Air conditioner supply and demand collaborative optimization scheduling method

Abstract

The application discloses a kind of air conditioner supply and demand collaborative optimization scheduling method, it is related to air conditioner supply and demand scheduling technical field, including sensing prediction module, acquisition comparison module, rectification drive module, control adjustment module, cold source control module, strategy adjustment module and exception detection module.The control adjustment module of the application is enhanced when the air conditioner terminal failure is detected by exception detection module peripheral equipment power fills up cold quantity gap, if water chiller unit fails, cold source control module enables spare unit to switch cold quantity output path, and linkage control adjustment module reduces low-priority area air volume to ensure core area cooling when cold quantity is insufficient, simultaneously, strategy adjustment module is automatically matched according to fault type predefined strategy chain, such as switching global average temperature control when temperature control fails, triggering load balancing strategy distribution to spare unit when equipment overload, and gradually rolled back to optimal strategy based on historical data after fault repair, and through cold quantity supply progressive improvement, avoid strategy conflict and equipment fluctuation.

Description

Technical Field

[0001] This invention relates to the field of air conditioning supply and demand scheduling technology, specifically to an air conditioning supply and demand collaborative optimization scheduling method. Background Technology

[0002] With the acceleration of urbanization and the continuous growth of building energy consumption, air conditioning systems, as a major component of building energy consumption, directly affect energy utilization and user comfort through their supply and demand scheduling efficiency. Traditional air conditioning systems mostly adopt fixed thresholds or periodic adjustment modes, controlling the cooling capacity supply through preset temperature and humidity parameters. However, these methods have significant shortcomings:

[0003] 1. Existing technologies rely on real-time monitoring data from environmental sensors, but lack the ability to predict dynamic factors such as pedestrian flow and meeting reservations. This results in insufficient accuracy in predicting cooling load demand. When actual pedestrian flow suddenly increases (such as during peak meeting times) or environmental parameters fluctuate, the system struggles to adjust cooling supply in a timely manner, often leading to over- or under-cooling, resulting in energy waste or decreased comfort.

[0004] 2. Traditional air conditioning systems typically employ a single operating strategy (such as full-time energy-saving mode or comfort-first mode), which cannot dynamically switch according to scenario requirements. For example, they may maintain high-power operation during low-load periods or lack a backup equipment linkage mechanism in case of emergency failures, making it difficult to balance energy efficiency and equipment reliability.

[0005] 3. In the event of equipment failure (such as chiller overload or temperature control malfunction), the existing system relies heavily on manual intervention or simple shutdown protection, lacking automated fault isolation, redundancy switching, and cooling capacity compensation mechanisms. This may lead to cooling interruptions in core areas or excessively long recovery times, impacting user experience.

[0006] 4. Frequent start-ups and shutdowns of cold source equipment (such as chillers and pumps) not only increase energy consumption but also accelerate equipment aging. In existing technologies, the application of energy storage equipment (such as ice storage) is mostly limited to peak-valley electricity price management and is not combined with real-time correction mechanisms, so it cannot effectively buffer sudden cooling demand or store redundant cooling capacity.

[0007] To address the aforementioned issues, while some studies in recent years have attempted to optimize cold load forecasting through intelligent algorithms or introduce multi-objective control strategies, these studies generally suffer from problems such as insufficient module coordination and a disconnect between forecasting and execution. For example, the forecasting model is not integrated with the real-time correction closed loop, the strategy switching lacks dynamic threshold design, and anomaly recovery still relies on fixed strategy rollback, resulting in limited overall system optimization effects. Summary of the Invention

[0008] The purpose of this invention is to provide a method for coordinated optimization scheduling of air conditioning supply and demand, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: an air conditioning supply and demand collaborative optimization scheduling method, including a sensing and prediction module, which obtains the flow distribution data of people in the floor through an access control system, a video recognition system, and a meeting reservation system. After obtaining the flow distribution data, the sensing and prediction module predicts the cooling load demand of each area of ​​the floor based on the flow distribution data, and controls the air conditioning equipment to cool each area according to the cooling load demand.

