Canteen equipment energy consumption monitoring management method and system based on internet of things

Abstract

The present application belongs to the technical field of energy consumption monitoring and management of Internet of Things devices, and provides a dining hall device energy consumption monitoring and management method and system based on Internet of Things, comprising: obtaining a total incoming line power curve in a historical feeding period, identifying transient power spikes and corresponding time periods appearing in the total incoming line power curve, and calibrating a start spike period; based on historical operation data of each dining hall device independent electric meter in the start spike period. A hierarchical sorting strategy of time margin priority and power proportion local adjustment is adopted, so that high-power devices are started as early as possible under the premise of meeting time constraints, and the transformer short-time overload tolerance capacity is taken as the safety boundary in the start time allocation process. The devices that cannot meet the capacity constraints are adjusted to the earliest time slot that meets the conditions, the upper limit of the number of parallel starts is dynamically controlled, the whole device start is orderly completed under the premise of ensuring that the transformer overload capacity is not exceeded, and the risk of tripping caused by simultaneous start of multiple devices is avoided.

Description

Technical Field

[0001] This invention belongs to the field of IoT device energy consumption monitoring and management technology, specifically an IoT-based method and system for monitoring and managing the energy consumption of canteen equipment. Background Technology

[0002] With the development of IoT technology and the increasing requirements for energy management, the energy consumption monitoring and management of canteen equipment has gradually received attention. Canteens are typical energy-intensive places with a wide variety of kitchen equipment, including steamers, ovens, induction cookers, exhaust fans, cold storage, warming tables, dishwashers, and disinfection cabinets. The equipment has high power, concentrated operating hours, and obvious batch-based meal supply characteristics. However, canteen meals are characterized by fixed time slots and concentrated equipment startup before meals. Within 30 to 40 minutes before meals, high-power equipment such as steamers, ovens, induction cookers, and exhaust fans need to be started simultaneously to enter the preheating state, causing a sharp increase in the instantaneous load of the transformer, which can easily lead to power spikes or even tripping accidents. According to the operating principle of power transformers, the starting current of motors can reach 4 to 7 times the rated current, and transformers need to withstand such impacts. Currently, the management of canteen equipment startup relies heavily on manual experience or fixed sequence control. On the one hand, it is difficult to predict the transformer load margin, and on the other hand, it ignores the tripping risk caused by the superposition effect of equipment startup impact current. Furthermore, during the gap between breakfast and lunch, and the interval between the end of lunch and dinner, the equipment is often in standby mode and continuously consumes electricity, resulting in hidden waste, but there is a lack of effective energy consumption monitoring methods to quantify such losses. To this end, the present invention provides a method and system for monitoring and managing the energy consumption of canteen equipment based on the Internet of Things. Summary of the Invention

[0003] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0004] The technical solution adopted by this invention to solve its technical problem is: an IoT-based method for monitoring and managing the energy consumption of canteen equipment, comprising: Obtain the total incoming power curve within the time window before the historical meal service period, identify the instantaneous power spikes that appear in the total incoming power curve and the corresponding time periods, and calibrate the start-up peak period; Based on the historical operating data of the independent electricity meters of each canteen equipment during the peak start-up period, the mapping relationship between the downtime and preheating time of each canteen equipment is extracted to determine the preheating time required for the current start-up. The latest start-up time of each canteen equipment is calculated with the fixed meal opening time as a countdown, and an effective scheduling window is obtained in combination with the peak start-up period. Before entering the pre-meal service startup phase, the energy consumption operation status of each canteen's equipment is monitored in real time, and equipment that is not restricted from startup is identified. Based on the equipment's power occupancy and the urgency of the latest startup time, the startup sequence is determined within the effective scheduling window. Based on the startup sequence and the startup time corresponding to the total load distribution at startup, the maximum number of unrestricted devices that can be started simultaneously in the current startup time slot is dynamically determined, and startup instructions are issued according to the startup time schedule. During the startup process, the power data of the main incoming line and each device are collected in real time through IoT smart meters to perform energy consumption monitoring and dynamic adjustment management.

[0005] The canteen equipment energy consumption monitoring and management system based on the Internet of Things includes: Start the peak period acquisition module: acquire the total incoming power curve within the time window before the historical meal service period, identify the instantaneous power peaks that appear in the total incoming power curve and the corresponding time periods, and calibrate the start peak period; Effective scheduling window acquisition module: Based on the historical operating data of the independent electricity meters of each canteen equipment during the peak start-up period, extract the mapping relationship between the downtime and preheating time of each canteen equipment, determine the preheating time required for the current start-up, calculate the latest start-up time of each canteen equipment with the fixed meal opening time as the countdown, and obtain the effective scheduling window in combination with the peak start-up period; Start-up sequence determination module: Before entering the pre-meal service start-up phase, monitor the energy consumption operation status of each canteen's equipment in real time, extract equipment that is not restricted from starting, and determine the start-up sequence within the effective scheduling window by combining the equipment's power occupancy and the urgency of the latest start-up time. Startup time allocation module: Based on the startup order and the startup time corresponding to the total load allocation at startup, dynamically determine the upper limit of the number of unrestricted devices that can be started simultaneously in the current startup time slot, and issue startup commands according to the startup time table; Start the energy consumption assessment module: During the startup process, the power data of the main incoming line and each device are collected in real time through the IoT smart meter to perform energy consumption monitoring and dynamic adjustment management.

