption control mechanism, the problems of fragmented equipment management and insufficient energy optimization in traditional methods are solved.

[0014] Optionally, based on the adjusted monitoring level and a preset energy consumption association rule, a plurality of different electrical devices are associated to obtain an associated device group, which specifically includes the following steps: Obtaining the operating timing, spatial location information and adjusted monitoring level of the electrical equipment; Based on the operation timing, spatial location information and the adjusted monitoring level, constructing an equipment operation correlation matrix, wherein a weight coefficient of the correlation matrix is ​​positively correlated with the adjusted monitoring level; According to the equipment operation correlation matrix and the preset energy consumption correlation rule, identifying equipment combinations having energy consumption impact relationships and whose monitoring levels are within a preset range; The device combination is marked as an associated device group, and the priority of the associated device group is determined according to the average monitoring level of the associated device group.

[0015] By adopting the above technical solution, it is difficult to accurately identify the device combination that truly has energy consumption correlation based on experience or simple physical connection relationships. This application first obtains the complete feature information of the equipment, then constructs a correlation matrix that takes into account the monitoring level weight, and then screens the device combination with similar monitoring levels through energy consumption association rules. Finally, the management priority of the associated device group is determined based on the average monitoring level, and a quantitative evaluation mechanism is established to improve the accuracy of device association identification.

[0016] In a second aspect, the present application provides a comprehensive energy monitoring and management system based on the Internet of Things, comprising: A monitoring level determination module, used to obtain energy consumption parameters of electrical equipment and determine the monitoring level according to the energy consumption parameters; A real-time data collection module, configured to collect data according to the monitoring level and configure collection rules, and collect real-time data according to the collection rules; A monitoring level updating module, used for acquiring historical data of the electric device, comparing the historical data with the real-time data, and updating the monitoring level based on the comparison result; A collection rule adjustment module, used to adjust the collection rule based on the updated monitoring level; The management strategy generation module is used to re-execute data collection according to the adjusted collection rules and generate an energy management strategy.

[0017] Optionally, the monitoring level determination module includes: An equipment data acquisition unit, used to acquire the peak-to-valley power difference value, operation time fluctuation value and load change rate of the electric equipment according to the energy consumption parameter; A dynamic coefficient acquisition unit, used to obtain a dynamic coefficient of the equipment according to the peak-to-valley power difference value, the operation time fluctuation value and the load change rate; The monitoring level determination unit is used to determine the monitoring level of the electrical equipment based on the equipment dynamic coefficient and the numerical range of the energy consumption parameter, and the monitoring level is positively correlated with the equipment dynamic coefficient and the numerical range of the energy consumption parameter.

[0018] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned comprehensive energy monitoring and management method based on the Internet of Things when executing the computer program.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned comprehensive energy monitoring and management method based on the Internet of Things.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: This application first performs initial classification based on the energy consumption parameters of the equipment, configures corresponding data collection schemes for different levels, and timely captures the changing trends of equipment operation characteristics through intelligent comparative analysis of historical data and real-time data. Finally, the monitoring level and collection rules are dynamically adjusted based on the analysis results, and energy management strategies that meet actual needs are generated accordingly; the application organically combines equipment feature recognition, dynamic hierarchical management and adaptive data collection, which improves monitoring accuracy while reducing system resource consumption; It is difficult to accurately reflect the actual monitoring needs of the equipment by relying on a single energy consumption parameter. For example, although the average energy consumption of some equipment is low, it fluctuates violently, or although the power of some equipment is large, the operation is stable; traditional methods often rely too much on static thresholds and cannot effectively capture the dynamic characteristics of equipment operation; this application first collects and calculates the peak-to-valley power difference value, operating time fluctuation value and load change rate of indicators reflecting the dynamic performance of the equipment, and then integrates these indicators into a device dynamic coefficient that can characterize the overall dynamic characteristics of the equipment through a specific algorithm. Finally, combined with the numerical range of the energy consumption parameter, a mapping relationship between the equipment characteristics and the monitoring level is established. The rationality of the monitoring level is ensured through multi-dimensional quantitative analysis, which effectively solves the problem that the traditional single indicator evaluation method cannot accurately reflect the actual monitoring needs of the equipment; Since the energy consumption characteristics of electrical equipment under different operating conditions will change over time, it is difficult to accurately determine whether the operating status of the equipment is abnormal relying solely on real-time data. For example, a sudden increase in energy consumption of an air conditioner under the same temperature setting and usage period may indicate a decline in equipment performance, and an accurate judgment cannot be made based on current data alone. This application first filters out historical operating data that matches the current operating conditions from the historical database, calculates the energy consumption deviation between the real-time data and the historical benchmark, and then dynamically adjusts the monitoring weight coefficient based on the deviation value, thereby updating the monitoring level more scientifically. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flow chart of a comprehensive energy monitoring and management method based on the Internet of Things according to an embodiment of the present application; Figure 2 It is a flow chart of step S100 in a comprehensive energy monitoring and management method based on the Internet of Things in an embodiment of the present application; Figure 3 It is a flowchart of step S300 in a comprehensive energy monitoring and management method based on the Internet of Things in an embodiment of the present application; Figure 4 It is a flowchart of step S400 in a comprehensive energy monitoring and management method based on the Internet of Things in an embodiment of the present application; Figure 5 It is a flowchart of step S500 in a comprehensive energy monitoring and management method based on the Internet of Things in an embodiment of the present application; Figure 6 It is a flowchart of step S510 in a comprehensive energy monitoring and management method based on the Internet of Things in an embodiment of the present application; Figure 7 This is a module schematic diagram of an integrated energy monitoring and management system based on the Internet of Things according to an embodiment of the present application; Figure 8 It is a diagram of the internal structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0023] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0024] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0025] In the first aspect, the present application provides a comprehensive energy monitoring and management method based on the Internet of Things, referring to Figure 1 , including the following steps: S100: Obtain energy consumption parameters of electrical equipment, and determine a monitoring level according to the energy consumption parameters.