[0010] Data Acquisition and Comparison Module: Acquires environmental sensor data, identifies temperature, humidity, and CO2 concentration parameters in each area, obtains real-time monitoring data, compares and analyzes the real-time monitoring data with the perception and prediction module, and determines whether the deviation between the actual monitoring data and the predicted data exceeds the set threshold.

[0011] Correction Drive Module: Acquires the cooling supply-demand deviation rate (DR). When the deviation exceeds the threshold, the acquisition and comparison module sends a signal to the correction drive module, triggering the correction mechanism of the correction drive module. The correction drive module dynamically adjusts the cooling load demand of the sensing and prediction module according to the degree of deviation, so that it matches the actual cooling load demand of the floor. At the same time, the correction drive module will record this correction behavior in the log. When encountering similar situations in the future, the correction drive module will send the log to the sensing and prediction module as a reference for the prediction of cooling load demand.

[0012] Control and regulation module: Based on the cooling load demand predicted by the sensing and prediction module, control the air conditioning equipment to adjust the air volume and cooling supply;

[0013] Cold source control module: Collects and summarizes the cooling load of all currently operating cold source equipment, compares it with the cooling load demand predicted by the sensing and prediction module, and flexibly adjusts cold source operating parameters such as chiller start-up and shutdown, water pump frequency, etc., to achieve supply based on demand;

[0014] Strategy Adjustment Module: Constructs operation strategies based on the actual conditions of each floor. The operation strategies are divided into energy saving priority, comfort priority, equipment protection priority, and area priority.

[0015] The anomaly detection module is used to monitor abnormal phenomena such as temperature control failure and equipment overload during system operation in real time. When an abnormal event occurs, the anomaly detection module will send the abnormal status to the strategy adjustment module, which will adjust the strategy or switch redundant devices according to the actual situation.

[0016] The strategy adjustment module receives the cold load demand prediction curve and the trend of people flow distribution from the perception and prediction module, constructs in advance the strategy combination for the future period. For example, it preloads the "comfort priority + area priority" strategy one hour before the peak of the meeting. According to the predicted amplitude of cold load fluctuation, it automatically adjusts the strategy switching threshold. For example, when the increase rate of people flow exceeds 30%, it forcibly locks the comfort priority strategy. In addition, the strategy adjustment module can dynamically adjust the strategy switching sensitivity according to the cold quantity supply-demand deviation rate (DR) fed back by the deviation correction drive module. When the deviation is low (DR ≤ 5%), it maintains the current strategy and only finely tunes the terminal control parameters. When the medium deviation occurs (5% < DR ≤ 15%), it switches to the energy-saving priority mode and reduces the cold quantity supply in the non-core areas. When the high deviation occurs (DR > 15%), it urgently enables the combined strategy of equipment protection + area priority.

[0017] Furthermore, the control and regulation module is linked with the anomaly detection module. If the anomaly detection module detects a failure of an air-conditioning terminal, the control and regulation module will fill the cold quantity gap by enhancing the output power of the surrounding equipment.

[0018] Furthermore, the cold source control module receives the cold load demand prediction curve from the perception and prediction module, adjusts the operating state of the cold source equipment in advance, and reduces the delay of cold quantity supply. For example, it obtains the change of people flow in each area in the future 1 hour and starts the preheating of the chiller 10 minutes in advance.

[0019] Furthermore, the cold source control module is linked with the strategy adjustment module and the perception and prediction module, and dynamically allocates the output ratio of the cold source equipment according to the area priority and the cold load prediction. For example, it preferentially allocates cold quantity to the meeting room area during the peak period of the meeting room.