[0006] The beneficial effects of this invention are as follows: This invention collects the total incoming power curve before historical meal service periods, identifies and marks the peak start-up periods formed by concentrated equipment startup, and deduces the short-term overload tolerance and available load margin of the transformer under actual operating conditions, thereby providing a capacity boundary for subsequent startup scheduling. By extracting the mapping relationship between the downtime and preheating time of each piece of equipment, the latest startup time of each piece of equipment is calculated with the meal service time as the anchor point, forming an effective scheduling window that takes into account both historical collective startup patterns and individual preheating needs, effectively reducing standby energy consumption caused by equipment startup too early or meal service delays caused by startup too late. This invention monitors the energy consumption and operating status of each device in real time before startup, selects devices with normal startup status that are not restricted from participating in the scheduling, excludes devices with abnormal status and pushes inspection reminders, thereby reducing ineffective scheduling or safety hazards. It adopts a hierarchical sorting strategy that prioritizes time margin and adjusts the power ratio locally, so that high-power devices can start as early as possible while meeting time constraints. In the process of allocating startup time, the transformer's short-term overload tolerance capacity is used as the safety boundary. Devices that cannot meet the capacity constraints are adjusted to the earliest time slot that meets the conditions. The upper limit of the number of parallel startups is dynamically controlled, and all devices are started in an orderly manner while ensuring that the transformer's overload capacity is not exceeded, thus avoiding the risk of tripping caused by the simultaneous startup of multiple devices. This invention continuously collects power data of the main incoming line and each device in real time through IoT smart meters during the startup process. It identifies startup inrush anomalies and startup delays by comparing the peak startup inrush current with historical benchmark values, dynamically adjusts the system by comparing the instantaneous power of the main incoming line with the overload capacity boundary in real time, and identifies abnormal energy consumption during the startup phase by comparing the total power consumption after startup with historical benchmark values. This enables multi-level energy consumption monitoring and dynamic adjustment management throughout the startup process. At the same time, the execution data is entered into a historical database to improve the accuracy and safety of subsequent scheduling. Attached Figure Description

[0007] The invention will now be further described with reference to the accompanying drawings.

[0008] Figure 1 This is a flowchart of the steps in Embodiment 1 of the present invention; Figure 2 This is an example diagram of the total incoming power curve in Embodiment 1 of the present invention; Figure 3 This is a system module diagram in Embodiment 2 of the present invention. Detailed Implementation

