A hotel weak current equipment energy consumption statistical analysis monitoring management method

Abstract

The application provides a hotel weak current equipment energy consumption statistical analysis monitoring management method, comprising: time sequence correlation analysis of the load change range of each regional equipment and the running power of each equipment of the weak current system to obtain a load correlation strength, and if the load correlation strength exceeds a set threshold, it is determined that there is a cross-regional energy consumption imbalance; identifying the severity of the cross-regional energy consumption imbalance, extracting mode switching records similar to the load correlation from historical energy consumption data, grouping according to time period and weak current equipment type to obtain a risk identification feature set; obtaining a weak current system equipment power adjustment range, extracting the numerical range of the load surge of the weak current system through historical data comparison, combining the power adjustment range and the resource allocation trigger condition to optimize the resource allocation ratio to obtain an adjusted power quota.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for statistical analysis, monitoring and management of energy consumption of low-voltage electrical equipment in hotels. Background Technology

[0002] As hotels expand and their customer base grows, energy management of their low-voltage electrical systems has become a fundamental element in ensuring sustainable development and customer satisfaction. Hotel low-voltage electrical systems mainly include core equipment such as lighting systems, HVAC systems, elevator systems, security monitoring systems, communication network systems, fire protection systems, water supply and drainage systems, and audio broadcasting systems. Current hotel low-voltage electrical energy management methods often rely on a globally unified monitoring framework. While this framework can cover overall data, it ignores the dynamic interaction of loads between different areas. For example, some systems only use average energy consumption indicators for scheduling, failing to capture how local peak loads affect surrounding areas, leading to delayed resource allocation and untimely responses. Furthermore, when dealing with multi-area coupling, these methods are prone to triggering chain reactions due to a lack of granular perception, resulting in uneven service and inefficiency. The core technical challenge of low-voltage system energy consumption monitoring lies in the real-time correlation between regional load peaks and the decline in service levels in adjacent areas. This correlation stems from the rapidly increasing power demands of low-voltage electrical equipment in high-load areas. For example, when events begin in meeting areas, lighting, audio, and air conditioning power consumption spike instantly, or elevator systems operate at peak times. These changes are transmitted to other areas via shared power supplies or control networks, forcing guest room equipment to automatically reduce operating power to maintain overall load balance. This cross-regional impact leads to unstable guest room temperature control, reduced lighting brightness, and a compromised user experience. Furthermore, the propagation mechanism of this cross-regional impact makes it difficult for the system to identify imbalance points in real time during peak periods, creating a vicious cycle where load surges trigger service degradation. To avoid power outages, the low-voltage management system automatically activates a load balancing mechanism, prioritizing power supply to public areas and correspondingly reducing the power consumption of equipment in back-office areas and some guest room floors. This allocation results in reduced kitchen ventilation speeds affecting food safety operations, slow laundry equipment operation delaying linen supply, and insufficient lighting in staff work areas impacting service efficiency, ultimately leading to a chain reaction of increased operating costs and decreased service quality. Therefore, accurately capturing the power value changes of each functional area at the peak load moment and their immediate impact on the power reduction of equipment in other areas in the low-voltage management system, so as to avoid service continuity interruption caused by cross-regional energy consumption imbalance, has become a key issue in improving the reliability of hotel operations. Summary of the Invention

[0003] This invention provides a method for statistical analysis, monitoring, and management of energy consumption of low-voltage electrical equipment in hotels, mainly including:

[0004] By aggregating the power of low-voltage equipment during the target time period, the power change trend of the hotel's low-voltage system equipment is obtained. Based on the power change trend, the frequency of switching of the working mode of low-voltage equipment in each area is identified. The operating power of low-voltage equipment in each floor and functional area at the time of the mode switching is obtained, and the load change range of equipment in each area is determined.

[0005] The load variation of equipment in each region is correlated with the operating power of each device in the weak current system in a time series analysis to obtain the load correlation strength. Based on the load correlation strength, the cross-regional energy consumption imbalance status is determined.

[0006] Extract mode switching records related to the load correlation strength from historical energy consumption data, group them according to time period and weak current equipment type, and generate risk identification feature set;

[0007] The peak load of the hotel's low-voltage system is obtained, the risk identification feature set is statistically evaluated, the risk level evaluation result is associated and matched with the peak load, a service interruption risk warning signal is generated, and the resource allocation triggering condition is determined based on the cross-regional energy consumption imbalance.

[0008] Based on the power adjustment range of the low-voltage system equipment and the resource allocation triggering conditions, an adjusted power quota is generated;

[0009] According to the adjusted power quota, the system sends equipment power adjustment instructions to the low-voltage management system to adjust the operating power of low-voltage equipment in each area and restore the balance between areas.

[0010] Identify the power changes of low-voltage equipment at the moment of mode switching to restore balance between regions, evaluate the power change trend, and obtain the operating efficiency of the low-voltage system and the regional energy consumption balance.

[0011] Furthermore, by aggregating the power of low-voltage equipment during the target time period, the power change trend of the hotel's low-voltage system equipment is obtained. Based on the power change trend, the frequency of switching of operating modes of low-voltage equipment in each area is identified, and the operating power of low-voltage equipment in each floor and functional area at the time of mode switching is obtained to determine the load change amplitude of equipment in each area, including:

[0012] The real-time power data of lighting equipment, air conditioning equipment and elevator equipment on each floor of the hotel's low-voltage system are divided into breakfast time, meeting time and peak check-in time. The power average of each time period is calculated using a fixed time window. The power change moment is identified based on the power average difference between adjacent time periods, and the instantaneous power value of each device at the power change moment is obtained.

[0013] Based on the instantaneous power value of the equipment at the moment of power change, the equipment is grouped into guest room area, conference area, catering area and public area. The power difference of each group of equipment before and after the moment of power change is calculated, and the number of equipment groups that switch modes in each area is counted as the mode switching frequency.

[0014] By calculating the total load change in each region based on the difference between the mode switching frequency and the power of the corresponding equipment group, and dividing it by the total power of the equipment in the region, the load change amplitude of the equipment in each region is obtained.

[0015] Furthermore, the step of performing time-series correlation analysis between the load variation amplitude of equipment in each region and the operating power of each device in the low-voltage system to obtain the load correlation strength, and determining the cross-regional energy consumption imbalance state based on the load correlation strength, includes:

[0016] The time series data of the load change amplitude of the equipment in each region are obtained, and the data are aligned according to the preset time granularity. The load change value of each region at the same time is extracted, and the operating power data of each device in the weak current system at the corresponding time are collected to construct the load change time series matrix and the power operation time series matrix.

[0017] A correlation coefficient matrix is ​​generated based on the load change timing matrix and the power operation timing matrix. The maximum correlation coefficient is calculated based on the offset position within the time window to determine the actual response delay.

[0018] Based on the correlation coefficient matrix and the actual response delay, a load correlation strength matrix is ​​generated, and off-diagonal elements in the load correlation strength matrix that exceed a preset threshold are identified to determine the corresponding inter-regional energy consumption imbalance state.

[0019] Furthermore, the process of extracting mode switching records related to the load correlation strength from historical energy consumption data, grouping them according to time period and type of low-voltage equipment, and generating a risk identification feature set includes:

[0020] Based on the load correlation strength and the number of affected areas, calculate the imbalance severity index, filter out events whose load correlation strength exceeds a preset threshold from historical energy consumption data, extract the regional load change sequence, equipment power sequence and mode switching time when the event occurs, and generate a historical imbalance event dataset.

[0021] For the aforementioned historical imbalance event dataset, it is grouped according to breakfast time, meeting time, peak check-in time, and late night time, and further grouped according to lighting equipment, air conditioning equipment, elevator equipment, monitoring equipment, and communication equipment to generate a two-dimensional grouping matrix of time and equipment.

[0022] Using the two-dimensional grouping matrix, the load change range, power change frequency, and mode switching duration of imbalance events within each group are extracted to generate the risk identification feature set.

[0023] Furthermore, the process of obtaining the peak load of the hotel's low-voltage system, statistically evaluating the risk identification feature set, associating and matching the risk level evaluation results with the peak load, generating a service interruption risk warning signal, and determining resource allocation triggering conditions based on the cross-regional energy consumption imbalance status includes:

[0024] The instantaneous power values ​​of each functional area are extracted from the real-time monitoring data of the hotel's low-voltage system. The maximum power value within the time window is calculated as the local peak value, and the value that is above the preset percentile after sorting is selected as the load peak value.

[0025] For the load peak and the risk identification feature set, the frequency of occurrence of risk patterns under different load levels is statistically analyzed, and high, medium and low risk levels are divided based on the frequency values ​​to generate the risk level assessment results;

[0026] By comparing the risk level assessment result with the load peak, it is determined whether the real-time load has reached a preset percentage of the load peak, and the service interruption risk warning signal is generated. Based on the cross-regional energy consumption imbalance state, a resource allocation trigger threshold is set, the power adjustment range and execution order are determined, and the resource allocation trigger condition is formed.

[0027] Furthermore, the step of setting a resource allocation trigger threshold based on the cross-regional energy consumption imbalance state, determining the power adjustment range and execution order, and forming the resource allocation trigger condition includes:

[0028] Based on the severity of the cross-regional energy consumption imbalance, trigger thresholds for routine allocation, emergency allocation, and emergency support are set.