[0020] Furthermore, the cold source control module is linked with the deviation correction drive module. When the deviation correction drive module corrects the cold load demand, the cold source control module synchronously adjusts the parameters of the cold source equipment such as the pump frequency and the cold water temperature, and calculates the cold quantity supply-demand deviation rate according to the cold quantity supply-demand deviation rate (DR) formula, and feeds it back to the strategy adjustment module to optimize the global strategy.

[0021] Furthermore, the cold source control module uses energy storage equipment such as ice storage to temporarily store the redundant cold quantity generated by the deviation correction for subsequent emergency use, and avoids the frequent start and stop of the cold source equipment.

[0022] Furthermore, the cold quantity supply-demand deviation rate (DR) formula is:

[0023]

[0024] where Q demand is the required cold quantity (unit: kW), provided by the perception and prediction module or the deviation correction drive module, and predicted based on the people flow data and the environmental parameters;

[0025] Q actual The actual cooling capacity (unit: kW) is measured in real time by sensors on the cooling source equipment, and the calculation formula is: Q actual =ρ·c·V·ΔT

[0026] ρ: Fluid density (kg / m³) 3 )

[0027] c: Specific heat capacity of the fluid (kJ / (kg·℃))

[0028] V: Fluid volumetric flow rate (m³) 3 / s)

[0029] ΔT: Supply and return water temperature difference (°C).

[0030] Furthermore, the cold source control module is linked with the anomaly detection module. When the anomaly detection module reports a chiller unit failure, the cold source control module automatically activates the backup unit and reallocates the cooling output path. If the failure results in a cooling shortage, the cold source control module will link with the control and adjustment module to temporarily reduce the air volume in low-priority areas to prioritize cooling the core areas.

[0031] Furthermore, when the strategy adjustment module selects comfort priority, the cold source control module will achieve high-precision temperature control by reducing the fluctuation range of cold water temperature. When the strategy adjustment module switches to energy saving priority, the cold source control module will achieve a tiered supply strategy of cold capacity by adjusting the cold water temperature on demand and in different time periods. In addition, the strategy adjustment module will dynamically rotate the cold source equipment according to the strategy running time to avoid long-term high-load operation of a single unit and extend the overall equipment life.

[0032] Furthermore, the strategy adjustment module is linked with the anomaly detection module. Based on the fault type reported by the anomaly detection module, it automatically matches a predefined strategy chain. For example, when the temperature control fails, the strategy adjustment module switches to the global average temperature control strategy. When the equipment is overloaded, the load balancing strategy is triggered to reduce the operating intensity of the same group of equipment and allocate it to the standby unit. After the fault is repaired, the strategy adjustment module will automatically roll back to the optimal strategy based on historical operating data.

[0033] This invention provides a method for coordinated and optimized scheduling of air conditioning supply and demand, which has the following beneficial effects:

[0034] 1. The acquisition and comparison module of the present invention obtains real-time monitoring data of each area through environmental sensors, and compares and analyzes it with the cold load prediction value of the perception and prediction module. When the deviation between the actual data and the predicted value exceeds the set threshold, this module sends a signal to the correction drive module to trigger the correction mechanism, and the latter dynamically corrects the cold load demand prediction value according to the degree of deviation to make it fit the actual demand. At the same time, the correction behavior is recorded in the log as a prediction reference for subsequent similar scenarios. This process constructs a closed-loop control of data acquisition - deviation analysis - strategy adjustment, significantly improving the system response speed and the matching accuracy of cold supply and demand compared with the traditional periodic adjustment mode.

