Energy consumption regulation method, device, lighting system, computer device, and storage medium

Abstract

The present application discloses an energy consumption regulation method, device, lighting system, computer device, and storage medium. The method comprises: determining target 5 energy consumption data and a target regulation period in response to an energy consumption restriction instruction for a target area; determining historical passenger flow data according to the current date and the target regulation period; performing energy consumption analysis based on the target energy consumption data and the historical passenger flow data to obtain an analysis result, wherein the analysis result is configured to characterize a corresponding 10 relationship between passenger flow and energy consumption that can meet the target energy consumption data; if the current time meets the target regulation period, determining current passenger flow data, and determining energy consumption regulation parameters based on the current passenger flow data and the analysis result; and controlling the operation of a target electrical appliance group in the target area based on the energy consumption regulation 15 parameters to dynamically control the operation of the target electrical appliance group. The method ensures that the energy consumption regulation of the target electrical appliance group does not affect customers' shopping experience, realizing balanced regulation between dynamic passenger flow and energy conservation. (Fig. 1) 32 20 25 22 07 27 20 A ug 2 02 5 A B S T R A C T 2 0 A u g 2 0 2 5 2 0 2 5 2 2 0 7 2 7 1 0 3 2

Description

This present application claims priority of Chinese patent application No. CN 202511073646.7 filed on July 31, 2025, the disclosures of which are incorporated herein by reference in its entirety. Field of Invention The present application relates to the technical field of intelligent control, and specifically relates to an energy consumption regulation method, a device, a lighting system, computer device, and a storage medium. Background of Invention In various modern business premises, lighting systems have evolved from a single basic lighting function to a composite tool that integrates atmosphere creation, commodity display, and user experience enhancement. For different areas, different lighting layouts need to be designed to improve customer experience. To match high-quality lighting, the required lighting energy consumption has increased simultaneously. However, the extensive energy consumption management of lighting systems has led to a huge waste of power resources. Summary of Invention According to a first aspect, the present application provides an energy consumption regulation method, the method comprises: determining target energy consumption data and a target 20 regulation period in response to an energy consumption restriction instruction for a target area; determining historical passenger flow data according to a current date and the target regulation period; performing energy consumption analysis based on the target energy consumption data and the historical passenger flow data to obtain an analysis result, wherein the analysis result is adapted to characterize a corresponding relationship between passenger flow and energy 25 consumption that can meet the target energy consumption data; if current time falls within the target regulation period: collecting data on a target group entering the target area to determine real-time entry data; determining a regional stay reference value corresponding to the target group based on a time-series interval period; marking the target group based on the real-time entry data to determine labeled data; adding the labeled data to a data pool and timing the 30 labeled data based on the regional stay reference value; removing the labeled data from the data pool if the timing of the labeled data counts to zero; determining current passenger flow data 1 2025220727   21 May 2026 by determining a cumulative count of labeled data remaining in the data pool based on the timeseries interval period; and determining energy consumption regulation parameters based on the current passenger flow data and the analysis result; controlling the operation of a target electrical appliance group in the target area based on the energy consumption regulation 5 parameters by periodically sending electrical appliance regulation instructions to corresponding target electrical appliances based on the energy consumption regulation parameters, causing the target electrical appliances to operate dynamically. In another optional embodiment, during the current time is within the target regulation period, the method further comprises: obtaining a first priority instruction, wherein the first priority 0 instruction is adapted to instruct to control the target electrical appliance group to execute a first regulation parameter in a first energy consumption period; determining first energy consumption data based on the first regulation parameter and the first energy consumption period; updating the target energy consumption data based on the first energy consumption data to obtain updated energy consumption data; feeding back the updated energy consumption data 15 to a target interface. In another optional embodiment, during the current time is within the target regulation period, the method further comprises: obtaining a second priority instruction, wherein the second priority instruction is adapted to instruct to control the target electrical appliance group to execute a second regulation parameter during a second energy consumption period; determining 20 second energy consumption data based on the second regulation parameter and the second energy consumption period; adjusting the target regulation period based on the second energy consumption period to obtain an adjusted target regulation period, wherein the adjusted target regulation period is adapted to determine the historical passenger flow data; adjusting the target energy consumption data based on the second energy consumption data to obtain adjusted target 25 energy consumption data, wherein the adjusted target energy consumption data is adapted for energy consumption analysis with the historical passenger flow data. In another optional embodiment, the step of determining historical passenger flow data according to the current date and the target regulation period, comprises: determining a query date range according to the current date, and determining a query time period range according 30 to the target regulation period; matching in a passenger flow database according to the query date range and the query time period range to determine the historical passenger flow data correspondingly, wherein the historical passenger flow data is characterized as time-series data. 2025220727   21 May 2026 In another optional embodiment, characterized in that, the method further comprises: forming candidate passenger flow data based on the current passenger flow data and corresponding collection time, time-series interval period and collection date; storing the candidate passenger flow data in the passenger flow database, wherein the candidate passenger flow data is adapted 5 to be determined as the historical passenger flow data through matching. In another optional embodiment, the step of determining the current passenger flow data comprises: collecting data on a target group entering the target area to determine real-time entry data; determining the current passenger flow data based on a time-series interval period and the real-time entry data. 0 In another optional embodiment, the step of determining the current passenger flow data based on the time-series interval period and the real-time entry data comprises: determining a regional stay reference value corresponding to the target group based on the time-series interval period; determining the current passenger flow data based on the real-time entry data and the regional stay reference value. 15 In another optional embodiment, the step of determining the regional stay reference value corresponding to the target group based on the time-series interval period comprises: determining a reference range corresponding to the target area, wherein the reference range comprises a plurality of reference regions; acquiring reference stay data of the target group in the reference regions based on the time-series interval period; determining the regional stay 20 reference value according to the reference stay data. In another optional embodiment, the step of determining the regional stay reference value according to the reference stay data comprises: determining influence factors of the reference regions relative to the target area; determining reference weights based on the influence factors; integrating the reference weights and the reference stay data to determine the regional stay 25 reference value. In another optional embodiment, the step of determining the regional stay reference value according to the reference stay data comprises: acquiring reference samples in the reference regions and target samples in the target area, wherein the reference samples correspond to the reference stay data, and the target samples correspond to the current passenger flow data; 30 performing model training based on the reference samples and the target samples to obtain a passenger flow stay prediction model; inputting the reference stay data and the current 2025220727   21 May 2026 passenger flow data into the passenger flow stay prediction model to obtain the regional stay reference value. In another optional embodiment, the step of determining the current passenger flow data based on the time-series interval period and the real-time entry data comprises: marking the target 5 group based on the real-time entry data to determine labeled data; adding the labeled data to a data pool and timing the labeled data based on the