An industrial air conditioner distributed intelligent collaborative energy-saving control method and device, electronic equipment and computer readable storage medium

Abstract

The application provides an industrial air conditioner distributed intelligent collaborative energy-saving control method and device, electronic equipment and computer readable storage medium, which are applied to the field of data processing. The application obtains industrial air conditioner operation related data, including environmental parameters and equipment state data; based on multi-device collaboration and hierarchical regulation requirements, an intelligent collaborative control mechanism is constructed, including a multi-device linkage strategy for dynamically allocating refrigeration resources according to regional cold and heat load, and a hierarchical regulation algorithm for the chilled water system and the cooling system, to form a control framework for realizing global optimization; the environmental parameters and the equipment state data are processed to generate a dynamic control strategy; based on the data analysis result and the dynamic control strategy, target energy-saving control information is generated; each item of operation data and control effect generated based on the target energy-saving control information is integrated and processed to generate a high energy consumption link optimization scheme, so that energy consumption data can be traced and aggregated, and a distributed air conditioning system can participate in grid frequency modulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, and in particular to an industrial air conditioner distributed intelligent collaborative energy-saving control method and device, electronic equipment and computer readable storage medium. BACKGROUND

[0002] In modern industrial production, the industrial air conditioning system is crucial to maintaining a suitable production environment. With the continuous expansion of industrial scale and the increasing demand for energy saving and emission reduction, the energy consumption problem of the industrial air conditioning system is increasingly prominent. At present, most industrial air conditioning systems have defects such as high energy consumption and inaccurate regulation and control.

[0003] On the one hand, the traditional industrial air conditioner has obvious shortcomings in data acquisition and transmission. For example, in a large-scale industrial scene, thousands of devices need to be connected, and conventional communication technologies cannot realize low-latency interconnection and data synchronization between devices. Common protocols such as MODBUS and BACNET are prone to communication bottlenecks in complex environments, and ordinary network technologies cannot meet the needs of stable data transmission of a large number of devices at the same time, resulting in that device state data and environmental parameters (such as supply and return water temperature, indoor temperature and humidity, etc.) cannot be obtained in time and accurately, affecting the timeliness and accuracy of subsequent control decisions.

[0004] On the other hand, the control mechanism lacks intelligent collaboration. Traditional control strategies are mostly based on simple rules, such as starting and stopping devices only according to a single temperature threshold, which cannot realize dynamic allocation of regional load. In the regulation of chilled water and cooling systems, fixed hierarchical mode is mostly used, without real-time adjustment according to actual load changes, making it difficult to form a global optimization framework, resulting in low overall operation efficiency of the system and serious energy waste. At the same time, the traditional system does not have the ability to generate dynamic control strategies according to real-time data, cannot optimize energy-saving schemes in real time according to operating conditions, lacks deep integration and analysis of energy consumption data, and cannot meet the needs of industrial enterprises for energy fine management and efficient use.

[0005] It should be noted that the information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore includes information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The purpose of the present application is to provide an industrial air conditioner distributed intelligent collaborative energy-saving control method and device, electronic equipment and computer readable storage medium, which at least to some extent overcomes the problems existing in the prior art, upgrades the industrial air conditioner from "single machine operation" to "global optimization" through distributed intelligent collaboration, and realizes energy fine management and control. At the same time, it realizes the traceable and reliable energy consumption data, aggregates the distributed air conditioning system to participate in the frequency regulation of the power grid, and has the function of virtual power plant.

[0007] Other features and advantages of the present application will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, which illustrate, by way of example, the principles of the application.

[0008] According to one aspect of the present application, an industrial air conditioner distributed intelligent collaborative energy-saving control method is provided, comprising: acquiring industrial air conditioner operation related data, including environmental parameters and equipment state data, wherein the environmental parameters include supply and return water temperature, pipeline pressure, and refrigerant flow; transmitting and processing the industrial air conditioner operation related data, using industrial protocols including MODBUS and BACNET and 5G / Wi-Fi6 technology to realize kilo-level device low-latency interconnection and data synchronization, and transmitting the collected data to the decision layer; based on multi-device collaboration and hierarchical regulation requirements, building an intelligent collaborative control mechanism, including a multi-device linkage strategy for dynamically allocating refrigeration resources according to regional cooling and heating load, and a hierarchical regulation algorithm for chilled water systems and cooling systems, forming a control framework for global optimization; processing the environmental parameters and equipment state data to generate a dynamic control strategy; based on the data analysis results and the dynamic control strategy, generating target energy-saving control information; integrating and processing each item of operation data and control effect generated based on the target energy-saving control information, displaying the energy consumption ranking of each region through a data visualization platform, and generating a high energy consumption link optimization scheme to realize credible traceability of energy consumption data and aggregation of distributed air conditioning systems participating in grid frequency regulation.

[0009] According to another aspect of the present application, an industrial air conditioner distributed intelligent collaborative energy-saving control device is provided, comprising: an acquisition module for acquiring industrial air conditioner operation related data, including environmental parameters and equipment state data; a processing module for transmitting and processing the industrial air conditioner operation related data, using industrial protocols including MODBUS and BACNET and 5G / Wi-Fi6 technology to realize kilo-level device low-latency interconnection and data synchronization, and transmitting the collected data to the decision layer; based on multi-device collaboration and hierarchical regulation requirements, building an intelligent collaborative control mechanism, including a multi-device linkage strategy for dynamically allocating refrigeration resources according to regional cooling and heating load, and a hierarchical regulation algorithm for chilled water systems and cooling systems, forming a control framework for global optimization; processing the environmental parameters and equipment state data to generate a dynamic control strategy; based on the data analysis results and the dynamic control strategy, generating target energy-saving control information; integrating and processing each item of operation data and control effect generated based on the target energy-saving control information, displaying the energy consumption ranking of each region through a data visualization platform, and generating a high energy consumption link optimization scheme to realize credible traceability of energy consumption data and aggregation of distributed air conditioning systems participating in grid frequency regulation.

[0010] According to another aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a second processor, implements the above-described distributed intelligent collaborative energy-saving control method for industrial air conditioning.

[0011] This application provides a distributed intelligent collaborative energy-saving control method, device, electronic equipment, and computer-readable storage medium for industrial air conditioning. By acquiring environmental and equipment data, it achieves low-latency interconnection of thousands of devices using MODBUS, BACNET protocols, and 5G / Wi-Fi 6. It constructs a collaborative mechanism including load sharing and system regulation algorithms to generate dynamic control strategies and energy-saving information. Through data integration and visualization, it generates high-energy-consuming optimization schemes, achieving energy consumption traceability and grid frequency regulation.

[0012] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0013] Figure 1 This document shows a flowchart illustrating a distributed intelligent collaborative energy-saving control method for industrial air conditioning according to an embodiment of this application.

[0014] Figure 2 This paper shows a schematic diagram of the structure of an industrial air conditioning distributed intelligent collaborative energy-saving control device according to an embodiment of this application. Detailed Implementation

[0015] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0016] The following is combined Figure 1 This application describes a distributed intelligent collaborative energy-saving control method for industrial air conditioning according to exemplary embodiments. For example... Figure 1 As shown:

[0017] S101, acquire relevant data on the operation of industrial air conditioning, including environmental parameters and equipment status data.

[0018] The environmental parameters include supply and return water temperatures, pipeline pressure, and refrigerant flow rate.

[0019] In one implementation, acquiring relevant operational data of the industrial air conditioning system is a fundamental step in the entire control method and system. The aim is to provide data support for subsequent intelligent collaborative control by comprehensively collecting environmental and equipment information. Specific details are as follows:

[0020] Environmental parameters focus on the external and piping environment conditions in which the air conditioning system operates, primarily including three core data categories: Supply and Return Water Temperature: This refers to the temperature of chilled or cooling water at the supply and return ends of the air conditioning system, a key indicator reflecting cooling capacity transfer efficiency. Piping Pressure: This is the pressure value within the pipes that transport refrigerant (such as chilled water or cooling water) in the air conditioning system, used to monitor whether the piping is operating normally and avoid system malfunctions caused by excessively high or low pressure. Refrigerant Flow Rate: This represents the volume or mass of refrigerant (chilled water, hot water, or refrigerant) passing through the piping per unit time, directly related to the cooling capacity transfer capability. Equipment Status Data mainly reflects the operating status of each component of the industrial air conditioning system, including compressor operating frequency, pump speed, fan start / stop status, and unit operating time, used to assess whether the equipment is operating normally and its energy efficiency level.

