Intelligent air conditioner resource energy-saving control method and system

Abstract

The application provides an intelligent air conditioner resource energy-saving control method and system, wherein the method comprises the following steps: performing functional-based area division on the performance place to obtain different functional areas, wherein the different functional areas at least include a core audience area and a corridor passage area; obtaining different running stages of the performance place, wherein the different running stages at least include a pre-cooling stage, a warm-up stage, a peak stage and a dispersal stage; predicting the area predicted cooling load of each functional area in each running stage based on the obtained multi-terminal data of the performance place, and comparing the obtained area predicted cooling load with the area actual cooling load of the same functional area and running stage in real time; and in response to the cooling load difference between the area actual cooling load and the area predicted cooling load at any time, performing dynamic energy-saving temperature control of the corresponding running stage on the functional area. The application at least improves the resource energy-saving efficiency.

Description

Technical Field

[0001] This invention relates to data processing technology, and more particularly to an intelligent air conditioning resource energy-saving control method and system. Background Technology

[0002] With the booming development of the cultural and entertainment industry, the frequency and scale of large-scale concerts, sports events and other activities continue to rise. As a public space where people gather in large numbers, the energy consumption of the air conditioning system in concert venues has reached 60%-70% of the total energy consumption of the venue. This data not only reflects the core position of the air conditioning system in the operation of the venue, but also exposes huge potential for energy-saving optimization.

[0003] From an energy consumption perspective, the high energy consumption of concert venue air conditioning systems is closely related to their unique usage scenarios. On one hand, during large-scale events, the density of people in the venue can reach 1.5-2 people per square meter, and the heat dissipation and moisture loss from the human body is 3-5 times higher than usual. Combined with the instantaneous power of stage lighting equipment reaching megawatt levels, this leads to a sharp increase in the indoor sensible heat load. Actual measurement data from a large stadium shows that within one hour of the performance starting, the air conditioning load surged by 40% compared to the preparation stage, with heat dissipation from people and equipment accounting for over 60%. On the other hand, to meet the demands of stage effects, venues often employ high-ceiling designs, with floor heights generally exceeding 20 meters. The resulting vertical temperature gradient causes stratification of hot and cold air, making it difficult for traditional constant air volume (CAV) air conditioning systems to control precisely, resulting in 30%-40% ineffective energy consumption.

[0004] This high energy consumption not only brings significant economic costs—the electricity cost for air conditioning in a single event at a venue with a capacity of 10,000 people can reach 150,000 to 200,000 yuan—but also creates a severe environmental burden. For a venue that hosts an average of 200 events per year, the annual carbon emissions can reach 5,000 to 8,000 tons, equivalent to the annual emissions of 3,000 to 5,000 family cars.

[0005] Therefore, there is an urgent need for an intelligent air conditioning resource-saving control method and system that can optimize resource conservation. Summary of the Invention

[0006] In view of the above problems, the present invention is proposed to provide an intelligent air conditioning resource energy-saving control method and system that overcomes or at least partially solves the above problems.

[0007] According to one aspect of the present invention, an intelligent air conditioning resource energy-saving control method is provided, comprising the following steps:

[0008] The performance venue is divided into functional areas to obtain different functional areas, including at least an audience core area and a corridor area.

[0009] acquiring different running stages of the corresponding performance venue, wherein the different running stages at least include a pre-cooling stage, a warm-up stage, a peak stage, and a dispersal stage;

[0010] predicting a regional predicted cooling load of each functional area in each running stage based on the acquired multi-terminal data of the corresponding performance venue, and comparing the obtained regional predicted cooling load with a regional actual cooling load corresponding to the same functional area and running stage in real time;

[0011] in response to a cooling load difference between the regional actual cooling load and the regional predicted cooling load at any moment, performing dynamic energy-saving temperature control of the corresponding running stage for the functional area.