regional stay reference value; removing the labeled data from the data pool if the timing of the labeled data counts to zero; determining the current passenger flow data by accumulating the labeled data in the data pool based on the timeseries interval period. In another optional embodiment, the step of determining the current passenger flow data based on the time-series interval period and the real-time entry data comprises: determining accumulated real-time entry data over the time-series interval period as periodic entry data; collecting data on the target group leaving the target area to determine real-time exit data; determining accumulated real-time exit data over the time-series interval period as periodic exit 15 data; integrating the periodic entry data and the periodic exit data to obtain the current passenger flow data. In another optional embodiment, the step of performing energy consumption analysis based on the target energy consumption data and the historical passenger flow data to obtain the analysis result comprises: determining a mapping relationship table between the historical passenger 20 flow data and the energy consumption regulation parameters, and determining initial regulation parameters based on the mapping relationship table; adjusting the initial regulation parameters proportionally based on the target energy consumption data to obtain the analysis result. In another optional embodiment, the target electrical appliance group comprises a plurality of target electrical appliances, and correspondingly, the step of controlling the operation of the 25 target electrical appliance group in the target area based on the energy consumption regulation parameters comprises: periodically sending electrical appliance regulation instructions to corresponding target electrical appliances based on the energy consumption regulation parameters, causing the target electrical appliances to operate dynamically. In another optional embodiment, the method further comprising: determining actual energy 30 consumption data corresponding to the operation of the target electrical appliance group; displaying the actual energy consumption data on a target interface. 2025220727   21 May 2026 According to a second aspect, the present application provides an energy consumption regulation device, the device comprises: a response module, configured to determine target energy consumption data and a target regulation period in response to an energy consumption restriction instruction for a target area; a determination module, configured to determine historical passenger flow data according to a current date and the target regulation period; an analysis module, configured to perform energy consumption analysis based on the target energy consumption data and the historical passenger flow data to obtain an analysis result, wherein the analysis result is adapted to characterize a corresponding relationship between passenger flow and energy consumption that can meet the target energy consumption data; the determination module, further configured to, when a current time meets the target regulation period collect data on a target group entering the target area to determine real-time entry data; determine a regional stay reference value corresponding to the target group based on a timeseries interval period; mark the target group based on the real-time entry data to determine labeled data; add the labeled data to a data pool and time the labeled data based on the regional stay reference value; remove the labeled data from the data pool if the timing of the labeled data counts to zero; determine current passenger flow data by accumulating the labeled data in the data pool based on the time-series interval period; and determine energy consumption regulation parameters based on the current passenger flow data and the analysis result; and determine energy consumption regulation parameters based on the current passenger flow data and the analysis result; a control module, configured to control the operation of a target electrical appliance group in the target area based on the energy consumption regulation parameters by periodically sending electrical appliance regulation instructions to corresponding target electrical appliances based on the energy consumption regulation parameters, causing the target electrical appliances to operate dynamically. According to a third aspect, the present application provides a lighting system, comprising a control center and a target light fixture group, wherein the control center is communicatively connected to the target light fixture group; the control center comprises: a response module, configured to determine target energy consumption data and a target regulation period in response to an energy consumption restriction instruction for a target area; a determination module, configured to determine historical passenger flow data according to a current date and the target regulation period; an analysis module, configured to perform energy consumption analysis based on the target energy consumption data and the historical passenger flow data to obtain an analysis result, wherein the analysis result is adapted to characterize a corresponding relationship between passenger flow and energy consumption that can meet the target energy 5 2025220727   21 May 2026 consumption data; the determination module, further configured to, when a current time meets the target regulation period: collect data on a target group entering the target area to determine real-time entry data; determine a regional stay reference value corresponding to the target group based on a time-series interval period; mark the target group based on the real-time entry data 5 to determine labeled data; add the labeled data to a data pool and time the labeled data based on the regional stay reference value; remove the labeled data from the data pool if the timing of the labeled data counts to zero; determine current passenger flow data by accumulating the labeled data in the data pool based on the time-series interval period; and determine energy consumption regulation parameters based on the current passenger flow data and the analysis 0 result; a control module, configured to control the operation of a target light fixture group in the target area based on the energy consumption regulation parameters by periodically sending electrical appliance regulation instructions to corresponding target electrical appliances based on the energy consumption regulation parameters, causing the target electrical appliances to operate dynamically. 15 According to a fourth aspect, the present application provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively interconnected, the memory is configured to store computer instructions, and the processor is configured to execute the computer instructions to thereby perform the energy consumption regulation method according to any one of the first aspect or any corresponding embodiments 20 thereof. According to a fifth aspect, the present application provides a computer-readable storage medium, wherein computer instructions are stored on the computer-readable storage medium, and the computer instructions are adapted to cause a computer to perform the energy consumption regulation method according to any one of the first aspect or any corresponding 25 embodiments thereof. Technical effects of the present invention The energy consumption regulation method, device, lighting system, computer device, and storage medium provided in the embodiments of the present application, based on target energy consumption data, obtain the corresponding relationship between historical passenger flow data 30 and energy consumption that can meet the target energy consumption data through correlation analysis between energy consumption and passenger flow. Moreover, based on this corresponding relationship, the energy consumption regulation parameters corresponding to the 2025220727   21 May 2026 15 current passenger flow data are determined, and the operation of the target electrical appliance group in the target area is dynamically controlled based on the energy consumption regulation parameters. Overview on drawings Hereinafter, the invention will be disclosed with reference to the drawings and exemplary embodiments, from which further features, technical effects and problems to be solved will become apparent. In the drawings: Fig. 1 is a first schematic flowchart of an energy consumption regulation method according to an embodiment of the present application. Fig. 2 is a second schematic flowchart of an energy consumption regulation method according to an embodiment of the present application. Fig. 3 is a third schematic flowchart of an energy consumption regulation method according to an embodiment of the present application. Fig. 4 is a fourth schematic flowchart of an energy consumption regulation method according to an embodiment of the present application. Fig. 5 is a fifth schematic flowchart of an energy consumption regulation method according to an embodiment of the present application. Fig. 6 is a schematic flowchart of an energy consumption regulation method according to another embodiment of the present application. 