[0029] Based on the trigger threshold, the power adjustment range of lighting equipment, air conditioning equipment, elevator equipment, and security equipment in each area is determined, and the power adjustment execution order is generated according to the weight of equipment category and the weight of area function.

[0030] By defining the power adjustment range and execution order, the resource allocation triggering conditions, which include the upper limit of the power of devices in each region, are generated.

[0031] Furthermore, the step of generating the adjusted power quota by combining the power adjustment range of the low-voltage system equipment and the resource allocation triggering conditions includes:

[0032] The power reduction amount and power reduction sequence are determined based on the power adjustment range of the low-voltage system equipment and the resource allocation triggering conditions.

[0033] By adjusting the power reduction amount and order, the power resources of each region are redistributed, the total available power of each region after adjustment is calculated, and the available power is allocated proportionally according to equipment type and functional requirements to obtain the adjusted power quota.

[0034] Furthermore, the step of sending equipment power adjustment instructions to the low-voltage management system according to the adjusted power quota, adjusting the operating power of low-voltage equipment in each area, and restoring the balance between areas includes:

[0035] Based on the adjusted power quota, construct the equipment power adjustment instruction, which includes the target equipment number, the current power value, and the target power value;

[0036] For each piece of equipment, set upper limits for the power consumption of the lighting system, the power consumption of the air conditioning system, the power consumption of the elevator operation, and the power consumption of the security equipment, and adjust the target power value of each piece of equipment to within the upper limit.

[0037] Based on the adjusted target power value, control parameters are sent to each device to adjust the device operating power. The device power is then fine-tuned based on the power difference between regions to determine whether the balance between regions has been restored.

[0038] Furthermore, the step of sending control parameters to each device using the adjusted target power value, adjusting the device operating power, fine-tuning the device power based on the power difference between regions, and determining the restoration of balance between regions includes:

[0039] The adjusted target power value is used to send control parameters to lighting equipment, air conditioning equipment, elevator equipment, and security equipment to adjust their operating power.

[0040] The actual power value of each device after adjustment is obtained, the real-time total power of each area is calculated, the power difference between areas is compared, and when the power difference is within a preset threshold range and continues for a preset duration, it is determined that the balance between areas has been restored.

[0041] Furthermore, identifying the power changes of low-voltage equipment at the time of mode switching during the balance restoration between regions, evaluating the power change trend, and obtaining the operating efficiency of the low-voltage system and the regional energy consumption balance include:

[0042] Monitor the power data of equipment in various areas of the hotel's low-voltage system, identify the mode switching time to restore balance, and calculate the power adjustment amount of lighting equipment, air conditioning equipment, and elevator equipment from an unbalanced state to a balanced state.

[0043] Based on the power adjustment amount and adjustment time, the power change trend is obtained by fitting the linear regression method. The system recovery speed is judged by the trend slope. The operating efficiency of the weak current system is obtained by calculating the ratio of the actual operating power to the rated power at each time. The deviation of the power in each region relative to the average power in the region is calculated. The standard deviation of the deviation is obtained to obtain the regional energy consumption balance.

[0044] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0045] This invention discloses a method for statistical analysis, monitoring, and management of energy consumption of low-voltage electrical equipment in hotels. Addressing the risk of cross-regional energy imbalance and service interruption caused by power fluctuations in hotel low-voltage electrical equipment, the method aggregates and analyzes equipment power change trends, identifies mode switching times and load variation amplitudes, and then performs time-series correlation analysis to determine the severity of cross-regional energy imbalance. By combining historical energy consumption data to extract risk feature sets, the method assesses load peaks and risk levels, and generates service interruption risk warning signals. This invention optimizes power quotas, dynamically adjusts the operating power of low-voltage electrical equipment, and implements upper limit constraints in conjunction with lighting, air conditioning, elevators, and security systems to restore energy balance between regions. This invention significantly improves the operating efficiency of low-voltage systems, reduces the risk of service interruption, ensures hotel operational continuity, and achieves intelligent management of energy consumption optimization and resource allocation. Attached Figure Description

[0046] Figure 1 This is a flowchart of a method for statistical analysis, monitoring and management of energy consumption of low-voltage electrical equipment in hotels according to the present invention.

[0047] Figure 2 This is a schematic diagram of a method for statistical analysis, monitoring and management of energy consumption of low-voltage electrical equipment in hotels according to the present invention.

[0048] Figure 3 This is another schematic diagram of a method for statistical analysis, monitoring and management of energy consumption of low-voltage electrical equipment in hotels according to the present invention. Detailed Implementation

[0049] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0050] like Figure 1-3 This embodiment of a method for statistical analysis, monitoring, and management of energy consumption of low-voltage electrical equipment in hotels may specifically include:

[0051] Step S101: By aggregating the power of low-voltage equipment during the target time period, the power change trend of the hotel's low-voltage system equipment is obtained. Based on the power change trend, the frequency of switching of the working mode of low-voltage equipment in each area is identified, the time of mode switching of power change is identified, the operating power of low-voltage equipment in each floor and functional area at the time of mode switching is obtained, and the load change amplitude of equipment in each area is identified.

[0052] Real-time power data for lighting, air conditioning, and elevator equipment on each floor of the hotel's low-voltage electrical system is divided into time periods: breakfast time, meeting time, and peak check-in time. A fixed time window is used to sum the power data for each time period, then divide by the number of sampling points to obtain the average power for each time period. The difference between the average power values ​​of adjacent time periods is calculated; if the difference exceeds a preset threshold, it is identified as a power surge, and the instantaneous power value of each device at the power surge moment is obtained. Based on the instantaneous power values ​​of the devices at the power surge moment, the area is divided into guest room area, meeting area, dining area, and public area. Low-voltage electrical equipment in each area is grouped according to equipment type, and the power difference before and after the power surge moment is calculated for each group of equipment. If the power difference exceeds a preset threshold, it is determined that the group of equipment has switched operating modes. The number of equipment groups that switch modes in each area is counted as the mode switching frequency. The total load change for each floor and functional area is obtained by multiplying the mode switching frequency by the power difference of the corresponding equipment group and summing the results. Dividing this by the total power of the equipment in that area yields the load change amplitude. The arithmetic mean of the load change amplitude data over consecutive time periods is calculated to obtain the load change amplitude of the equipment in each area.

[0053] For example, in one implementation, power data acquisition for the hotel's low-voltage system is achieved through smart meters installed in the distribution boxes on each floor, collecting the instantaneous power values ​​of lighting equipment, air conditioning equipment, and elevator equipment every 5 seconds. The time periods are determined based on the hotel's operational schedule: breakfast is scheduled from 6:00 AM to 10:00 AM, meeting sessions are scheduled from 9:00 AM to 12:00 PM and 2:00 PM to 5:00 PM, and peak check-in time is scheduled from 3:00 PM to 9:00 PM.

[0054] Specifically, a fixed time window is set to 10 minutes, with 120 sampling points within each window. The power average for that window is obtained by summing the power values ​​of all sampling points within the window and then dividing by 120. When the difference between the power averages of two adjacent time windows exceeds 15% of the rated power of that area, it is determined to be a power surge. At this time, the instantaneous power values ​​of each device before and after the surge are immediately recorded. These values ​​reflect the actual load state of the device at the moment of operating mode switching.

[0055] For example, the hotel's zoning is based on the building's functional layout. The guest room area includes standard rooms and suites on floors 3 to 12; the conference area covers meeting rooms of various sizes on the 2nd floor; the dining area includes Chinese and Western restaurants and a banquet hall on the 1st floor; and the public areas include the lobby, corridors, elevator lobbies, and other spaces. Within each area, lighting equipment is grouped into downlights, chandeliers, and wall lights; air conditioning equipment is grouped into central air conditioning terminals, split air conditioners, and fresh air systems; and elevator equipment is grouped into passenger elevators, freight elevators, and escalators.

[0056] It should be noted that the power difference is calculated as the difference between the average power of the 10 seconds before and the average power of the 10 seconds after the abrupt change. When the power difference of a certain equipment group exceeds 20% of the rated power of that group, it is determined that the equipment group has switched operating modes. For example, when the conference room lighting switches from full-on mode to energy-saving mode, the lighting power drops from 5 kW to 2 kW, and the power difference reaches 60%, which significantly exceeds the preset threshold.

[0057] In one possible implementation, the total load change is obtained by multiplying the mode switching frequency of each equipment group by its corresponding power difference and then summing the results. For example, in a guest room area, if the lighting equipment group switches 3 times within one hour, with a power difference of 2 kW each time, and the air conditioning equipment group switches 2 times, with a power difference of 5 kW each time, then the total load change for this area is 16 kW. Dividing this total by the total power of the equipment in this area, 30 kW, yields a load change amplitude of 53%. By calculating the arithmetic mean of the load change amplitudes over six consecutive time periods, the influence of instantaneous fluctuations is eliminated, and a stable regional load change characteristic is obtained.

[0058] During the operation of the hotel's low-voltage electrical system, the time points when the lighting equipment on each floor switches from normal lighting to energy-saving mode are monitored. At the same time, the actual power consumption values ​​of the security monitoring system on the guest room floors, the audio equipment in the conference area, and the network communication equipment in the lobby area are collected. The power consumption difference of the air conditioning control panel in the guest room area, the elevator car lighting system, and the background music playback equipment in the restaurant area are compared to determine the degree of power reduction of the equipment in each functional area when switching from high-load operation to low-load standby.