[0012] Optionally, in the method according to the present application, the different running stages of the corresponding performance venue are acquired, including:

[0013] determining different running stages and stage time periods corresponding to each running stage based on an activity flow table of the corresponding performance venue;

[0014] based on a comparison result of the current moment and the different stage time periods, determining a running stage corresponding to the current moment, and dynamically verifying the running stage based on real-time data acquisition of the corresponding functional area;

[0015] in response to a verification result being a stage deviation, determining a running stage corresponding to a next stage time period as an updated running stage based on the activity flow table.

[0016] Optionally, in the method according to the present application, the dynamic verification of the running stage based on the real-time data acquisition of the corresponding functional area includes:

[0017] in response to the running stage being a pre-cooling stage, determining a verification result as a stage deviation based on the acquired current ticketing total of the corresponding audience core area being greater than a preset ticketing total;

[0018] in response to the running stage being a warm-up stage, acquiring a regional heat load and a regional carbon concentration of the corresponding audience core area based on thermal imaging technology and breath detection technology, and determining a verification result as a stage deviation based on the regional heat load and the regional carbon concentration being greater than a preset heat load and a preset carbon concentration, respectively;

[0019] in response to the running stage being a peak stage, determining a verification result as a stage deviation based on the acquired regional traffic flow of the corridor passage area being greater than a preset traffic flow.

[0020] Optionally, in the method according to the present application, the regional predicted cooling load of each functional area in each running stage is predicted based on the acquired multi-terminal data of the corresponding performance venue, including:

[0021] A BIM model is established based on BIM data of a corresponding performance venue, and the BIM model is meshed to obtain each mesh unit;

[0022] Real-time equipment power, seat distribution data and real-time weather data output by a performance equipment terminal, a ticket terminal and a weather terminal corresponding to the performance venue are based on, and the real-time weather data and the determined unit personnel density and unit power density corresponding to each mesh unit form mesh prediction data;

[0023] Historical load data corresponding to different operating stages are obtained, and each load prediction model corresponding to different operating stages is obtained based on training of the historical load data;

[0024] Each functional area is determined to have a regional predicted cooling load in each operating stage based on each prediction result obtained by sequentially inputting each mesh prediction data into each load prediction model.

[0025] Optionally, in the method according to the present application, the method further comprises:

[0026] Media text data related to the performance venue output by a social media terminal is extracted based on, and a dressing type corresponding to the largest dressing proportion is determined as a target dressing based on the extraction result;

[0027] A suitable dressing is determined based on the regional predicted cooling load of any functional area in any operating stage, and the suitable dressing is compared with the target dressing;

[0028] In response to the existence of a dressing deviation between the suitable dressing and the target dressing, the regional predicted cooling load is updated based on a deviation degree corresponding to the dressing deviation.

[0029] Optionally, in the method according to the present application, determining each functional area to have a regional predicted cooling load in each operating stage based on each prediction result obtained by sequentially inputting each mesh prediction data into each load prediction model comprises:

[0030] Each mesh prediction data is input into a load prediction model corresponding to any operating stage to obtain a mesh predicted cooling load corresponding to the operating stage;

[0031] Air conditioner distribution data corresponding to the same functional area are obtained, and each mesh unit within the coverage range of an air conditioner in the same air conditioner equipment unit is determined to be an air conditioner radiation group based on the air conditioner distribution data;

[0032] Each mesh prediction cooling load corresponding to each mesh unit in the same air conditioner radiation group is subjected to mean value calculation, and all air conditioner predicted cooling loads corresponding to the same functional area obtained are determined to be the regional predicted cooling load in the operating stage.

[0033] Optionally, in the method according to the present application, in response to the fact that there is a cooling load difference between the actual cooling load of the region at any moment and the predicted cooling load of the region, dynamic energy-saving temperature control in the corresponding operation stage is performed on the functional region, including:

[0034] In response to the fact that the cooling load difference between the actual cooling load of the region at any moment and the predicted cooling load of the region is a positive difference, the positive difference is compared with a preset upper limit value, and in the case that the positive difference is less than the preset upper limit value, the air conditioning flow of the corresponding region is increased;

[0035] In response to the fact that the cooling load difference between the actual cooling load of the region at any moment and the predicted cooling load of the region is a negative difference, the negative difference is compared with a preset lower limit value, and in the case that the negative difference is less than the preset lower limit value, the air conditioning flow of the corresponding region is reduced.