20 Fig. 7 is a structural block diagram of an energy consumption regulation device according to an embodiment of the present application. Fig. 8 is a system block diagram of an energy consumption regulation device according to an embodiment of the present application. Fig. 9 is a schematic diagram of the hardware structure of computer device according to 25             an embodiment of the present application. Throughout the drawings, like reference numerals designated identical or substantially equivalent elements or groups of elements. Detailed description of preferred embodiments 2025220727   21 May 2026 15 In the following description, to illustrate rather than limit, specific details such as a particular system structure, and a technology are provided to make a thorough understanding of embodiments of this application. However, a person skilled in the art should know that this application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted, so that this application is described without being obscured by unnecessary details. Fig. 1 is a first schematic flowchart of an energy consumption regulation method according to an embodiment of the present application. Referring to Fig. 1, in a first aspect, an embodiment of the present application provides an energy consumption regulation method, which comprises: Operation 100: in response to an energy consumption restriction instruction for a target area, determining target energy consumption data and a target regulation period; Operation 200: determining historical passenger flow data according to the current date and the target regulation period; Operation 300: performing energy consumption analysis based on the target energy consumption data and the historical passenger flow data to obtain an analysis result, wherein the analysis result is adapted to characterize a corresponding relationship between passenger flow and energy consumption that can meet the target energy consumption data; Operation 400: if the current time meets the target regulation period, determining current 20 passenger flow data, and determining energy consumption regulation parameters based on the current passenger flow data and the analysis result; Operation 500: controlling the operation of a target electrical appliance group in the target area based on the energy consumption regulation parameters. The energy consumption regulation method provided in the embodiment of the present 25 application can obtain a corresponding relationship between historical passenger flow data and energy consumption that can meet the target energy consumption data through correlation analysis between energy consumption and passenger flow based on the target energy consumption data. Moreover, based on this corresponding relationship, energy consumption regulation parameters corresponding to the current passenger flow data are determined, and the 30 operation of the target electrical appliance group in the target area is dynamically controlled based on the energy consumption regulation parameters, so that the energy consumption regulation of the target electrical appliance group does not affect customers' shopping experience, realizing balanced regulation between dynamic passenger flow and energy conservation. This method is applied to control device with data processing functions, and is 5 suitable for target scenarios including business premises with dynamic passenger flow such as shopping malls, supermarkets, and stores, or public service facilities such as hospitals. 2025220727   21 May 2026 In Operation 100 of this method, according to the actual division of the target scenario, the target area may be the entire target scenario or a part of the target scenario. The energy consumption restriction instruction comes from a target object, wherein the target object is a user or an intelligent device. The energy consumption restriction instruction is adapted to instruct the control device to limit the energy consumption value of the target electrical appliance group within the target time period to the target energy consumption data. The energy consumption restriction instruction may directly carry the target energy consumption data and the target regulation period. Alternatively, the target energy consumption data and the target 15 regulation period preset by the target object and stored in advance in the control device may be used. In response to the energy consumption restriction instruction, the control device retrieves the stored target energy consumption data and target regulation period. The target energy consumption period may be one hour, one day, one week, or other periods set as needed. When the target area is a supermarket, the target energy consumption period may 20 be the opening hours of the supermarket, such as 8:00-22:00. It may also be a specific period within the opening hours of the supermarket, such as 8:00-12:00. The target energy consumption data may be characterized by means of ratio, range, value, etc. For example, when characterized by ratio, the target energy consumption data may be represented as an energy-saving ratio of 20% or a consumption ratio of 80%; when 25 characterized by a ratio range, the target energy consumption data may be represented as an energy-saving ratio of 20-30% or an energy consumption ratio of 70-80%; when characterized by a value, the target energy consumption data may be a specific electric energy value to be consumed in the target regulation period. When the target energy consumption data is a range value, the control device may select the middle value of the range for subsequent data 30 processing and calculation, or select two endpoint values for two calculations. In Operation 200 of this method, the historical passenger flow data is generally adapted to characterize passenger flow data within a period before the current date. The specific length of 2025220727   21 May 2026 the period may be preset. In a supermarket scenario, passenger flow collectors are usually turned on during opening hours, and the target regulation period may be the entire opening hours or a certain period within the opening hours. Therefore, it is necessary to determine passenger flow data by combining the current date and the target regulation period, so that both the date and period of the passenger flow data can correspond to the target energy consumption data. In Operation 300 of this method, the energy consumption analysis may be performed through preset template rules or a neural network model. The analysis result may be characterized by means of a correlation function, a curve graph, a line graph, etc., or by the analysis result output by the neural network model. The analysis result is adapted to characterize the corresponding relationship between passenger flow variation and energy consumption regulation when the historical passenger flow data can meet the target energy consumption data. For example, in the first time-series interval period, when the historical passenger flow data is A1, the corresponding energy consumption data is B1; in the second time-series interval period, when the historical passenger flow data is A2, the corresponding energy consumption data is B2, and so on, the following are similar and will not be repeated.. In Operation 400, when the current time is within the target regulation period, the control device collects and determines current passenger flow data through the passenger flow collector. The passenger flow collector is generally adapted to at least collect area entry data. The current passenger flow data is time-series data, adapted to characterize the corresponding value of the number of people staying in the target area within a set time-series interval period. The passenger flow data may be represented as a specific number of people or a proportion of people. For example, every 5 minutes, the data of customers staying in the target area within 5 minutes is determined. By integrating the current passenger flow data of the time-series interval period with the analysis result in real time, corresponding energy consumption regulation parameters can be determined. The energy consumption regulation parameters may be adapted to characterize the available energy consumption value or available energy consumption ratio in the next timeseries interval period. In Operation 500, the target electrical appliance group in the target area comprises but is not limited to: various types of light fixtures, power supplies, and other electrical appliances. Different types of target electrical appliances may be set with different energy consumption 10 2025220727   21 May 2026 allocation priorities, and energy consumption is allocated to the target electrical appliance group based on the available energy consumption value or available energy consumption ratio, thereby realizing dynamic energy consumption control of different electrical appliances in the target electrical appliance group. 5 In an optional embodiment, the method further comprises: first, determining actual energy consumption data corresponding to the operation of the target electrical appliance group; then, displaying the actual energy consumption data on a target interface. In practical application scenarios, since the energy consumption regulation throughout the target regulation period is based on historical passenger flow data as a reference, the current 0 passenger flow data may not be completely consistent with the historical passenger flow data. Therefore, the actual energy consumption data obtained from regulating the operation of the target electrical appliance group based on the analysis result and current passenger flow data may not be completely consistent with the target energy consumption data. Therefore, it is necessary to monitor the actual energy consumption generated by the operation 15 of the target electrical appliance group to determine the actual energy consumption data corresponding to the operation of the target electrical appliance group. The actual energy consumption data can also be characterized as an energy consumption ratio or a specific energy consumption value. Further, there can be multiple pieces of actual energy consumption data, including but not 20 limited to actual energy consumption data corresponding to a certain target electrical appliance, actual energy consumption data corresponding to the target regulation period, actual energy consumption data corresponding to a period of dates, etc. If needed, comparison information between the actual energy consumption data and the target energy consumption data can also be displayed. 