[0021] Taking a large machining workshop as an example, in the workshop's air conditioning chilled water system, the temperature sensor on the supply water pipe shows a supply water temperature of 8℃, and the temperature sensor on the return water pipe shows a return water temperature of 13℃, a temperature difference of 5℃, reflecting the heat generated by the workshop equipment and its consumption of cooling capacity. The pressure transmitter on the chilled water pipeline monitors a pressure of 0.35MPa, which is within the normal operating pressure range of the system (0.3-0.4MPa), indicating that there are no abnormalities such as blockages or leaks in the pipeline. The flow meter on the refrigerant pipeline shows a flow rate of 60m³ / h. Combined with the supply and return water temperatures, it can be calculated that the current cooling capacity delivery can meet the heat dissipation needs of the core processing areas of the workshop (such as the CNC machine tool cluster). Of the three air conditioning units in the workshop, Unit 1 has accumulated 800 hours of operation with a compressor frequency of 45Hz; Unit 2 has accumulated 750 hours of operation with a compressor frequency of 40Hz; and Unit 3 is in standby mode (accumulated 600 hours of operation). The chilled water pump speed is 1200 r / min, the cooling fan is running, and the fan speed level is 3, both of which meet the current cooling capacity distribution requirements.

[0022] Based on the system's perception layer and network layer architecture, the aforementioned data is collected and transmitted in the following ways: Perception layer deployment: Temperature sensors, pressure transmitters, flow meters, and other sensing elements are installed at key nodes of the air conditioning system (such as supply and return water pipes, equipment inlets and outlets) to capture environmental parameters and equipment status data in real time. Network layer transmission utilizes industrial protocols such as MODBUS and BACNET, as well as 5G / Wi-Fi 6 technology, to achieve low-latency interconnection and synchronization of the collected data from thousands of devices, ultimately transmitting it to the decision layer for processing.

[0023] S102 processes and transmits relevant data on the operation of industrial air conditioning, and transmits the collected data to the decision-making level.

[0024] In one implementation, industrial protocols including MODBUS and BACNET are used to standardize the data format of industrial air conditioning operation-related data, ensuring that data collected by different types of sensors and devices conforms to a unified transmission standard. By using industrial protocols such as MODBUS and BACNET, raw data collected by different sensors and devices is converted into a unified format, eliminating data heterogeneity and ensuring that output data from different types of devices such as temperature sensors, pressure transmitters, and flow meters can be uniformly identified and processed by the system.

[0025] The supply and return water temperature data collected by the temperature sensor in the workshop (output signal is 4-20mA analog quantity) is converted into 16-bit binary format through the MODBUS protocol; the pipeline pressure data of the pressure transmitter (RS485 interface) is converted into the same binary format through the BACNET protocol. Finally, both types of data are stored in a unified structure of "device ID + timestamp + value" for easy subsequent transmission and analysis.

[0026] Leveraging 5G / Wi-Fi 6 technology, and based on the distribution density of equipment and data transmission bandwidth requirements in industrial scenarios, communication frequency bands are adapted and network access points are deployed to ensure the compatibility of data transmission channels. Depending on the distribution density of equipment in the workshop (e.g., densely packed machine tool clusters versus sparsely packed warehouse areas) and data transmission bandwidth requirements, communication frequency bands are adapted and network access points are deployed to ensure stable transmission of high-concurrency data and avoid data loss due to signal blind spots or insufficient bandwidth.

[0027] Specifically, base stations supporting the 5G Sub-6GHz frequency band are deployed in the machine tool cluster area of ​​the workshop (equipment density of up to 50 units / 100㎡) to meet the data transmission needs of high-density equipment, leveraging its wide coverage characteristics; Wi-Fi 6 access points are deployed in the warehouse area (low equipment density), using the 5.8GHz frequency band to provide high-bandwidth connectivity. The network access points in the two types of areas are interconnected through fiber optic backhaul links to form a fully covered communication network.

[0028] A protocol conversion gateway is used to achieve interface adaptation between industrial protocols and communication technologies, establishing a transmission link adaptation mechanism for environmental parameters and equipment status data. Deploying the protocol conversion gateway ensures interface compatibility between industrial protocols (MODBUS / BACNET) and communication technologies (5G / Wi-Fi 6), establishing a data transmission link. This resolves the compatibility issues between industrial equipment protocols and wireless communication standards, ensuring smooth data transmission from the perception layer to the network layer.

[0029] Specifically, the protocol conversion gateway in the workshop converts temperature data from the MODBUS protocol into IP data packet format supported by the 5G network, and pressure data from the BACNET protocol into IEEE 802.11ax frame format supported by Wi-Fi 6. At the same time, through the built-in link detection function of the gateway, it automatically selects the optimal transmission path (such as switching to Wi-Fi 6 when the 5G network is congested).

[0030] For diverse data types from thousands of devices, we coordinate and match transmission protocols and communication standards to ensure data format consistency and transmission stability during transmission. For diverse data types (environmental parameters, device status) from thousands of devices (such as hundreds of sensors or dozens of air conditioning units), we coordinate the parameter configurations of transmission protocols and communication standards to ensure consistent data format and stable transmission. This avoids data corruption or transmission interruptions caused by protocol and standard incompatibility.

[0031] Specifically, among the 1,000 pieces of equipment in the workshop, the status data of the chilled water pumps (updated frequently, every 1 second) is transmitted using the MODBUS protocol + 5GURLLC (ultra-low latency) standard; the historical data of refrigerant flow (updated infrequently, every 10 seconds) is transmitted using the BACNET protocol + Wi-Fi 6 (high throughput) standard. The system ensures that both types of data maintain "time synchronization + uniform format" during transmission through preset rules, such as uniformly using JSON format encapsulation.

[0032] Through low-latency transmission optimization mechanisms, interconnection and data synchronization of thousands of devices are achieved, establishing data transmission channels between devices. These mechanisms (such as local caching at edge nodes and data fragmentation transmission) enable real-time interconnection and data synchronization of thousands of devices. This ensures the real-time nature of control commands and feedback data, meeting the timeliness requirements of dynamic adjustment in industrial air conditioning systems.

[0033] Specifically, the workshop deploys 10 edge computing nodes, each responsible for managing 100 devices. When the supply and return water temperature in a certain area suddenly rises, the edge nodes first cache local sensor data, and then use 5G slicing technology to transmit the data in segments to adjacent nodes, achieving data synchronization across the entire workshop within 50ms, ensuring that the decision-making level can respond quickly to load changes.

[0034] Utilizing the aforementioned transmission channels, the collected environmental parameters and equipment status data are directionally transmitted to the decision-making layer, completing the data flow from the sensing end to the processing end. Similarly, the environmental parameters (supply and return water temperatures, pipeline pressures, etc.) and equipment status data (compressor frequency, pump speed, etc.) collected by the sensing layer are directionally transmitted to the decision-making layer (edge ​​computing and cloud computing platform). This completes the data flow from the collection end to the processing end, providing input for subsequent AI algorithms to generate control strategies.

[0035] Real-time data collected by the workshop's sensing layer (such as 8°C water supply temperature, 0.35MPa pipeline pressure, and 50Hz compressor frequency) is aggregated through transmission channels and then sent to the cloud computing platform in the workshop's central control room and regional edge servers. High-frequency pressure data is prioritized for transmission to the edge servers for rapid local adjustment, while historical energy consumption data is transmitted to the cloud computing platform for long-term trend analysis.

[0036] S103, based on the needs of multi-device collaboration and hierarchical adjustment, constructs an intelligent collaborative control mechanism, including a multi-device linkage strategy that dynamically allocates refrigeration resources according to regional heating and cooling loads, and hierarchical adjustment algorithms for chilled water system and cooling system, forming a control framework to achieve global optimization.

[0037] In one implementation, regional heating and cooling load data and equipment cooling capacity parameters are dynamically matched to generate a multi-equipment linkage resource allocation model. This model is constructed from load data calculated using a supply-demand balance algorithm and equipment capacity parameters. The heating and cooling load data for each area of ​​the workshop are dynamically matched with the cooling capacity parameters of the air conditioning equipment. A resource allocation scheme is calculated using a supply-demand balance algorithm to generate the multi-equipment linkage resource allocation model. Specifically, the multi-equipment linkage resource allocation model is a dynamic matching model for supply-demand balance. Its model structure is as follows: Input layer (regional load data, equipment cooling capacity parameters) → Calculation layer (supply-demand balance algorithm) → Output layer (cooling capacity allocation scheme). Key parameters of the model include the regional load deviation rate (≤5%) and the equipment cooling efficiency coefficient (0-1, representing the proportion of the equipment's current cooling capacity to its rated value).