[0036] According to still another aspect of the present application, an intelligent-based air conditioning resource energy-saving control system is provided, including:

[0037] The division module is configured to perform functional-based regional division on the performance venue to obtain different functional regions, at least including a core audience area and a corridor passage area;

[0038] The acquisition module is configured to acquire different operation stages of the corresponding performance venue, at least including a pre-cooling stage, a warm-up stage, a peak stage and a dispersal stage;

[0039] The comparison module is configured to predict the predicted cooling load of each functional region in each operation stage based on the acquired multi-terminal data of the corresponding performance venue, and compare the obtained predicted cooling load with the actual cooling load of the corresponding same functional region in the operation stage in real time;

[0040] The regulation and control module is configured to perform dynamic energy-saving temperature control in the corresponding operation stage on the functional region in response to the fact that there is a cooling load difference between the actual cooling load of the region at any moment and the predicted cooling load of the region.

[0041] Optionally, in the system according to the present application, the different operation stages of the corresponding performance venue are acquired, including:

[0042] The different operation stages and the stage time periods of each operation stage are determined based on the activity flow table of the corresponding performance venue;

[0043] Based on the comparison result of the current moment and the different stage time periods, the operation stage in which the current moment is located is determined, and the operation stage is dynamically verified based on the real-time data acquisition of the corresponding functional region;

[0044] In response to the verification result being stage deviation, determining, based on the activity flow table, an operation stage corresponding to a next stage period as an updated operation stage.

[0045] Optionally, in the system according to the present application, the operation stage is dynamically verified based on real-time data acquisition of the corresponding functional area, including:

[0046] In response to the operation stage being a pre-cooling stage, determining the verification result as stage deviation based on the obtained current total number of ticket checks of the corresponding audience core area being greater than a preset total number of ticket checks;

[0047] In response to the operation stage being a warm-up stage, obtaining, based on thermal imaging technology and breath detection technology, regional heat load and regional carbon concentration of the corresponding audience core area, and determining the verification result as stage deviation based on the regional heat load and the regional carbon concentration being greater than a preset heat load and a preset carbon concentration, respectively;

[0048] In response to the operation stage being a peak stage, determining the verification result as stage deviation based on the pressure sensor arranged in the corridor passage area determining that the regional traffic flow is greater than a preset traffic flow. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 A flow chart of an intelligent air conditioning resource energy-saving control method according to an embodiment of the present application is shown;

[0050] Figure 2 A structural block diagram of an intelligent air conditioning resource energy-saving control system according to another embodiment of the present application is shown. DETAILED DESCRIPTION

[0051] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0052] To solve the problems in the prior art, the inventors propose the present application. An embodiment of the present application provides an intelligent air conditioning resource energy-saving control method and system, which can be executed in a computing device.

[0053] According to an aspect of the present application, an intelligent air conditioning resource energy-saving control method is provided, including the following steps:

[0054] The performance venue is functionally divided into different functional areas, including at least an audience core area and a corridor passage area;

[0055] acquiring different running stages of the corresponding performance venue, wherein at least includes pre-cooling stage, warm-up stage, peak stage and off-stage stage;

[0056] predicting the regional predicted cooling load of each functional area in each running stage based on the acquired multi-terminal data of the corresponding performance venue, and comparing the obtained regional predicted cooling load with the regional actual cooling load corresponding to the same functional area and running stage in real time;

[0057] in response to the cooling load difference between the regional actual cooling load and the regional predicted cooling load at any time, performing dynamic energy-saving temperature control of the corresponding running stage for the functional area.