[0009] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0010] Example 1

[0011] Please see Figure 1 and Figure 2 As shown in the embodiment of the present invention, the method for monitoring and managing the energy consumption of canteen equipment based on the Internet of Things includes the following steps: Step 1: Obtain the total incoming power curve within the time window before the historical meal service period, identify the instantaneous power spikes that appear in the total incoming power curve and the corresponding time periods, and mark the start-up peak period; The specific process of step one includes: continuously collecting power data of the main incoming line through the Internet of Things smart meter installed at the main incoming line of the canteen at a sampling interval of ≤1 second; The power data should include at least the instantaneous power value at each sampling time and the corresponding timestamp; The collected power data is categorized and stored according to date and meal service time to build a historical total incoming power database; Meal service times include breakfast, lunch, and dinner. Based on the historical data for each meal service day, extract the total incoming power curve within a preset duration before the start of each meal service period, where the preset duration is set to 30 minutes or 40 minutes. It should be noted that the start time of the meal service period is the fixed opening time of the canteen, and the number of power curve segments extracted on each meal service day is the same as the number of meal service periods on that day. For each extracted total incoming power curve, a median filtering method is used for smoothing and noise reduction to eliminate random interference caused by power grid fluctuations or measurement noise, resulting in a smoothed total incoming power curve. For each smoothed total input power curve, peak distribution identification is performed, including: Calculate the lower quartile of all power values ​​in the total incoming power curve and use it as the reference power value; The lower quartile refers to the value at the 25th percentile after arranging a set of data in ascending order. If the total number of values ​​is not an integer multiple, the average of the values ​​at the two adjacent positions is taken. Calculate the average power value and the standard deviation of power values ​​for all power values ​​in the total incoming power curve; According to statistical principles, when the power curve fluctuates normally and follows a normal distribution, approximately 99.7% of the power values ​​at the sampling points should fall within the range of the average power value plus or minus three times the power standard deviation. Sampling points that exceed this range are statistically outliers. Based on this, the sum of the average power value and three times the power standard deviation is used as the initial peak determination threshold. At the same time, the peak determination threshold is obtained by adjusting the number of sampling points: when the number of sampling points is small, the critical value of the t-distribution in statistics is used instead of the critical value of the normal distribution. That is, the critical value table of the t-distribution is consulted according to the number of sampling points, and the three standard deviations are adjusted to a multiple corresponding to the number of sampling points. The fewer the number of sampling points, the larger the multiple. The multiple ranges from 2.8 to 3.5, and is automatically determined by the number of sampling points; Compare the reference power value with the average power value; If the absolute value of the difference between the reference power value and the average power value is less than the set percentage of the average power value, it indicates that the total incoming power curve distribution is relatively symmetrical and there is no situation where a large number of high power peaks raise the average value. Peak determination threshold is used for peak identification. If the absolute value of the difference between the reference power value and the average power value is greater than or equal to the set percentage of the average power value, it indicates that the total incoming power curve is right-skewed, meaning that there are many high power spikes that pull up the average value. In this case, based on the reference power value, the overall peak judgment threshold is adjusted downward. The adjustment method is as follows: the difference between the lower quartile and the average power value is used as the correction amount, and a portion of the correction amount is subtracted from the original peak judgment threshold. This ensures that the adjusted peak judgment threshold is not affected by a few extreme high power spikes, while maintaining tolerance for normal fluctuations. The percentage is set between 5% and 15%, with 10% being preferred. Traverse each sampling point in the total incoming power curve and compare the power value of the sampling point with the peak determination threshold; If the power value of a sampling point is greater than the peak determination threshold, and the power value of the sampling point is greater than the power values ​​of the previous and next adjacent sampling points, it is the maximum value point in the local range, and thus a peak candidate point. For each calibrated peak candidate point, trace back along the time axis to the point where the power value first drops to a set percentage between the peak determination threshold and the baseline power value, and use this as the starting time of the peak; Tracing back to where the power value drops back to the same set percentage position mentioned above, this is taken as the end time of the peak; The continuous time period from the start time to the end time is defined as the duration of the instantaneous power spike; The above smoothing and noise reduction processing and peak distribution identification steps are repeated on the total incoming power curve of multiple consecutive historical meal supply days to obtain the duration of all instantaneous power peaks before each meal supply period on each meal supply day. The timeline before each meal service period is divided into multiple sub-time periods according to a preset time granularity, and the number of instantaneous power spikes recorded in each sub-time period is counted. Multiple consecutive sub-time periods with a frequency of peak occurrence exceeding a preset frequency threshold are identified as candidate peak-dense areas; The preset frequency threshold is determined by: counting the upper quartile of the number of peaks in all sub-time periods, using the upper quartile as the frequency threshold, or by directly presetting a fixed value, for example, under the data of 30 historical meal supply days, the frequency threshold is preset to 10 times. Multiple consecutive sub-time periods refer to two or more adjacent sub-time periods where the number of peak occurrences exceeds a preset frequency threshold. For each candidate peak-dense region, calculate the average of the start times of all peaks in the region as the start time of the peak initiation period, calculate the average of the end times of all peaks in the region as the end time of the peak initiation period, and use the difference between the end time and the start time as the duration of the peak initiation period. When there are multiple candidate peak density areas, the candidate peak density area with the highest peak occurrence frequency is selected as the final start peak period, or the union of all candidate peak density areas is used as the start peak period. Extract the peak power of all instantaneous power spikes during the startup peak period and take the maximum value as the peak maximum value; Obtain the rated capacity of the transformer. Based on the transformer's short-time overload characteristics, calculate the transformer's short-time overload tolerance assessment value. The short-time overload tolerance assessment value is 1.5 times the transformer's rated capacity. Compare the maximum peak value with the short-time overload tolerance assessment value, and take the minimum value as the actual constraint boundary of the transformer's short-time overload tolerance. The maximum value among the reference power values ​​of each power curve segment is taken as the base load. The difference between the base load and the actual constraint boundary of the transformer's short-time overload tolerance is the actual available load margin of the transformer.

[0012] Step one involves collecting, smoothing, and identifying peak distributions of historical total incoming power curves to analyze the historical behavior patterns of concentrated startup of equipment before canteen meal service. This process identifies peak periods and time boundaries for concentrated startup of multiple devices and deduces the short-term overload tolerance and available load margin of transformers under actual operating conditions. This provides a data foundation and boundary constraints for determining the effective scheduling window, allocating startup times for each device, and verifying the capacity of the total load at startup in subsequent steps.