[0038] The large-scale machining workshop is divided into three areas: Area A (machine tool cluster, real-time cooling load 800kW), Area B (assembly area, cooling load 500kW), and Area C (warehousing area, cooling load 300kW). The workshop has three air conditioning units with cooling capacities of 600kW, 600kW, and 500kW respectively. Through a supply-demand balance algorithm, Area A is allocated to Unit 1 (600kW) plus a portion of Unit 3's load (200kW); Area B is allocated to Unit 2 (500kW); and Area C is covered by the remaining 300kW load from Unit 3. This achieves precise matching between load and equipment capacity, avoiding redundant equipment operation.

[0039] A composite analysis of temperature and pressure difference signals from the chilled water system is performed to generate a temperature-pressure difference composite control algorithm. This algorithm is generated by linking temperature and pressure difference parameters to a pump frequency correlation model through collaborative adjustment logic. The supply and return water temperature difference and pipeline pressure difference signals of the chilled water system are analyzed in combination. A control algorithm for adjusting the pump frequency is generated through collaborative adjustment logic and a pump frequency correlation model. The algorithm is primarily driven by the temperature difference signal (reflecting cooling demand) and constrained by the pressure difference signal (reflecting pipeline resistance), dynamically calculating the optimal pump frequency. The target temperature difference is 5℃ ± 0.5℃, the minimum safe pressure difference is 0.2MPa, and the pump frequency adjustment step is 1Hz / cycle.

[0040] The current supply and return water temperature difference in the workshop's chilled water system is 3℃ (lower than the target of 5℃), indicating insufficient cooling capacity transfer; the pipeline pressure difference is 0.3MPa (higher than the minimum safe pressure difference of 0.2MPa). The algorithm, through coordinated adjustment logic, determines that the pump frequency needs to be increased to increase flow rate and improve cooling capacity transfer efficiency. Combining this with the pump frequency correlation model, the frequency was adjusted from 30Hz to 35Hz, ultimately raising the temperature difference to 4.8℃ and stabilizing the pressure difference at 0.28MPa, meeting the optimization objectives.

[0041] The temperature difference data of the cooling system and the ambient temperature parameters are logically integrated to generate a fusion algorithm for temperature difference-triggered fan speed regulation and antifreeze protection. This fusion algorithm is constructed by fusing temperature difference signals according to safety and energy efficiency priority rules. The inlet and outlet water temperature difference of the cooling system and the ambient temperature parameters are logically integrated, and a control algorithm for fusion fan speed regulation and antifreeze protection is generated based on safety and energy efficiency priority rules.

[0042] When the ambient temperature is >5℃, energy efficiency takes priority: the fan speed is adjusted according to the temperature difference (target temperature difference 8℃), with the speed increasing by 10% for every 1℃ increase in temperature difference. When the ambient temperature is ≤5℃, safety takes priority: anti-freeze logic is triggered, limiting the minimum fan speed (not lower than 30% of rated speed) to prevent pipe icing. The target temperature difference is 8℃, the anti-freeze trigger threshold is ambient temperature ≤5℃, and the fan speed adjustment range is 30%-100%.

[0043] In summer, the temperature difference between the inlet and outlet water of the cooling system is 10℃ (8℃ higher than the target), and the ambient temperature is 25℃. The algorithm prioritizes energy efficiency and adjusts the fan speed from 50% to 70%, reducing the temperature difference to 8.2℃. In winter, the ambient temperature is 3℃ and the temperature difference is 6℃ (lower than the target). The algorithm triggers the anti-freeze logic, and even if the temperature difference does not meet the target, the fan speed is still maintained at 30% to prevent the pipeline from freezing.

[0044] By integrating a multi-device linkage resource allocation model, a temperature difference-pressure difference composite control algorithm, and a temperature difference-triggered fan speed regulation and anti-freezing logic fusion algorithm, the core mechanism achieves an organic connection between "equipment collaborative scheduling" and "precise adjustment of a single system" through a global optimization objective function. This process, with the core objective of "minimizing total energy consumption cost + equipment maintenance coefficient × operating time," breaks through the limitations of traditional industrial air conditioning's "independent single-unit control," forming a global control mode of "regional linkage + system collaboration."

[0045] The core function of the equipment linkage strategy is to achieve dynamic scheduling of cooling capacity across regions. Based on the real-time calculation results of the multi-equipment linkage resource allocation model, it allocates cooling resources from areas with redundant cooling capacity to areas with load gaps, while ensuring the load demand of each region, thus avoiding equipment idling or redundant operation. The scheduling is based on the real-time difference between regional cooling and heating loads (e.g., the current cooling demand of region A is 500kW, the actual output is 700kW, i.e., 200kW is redundant) and the pipeline transmission loss coefficient (the loss rate of cooling capacity during pipeline transmission, usually taken as 5%-8%). During the cooling capacity scheduling process, the supply and return water temperature difference must be ≥3℃ (to ensure effective cooling capacity transmission) and the pipeline pressure must be stable at 0.3-0.5MPa (to avoid pressure fluctuations affecting system safety).

[0046] For subsystems such as chilled water systems and cooling systems, parameter optimization is achieved through independent algorithm models to ensure maximum operating efficiency of single systems, while providing a stable basis for equipment linkage strategies.

[0047] The chilled water system is based on a temperature difference-pressure difference composite control algorithm. When the flow rate changes due to the cooling capacity scheduling, the pump frequency is automatically adjusted (e.g., if the cooling capacity increases by 10%, the pump frequency is increased by 8%-10%) to maintain the coordinated balance between temperature difference and pressure difference.

[0048] The cooling system uses a temperature difference-triggered fan speed regulation and anti-freeze logic fusion algorithm to dynamically adjust the fan speed according to changes in cooling output (e.g., if cooling output decreases by 20%, the fan speed will decrease by 15%-20% simultaneously). At the same time, the anti-freeze logic is activated when the ambient temperature is ≤5℃ to prioritize equipment safety.

[0049] In the objective function Min(Total Energy Cost + Equipment Maintenance Coefficient × Operating Time), the specific meanings and value bases of each parameter are as follows: Total energy cost is the real-time electricity price (e.g., 1.2 yuan / kWh during peak hours and 0.5 yuan / kWh during off-peak hours) × the actual power consumption of the equipment (cumulative power consumption of compressors, water pumps, fans, etc.), reflecting the cost control objective of "reducing peak electricity consumption through time-of-use pricing strategy". The equipment maintenance coefficient is set according to the equipment type (compressor 1.2, water pump 0.8, fan 0.6). The longer the operating time, the higher the maintenance cost, which matches the energy-saving function of "rotating start-stop units based on equipment operating time balancing algorithm to extend equipment life".

[0050] Taking a large-scale machining workshop as an example, the operation of the intelligent collaborative control mechanism is explained as follows: Initially, the cooling load in Zone A is 800kW (borne by Unit 1 (600kW) + Unit 3 (200kW)), Zone B has 500kW (borne by Unit 2), and Zone C has 300kW (borne by the remaining 300kW of Unit 3). The chilled water system pump frequency is 35Hz, the cooling system fan speed is 70%, and the total energy consumption is 120kW / h. The cumulative operating time of the equipment is: Unit 1 800h, Unit 2 750h, and Unit 3 600h. The trigger condition is that due to equipment shutdown in Zone A, the cooling load suddenly drops to 500kW, and Unit 1 (600kW) has a 100kW cooling capacity redundancy (considering a 5% pipeline transmission loss, the actual dispatchable cooling capacity is 95kW).

[0051] The current load of Zone B is calculated to be 500kW. If 95kW of cooling capacity is received from Zone A, the load of Unit #2 can be reduced to 405kW. The system verifies through a multi-device linkage resource allocation model that after scheduling, the supply and return water temperature difference in Zone B is maintained at 5℃ (meeting the cooling capacity transmission requirements), and the pipeline pressure is 0.38MPa (within the safe range). Therefore, scheduling is executed.

[0052] Due to chilled water capacity scheduling, the pipeline flow rate changed. The temperature-pressure differential control algorithm detected that the supply and return water temperature difference increased from 5℃ to 5.5℃, and the pressure difference increased from 0.35MPa to 0.38MPa. It automatically adjusted the pump frequency from 35Hz to 32Hz, causing the temperature difference to drop back to 5.2℃ and the pressure difference to stabilize at 0.36MPa. In the cooling system, the output load of Unit #1 decreased from 600kW to 500kW, and the cooling temperature difference decreased from 8℃ to 7℃. This temperature difference triggered the fan speed regulation algorithm, adjusting the fan speed from 70% to 60%. Simultaneously, the ambient temperature was 15℃ (above the antifreeze threshold of 5℃), so the antifreeze logic did not need to be activated.

[0053] The intelligent collaborative control mechanism is encapsulated into a framework to generate a globally optimized control framework. This framework encapsulation uses hierarchical control logic to define the function and interface of each module, thus forming a globally optimized control framework.