[0026] In this embodiment, energy consumption parameters refer to key data indicators that reflect the energy consumption characteristics of electrical equipment, including but not limited to the rated power, actual operating power, peak power, valley power, daily energy consumption, monthly energy consumption, annual energy consumption, load rate, etc. of the equipment. These parameters are collected in real time by IoT sensor nodes distributed on various electrical equipment, and transmitted to the data processing center through the communication network for evaluating the energy consumption status and change trend of the equipment. The monitoring level is to divide the equipment into different monitoring priorities according to the importance and change characteristics of the energy consumption parameters, so as to achieve differentiated monitoring and management.

[0027] Specifically, taking a university chemistry laboratory as an example, the system first collects the operating data of the experimental equipment through smart meters and power sensors. For example, the rated power of a precision thermostat for an experiment is 3kW, and the actual operating power during the experiment fluctuates between 1.5-2.8kW, the average daily energy consumption is about 35kWh, and the monthly energy consumption varies in the range of 800-1000kWh. Through the analysis of these energy consumption parameters, it can be found that the thermostat has a large energy consumption fluctuation range and has strict requirements on the experimental temperature. At the same time, considering that the thermostat stores important experimental samples, temperature fluctuations may lead to deviations in experimental results, so the system classifies it as a higher monitoring level to ensure that abnormal energy consumption can be discovered and handled in time. In contrast, ordinary lighting equipment and desktop computers in the same laboratory can be classified as a lower monitoring level because their energy consumption parameters are relatively stable and the values ​​are small.

[0028] In the process of determining the monitoring level, the system adopts a grading method based on multi-dimensional evaluation, comprehensively considering factors such as the energy consumption value of the equipment, the degree of fluctuation, and the importance of the experiment, and obtains the final monitoring level through a specific calculation model. For example, for the power index in the energy consumption parameter, multiple threshold intervals are set: when the actual operating power of the experimental equipment exceeds 75% of the rated power, a higher evaluation score will be obtained; when the power fluctuation range exceeds 25% of the average value, its evaluation score will also be improved. In particular, considering the particularity of laboratory equipment, the degree of influence of the equipment on the experimental results must also be used as an important evaluation factor. Finally, the system divides the laboratory equipment into three basic monitoring levels: A, B, and C based on the comprehensive evaluation score.

[0029] S200. Configure collection rules according to the monitoring level, and collect real-time data according to the collection rules.

[0030] In this embodiment, the collection rules refer to data collection schemes set based on different monitoring levels, mainly including sampling frequency, collection parameter type, data accuracy requirements and alarm thresholds. Real-time data refers to various types of real-time operation data obtained from the device end according to the collection rules.