[0013] Step 2: Based on the historical operating data of the independent electricity meters of each canteen equipment during the peak start-up period, extract the mapping relationship between the downtime and preheating time of each canteen equipment, determine the preheating time required for each canteen equipment to start at the current time, calculate the latest start-up time of each canteen equipment with the fixed meal opening time as the countdown, and obtain the effective scheduling window by combining the analysis of the peak start-up period. Step two involves extracting the time intervals for each meal service day and meal service period corresponding to the peak start-up period from the historical total incoming line power database, and retrieving the power data of each canteen device within the time interval. The canteen equipment should include at least a steamer, oven, induction cooker, exhaust fan, warming table, dishwasher, and sterilizer. The extracted power data of each canteen's equipment is stored according to equipment number, meal service date, and meal service time, and a sub-database of equipment historical operation is constructed. Power data for each device during peak hours on each meal service day and during each meal service period; The latter third of the peak period is taken as the steady period. The average power value of all sampling points during the steady period is calculated as the steady-state operating power. The product of the stable operating power and the start-up judgment ratio coefficient is used as the start-up judgment power threshold. The start-up judgment ratio coefficient ranges from 10% to 20%, preferably 15%. Starting from the beginning of the peak startup period, the power values ​​are traversed forward along the time axis. When the power value of a certain sampling point is greater than or equal to the startup judgment power threshold for the first time, and the power values ​​of multiple consecutive sampling points after the sampling point all show an upward trend, the corresponding time is marked as the startup response time of the equipment on the meal supply day. Continue traversing forward along the time axis. When the power value reaches the set percentage range of the steady-state operating power (e.g., 95% to 105%) and subsequent consecutive sampling points are stable within the range, the moment when the device reaches the steady-state operating power is calibrated. The difference between the moment when steady-state operating power is reached and the moment of startup response is calculated to obtain the startup duration. For each canteen device, the downtime between adjacent meal service periods on each meal service day, and the preheating time before starting the next meal service period are extracted to construct a historical mapping dataset of downtime and preheating time. Specifically: For a given meal service period (e.g., lunch), the time difference between the end of the previous meal service period (e.g., breakfast) and the start of the response for the current meal service period is the downtime. The end time of the previous meal service period is determined by detecting that the power value of the equipment drops below the set percentage of the steady-state operating power and remains below the set time. During the peak period of the current meal service period, the time difference required from the start-up response time to the steady-state operating power time is the preheating time of the equipment under the condition of shutdown duration. The downtime and preheating time of the same equipment collected from multiple sets of historical meal service days are used to form a data point pair, forming a historical mapping dataset of equipment downtime-preheating time. For each device, a mapping relationship between downtime and preheating time is constructed by curve fitting or piecewise linear interpolation. That is, for any given downtime, the corresponding preheating time can be estimated through this mapping relationship. Before each meal service period starts, the time difference between the current time of each canteen equipment and the time when the equipment last ran and ended is obtained as the current downtime. The current downtime is substituted into the mapping relationship to calculate the preheating time required for this start-up under the current downtime state. Using the fixed meal opening time as the countdown anchor point, for each canteen equipment, the fixed meal opening time is subtracted from the preheating time required for this start-up, and then the preset time redundancy is subtracted to obtain the latest start-up time; The preset time redundancy is used to offset the fluctuation of preheating time caused by factors such as equipment aging and changes in ambient temperature. The value range is 1 to 3 minutes, with 2 minutes being preferred. The meaning of the latest start time is: it must be started before the latest start time, otherwise preheating will not be completed before the meal service, thus affecting the normal meal service. The start time of the peak period is used as the fixed start time of the effective scheduling window, the duration of the peak period is used as the initial scheduling window length, and the initial end time is the sum of the fixed start time of the effective scheduling window and the initial scheduling window length. The minimum value between the latest start time and the initial deadline of all devices to be started is taken as the deadline of the effective scheduling window. In other words, the smaller value between the latest start time and the initial deadline of the most urgent (i.e. the smallest value) device in all canteen equipment is determined as the final deadline of the effective scheduling window. It is important to note that the duration of the effective scheduling window is calculated based on the fixed start time and the end time of the effective scheduling window. If the duration of the effective window is greater than or equal to the shortest scheduling duration, the effective scheduling window is valid. If the duration of the effective window is less than the shortest scheduling duration, the effective scheduling window is invalid and the startup time allocation for all devices cannot be completed within the window. The administrator is prompted to intervene manually or extend the start time of the startup window, and the adjusted window is output as the effective scheduling window to subsequent steps.

[0014] Step two involves extracting historical operating data from the individual meters of each device during peak startup periods, identifying the startup response time and the time when steady-state power is reached for each device, and establishing a mapping relationship between the downtime and preheating time of each device. This allows for accurate prediction of the required preheating time based on the current downtime of the devices before each meal service starts. By using the fixed meal service time as an anchor point, the latest startup time for each device is derived. The peak startup period is used as the initial boundary, and the latest startup time among all devices is used to correct the initial cutoff time. This forms an effective scheduling window that takes into account both historical collective startup patterns and the current individual preheating needs of each device. This provides a time boundary and scheduling range for determining the device startup order and allocating startup times in subsequent steps, reducing standby energy consumption caused by premature device startup or meal service delays caused by late startup.