[0059] The operating status of lighting equipment is monitored in real time by smart meters deployed in the power distribution cabinets on each floor. When the current in the lighting circuit drops from the rated value to a preset threshold, the mode switching time point is recorded. The average power of the lighting equipment in the preset period before the switch is obtained as the normal lighting power, and the average power in the same period after the switch is obtained as the energy-saving mode power. At the same time, a data acquisition command is triggered to start the power acquisition program for the security monitoring equipment on the guest room floors, the audio equipment in the conference area, and the network communication equipment in the lobby area. Instantaneous power values ​​are collected at a preset frequency to obtain the power data of each device during the mode switching period. Based on the power data, the power values ​​of each preset number of sampling points before and after the instant of mode switching are extracted. The difference between the peak power and the valley power of the air conditioning control panel in the guest room area at the switching time is calculated. The power change amplitude of the elevator car lighting at the same time is obtained. The power fluctuation range of the background music playback equipment in the restaurant area is collected. The power consumption difference is obtained by the ratio of the peak-valley power difference of each device to its rated power. Using the power consumption difference, the system is categorized and statistically analyzed according to equipment type and functional area. The percentage power reduction for each type of equipment when transitioning from a high-load to a low-load state is calculated. A weighted average of the power reduction percentages for all equipment within each area is then calculated, with the weights determined by the proportion of the equipment's rated power to the total power of that area. This yields the power reduction extent for each functional area. Based on the power reduction extent for each functional area, areas where the power reduction exceeds a preset threshold are identified and marked as load transition complete. The distribution of power reduction extent when all functional area equipment transitions from a high-load operating state to a low-load standby state is then statistically analyzed to determine the power reduction extent of the hotel's low-voltage electrical equipment during mode switching.

[0060] For example, in one implementation, the mode switching monitoring of the hotel's low-voltage electrical system is achieved through a distributed power monitoring network. This network consists of smart meters deployed in the distribution cabinets on each floor, power sensors installed at the device ends, and a central data processing server. The smart meters use high-precision current transformers to monitor current changes in the lighting circuits in real time. When the detected current value drops from the rated value during normal operation to a preset threshold, it is determined that the lighting equipment has begun the energy-saving mode switching process.

[0061] Specifically, the recording accuracy of the mode switching time point reaches the millisecond level. Upon detecting a current decrease trend, a high-frequency sampling mode is immediately activated, increasing the sampling frequency from the usual once per second to 100 times per second, continuously monitoring the current change curve. When the current decrease slope reaches a preset threshold, this moment is recorded as the start time point of the mode switching. Simultaneously, power data from a preset period before the switch is extracted, and a normal lighting power baseline value is calculated using a moving average algorithm. Power data from the same period after the switch undergoes the same processing to obtain the energy-saving mode power value.

[0062] For example, when a lighting mode switch is detected, a synchronization acquisition command is sent to each monitoring terminal via the Ethernet communication protocol. The security monitoring system on the guest room floors includes corridor cameras, access control card readers, smoke detectors, and other devices. After receiving the acquisition command, these devices read their current power consumption through their built-in power measurement modules. The audio equipment in the conference area includes amplifiers, mixing consoles, wireless microphone receivers, etc., and each device uploads real-time power data to the data acquisition unit via an RS485 bus. The network communication equipment in the lobby area includes wireless routers, switches, information display screens, etc., which report their respective power consumption status via the SNMP protocol.

[0063] It should be noted that power data is collected at a preset frequency, typically once per second, for a predetermined duration. A preset number of sampling points are extracted before and after mode switching, covering the entire process of equipment state transition. For guest room air conditioning control panels, the system records not only the power consumption of the display screen and control chip, but also the instantaneous power consumption when relays operate. The monitoring range for elevator car lighting systems covers all lighting components, including ceiling lights, indicator lights, and floor displays. The power data for background music playback equipment in the restaurant area includes the power of the audio player, zone controller, and ceiling speakers.

[0064] In one possible implementation, the power peak-valley difference is calculated using a dynamic window algorithm. After obtaining the power data sequence for each device, local maxima and minima are found by moving the window. The window size is dynamically adjusted according to the device type; a smaller window is used for fast-responding lighting equipment, while a larger window is used for air conditioning equipment with thermal inertia. The peak value is defined as the maximum power within the window, and the valley value as the minimum; the difference between the two is the power peak-valley difference. This difference is compared with the rated power of the equipment to obtain a relative percentage of the power consumption difference, which reflects the drastic change in equipment load.

[0065] Preferably, a hierarchical weighted average method is used when calculating the power reduction. Equipment within each functional area is first grouped by equipment type, such as lighting equipment group, air conditioning equipment group, power equipment group, etc. Within each group, the power reduction value for all equipment transitioning from a high-load state to a low-load state is calculated; this reduction value is the difference between the stable power before and after the transition. The reduction value is divided by the power before the transition to obtain the percentage power reduction for that equipment. The arithmetic mean of the percentage reductions for all equipment within a group is then calculated to obtain the average reduction for that equipment group. Furthermore, the average reduction for each equipment group needs to be weighted to obtain the overall power reduction degree of the functional area. The weights are determined based on two factors: the proportion of the rated power of the equipment group to the total power of the area, and the importance coefficient of the equipment group to the area's function.

[0066] For example, in the guest room area, although the air conditioning unit accounts for a large proportion of power, the lighting unit has a more direct impact on the customer experience; therefore, the lighting unit is assigned a higher importance coefficient. The weight value is obtained by multiplying the rated power proportion by the importance coefficient. The average power reduction of each unit is multiplied by its corresponding weight and then summed to obtain the overall power reduction degree for that functional area.

[0067] Understandably, determining the power descent characteristics requires statistical analysis of the descent levels across all functional areas. The power descent levels of each area are sorted by numerical value, and areas with descent exceeding a preset threshold are identified; these areas are marked as having completed load transition. By calculating the mean, variance, skewness, and other statistical measures of the descent levels across all areas, a probability distribution model of the power descent is constructed. This model reflects the overall load change characteristics of the hotel's low-voltage system during mode switching, including the central tendency, dispersion, and distribution pattern of the descent.

[0068] For example, when a meeting ends, the conference room lights switch from full brightness to off, the sound system switches from working to standby, and the air conditioning switches from cooling to ventilation mode. This series of changes can cause a power reduction of over 70% in the meeting area. At the same time, the adjacent dining area may increase lighting and background music volume as dinner time approaches, resulting in a power increase.

[0069] Step S102: Perform time-series correlation analysis between the load change amplitude of each area equipment and the operating power of each equipment in the weak current system to obtain the load correlation strength. If the load correlation strength exceeds the set threshold, it is determined that there is a cross-regional energy consumption imbalance.

[0070] Time-series data on load changes in equipment across different regions are acquired. The data is aligned according to a preset time granularity, and the load change value for each region at the same time is extracted. Simultaneously, operating power data for each device in the low-voltage system at the corresponding time is collected. A load change time-series matrix and a power operation time-series matrix are constructed. Rows in the matrices represent different regions or devices, and columns represent time sampling points. A timestamp synchronization mechanism ensures complete correspondence in the time dimensions of the two matrices. For the load change time-series matrix and the power operation time-series matrix, the Pearson correlation coefficient is used to calculate the correlation between the load change sequence of each region and the power sequences of devices in other regions, obtaining a correlation coefficient matrix. The correlation coefficients are then corrected for time delay by gradually moving the time-series data of one region within a preset time window, calculating the correlation coefficient at each offset position, and selecting the offset corresponding to the largest absolute value of the correlation coefficient as the actual response delay. Based on the correlation coefficient matrix and the response delay, the load correlation strength between regions is calculated. The absolute value of the correlation coefficient is used as the base correlation value. When the response delay is less than a preset threshold, the base correlation value remains unchanged; when the response delay exceeds the preset threshold, the base correlation value is reduced proportionally, resulting in a load correlation strength matrix reflecting the real-time impact between regions. By using the load correlation strength matrix, the positions of off-diagonal elements that exceed a preset threshold are identified. If there are elements that exceed the threshold, it is determined that there is a cross-regional energy consumption imbalance between the two corresponding areas, thus determining the cross-regional energy consumption imbalance status of the hotel's low-voltage system.

[0071] For example, in one implementation, the time-series correlation analysis of the hotel's low-voltage electrical system is achieved by constructing a multi-dimensional time-series data matrix. Load change amplitude data are collected from monitoring points on each floor and in each functional area. Each monitoring point represents a specific area, such as the guest room area on the 3rd floor, the conference area on the 5th floor, and the dining area on the 1st floor. The time granularity is set to 1 minute, meaning the load change amplitude value for each area is recorded once per minute. The number of rows in the load change time-series matrix equals the number of monitoring areas, and the number of columns equals the number of time sampling points. Each element in the matrix represents the percentage of load change amplitude for a specific area at a specific time.

[0072] Specifically, time alignment is achieved through a unified clock reference. The central server of the hotel's low-voltage system is configured with a network time protocol synchronization function, ensuring that the clocks of all monitoring terminals are synchronized with the central server, with time deviations controlled within 100 milliseconds. When timestamps are missing in load change data for a certain area due to network latency or equipment failure, linear interpolation is used to fill in the missing data. The construction method of the power operation timing matrix is ​​similar to that of the load change timing matrix, except that the matrix elements record the actual operating power values ​​of each device rather than the magnitude of change.