[0054] The control framework includes the following: Decision layer: edge computing nodes (responsible for regional optimization) + cloud computing platform (responsible for global policy generation); Execution layer: device controllers (receiving instructions and adjusting units, pumps, and fans); Interface adaptation: standardized communication interfaces (supporting data interaction with the perception layer and network layer). Functional positioning is as follows: Decision layer: updates the global policy every 10 seconds and handles cross-regional collaboration needs; Execution layer: responds to adjustment instructions every 1 second to ensure real-time parameter optimization.

[0055] After being encapsulated in a framework, the central control room of the workshop monitors the whole situation through a data visualization platform: the decision-making cloud computing platform generates a "peak-off operation" strategy (avoiding the peak electricity price from 18:00 to 22:00), and the edge nodes fine-tune regional parameters according to the real-time load; the unit controllers, water pump frequency converters, etc. of the execution layer receive instructions through standardized interfaces to realize closed-loop control of "global strategy → regional execution → parameter feedback", ultimately achieving a 20% reduction in energy consumption during peak periods.

[0056] S104 processes environmental parameters and equipment status data to generate dynamic control strategies.

[0057] In one implementation, feature extraction is performed on environmental parameters and equipment status data based on an edge computing and cloud computing convergence platform. A load forecasting model then maps historical cooling demand data and real-time load parameters to the unit start-up and shutdown optimization space with time-series weights. The historical cooling demand is... The real-time ambient temperature is The heat generated by the equipment is Then the formula for predicting cooling demand at time t is: , These are the weighting coefficients. For historical data time series weights, The reference temperature is used. The technical logic of calculating the cooling demand at time t through the load forecasting model is the core step in the "generating dynamic control strategy" process. Its core is to accurately predict the cooling demand based on the fusion analysis of historical data and real-time parameters, providing a basis for unit start-up and shutdown and load allocation.

[0058] The types of input data are as follows: historical cooling demand. The data shows the air conditioning cooling output of the workshop over a past period (e.g., the last 1 hour, 3 hours, 8 hours), reflecting the load patterns at different times (e.g., higher cooling demand due to concentrated equipment startup between 9-11 am). Real-time ambient temperature. The ambient temperature is collected by temperature sensors deployed in the workshop. For example, if the ambient temperature in the workshop rises to 32°C in the afternoon during summer, it will directly increase the cooling demand. (Equipment heat generation) The heat generated by machine tools, production lines, and other equipment in the workshop during operation; for example, a batch of heavy machine tools can generate up to 200kW of heat per hour when running at full load. Based on a converged edge computing and cloud computing platform, the above data is preprocessed (e.g., outlier filtering, time-series alignment) to extract key features (e.g., the correlation between ambient temperature and equipment heat generation, and historical cooling fluctuation cycles) to provide high-quality input for the prediction model. The load prediction model weights and fuses the patterns of historical cooling data, the impact of real-time ambient temperature, and the immediate contribution of equipment heat generation to generate a predicted cooling demand value at time t (e.g., 15 minutes or 1 hour in the future), avoiding energy waste or insufficient cooling due to load estimation errors.

[0059] Weighting coefficients ( These represent the impact of historical cooling data, changes in ambient temperature, and equipment heat generation on current cooling demand, respectively. For example, in a densely populated processing area, (Equipment heat generation weight) is set to a high value (e.g., 0.3); in the storage area, (Historical data weight) takes a higher value (e.g., 0.6). Time series weight. Different weights are assigned to historical cooling data from different time periods, with more recent data (e.g., within the last hour) having a higher weight (e.g., 0.7) and more distant data (e.g., 8 hours ago) having a lower weight (e.g., 0.1), ensuring the model is more sensitive to recent trends. Baseline temperature ( The standard ambient temperature set in the workshop (e.g., 25℃) is used when the real-time temperature... Higher than At that time, additional cooling capacity is needed to compensate for the increased load caused by the temperature difference.

[0060] The predicted cooling demand output by the model serves as a crucial basis for subsequent "unit start-up and shutdown optimization" and "dynamic cooling capacity allocation." For example, if a 30% surge in cooling demand is predicted for a certain period, the system can activate standby units in advance, avoiding energy consumption peaks caused by temporary additional units. The prediction accuracy directly supports the "dynamic load matching" function, ensuring temperature fluctuations are controlled within ±0.5℃, meeting workshop production environment requirements while avoiding energy waste caused by over-cooling. Through the fusion analysis of historical and real-time data, a precise load benchmark is provided for the "global optimization" of the industrial air conditioning system.

[0061] The data preprocessing layer and branching algorithm structure output COP extreme value search results, neural network predictions, and equipment runtime balancing results (unit rotation priority). The COP extreme value search results represent the frequency adjustment parameters of the inverter equipment, and the neural network predictions represent the precise cooling capacity demand. The inverter equipment frequency is f, and the cooling capacity is... The power consumption is P Energy efficiency ratio Searching for extreme values ​​using gradient ascent , This is the step size coefficient. Environmental parameters (such as supply and return water temperatures and pipeline pressure) and equipment status data (such as compressor frequency and pump speed) collected by the sensing layer are cleaned (filtering outliers), normalized (unifying data volume), and time-series aligned (ensuring consistency in the time dimension) to provide high-quality input for subsequent algorithms. For example, instantaneous pressure jumps caused by sensor malfunctions are removed, and the operating frequencies of different devices are uniformly converted to a standardized range of 0-100Hz. The branch algorithm adopts a parallel processing architecture, inputting the preprocessed data into three independent algorithm modules (COP extreme value search, neural network prediction, and unit rotation priority calculation), simultaneously outputting three types of results, which are then integrated into unified control parameters through collaborative logic to ensure decision-making efficiency and accuracy.

[0062] The COP extreme value search algorithm targets variable frequency equipment (such as variable frequency compressors and variable frequency water pumps) in the workshop. It calculates the energy efficiency ratio (COP = cooling capacity Q(f) / power consumption P(f)) in real time and uses a gradient ascent method to search for the optimal operating frequency, ensuring the equipment maintains maximum energy efficiency under the current load. For example, when the workshop's cooling demand is stable, the algorithm dynamically adjusts the compressor frequency to avoid energy waste due to excessively high frequency ("over-powered" operation) or insufficient cooling due to excessively low frequency. The frequency adjustment parameters output by the algorithm are directly used for equipment control; for example, fine-tuning the water pump frequency from 35Hz to 32Hz reduces power consumption while meeting cooling demand.

[0063] Based on historical cooling data and real-time environmental characteristics (such as equipment heat generation and ambient temperature), a neural network model outputs accurate predictions of cooling demand, providing a basis for unit start-up and shutdown and load allocation. For example, if it is predicted that the cooling demand in the machine tool cluster area of ​​the workshop will increase by 20% in one hour due to the equipment operating at full load, cooling resources can be reserved in advance for this area to avoid temperature fluctuations caused by temporary adjustments.

[0064] For multiple air conditioning units (such as 3 refrigeration units) in the workshop, a rotation priority coefficient is calculated based on the cumulative running time, prioritizing the use of units with shorter running times to balance equipment wear and tear. For example, when Unit 1 has run for 800 hours, Unit 2 for 750 hours, and Unit 3 for 600 hours, the algorithm determines that Unit 3 has the highest priority and is put into operation first.

[0065] The variable frequency drive (VFD) adjustment parameters, precise cooling demand values, and unit rotation priorities output by the aforementioned algorithms collectively constitute the core of the dynamic control strategy, directly guiding equipment operation adjustments (such as compressor frequency and water pump speed) and unit start-up and shutdown planning. The multi-algorithm collaborative mechanism ensures a balance between "energy efficiency optimization" and "equipment safety." For example, COP extreme value search improves the energy efficiency of individual equipment, unit rotation reduces maintenance costs, and neural network prediction ensures matching of cooling supply and demand. These three elements jointly support the system's upgrade from "single-unit adjustment" to "global optimization."

[0066] The cumulative operating time of k generating units is , ... The goal is to minimize the deviation at each time interval, and the rotation priority coefficient is... (j=1,2,...,k) The larger the value, the higher the priority for unit operation. By dynamically calculating the rotation priority of each unit, units with shorter cumulative operating time are prioritized for activation, minimizing the deviation in operating time among units, avoiding premature aging of some equipment due to overuse, thereby reducing maintenance costs and extending the overall equipment lifespan. The cumulative operating time of k air conditioning units in the workshop ( , ... For example, three refrigeration units deployed in a large machining workshop have accumulated operating times of 800 hours, 750 hours, and 600 hours, respectively. Rotation priority coefficient ( This value is used to quantify the priority of each unit's activation. The larger the value, the greater the difference between the unit's cumulative operating time and its longest operating time, and the more it needs to be put into operation first to balance losses. Based on the maximum and minimum operating times of each unit, a coefficient is calculated through standardization (e.g., the closer a unit's operating time is to the minimum value, the closer the coefficient is to 1; the closer it is to the maximum value, the closer the coefficient is to 0).