[0058] Optionally, in the method according to the present application, the different running stages of the corresponding performance venue are acquired, including:

[0059] determining different running stages and stage time periods corresponding to each running stage based on the activity flow table of the corresponding performance venue;

[0060] based on the comparison result of the current time and the different stage time periods, determining the running stage corresponding to the current time, and dynamically verifying the running stage based on the real-time data acquisition of the corresponding functional area;

[0061] in response to the verification result being stage deviation, determining the running stage corresponding to the next stage time period as the updated running stage based on the activity flow table.

[0062] Optionally, in the method according to the present application, the dynamic verification of the running stage based on the real-time data acquisition of the corresponding functional area includes:

[0063] in response to the running stage being the pre-cooling stage, determining the verification result as stage deviation based on the acquired current ticket checking total of the corresponding audience core area being greater than the preset ticket checking total;

[0064] in response to the running stage being the warm-up stage, acquiring the regional heat load and the regional carbon concentration of the corresponding audience core area based on the thermal imaging technology and the breath detection technology, and determining the verification result as stage deviation based on the regional heat load and the regional carbon concentration being greater than the preset heat load and the preset carbon concentration, respectively;

[0065] in response to the running stage being the peak stage, determining the verification result as stage deviation based on the acquired regional traffic flow of the corridor passage area being greater than the preset traffic flow.

[0066] Optionally, in the method according to the present application, the regional predicted cooling load of each functional area in each running stage is predicted based on the acquired multi-terminal data of the corresponding performance venue, including:

[0067] establishing a BIM model based on BIM data corresponding to the performance venue, and performing meshing processing on the BIM model to obtain each mesh unit;

[0068] based on real-time equipment power, seat distribution data and real-time weather data respectively output by performance equipment terminals, ticket terminals and weather terminals corresponding to the performance venue, and the real-time weather data and the determined unit personnel density and unit power density corresponding to each mesh unit, forming mesh prediction data;

[0069] obtaining historical load data corresponding to different operating stages, and training based on the historical load data to obtain each load prediction model corresponding to different operating stages;

[0070] based on each prediction result obtained by sequentially inputting each mesh prediction data into each load prediction model, determining the regional predicted cooling load of each functional area under each operating stage.

[0071] Optionally, in the method according to the present application, the method further comprises:

[0072] based on the media text data output by the social media terminal and related to the performance venue, extracting, and based on the extraction result, determining the dressing type corresponding to the largest dressing proportion as the target dressing;

[0073] based on the regional predicted cooling load of any functional area under any operating stage, determining adaptive dressing, and comparing the adaptive dressing with the target dressing;

[0074] in response to the existence of dressing deviation between the adaptive dressing and the target dressing, updating the regional predicted cooling load based on the deviation degree corresponding to the dressing deviation.

[0075] Optionally, in the method according to the present application, based on each prediction result obtained by sequentially inputting each mesh prediction data into each load prediction model, determining the regional predicted cooling load of each functional area under each operating stage, comprising:

[0076] inputting each mesh prediction data into the load prediction model corresponding to any operating stage to obtain the mesh predicted cooling load corresponding to the operating stage;

[0077] obtaining air conditioning distribution data corresponding to the same functional area, and based on the air conditioning distribution data, determining each mesh unit within the coverage range of the air conditioning equipment unit in the same air conditioning radiation group as an air conditioning radiation group;

[0078] performing mean calculation on each mesh predicted cooling load corresponding to each mesh unit located in the same air conditioning radiation group, and determining all air conditioning predicted cooling loads corresponding to the same functional area as the regional predicted cooling load under the operating stage.