[0073] For example, the calculation of the Pearson correlation coefficient involves the covariance and standard deviation of two time series. For a load change series in region A and a power series in region B, the average of the two series is first calculated. Then, the sum of the products of the deviations at each time point is calculated and divided by the series length to obtain the covariance. The standard deviations of the two series are calculated separately, and the product of the covariance and the two standard deviations is the Pearson correlation coefficient. The correlation coefficient ranges from -1 to 1, and the closer the absolute value is to 1, the stronger the linear correlation between the two series. When the correlation coefficient is positive, it indicates that the load changes in the two regions are positively correlated, that is, when the load in one region increases, the power in the other region also increases; a negative value indicates a negative correlation.

[0074] It's important to note that time delay correction is a crucial step in identifying the propagation characteristics of cross-regional impacts. In a hotel's low-voltage electrical system, when the audio and lighting equipment in a conference room is turned on simultaneously, causing a surge in load in that area, the air conditioning systems in adjacent guest room areas may automatically reduce their operating power after a few seconds to maintain overall load balance. This response is not instantaneous but involves a time delay. Within a preset time window, the time-series data of a region is progressively shifted forward or backward, one time unit at a time, and the correlation coefficient is recalculated after the shift. The time window is typically set to 5 minutes forward and backward to cover the possible delay range. When the absolute value of the correlation coefficient at a certain offset position reaches its maximum, that offset is the actual response delay time.

[0075] Preferably, the calculation of load correlation strength comprehensively considers both correlation strength and response timeliness. The basic correlation value directly uses the absolute value of the correlation coefficient, reflecting the degree of correlation between load changes in two regions. The impact of response delay on correlation strength is determined through a threshold judgment. When the response delay is less than a preset threshold, such as 2 minutes, the influence between regions is considered to propagate rapidly, and the basic correlation value remains unchanged. When the response delay is between 2 and 5 minutes, the correlation value is reduced linearly, with a larger reduction for longer delays. When the response delay exceeds 5 minutes, the correlation between regions is considered weak, and the correlation value drops to below 30% of the basic value. This approach reflects the importance of real-time performance in influencing cross-regional energy consumption.

[0076] In one possible implementation, the load correlation strength matrix is ​​a symmetric matrix where rows and columns represent different functional areas. Diagonal elements represent the correlation strength within each area, typically set to 1. Off-diagonal elements represent the correlation strength between different areas, ranging from 0 to 1. By traversing all off-diagonal elements of the matrix, locations exceeding a preset threshold are identified. The threshold is determined based on the hotel's operational characteristics, typically set to 0.6, indicating that a significant cross-regional impact is considered present when the correlation strength between two areas exceeds 60%. Furthermore, the determination of cross-regional energy imbalance relies not only on a single threshold but also on the duration and scope of the imbalance. When the correlation strength between a pair of areas exceeds the threshold and lasts for more than 10 minutes, it is considered a persistent imbalance; when multiple pairs of areas simultaneously exhibit correlations exceeding the threshold, it is considered a widespread imbalance. The location information, correlation strength values, start time, and duration of all imbalanced pairs are recorded to generate a cross-regional energy imbalance status report.

[0077] For example, during a large conference held at the hotel, all the lighting, sound, and projection equipment in the second-floor conference hall were turned on, causing a surge in power demand. Time-series correlation analysis revealed that the air conditioning power in the guest room areas on floors 3 to 5 generally decreased by 20% within three minutes of the conference starting, with a correlation coefficient of -0.75 and a load correlation strength of 0.68, exceeding the set threshold. Simultaneously, the lighting brightness in the lobby on the first floor also decreased, with a correlation strength of 0.62. Based on this, it was determined that there was a cross-regional energy consumption imbalance between the conference hall, guest room areas, and lobby area. The identification of this imbalance provided data for subsequent power allocation optimization.

[0078] Understandably, determining cross-regional energy imbalances also requires considering the priority of regional functions. In hotel operations, certain areas have higher priority in terms of electricity demand, such as fire protection systems, emergency lighting, and elevator systems. When energy imbalances occur between these high-priority areas and other areas, they are specifically marked to ensure that the normal operation of these areas is prioritized in subsequent resource allocation.

[0079] Step S103: Identify the severity of cross-regional energy consumption imbalance, extract similar load-related mode switching records from historical energy consumption data, group them according to time period and type of weak electrical equipment, and obtain a risk identification feature set.

[0080] Based on the correlation strength value and the number of affected areas of cross-regional energy consumption imbalance, an imbalance severity index is obtained by multiplying the correlation strength value by the number of affected areas. Energy consumption records within a preset time period are retrieved from the hotel's low-voltage system historical database. Historical events with load correlation strength exceeding a preset threshold are filtered out. The regional load change sequence, equipment power sequence, and mode switching time at the time of each event are extracted to obtain a historical imbalance event dataset. This historical imbalance event dataset is then grouped according to the time period of the events, including breakfast time, meeting time, peak check-in time, and late-night time. It is further grouped according to the type of low-voltage equipment involved, including lighting equipment, air conditioning equipment, elevator equipment, monitoring equipment, and communication equipment. A two-dimensional grouping matrix of time and equipment is constructed, with the matrix elements representing the set of imbalance events within the corresponding group. By using the two-dimensional grouping matrix, common features of imbalance events within each group are extracted. These features include the maximum and minimum values ​​of load change amplitude, the number of power changes per unit time, the duration of mode switching, and the response delay between regions. The K-means clustering algorithm is used to classify the feature vectors and identify typical imbalance pattern types. The pattern type label, the range of each feature value, and the frequency of the pattern in historical data are combined to form a risk identification feature set.

[0081] For example, in one implementation, the severity index of the imbalance is derived by comprehensively assessing the imbalance characteristics across multiple dimensions. The correlation strength value reflects the degree of mutual influence between energy consumption regions, ranging from 0 to 1; the number of affected regions indicates the number of regions where energy consumption imbalance occurs simultaneously. The severity index obtained by multiplying the two values ​​simultaneously reflects both the intensity and scope of the imbalance; a larger index value indicates a more severe imbalance problem that requires priority attention.

[0082] Specifically, the construction of the historical imbalance event dataset relies on the hotel's low-voltage electrical system's historical database. The database stores minute-by-minute load data, equipment power data, and mode switching records for each area. All records within a preset time period are iterated through. When the load correlation strength at a certain moment exceeds a preset threshold of 0.6, the complete data for the 10 minutes before and after that moment is extracted as an imbalance event. Each event includes the event occurrence time, a list of affected areas, a sequence of load changes in each area, a sequence of power changes for each device, and the precise time of the mode switch.

[0083] For example, the construction of the two-dimensional grouping matrix of time and equipment involves dual classification. Breakfast time is defined as 6:00 to 10:00, meeting time as 9:00 to 17:00, peak check-in time as 15:00 to 21:00, and late-night time as 23:00 to 5:00 the next day. Equipment type classification includes lighting equipment such as downlights, chandeliers, and emergency lights; air conditioning equipment such as central air conditioning, split air conditioning, and fresh air units; and elevator equipment such as passenger elevators, freight elevators, and escalators. Each element of the matrix is ​​a set of imbalance events, containing all imbalance events caused by that type of equipment within that time period. Feature extraction is performed on the set of imbalance events within each group. The maximum and minimum values ​​of load change amplitude are obtained by traversing all load data in the event set; the number of power changes per unit time is calculated by the number of inflection points on the statistical power curve; the mode switching duration is the time interval from when the equipment switches to a stable state; and the inter-regional response delay is the time difference between a change in one region and a response in another region. These four features form a feature vector, which serves as the input to the K-means clustering algorithm.

[0084] Preferably, the K-means clustering algorithm sets the number of clusters to 5, corresponding to five pattern types: slight imbalance, local imbalance, regional imbalance, widespread imbalance, and severe imbalance. The algorithm iteratively optimizes the clustering process, grouping imbalance events with similar feature vectors into the same category. Each cluster center represents a typical imbalance pattern, and the range of all feature values ​​within the cluster constitutes the feature interval of that pattern. Furthermore, the risk identification feature set integrates the results of cluster analysis and statistical information. Each risk feature includes a pattern type label, the numerical range of four features, and the frequency of occurrence of that pattern in historical data. The frequency is obtained by dividing the number of imbalance events of that type by the total number of events, reflecting the probability of occurrence of that risk pattern.

[0085] Historical energy consumption imbalance events are divided into time dimensions according to each operating period. Various types of low-voltage equipment are classified and organized separately. Partial power outage events caused by equipment failures are extracted from the historical operation records of each type of equipment. Partial power outage events include service interruption events, system overload events caused by load surges, and partial power outage events caused by uneven power distribution between areas, forming a risk event library covering fault types and durations.