[0015] Step 3: Before entering the pre-meal service start-up phase, monitor the energy consumption operation status of each canteen's equipment in real time, identify equipment that is not restricted from starting, and determine the start-up sequence of all unrestricted equipment within the effective scheduling window, taking into account the equipment's power occupancy and the urgency of the latest start-up time. Step 3 includes the following steps: before starting the meal service, acquiring the real-time power value of each canteen device at the current moment; If the real-time power at the current moment is within the set ratio range of the steady-state operating power, it is determined that the device is currently in standby mode and in normal condition, and will be marked as an unrestricted device for startup, allowing it to participate in this startup scheduling. If the real-time power is not within the set ratio range of steady-state operating power, it is determined that the device is not in normal standby mode, is marked as a device with restricted startup, and an abnormal status alert is generated and pushed to the management terminal to prompt the administrator to check. The set percentage range for steady-state operating power is set to 5%-15%, with 10% being the preferred value. Extract all boot-unrestricted devices and determine the boot order, including: Obtain the rated power value of the unrestricted starting device, and extract the maximum value among all the rated power values ​​of the unrestricted starting devices, using the maximum value as the base value; For each device with no start-up restrictions, calculate the percentage of the rated power value to the baseline value as the power ratio coefficient; Among them, the larger the power proportion coefficient, the higher the power consumption of the corresponding canteen equipment among all unrestricted start-up equipment; For each device that is not restricted from starting, calculate the difference between the latest start time and the start time of the effective scheduling window as the time margin; The shorter the time margin, the shorter the corresponding canteen equipment needs to be started within a short period of time, and the higher the urgency. The unrestricted devices are arranged in ascending order of their time margin to obtain the first order; Based on the first order, the absolute value of the time margin difference between adjacent unrestricted start-up devices is calculated as the time margin deviation value; Starting from the first device in the first order, traverse sequentially, grouping consecutive devices with time margin deviation values ​​less than or equal to the preset deviation limit into the same adjustment group, until a device with a time margin deviation value greater than the deviation limit is encountered. After forming an independent group, start a new group division from the cut-off device. The preset deviation limit ranges from 0.5 to 2 minutes, with 1 minute being preferred; For each group to be adjusted, if the group contains only a single device, the original order remains unchanged; If a group contains two or more devices, then all devices in the group are reordered in descending order of power ratio coefficient to obtain a second order. Arrange each group in the original timeline order and combine them to generate the final startup order list; It should be noted that if there are devices with the same power percentage coefficient but different time margins in the second order, the device with the smaller time margin will be ranked first. If there are devices with the same power percentage coefficient and the same time margin, they will be randomly ranked.

[0016] Step 3 involves monitoring the real-time power values ​​of each device before startup to determine whether the device is in a normal standby state. Devices in normal state are marked as unrestricted startup devices and participate in scheduling, while devices in abnormal state are excluded and a check reminder is pushed to ensure that all devices participating in startup are available, thereby reducing ineffective scheduling or safety hazards. A hierarchical sorting strategy is adopted, prioritizing time margin and adjusting power proportion locally: first, sorting is done according to time urgency, and then the equipment groups with similar time urgency are adjusted from large to small according to the power proportion coefficient. This allows high-power equipment to start as early as possible while meeting time constraints, providing a scheduling order that balances time rationality and capacity safety for the allocation of start-up time in subsequent steps, and also providing an optimized basis for transformer load balancing.

[0017] Step 4: Based on the startup sequence and the total load at startup, allocate corresponding startup times to the unrestricted devices, dynamically determine the maximum number of unrestricted devices that can be started simultaneously in the current startup time slot, and issue startup commands according to the allocated startup time schedule. Step four involves the following process: Each unrestricted device is retrieved sequentially from the beginning of the startup order list, and a startup time is assigned to the current device. The process is as follows: The start time of the valid window is used as the allocated start time for starting unrestricted devices; The instantaneous total load of the transformer after startup is obtained by summing the base load of the currently unrestricted equipment and the startup impact power. The starting impact power is the rated power multiplied by the starting impact current ratio, which can be found in the equipment nameplate parameters. The instantaneous total load of the transformer after startup is compared with the actual constraint boundary of the transformer's short-time overload tolerance capability; If the instantaneous total load of the transformer is less than or equal to the actual constraint boundary of the transformer's short-term overload tolerance capacity, the start time is determined as the current time slot pointer, the device is added to the set of started devices, the current transformer load is updated to the current transformer load plus the steady-state operating power of the device, the time slot pointer is advanced by one sampling interval or the impact duration of the device, and the next device is processed. If the instantaneous total load of the transformer is greater than the actual constraint boundary of the transformer's short-term overload withstand capability, the start time will be adjusted backward. Specifically, the time slot pointer will be pushed back to the earliest time when the current transformer load plus the start-up impact power is less than or equal to the actual constraint boundary of the transformer's short-term overload withstand capability. The earliest time will be determined as the start time, added to the set of started equipment, and the current transformer load and time slot pointer will be updated. Does the adjusted start time meet the following boundary constraints: it must not be later than the latest start time of the corresponding device, and it must not be later than the deadline of the effective scheduling window; If the adjusted start time satisfies the above boundary constraints, then it is determined as the start time of the device, added to the set of started devices, the current transformer load and time slot pointer are updated, and the next device is processed. If the adjusted start time does not meet the above boundary constraints, that is, the adjusted time is later than the latest start time or later than the deadline of the effective scheduling window, it is determined that there is no feasible solution for this start scheduling within the effective scheduling window, and the administrator is prompted to extend the effective scheduling window or reduce the number of devices started at the same time, and the allocation process is terminated. Calculate the current available load margin based on the current transformer load under the current time slot pointer; According to the order of devices that have not yet been assigned a start time in the start order list, the start impact power of each device is accumulated sequentially from front to back. If the total accumulated value still does not exceed the current available load margin when the Nth device is accumulated, and the total accumulated value exceeds the current available load margin when the N+1th device is accumulated, then it is determined that the current time slot can start a maximum of N devices at the same time, where N is an integer greater than or equal to zero. If N is greater than or equal to two, then the N consecutive devices in the current time slot pointer in the startup sequence list will be assigned to the same startup time slot. The startup instructions of the N devices will be issued in parallel at the same time. The sum of the startup impact power of each device will not exceed the actual constraint boundary of the transformer's short-term overload withstand capability. If N equals one, only one device will be started in the current time slot. After the device startup impact ends and the current transformer load is updated, the next device will be processed, which means switching to single serial startup mode. If N equals zero, it is determined that there is no available capacity in the current time slot, the time slot pointer is advanced to the earliest moment when the current available load margin is greater than zero, and the above determination process is repeated. Once all boot-unrestricted devices have completed their boot time allocation, a boot time table is generated.