[0031] Specifically, taking the laser equipment in a physics laboratory of a certain university as an example, when it is judged to be a Class A monitoring level, the system will configure a higher frequency collection rule, for example, power data is collected every 10 seconds, with an accuracy requirement of 0.1kW; temperature data is collected every 30 seconds, with an accuracy requirement of 0.1°C; current data is collected once a minute, with an accuracy requirement of 0.1A. At the same time, the alarm is set to be triggered immediately when the power exceeds 90% of the rated value or the temperature exceeds 40°C. For electron microscopes monitored at Class B, the collection frequency can be appropriately reduced: power data is collected once a minute, and temperature data is collected once every 5 minutes. For ordinary computer equipment monitored at Class C, it may only be necessary to record total energy consumption data once an hour.

[0032] S300, obtaining historical data of the electrical equipment, comparing the historical data with the real-time data, and updating the monitoring level based on the comparison result.

[0033] In this embodiment, historical data refers to the operation records of the equipment in the past period of time, including energy consumption data and operation status data. The system compares and analyzes the differences between historical data and real-time data, evaluates the changing trend of the equipment operation characteristics, and dynamically adjusts the monitoring level accordingly.

[0034] Specifically, taking a centrifuge in a chemical laboratory as an example, the system will compare its recent one-month operating data with historical data for the same period. If it is found that the average daily energy consumption of the equipment has increased from the original 2kWh to 3kWh, and the power fluctuation frequency has increased by 50%, this may indicate that the performance of the equipment has declined or the usage pattern has changed. At this time, the system will upgrade the monitoring level of the centrifuge from Class B to Class A, and strengthen the monitoring of its operating status. On the contrary, if a constant temperature water bath that was originally monitored at Class A has stable energy consumption and small fluctuations for three consecutive months, it can be considered to be downgraded to Class B monitoring.

[0035] S400: Adjust collection rules based on the updated monitoring level.

[0036] In this embodiment, the collection rule adjustment refers to optimizing and improving the data collection plan accordingly according to the updated monitoring level.

[0037] Specifically, when an analytical balance in a laboratory that was originally monitored at Class B is upgraded to Class A monitoring, the system will automatically increase its data collection frequency: the power collection interval is shortened from 5 minutes to 1 minute, and the current, voltage and other collection parameters are added, and temperature drift monitoring is added. At the same time, the system will also adjust the alarm threshold to make it more sensitive to abnormal changes in the equipment status.

[0038] S500: re-execute data collection according to the adjusted collection rules and generate an energy management strategy.

[0039] In this embodiment, the energy management strategy refers to an energy-saving optimization plan developed based on the analysis results of the collected data, including equipment operation time adjustment suggestions, energy consumption abnormality warnings, and maintenance reminders.

[0040] Specifically, by analyzing the data collected by a PCR instrument in a biological laboratory, the system found that the device was still running at a high power during the weekend, but no experiments were actually performed. Based on this finding, the system generated suggestions for optimizing the device usage time, suggesting that the device power be reduced or enter standby mode during non-experimental periods. At the same time, the system will also predict the possible risk of failure based on the energy consumption trend of the equipment, and issue maintenance reminders in advance to avoid energy waste due to reduced equipment performance.

[0041] In one embodiment, referring to Figure 2 In step S100, the monitoring level is determined according to the energy consumption parameter, which specifically includes the following steps: S110. Obtain peak-to-valley power difference values, operating time fluctuation values, and load change rates of electrical equipment according to energy consumption parameters.

[0042] In this embodiment, the peak-to-valley power difference value reflects the amplitude of power fluctuation during the operation of the device, which is often directly related to the working status and utilization efficiency of the device. The operating time fluctuation value reflects the degree of deviation between the actual use of the device and the expected use plan. This deviation may indicate equipment utilization efficiency problems or maintenance needs. The load change rate, as an indicator to measure the severity of equipment load changes, is directly related to the energy utilization efficiency and service life of the equipment. The comprehensive analysis of these three parameters can fully reflect the operating characteristics and energy utilization efficiency of the equipment.