25 In an optional embodiment, when the current time is within the target regulation period, the method further comprises: first, obtaining a first priority instruction, wherein the first priority instruction is adapted to instruct controlling the target electrical appliance group to execute first regulation parameters within a first energy consumption period; then, determining first energy consumption data based on the first regulation parameters and the first energy consumption 30 period; then, updating the target energy consumption data based on the first energy consumption data to obtain updated energy consumption data; afterwards, feeding back the updated energy consumption data to the target interface. 2025220727   21 May 2026 In a practical implementation scenario, users can issue first priority instructions in real-time due to specific needs. For example, during specific periods such as holidays or maintenance time, it is necessary to set a first regulation period in real-time within the target regulation period. The regulation priority of the first regulation period is higher than that of the target energy consumption period. During the first regulation period, the control device regulates the target electrical appliance group according to the first regulation parameters provided by the user. The first regulation parameters may include but are not limited to: type parameters of target electrical appliances, regulation parameters of target electrical appliances, etc. For example, if the target regulation period is 8:00-22:00, and the user sets 17:00-19:00 as the first regulation period, during the first regulation period, the target light fixtures in the target electrical appliance group need to operate at 100% brightness. When the control device executes the first priority instruction, it instructs the target electrical appliance group to operate based on the first regulation parameters during the first regulation period. For other periods within the target energy consumption period, Operations 101 to 105 are executed. Furthermore, there may be a difference between the actual energy consumption data of the target electrical appliance group and the target energy consumption data. In this case, first energy consumption data can be determined based on the first regulation parameters and the first energy consumption period, wherein the first energy consumption data is adapted to characterize the energy consumption data corresponding to the target electrical appliance group executing the first regulation parameters during the first energy consumption period. By integrating the first energy consumption data and the target energy consumption data, updated energy consumption data can be obtained, which is adapted to characterize the theoretical energy consumption data that needs to be consumed after adding the first priority instruction within the target energy consumption period. Integration methods comprise but are not limited to direct addition, or subtracting the theoretical energy consumption data of the first regulation period from the target regulation period and then adding it to the energy consumption data of the first regulation period. The updated energy consumption data is fed back to the target interface, which can be the display screen of the control device or various intelligent terminals communicatively connected to the control device, such as mobile phones, tablets, wearable devices, etc. Users can learn about the updated energy consumption data from the target interface. In an optional embodiment, when the current time is within the target regulation period, the method further comprises: first, obtaining a second priority instruction, wherein the second 12 2025220727   21 May 2026 priority instruction is adapted to instruct controlling the target electrical appliance group to execute second regulation parameters within a second energy consumption period; then, determining second energy consumption data based on the second regulation parameters and the second energy consumption period; then, adjusting the target regulation period based on the second energy consumption period to obtain an adjusted target regulation period, wherein the adjusted target regulation period is configured to determine historical passenger flow data; afterwards, adjusting the target energy consumption data based on the second energy consumption data to obtain adjusted target energy consumption data, wherein the adjusted target energy consumption data is used for energy consumption analysis with historical passenger flow data. In another implementation scenario, the method can meet the requirement of adding a priority regulation period while ensuring that the control device still makes the operating energy consumption of the target electrical appliance group meet the target energy consumption data. It should be explained that the operating energy consumption of the target electrical appliance group meeting the target energy consumption data here means that the actual operating energy consumption is within the range corresponding to the target energy consumption data. Based on this, users may also issue second priority instructions. The first priority instruction and the second priority instruction can be distinguished by instruction identifiers; alternatively, the priority instruction issued before executing Operations 101 to 105 can be determined as the second priority instruction, and the priority instruction issued during the execution of Operations 101 to 105 can be determined as the first priority instruction; it is also possible to determine the type of priority instruction according to the proportion of the priority energy consumption data corresponding to the priority regulation parameters relative to the target energy consumption data and / or the proportion of the priority regulation period relative to the target regulation period. For example, if the proportion does not exceed a set value, the second priority instruction can be adopted; if the proportion exceeds the set value, the first priority instruction can be adopted. Under the second priority instruction, the control device determines second energy consumption data based on the second regulation parameters and the second energy consumption period. The adjusted target regulation period is determined by removing the second energy consumption period from the target regulation period, and the adjusted target energy consumption data is obtained by subtracting the second energy consumption data from the target energy consumption data. Historical passenger flow data corresponding to the 13 2025220727   21 May 2026 adjusted target regulation period is determined based on the adjusted target regulation period. Afterwards, Operations 103 to 105 are executed according to the adjusted target energy consumption data and the historical passenger flow data corresponding to the adjusted target regulation period to perform energy consumption analysis, so as to implement dynamic 5 regulation of the target electrical appliance group. In an optional embodiment, Operation 200 comprises: first, determining a query date range according to the current date, and determining a query time period range according to the target regulation period; then, matching in the passenger flow database according to the query date range and the query time period range to determine corresponding historical passenger flow 0 data, wherein the historical passenger flow data is characterized as time-series data. The query date range can be adaptively preset according to the storage capacity of the storage module of the control device and the calculation amount of data processing. For example, the query date range can be set as passenger flow data from the first half of the month before the current date. 15 Considering that the target regulation period can be adjusted, the corresponding period of the historical passenger flow data also needs to correspond to the target regulation period. For example, when the target regulation period is the daily opening hours, the historical passenger flow data corresponds to the same opening hours. When the target regulation period is a specific time period, such as 9:00-12:00, the historical passenger flow data is the passenger flow data 20 from 9:00-12:00 within the query date range of the current date. In an optional embodiment, the method further comprises: first, forming candidate passenger flow data based on the current passenger flow data and its corresponding collection time, timeseries interval period, and collection date; then, storing the candidate passenger flow data in the passenger flow database, wherein the candidate passenger flow data is configured to be 25 determined as historical passenger flow data through matching. In the embodiment of the present application, the collected and determined current passenger flow data, as well as its corresponding collection date, collection time, and time-series interval period, are determined as candidate passenger flow data and stored in the passenger flow database. The historical passenger flow data is obtained by filtering the candidate passenger 30 flow data through matching the query date range and query time period range with the collection date and collection time of the time-series data. 2025220727   21 May 2026 In another implementation scenario, the data storage capacity of the storage module may directly correspond to the collection date range, and the candidate passenger flow data corresponding to the current date may overwrite the candidate passenger flow data of the earliest day in the storage date, so that only the query time period range needs to be matched. 