[0018] Step 4: Based on the determined startup sequence, with the transformer's short-term overload tolerance capacity as the safety constraint boundary and the effective scheduling window as the time boundary, allocate specific startup times to each unrestricted startup device according to the startup sequence. During the allocation process, by comparing the transformer's instantaneous total load after the device starts with the actual constraint boundary of the overload tolerance capacity, devices that cannot meet the capacity constraints are adjusted to the earliest time slot that meets the conditions. Simultaneously, during the allocation process, the upper limit of the number of devices that can be started at the same time in the current time slot is dynamically calculated based on the current available load margin. Under the premise of capacity allowance, multiple devices are allowed to start in parallel. When the capacity is tight, it automatically reduces to single-device serial start. When the capacity is zero, it automatically searches for a startable time slot. In this way, the start-up time allocation of all unrestricted devices is completed with the smallest possible start-up time span without exceeding the transformer's short-term overload tolerance capacity. This ensures that each device starts no later than its latest start-up time, guaranteeing that all devices can be preheated before the meal service begins.

[0019] Step 5: During the startup process, the power data of the main incoming line and each device are collected in real time through the IoT smart meter to perform energy consumption monitoring and dynamic adjustment management; Step 5 includes the following steps: after the start command is issued and executed, collect the instantaneous power data of the main incoming line and simultaneously collect the instantaneous power data of each canteen equipment that has been started and is starting up. For the device currently executing the start command, record the power change curve in real time from the moment the start command is issued until the moment the steady-state operating power is reached; Extract the actual peak value of the inrush current during this startup process and compare the actual peak value of the inrush current with the historical startup inrush current benchmark value; The historical starting inrush current benchmark value is obtained by extracting the peak value of the inrush current for each start-up from the historical data of the independent meters of each device, and taking the average value of multiple starts as the historical starting inrush current benchmark value. If the actual peak inrush current deviates from the historical benchmark value by more than the corresponding set threshold, it is determined that there is an inrush anomaly during this startup, a startup inrush anomaly alarm is generated and pushed to the management terminal; If the actual peak inrush current is normal, but the time from the issuance of the start command to reaching the steady-state operating power exceeds 1.5 times the historical average start duration, then a start delay is determined, a start delay alarm is generated and pushed to the management terminal. If the actual peak inrush current and startup duration are both normal, then record the startup as successful and mark the startup data as valid data. During the startup process of each device, the instantaneous power of the total incoming line is simultaneously compared with the actual constraint boundary of the transformer's short-time overload withstand capability. If the instantaneous power of the total incoming line does not exceed the actual constraint boundary, the start-up instructions of subsequent equipment will continue to be executed according to the start-up timetable; If the instantaneous power of the total incoming line exceeds the actual constraint boundary, the start command of the device that has not yet started in the current time slot will be suspended, the start of the current device will be delayed to the next time slot, and the start time of the remaining devices will be recalculated to see if the latest start time constraint is still met. If not, an early warning message will be generated and pushed to the management terminal. If the duration exceeds the corresponding set threshold, the start command of all devices will be interrupted, triggering the main incoming line overload protection alarm. After all devices with no startup restrictions have started up, the total power consumption during this startup phase is calculated and compared with the average power consumption during historical startup peak periods. If the total power consumption during this startup deviates from the historical average by more than a set threshold, it is determined that there is abnormal energy consumption during the startup phase, and an abnormal startup energy consumption alarm is generated and pushed to the management terminal. The historical average start-up electricity consumption is obtained by averaging the total electricity consumption during the peak start-up period of each meal service day in step one. If the total power consumption during this startup is within a reasonable range, the energy consumption data for this startup phase will be marked as valid data and entered into the historical database.

[0020] Step 5 identifies abnormal starting impact and slow starting by comparing the peak value of the starting impact current of a single device with the historical benchmark value. It also performs safety monitoring and dynamic adjustment by comparing the instantaneous power of the total incoming line with the actual constraint boundary of the transformer's short-term overload tolerance capacity in real time. Finally, it identifies abnormal energy consumption during the starting phase by comparing the total power consumption after starting with the historical benchmark value, thus realizing multi-level energy consumption monitoring and dynamic adjustment management throughout the entire starting process.