[0035] 2. The strategy adjustment module of the present invention constructs a strategy combination in advance (such as preloading the comfort priority + area priority strategy before the meeting peak) based on the cold load demand prediction curve and the trend of the personnel flow distribution provided by the perception and prediction module, reducing the strategy switching delay and improving the response efficiency. At the same time, it automatically adjusts the threshold according to the fluctuation range of the cold load (such as forcibly locking the comfort priority strategy when the personnel flow increase exceeds 30%), avoiding the influence of strategy lag on comfort. In addition, combined with the cold supply and demand deviation rate (DR) fed back by the correction drive module, it dynamically adjusts the sensitivity: when the deviation is low (DR ≤ 5%), it fine-tunes the terminal parameters to maintain stability; when the deviation is medium (5% < DR ≤ 15%), it switches to the energy-saving priority mode to balance energy efficiency; when the deviation is high (DR > 15%), it urgently enables the "equipment protection + area priority" combined strategy to prioritize the cooling supply of the core area and prevent equipment overload. This design solves the pain points of traditional air conditioners such as response lag, rigid strategies, and imbalance between energy efficiency and comfort through the deep coordination of prediction data and real-time monitoring.

[0036] 3. When the abnormal detection module of the present invention detects a failure of the air conditioner terminal, the control and regulation module fills the cold quantity gap by increasing the power of the surrounding equipment. If the chiller fails, the cold source control module enables the standby unit and switches the cold quantity output path. When the cold quantity is insufficient, it联动controls the control and regulation module to reduce the air volume of the low-priority area to prioritize the cooling supply of the core area. At the same time, the strategy adjustment module automatically matches the predefined strategy chain according to the type of failure (such as switching to the global average temperature control when the temperature control fails, and triggering the load balancing strategy to allocate to the standby unit when the equipment is overloaded), and gradually rolls back to the optimal strategy based on historical data after the failure is repaired. By adjusting the parameters in stages (such as gradually increasing the cold quantity supply), it avoids strategy conflicts and equipment fluctuations and achieves a smooth transition.

[0037] 4. In this invention, the control and regulation module and the anomaly detection module work together. When an air conditioning terminal malfunction is detected, the power of peripheral equipment is increased to fill the cooling capacity gap. The cold source control module dynamically allocates the cold source output ratio based on the regional priority provided by the strategy adjustment module and the cooling load prediction data from the perception and prediction module (e.g., prioritizing cooling during peak hours in conference rooms). It also utilizes energy storage devices such as ice storage to store redundant cooling capacity for sudden demand, avoiding frequent equipment start-ups and shutdowns. If the anomaly detection module reports a chiller unit malfunction, the cold source control module automatically activates the backup unit and switches the output path. When cooling capacity is insufficient, the linkage control and regulation module reduces the airflow in low-priority areas to ensure cooling in core areas. When the strategy switching module selects "comfort priority," the cold source control module achieves high-precision temperature control by narrowing the chilled water temperature fluctuation range. When switching to "energy saving priority," it adjusts the chilled water temperature according to demand and time periods to achieve tiered cooling. Furthermore, the cold source equipment dynamically rotates between primary and backup roles based on the strategy's operating time, avoiding long-term high-load operation of a single unit and thus extending its overall lifespan. Through deep inter-module collaboration, the system achieves precise cooling capacity matching, rapid fault response, and continuous energy efficiency optimization. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the real-time monitoring and deviation correction process of an air conditioning supply and demand coordinated optimization scheduling method according to the present invention;

[0039] Figure 2 This is a schematic diagram illustrating the strategy formulation and dynamic switching process of an air conditioning supply and demand collaborative optimization scheduling method according to the present invention.

[0040] Figure 3 This is a schematic diagram of the anomaly detection and emergency response process of an air conditioning supply and demand collaborative optimization scheduling method according to the present invention. Detailed Implementation

[0041] Please see Figures 1 to 3 The present invention provides a technical solution: an air conditioning supply and demand collaborative optimization scheduling method, including a sensing and prediction module, which obtains the flow distribution data of people in the floor through an access control system, a video recognition system, and a meeting reservation system. After obtaining the flow distribution data, the sensing and prediction module predicts the cooling load demand of each area of ​​the floor based on the flow distribution data, and controls the air conditioning equipment to cool each area according to the cooling load demand.