[0067] When the cooling demand in the workshop increases and a standby unit needs to be activated, the system calculates the priority using this algorithm: if the operating times of the three units are 800h, 750h, and 600h respectively, with the longest operating time being 800h and the shortest being 600h, then the unit with the shortest operating time (600h) has the highest priority coefficient and will be activated first; while the unit with the longest operating time (800h) has the lowest priority coefficient and enters standby mode. This algorithm directly supports the goal of extending equipment life in "energy efficiency optimization and cost control," reducing the risk of failure due to overuse of a single piece of equipment (such as compressor wear and pipe aging) by balancing operating times, which is consistent with the function of "reducing maintenance costs" in "full-link energy efficiency management." At the same time, priority adjustment will trigger the coordinated adjustment of parameters of the chilled water system and cooling system (such as water pump frequency and fan speed) to ensure optimal overall system energy efficiency. By quantifying priorities, it ensures that resource scheduling meets cooling demand while also balancing equipment wear, providing equipment-level guarantees for the "global optimization" of the industrial air conditioning system.

[0068] By combining equipment safety constraints with upper and lower limit verification of adjustment parameters to avoid overload, and real-time matching verification of cooling output and load demand to ensure supply-demand coordination, a dynamic control strategy is generated. Ensuring the reliability of control parameters through dual verification (equipment safety constraint verification and supply-demand coordination verification) is a crucial step in connecting algorithm output with actual equipment control. Equipment safety constraint verification (avoiding overload) sets upper and lower thresholds for adjustment parameters (compressor frequency, pump speed, fan speed) output by load prediction models, COP extreme value search algorithms, etc., to prevent equipment from overloading, damaging, or causing safety accidents due to operation beyond rated conditions.

[0069] Based on the equipment's factory parameters and industrial safety standards, for example, the compressor's operating frequency is set to an upper limit of 60Hz (to prevent motor overheating); the chilled water pump's pressure is set to an upper limit of 0.6MPa (to prevent pipe bursts); and the cooling fan's speed is set to a lower limit of 30% of its rated speed (for winter anti-freeze logic requirements). If the COP extreme value search algorithm finds that the optimized frequency for a compressor is 65Hz (exceeding the safe upper limit of 60Hz), the system will automatically trigger a safety check, correcting the frequency to 60Hz to ensure the equipment operates within a safe range.

[0070] Verify that the cooling output plan generated by the algorithm (such as unit start-up and shutdown combinations, and variable frequency drive adjustment range) is consistent with the real-time cooling demand of each area to avoid energy waste caused by "supply exceeding demand" or temperature fluctuations caused by "supply falling short of demand". Based on the cooling demand output by the load forecasting model, the allowable deviation range is ≤5%. For example, if the real-time cooling load in area A is 500kW, the cooling output planned by the algorithm should be within the range of 475-525kW; the supply and return water temperature difference should be maintained at 3-7℃ (to ensure effective cooling transfer).

[0071] If the algorithm plans a cooling output of 550kW for Zone B, while the actual load demand is 500kW (a deviation of 10%, exceeding the allowable range), the system will trigger a matching degree verification. By reducing the compressor frequency of the corresponding unit or shutting down some equipment, the cooling output will be corrected to 510kW to ensure coordination with the load demand and meet the dynamic load matching requirement of "temperature fluctuation control with an accuracy of ±0.5℃".

[0072] Dual verification serves as the "last line of defense" before the dynamic control strategy takes effect. It ensures both the safety of equipment operation (e.g., preventing compressor overload and burnout) and the feasibility of energy-saving targets (e.g., imbalance between cooling supply and demand directly leads to increased energy consumption). The verification results are fed back to the decision-making level. If parameter exceedances or insufficient matching occur frequently, the system will automatically adjust the weight coefficients (e.g., α, β, γ) of the load forecasting model or the step size coefficient (η) of the COP algorithm, forming a closed loop of "algorithm optimization → verification feedback → model iteration".

[0073] S105 generates target energy-saving control information based on data analysis results and dynamic control strategies.

[0074] In one implementation, preliminary energy-saving control parameters are generated based on the unit start-up and shutdown sequence and variable frequency drive (VFD) adjustment parameters in the dynamic control strategy, combined with real-time supply and return water temperatures, pipeline pressures, equipment runtime, and equipment energy consumption information. Preliminary equipment control parameters are generated based on the unit start-up and shutdown plan (e.g., priority start-up of Unit #3) and VFD adjustment parameters (e.g., compressor frequency 45Hz) in the dynamic control strategy, integrating real-time environmental parameters (supply and return water temperatures, pipeline pressures) and equipment status data (runtime, energy consumption).

[0075] The start-up and shutdown sequence of the units must match the real-time cooling capacity demand (e.g., if the predicted cooling load for Zone A is 500kW, start one 600kW unit). The variable frequency drive (VFD) adjustment parameters must be correlated with the supply and return water temperature difference (e.g., if the temperature difference is 5℃, set the pump frequency to 35Hz).

[0076] The workshop dynamic control strategy requires "starting Unit #3 (500kW) at 15:00, with the compressor frequency at 40Hz and the chilled water pump frequency at 32Hz." Based on real-time data: supply and return water temperatures of 8℃ / 13℃ (temperature difference of 5℃), pipeline pressure of 0.35MPa, and Unit #3's cumulative operating hours of 600 hours, preliminary parameters are generated: Unit #3 to operate at 40Hz, water pumps to operate at 32Hz, with a planned operation time of 2 hours.

[0077] The initial energy-saving control parameters are verified against equipment safety thresholds to filter out parameters exceeding the safe range and generate safe and compliant control parameters. The safety thresholds are set based on: compressor frequency ≤ 50Hz (to avoid motor overload); pipeline pressure ≤ 0.5MPa (to prevent pipe bursts); and chilled water temperature ≥ 5℃ (to prevent freezing). The initial parameter "cooling fan speed 90%" was verified to be 80% of the maximum safe fan speed (to avoid mechanical wear) at the current ambient temperature of 35℃. Therefore, it was corrected to 80%, generating safe and compliant parameters.

[0078] Based on safety and compliance control parameters, and combined with time-of-use pricing strategies and cooling capacity scheduling requirements, the unit operation mode and equipment adjustment range are optimized and adapted to generate adaptive energy-saving control parameters. Based on these safety and compliance parameters, and combined with time-of-use pricing strategies (e.g., peak price of 1.2 yuan / kWh from 18:00-22:00, and off-peak price of 0.5 yuan / kWh from 00:00-08:00) and cooling capacity scheduling requirements (e.g., redundant cooling capacity from area A to area B), the unit operation mode and equipment adjustment range are optimized. The adaptation logic prioritizes the operation of units with high energy efficiency ratios during peak hours, reducing the load on inverter equipment (e.g., reducing compressor frequency from 40Hz to 35Hz). During off-peak hours, the equipment load is increased to reserve cooling capacity (e.g., chilled water tank storage). Redundant cooling capacity from high-temperature areas (e.g., machine tool areas) is allocated to low-temperature areas (e.g., storage areas).

[0079] Specifically, the safety compliance parameters are "Unit 3 operates at 40Hz until 18:00". Taking into account the peak electricity price after 18:00, the optimization is "Reducing the frequency of Unit 3 to 35Hz at 17:30 and stopping operation at 18:00, with Unit 2 (with a higher energy efficiency ratio) taking over the 300kW load". At the same time, the 100kW of redundant cooling capacity in Zone A is allocated to Zone B to reduce energy consumption during peak hours.

[0080] By integrating end-to-end energy efficiency management goals, the system assigns weights and coordinates adjustments to adaptable energy-saving control parameters to generate target energy-saving control information. This involves integrating end-to-end energy efficiency management goals (such as a 15% reduction in total energy consumption and temperature fluctuations in each region ≤ ±0.5℃), assigning weights to adaptable parameters (such as energy-saving priority 60%, temperature stability 30%, and equipment lifespan 10%), and coordinating adjustments to generate final, executable control information.

[0081] After the adaptability parameters are weighted, the target information is finally generated: "Unit 3 runs at 35Hz until 17:30, and then switches to Unit 2 at 30Hz after 17:30; the chilled water pump frequency is finely adjusted from 32Hz to 30Hz according to the cooling capacity scheduling; the cooling capacity scheduling from Zone A to Zone B is 100kW to ensure that the temperature in Zone B is stable at 25℃±0.5℃".