[0021] Example 2

[0022] Based on the same inventive concept as the IoT-based canteen equipment energy consumption monitoring and management method in the foregoing embodiments, such as Figure 3 As shown, this application provides an IoT-based energy consumption monitoring and management system for canteen equipment, wherein the system specifically includes: Start the peak period acquisition module: acquire the total incoming power curve within the time window before the historical meal service period, identify the instantaneous power peaks that appear in the total incoming power curve and the corresponding time periods, and mark the start peak period; Effective scheduling window acquisition module: Based on the historical operating data of the independent electricity meters of each canteen equipment during the peak start-up period, extract the mapping relationship between the downtime and preheating time of each canteen equipment, determine the preheating time required for each canteen equipment to start at the current time, calculate the latest start time of each canteen equipment with the fixed meal opening time as a countdown, and obtain the effective scheduling window by combining the analysis of the peak start-up period; Start-up sequence determination module: Before entering the pre-meal service start-up phase, monitor the energy consumption operation status of each canteen's equipment in real time, extract the equipment that is not restricted from starting, and determine the start-up sequence of all unrestricted equipment within the effective scheduling window by combining the equipment's power occupancy and the urgency of the latest start-up time. Startup time allocation module: Based on the startup order and combined with the total load at startup, it allocates corresponding startup times to unrestricted devices, dynamically determines the upper limit of the number of unrestricted devices that can be started simultaneously in the current startup time slot, and issues startup commands according to the allocated startup time table; Start the energy consumption assessment module: During the startup process, the power data of the main incoming line and each device are collected in real time through the IoT smart meter to perform energy consumption monitoring and dynamic adjustment management.

[0023] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring and managing the energy consumption of canteen equipment based on the Internet of Things, characterized in that: include: Obtain the total incoming power curve within the time window before the historical meal service period, identify the instantaneous power spikes that appear in the total incoming power curve and the corresponding time periods, and calibrate the start-up peak period; Based on the historical operating data of the independent electricity meters of each canteen equipment during the peak start-up period, the mapping relationship between the downtime and preheating time of each canteen equipment is extracted to determine the preheating time required for the current start-up. The latest start-up time of each canteen equipment is calculated with the fixed meal opening time as a countdown, and an effective scheduling window is obtained in combination with the peak start-up period. Before entering the pre-meal service startup phase, the energy consumption operation status of each canteen's equipment is monitored in real time, and equipment that is not restricted from startup is identified. Based on the equipment's power occupancy and the urgency of the latest startup time, the startup sequence is determined within the effective scheduling window. Based on the startup sequence and the startup time corresponding to the total load distribution at startup, the maximum number of unrestricted devices that can be started simultaneously in the current startup time slot is dynamically determined, and startup instructions are issued according to the startup time schedule. During the startup process, the power data of the main incoming line and each device are collected in real time through IoT smart meters to perform energy consumption monitoring and dynamic adjustment management.

2. The method for monitoring and managing the energy consumption of canteen equipment based on the Internet of Things according to claim 1, characterized in that: The process of acquiring peak hours is as follows: Peak distribution is identified in the total incoming power curves of multiple consecutive historical meal supply days to obtain the duration of all instantaneous power peaks before each meal supply period; The timeline before each meal service period is divided into multiple sub-time periods according to a preset time granularity, and the number of instantaneous power spikes recorded in each sub-time period is counted. Multiple consecutive sub-time periods with excessive frequency of spike occurrence are identified as candidate spike-dense areas; The average of the start times of all peaks within each candidate peak density area is taken as the start time of the peak initiation period, and the average of the end times of all peaks is taken as the end time of the peak initiation period. When there are multiple candidate peak density areas, the one with the highest peak occurrence frequency is selected as the final start peak period, or the union of all candidate peak density areas is used as the start peak period.

3. The method for monitoring and managing the energy consumption of canteen equipment based on the Internet of Things according to claim 2, characterized in that: The process of identifying peak distribution is as follows: Traverse the sampling points in the total incoming power curve and mark the sampling points whose power values ​​are greater than the peak determination threshold and are also greater than the power values ​​of the adjacent sampling points as peak candidate points. For each candidate peak, the peak is started when the power value first drops to a set percentage between the peak determination threshold and the baseline power value, and ended when the power value falls back to the same set percentage.

4. The method for monitoring and managing the energy consumption of canteen equipment based on the Internet of Things according to claim 1, characterized in that: The process of obtaining a valid scheduling window is as follows: Extract the downtime of each canteen equipment between adjacent meal service periods on each meal service day, and the preheating time when it is started in the next meal service period, and construct a historical mapping dataset of equipment downtime and preheating time; Before each meal service period starts, the current downtime of each device is obtained and substituted into the historical mapping dataset to calculate the preheating time required for this start-up. Using the fixed meal opening time as the anchor point, subtract the preheating time required for this startup and the preset time redundancy to obtain the latest startup time of each device; The start time of the startup peak period is used as the fixed start time of the effective scheduling window, the duration of the startup peak period is used as the initial scheduling window length, and the minimum value between the latest start time of all devices to be started and the initial cutoff time is used as the cutoff time of the effective scheduling window.