[0043] Specifically, these parameters have different application values ​​in different types of building scenarios. For example, in hospital medical equipment, large imaging equipment such as MRI instruments have obvious peak-to-valley power difference characteristics. The power can reach 8kW during the startup preheating stage, while only 1kW is required in the standby state. This significant power difference often leads to higher energy consumption. At the same time, the operating room air-conditioning system needs to maintain a constant temperature and humidity environment, so its operating time fluctuation value should be kept at a low level. If there is a large fluctuation, it may mean that the system efficiency is reduced or there is a fault. In the central air-conditioning system of a shopping mall, the load change rate will fluctuate significantly with the change of the flow of people. The load may reach 85% during the peak hours of the morning and evening flow, and may drop to 30% during the off-season. This load characteristic directly affects the energy efficiency. By analyzing these three characteristic parameters, key energy-consuming equipment and energy consumption characteristics in different scenarios can be identified. For example, for medical equipment, the system focuses on the peak-to-valley power difference value, and reduces unnecessary power fluctuations by optimizing the equipment on-off timing; for places with high requirements for constant temperature and humidity, it mainly monitors the operating time fluctuation value, promptly detects abnormal fluctuations and makes adjustments; and for commercial places with obvious load changes, it focuses on analyzing the load change rate and improves equipment efficiency by adjusting the operation strategy. This targeted analysis and management method can effectively improve the overall energy utilization efficiency and reduce operating costs. In addition, the historical data accumulation of these three parameters can be used to predict the maintenance needs of equipment and the direction of energy efficiency optimization. For example, when the peak-to-valley power difference value of a certain device gradually increases, or the operating time fluctuation value increases abnormally, it may indicate that the performance of the equipment has begun to decline and timely maintenance is required. Similarly, by analyzing the long-term trend of the load change rate, the capacity configuration and operation strategy of the equipment can be optimized to avoid energy waste caused by being in a low-load state for a long time.

[0044] S120, obtaining a dynamic coefficient of the equipment according to the peak-to-valley power difference value, the operating time fluctuation value and the load change rate.

[0045] In this embodiment, the equipment dynamic coefficient is a dimensionless indicator that comprehensively reflects the fluctuation characteristics of equipment operation. By weighted integration of the three parameters of peak-to-valley power difference value, operating time fluctuation value, and load change rate, a value that can characterize the dynamic characteristics of the equipment is obtained. The larger the equipment dynamic coefficient, the more significant the fluctuation of the equipment's operating state, and the lower the energy efficiency may be.

[0046] Specifically, for different types of electrical equipment, the calculation method of the equipment dynamic coefficient needs to take into account its specific operating characteristics. For example, for HVAC systems in buildings, since their operating characteristics are mainly affected by the environment and usage requirements, the weight of the load change rate should be relatively high and can be set to 0.4, while the weights of the peak-to-valley power difference value and the operating time fluctuation value are 0.3 each. For example, for the central air-conditioning system of an office building, when its peak-to-valley power difference value is 12kW (0.7 after standardization), the operating time fluctuation value is ±15% (0.5 after standardization), and the load change rate is 65% (0.8 after standardization), the calculated equipment dynamic coefficient is 0.68. For precision instruments and equipment in school laboratories, since they have high requirements for operating stability and precise control of operating time is particularly important, the weight of the operating time fluctuation value should be relatively large and can be set to 0.4, and the weights of the peak-to-valley power difference value and the load change rate are 0.35 and 0.25 respectively.

[0047] S130. Determine a monitoring level of the electrical equipment based on the numerical range of the equipment dynamic coefficient and the energy consumption parameter, wherein the monitoring level is positively correlated with the numerical range of the equipment dynamic coefficient and the energy consumption parameter.

[0048] In this embodiment, the monitoring level is determined by evaluating the numerical range of the equipment dynamic coefficient and the energy consumption parameter, taking into account the dynamic characteristics of the equipment operation and the absolute level of energy consumption. The monitoring level is usually divided into three levels: A, B, and C, where A indicates that the most stringent monitoring management is required, and C indicates that a more relaxed monitoring plan can be adopted.

[0049] Specifically, the system sets the classification threshold of the dynamic coefficient based on a large amount of statistical data: when the dynamic coefficient is greater than 0.6 and the energy consumption parameters are at a high level, the equipment is classified as Class A monitoring; when the dynamic coefficient is between 0.3-0.6 and the energy consumption parameters are at a medium level, it is classified as Class B monitoring; when the dynamic coefficient is less than 0.3 and the energy consumption parameters are low, it is classified as Class C monitoring.

[0050] In one embodiment, referring to Figure 3In step S300, historical data of the electrical equipment is obtained, and the historical data is compared with the real-time data, and the monitoring level is updated based on the comparison result, which specifically includes the following steps: S310, obtaining historical data of the electrical equipment in the same time period and under similar load conditions, and calculating energy consumption deviation values ​​between the historical data and the real-time data.