[0082] S106 integrates and processes various operational data and control effects generated based on target energy-saving control information, displays energy consumption rankings for each region through a data visualization platform, and generates optimization schemes for high-energy-consuming processes.

[0083] In one implementation, the energy consumption data of each area generated from the target energy-saving control information is quantitatively and statistically analyzed to generate quantitative energy consumption indicators. The energy consumption data (such as hourly power consumption and cooling loss) of each area in the workshop (Area A machine tool cluster, Area B assembly area, Area C storage area) after the implementation of the target energy-saving control information are quantitatively and statistically analyzed to generate comparable energy consumption indicators. Area unit area energy consumption (kWh / ㎡): reflects the area's energy density; cooling utilization rate (actual cooling consumption / total cooling output): measures cooling transfer efficiency; equipment unit cooling capacity energy consumption (kWh / kW): evaluates the energy efficiency of a single piece of equipment.

[0084] After implementing the target control information, the statistics show that: the energy consumption per unit area in Zone A is 8 kWh / m², in Zone B it is 5 kWh / m², and in Zone C it is 3 kWh / m²; the cooling capacity utilization rate is 75% in Zone A, 82% in Zone B, and 90% in Zone C, providing a quantitative basis for subsequent energy consumption ranking.

[0085] The effectiveness of adjusting the operating parameters of each device generated from the target energy-saving control information is evaluated and analyzed to generate device efficiency factors. These factors characterize the contribution of optimized device operation to overall energy saving. The energy-saving contribution of each device (compressor, water pump, fan) after parameter optimization is assessed, generating device efficiency factors (range 0-1, with higher values ​​indicating greater contribution). The percentage reduction in power consumption after frequency adjustment of variable frequency equipment (e.g., a 15% reduction in power consumption when the water pump frequency is reduced from 35Hz to 32Hz). Maintenance cost savings after balancing device operating time (e.g., a 20% extension of the maintenance cycle for unit 1 due to priority operation of unit 3). The efficiency factors are: chilled water pump 0.8 (significant energy saving due to frequency optimization), cooling fan 0.6 (smaller speed adjustment range), and compressor 0.7 (optimized load distribution), reflecting the differences in the contribution of different devices to overall energy saving.

[0086] Historical energy consumption data associated with the target energy-saving control information is compared and analyzed with current energy consumption trends to generate an energy consumption trend similarity factor. The energy consumption trends before and after the implementation of the target control information (such as daily energy consumption curves and peak-valley energy consumption ratios) are compared, and a trend similarity factor (range 0-1, with smaller values ​​indicating more significant optimization effects) is calculated. This includes the reduction in peak energy consumption (e.g., energy consumption during peak hours decreasing from 120kW to 90kW) and the smoothness of the energy consumption curve (reduced curve fluctuations after optimization indicate a stable control strategy). Before implementation, the energy consumption curve in area A fluctuated significantly between 10:00 and 16:00 (±20kW); after implementation, the fluctuation decreased to ±5kW, with a trend similarity factor of 0.3, indicating that the energy-saving strategy effectively smoothed out energy consumption fluctuations.

[0087] An impact analysis is conducted on the implementation effects of key control strategies in the target energy-saving control information to generate key strategy impact factors. These key strategy impact factors characterize the degree of impact of key control strategies on energy consumption reduction. The key strategy impact factor is a quantitative indicator (range 0-1) that measures the contribution of core strategies such as cooling load scheduling and time-of-use pricing to energy consumption reduction. Its calculation process needs to combine the analysis of single-strategy isolation and superposition effects to ensure that the evaluation results objectively reflect the actual energy-saving value of the strategies.

[0088] The core of the single-strategy isolation method is to isolate the independent impact of a single strategy through closed-loop verification of "pausing a strategy → monitoring energy consumption changes → calculating energy-saving contribution". The specific operation is as follows: Strategy Pause Mechanism: While keeping other control parameters unchanged, the target strategy (such as cooling capacity scheduling) is temporarily shut down, restoring the system to its operating state before the strategy was activated (e.g., each unit provides independent cooling, without cross-regional cooling capacity allocation). Energy Consumption Recovery Ratio Calculation: Record the energy consumption per unit time (e.g., kW / h) before and after the strategy pause. Calculate the independent energy-saving effect of the strategy using the formula "Energy Saving Contribution Rate = (Energy Consumption After Pause - Energy Consumption Before Pause) / Energy Consumption After Pause". Then, standardize it to the range of 0-1 as an influence factor (the higher the energy saving contribution rate, the larger the factor value).

[0089] Strategy superposition effect analysis is used to evaluate whether the energy-saving gain of multi-strategy synergy is greater than the sum of the effects of a single strategy, avoiding the cancellation of effects due to mutual constraints between strategies. Specifically, it includes: Calculating the superposition energy-saving rate: Energy consumption is calculated under three modes: "cooling load scheduling only," "time-sharing peak shaving only," and "cooling load scheduling + time-sharing peak shaving." The superposition energy-saving rate is calculated as: (1 - superposition mode energy consumption / baseline energy consumption) - (single scheduling energy-saving rate + single peak shaving energy-saving rate). Correction for interaction effects: If the superposition energy-saving rate is positive (e.g., 10%), it indicates synergistic effect of strategies, requiring an increase in the weight of the influence factors of each strategy; if it is negative (e.g., -5%), it indicates constraints, requiring a decrease in weight and optimization of the strategy combination logic.

[0090] Taking Area B (assembly area) and the overall system of a large machining workshop as an example, the generation process of the influencing factors is explained in detail. Specifically, the influencing factor of the cooling capacity scheduling strategy is calculated as follows: Under single-strategy isolation, when the strategy is activated, the energy consumption of Area B is 500 kW / h (by receiving 200 kW of redundant cooling capacity from Area A, the load of its own units is reduced); after the strategy is suspended, Area B needs to supply cooling independently, and the energy consumption rises to 610 kW / h. The energy saving contribution rate = (610-500) / 610≈18%, and the standardized influencing factor is 0.6 (18% corresponds to 0.6, since the maximum energy saving contribution rate is set to 30%, i.e., 0.6=18% / 30%). Verification of the superposition effect: after superposition with the time-sharing peak-shaving strategy, the energy consumption of Area B drops to 420 kW / h, and the superposition energy saving rate = (1-420 / 610)-(18%+12%)≈(31%)-30%=1%, which is a positive effect. Therefore, the influencing factor remains at 0.6.

[0091] The impact factor of the time-of-use pricing peak-shifting strategy is calculated as follows: When the strategy is activated (avoiding peak electricity prices from 18:00-22:00 and storing cold energy in the off-peak period), the overall system energy consumption is 1200 kW / h. After the strategy is suspended, the system operates at full load during peak hours, increasing energy consumption to 1360 kW / h. The energy-saving contribution rate is approximately 12% (1360-1200) / 1360, and the standardized impact factor is 0.4 (12% / 30%). The superposition effect is verified: after being superimposed with cooling capacity scheduling, the overall energy consumption drops to 1050 kW / h. The superposition energy-saving rate is approximately 7.2% (1-1050 / 1360)-(18%+12%)≈(22.8%)-30%, with a slight constraint (due to some cooling loss caused by the shared pipeline between cold storage and cooling capacity scheduling). Therefore, the factor is maintained at 0.4, but further optimization of pipeline insulation is needed to reduce this constraint.

[0092] Based on energy consumption quantification indicators, equipment efficiency factors, energy consumption trend similarity factors, and key strategy influencing factors, combined with the framework of the energy consumption analysis model, the energy consumption situation of each region is integrated and processed to generate comprehensive energy consumption analysis characteristics. The four types of factors need to be converted into standardized values ​​in the range of 0-1 to eliminate dimensional differences and ensure the rationality of weighted calculation. The specific conversion rules are as follows: Taking the energy consumption per unit area of ​​a region as an example, the highest energy consumption threshold for the workshop is set at 10 kWh / m² (corresponding to 0), and the lowest threshold is set at 2 kWh / m² (corresponding to 1). The standardized value is calculated using the linear mapping formula: Standardized value = (highest threshold - actual value) / (highest threshold - lowest threshold). For example, the actual energy consumption per unit area in area A is 8 kWh / m², and the standardized value is (10-8) / (10-2) = 0.25; for area B, it is 5 kWh / m², and the standardized value is (10-5) / 8 = 0.625.