[0086] Historical energy imbalance events were categorized by time dimension according to the hotel's operating hours, including breakfast service, daytime meeting hours, peak dinner hours, and nighttime rest hours. Records of all imbalance events occurring within each time period were obtained, and the start and end times of each event, as well as the functional areas involved, were extracted to establish a time period-event mapping table. Based on this mapping table, low-voltage electrical equipment was categorized according to its functional attributes: lighting equipment, air conditioning and ventilation equipment, elevator transportation equipment, security monitoring equipment, and communication network equipment. Abnormal shutdown records were filtered from the historical operating logs of each type of equipment. These records included the equipment number, shutdown time, and recovery time. These abnormal shutdown records were used to identify partial power outage events caused by equipment failures. A service interruption event was identified when lighting equipment abnormally shut down and the power of other equipment in the same area decreased beyond a preset threshold. A system overload event was identified when multiple high-power devices started simultaneously, causing the power distribution switch to trip. A partial power outage event was identified when the power of equipment in a certain area was forcibly reduced while equipment in other areas operated normally. Based on the type of the partial power outage event, the duration of each event from the onset of equipment malfunction to full restoration of normal operation is calculated. The fault type identifier, event occurrence time, list of involved equipment, list of affected areas, and duration value are integrated to construct a risk event library covering fault type and duration.

[0087] For example, in one implementation, historical energy consumption imbalance events of the hotel's low-voltage electrical system are finely divided according to operating periods. Breakfast service hours are typically set from 6:00 AM to 10:00 AM, characterized by frequent use of restaurant lighting, kitchen equipment, and elevators; daytime meeting hours are from 9:00 AM to 5:00 PM, mainly characterized by concentrated loads on meeting room audio equipment, projection equipment, and air conditioning; the peak dinner period is from 5:00 PM to 9:00 PM, when the electricity demand of both the dining and guest room areas reaches its peak simultaneously; and the nighttime rest period is from 10:00 PM to 6:00 AM the next day, during which most public areas enter energy-saving mode. The time period-event mapping table adopts a two-dimensional data structure, with row indexes representing time periods and columns containing fields such as event number, start time, end time, and a list of affected areas.

[0088] Specifically, the classification and organization of low-voltage electrical equipment follows the principle of functional attributes. Lighting equipment includes guest room lighting, corridor lighting, lobby chandeliers, emergency lighting, and outdoor landscape lighting; air conditioning and ventilation equipment includes central air conditioning units, fan coil units, fresh air units, and exhaust fans; elevator transportation equipment includes passenger elevators, freight elevators, escalators, and dumbwaiters; security monitoring equipment includes cameras, access control systems, alarms, and fire detectors; and communication network equipment includes switches, routers, wireless access points, and telephone exchanges. Each type of equipment has an independent operation log file, recording information such as equipment start-up and shutdown times, operating parameters, and error codes.

[0089] For example, the filtering of abnormal shutdown records is achieved through multi-condition filtering. First, the equipment operation log is read to identify records where the status abruptly changes from "running" to "stopped". Then, it checks whether there were any abnormal alarm signals before the shutdown, such as overcurrent protection, overheat protection, or communication interruption. For each abnormal shutdown record, the system extracts the equipment number to locate the specific equipment, the shutdown time is accurate to the second for time series analysis, and the recovery time is used to calculate the shutdown duration. These records are arranged in chronological order to form a time series of equipment failures.

[0090] It should be noted that the identification of partial power outage events involves complex judgment logic. The criteria for determining a service interruption event include: lighting equipment suddenly shutting down at an unplanned time, while the power of other electrical equipment in the area, such as air conditioners and sockets, drops by more than 50% of its rated power within 5 seconds, and this state persists for more than 1 minute. This situation typically occurs when there is a fault in the power distribution line or a circuit breaker malfunctions, causing a power outage in the entire area. Overload events are determined based on current monitoring data. When the total current of the distribution cabinet exceeds 120% of the rated current, and the overcurrent protection device is triggered within 10 seconds, and multiple high-power devices such as elevators, air conditioner compressors, and kitchen equipment start simultaneously, it is determined to be a system overload caused by a load surge. Partial power outage events caused by uneven power distribution manifest as: the power of equipment in some non-critical areas is forcibly reduced to below 30% of its rated power by the management system, while critical areas such as fire protection systems and emergency lighting maintain normal power supply. This situation often occurs when the total load is close to the transformer capacity limit.

[0091] Preferably, when constructing the risk event database, multi-dimensional information is extracted for each power outage event. Fault type identification uses a coding method, such as "SI-001" for a service interruption event, "OL-002" for a system overload event, and "PD-003" for an uneven power distribution event. Precise start and end timestamps are recorded for the event's occurrence time period, facilitating subsequent time-period feature analysis. The equipment list details all affected equipment numbers, names, categories, and rated power. The affected area list marks all functional areas affected by the power outage, including floor numbers, room numbers, and area functional attributes. Furthermore, the duration calculation considers the recovery characteristics of different equipment. For lighting equipment, the duration is calculated from the moment of power outage until the lighting returns to normal brightness; for air conditioning equipment, due to thermal inertia, the duration extends until the indoor temperature returns to the set value; for elevator equipment, the duration includes downtime plus the time for readjustment and safety checks. This differentiated duration calculation method more accurately reflects the actual impact of power outage events on hotel operations.

[0092] In one possible implementation, the risk event database is stored using a relational data structure. The main table records basic event information, including event number, fault type, occurrence time, and duration; related tables store information about the involved equipment, affected areas, and processing records, respectively. This structure facilitates quick retrieval of specific types of historical events and supports multi-dimensional statistical analysis.

[0093] For example, a system overload event that occurred during a meeting was recorded in the risk event database as follows: the event number was "EV-20231015-042", the fault type was "OL-002", it occurred at 14:35:20 during the daytime meeting period, and involved the sound system, projection equipment and air conditioning units in the meeting hall on the 2nd floor. At the same time, the two passenger elevators on the 3rd floor and the air conditioning units in some guest rooms on the 4th floor were forced to operate at reduced load. The affected area included all meeting rooms on the 2nd floor and the guest room corridors on the 3rd to 5th floors, and the duration was 18 minutes.

[0094] Step S104: Obtain the peak load of the hotel's low-voltage system, perform statistical evaluation on the risk identification feature set, obtain the risk level assessment result, associate and match the risk level assessment result with the peak load of the low-voltage system to obtain a service interruption risk warning signal, and determine the resource allocation triggering condition based on the severity of cross-regional energy consumption imbalance.

[0095] Instantaneous power values ​​of each functional area are extracted from real-time monitoring data of the hotel's low-voltage electrical system. A moving average power value is calculated according to a preset time window, and the maximum power value within each time window is identified as a local peak. All local peaks are sorted, and values ​​above a preset percentile are selected as system load peaks. The time of occurrence, duration, and equipment category involved in each peak are recorded. For the system load peaks and risk identification feature set, the frequency of occurrence of each risk pattern under different load levels is statistically analyzed. The frequency is divided by the total number of events to obtain the occurrence frequency. Risk levels are classified according to the frequency value: frequencies exceeding a preset high threshold are marked as high risk, frequencies between the high and low thresholds are marked as medium risk, and frequencies below the low threshold are marked as low risk. This yields a risk level assessment result including the risk pattern type and corresponding level. By comparing the risk level assessment result with the system load peak, the currently monitored real-time load value is obtained. It is determined whether the real-time load has reached a preset percentage of the load peak. If it has, and the corresponding risk level is high risk, a service interruption risk warning signal is generated. The warning signal includes the warning level, the potentially affected area, and the expected duration of the impact. Based on the warning level of the service interruption risk warning signal and the severity value of cross-regional energy consumption imbalance, different levels of resource allocation trigger thresholds are set. When the severity value exceeds the first-level threshold, regular allocation is initiated; when it exceeds the second-level threshold, emergency allocation is initiated; and when it exceeds the third-level threshold, emergency protection is initiated. The power adjustment range and execution order corresponding to each level are determined to form the resource allocation trigger conditions.

[0096] For example, in one implementation, the peak load of the hotel's low-voltage system is acquired through real-time data stream processing. Instantaneous power values ​​for each functional area are collected once per second, including lighting power, air conditioning power, and elevator power in guest room floors, meeting areas, dining areas, and public areas. A preset time window is set to 5 minutes, with 300 sampling points within each window. The moving average is calculated using an exponentially weighted moving average method, with new data points having higher weights than historical data, enabling rapid response to power change trends. The maximum power value within each time window is identified; these local peaks reflect the load characteristics at different times.

[0097] Specifically, the sorting and selection process for local peak loads involves statistical analysis. Local peak loads across 288 time windows within a day are sorted in descending order, with a preset percentile of 90%, meaning the top 10% of values ​​after sorting are selected as the candidate set for system load peaks. The load peaks extracted from the candidate set include not only their numerical value but also the specific time of their occurrence, such as the start of a meeting at 10:30 AM or the peak check-in time at 3:00 PM. The duration is calculated by identifying periods where power values ​​remain above 90% of the peak value. The types of equipment involved are determined by analyzing the power share of each type of equipment at peak times; when the power share of a certain type of equipment exceeds 30%, it is marked as a major contributing device.

[0098] For example, the risk identification feature set includes pattern characteristics and statistical information of historical imbalance events. Each risk pattern corresponds to a specific imbalance scenario, such as regional overload caused by a peak conference period or cascading power outages caused by equipment failure late at night. The frequency of occurrence of each risk pattern under different load levels is statistically analyzed. Load levels are divided into low load range (0-40%), medium load range (40%-70%), and high load range (70%-100%) according to their percentage of transformer capacity. Within each load range, the frequency of occurrence of the risk pattern is calculated by dividing the frequency by the total operating time within that range to obtain the occurrence frequency per unit time.