5 If no query time period range is set, all candidate passenger flow data can be directly processed, thus to facilitate data processing. In an optional embodiment, Operation 300 comprises: first, determining a mapping relationship table between historical passenger flow data and energy consumption regulation parameters, and determining initial regulation parameters based on the mapping relationship table; then, 0 performing proportional adjustment on the initial regulation parameters based on the target energy consumption data to obtain an analysis result. The energy consumption analysis in the embodiment of the present application may comprise constructing a mapping relationship based on historical passenger flow data and energy consumption regulation parameters. Generally, the larger the passenger flow corresponding to 15 the historical passenger flow data, the more energy consumption the target electrical appliance group needs. And initial regulation parameters corresponding to each time-series interval period of historical passenger flow data are obtained. The initial regulation parameters may be the energy consumption value that needs to be consumed corresponding to the historical passenger flow data of that time sequence. For convenience of calculation, the historical passenger flow 20 data can be averaged. Afterwards, proportional adjustment of energy consumption is performed on the initial regulation parameters based on the target energy consumption data, so that all regulation parameters integrated over the target regulation period meet the target energy consumption data, and an analysis result is obtained. The mapping relationship of the analysis result comprises 25 different historical passenger flow data corresponding to different or the same target regulation parameters, wherein the target regulation parameters are characterized as available energy consumption data corresponding to the historical passenger flow data. Fig. 2 is a second schematic flowchart of an energy consumption regulation method according to an embodiment of the present application. 30 Referring to Fig. 2, in an optional embodiment, in Operation 400, determining current passenger flow data comprises: 2025220727   21 May 2026 15 Operation 410: collecting target groups entering the target area to determine real-time entry data; Operation 420: determining current passenger flow data based on the time-series interval period and the real-time entry data. In Operation 410, the target group is configured to characterize people entering the target area, and the passenger flow collector can collect the number of people entering the store to obtain real-time entry data. In a specific scenario, the passenger flow collector is used to collect and accumulate the number of real-time entries; the control device reads the accumulated number from the passenger flow collector at each time-series interval period and resets the collector for re-counting, thereby obtaining corresponding real-time entry data. In Operation 420, the control device determines current passenger flow data based on the timeseries interval period and the real-time entry data. Fig. 3 is a third schematic flowchart of an energy consumption regulation method according to an embodiment of the present application. Fig. 4 is a fourth schematic flowchart of an energy consumption regulation method according to an embodiment of the present application. Referring to Fig. 3 and Fig. 4, in an optional embodiment, Operation 420, determining current passenger flow data based on the time-series interval period and the real-time entry data, comprises: Operation 421: determining a regional stay reference value corresponding to the target group 20 based on the time-series interval period, and which specifically comprises: Operation 4211: determining a reference range corresponding to the target area, wherein the reference range contains multiple reference regions. The reference range may be set as a specific range around the target area, and the reference regions may be business premises or public facilities within the reference range. For example, when the target area is a shopping 25 mall, the reference range can be the same business district or the same street, and the reference regions can be shopping malls, office buildings, cinemas, etc., within that business district or street. Operation 4212: acquiring reference stay data of the target group in the reference regions based on the time-series interval period. The reference stay data includes reference entry data and 30 reference exit data. The reference stay data can be obtained by subtracting the reference exit 2025220727   21 May 2026 data from the reference entry data. For instance, with a 15-minute time-series interval period, the number of entries and exits within 15 minutes is both detected, and by subtracting the two, the reference stay data corresponding to each reference region and different time-series interval period can be calculated. 5 Fig. 5 is a fifth schematic flowchart of an energy consumption regulation method according to an embodiment of the present application. Referring to Fig. 5, Operation 4213: determining a regional stay reference value according to the reference stay data, specifically comprising: Operation 42131: determining influence factors of the reference regions relative to the target 0 area. Influence factors may refer the reference region's own size, the specific time corresponding to the time-series interval period, distance from the target area, and type of the target area. These factors need to be quantified in terms of their correlation with the target area to obtain factor weights for different influence factors. It is understandable that the greater the correlation between an influence factor and the target area, the higher the factor weight, for 15 example, reference regions of the same type as the target area have higher factor weights than those of different types. Operation 42132: determining reference weights based on the influence factors. Specifically, integrating the factor weights for each reference region yields a reference weight for each reference region. Integration methods include but are not limited to addition, subtraction, 20 multiplication, and normalization. Operation 42133: integrating the reference weights and reference stay data to determine a stay reference value for a single reference region. Specifically, integrating the reference stay data and reference weight for the same reference region yields a regional stay impact value for that reference region. Similarly, integration methods include but are not limited to addition, 25 subtraction, multiplication, and normalization, which will not be repeated below. Operation 42134: integrating the stay reference values of all reference regions to obtain the regional stay reference value. Through this operation, the regional stay reference value can serve as a reference duration for the target group's stay in the target area. Depending on granularity requirements, regional stay reference values may be the same or different across 30 different time periods, i.e., one or more different regional stay reference values may exist within a single day. 2025220727   21 May 2026 15 Fig. 6 is a schematic flowchart of an energy consumption regulation method according to another embodiment of the present application. Referring to Fig. 6, in another optional embodiment, Operation 4213, determining a regional stay reference value according to the reference stay data, may also be predicted through a neural network model, and specifically comprises: Operation 42135: acquiring reference samples in the reference regions and target samples in the target area, wherein the reference samples correspond to the reference stay data, and the target samples correspond to the historical passenger flow data. Reference samples corresponding to the reference stay data and target samples corresponding to the historical passenger flow data are used as training samples. In the early stage, when data of the target area is insufficient, reference samples can serve as the main training samples; in the later stage, as the number of target samples in the target area increases, target samples can become the main training samples. Through this operation, the model can be updated at intervals, enabling it to flexibly adjust based on actual data samples. Operation 42136: performing model training based on the reference samples and target samples to obtain a passenger flow stay prediction model. The model can be a hybrid model of a long-short term memory network model and a time-series decomposition model. Specifically, the LSTM-Prophet hybrid model is trained using reference samples and target samples to obtain the passenger flow stay prediction model. 20 Operation 42137: inputting the reference stay data and current passenger flow data into the passenger flow stay prediction model to obtain a regional stay reference value. After obtaining the passenger flow stay prediction model, input the reference stay data and current passenger flow data into the model to yield a regional stay reference value corresponding to the input reference stay data and current passenger flow data. 25 Compared with the method for determining the regional stay reference value in Operations 42131 to 42134, the prediction method using the neural network model in Operations 42135 to 42137 achieves more accurate and flexible prediction results for the regional stay reference value. In a practical application scenario, depending on the operating hours of the target premises, either Operations 42131 to 42134 or Operations 42135 to 42137 can be adopted. 2025220727   21 May 2026 In Operation 422, current passenger flow data is determined based on the real-time entry data and the regional stay reference value. That is, the current passenger flow data of the target area at a specific time can be determined using the real-time entry data and the regional stay reference value. 