[0042] Data Acquisition and Comparison Module: Acquires environmental sensor data, identifies temperature, humidity, and CO2 concentration parameters in each area, obtains real-time monitoring data, compares and analyzes the real-time monitoring data with the perception and prediction module, and determines whether the deviation between the actual monitoring data and the predicted data exceeds the set threshold.

[0043] Correction drive module: acquires the cooling supply and demand deviation rate (DR). When the deviation exceeds the threshold, the acquisition and comparison module will send a signal to the correction drive module to trigger the correction mechanism of the correction drive module. The correction drive module dynamically adjusts the cooling load demand of the sensing and prediction module according to the degree of deviation to meet the actual cooling load demand of the composite floor.

[0044] Control and regulation module: Based on the cooling load demand predicted by the sensing and prediction module, control the air conditioning equipment to adjust the air volume and cooling supply;

[0045] Cold source control module: Collects and summarizes the cooling load of all currently operating cold source equipment, compares it with the cooling load demand predicted by the sensing and prediction module, and flexibly adjusts cold source operating parameters such as chiller start-up and shutdown, water pump frequency, etc., to achieve supply based on demand;

[0046] Strategy Adjustment Module: Constructs operation strategies based on the actual conditions of each floor. The operation strategies are divided into energy saving priority, comfort priority, equipment protection priority, and area priority.

[0047] The anomaly detection module is used to monitor abnormal phenomena such as temperature control failure and equipment overload during system operation in real time. When an abnormal event occurs, the anomaly detection module will send the abnormal status to the policy adjustment module, which will adjust the policy or switch redundant equipment according to the actual situation.

[0048] The specific operation is as follows: When the system starts up, the perception and prediction module, the data acquisition and comparison module, the correction and drive module, the control and adjustment module, the cold source control module, the strategy adjustment module, and the anomaly detection module enter the normal working state. The perception and prediction module works by acquiring real-time data on the distribution of people in the floor through the access control system, video recognition system, and meeting reservation system. Based on the collected data on the distribution of people, the perception and prediction module uses a built-in algorithm to predict the cooling load demand of each area of ​​the floor. The control and adjustment module controls the air conditioning equipment to adjust the air volume and cooling supply according to the prediction results of the perception and prediction module to meet the preliminary predicted cooling load demand.

[0049] During the continuous operation of the system, the acquisition and comparison module works. Using the environmental sensors located in the floors, it continuously monitors the temperature, humidity, and CO2 concentration parameters in each floor area to obtain real-time monitoring data. The acquisition and comparison module compares and analyzes the real-time monitoring data with the predicted data of the perception and prediction module to determine whether the deviation between the actual monitoring data and the predicted data exceeds the set threshold. If the deviation is within the set threshold, the current scheduling strategy is continued. When the deviation exceeds the threshold, the acquisition and comparison module sends a signal to the deviation correction drive module to trigger the deviation correction mechanism. The deviation correction drive module dynamically adjusts the predicted value of the cooling load demand of the perception and prediction module according to the degree of deviation to make it conform to the actual cooling load demand of the floor. At the same time, the deviation correction drive module will record this deviation correction behavior in the log. When encountering a similar situation later, it can send this log to the perception and prediction module as a reference for the prediction of the cooling load demand. Through the above operations, the system can build an execution closed-loop from data acquisition - deviation calculation - strategy adjustment, enabling the air conditioning system to have the ability of real-time dynamic deviation correction. Compared with the periodic adjustment mode of traditional air conditioners, it can effectively improve the response speed and enhance the matching accuracy of cooling supply and demand;