[0051] In this embodiment, the system matches the corresponding historical data according to the time characteristics and load conditions, and calculates the energy consumption deviation through comparative analysis. The time characteristics include periodic factors such as working days, holidays, and seasons, and the load conditions consider the actual working status of the equipment, such as production load, operation mode, etc.

[0052] S320. Adjust the monitoring weight coefficient based on the energy consumption deviation value, and update the monitoring level according to the monitoring weight coefficient.

[0053] In this embodiment, the system adopts a hierarchical adjustment mechanism to dynamically adjust the monitoring weight coefficient according to the size of the energy consumption deviation value. When the energy consumption deviation value is in different intervals, the system will adjust the monitoring weight coefficient accordingly, thereby affecting the monitoring level of the device.

[0054] In one embodiment, referring to Figure 4 In step S400, the collection rules are adjusted based on the updated monitoring level, specifically including the following steps: S410: Based on the updated monitoring level, obtain historical collection rules and historical monitoring data of the electrical equipment.

[0055] In this embodiment, the system retrieves the historical collection rules and monitoring data records of the device at the same monitoring level from the database according to the current monitoring level of the device. The historical collection rules include collection frequency, collection parameter type and data accuracy, and the historical monitoring data includes energy consumption data, operating status and alarm records.

[0056] Specifically, taking an electron microscope in a university's materials laboratory as an example, when its monitoring level is "A-level monitoring", the system will retrieve the collection rule configuration of the device when it was in the "A-level monitoring" state in the past semester, including collecting power data every 10 minutes, recording vacuum parameters every half an hour, etc. At the same time, it will obtain monitoring data such as power consumption curves and experimental records during that period.

[0057] S420. Compare the monitoring level of the electric equipment with the historical monitoring level, and mark the electric equipment with the same comparison result as stable equipment.

[0058] In this embodiment, the system compares the current monitoring level of the device with the changes in the monitoring level in the historical period. If the monitoring level remains stable within a specified time window (such as nearly one semester), the device is marked as a stable device.

[0059] Specifically, if the electron microscope has maintained an "A-level monitoring" rating for the past semester and the fluctuations of various indicators are within a reasonable range, the system will mark it as a stable device. This indicates that the equipment is used in a standardized manner and its status is controllable.

[0060] S430. According to the historical monitoring data of the stable device, adjust the collection frequency and collection parameters of the corresponding stable device.

[0061] In this embodiment, the system analyzes the historical monitoring data of the stable equipment, evaluates the applicability of the existing collection rules, and optimizes the collection frequency and parameter configuration accordingly.

[0062] Specifically, the analysis found that during the experiment, the 10-minute acquisition frequency of power data of the electron microscope can be adjusted to once every 20 minutes, because the power changes slowly after the device is started. As a key parameter, vacuum degree needs to maintain a high acquisition frequency (once every 5 minutes) during sample replacement, and can be reduced to once an hour during other periods. The acquisition frequency of environmental parameters such as temperature can be appropriately adjusted according to the operation of the laboratory air conditioning.

[0063] S440: Update the collection rules of the corresponding stable device according to the adjusted collection frequency and collection parameters.

[0064] In this embodiment, the system automatically updates the collection rule configuration of the stable device according to the optimized collection scheme. The new collection rule realizes the optimized allocation of data collection resources while ensuring the monitoring effect.

[0065] In one embodiment, referring to Figure 5 In step S500, an energy management strategy is generated, which specifically includes the following steps: S510: Based on the adjusted monitoring level and the preset energy consumption association rule, several different electrical devices are associated to obtain an associated device group.

[0066] In this embodiment, the system identifies equipment with usage association in the laboratory through energy consumption association rules.

[0067] Specifically, taking a university's optical laboratory as an example, the system identified a usage association between the laser, the optical platform temperature control system, and the purification air conditioner. When the monitoring levels of these devices are all "A-level monitoring", the system combines them into a precision optical experimental equipment group.

[0068] S520: Generate energy consumption control parameters for the electrical equipment according to a preset energy consumption threshold of the electrical equipment.

[0069] In this embodiment, the system sets scientific energy consumption thresholds based on the technical specifications, historical usage data and laboratory management specifications of each device, and generates corresponding control parameters accordingly.