[0099] It should be noted that the risk level classification uses a three-tier standard. High risk is defined as an occurrence frequency exceeding 0.1 times per hour, meaning such an imbalance event occurs on average once every 10 hours; medium risk is between 0.01 and 0.1 times per hour; and low risk is less than 0.01 times per hour. The risk level assessment results form a two-dimensional mapping table, with the horizontal axis representing the load level range and the vertical axis representing the risk pattern type. Each cell in the table is labeled with its corresponding risk level. This mapping relationship allows the system to quickly query the possible risk types and levels based on the current load level.

[0100] Preferably, the generation of service interruption risk warning signals is based on real-time monitoring and historical pattern matching. The current real-time load value is continuously acquired, and the ratio of real-time load to the system load peak is calculated every minute. When the ratio reaches a preset percentage, such as 85%, a warning preparation state is entered. At this time, the risk level assessment results are queried to identify all high-risk patterns corresponding to the current load level. The warning signal contains three elements: the warning level is determined according to the risk level, divided into Level 1, Level 2, and Level 3 warnings; the potentially affected areas are inferred from the impact range of similar historical events; and the expected impact duration is estimated based on the historical average duration of similar risk patterns. Furthermore, the severity value of cross-regional energy consumption imbalance is calculated by comprehensively considering multiple indicators. The severity value is equal to the weighted sum of three factors: correlation strength value, number of affected areas, and duration. The correlation strength value reflects the degree of mutual influence between regions, ranging from 0 to 1; the number of affected areas indicates the number of regions experiencing imbalance simultaneously; and the duration is in minutes. The weight coefficients of the three factors are set to 0.5, 0.3, and 0.2, respectively, to ensure that correlation strength plays a dominant role in the assessment.

[0101] In one possible implementation, the determination of resource allocation trigger conditions adopts a tiered response mechanism. A Level 1 threshold, corresponding to a severity value exceeding 2.0, triggers routine allocation. In this case, the system automatically reduces the air conditioning setpoints in non-critical areas such as staff rest rooms and storage rooms by 2 degrees Celsius and reduces corridor lighting brightness by 20%. This allocation has minimal impact on the hotel's normal operations. A Level 2 threshold, corresponding to a severity value exceeding 4.0, triggers emergency allocation, suspending some elevator operations, turning off landscape lighting, and restricting the simultaneous use of high-power kitchen equipment. A Level 3 threshold, corresponding to a severity value exceeding 6.0, activates the emergency backup mechanism, prioritizing power supply to the fire protection system, emergency lighting, and critical communication equipment, while other equipment is gradually restricted according to a preset priority order.

[0102] For example, during a large conference, the system detected that the real-time load reached 88% of the transformer capacity, exceeding the 85% peak load threshold. A risk assessment indicated that the risk of "overload in the conference area causing power outages on adjacent floors" was high under the current load level. A Level 1 warning signal was immediately generated, indicating that the conference area on the 2nd floor and guest room areas on the 3rd and 4th floors might be affected, with an estimated impact duration of 20 minutes. Simultaneously, the severity score was calculated to be 2.5, exceeding the Level 1 threshold, automatically triggering standard resource allocation measures to proactively mitigate potential imbalance risks. Through a tiered resource allocation strategy, the response intensity can be flexibly adjusted according to the severity of the imbalance, achieving dynamic optimization of energy allocation while ensuring critical services.

[0103] Step S105: Obtain the power adjustment range of the low-voltage system equipment, extract the range of the load surge of the low-voltage system by comparing historical data, and optimize the resource allocation ratio by combining the power adjustment range and resource allocation trigger conditions to obtain the adjusted power quota.

[0104] The system acquires the power adjustment range of various devices in the low-voltage system. The adjustable range for lighting equipment is from a preset lower limit to the rated power, and for air conditioning equipment, it is also from the preset lower limit to the rated power. Elevator equipment can be reduced to a preset percentage of its rated power during off-peak hours. Load change records within a preset time period are extracted from the historical database. Events where the load rises above a preset threshold in a short period are identified. The difference between the initial and peak values ​​of the load surge is calculated to obtain the numerical range of the load surge. Based on the numerical range of the load surge and the device power adjustment range, combined with the thresholds at various levels in the resource allocation trigger conditions, different power reduction ratios are assigned when the load surge value reaches different thresholds. The current power of the device is multiplied by the corresponding reduction ratio to obtain the power reduction amount. The reduction order is determined according to preset device category weights and area function weights. Using the power reduction amount and reduction order, power resources in each area are redistributed. The total available power in each area after adjustment is calculated. The available power is allocated proportionally according to device type and functional requirements. Fire protection equipment, emergency lighting, and communication equipment maintain a higher quota ratio, while landscape lighting and background music equipment have a lower quota ratio, resulting in an adjusted power quota that includes the upper limit of the power for each type of equipment in each area.

[0105] For example, in one implementation, the power adjustment range of the low-voltage system equipment is obtained based on the equipment's technical parameters and operating characteristics. Lighting equipment uses a dimmable driver, whose adjustment range is determined by the driver's minimum output power and rated power. The preset lower limit is typically 20% of the rated power, ensuring that the lighting brightness still meets basic visual needs. Air conditioning equipment uses frequency converter control to achieve power adjustment; the compressor frequency can be continuously adjusted within a preset range, with the lowest operating frequency corresponding to approximately 30% of the rated power. Elevator equipment reduces power consumption during off-peak periods by reducing the number of elevators in operation or lowering the operating speed.

[0106] Specifically, load surges are identified through statistical analysis of historical data. Load records within a preset time period are extracted from the database, each record containing a timestamp and the corresponding total load value. Historical data is traversed using a sliding time window, and when a sustained increase in load is detected across several consecutive sampling points exceeding a preset threshold, it is marked as a load surge event. The numerical range of load surges is obtained by calculating the difference between the initial load value and the peak load value for all surge events; this range reflects the intensity of the load impact the system may face.

[0107] For example, the power reduction ratio is determined based on the level of the resource allocation trigger condition. When the load surge reaches the first-level threshold, the corresponding reduction ratio is 10%; when it reaches the second-level threshold, the reduction ratio increases to 25%; and when it reaches the third-level threshold, the reduction ratio can reach 40%. Equipment category weights are set according to the importance of the equipment to hotel operations, with fire protection equipment having the highest weight, followed by emergency lighting and communication equipment, and landscape lighting and background music equipment having the lowest weight. Area function weights consider the service priority of different areas, with guest room areas and meeting areas having a higher weight than back-of-house areas.

[0108] It should be noted that the redistribution of power resources adopts a proportional allocation method. First, the total available power in each area after reduction is calculated, which equals the original quota minus the reduction. Then, the allocation ratio is determined according to the importance of equipment type: fire-fighting equipment maintains 100% power quota to ensure safety, emergency lighting maintains more than 80% quota to meet evacuation needs, and communication equipment maintains 70% quota to maintain basic communication functions. Non-critical equipment such as landscape lighting can be reduced to 30% quota, and background music systems can be temporarily turned off.

[0109] Preferably, the adjusted power quotas form a quota table, which records the upper limit of power for various types of equipment in each area. These upper limits are sent as control commands to the power distribution controllers in each area, and the controllers automatically adjust the operating parameters of the equipment according to the quotas. When the actual power approaches the upper limit of the quota, the controller will reduce the operating intensity of the equipment or shut down some equipment to ensure that the quota is not exceeded, thereby realizing dynamic power management of the hotel's low-voltage system.

[0110] Step S106: Send equipment power adjustment instructions to the low-voltage management system according to the adjusted power quota, and implement upper limit constraints in combination with the power consumption of lighting system, air conditioning system, elevator operation, and security equipment. At the same time, adjust the operating power of low-voltage equipment in each area according to the equipment power adjustment instructions to restore the balance between areas.

[0111] Based on the adjusted power quota, equipment power adjustment instructions are constructed. These instructions include the target equipment number, current power value, target power value, and adjustment time limit. These instructions are sent to the power distribution controllers in each area through the low-voltage management system. The controllers parse the instructions and identify the lighting, air conditioning, elevator, and security monitoring equipment under their jurisdiction. For each device, the power limit for the lighting system is set as a preset percentage of the rated power; the power limit for the air conditioning system is set as the maximum allowable power in cooling or heating mode; the power limit for elevator operation is set as the full-load operating power; and the power limit for security equipment is set as the power requirement when all functions are enabled. If the target power value exceeds the corresponding limit, it is adjusted to the upper limit value. Based on the adjusted target power value, the power distribution controllers send control parameters to each device, adjusting the output power of the lighting equipment, the operating frequency of the air conditioning equipment, the operating parameters of the elevator equipment, and the operating mode of the security equipment, thus obtaining the actual adjusted power value for each device. The real-time total power of each region is calculated using the actual power value. The power difference between different regions is compared. If the difference exceeds a preset threshold, the device power is fine-tuned. If the difference is within the threshold range, it is determined that the regions have reached a balanced state. When the power fluctuation does not exceed the allowable range within a preset time period, the balance between regions is confirmed to be restored.