[0079] Optionally, in the method according to the present application, in response to the fact that there is a cooling load difference between the actual cooling load of the region at any moment and the predicted cooling load of the region, dynamic energy-saving temperature control in the corresponding operation stage is performed on the functional region, including:

[0080] In response to the fact that the cooling load difference between the actual cooling load of the region at any moment and the predicted cooling load of the region is a positive difference, the positive difference is compared with a preset upper limit value, and in the case where the positive difference is less than the preset upper limit value, the air conditioning flow of the corresponding region is increased;

[0081] In response to the fact that the cooling load difference between the actual cooling load of the region at any moment and the predicted cooling load of the region is a negative difference, the negative difference is compared with a preset lower limit value, and in the case where the negative difference is less than the preset lower limit value, the air conditioning flow of the corresponding region is reduced.

[0082] According to still another aspect of the present application, an intelligent-based air conditioning resource energy-saving control system is provided, including:

[0083] The division module is configured to perform functional-based regional division on the performance venue to obtain different functional regions, at least including a core audience area and a corridor passage area;

[0084] The acquisition module is configured to acquire different operation stages of the corresponding performance venue, at least including a pre-cooling stage, a warm-up stage, a peak stage and a dispersal stage;

[0085] The comparison module is configured to predict the predicted cooling load of each functional region in each operation stage based on the acquired multi-terminal data of the corresponding performance venue, and to compare the obtained predicted cooling load with the actual cooling load of the corresponding same functional region in the operation stage in real time;

[0086] The regulation and control module is configured to perform dynamic energy-saving temperature control in the corresponding operation stage on the functional region in response to the fact that there is a cooling load difference between the actual cooling load of the region at any moment and the predicted cooling load of the region.

[0087] Optionally, in the system according to the present application, the different operation stages of the corresponding performance venue are acquired, including:

[0088] The different operation stages and the stage time periods of each operation stage are determined based on the activity flow table of the corresponding performance venue;

[0089] Based on the comparison result of the current moment and the different stage time periods, the operation stage in which the current moment is located is determined, and the operation stage is dynamically verified based on the real-time data acquisition of the corresponding functional region;

[0090] In response to the verification result being stage deviation, determining, based on the active flow table, an operation stage corresponding to a next stage period as an updated operation stage.

[0091] Optionally, in the system according to the present application, the operation stage is dynamically verified based on real-time data acquisition of a corresponding functional area, comprising:

[0092] In response to the operation stage being a pre-cooling stage, determining the verification result as stage deviation based on the acquired current total number of ticket checks of the corresponding audience core area being greater than a preset total number of ticket checks;

[0093] In response to the operation stage being a warm-up stage, determining the verification result as stage deviation based on the acquired regional heat load and regional carbon concentration of the corresponding audience core area by thermal imaging technology and breath detection technology, and based on the regional heat load and the regional carbon concentration being respectively greater than a preset heat load and a preset carbon concentration;

[0094] In response to the operation stage being a peak stage, determining the verification result as stage deviation based on the acquired regional traffic flow being greater than a preset traffic flow by the pressure sensor arranged in the corridor passage area.

[0095] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application can be practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

[0096] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application can be practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

[0097] Similarly, it is to be understood that the various features of the inventive aspects sometimes described in the specification in the context of separate embodiments can also be implemented in combination in a single embodiment. In addition, it is to be understood that the features of the embodiments sometimes described in the specification in the context of one of the inventive aspects can also be implemented and used in other ones of the inventive aspects.

[0098] It will be appreciated by those skilled in the art that the modules or units of the devices in the examples disclosed herein can be arranged in a device as described in the examples, or alternatively can be located in one or more devices different from the devices in the examples. The modules in the foregoing examples can be combined as one module or further divided into multiple sub-modules.

[0099] Those skilled in the art will appreciate that modules in the apparatus in the embodiments can be adapted and placed in one or more apparatuses other than the embodiments. Modules or units or components in the embodiments can be combined into one module or unit or component and furthermore can be divided into multiple sub-modules or sub-units or sub-components.

[0100] Furthermore, those skilled in the art will appreciate that the features of the different embodiments described herein can be combined with each other, meaning within the scope of the application and forming different embodiments, unless otherwise indicated.