[0070] S530: Adjust the energy consumption control parameters of the associated device group according to the preset energy consumption threshold of the associated device group.

[0071] In this embodiment, the system further considers the overall energy consumption characteristics of the equipment group on the basis of controlling a single device, and uses a collaborative optimization method to adjust the control parameters of each device.

[0072] Specifically, for example, system analysis found that there is room for optimization in the linkage operation of the laser, temperature control system, and purification air conditioner. By adjusting the start-up advance of the temperature control system to 45 minutes and setting the purification air conditioner to variable frequency operation, the total energy consumption of the entire equipment group can be reduced by 12% while ensuring the experimental conditions.

[0073] S540: Generate an energy management strategy based on the energy consumption control parameters.

[0074] In this embodiment, the system converts the optimized control parameters into specific management strategies, including equipment start and stop timing, operation mode switching, automatic parameter adjustment and other specific measures.

[0075] In one embodiment, referring to Figure 6 In step S510, based on the adjusted monitoring level and the preset energy consumption association rule, a number of different electrical devices are associated to obtain an associated device group, which specifically includes the following steps: S511. Obtain the operating sequence, spatial location information and adjusted monitoring level of the electrical equipment.

[0076] S512: construct an equipment operation correlation matrix based on the operation sequence, spatial location information and the adjusted monitoring level.

[0077] Among them, the weight coefficient of the correlation matrix is ​​positively correlated with the adjusted monitoring level.

[0078] In this embodiment, the association relationship between devices is quantified into a matrix form. Each element in the matrix represents the degree of association between two devices, and the combined influence of time overlap, spatial distance and monitoring level is considered during the calculation. The higher the monitoring level, the greater the weight coefficient in the association calculation.

[0079] S513. According to the equipment operation correlation matrix and the preset energy consumption correlation rules, identify equipment combinations that have energy consumption impact relationships and whose monitoring levels are within a preset range.

[0080] In this embodiment, the system combines the correlation matrix with the predefined energy consumption correlation rules to screen out equipment combinations that have both strong operational correlation and significant energy consumption impact. The energy consumption association rules include energy transfer relationships between equipment, environmental impact factors, etc., and require that the monitoring level of the equipment in the combination meet the preset conditions. Taking a physics laboratory in a certain university as an example, the system obtains the operating data of an electron microscope, a vacuum pump, and a thermostat. The electron microscope is located in the 405 laboratory, with a monitoring level of "A-level monitoring", usually started at 9 am and runs for 6 hours; the matching vacuum pump is located in the same laboratory, with a monitoring level of "B-level monitoring", which needs to be started 15 minutes in advance; the thermostat is installed in the adjacent 406 laboratory, with a monitoring level of "A-level monitoring", and runs all day to maintain a constant temperature environment. The system constructs a 3×3 correlation matrix for the above three devices. The electron microscope and the vacuum pump have a high correlation of 0.9 due to complete time overlap, spatial proximity, and at least one of them is a key monitoring device; while the correlation with the thermostat is reduced to 0.6 due to the long spatial distance, even though it is also a key monitoring device. System analysis found that electron microscopes and vacuum pumps are not only highly correlated, but also have a direct impact on energy consumption: the operating status of the vacuum pump directly affects the working environment of the microscope, and the energy consumption fluctuations of the two show obvious synergistic characteristics. Although the vacuum pump is a "B-level monitoring", it is closely related to the "A-level monitoring" microscope, so the system identifies it as an effective equipment combination.

[0081] S514: Mark the device combination as an associated device group, and determine the priority of the associated device group according to the average monitoring level of the associated device group.

[0082] In this embodiment, the system marks and classifies the identified equipment combinations and creates associated equipment group files. The priority of each associated equipment group is determined by the average monitoring level of the equipment in the group, ensuring that energy management measures can preferentially cover equipment combinations with higher importance.

[0083] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0084] On the second aspect, the present application provides a comprehensive energy monitoring and management system based on the Internet of Things. The comprehensive energy monitoring and management system based on the Internet of Things of the present application is described below in combination with the above-mentioned comprehensive energy monitoring and management method based on the Internet of Things.