[0112] For example, in one implementation, the power adjustment commands of the hotel's low-voltage electrical management system adopt a structured data format. Each command contains four key fields: the target device number uses a hierarchical coding method, with the first two digits representing the floor, the middle two digits representing the area, and the last four digits representing the specific device; the current power value is obtained through real-time acquisition with an accuracy of 0.1 kilowatts; the target power value is calculated based on the power quota; and the adjustment time limit is dynamically determined according to the device type and adjustment range, typically between 30 seconds and 5 minutes. Commands are transmitted to the power distribution controllers in each area via Ethernet or RS485 bus, using the industry-standard Modbus protocol to ensure the reliability and real-time performance of data transmission.

[0113] Specifically, the power distribution controller is the core execution unit for power regulation. Each controller manages all low-voltage electrical equipment within a functional area, and its built-in microprocessor is responsible for parsing received instructions and identifying target equipment within its area. The controller maintains an equipment inventory database, recording the addresses and models of the managed lighting equipment (including downlights, chandeliers, and emergency lights), the control interfaces of air conditioning equipment (including fan coil units and fresh air units), the connection methods of elevator equipment's operation control cabinets, and the power supply circuits of security monitoring equipment such as cameras and access control card readers. Upon receiving a regulation instruction, the controller selects the appropriate control method based on the equipment type.

[0114] For example, power limits are set considering the operating characteristics of the equipment and service requirements. The power limit for lighting systems is typically set between 80% and 100% of the rated power, with the specific value determined based on illuminance requirements. Public area lighting can be reduced to 80%, while guest room lighting remains above 90%. The maximum allowable power for air conditioning systems in cooling mode is the compressor's rated power plus the fan power; in heating mode, the electric heater power must also be added. The system automatically switches the upper limit value according to the season. The full-load operating power of elevators includes the sum of the traction machine power, control system power, and car lighting power. This upper limit ensures that the elevator can still operate normally under full load. The full-function power of security equipment includes the maximum power consumption of cameras, infrared supplementary lighting power, pan / tilt rotation power, etc., ensuring that all security functions can operate normally at night or in emergency situations.

[0115] It should be noted that the method of power regulation varies depending on the type of equipment. Lighting equipment adjusts its output power through SCR dimmers or PWM dimming drivers. The controller sends an analog or digital dimming signal of 0-10V, and the driver linearly adjusts the output voltage or duty cycle according to the signal strength. Air conditioning equipment's inverter receives a frequency setpoint and adjusts the compressor speed by changing the output frequency. The frequency is continuously adjustable from 25Hz to 50Hz, corresponding to different cooling capacities and power consumption. Elevator control cabinets receive operating parameter adjustment commands and can limit the elevator's speed, acceleration, or load capacity, thereby reducing operating power. Security equipment controls power consumption by adjusting operating modes, such as reducing the camera's frame rate from 30fps to 15fps, disabling unnecessary image processing functions, and reducing the brightness of infrared supplementary lights.

[0116] Preferably, during power adjustment, the system monitors the response of each device in real time. The controller collects the actual power value of the device every second and compares it with the target power value. If the deviation between the actual power and the target power exceeds 5%, the controller will perform a secondary adjustment, fine-tuning the control parameters until the target value is reached. For devices with slower response, such as air conditioning systems, a longer adjustment time is allowed, usually completed within 3-5 minutes; for devices with faster response, such as lighting systems, the target power must be reached within 30 seconds. Furthermore, the determination of inter-area balance adopts a multi-index comprehensive evaluation method. First, the real-time total power of each area is calculated, which is the sum of the actual power of all devices in that area. Then, the power difference between adjacent areas is calculated, such as the difference between the guest room area and the corridor area, or the meeting area and the catering area. When the power difference between all adjacent areas is less than a preset threshold, a preliminary balance is determined. The stability of this balance is continuously monitored. If the power fluctuation does not exceed ±3% of the allowable range within 5 consecutive minutes, the balance between areas is finally confirmed.

[0117] In one possible implementation, if the power difference in certain areas consistently fails to fall below a threshold during the adjustment process, the system will activate a fine-tuning mode. In this mode, the specific devices causing the imbalance are analyzed, and their power allocation is adjusted accordingly.

[0118] For example, if a high-powered projection device in the conference room causes excessive power consumption in that area, the system may appropriately lower the set temperature of the air conditioner in that area or reduce the brightness of the decorative lighting, achieving regional balance through coordinated adjustment of multiple devices.

[0119] Understandably, this power quota-based equipment regulation mechanism enables intelligent management of the hotel's low-voltage electrical system.

[0120] Step S107: Identify the power changes of low-voltage equipment at the time of mode switching to restore inter-regional balance, evaluate the power change trend to obtain the operating efficiency of the low-voltage system and the regional energy consumption balance, and combine the service interruption risk warning signal to ensure the continuity of hotel operation services.

[0121] The system monitors the power data of equipment in each area of ​​the hotel's low-voltage electrical system. When the power difference between areas gradually decreases from exceeding a preset threshold to below the threshold, this moment is recorded as the mode switching moment for restoring balance. The power change sequence of various low-voltage electrical devices within a preset time period before and after this moment is obtained, and the power adjustment amount of lighting equipment, air conditioning equipment, and elevator equipment from an unbalanced state to a balanced state is calculated. Based on the power adjustment amount and adjustment time, the power change rate is calculated. A linear regression method is used to fit the power change rate data to obtain the power change trend. The system recovery speed is judged by the trend slope. The ratio of actual operating power to rated power at each moment is calculated to obtain the low-voltage system operating efficiency. The deviation of power in each area relative to the area average power is calculated, and the standard deviation of the deviation is obtained to obtain the area energy consumption balance. Using the operating efficiency and area energy consumption balance, combined with the warning level of the service interruption risk warning signal, if the warning level is high and the balance is below the preset threshold, the power recovery time interval is increased and the recovery rate is decreased. If the warning level is low and the balance is above the preset threshold, the power recovery time interval is shortened and the recovery rate is increased. By adjusting the recovery rate, the continuity of hotel operation services is ensured.

[0122] For example, in one implementation, the identification of the hotel's low-voltage system's restoration of balance is based on real-time power monitoring data. The power difference between each area is continuously calculated. When the power difference between the guest room area and the meeting area, and the power difference between the dining area and the public area, gradually decreases from exceeding a preset threshold, it indicates a transition from an unbalanced state to a balanced state. When the power difference between all areas drops below the threshold and remains stable for more than 30 seconds, the system records this moment as the mode switching moment for restoring balance.

[0123] Specifically, the calculation of power adjustment involves comparing data from multiple time points. Power data is extracted for 5 minutes before and after the mode switch, forming a power change sequence. For lighting equipment, the adjustment amount required to restore normal lighting power from the reduced power during the imbalance period is calculated; for air conditioning equipment, the adjustment amount required to restore the power required to reach a comfortable temperature from the limited operating power is calculated; for elevator equipment, the power increment required to restore the elevator from reduced load operation to full speed operation is calculated. These adjustments reflect the changes in power demand of various types of equipment during the recovery process.

[0124] For example, linear regression is used to analyze power change trends. The rate of power change is used as the dependent variable, and time as the independent variable. A regression line is obtained by fitting the data using the least squares method. The slope of the regression line characterizes the system's recovery speed; a positive slope indicates that power is increasing, and a larger absolute value of the slope indicates faster recovery. Operating efficiency is obtained by calculating the ratio of actual operating power to the equipment's rated power at each moment. The closer the ratio is to 1, the closer the equipment is to full-load operation.

[0125] It should be noted that the calculation of regional energy balance uses statistical methods. First, the average power of each region is calculated, and then the deviation of each region's power from the average value is calculated. By obtaining the standard deviation of all deviations, a balance index reflecting the uniformity of power distribution among regions is obtained. The smaller the standard deviation, the more balanced the power distribution among regions, and the higher the balance.

[0126] Preferably, the recovery rate is determined based on a combination of risk warning level and balance. When the warning level is high risk and the balance is below the threshold, the current state is considered fragile, and a slow recovery strategy is adopted, with the interval between each power adjustment extended to 5 minutes and the adjustment range limited to within 10%. When the warning level is low risk and the balance is above the threshold, the state is considered stable, and the recovery process can be accelerated, with the adjustment interval shortened to 1 minute and the adjustment range reaching 30%. Furthermore, this dynamic adjustment mechanism ensures the continuity of hotel services. Through real-time monitoring and evaluation, the normal power supply level of each area can be restored as quickly as possible while ensuring stable operation, allowing services such as room temperature, lighting brightness, and elevator operation to gradually return to normal standards, avoiding secondary imbalances or service interruptions caused by overly rapid recovery.