[0093] Equipment performance factors are used directly from their original values ​​(already in the range of 0-1), such as pump performance factor 0.5 for area A and 0.8 for area B. Energy consumption trend similarity factors are originally in the range of 0-1 (smaller values ​​indicate better optimization). To maintain consistency, they are converted to "1 - original value", such as cause factor 0.3 for area A → standardized value 0.7; cause factor 0.5 for area B → standardized value 0.5. Key strategy impact factors are also used directly from their original values ​​(already in the range of 0-1), such as cooling capacity scheduling impact factor 0.6 for area A and 0.7 for area B.

[0094] The weight matrix (energy consumption quantification index 0.4, equipment efficiency 0.2, trend similarity 0.2, key strategy 0.2) is set based on the priority of each factor's influence on energy consumption characteristics. Specifically, the energy consumption quantification index (0.4) has the highest weight because the absolute value of the actual energy consumption in a region is the core basis for judging high-energy-consuming links, directly reflecting the urgency of energy-saving needs. The equipment efficiency factor (0.2) is the second most important because equipment operating efficiency is a key cause of energy consumption differences (e.g., inefficient water pumps can lead to a surge in energy consumption). The energy consumption trend similarity factor (0.2) reflects the stability after the strategy is implemented; the better the trend (the smaller the fluctuation), the more sustainable the energy-saving effect. The key strategy influence factor (0.2) measures the region's responsiveness to energy-saving strategies; the better the response (e.g., high acceptance of cooling capacity scheduling), the greater the potential for subsequent optimization.

[0095] Overall score = (Standardized value of energy consumption index × 0.4) + (Equipment efficiency factor × 0.2) + (Standardized value of trend similarity × 0.2) + (Key strategy impact factor × 0.2) × 100. Area A: (0.25 × 0.4) + (0.5 × 0.2) + (0.7 × 0.2) + (0.6 × 0.2) = 0.1 + 0.1 + 0.14 + 0.12 = 0.46 → 0.46 × 100 = 46 points. If the cooling capacity utilization rate of area A is only 75% (standardized value 0.3), equipment efficiency 0.6, trend similarity 0.6, and key strategy 0.5, then the score = (0.3×0.4) + (0.6×0.2) + (0.6×0.2) + (0.5×0.2) = 0.12 + 0.12 + 0.12 + 0.1 = 0.46 → 46 points, which meets the characteristics of "high energy consumption".

[0096] Based on the comprehensive score range (0-60 points for "high energy consumption", 60-85 points for "medium energy consumption", and 85-100 points for "low energy consumption") and the shortcomings of each factor, labels are automatically generated: If the standardized value of the energy consumption quantitative index of a certain region is <0.3 (high energy consumption) and the standardized value of the cooling capacity utilization rate is <0.4 (low utilization rate), then the label is "high energy consumption - low cooling capacity utilization rate" (e.g., region A). If the score is 60-85 points (medium energy consumption) and the key strategy impact factor is >0.6 (good response), then the label is "medium energy consumption - good strategy response" (e.g., region B).

[0097] Based on the energy consumption analysis model, the comprehensive energy consumption characteristics are deeply analyzed to generate energy consumption ranking information for each region, which is then displayed through a data visualization platform. Based on the comprehensive energy consumption analysis characteristics, each region is ranked from highest to lowest according to its comprehensive score, and the data is displayed through a data visualization platform (such as heat maps and bar charts) to intuitively present energy consumption differences. The ranking of absolute regional energy consumption is as follows: Region A > Region B > Region C. The ranking of energy-saving potential is as follows: Region A has a potential of 30% > Region B has 15% > Region C has 5%. The visualization platform shows that Region A ranks first in energy consumption, but its cooling capacity utilization rate is only 75% (lower than the average of 82%), and it is marked as a "key optimization area".

[0098] Targeted extraction and analysis of data related to high-energy-consuming links in the comprehensive energy consumption analysis characteristics are conducted. Based on energy-saving targets and actual operating conditions, optimization schemes for high-energy-consuming links are generated. For high-energy-consuming links in the comprehensive energy consumption analysis characteristics (such as high cooling loss in area A and low water pump efficiency), targeted optimization measures are formulated based on energy-saving targets (a 15% reduction in total energy consumption) and actual operating data.

[0099] Equipment level: Replace the inefficient water pumps in Zone A (efficiency factor 0.5) with high-efficiency variable frequency models. Strategy level: Increase the cooling capacity dispatched from Zone A to Zone C (currently only 50kW, can be increased to 80kW). Maintenance level: Perform insulation upgrades on the pipelines in Zone A to reduce cooling loss (target: increase cooling capacity utilization to 85%). Addressing the "high energy consumption - low utilization" characteristics of Zone A, the solutions include: ① Directly dispatching part of the load (100kW) of Unit 3 to Zone A; ② Optimizing the water pump frequency to 30Hz, combined with a temperature difference-pressure difference algorithm to improve flow stability; ③ Calibrating the pipeline pressure in Zone A weekly to reduce leakage losses.

[0100] This application achieves global energy consumption optimization through multi-device collaboration and dynamic control. First, it acquires environmental parameters (supply and return water temperatures, etc.) and equipment status data, employing MODBUS, BACNET protocols, and 5G / Wi-Fi 6 technologies to achieve low-latency interconnection and data synchronization for thousands of devices. Next, it constructs an intelligent collaborative control mechanism, including a multi-device linkage strategy for dynamic regional load allocation and hierarchical adjustment algorithms for chilled water and cooling systems, forming a global optimization framework. Then, it processes the data to generate dynamic control strategies and combines the analysis results to generate target energy-saving control information. Finally, it integrates operational data and control effects, displays energy consumption rankings through a visualization platform, generates optimization schemes for high-energy-consuming links, and achieves energy consumption traceability and participation in grid frequency regulation.

[0101] In one implementation, such as Figure 2 As shown, this application also provides a distributed intelligent collaborative energy-saving control device for industrial air conditioning, comprising:

[0102] The acquisition module 201 is used to acquire relevant data on the operation of industrial air conditioning, including environmental parameters and equipment status data;

[0103] The processing module 202 is used to transmit and process industrial air conditioning operation-related data. It adopts industrial protocols including MODBUS and BACNET, as well as 5G / Wi-Fi 6 technology, to achieve low-latency interconnection and data synchronization of thousands of devices, and transmits the collected data to the decision-making layer. Based on the needs of multi-device collaboration and hierarchical adjustment, it constructs an intelligent collaborative control mechanism, including a multi-device linkage strategy for dynamically allocating cooling resources according to regional heating and cooling loads, and hierarchical adjustment algorithms for chilled water systems and cooling systems, forming a control framework for global optimization. It processes environmental parameters and equipment status data to generate dynamic control strategies. Based on data analysis results and dynamic control strategies, it generates target energy-saving control information. It integrates and processes various operating data and control effects generated based on target energy-saving control information, displays energy consumption rankings for each region through a data visualization platform, generates optimization schemes for high-energy-consuming links, achieves reliable traceability of energy consumption data, and aggregates distributed air conditioning systems to participate in power grid frequency regulation.

[0104] The various embodiments in this application are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of the industrial air conditioning distributed intelligent collaborative energy-saving control device, electronic device, and computer-readable storage medium are basically similar to the above-described embodiments of the industrial air conditioning distributed intelligent collaborative energy-saving control method, so the description is relatively simple. Relevant parts can be referred to in the description of the above-described embodiments of the industrial air conditioning distributed intelligent collaborative energy-saving control method.

Claims

1. A distributed intelligent collaborative energy-saving control method for industrial air conditioning, characterized in that, include: Acquire relevant data on the operation of industrial air conditioning, including environmental parameters and equipment status data. Among them, environmental parameters include supply and return water temperature, pipeline pressure, and refrigerant flow rate. The system transmits and processes data related to the operation of industrial air conditioners, using industrial protocols including MODBUS and BACNET, as well as 5G / Wi-Fi 6 technology, to achieve low-latency interconnection and data synchronization for thousands of devices, and transmits the collected data to the decision-making level. Based on the requirements of multi-device collaboration and hierarchical adjustment, an intelligent collaborative control mechanism is constructed, including a multi-device linkage strategy that dynamically allocates refrigeration resources according to regional heating and cooling loads, and hierarchical adjustment algorithms for chilled water system and cooling system, forming a control framework for achieving global optimization. The system processes environmental parameters and equipment status data to generate dynamic control strategies. Based on data analysis results and dynamic control strategies, target energy-saving control information is generated. The system integrates and processes various operational data and control effects generated based on target energy-saving control information, displays energy consumption rankings for each region through a data visualization platform, generates optimization schemes for high-energy-consuming links, achieves reliable traceability of energy consumption data, and aggregates distributed air conditioning systems to participate in power grid frequency regulation.