[0085] Reference Figure 7 , a comprehensive energy monitoring and management system based on the Internet of Things, including: A monitoring level determination module is used to obtain energy consumption parameters of electrical equipment and determine the monitoring level according to the energy consumption parameters; The real-time data collection module is used to configure the collection rules according to the monitoring level and collect real-time data according to the collection rules; A monitoring level update module is used to obtain historical data of electrical equipment, compare the historical data with real-time data, and update the monitoring level based on the comparison results; A collection rule adjustment module, used to adjust the collection rules based on the updated monitoring level; The management strategy generation module is used to re-execute data collection according to the adjusted collection rules and generate an energy management strategy.

[0086] In one embodiment, the monitoring level determination module includes: The equipment data acquisition unit is used to obtain the peak-to-valley power difference value, operation time fluctuation value and load change rate of the electric equipment according to the energy consumption parameters; A dynamic coefficient acquisition unit is used to obtain the dynamic coefficient of the equipment according to the peak-valley power difference value, the operation time fluctuation value and the load change rate; The monitoring level determination unit is used to determine the monitoring level of the electrical equipment based on the numerical range of the equipment dynamic coefficient and the energy consumption parameter, and the monitoring level is positively correlated with the numerical range of the equipment dynamic coefficient and the energy consumption parameter.

[0087] In one embodiment, the monitoring level update module includes: A historical data comparison unit is used to obtain historical data of electrical equipment under the same period and similar load conditions, and calculate the energy consumption deviation value between historical data and real-time data; The level updating unit is used to adjust the monitoring weight coefficient based on the energy consumption deviation value and update the monitoring level according to the monitoring weight coefficient.

[0088] In one embodiment, the collection rule adjustment module includes: A historical rule acquisition unit, used to acquire historical collection rules and historical monitoring data of the electric equipment based on the updated monitoring level; A stable equipment marking unit is used to compare the monitoring level of the electric equipment with the historical monitoring level, and mark the electric equipment with the same comparison result as a stable equipment; A collection parameter adjustment unit, used to adjust the collection frequency and collection parameters of the corresponding stable device according to the historical monitoring data of the stable device; The collection rule updating unit is used to update the collection rules of the corresponding stable device according to the adjusted collection frequency and collection parameters.

[0089] In one embodiment, the management policy generation module includes: An associated device group acquisition unit is used to associate a number of different electrical devices based on the adjusted monitoring level and the preset energy consumption association rule to obtain an associated device group; The device control parameter generating unit is used to generate energy consumption control parameters for the power-consuming device according to a preset energy consumption threshold of the power-consuming device; A group control parameter adjustment unit, used to adjust the energy consumption control parameters of the corresponding associated device group according to a preset energy consumption threshold of the associated device group; The strategy generating unit is used to generate an energy management strategy based on energy consumption control parameters.

[0090] In one embodiment, the associated device group obtaining unit includes: The device information acquisition subunit is used to obtain the operating sequence, spatial location information and adjusted monitoring level of the electrical equipment; The association matrix construction subunit is used to construct the equipment operation association matrix based on the operation sequence, the spatial location information and the adjusted monitoring level, wherein the weight coefficient of the association matrix is ​​positively correlated with the adjusted monitoring level; The equipment combination identification subunit is used to identify equipment combinations having energy consumption impact relationships and whose monitoring levels are within a preset range according to the equipment operation correlation matrix and preset energy consumption correlation rules; The priority determination subunit is used to mark the device combination as an associated device group and determine the priority of the associated device group according to the average monitoring level of the associated device group.

[0091] In one embodiment, the present application provides an electronic device, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The electronic device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a comprehensive energy monitoring and management method based on the Internet of Things is implemented.

[0092] Those skilled in the art will understand that Figure 8 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the electronic device to which the scheme of the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0093] In one embodiment, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0094] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0095] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A comprehensive energy monitoring and management method based on the Internet of Things, characterized in that: The steps include: Obtaining energy consumption parameters of electrical equipment, and determining a monitoring level according to the energy consumption parameters; Configure collection rules according to the monitoring level, and collect real-time data according to the collection rules; Acquire historical data of the electric device, compare the historical data with the real-time data, and update the monitoring level based on the comparison result; adjusting the collection rules based on the updated monitoring level; Re-execute data collection according to the adjusted collection rules and generate an energy management strategy.

2. The comprehensive energy monitoring and management method based on the Internet of Things according to claim 1 is characterized in that: Determining the monitoring level according to the energy consumption parameter specifically includes the following steps: According to the energy consumption parameters, the peak-to-valley power difference value, the operation time fluctuation value and the load change rate of the electrical equipment are obtained; Obtaining a dynamic coefficient of the equipment according to the peak-to-valley power difference value, the operating time fluctuation value and the load change rate; Based on the numerical range of the equipment dynamic coefficient and the energy consumption parameter, the monitoring level of the electrical equipment is determined, and the monitoring level is positively correlated with the numerical range of the equipment dynamic coefficient and the energy consumption parameter.