[0127] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for statistical analysis, monitoring, and management of energy consumption of low-voltage electrical equipment in hotels, characterized in that, include: By aggregating the power data of the hotel's low-voltage system equipment during the target time period, the power change trend of the hotel's low-voltage system equipment is obtained. Based on the power change trend, the switching frequency of the working mode of the low-voltage equipment in each area is identified, and the operating power of the low-voltage equipment in each floor and functional area at the switching time of the working mode of the low-voltage equipment in each area is obtained, thus determining the load change range of the equipment in each area. The load variation of equipment in each region is correlated with the operating power of each device in the weak current system in a time series analysis to obtain the load correlation strength. Based on the load correlation strength, the cross-regional energy consumption imbalance status is determined. Extract mode switching records related to the load correlation strength from historical energy consumption data, and then group them according to time period and type of weak current equipment to generate a risk identification feature set; The peak load of the hotel's low-voltage system is obtained, the risk identification feature set is statistically evaluated, the risk level evaluation result is associated and matched with the peak load, a service interruption risk warning signal is generated, and the resource allocation triggering conditions are determined based on the service interruption risk warning signal and the cross-regional energy consumption imbalance status. Based on the power adjustment range of the low-voltage system equipment and the resource allocation triggering conditions, an adjusted power quota is generated; According to the adjusted power quota, the system sends equipment power adjustment instructions to the low-voltage management system to adjust the operating power of low-voltage equipment in each area and restore the balance between areas. Identify the power changes of hotel low-voltage system equipment at the time of mode switching to restore inter-regional balance, evaluate the power change trend, and obtain the low-voltage system operating efficiency and regional energy consumption balance. The step of performing time-series correlation analysis between the load variation of equipment in each region and the operating power of each device in the low-voltage system to obtain the load correlation strength, and determining the cross-regional energy consumption imbalance state based on the load correlation strength, includes: The time series data of the load change amplitude of the equipment in each region are obtained, and after being aligned according to the preset time granularity, the load change value of each region at the same time is extracted. The operating power data of each device in the weak current system at the corresponding time are collected, and the load change time series matrix and the power operation time series matrix are constructed. A correlation coefficient matrix is ​​generated based on the load change timing matrix and the power operation timing matrix. The maximum correlation coefficient is calculated based on the offset position of the correlation coefficient matrix within the time window to determine the actual response delay. Based on the correlation coefficient matrix and the actual response delay, a load correlation strength matrix is ​​generated, and off-diagonal elements in the load correlation strength matrix that exceed a preset threshold are identified to determine the corresponding inter-regional energy consumption imbalance state.

2. The method for statistical analysis, monitoring, and management of energy consumption of low-voltage electrical equipment in hotels according to claim 1, characterized in that, The process involves aggregating data on the power consumption of the hotel's low-voltage system equipment during a target time period to obtain the power consumption trend. Based on this trend, the switching frequency of the operating modes of the low-voltage equipment in each area is identified. The operating power of the low-voltage equipment on each floor and in each functional area is then obtained at the switching times of the operating modes, and the load variation of the equipment in each area is determined. This includes: The real-time power data of lighting equipment, air conditioning equipment and elevator equipment on each floor of the hotel's low-voltage system are divided into breakfast time, meeting time and peak check-in time. The power average of each time period is calculated using a fixed time window, the power average of adjacent time periods is calculated, the power change time is identified based on the difference of the power average of adjacent time periods, and the instantaneous power value of each device at the power change time is obtained. Based on the instantaneous power value of the equipment at the moment of power change, the equipment is grouped into guest room area, conference area, catering area and public area. The power difference of each group of equipment before and after the moment of power change is calculated. The number of equipment groups that switch modes in each area is counted and used as the mode switching frequency. The total load change in each region is calculated by the difference between the mode switching frequency and the power of the corresponding equipment group, and the total load change in each region is divided by the total power of the equipment in the region to obtain the load change amplitude of the equipment in each region.

3. The method for statistical analysis, monitoring, and management of energy consumption of low-voltage electrical equipment in hotels according to claim 1, characterized in that, The process involves extracting mode switching records related to the load correlation strength from historical energy consumption data, and then grouping them according to time period and type of low-voltage equipment to generate a risk identification feature set, including: Based on the load correlation strength and the number of affected areas, calculate the imbalance severity index, filter out events whose load correlation strength exceeds a preset threshold from historical energy consumption data, extract the regional load change sequence, equipment power sequence and mode switching time when the event occurs, and generate a historical imbalance event dataset. For the aforementioned historical imbalance event dataset, it is grouped according to breakfast time, meeting time, peak check-in time and late night time, and further grouped according to lighting equipment, air conditioning equipment, elevator equipment, monitoring equipment and communication equipment to generate a two-dimensional grouping matrix of time and equipment; The risk identification feature set is generated by extracting the load change range, power change frequency, and mode switching duration of imbalance events within each group using the two-dimensional grouping matrix.

4. The method for statistical analysis, monitoring, and management of energy consumption of low-voltage electrical equipment in hotels according to claim 1, characterized in that, The process of obtaining the peak load of the hotel's low-voltage system, statistically evaluating the risk identification feature set, associating and matching the risk level evaluation results with the peak load, generating a service interruption risk warning signal, and determining resource allocation triggering conditions based on the service interruption risk warning signal and the cross-regional energy consumption imbalance status includes: After extracting the instantaneous power values ​​of each functional area from the real-time monitoring data of the hotel's low-voltage system, the maximum power value within each time window is taken as the local peak value and sorted. The value that is above the last preset percentile in the sort is selected as the load peak value. For the load peak and the risk identification feature set, the frequency of occurrence of risk patterns under different load levels is statistically analyzed, and high, medium and low risk levels are divided based on the frequency values ​​to generate the risk level assessment results. By comparing the risk level assessment result with the load peak, it is determined whether the real-time load has reached a preset percentage of the load peak. If it has, and the corresponding risk level is high risk, a service interruption risk warning signal is generated. Based on the service interruption risk warning signal and the cross-regional energy consumption imbalance status, a resource allocation trigger threshold is set, the power adjustment range and execution order are determined, and the resource allocation trigger condition is formed.

5. The method for statistical analysis, monitoring, and management of energy consumption of low-voltage electrical equipment in hotels according to claim 4, characterized in that, The process of setting a resource allocation trigger threshold based on the service interruption risk warning signal and the cross-regional energy consumption imbalance state, determining the power adjustment range and execution order, and forming the resource allocation trigger conditions includes: Based on the service interruption risk warning signal and the severity value of the cross-regional energy consumption imbalance, trigger thresholds for routine allocation, emergency allocation, and emergency support are set. Based on the trigger threshold, the power adjustment range of lighting equipment, air conditioning equipment, elevator equipment and security equipment in each area is determined, and the power adjustment execution order is generated according to the weight of equipment category and the weight of area function. By defining the power adjustment range and execution order, the resource allocation triggering conditions, which include the upper limit of the power of devices in each region, are generated.

6. The method for statistical analysis, monitoring, and management of energy consumption of low-voltage electrical equipment in hotels according to claim 1, characterized in that, The process of generating an adjusted power quota by combining the power adjustment range of the low-voltage system equipment and the resource allocation triggering conditions includes: The power reduction amount and power reduction sequence are determined based on the power adjustment range of the low-voltage system equipment and the resource allocation triggering conditions. By using the power reduction amount and power reduction order, the power resources of each region are redistributed, the total available power of each region after adjustment is calculated, and the total available power is allocated proportionally according to equipment type and functional requirements to obtain the adjusted power quota.

7. The method for statistical analysis, monitoring, and management of energy consumption of low-voltage electrical equipment in hotels according to claim 1, characterized in that, The step of sending equipment power adjustment instructions to the low-voltage management system according to the adjusted power quota, adjusting the operating power of low-voltage equipment in each area, and restoring the balance between areas includes: Based on the adjusted power quota, construct the equipment power adjustment instruction, which includes the target equipment number, the current power value, and the target power value; For each piece of equipment, set upper limits for the power consumption of the lighting system, the power consumption of the air conditioning system, the power consumption of the elevator operation, and the power consumption of the security equipment, and adjust the target power value of each piece of equipment to within the upper limit. Based on the adjusted target power value, control parameters are sent to each device to adjust the device operating power. The device power is then fine-tuned based on the power difference between regions to determine whether the balance between regions has been restored.

8. The method for statistical analysis, monitoring, and management of energy consumption of low-voltage electrical equipment in hotels according to claim 7, characterized in that, The process of sending control parameters to each device using the adjusted target power value, adjusting the device operating power, fine-tuning the device power based on the power difference between regions, and determining the restoration of inter-regional balance includes: The adjusted target power value is used to send control parameters to lighting equipment, air conditioning equipment, elevator equipment, and security equipment to adjust their operating power. Obtain the actual power value of each device after adjustment, calculate the real-time total power of each area, compare the power difference between areas, and determine that the balance between areas is restored when the power difference is within a preset threshold range and continues for a preset duration.

9. The method for statistical analysis, monitoring, and management of energy consumption of low-voltage electrical equipment in hotels according to claim 1, characterized in that, The process of identifying the power changes of the hotel's low-voltage system equipment at the time of mode switching during the restoration of inter-regional balance, evaluating the trend of these power changes, and obtaining the operating efficiency of the low-voltage system and the regional energy consumption balance includes: Monitor the power data of equipment in each area of ​​the hotel's low-voltage system, identify the mode switching time to restore balance, and calculate the power adjustment amount of lighting equipment, air conditioning equipment and elevator equipment from an unbalanced state to a balanced state. Based on the power adjustment amount and adjustment time, a linear regression method is used to fit the power change trend, and the power recovery speed of the system is determined by the trend slope. The operating efficiency of the low-voltage system can be obtained by calculating the ratio of the actual operating power to the rated power at each moment. Calculate the deviation of power in each region from the average power in the region, and obtain the standard deviation of the deviation to obtain the regional energy balance. The power recovery speed of the system is adjusted according to the operating efficiency of the low-voltage system and the regional energy consumption balance.

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