[0101] Furthermore, some of the described embodiments can be method or process embodiments according to any combination of the elements of the described embodiments. Although each of the described embodiments can represent a

[0102] As used herein, unless otherwise indicated, the use of the ordinal adjectives "first", "second", "third" etc., merely to distinguish different instances of an object to which the adjective refers, and are not intended to denote a given sequence or order of such objects. Thus, a reference to first and second components, is not intended to mean that separately referenced or discussed components can not be integrated in some embodiments.

[0103] While the application has been described in terms of what are presently considered to be the most practical and preferred embodiments, it is to be understood that the application is not to be limited to the disclosed embodiments, but it is intended to cover various modifications, equivalences, and alternatives included within the spirit and scope of the application. Furthermore, language used in this specification should not be used to limit the scope of the application.

Claims

1. A method for intelligent energy-saving control of air conditioning resources, characterized in that, Includes the following steps: The performance venue is divided into functional zones, including at least the audience core area and the corridor area. Obtain the different operational phases of the corresponding performance venue, including at least the pre-cooling phase, the warm-up phase, the peak phase, and the end phase; Based on the multi-terminal data of the corresponding performance venues, the predicted regional cooling load of each functional area under each operation stage is predicted, and the predicted regional cooling load is compared with the actual regional cooling load of the corresponding functional area and operation stage in real time. If there is a cooling load difference between the actual cooling load of the area and the predicted cooling load of the area at any given time, dynamic energy-saving temperature control is performed on the functional area according to the corresponding operating stage. This includes forecasting the regional cooling load for each functional area at each operational stage based on multi-terminal data acquired from the corresponding performance venues, including: A BIM model is established based on the BIM data of the corresponding performance venue, and the BIM model is then gridded to obtain each grid cell. Based on the real-time equipment power, seat distribution data and real-time weather data output by the performance equipment terminal, ticketing terminal and weather terminal of the corresponding performance venue, the real-time weather data is combined with the determined unit personnel density and unit power density corresponding to each grid unit to form grid prediction data. Historical load data corresponding to different operating stages are obtained, and load prediction models corresponding to different operating stages are trained based on the historical load data. Based on the prediction results obtained by sequentially inputting the prediction data of each grid into each load prediction model, the regional predicted cooling load of each functional area under each operating stage is determined. The method further includes: Based on the extraction of media text data related to the performance venue from social media terminals, the clothing type with the largest corresponding clothing proportion is determined as the target clothing based on the extraction results; Based on the regional predicted cooling load of any functional area under any operating stage, the appropriate clothing is determined, and the appropriate clothing is compared with the target clothing. In response to a clothing deviation between the adapted clothing and the target clothing, the predicted cooling load for the region is updated based on the degree of deviation of the corresponding clothing deviation.

2. The intelligent air conditioning resource energy-saving control method according to claim 1, characterized in that, Obtain the different operational stages of the corresponding performance venue, including: Based on the activity flow chart of the corresponding performance venue, determine the different operation stages and the corresponding time periods for each operation stage; Based on the comparison results between the current time and different time periods, the operation stage corresponding to the current time is determined, and the operation stage is dynamically verified based on the real-time data collection of the corresponding functional area. The response verification result is a phase deviation. Based on the activity flow table, the running phase corresponding to the next phase period is determined as the updated running phase.

3. The intelligent air conditioning resource energy-saving control method according to claim 2, characterized in that, The operation phase is dynamically verified based on real-time data acquisition from corresponding functional areas, including: In response to the operation phase being the pre-cooling phase, based on the fact that the current total number of tickets checked in the corresponding audience core area is greater than the preset total number of tickets checked, the verification result is determined as the phase deviation. In response to the operation phase being the warm-up phase, the regional heat load and regional carbon concentration of the corresponding audience core area are obtained based on thermal imaging technology and breathing detection technology. Based on the fact that the regional heat load and regional carbon concentration are greater than the preset heat load and preset carbon concentration, the verification results are determined as the phase deviation. If the operation phase is a peak phase, and the pressure sensor installed in the corridor channel area determines that the traffic flow in the area is greater than the preset traffic flow, the verification result is determined as the phase deviation.