3. The comprehensive energy monitoring and management method based on the Internet of Things according to claim 1 is characterized in that: Acquiring historical data of the electric device, comparing the historical data with the real-time data, and updating the monitoring level based on the comparison result, specifically includes the following steps: Obtaining historical data of the electrical equipment in the same period and under similar load conditions, and calculating an energy consumption deviation value between the historical data and the real-time data; The monitoring weight coefficient is adjusted based on the energy consumption deviation value, and the monitoring level is updated according to the monitoring weight coefficient.

4. The comprehensive energy monitoring and management method based on the Internet of Things according to claim 1 is characterized in that: Adjusting the collection rules based on the updated monitoring level specifically includes the following steps: Based on the updated monitoring level, obtain the historical collection rules and historical monitoring data of the power-consuming equipment; Comparing the monitoring level of the electric equipment with the historical monitoring level, and marking the electric equipment with the same comparison result as a stable equipment; According to the historical monitoring data of the stable device, adjusting the collection frequency and collection parameters corresponding to the stable device; According to the adjusted acquisition frequency and acquisition parameters, the acquisition rules corresponding to the stable device are updated.

5. The comprehensive energy monitoring and management method based on the Internet of Things according to claim 1 is characterized in that: Generate an energy management strategy, which includes the following steps: Based on the adjusted monitoring level and the preset energy consumption association rule, a number of different electrical devices are associated to obtain an associated device group; Generate energy consumption control parameters corresponding to the electrical equipment according to a preset energy consumption threshold of the electrical equipment; According to the preset energy consumption threshold of the associated device group, adjusting the energy consumption control parameter corresponding to the associated device group; An energy management strategy is generated based on the energy consumption control parameters.

6. The comprehensive energy monitoring and management method based on the Internet of Things according to claim 5 is characterized in that: Based on the adjusted monitoring level and the preset energy consumption association rule, a number of different electrical devices are associated to obtain an associated device group, which specifically includes the following steps: Obtaining the operating timing, spatial location information and adjusted monitoring level of the electrical equipment; Based on the operation timing, spatial location information and the adjusted monitoring level, constructing an equipment operation correlation matrix, wherein a weight coefficient of the correlation matrix is ​​positively correlated with the adjusted monitoring level; According to the equipment operation correlation matrix and the preset energy consumption correlation rule, identifying equipment combinations having energy consumption impact relationships and whose monitoring levels are within a preset range; The device combination is marked as an associated device group, and the priority of the associated device group is determined according to the average monitoring level of the associated device group.

7. A comprehensive energy monitoring and management system based on the Internet of Things, characterized in that: include: A monitoring level determination module, used to obtain energy consumption parameters of electrical equipment and determine the monitoring level according to the energy consumption parameters; A real-time data collection module, configured to collect data according to the monitoring level and configure collection rules, and collect real-time data according to the collection rules; A monitoring level updating module, used for acquiring historical data of the electric device, comparing the historical data with the real-time data, and updating the monitoring level based on the comparison result; A collection rule adjustment module, used to adjust the collection rule based on the updated monitoring level; The management strategy generation module is used to re-execute data collection according to the adjusted collection rules and generate an energy management strategy.

8. The comprehensive energy monitoring and management system based on the Internet of Things according to claim 7 is characterized in that: The monitoring level determination module includes: An equipment data acquisition unit, used to acquire the peak-to-valley power difference value, operation time fluctuation value and load change rate of the electric equipment according to the energy consumption parameter; A dynamic coefficient acquisition unit, used to obtain a dynamic coefficient of the equipment according to the peak-to-valley power difference value, the operation time fluctuation value and the load change rate; The monitoring level determination unit is used to determine the monitoring level of the electrical equipment based on the equipment dynamic coefficient and the numerical range of the energy consumption parameter, and the monitoring level is positively correlated with the equipment dynamic coefficient and the numerical range of the energy consumption parameter.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the comprehensive energy monitoring and management method based on the Internet of Things described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the comprehensive energy monitoring and management method based on the Internet of Things described in any one of claims 1-6 are implemented.

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