5 Operation 422 specifically comprises: first, marking the target group based on the real-time entry data to determine label data; then, adding the label data to a data pool and timing the label data based on the regional stay reference value; afterwards, if the timing of the label data counts to zero, removing the label data from the data pool; then, determining the cumulative value of the label data in the data pool as current passenger flow data based on the time-series interval 0 period. For example, each time a customer enters the target area and is collected by the collector, the control device adds a label data to the data pool, e.g., 1, 2, 3.... Meanwhile, each label data is assigned a regional stay reference value and counts down based on this value, such as 1 (18 minutes and 30 seconds), 2 (18 minutes and 40 seconds), 3 (19 minutes and 00 seconds)... When 15 the timing of any label data counts to zero, that label data is removed from the data pool. For instance, when label data 1 shows 1 (0 minutes and 0 seconds), it is removed from the data pool, leaving label data 2 (10 seconds), 3 (30 seconds), etc. in the pool. By analogy, the control device queries the cumulative value of label data in the data pool at each time-series interval period and determines the current passenger flow data based on this cumulative value. 20 In an optional embodiment, Operation 420 comprises: first, determining the cumulative value of real-time entry data as periodic entry data based on the time-series interval period; then, collecting target groups leaving the target area to determine real-time exit data; then, determining the cumulative value of real-time exit data as periodic exit data based on the timeseries interval period; afterwards, integrating the periodic entry data and periodic exit data to 25 obtain current passenger flow data. In another implementation scenario, for convenient data collection, multiple passenger flow collectors can be installed to separately collect real-time entry data and real-time exit data, thereby obtaining periodic entry data and periodic exit data corresponding to the time-series interval period. Current passenger flow data for the corresponding time-series interval period 30 can be obtained simply by integrating the periodic entry data and periodic exit data. Fig. 7 is a structural block diagram of an energy consumption regulation device according to an embodiment of the present application. 2025220727   21 May 2026 Referring to Fig. 7, in a second aspect, an embodiment of the present application provides an energy consumption regulation device, which comprises: a response module 701, configured to respond to an energy consumption restriction instruction for a target area and determine target energy consumption data and a target regulation period; a determination module 702, configured to determine historical passenger flow data according to the current date and the target regulation period; an analysis module 703, configured to perform energy consumption analysis based on the target energy consumption data and the historical passenger flow data to obtain an analysis result, wherein the analysis result is configured to characterize a corresponding relationship between passenger flow and energy consumption that can meet the target energy consumption data; the determination module 702 is further configured to, when the current time meets the target regulation period, determine current passenger flow data and determine energy consumption regulation parameters based on the current passenger flow data and the analysis result; a control module 704, configured to control the operation of a target electrical appliance group in the target area based on the energy consumption regulation parameters. In an optional embodiment, the determination module 702 comprises: an acquisition submodule 7021, configured to acquire a first priority instruction, wherein the first priority instruction is used to instruct controlling the target electrical appliance group to execute first regulation parameters within a first energy consumption period; a determination sub-module 7022, configured to determine first energy consumption data based on the first regulation parameters and the first energy consumption period; an update sub-module 7023, configured to update the target energy consumption data based on the first energy consumption data to obtain updated energy consumption data; a feedback sub-module 7024, configured to feed back the updated energy consumption data to a target interface. In an optional embodiment, the acquisition sub-module 7021 is further configured to acquire a second priority instruction, wherein the second priority instruction is configured to instruct controlling the target electrical appliance group to execute second regulation parameters within a second energy consumption period; the determination sub-module 7022 is further configured to determine second energy consumption data based on the second regulation parameters and the second energy consumption period; and the determination module 702 further comprises: an adjustment sub-module 7025, configured to adjust the target regulation period based on the second energy consumption period to obtain an adjusted target regulation period, wherein the adjusted target regulation period is used to determine historical passenger flow data; the 20 2025220727   21 May 2026 adjustment sub-module 7025 is further configured to adjust the target energy consumption data based on the second energy consumption data to obtain adjusted target energy consumption data, wherein the adjusted target energy consumption data is used for energy consumption analysis with historical passenger flow data. 5 In an optional embodiment, the determination sub-module 7022 is configured to determine a query date range according to the current date and determine a query time period range according to the target regulation period; the determination module 702 further comprises a matching sub-module 7026, configured to match in the passenger flow database according to the query date range and the query time period range to determine corresponding historical 0 passenger flow data, wherein the historical passenger flow data is characterized as time-series data. In an optional embodiment, the device further comprises: a formation module 705, configured to form candidate passenger flow data based on current passenger flow data and its corresponding collection time, time-series interval period, and collection date; a storage module 15   706, configured to store the candidate passenger flow data in the passenger flow database, wherein the candidate passenger flow data is used to be determined as historical passenger flow data through matching. In an optional embodiment, the determination module 702 comprises: a collection sub-module 7027, configured to collect target groups entering the target area to determine real-time entry 20 data; the determination sub-module 7022 is further configured to determine current passenger flow data based on the time-series interval period and the real-time entry data. In an optional embodiment, the determination sub-module 7022 is further configured to: determine a regional stay reference value corresponding to the target group based on the timeseries interval period; and determine current passenger flow data based on the real-time entry 25 data and the regional stay reference value. In an optional embodiment, the determination sub-module 7022 is further configured to: determine a reference range corresponding to the target area, wherein the reference range contains multiple reference regions; acquire reference stay data of the target group in the reference regions based on the time-series interval period; and determine a regional stay 30 reference value according to the reference stay data. 2025220727   21 May 2026 In an optional embodiment, the determination sub-module 7022 is further configured to: determine influence factors of the reference regions relative to the target area; determine reference weights based on the influence factors; and integrate the reference weights and reference stay data to determine a regional stay reference value. 5 In an optional embodiment, the determination sub-module 7022 is further configured to: acquire reference samples in the reference regions and target samples in the target area, wherein the reference samples correspond to the reference stay data and the target samples correspond to the historical passenger flow data; perform model training based on the reference samples and target samples to obtain a passenger flow stay prediction model; and input the reference stay 0 data and current passenger flow data into the passenger flow stay prediction model to obtain a regional stay reference value. In an optional embodiment, the determination sub-module 7022 is further configured to: mark the target group based on the real-time entry data to determine label data; add the label data to a data pool and time the label data based on the regional stay reference value; if the timing of 15 the label data counts to zero, remove the label data from the data pool; and determine the cumulative value of the label data in the data pool as current passenger flow data based on the time-series interval period. In an optional embodiment, the determination sub-module 7022 is further configured to: determine the cumulative value of real-time entry data as periodic entry data based on the time- 20 series interval period; collect target groups leaving the target area to determine real-time exit data; determine the cumulative value of real-time exit data as periodic exit data based on the time-series interval period; and integrate the periodic entry data and periodic exit data to obtain current passenger flow data. In an optional embodiment, the analysis module 703 is further configured to: determine a 25 mapping relationship table between historical passenger flow data and energy consumption regulation parameters, and determine initial regulation parameters based on the mapping relationship table; and perform proportional adjustment on the initial regulation parameters based on the target energy consumption data to obtain an analysis result. In an optional embodiment, the target electrical appliance group