[0050] In addition, the strategy adjustment module constructs operation strategies according to the actual situation of the floors, including energy-saving priority, comfort priority, equipment protection, and area priority strategies. The strategy adjustment module receives the predicted curve of the cooling load demand and the trend of the personnel flow distribution from the perception and prediction module and constructs a strategy combination for the future period in advance. For example, one hour before the peak of the meeting, it preloads the "comfort priority + area priority" strategy. This enables the system to significantly reduce the strategy switching delay and effectively improve the system response efficiency compared with the passive scheduling method adopted by traditional air conditioners. In addition, the strategy adjustment module can automatically adjust the strategy switching threshold according to the predicted fluctuation range of the cooling load by the perception and prediction module. For example, when the increase in the personnel flow exceeds 30%, the comfort priority strategy is forcibly locked. This can avoid the strategy lag caused by sudden demands and affect the comfort in the space. In addition, the strategy switching module can dynamically adjust the strategy switching sensitivity according to the cooling supply and demand deviation rate (DR) feedback by the deviation correction drive module. When the deviation is low (DR ≤ 5%), the current strategy is maintained, and only the terminal control parameters are slightly adjusted to maintain the stability of the strategy. When the medium deviation (5% < DR ≤ 15%) occurs, it switches to the energy-saving priority mode to reduce the cooling supply in the non-core areas and achieve the balance of energy efficiency and demand. When the high deviation (DR > 15%) occurs, the strategy switching module will urgently enable the equipment protection + area priority combined strategy to prioritize the cooling supply in the core areas and prevent equipment overload. Through the above operations, it not only avoids equipment damage but also ensures the comfort in the key areas, solving the drawback that the traditional single strategy cannot balance safety and efficiency. By deeply binding the predicted data, real-time monitoring, and dynamic strategies, the system solves the problems of lagging response, rigid strategies, and difficulty in balancing energy efficiency and comfort in traditional air conditioning systems through forward-looking preloading, hierarchical response, and multi-strategy coordination;

[0051] During system operation, the anomaly detection module can monitor abnormal conditions in real time, such as temperature control failure and equipment overload. When the anomaly detection module detects a fault in the air conditioning terminal, the control and regulation module can fill the cooling capacity gap by increasing the output power of peripheral equipment. When the anomaly detection module detects a fault in the chiller unit, the cold source control module automatically activates the backup unit and reallocates the cooling capacity output path. If the fault causes a cooling capacity gap, the control and regulation module will temporarily reduce the airflow in low-priority areas to prioritize cooling the core areas. In addition, based on the fault type reported by the anomaly detection module, the strategy adjustment module... It will automatically match the predefined strategy chain. For example, when the temperature control fails, the strategy switching module switches to the global average temperature control strategy. When the equipment is overloaded, the load balancing strategy is triggered to reduce the operating intensity of the equipment in the same group and allocate it to the standby unit. After the fault is repaired, it will automatically roll back to the optimal strategy based on historical operating data. This can effectively prevent strategy conflicts. For example, if the emergency strategy of equipment protection + area priority is temporarily enabled during the fault, directly switching back to the normal strategy may cause sudden changes in cooling supply and demand. Automatic rollback can smoothly transition to the optimal strategy by gradually adjusting parameters (increasing cooling supply in stages) and avoid frequent equipment start-ups and shutdowns or temperature and humidity fluctuations.

[0052] Furthermore, the control and regulation module can be linked with the anomaly detection module. If the anomaly detection module detects a fault in an air conditioning terminal, the control and regulation module will compensate for the cooling capacity shortfall by increasing the output power of surrounding equipment. The cold source control module can also be linked with the strategy adjustment module and the sensing and prediction module to dynamically allocate the output ratio of cold source equipment based on regional priority and cooling load prediction. For example, during peak hours in the conference room, priority can be given to allocating cooling capacity to the conference room area. At the same time, the cold source control module can temporarily store redundant cooling capacity generated by correction using energy storage devices such as ice storage for subsequent emergency needs, avoiding frequent start-ups and shutdowns of cold source equipment. In addition, the cold source control module can also be linked with the anomaly detection module. When the anomaly detection module reports a fault in a chiller unit, the cold source control module automatically activates the backup. The system utilizes the chiller units and reallocates the cooling output paths. If a fault causes a cooling shortage, the chiller source control module will work in conjunction with the control and adjustment module to temporarily reduce the airflow in low-priority areas, prioritizing cooling for the core areas. When the strategy switching module selects comfort priority, the chiller source control module will achieve high-precision temperature control by reducing the range of chilled water temperature fluctuations. When the strategy switching module switches to energy-saving priority, the chiller source control module will implement a tiered cooling supply strategy by adjusting the chilled water temperature on demand and in different time periods. In addition, the strategy switching module will dynamically rotate the chiller equipment according to the strategy's running time to avoid long-term high-load operation of a single unit and extend the overall equipment lifespan. Through the above operations, the system can achieve continuous optimization and energy efficiency improvement through the cooperation between modules.