2. The method as described in claim 1, characterized in that, The system transmits and processes data related to industrial air conditioning operation, employing industrial protocols including MODBUS and BACNET, as well as 5G / Wi-Fi 6 technology, to achieve low-latency interconnection and data synchronization for thousands of devices. The collected data is then transmitted to the decision-making level, including: Industrial protocols including MODBUS and BACNET are used to standardize and convert the data formats of industrial air conditioning operation-related data to ensure that the data collected by different types of sensors and equipment conforms to the unified transmission standard. Based on 5G / Wi-Fi 6 technology, and according to the distribution density of equipment and the data transmission bandwidth requirements in industrial scenarios, communication frequency bands are adapted and network access points are deployed to ensure the compatibility of data transmission channels. A protocol conversion gateway is used to achieve interface adaptation between industrial protocols and communication technologies, and to establish a transmission link adaptation mechanism for environmental parameters and equipment status data. For the diverse data types from thousands of devices, we complete the coordinated matching of transmission protocols and communication standards to ensure data format consistency and transmission stability during the transmission process; Through a low-latency transmission optimization mechanism, interconnection and data synchronization of thousands of devices are achieved, and a data transmission channel between devices is built. Relying on the aforementioned transmission channels, the collected environmental parameters and equipment status data are transmitted to the decision-making level, completing the data flow from the sensing end to the processing end.

3. The method as described in claim 1, characterized in that, Based on the requirements of multi-device collaboration and hierarchical adjustment, an intelligent collaborative control mechanism is constructed, including a multi-device linkage strategy for dynamically allocating refrigeration resources according to regional heating and cooling loads, and hierarchical adjustment algorithms for the chilled water system and cooling system, forming a control framework for achieving global optimization, including: The regional heating and cooling load data and equipment cooling capacity parameters are dynamically matched and processed to generate a multi-equipment linkage resource allocation model. The multi-equipment linkage resource allocation model is composed of load data calculated by supply and demand balance algorithm and equipment capacity parameters. The temperature difference and pressure difference signals of the chilled water system are analyzed and processed to generate a temperature difference-pressure difference composite control algorithm. The temperature difference-pressure difference composite control algorithm is generated by the temperature difference and pressure difference parameters through a collaborative adjustment logic and a water pump frequency correlation model. The temperature difference data of the cooling system and the ambient temperature parameters are logically integrated and processed to generate a temperature difference-triggered fan speed regulation and antifreeze logic fusion algorithm. The temperature difference-triggered fan speed regulation and antifreeze logic fusion algorithm is composed of temperature difference signals fused by safety and energy efficiency priority judgment rules. The multi-device linkage resource allocation model, temperature difference-pressure difference composite control algorithm, and temperature difference triggered fan speed regulation and antifreeze logic fusion algorithm are integrated to generate an intelligent collaborative control mechanism. The intelligent collaborative control mechanism is composed of equipment linkage strategy and hierarchical adjustment algorithm fused through a global optimization objective function. The intelligent collaborative control mechanism is encapsulated in a framework to generate a control framework that achieves global optimization. The framework encapsulation process uses hierarchical control logic to locate the functions and adapt the interfaces of each module of the mechanism.

4. The method as described in claim 1, characterized in that, The system processes environmental parameters and equipment status data to generate dynamic control strategies, including: Based on an edge computing and cloud computing convergence platform, features are extracted from environmental parameters and equipment status data. A load forecasting model maps historical cooling demand data and real-time load parameters to the unit start-up and shutdown optimization space with time-series weights. The historical cooling demand is... The real-time ambient temperature is The heat generated by the equipment is Then the formula for predicting cooling demand at time t is: , These are the weighting coefficients. For historical data time series weights, Reference temperature; The data preprocessing layer and branching algorithm structure output COP extreme value search results, neural network predictions, and equipment runtime balancing results. The COP extreme value search results represent the frequency adjustment parameters of the inverter equipment, and the neural network predictions represent the precise value of the cooling capacity demand. The inverter equipment frequency is f, and the cooling capacity is... The power consumption is P Energy efficiency ratio Searching for extreme values ​​using gradient ascent , This is the step size coefficient; The cumulative operating time of k generating units is , ... The goal is to minimize the deviation at each time interval, and the rotation priority coefficient is... (j=1,2,...,k) The larger the capacity, the higher the priority for putting the unit into operation; By combining equipment safety constraints to perform upper and lower limit verification of adjustment parameters to avoid overload, and real-time matching verification of cooling output and load demand to ensure supply and demand coordination, a dynamic control strategy is generated.

5. The method as described in claim 1, characterized in that, Based on data analysis results and dynamic control strategies, target energy-saving control information is generated, including: Based on the unit start-up and shutdown sequence and variable frequency equipment adjustment parameters in the dynamic control strategy, combined with real-time supply and return water temperature, pipeline pressure, equipment running time, and equipment energy consumption information, preliminary energy-saving control parameters are generated. The initial energy-saving control parameters are verified for equipment safety thresholds, parameters that exceed the safety range are filtered out, and safe and compliant control parameters are generated. Based on safe and compliant control parameters, and combined with time-of-use electricity pricing strategies and cooling capacity scheduling requirements, the unit operation mode and equipment adjustment range are optimized and adapted to generate adaptive energy-saving control parameters. By integrating the energy efficiency management objectives across the entire value chain, weighting and coordinating the adjustment of adaptable energy-saving control parameters, target energy-saving control information is generated.

6. The method as described in claim 5, characterized in that, The system integrates and processes various operational data and control effects generated based on target energy-saving control information, displays energy consumption rankings for different regions through a data visualization platform, generates optimization schemes for high-energy-consuming processes, achieves reliable traceability of energy consumption data, and aggregates distributed air conditioning systems to participate in grid frequency regulation, including: Quantitative statistical analysis is performed on the energy consumption data of each region generated from the target energy-saving control information to generate quantitative energy consumption indicators; The effectiveness of adjusting the operating parameters of each device generated from the target energy-saving control information is evaluated and analyzed to generate device effectiveness factors. The device effectiveness factors are used to characterize the degree of contribution of device operation optimization to overall energy saving. The historical energy consumption data associated with the target energy-saving control information is compared and analyzed with the current energy consumption trend to generate an energy consumption trend similarity factor. An impact analysis is conducted on the implementation effect of key control strategies in the target energy-saving control information to generate key strategy impact factors. The key strategy impact factors are used to characterize the degree of impact of key control strategies on energy consumption reduction. Based on energy consumption quantitative indicators, equipment efficiency factors, energy consumption trend similarity factors, and key strategy influencing factors, combined with the architecture of the energy consumption analysis model, the energy consumption situation in each region is integrated and processed to generate comprehensive energy consumption analysis characteristics. Based on the energy consumption analysis model, the comprehensive energy consumption analysis characteristics are deeply explored to generate energy consumption ranking information for each region, which is then displayed through a data visualization platform. Targeted extraction and analysis of relevant data from high-energy-consuming links in the comprehensive energy consumption analysis characteristics, combined with energy-saving targets and actual operation conditions, generate optimization schemes for high-energy-consuming links.

7. A distributed intelligent collaborative energy-saving control device for industrial air conditioning, characterized in that, The device includes: The acquisition module is used to acquire relevant data on the operation of industrial air conditioners, including environmental parameters and equipment status data; The processing module is used to transmit and process data related to the operation of industrial air conditioning. It adopts industrial protocols including MODBUS and BACNET, as well as 5G / Wi-Fi 6 technology, to achieve low-latency interconnection and data synchronization of thousands of devices, and transmits the collected data to the decision-making level. Based on the needs of multi-device collaboration and hierarchical adjustment, an intelligent collaborative control mechanism is constructed, including a multi-device linkage strategy for dynamically allocating cooling resources according to regional heating and cooling loads, and hierarchical adjustment algorithms for chilled water systems and cooling systems, forming a control framework for global optimization. It processes environmental parameters and equipment status data to generate dynamic control strategies. Based on data analysis results and dynamic control strategies, it generates target energy-saving control information. It integrates and processes various operating data and control effects generated based on target energy-saving control information, displays energy consumption rankings for each region through a data visualization platform, generates optimization schemes for high-energy-consuming links, achieves reliable traceability of energy consumption data, and aggregates distributed air conditioning systems to participate in power grid frequency regulation.

8. An electronic device, characterized in that, include: First processor; and memory for storing executable instructions of the first processor; The first processor is configured to execute the industrial air conditioning distributed intelligent collaborative energy-saving control method according to any one of claims 1 to 6 by executing the executable instructions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the second processor, it implements the distributed intelligent collaborative energy-saving control method for industrial air conditioning as described in any one of claims 1 to 6.

Images