contains multiple target 30 electrical appliances; correspondingly, the control module 704 is further configured to periodically send electrical appliance regulation instructions to corresponding target electrical 2025220727   21 May 2026 15 appliances based on the energy consumption regulation parameters, enabling dynamic operation of the target electrical appliances. The energy consumption regulation device in this embodiment is presented in the form of functional units. Herein, a "unit" refers to an ASIC (Application Specific Integrated Circuit) circuit, processors and memories executing one or more software or fixed programs, and / or other devices capable of providing the above-mentioned functions. Fig. 8 is a system block diagram of an energy consumption regulation device according to an embodiment of the present application. Referring to Fig. 8, in a third aspect, an embodiment of the present application provides a lighting system, which comprises a control center 810 and a target light fixture group 820, wherein the control center is communicatively connected to the target light fixture group; the control center 810 comprises: a response module 811, configured to respond to an energy consumption restriction instruction for a target area and determine target energy consumption data and a target regulation period; a determination module 812, configured to determine historical passenger flow data according to the current date and the target regulation period; an analysis module 813, configured to perform energy consumption analysis based on the target energy consumption data and the historical passenger flow data to obtain an analysis result, where the analysis result is used to characterize a corresponding relationship between passenger 20 flow and energy consumption that can meet the target energy consumption data; the determination module 812 is further configured to, when the current time meets the target regulation period, determine current passenger flow data and determine energy consumption regulation parameters based on the current passenger flow data and the analysis result; a control module 814, configured to control the operation of the target electrical appliance group 25 in the target area based on the energy consumption regulation parameters. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments above, and will not be repeated again here. In a fourth aspect, the present application provides a computer device, comprising: a memory and a processor, where the memory and the processor are communicatively interconnected, the 2025220727   21 May 2026 memory stores computer instructions, and the processor executes the computer instructions to thereby perform the energy consumption regulation method according to the first aspect or any corresponding implementation manner thereof. An embodiment of the present application further provides a computer device, which includes the energy consumption regulation device shown in Fig. 9 above. Please refer to Fig. 9, which is a schematic structural diagram of a computer device provided by an optional embodiment of the present application. As shown in Fig. 9, the computer device comprises: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. The various components are communicatively interconnected via different buses and may be mounted on a common motherboard or installed in other ways as needed. The processor may process instructions executed within the computer device, including instructions stored in or on the memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, multiple processors and / or multiple buses may be used together with multiple memories and multiple memories if needed. Similarly, multiple computer devices may be connected, with each device providing part of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Fig. 9 shows one processor 10 as an example. The processor 10 may be a central processing unit, a network processor, or a combination thereof. The processor 10 may further include a hardware chip. The above hardware chip may be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device may be a complex programmable logic device, a field-programmable gate array, a generic array logic, or any combination thereof. The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments. The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some optional implementations, the memory 20 may optionally include memories remotely 24 2025220727   21 May 2026 disposed relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. 5 The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive; the memory 20 may also include a combination of the above types of memories. The computer device further comprises an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30, and the output device 40 may be connected 0 via a bus or in other ways, with bus connection taken as an example in Fig. 9. The input device 30 may receive input digital or character information and generate key signal input related to user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary 15 lighting device (for example, an LED), a haptic feedback device (for example, a vibration motor), etc. The above display device includes but is not limited to a liquid crystal display, a light-emitting diode display, and a plasma display. In some optional implementations, the display device may be a touch screen. An embodiment of the present application further provides a computer-readable storage 20 medium. The method according to the embodiment of the present application may be implemented in hardware, firmware, or recorded in a storage medium, or implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded via a network to be stored in a local storage medium, so that the method described herein may be stored in such software processing on a storage 25 medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium may be a magnetic disk, an optical disc, a read-only memory, a random access memory, a flash memory, a hard disk, a solid-state drive, etc.; further, the storage medium may also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable 30 hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, processor, or hardware, the method shown in the above embodiments is implemented. 2025220727   21 May 2026 15 The foregoing embodiments are merely intended to describe the technical solutions of this application, but are not to limit this application. Although this application is described in detail with reference to the foregoing embodiments, a person of ordinary skill in the art should understand that they may still make modifications to the technical solutions described in the foregoing embodiments or make equivalent replacements to some technical features thereof, without departing from the scope of the technical solutions of embodiments of this application, and these modifications and replacements shall fall within the protection scope of this application. Throughout this specification and the claims which follow, unless the context requires otherwise, the word "comprise", and variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps. The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as, an acknowledgement or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

Claims

1. An energy consumption regulation method, the method comprising:determining target energy consumption data and a target regulation period in response to an energy consumption restriction instruction for a target area;5 determining historical passenger flow data according to a current date and the target regulation period;performing energy consumption analysis based on the target energy consumption data and the historical passenger flow data to obtain an analysis result, wherein the analysis result is adapted to characterize a corresponding relationship between passenger flow and energy consumption0 that can meet the target energy consumption data;if current time falls within the target regulation period:collecting data on a target group entering the target area to determine real-time entry data;determining a regional stay reference value corresponding to the target group15           based on a time-series interval period;marking the target group based on the real-time entry data to determine labeled data;adding the labeled data to a data pool and timing the labeled data based on the regional stay reference value;20                 counting down the timing of the labeled data based on the regional stayreference value;removing the labeled data from the data pool if the timing of the labeled data counts to zero;determining current passenger flow data by determining a cumulative count of25          labeled data remaining in the data pool based on the time-series interval period; anddetermining energy consumption regulation parameters based on the currentpassenger flow data and the analysis result;controlling the operation of a target electrical appliance group in the target area based on the energy consumption regulation parameters by periodically sending electrical appliance30 regulation instructions to corresponding target electrical appliances based on the energy consumption regulation parameters, causing the target electrical appliances to operate dynamically.2025220727   21 May 20262.     The method as claimed in claim 1, wherein, during the current time is within the targetregulation period, the method further comprises:obtaining a first priority instruction, wherein the first priority instruction is adapted to instruct to control the target electrical appliance group to execute a first regulation parameter in a first energy consumption period;determining first energy consumption data based on the first regulation parameter and the first energy consumption period;updating the target energy consumption data based on the first energy consumption data to obtain updated energy consumption data;feeding back the updated energy consumption data to a target interface.