[0053] It should be noted that, in this document, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0054] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only for the purpose of helping to understand the method and core ideas of the present invention. The above descriptions are only preferred embodiments of the present invention. It should be noted that due to the limitations of textual expression, while there are objectively infinite specific structures, those skilled in the art can make several improvements, modifications, or changes without departing from the principles of the present invention, and can also combine the above technical features in an appropriate manner. These improvements, modifications, changes, or combinations, or the direct application of the inventive concept and technical solution to other situations without modification, should all be considered within the scope of protection of the present invention.

Claims

1. A method for coordinated and optimized scheduling of air conditioning supply and demand, characterized in that, The method includes: A perception and prediction module that obtains the people flow distribution data within the floor through an access control system, a video recognition system, and a meeting reservation system. After obtaining the people flow distribution data, the perception and prediction module predicts the cooling load requirements of each area on the floor based on the people flow distribution data, and controls the air conditioning equipment to cool each area according to the cooling load requirements; A strategy adjustment module: constructs an operation strategy according to the actual situation of the floor. The operation strategies are divided into energy conservation priority, comfort priority, equipment protection, and area priority; The strategy adjustment module receives the cooling load demand prediction curve and the people flow distribution trend of the perception and prediction module, constructs a strategy combination for the future period in advance, pre-loads the "comfort priority + area priority" strategy one hour before the meeting peak, automatically adjusts the strategy switching threshold according to the predicted cooling load fluctuation range, and forcibly locks the comfort priority strategy when the people flow increase exceeds 30%. In addition, the strategy adjustment module can dynamically adjust the strategy switching sensitivity according to the cooling capacity supply-demand deviation rate fed back by the deviation correction drive module, maintain the current strategy when the low deviation DR ≤ 5%, only fine-tune the terminal control parameters, switch to the energy conservation priority mode when the medium deviation 5% < DR ≤ 15% to reduce the cooling capacity supply in non-core areas, and urgently enable the equipment protection + area priority combined strategy when the high deviation DR > 15%; The formula for the cooling capacity supply-demand deviation rate DR is: Q demand The demand for cooling is provided by the perception and prediction module or the correction and drive module, and is predicted based on pedestrian flow data and environmental parameters. Q actual The actual cooling capacity is measured in real time by sensors in the cooling source equipment, and the calculation formula is: Q actual =ρ·c·V·ΔT ρ: fluid density c: specific heat capacity of the fluid V: fluid volume flow rate ΔT: supply and return water temperature difference; An acquisition and comparison module: obtains environmental sensor data, identifies the temperature, humidity, and CO2 concentration parameters of each area to obtain real-time monitoring data, and compares and analyzes the real-time monitoring data with the perception and prediction module to determine whether the deviation between the actual monitoring data and the predicted data exceeds the set threshold; A deviation correction drive module: obtains the cooling capacity supply-demand deviation rate DR. When the deviation exceeds the threshold, the acquisition and comparison module sends a signal to the deviation correction drive module to trigger the deviation correction mechanism of the deviation correction drive module. The deviation correction drive module dynamically adjusts the cooling load demand of the perception and prediction module according to the deviation degree to make it conform to the actual cooling load demand of the floor; A control and adjustment module: controls the air conditioning equipment to adjust the air volume and cooling capacity supply according to the cooling load demand predicted by the perception and prediction module; A cold source control module: collects and summarizes the cooling load of all currently operating cold source equipment, compares it with the cooling load demand predicted by the perception and prediction module, and flexibly adjusts cold source operation parameters such as the start and stop of chillers and the frequency of pumps to achieve supply based on demand; An anomaly detection module is used to monitor anomalies such as temperature control failure and equipment overload in real time during system operation. When an anomaly event occurs, the anomaly detection module sends the anomaly status to the strategy adjustment module, and the strategy adjustment module adjusts the strategy or switches redundant equipment according to the actual situation.