5. The method for monitoring and managing the energy consumption of canteen equipment based on the Internet of Things according to claim 4, characterized in that: The process for extracting and determining the startup order of unrestricted devices is as follows: Before entering the pre-meal service start-up phase, the real-time power value of each canteen's equipment is obtained. If the real-time power is within the set ratio range of steady-state operating power, it is marked as an unrestricted start-up device. Calculate the time margin and power ratio for starting up unrestricted devices; The unrestricted devices are arranged in ascending order of time margin to obtain the first order. The absolute value of the time margin difference between adjacent devices is calculated. Consecutive devices with an absolute difference less than or equal to the preset deviation limit are grouped into the same adjustment group. The devices in the group are reordered in descending order of power ratio coefficient. The groups are arranged in the original time axis order and combined to generate the final start order list.

6. The method for monitoring and managing the energy consumption of canteen equipment based on the Internet of Things according to claim 5, characterized in that: The calculation process for time margin and power ratio factor is as follows: The maximum rated power of all unrestricted starting devices is taken as the benchmark value, and the percentage of the rated power of each device to the benchmark value is calculated as the power ratio coefficient. Obtain the latest start time of each unrestricted device and calculate the difference between the latest start time and the start time of the effective scheduling window as the time margin.

7. The method for monitoring and managing the energy consumption of canteen equipment based on the Internet of Things according to claim 1, characterized in that: The process of allocating startup time is as follows: Each unrestricted device is retrieved sequentially from the startup order list, with the start time of the effective scheduling window serving as the current time slot pointer. The current transformer load is summed with the starting impact power of the current equipment to obtain the instantaneous total load of the transformer after startup; If the instantaneous total load of the transformer after startup is less than or equal to the actual constraint boundary of the transformer's short-term overload withstand capability, then the current time slot pointer is determined as the startup time. If the instantaneous total load of the transformer after startup exceeds the actual constraint boundary of the transformer's short-term overload tolerance capacity, the time slot pointer will be pushed back to the earliest moment that satisfies the capacity constraint, and the earliest moment will be determined as the startup moment.

8. The method for monitoring and managing the energy consumption of canteen equipment based on the Internet of Things according to claim 7, characterized in that: The process for determining the maximum number of devices that can be started simultaneously in the current time slot is as follows: Calculate the current available load margin based on the current transformer load under the current time slot pointer; According to the order of devices in the startup sequence list that have not yet been assigned a startup time, the startup impact power of each device is accumulated sequentially from front to back. If the total accumulated value still does not exceed the current available load margin when accumulated to the Nth device, but exceeds the current available load margin when accumulated to the N+1th device, then it is determined that the current time slot can start up to N devices at the same time.

9. The method for monitoring and managing the energy consumption of canteen equipment based on the Internet of Things according to claim 1, characterized in that: The process of implementing energy consumption monitoring and dynamic adjustment management is as follows: After the start command is issued, the instantaneous power of the main incoming line and each device is collected; For equipment that is starting up, extract the actual peak value of the inrush current and compare it with the historical benchmark value. If the deviation exceeds the threshold, an inrush anomaly is determined and an alarm is triggered. If the impact is normal but the startup time exceeds a set multiple of the historical time, the startup is considered slow and an alarm is triggered; if everything is normal, the startup is recorded as successful. During startup, the instantaneous power of the total incoming line is compared in real time with the actual constraint boundary of the transformer's short-term overload withstand capability: if it is not exceeded, the process continues. If the timeout exceeds the threshold, all startup commands will be interrupted and an overload protection alarm will be triggered. After all devices have started up, the total power consumption of this startup is calculated and compared with the historical average startup power consumption. If the deviation exceeds the threshold, abnormal energy consumption is identified and an alarm is triggered. If it is within a reasonable range, the energy consumption data of this startup is entered into the historical database.

10. An IoT-based canteen equipment energy consumption monitoring and management system, characterized in that, The system is used to perform the method according to any one of claims 1-9, the system comprising: Start the peak period acquisition module: acquire the total incoming power curve within the time window before the historical meal service period, identify the instantaneous power peaks that appear in the total incoming power curve and the corresponding time periods, and calibrate the start peak period; Effective scheduling window acquisition module: Based on the historical operating data of the independent electricity meters of each canteen equipment during the peak start-up period, extract the mapping relationship between the downtime and preheating time of each canteen equipment, determine the preheating time required for the current start-up, calculate the latest start-up time of each canteen equipment with the fixed meal opening time as the countdown, and obtain the effective scheduling window in combination with the peak start-up period; Start-up sequence determination module: Before entering the pre-meal service start-up phase, monitor the energy consumption operation status of each canteen's equipment in real time, extract equipment that is not restricted from starting, and determine the start-up sequence within the effective scheduling window by combining the equipment's power occupancy and the urgency of the latest start-up time. Startup time allocation module: Based on the startup order and the startup time corresponding to the total load allocation at startup, dynamically determine the upper limit of the number of unrestricted devices that can be started simultaneously in the current startup time slot, and issue startup commands according to the startup time table; Start the energy consumption assessment module: During the startup process, the power data of the main incoming line and each device are collected in real time through the IoT smart meter to perform energy consumption monitoring and dynamic adjustment management.

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