4. The intelligent air conditioning resource energy-saving control method according to claim 1, characterized in that, Based on the prediction results obtained by sequentially inputting the prediction data of each grid into each load prediction model, the regional predicted cooling load of each functional area under each operating stage is determined, including: Each grid prediction data is input into the load prediction model corresponding to any operating phase to obtain the grid prediction cold load for that operating phase. Obtain the air conditioner distribution data corresponding to the same functional area, and determine each grid unit within the air conditioner coverage area of ​​the same air conditioner equipment unit as an air conditioner radiation group based on the air conditioner distribution data; The average value of the predicted cooling load of each grid unit located in the same air conditioning radiant group is calculated, and the predicted cooling load of all air conditioning units corresponding to the same functional area is determined as the regional predicted cooling load under this operation phase.

5. The intelligent air conditioning resource energy-saving control method according to claim 1, characterized in that, If there is a cooling load difference between the actual cooling load and the predicted cooling load of the area at any given time, dynamic energy-saving temperature control is performed on the functional area according to the corresponding operating phase, including: The difference between the actual cooling load of the region and the predicted cooling load of the region at any given time is a positive difference value. The positive difference value is compared with a preset upper limit value. If the positive difference value is less than the preset upper limit value, the air conditioning flow rate of the corresponding region is increased. The difference between the actual cooling load of the region and the predicted cooling load of the region at any given time is a negative difference. The negative difference is compared with a preset lower limit. If the negative difference is less than the preset lower limit, the air conditioning flow rate of the corresponding region is reduced.

6. An intelligent air conditioning resource energy-saving control system, characterized in that, The system uses the method of claim 1, the system comprising: The partitioning module is configured to divide the performance venue into functional areas, resulting in different functional areas, including at least an audience core area and a corridor area. The acquisition module is configured to acquire different operational phases of the corresponding performance venue, including at least the pre-cooling phase, the warm-up phase, the peak phase, and the end phase. The comparison module is configured to predict the regional predicted cooling load of each functional area at each operating stage based on the acquired multi-terminal data of the corresponding performance venue, and to compare the obtained regional predicted cooling load with the actual regional cooling load of the corresponding functional area at the same operating stage in real time. The control module is configured to perform dynamic energy-saving temperature control on the functional area in response to the difference between the actual cooling load of the area and the predicted cooling load of the area at any given time.

7. The intelligent air conditioning resource energy-saving control system according to claim 6, characterized in that, Obtain the different operational stages of the corresponding performance venue, including: Based on the activity flow chart of the corresponding performance venue, determine the different operation stages and the corresponding time periods for each operation stage; Based on the comparison results between the current time and different time periods, the operation stage corresponding to the current time is determined, and the operation stage is dynamically verified based on the real-time data collection of the corresponding functional area. The response verification result is a phase deviation. Based on the activity flow table, the running phase corresponding to the next phase period is determined as the updated running phase.

8. The intelligent air conditioning resource energy-saving control system according to claim 7, characterized in that, The operation phase is dynamically verified based on real-time data acquisition from corresponding functional areas, including: In response to the operation phase being the pre-cooling phase, based on the fact that the current total number of tickets checked in the corresponding audience core area is greater than the preset total number of tickets checked, the verification result is determined as the phase deviation. In response to the operation phase being the warm-up phase, the regional heat load and regional carbon concentration of the corresponding audience core area are obtained based on thermal imaging technology and breathing detection technology. Based on the fact that the regional heat load and regional carbon concentration are greater than the preset heat load and preset carbon concentration, the verification results are determined as the phase deviation. If the operation phase is a peak phase, and the pressure sensor installed in the corridor channel area determines that the traffic flow in the area is greater than the preset traffic flow, the verification result is determined as the phase deviation.

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