2. The air conditioning supply and demand coordinated optimization scheduling method according to claim 1, characterized in that, The control and adjustment module is linked with the anomaly detection module. If the anomaly detection module detects a failure of an air conditioning terminal, the control and adjustment module will fill the cooling capacity gap by increasing the output power of surrounding equipment.

3. The air conditioning supply and demand coordinated optimization scheduling method according to claim 2, characterized in that, The cold source control module receives the cold load demand prediction curve from the sensing and prediction module, adjusts the operating status of the cold source equipment in advance, reduces the delay in cold supply, obtains the changes in pedestrian flow in each area in the next hour, and starts the chiller unit to preheat 10 minutes in advance.

4. The air conditioning supply and demand coordinated optimization scheduling method according to claim 2, characterized in that, The cold source control module, in conjunction with the strategy adjustment module and the sensing and prediction module, adjusts the cold source control module based on regional priority and cold source conditions. Load forecasting and dynamic allocation of cooling equipment output ratios prioritize the allocation of cooling capacity to the conference room area during peak hours.

5. The air conditioning supply and demand coordinated optimization scheduling method according to claim 2, characterized in that, The cold source control module is linked with the correction drive module. When the correction drive module corrects the cooling load demand, the cold source control module synchronously adjusts parameters of the cold source equipment, such as water pump frequency and chilled water temperature. It also calculates the cooling supply and demand deviation rate according to the formula and feeds it back to the strategy adjustment module to optimize the global strategy. The cold source control module uses energy storage devices such as ice storage to temporarily store the redundant cooling capacity generated by the correction for use in subsequent emergencies, thus avoiding frequent start-ups and shutdowns of the cold source equipment.

6. The air conditioning supply and demand coordinated optimization scheduling method according to claim 2, characterized in that, The cold source control module is linked with the anomaly detection module. When the anomaly detection module reports a chiller unit failure, the cold source control module automatically activates the backup unit and reallocates the cooling output path. If the failure causes a cooling shortage, the cold source control module will link with the control and adjustment module to temporarily reduce the air volume in low-priority areas to prioritize cooling the core areas.

7. The air conditioning supply and demand coordinated optimization scheduling method according to claim 2, characterized in that, When the strategy adjustment module selects comfort priority, the cold source control module will achieve high-precision temperature control by reducing the range of cold water temperature fluctuations. When the strategy adjustment module switches to energy saving priority, the cold source control module will achieve a tiered supply strategy of cold capacity by adjusting the cold water temperature on demand and in different time periods. In addition, the strategy adjustment module will dynamically rotate the cold source equipment according to the strategy running time to avoid long-term high-load operation of a single unit and extend the overall equipment life.

8. The air conditioning supply and demand coordinated optimization scheduling method according to claim 2, characterized in that, The strategy adjustment module works in conjunction with the anomaly detection module. Based on the fault type reported by the anomaly detection module, it automatically matches a predefined strategy chain. For example, when the temperature control fails, the strategy adjustment module switches to the global average temperature control strategy. When the equipment is overloaded, the load balancing strategy is triggered to reduce the operating intensity of the same group of equipment and allocate it to the standby unit. After the fault is repaired, the strategy adjustment module will automatically roll back to the optimal strategy based on historical operating data.

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