3. The method as claimed in claim 2, wherein, during the current time is within the targetregulation period, the method further comprises:obtaining a second priority instruction, wherein the second priority instruction is adapted to15 instruct to control the target electrical appliance group to execute a second regulation parameter during a second energy consumption period;determining second energy consumption data based on the second regulation parameter and the second energy consumption period;adjusting the target regulation period based on the second energy consumption period to obtain20 an adjusted target regulation period, wherein the adjusted target regulation period is adapted to determine the historical passenger flow data;adjusting the target energy consumption data based on the second energy consumption data to obtain adjusted target energy consumption data, wherein the adjusted target energy consumption data is adapted for energy consumption analysis with the historical passenger flow25 data.

4. The method as claimed in any one of the preceding claims, wherein, the step ofdetermining historical passenger flow data according to the current date and the target regulation period, comprises:30 determining a query date range according to the current date, and determining a query time period range according to the target regulation period;matching in a passenger flow database according to the query date range and the query time period range to determine the historical passenger flow data correspondingly, wherein the historical passenger flow data is characterized as time-series data.2025220727   21 May 20265.     The method as claimed in claim 4, wherein, the method further comprises:forming candidate passenger flow data based on the current passenger flow data and corresponding collection time, time-series interval period and collection date;5 storing the candidate passenger flow data in the passenger flow database, wherein the candidate passenger flow data is adapted to be determined as the historical passenger flow data through matching.

6. The method as claimed in any one of the preceding claims, wherein, the step of0 determining the regional stay reference value corresponding to the target group based on the time-series interval period comprises:determining a reference range corresponding to the target area, wherein the reference range comprises a plurality of reference regions;acquiring reference stay data of the target group in the reference regions based on the time-15 series interval period;determining the regional stay reference value according to the reference stay data.

7. The method as claimed in claim 6, wherein, the step of determining the regional stayreference value according to the reference stay data comprises:20 determining influence factors of the reference regions relative to the target area;determining reference weights based on the influence factors;integrating the reference weights and the reference stay data to determine the regional stay reference value.25   8.     The method as claimed in claim 6, wherein, the step of determining the regional stayreference value according to the reference stay data comprises:acquiring reference samples in the reference regions and target samples in the target area, wherein the reference samples correspond to the reference stay data, and the target samples correspond to the current passenger flow data;30 performing model training based on the reference samples and the target samples to obtain a passenger flow stay prediction model;inputting the reference stay data and the current passenger flow data into the passenger flow stay prediction model to obtain the regional stay reference value.2025220727   21 May 20269.     The method as claimed in any one of the preceding claims, wherein, the step ofperforming energy consumption analysis based on the target energy consumption data and the historical passenger flow data to obtain the analysis result comprises:5 determining a mapping relationship table between the historical passenger flow data and the energy consumption regulation parameters, and determining initial regulation parameters based on the mapping relationship table;adjusting the initial regulation parameters proportionally based on the target energy consumption data to obtain the analysis result.

10. The method as claimed in any one of the preceding claims, wherein the method further comprises:determining actual energy consumption data corresponding to the operation of the target 15 electrical appliance group;displaying the actual energy consumption data on a target interface.

11. An energy consumption regulation device, wherein the device comprises:a response module, configured to determine target energy consumption data and a target 20 regulation period in response to an energy consumption restriction instruction for a target area;a determination module, configured to determine historical passenger flow data according to a current date and the target regulation period;an analysis module, configured to perform energy consumption analysis based on the target energy consumption data and the historical passenger flow data to obtain an analysis result, 25 wherein the analysis result is adapted to characterize a corresponding relationship between passenger flow and energy consumption that can meet the target energy consumption data;the determination module, further configured to, when a current time meets the target regulation period:collect data on a target group entering the target area to determine real-time30           entry data;determine a regional stay reference value corresponding to the target group based on a time-series interval period;mark the target group based on the real-time entry data to determine labeled data;2025220727   21 May 2026add the labeled data to a data pool and time the labeled data based on the regional stay reference value;remove the labeled data from the data pool if the timing of the labeled data counts to zero;5                 determine current passenger flow data by accumulating the labeled data in thedata pool based on the time-series interval period; anddetermine energy consumption regulation parameters based on the current passenger flow data and the analysis result;a control module, configured to control the operation of a target electrical appliance group in0 the target area based on the energy consumption regulation parameters by periodically sending electrical appliance regulation instructions to corresponding target electrical appliances based on the energy consumption regulation parameters, causing the target electrical appliances to operate dynamically.15   12. A lighting system comprising a control center and a target light fixture group, whereinthe control center is communicatively connected to the target light fixture group;the control center comprises:a response module, configured to determine target energy consumption data and a target regulation period in response to an energy consumption restriction instruction for a target area;20 a determination module, configured to determine historical passenger flow data according to a current date and the target regulation period;an analysis module, configured to perform energy consumption analysis based on the target energy consumption data and the historical passenger flow data to obtain an analysis result, wherein the analysis result is adapted to characterize a corresponding relationship between25 passenger flow and energy consumption that can meet the target energy consumption data;the determination module, further configured to, when a current time meets the target regulation period:collect data on a target group entering the target area to determine real-time entry data;30                 determine a regional stay reference value corresponding to the target groupbased on a time-series interval period;mark the target group based on the real-time entry data to determine labeled data;2025220727   21 May 2026add the labeled data to a data pool and time the labeled data based on the regional stay reference value;remove the labeled data from the data pool if the timing of the labeled data counts to zero;5                 determine current passenger flow data by accumulating the labeled data in thedata pool based on the time-series interval period; anddetermine energy consumption regulation parameters based on the current passenger flow data and the analysis result;a control module, configured to control the operation of a target light fixture group in the target 0 area based on the energy consumption regulation parameters by periodically sending light fixture regulation instructions to corresponding target light fixtures based on the energy consumption regulation parameters, causing the target light fixtures to operate dynamically.

13. A computer device comprising:15 a memory and a processor, wherein the memory and the processor are communicatively interconnected, the memory is configured to store computer instructions, and the processor is configured to execute the computer instructions to thereby perform the energy consumption regulation method according to any one of claims 1 to 10.20   14. A computer-readable storage medium, wherein computer instructions are stored on thecomputer-readable storage medium, and the computer instructions are adapted to cause a computer to perform the energy consumption regulation method according to any one of claims 1 to 10.

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